[
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the drug CC[C@@H](C)CO's dissolution process. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H](C)CO\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC[C@@H](C)CO\",\"value\": -4.42}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC[C@@H](C)CO is -4.42."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the energy change in the dissolution of the molecule CCC[C@H](C)CC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC[C@H](C)CC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[C@H](C)CC\",\"value\": 2.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[C@H](C)CC is 2.71."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the thermodynamic change in free energy during the drug CN(C)c1ccccc1 dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)c1ccccc1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CN(C)c1ccccc1\",\"value\": -3.45}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CN(C)c1ccccc1 is -3.45."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the compound C[C@@H](CO[N+](=O)[O-])O[N+](=O)[O-]'s dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](CO[N+](=O)[O-])O[N+](=O)[O-]\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C[C@@H](CO[N+](=O)[O-])O[N+](=O)[O-]\",\"value\": -4.95}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C[C@@H](CO[N+](=O)[O-])O[N+](=O)[O-] is -4.95."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the enthalpy and entropy changes for the drug CCOP(=S)(OCC)S[C@@H](CCl)N1C(=O)c2ccccc2C1=O dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOP(=S)(OCC)S[C@@H](CCl)N1C(=O)c2ccccc2C1=O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOP(=S)(OCC)S[C@@H](CCl)N1C(=O)c2ccccc2C1=O\",\"value\": -5.74}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOP(=S)(OCC)S[C@@H](CCl)N1C(=O)c2ccccc2C1=O is -5.74."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the dissolution free energy for the compound Cc1cc(cc(c1)C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(cc(c1)C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cc(cc(c1)C)C\",\"value\": -0.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cc(cc(c1)C)C is -0.9."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the change in Gibbs free energy for the drug CC(=C)C(=C)C's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=C)C(=C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(=C)C(=C)C\",\"value\": 0.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(=C)C(=C)C is 0.4."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the equilibrium Gibbs free energy change for the drug c1ccc(c(c1)N)[N+](=O)[O-] dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc(c(c1)N)[N+](=O)[O-]\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(c(c1)N)[N+](=O)[O-]\",\"value\": -7.37}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(c(c1)N)[N+](=O)[O-] is -7.37."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is required for the drug CN1CCNCC1 to dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN1CCNCC1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CN1CCNCC1\",\"value\": -7.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CN1CCNCC1 is -7.77."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the dissolution free energy for the given compound CCNc1nc(nc(n1)SC)NC(C)(C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCNc1nc(nc(n1)SC)NC(C)(C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCNc1nc(nc(n1)SC)NC(C)(C)C\",\"value\": -6.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCNc1nc(nc(n1)SC)NC(C)(C)C is -6.68."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the Gibbs free energy of dissolution for the molecule CCCCC#C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCC#C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCC#C\",\"value\": 0.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCC#C is 0.29."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change for dissolving the drug CCSCC in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCSCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCSCC\",\"value\": -1.46}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCSCC is -1.46."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the free energy associated with the compound CCCC(=C)C's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(=C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCC(=C)C\",\"value\": 1.47}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCC(=C)C is 1.47."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the free energy required for the dissolution of the molecule CCCCCC(=O)OCC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCC(=O)OCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCC(=O)OCC\",\"value\": -2.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCC(=O)OCC is -2.23."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the total free energy required for the drug CCc1cccc(c1N(COC)C(=O)CCl)CC's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCc1cccc(c1N(COC)C(=O)CCl)CC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCc1cccc(c1N(COC)C(=O)CCl)CC\",\"value\": -8.21}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCc1cccc(c1N(COC)C(=O)CCl)CC is -8.21."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change during the molecule CCCCCCOC(=O)C's dissolution process? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCCOC(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCOC(=O)C\",\"value\": -2.26}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCOC(=O)C is -2.26."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change involved in dissolving the drug CC(C)C(=O)C in solution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)C(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)C(=O)C\",\"value\": -3.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)C(=O)C is -3.24."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the total free energy change involved in the dissolution of the drug CC(C)Cc1cnccn1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)Cc1cnccn1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)Cc1cnccn1\",\"value\": -5.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)Cc1cnccn1 is -5.04."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the overall free energy change for the drug COCCO's dissolution process? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COCCO\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COCCO\",\"value\": -6.619999999999999}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COCCO is -6.619999999999999."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy for the dissolution of the drug CCc1cccc(c1N(COC)C(=O)CCl)CC molecule. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCc1cccc(c1N(COC)C(=O)CCl)CC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCc1cccc(c1N(COC)C(=O)CCl)CC\",\"value\": -8.21}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCc1cccc(c1N(COC)C(=O)CCl)CC is -8.21."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy of the dissolution process for the drug CC1CCCC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1CCCC1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC1CCCC1\",\"value\": 1.59}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC1CCCC1 is 1.59."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy change in the molecule CF's dissolution process. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CF\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CF\",\"value\": -0.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CF is -0.22."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the dissolution process of the drug Cc1ccc(cc1)O affect free energy? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(cc1)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc(cc1)O\",\"value\": -6.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc(cc1)O is -6.13."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the free energy change for the dissolution of the molecule CC(=O)N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)N\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(=O)N\",\"value\": -9.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(=O)N is -9.71."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy for the compound CC(=O)C(C)(C)C's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)C(C)(C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(=O)C(C)(C)C\",\"value\": -3.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(=O)C(C)(C)C is -3.11."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the thermodynamic energy change for the molecule CCCCCC(=O)C's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCC(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCC(=O)C\",\"value\": -3.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCC(=O)C is -3.04."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the total free energy involved in the drug C(Br)(Br)Br's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(Br)(Br)Br\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(Br)(Br)Br\",\"value\": -2.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(Br)(Br)Br is -2.13."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the Gibbs free energy calculated for the drug CCCCCC=O's dissolution in solution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCC=O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCC=O\",\"value\": -2.81}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCC=O is -2.81."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the free energy for the compound CC(C)I dissolution reaction. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)I\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)I\",\"value\": -0.46}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)I is -0.46."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy does the molecule CCc1cnccn1 require to dissolve? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCc1cnccn1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCc1cnccn1\",\"value\": -5.45}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCc1cnccn1 is -5.45."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is released when the drug COc1c(ccc(c1C(=O)O)Cl)Cl dissolves? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1c(ccc(c1C(=O)O)Cl)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COc1c(ccc(c1C(=O)O)Cl)Cl\",\"value\": -9.86}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COc1c(ccc(c1C(=O)O)Cl)Cl is -9.86."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the contribution of free energy to the dissolution of the drug CC(C)CCC(C)(C)C? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CCC(C)(C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CCC(C)(C)C\",\"value\": 2.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)CCC(C)(C)C is 2.93."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy change calculated for the dissolution of the drug c1ccc(cc1)O? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc(cc1)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(cc1)O\",\"value\": -6.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(cc1)O is -6.6."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the entropic and enthalpic contributions to the drug N's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"N\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"N\",\"value\": -4.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug N is -4.29."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the free energy of dissolution for the compound CCOC(=O)CC(=O)OCC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOC(=O)CC(=O)OCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOC(=O)CC(=O)OCC\",\"value\": -6.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOC(=O)CC(=O)OCC is -6.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the enthalpy change for the drug COCCN's dissolution process? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COCCN\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COCCN\",\"value\": -6.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COCCN is -6.55."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the free energy change associated with the dissolution of the drug COC(=O)C1CC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COC(=O)C1CC1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COC(=O)C1CC1\",\"value\": -4.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COC(=O)C1CC1 is -4.1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the energy change during the dissolution of the drug CC(OC(=O)C)OC(=O)C in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(OC(=O)C)OC(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(OC(=O)C)OC(=O)C\",\"value\": -4.97}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(OC(=O)C)OC(=O)C is -4.97."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the change in free energy as the compound CCC[N+](=O)[O-] dissolves. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC[N+](=O)[O-]\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[N+](=O)[O-]\",\"value\": -3.34}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[N+](=O)[O-] is -3.34."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy is released during the molecule C=C's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C=C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C=C\",\"value\": 1.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C=C is 1.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the change in free energy as the molecule C(C(Cl)Cl)Cl undergoes dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C(Cl)Cl)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(C(Cl)Cl)Cl\",\"value\": -1.99}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(C(Cl)Cl)Cl is -1.99."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the enthalpy contribution to the dissolution of the drug CC/C=C\\C? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC/C=C\\C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC/C=C\\C\",\"value\": 1.31}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC/C=C\\C is 1.31."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the amount of free energy required for compound Cc1cc2ccccc2cc1C dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2ccccc2cc1C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cc2ccccc2cc1C\",\"value\": -2.78}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cc2ccccc2cc1C is -2.78."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the Gibbs free energy for the compound c1c(c(=O)[nH]c(=O)[nH]1)I's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1c(c(=O)[nH]c(=O)[nH]1)I\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1c(c(=O)[nH]c(=O)[nH]1)I\",\"value\": -18.72}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1c(c(=O)[nH]c(=O)[nH]1)I is -18.72."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy calculated during the drug C=CCO dissolution process? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C=CCO\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C=CCO\",\"value\": -5.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C=CCO is -5.03."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the free energy for dissolving the drug Cc1cccnc1 in solution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cccnc1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cccnc1\",\"value\": -4.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cccnc1 is -4.77."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change for the dissolution of the compound COCC(OC)(OC)OC molecule? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COCC(OC)(OC)OC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COCC(OC)(OC)OC\",\"value\": -5.73}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COCC(OC)(OC)OC is -5.73."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy change when the drug CCOC=O is dissolved in solvent. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOC=O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOC=O\",\"value\": -2.56}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOC=O is -2.56."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the free energy of dissolution for the compound Cc1cccs1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cccs1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cccs1\",\"value\": -1.38}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cccs1 is -1.38."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy of dissolution calculated for the molecule C1CCCCC1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1CCCCC1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C1CCCCC1\",\"value\": 1.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C1CCCCC1 is 1.23."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the Gibbs free energy for dissolving the drug CCC(C)CC and CCCCI in solution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)CC\", \"CCCCI\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC(C)CC\",\"value\": 2.51}, {\"smile\": \"CCCCI\",\"value\": -0.25}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC(C)CC and CCCCI are 2.51 and -0.25 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the standard free energy of dissolution for the drug C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O and CC(C)(C)C(=O)OC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O\", \"CC(C)(C)C(=O)OC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O\",\"value\": -10.21}, {\"smile\": \"CC(C)(C)C(=O)OC\",\"value\": -2.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C[C@@H](c1ccc2cc(ccc2c1)OC)C(=O)O and CC(C)(C)C(=O)OC are -10.21 and -2.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do the enthalpy and entropy changes impact the free energy of the drug Cc1cccc(c1)C and CCI's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc(c1)C\", \"CCI\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cccc(c1)C\",\"value\": -0.83}, {\"smile\": \"CCI\",\"value\": -0.74}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cccc(c1)C and CCI are -0.83 and -0.74 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug C=C(Cl)Cl and Cc1cccc2c1cccc2's dissolution relate to free energy changes? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C=C(Cl)Cl\", \"Cc1cccc2c1cccc2\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C=C(Cl)Cl\",\"value\": 0.25}, {\"smile\": \"Cc1cccc2c1cccc2\",\"value\": -2.44}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C=C(Cl)Cl and Cc1cccc2c1cccc2 are 0.25 and -2.44 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the free energy change involved in dissolving the compound CC(C)C and CCC=C? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)C\", \"CCC=C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)C\",\"value\": 2.3}, {\"smile\": \"CCC=C\",\"value\": 1.38}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)C and CCC=C are 2.3 and 1.38 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the standard Gibbs free energy change for the drug c1cc(c(c(c1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl)Cl and CCCCCC(=O)C's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1cc(c(c(c1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl)Cl\", \"CCCCCC(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(c(c(c1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl)Cl\",\"value\": -4.4}, {\"smile\": \"CCCCCC(=O)C\",\"value\": -3.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(c(c(c1c2cc(c(c(c2Cl)Cl)Cl)Cl)Cl)Cl)Cl and CCCCCC(=O)C are -4.4 and -3.04 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy is required to dissolve the drug CCOP(=S)(OCC)Oc1ccc(cc1)[N+](=O)[O-] and c1ccc(cc1)S in solution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOP(=S)(OCC)Oc1ccc(cc1)[N+](=O)[O-]\", \"c1ccc(cc1)S\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOP(=S)(OCC)Oc1ccc(cc1)[N+](=O)[O-]\",\"value\": -6.74}, {\"smile\": \"c1ccc(cc1)S\",\"value\": -2.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOP(=S)(OCC)Oc1ccc(cc1)[N+](=O)[O-] and c1ccc(cc1)S are -6.74 and -2.55 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the Gibbs free energy of dissolution for the given drug CC(C)SC(C)C and C1CCC(=O)C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)SC(C)C\", \"C1CCC(=O)C1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)SC(C)C\",\"value\": -1.21}, {\"smile\": \"C1CCC(=O)C1\",\"value\": -4.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)SC(C)C and C1CCC(=O)C1 are -1.21 and -4.7 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the thermodynamic change in free energy for the compound C1CCC(=O)C1 and C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1CCC(=O)C1\", \"C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C1CCC(=O)C1\",\"value\": -4.7}, {\"smile\": \"C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl\",\"value\": -0.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C1CCC(=O)C1 and C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl are -4.7 and -0.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy required for the dissolution of the molecule CCOC(OCC)Oc1ccccc1 and CCCCCCCCl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOC(OCC)Oc1ccccc1\", \"CCCCCCCCl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOC(OCC)Oc1ccccc1\",\"value\": -5.23}, {\"smile\": \"CCCCCCCCl\",\"value\": 0.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOC(OCC)Oc1ccccc1 and CCCCCCCCl are -5.23 and 0.29 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the thermodynamic change in free energy for dissolving the drug C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O and CS(=O)(=O)Cl? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\", \"CS(=O)(=O)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\",\"value\": -8.42}, {\"smile\": \"CS(=O)(=O)Cl\",\"value\": -4.87}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O and CS(=O)(=O)Cl are -8.42 and -4.87 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy change for the compound CCOCCOCC and CCCOC's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOCCOCC\", \"CCCOC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOCCOCC\",\"value\": -3.54}, {\"smile\": \"CCCOC\",\"value\": -1.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOCCOCC and CCCOC are -3.54 and -1.66 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the dissolution free energy of the compound Cc1ccc(cc1)C(=O)C and CCNCC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(cc1)C(=O)C\", \"CCNCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc(cc1)C(=O)C\",\"value\": -4.7}, {\"smile\": \"CCNCC\",\"value\": -4.07}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc(cc1)C(=O)C and CCNCC are -4.7 and -4.07 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the free energy of dissolution of the drug Cc1cnccn1 and C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O molecule. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cnccn1\", \"C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cnccn1\",\"value\": -5.51}, {\"smile\": \"C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O\",\"value\": -23.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cnccn1 and C([C@H]([C@H]([C@@H]([C@@H](CO)O)O)O)O)O are -5.51 and -23.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the enthalpy and entropy contribution to the free energy of the drug CC(C)(C)C(=O)OC and Cc1ccc(cc1)C(C)C dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)C(=O)OC\", \"Cc1ccc(cc1)C(C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)(C)C(=O)OC\",\"value\": -2.4}, {\"smile\": \"Cc1ccc(cc1)C(C)C\",\"value\": -0.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)(C)C(=O)OC and Cc1ccc(cc1)C(C)C are -2.4 and -0.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the Gibbs free energy of dissolution for the compound c1ccc2c(c1)Oc3ccccc3O2 and [C@@H](C(F)(F)F)(Cl)Br. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc2c(c1)Oc3ccccc3O2\", \"[C@@H](C(F)(F)F)(Cl)Br\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc2c(c1)Oc3ccccc3O2\",\"value\": -3.15}, {\"smile\": \"[C@@H](C(F)(F)F)(Cl)Br\",\"value\": -0.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc2c(c1)Oc3ccccc3O2 and [C@@H](C(F)(F)F)(Cl)Br are -3.15 and -0.11 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy associated with the molecule c1c(c(=O)[nH]c(=O)[nH]1)C(F)(F)F and COc1ccccc1's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1c(c(=O)[nH]c(=O)[nH]1)C(F)(F)F\", \"COc1ccccc1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1c(c(=O)[nH]c(=O)[nH]1)C(F)(F)F\",\"value\": -15.46}, {\"smile\": \"COc1ccccc1\",\"value\": -2.45}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1c(c(=O)[nH]c(=O)[nH]1)C(F)(F)F and COc1ccccc1 are -15.46 and -2.45 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the free energy balance for the drug CN and Cc1ccc2cc(ccc2c1)C dissolution process? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN\", \"Cc1ccc2cc(ccc2c1)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CN\",\"value\": -4.55}, {\"smile\": \"Cc1ccc2cc(ccc2c1)C\",\"value\": -2.63}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CN and Cc1ccc2cc(ccc2c1)C are -4.55 and -2.63 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is needed for the drug C(C(F)(Cl)Cl)(F)(F)Cl and c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl to undergo dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C(F)(Cl)Cl)(F)(F)Cl\", \"c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(C(F)(Cl)Cl)(F)(F)Cl\",\"value\": 1.77}, {\"smile\": \"c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl\",\"value\": -16.43}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(C(F)(Cl)Cl)(F)(F)Cl and c1ccc(cc1)n2c(=O)c(c(cn2)N)Cl are 1.77 and -16.43 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the change in free energy when the molecule c1ccc(cc1)CCCO and COC(=O)CC#N dissolves. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc(cc1)CCCO\", \"COC(=O)CC#N\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(cc1)CCCO\",\"value\": -6.92}, {\"smile\": \"COC(=O)CC#N\",\"value\": -6.72}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(cc1)CCCO and COC(=O)CC#N are -6.92 and -6.72 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy change for the molecule CCC[N@@](CC1CC1)c2c(cc(cc2[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-] and Cc1ccccc1's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC[N@@](CC1CC1)c2c(cc(cc2[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-]\", \"Cc1ccccc1\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[N@@](CC1CC1)c2c(cc(cc2[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-]\",\"value\": -2.45}, {\"smile\": \"Cc1ccccc1\",\"value\": -0.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[N@@](CC1CC1)c2c(cc(cc2[N+](=O)[O-])C(F)(F)F)[N+](=O)[O-] and Cc1ccccc1 are -2.45 and -0.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change for the dissolution of the molecule C(=C/Cl)\\Cl and C=CCl in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(=C/Cl)\\Cl\", \"C=CCl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(=C/Cl)\\Cl\",\"value\": -0.78}, {\"smile\": \"C=CCl\",\"value\": -0.59}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(=C/Cl)\\Cl and C=CCl are -0.78 and -0.59 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change during the compound CCl and C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCl\", \"C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCl\",\"value\": -0.55}, {\"smile\": \"C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl\",\"value\": -0.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCl and C(C(Cl)(Cl)Cl)(Cl)(Cl)Cl are -0.55 and -0.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the change in Gibbs free energy for dissolving the drug CCl and CC[C@H](C)O in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCl\", \"CC[C@H](C)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCl\",\"value\": -0.55}, {\"smile\": \"CC[C@H](C)O\",\"value\": -4.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCl and CC[C@H](C)O are -0.55 and -4.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the energy change in the compound c1ccc(c(c1)N)[N+](=O)[O-] and CC(C)O's dissolution reaction. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc(c(c1)N)[N+](=O)[O-]\", \"CC(C)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(c(c1)N)[N+](=O)[O-]\",\"value\": -7.37}, {\"smile\": \"CC(C)O\",\"value\": -4.74}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(c(c1)N)[N+](=O)[O-] and CC(C)O are -7.37 and -4.74 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the energy change during the compound CC(C)CCO and CCCCCCCO's dissolution process. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CCO\", \"CCCCCCCO\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CCO\",\"value\": -4.42}, {\"smile\": \"CCCCCCCO\",\"value\": -4.21}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)CCO and CCCCCCCO are -4.42 and -4.21 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy is involved in the compound CCCC[N+](=O)[O-] and C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC[N+](=O)[O-]\", \"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCC[N+](=O)[O-]\",\"value\": -3.09}, {\"smile\": \"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\",\"value\": -8.42}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCC[N+](=O)[O-] and C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O are -3.09 and -8.42 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the energy change involved in the molecule c1cc(ccc1N)N(=O)=O and CC(C)OC's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1cc(ccc1N)N(=O)=O\", \"CC(C)OC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(ccc1N)N(=O)=O\",\"value\": -9.82}, {\"smile\": \"CC(C)OC\",\"value\": -2.01}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(ccc1N)N(=O)=O and CC(C)OC are -9.82 and -2.01 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much Gibbs free energy is involved in the dissolution of the drug CCCCC(C)C and COCC(OC)(OC)OC? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCC(C)C\", \"COCC(OC)(OC)OC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCC(C)C\",\"value\": 2.93}, {\"smile\": \"COCC(OC)(OC)OC\",\"value\": -5.73}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCC(C)C and COCC(OC)(OC)OC are 2.93 and -5.73 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the free energy of dissolution for the compound CC and CN? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC\", \"CN\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC\",\"value\": 1.83}, {\"smile\": \"CN\",\"value\": -4.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC and CN are 1.83 and -4.55 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy associated with the dissolution process of the compound COc1ccccc1O and CCOCC? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccccc1O\", \"CCOCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COc1ccccc1O\",\"value\": -5.94}, {\"smile\": \"CCOCC\",\"value\": -1.59}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COc1ccccc1O and CCOCC are -5.94 and -1.59 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the value of free energy for the dissolution of the molecule c1ccc(c(c1)O)F and CCCCCCI? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc(c(c1)O)F\", \"CCCCCCI\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(c(c1)O)F\",\"value\": -5.29}, {\"smile\": \"CCCCCCI\",\"value\": 0.08}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(c(c1)O)F and CCCCCCI are -5.29 and 0.08 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the Gibbs free energy change when the drug CCC[C@@H](C)O and CCCC(C)C dissolves. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC[C@@H](C)O\", \"CCCC(C)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[C@@H](C)O\",\"value\": -4.39}, {\"smile\": \"CCCC(C)C\",\"value\": 2.51}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[C@@H](C)O and CCCC(C)C are -4.39 and 2.51 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the dissolution free energy for the molecule CCCCCCCCC=O and c1ccc2c(c1)cccc2O at standard conditions. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCCCCC=O\", \"c1ccc2c(c1)cccc2O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCCCC=O\",\"value\": -2.07}, {\"smile\": \"c1ccc2c(c1)cccc2O\",\"value\": -7.67}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCCCC=O and c1ccc2c(c1)cccc2O are -2.07 and -7.67 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the change in free energy as the molecule CN(C)CCOC(c1ccccc1)c2ccccc2 and CCCCCC=C dissolves in solution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)CCOC(c1ccccc1)c2ccccc2\", \"CCCCCC=C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CN(C)CCOC(c1ccccc1)c2ccccc2\",\"value\": -9.34}, {\"smile\": \"CCCCCC=C\",\"value\": 1.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CN(C)CCOC(c1ccccc1)c2ccccc2 and CCCCCC=C are -9.34 and 1.66 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy at equilibrium for the drug CCCCI and C(F)Cl's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCI\", \"C(F)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCI\",\"value\": -0.25}, {\"smile\": \"C(F)Cl\",\"value\": -0.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCI and C(F)Cl are -0.25 and -0.77 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the free energy of the compound CC(=O)C1CCCCC1 and CC(C)CCOC=O's dissolution process. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)C1CCCCC1\", \"CC(C)CCOC=O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(=O)C1CCCCC1\",\"value\": -3.9}, {\"smile\": \"CC(C)CCOC=O\",\"value\": -2.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(=O)C1CCCCC1 and CC(C)CCOC=O are -3.9 and -2.13 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the free energy of the dissolution reaction for the drug CCCC(=C)C and CCSSCC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(=C)C\", \"CCSSCC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCC(=C)C\",\"value\": 1.47}, {\"smile\": \"CCSSCC\",\"value\": -1.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCC(=C)C and CCSSCC are 1.47 and -1.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the thermodynamic free energy change for the molecule CCC(C)(C)O and c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)(C)O\", \"c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC(C)(C)O\",\"value\": -4.43}, {\"smile\": \"c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N\",\"value\": -9.53}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC(C)(C)O and c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N are -4.43 and -9.53 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the thermodynamic free energy of the compound Cc1ccc(c(c1)C)C and CC(C)(C)c1ccc(cc1)O's dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(c(c1)C)C\", \"CC(C)(C)c1ccc(cc1)O\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc(c(c1)C)C\",\"value\": -0.86}, {\"smile\": \"CC(C)(C)c1ccc(cc1)O\",\"value\": -5.91}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc(c(c1)C)C and CC(C)(C)c1ccc(cc1)O are -0.86 and -5.91 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy is absorbed during the molecule C1CCC=CC1 and c1ccnc(c1)Cl's dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1CCC=CC1\", \"c1ccnc(c1)Cl\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C1CCC=CC1\",\"value\": 0.14}, {\"smile\": \"c1ccnc(c1)Cl\",\"value\": -4.39}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C1CCC=CC1 and c1ccnc(c1)Cl are 0.14 and -4.39 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the free energy involved in dissolving the molecule CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-] and CCCOC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-]\", \"CCCOC\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-]\",\"value\": -5.66}, {\"smile\": \"CCCOC\",\"value\": -1.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-] and CCCOC are -5.66 and -1.66 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the equilibrium free energy change for the drug CCC(=O)O and CCCCC(=O)C dissolution? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(=O)O\", \"CCCCC(=O)C\"], \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC(=O)O\",\"value\": -6.46}, {\"smile\": \"CCCCC(=O)C\",\"value\": -3.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC(=O)O and CCCCC(=O)C are -6.46 and -3.28 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the drug's dissolution process. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccccc1\",\"value\": -0.9}, {\"smile\": \"CCc1cccc(c1)O\",\"value\": -6.25}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccccc1 and CCc1cccc(c1)O are -0.9 and -6.25 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the energy change in the dissolution of the molecule. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[C@H](CC)O\",\"value\": -4.06}, {\"smile\": \"CC(=C)C(=C)C\",\"value\": 0.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[C@H](CC)O and CC(=C)C(=C)C are -4.06 and 0.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the thermodynamic change in free energy during the drug dissolution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCc1cccc(c1)O\",\"value\": -6.25}, {\"smile\": \"c1ccc(c(c1)O)F\",\"value\": -5.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCc1cccc(c1)O and c1ccc(c(c1)O)F are -6.25 and -5.29 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the compound's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CC(=O)O\",\"value\": -6.09}, {\"smile\": \"S\",\"value\": -0.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)CC(=O)O and S are -6.09 and -0.7 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the enthalpy and entropy changes for the drug dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCc1cccnc1\",\"value\": -4.59}, {\"smile\": \"CCC(C)(C)CC\",\"value\": 2.56}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCc1cccnc1 and CCC(C)(C)CC are -4.59 and 2.56 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the dissolution free energy for the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COCOC\",\"value\": -2.93}, {\"smile\": \"CN(C)C(=O)c1ccc(cc1)[N+](=O)[O-]\",\"value\": -11.95}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COCOC and CN(C)C(=O)c1ccc(cc1)[N+](=O)[O-] are -2.93 and -11.95 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the change in Gibbs free energy for the drug's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COc1cccc(c1O)OC\",\"value\": -6.96}, {\"smile\": \"CC[C@H](C(C)C)O\",\"value\": -3.88}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COc1cccc(c1O)OC and CC[C@H](C(C)C)O are -6.96 and -3.88 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the equilibrium Gibbs free energy change for the drug dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(ccc1O)Br\",\"value\": -5.85}, {\"smile\": \"CF\",\"value\": -0.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(ccc1O)Br and CF are -5.85 and -0.22 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is required for the drug to dissolve in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COC(CCl)(OC)OC\",\"value\": -4.59}, {\"smile\": \"C1CCC(=O)C1\",\"value\": -4.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COC(CCl)(OC)OC and C1CCC(=O)C1 are -4.59 and -4.7 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the dissolution free energy for the given compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(ccc1C#N)O\",\"value\": -10.17}, {\"smile\": \"CCCC(=O)OCC\",\"value\": -2.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(ccc1C#N)O and CCCC(=O)OCC are -10.17 and -2.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the Gibbs free energy of dissolution for the molecule. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc2cc(ccc2c1)C\",\"value\": -2.63}, {\"smile\": \"CC[C@H](C)n1c(=O)c(c([nH]c1=O)C)Br\",\"value\": -9.73}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc2cc(ccc2c1)C and CC[C@H](C)n1c(=O)c(c([nH]c1=O)C)Br are -2.63 and -9.73 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change for dissolving the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)OC\",\"value\": -2.01}, {\"smile\": \"CCN\",\"value\": -4.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)OC and CCN are -2.01 and -4.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the free energy associated with the compound's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCNc1nc(nc(n1)SC)NC(C)C\",\"value\": -7.65}, {\"smile\": \"CCC(=O)CC\",\"value\": -3.41}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCNc1nc(nc(n1)SC)NC(C)C and CCC(=O)CC are -7.65 and -3.41 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the free energy required for the dissolution of the molecule. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc2cc3ccccc3cc2c1\",\"value\": -3.95}, {\"smile\": \"C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O)O)O)O)O\",\"value\": -25.47}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc2cc3ccccc3cc2c1 and C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O)O)O)O)O are -3.95 and -25.47 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the total free energy required for the drug's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCNCCCC\",\"value\": -3.24}, {\"smile\": \"CC(C)CBr\",\"value\": -0.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCNCCCC and CC(C)CBr are -3.24 and -0.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change during the molecule's dissolution process? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C1CCNC1\",\"value\": -5.48}, {\"smile\": \"Cc1ccnc(c1)C\",\"value\": -4.86}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C1CCNC1 and Cc1ccnc(c1)C are -5.48 and -4.86 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change involved in dissolving the drug in solution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCCCCCO\",\"value\": -3.64}, {\"smile\": \"C1CCOCC1\",\"value\": -3.12}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCCCCCO and C1CCOCC1 are -3.64 and -3.12 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the total free energy change involved in the dissolution of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCCCCCO\",\"value\": -3.64}, {\"smile\": \"CCc1ccccc1O\",\"value\": -5.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCCCCCO and CCc1ccccc1O are -3.64 and -5.66 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the overall free energy change for the drug's dissolution process? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cc(c2ccccc2c1)C\",\"value\": -2.47}, {\"smile\": \"c1cc(ccc1O)Br\",\"value\": -5.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1cc(c2ccccc2c1)C and c1cc(ccc1O)Br are -2.47 and -5.85 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy for the dissolution of the drug molecule. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(ccc1N)Cl\",\"value\": -5.9}, {\"smile\": \"CCBr\",\"value\": -0.74}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(ccc1N)Cl and CCBr are -5.9 and -0.74 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy of the dissolution process for the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CN(C)CCOC(c1ccccc1)c2ccccc2\",\"value\": -9.34}, {\"smile\": \"Cc1ccccc1C=O\",\"value\": -3.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CN(C)CCOC(c1ccccc1)c2ccccc2 and Cc1ccccc1C=O are -9.34 and -3.93 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy change in the molecule's dissolution process. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCOCCCC\",\"value\": -0.83}, {\"smile\": \"CCCC(=O)OC\",\"value\": -2.83}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCOCCCC and CCCC(=O)OC are -0.83 and -2.83 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the dissolution process of the drug affect free energy? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)COC=O\",\"value\": -2.22}, {\"smile\": \"C=CCl\",\"value\": -0.59}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)COC=O and C=CCl are -2.22 and -0.59 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the free energy change for the dissolution of the molecule. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(CO[N+](=O)[O-])CO[N+](=O)[O-]\",\"value\": -4.8}, {\"smile\": \"CCOP(=S)(OCC)Oc1c(cc(c(n1)Cl)Cl)Cl\",\"value\": -5.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(CO[N+](=O)[O-])CO[N+](=O)[O-] and CCOP(=S)(OCC)Oc1c(cc(c(n1)Cl)Cl)Cl are -4.8 and -5.04 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the Gibbs free energy for the compound's dissolution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc(c2c1cccc2)C\",\"value\": -2.82}, {\"smile\": \"CCCNCCC\",\"value\": -3.65}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc(c2c1cccc2)C and CCCNCCC are -2.82 and -3.65 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the thermodynamic energy change for the molecule's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC(C)C\",\"value\": 2.38}, {\"smile\": \"Cc1cccc(c1)O\",\"value\": -5.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC(C)C and Cc1cccc(c1)O are 2.38 and -5.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the total free energy involved in the drug's dissolution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(cc1)Cl\",\"value\": -1.12}, {\"smile\": \"CCCCCCCC(=O)C\",\"value\": -2.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(cc1)Cl and CCCCCCCC(=O)C are -1.12 and -2.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the Gibbs free energy calculated for the drug's dissolution in solution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cn1cccc1\",\"value\": -2.89}, {\"smile\": \"CCCCCl\",\"value\": -0.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cn1cccc1 and CCCCCl are -2.89 and -0.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the free energy for the compound dissolution reaction. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOC(=O)c1ccccc1\",\"value\": -3.64}, {\"smile\": \"C(C(Cl)(Cl)Cl)(Cl)Cl\",\"value\": -1.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCOC(=O)c1ccccc1 and C(C(Cl)(Cl)Cl)(Cl)Cl are -3.64 and -1.23 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy does the molecule require to dissolve? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCO\",\"value\": -5.0}, {\"smile\": \"c1ccc(c(c1)C=O)O\",\"value\": -4.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCO and c1ccc(c(c1)C=O)O are -5.0 and -4.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is released when the drug dissolves? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)OC(=O)C\",\"value\": -2.64}, {\"smile\": \"C[C@@H]1CC[C@H](CC1=O)C(=C)C\",\"value\": -3.75}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)OC(=O)C and C[C@@H]1CC[C@H](CC1=O)C(=C)C are -2.64 and -3.75 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the contribution of free energy to the dissolution of the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COC(CCl)(OC)OC\",\"value\": -4.59}, {\"smile\": \"Cc1cccnc1\",\"value\": -4.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug COC(CCl)(OC)OC and Cc1cccnc1 are -4.59 and -4.77 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy change calculated for the dissolution of the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\",\"value\": -8.42}, {\"smile\": \"Cc1ccccc1C\",\"value\": -0.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O and Cc1ccccc1C are -8.42 and -0.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the entropic and enthalpic contributions to the drug's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O\",\"value\": -8.42}, {\"smile\": \"CNC\",\"value\": -4.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C[C@@H](c1ccc(c(c1)F)c2ccccc2)C(=O)O and CNC are -8.42 and -4.29 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the free energy of dissolution for the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCC#C\",\"value\": 0.6}, {\"smile\": \"CC(=C)c1ccccc1\",\"value\": -1.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCC#C and CC(=C)c1ccccc1 are 0.6 and -1.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the enthalpy change for the drug's dissolution process? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)(C)C(=O)OC\",\"value\": -2.4}, {\"smile\": \"c1ccccc1\",\"value\": -0.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)(C)C(=O)OC and c1ccccc1 are -2.4 and -0.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the free energy change associated with the dissolution of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CC(=O)C\",\"value\": -3.05}, {\"smile\": \"C=CCO\",\"value\": -5.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)CC(=O)C and C=CCO are -3.05 and -5.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the energy change during the dissolution of the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccc(cc1)Br\",\"value\": -1.39}, {\"smile\": \"CCCc1ccccc1\",\"value\": -0.53}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cc1ccc(cc1)Br and CCCc1ccccc1 are -1.39 and -0.53 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the change in free energy as the compound dissolves. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(=C(F)F)(C(F)(F)F)F\",\"value\": 2.93}, {\"smile\": \"Cc1ccc(cc1)N\",\"value\": -5.57}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug C(=C(F)F)(C(F)(F)F)F and Cc1ccc(cc1)N are 2.93 and -5.57 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much free energy is released during the molecule's dissolution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC#C\",\"value\": -0.48}, {\"smile\": \"CC(=C)C=C\",\"value\": 0.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC#C and CC(=C)C=C are -0.48 and 0.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the change in free energy as the molecule undergoes dissolution? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cn1cccc1\",\"value\": -2.89}, {\"smile\": \"CCCCOC(=O)C\",\"value\": -2.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug Cn1cccc1 and CCCCOC(=O)C are -2.89 and -2.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the enthalpy contribution to the dissolution of the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CBr\",\"value\": -0.03}, {\"smile\": \"c1cc(c(cc1c2ccc(cc2F)F)C(=O)O)O\",\"value\": -9.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CC(C)CBr and c1cc(c(cc1c2ccc(cc2F)F)C(=O)O)O are -0.03 and -9.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the amount of free energy required for compound dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(cc(c1)Cl)O\",\"value\": -6.62}, {\"smile\": \"c1ccc-2c(c1)Cc3c2cccc3\",\"value\": -3.35}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1cc(cc(c1)Cl)O and c1ccc-2c(c1)Cc3c2cccc3 are -6.62 and -3.35 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the Gibbs free energy for the compound's dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC[C@@H](C)O\",\"value\": -4.39}, {\"smile\": \"CCOC(OCC)Oc1ccccc1\",\"value\": -5.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCC[C@@H](C)O and CCOC(OCC)Oc1ccccc1 are -4.39 and -5.23 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy calculated during the drug dissolution process? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCO[N+](=O)[O-]\",\"value\": -1.66}, {\"smile\": \"CCNc1nc(nc(n1)Cl)NCC\",\"value\": -10.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCO[N+](=O)[O-] and CCNc1nc(nc(n1)Cl)NCC are -1.66 and -10.22 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the free energy for dissolving the drug in solution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCCCl\",\"value\": 0.29}, {\"smile\": \"CCCCNCCCC\",\"value\": -3.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCCCl and CCCCNCCCC are 0.29 and -3.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change for the dissolution of the compound molecule? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc(c(c1)O)I\",\"value\": -6.2}, {\"smile\": \"C1CCC(C1)O\",\"value\": -5.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc(c(c1)O)I and C1CCC(C1)O are -6.2 and -5.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the free energy change when the drug is dissolved in solvent. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCCCC(=O)C\",\"value\": -2.49}, {\"smile\": \"CCCCCCCCC\",\"value\": 3.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCCCC(=O)C and CCCCCCCCC are -2.49 and 3.13 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the free energy of dissolution for the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc2c(c1)Oc3cc(c(cc3O2)Cl)Cl\",\"value\": -3.56}, {\"smile\": \"CC#N\",\"value\": -3.88}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug c1ccc2c(c1)Oc3cc(c(cc3O2)Cl)Cl and CC#N are -3.56 and -3.88 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How is the free energy of dissolution calculated for the molecule? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCCCCc1ccccc1\",\"value\": -0.23}, {\"smile\": \"CCCCc1ccccc1\",\"value\": -0.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drug CCCCCc1ccccc1 and CCCCc1ccccc1 are -0.23 and -0.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the optimized drugs' dissolution process. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COc1ccccc1OC\",\"value\": -3.7863657}, {\"smile\": \"C(C(F)(F)F)Cl\",\"value\": -4.419887}, {\"smile\": \"CCCc1ccccc1\",\"value\": -3.9381607}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.7863657, -4.419887 and -3.9381607 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the energy change in the dissolution of the molecule. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CCO\",\"value\": -3.906902}, {\"smile\": \"CCCCCCOC(=O)C\",\"value\": -4.1855106}, {\"smile\": \"c1ccc(c(c1)C(F)(F)F)C(F)(F)F\",\"value\": -4.5094824}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.906902, -4.1855106 and -4.5094824 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the thermodynamic change in free energy during the generated drugs dissolution? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)OC\",\"value\": -3.7638836}, {\"smile\": \"CNC\",\"value\": -3.5803385}, {\"smile\": \"CCCOC(=O)CC\",\"value\": -3.9080462}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.7638836, -3.5803385 and -3.9080462 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy of the compound' dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cn1cnc2c1c(=O)n(c(=O)n2C)C\",\"value\": -3.8000236}, {\"smile\": \"CCCCCCCCC=O\",\"value\": -4.175282}, {\"smile\": \"CC(C)(C)Cl\",\"value\": -4.574432}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.8000236, -4.175282 and -4.574432 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the enthalpy and entropy changes for the optimized drugs dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOC(=O)C\",\"value\": -4.120806}, {\"smile\": \"CCC(CC)O\",\"value\": -3.8772614}, {\"smile\": \"COC(OC)OC\",\"value\": -3.6306243}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.120806, -3.8772614 and -3.6306243 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the dissolution free energy for the compound. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"COc1ccc(cc1)C(=O)OC\",\"value\": -3.767413}, {\"smile\": \"CC[C@H](C(C)C)O\",\"value\": -3.7546344}, {\"smile\": \"CCOCCO\",\"value\": -3.7478275}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.767413, -3.7546344 and -3.7478275 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the change in Gibbs free energy for the optimized drugs' dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1c(nc(nc1OC(=O)N(C)C)N(C)C)C\",\"value\": -4.0493255}, {\"smile\": \"C[C@@H](CCl)Cl\",\"value\": -3.9268203}, {\"smile\": \"COc1ccc(cc1)C(=O)OC\",\"value\": -3.920445}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.0493255, -3.9268203 and -3.920445 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the equilibrium Gibbs free energy change for the optimized drugs dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1cc(cnc1)C\",\"value\": -3.6172967}, {\"smile\": \"COC(CC#N)(OC)OC\",\"value\": -3.7855794}, {\"smile\": \"COCC(OC)(OC)OC\",\"value\": -3.724927}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.6172967, -3.7855794 and -3.724927 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much energy is required for the generated drugs to dissolve in water? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1ccc2c(c1)Oc3ccccc3O2\",\"value\": -3.932107}, {\"smile\": \"CO\",\"value\": -3.4757125}, {\"smile\": \"CCCCC#C\",\"value\": -4.1605487}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.932107, -3.4757125 and -4.1605487 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the dissolution free energy for the given compound. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(C)CBr\",\"value\": -3.9264913}, {\"smile\": \"c1(c(c(c(c(c1Cl)Cl)Cl)Cl)Cl)N(=O)=O\",\"value\": -4.2554135}, {\"smile\": \"CCCC(C)(C)C\",\"value\": -4.2911944}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.9264913, -4.2554135 and -4.2911944 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the Gibbs free energy of dissolution for the molecule. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C(C(F)(F)F)Cl\",\"value\": -4.5475507}, {\"smile\": \"CCOCC\",\"value\": -3.6895928}, {\"smile\": \"c1ccc(cc1)c2ccccc2Cl\",\"value\": -3.9106193}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.5475507, -3.6895928 and -3.9106193 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change for dissolving the generated drugs in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"Cc1ccnc(c1)C\",\"value\": -4.0837364}, {\"smile\": \"c1cc(ccc1C#N)O\",\"value\": -4.139836}, {\"smile\": \"CC(=O)c1cccnc1\",\"value\": -4.23997}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.0837364, -4.139836 and -4.23997 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the free energy associated with the compound' dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc(cc(c1)Cl)Cl\",\"value\": -3.9409826}, {\"smile\": \"c1cc(c(c(c1Cl)Cl)Cl)Cl\",\"value\": -3.9790606}, {\"smile\": \"C(C(Cl)(Cl)Cl)Cl\",\"value\": -4.3741374}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.9409826, -3.9790606 and -4.3741374 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the free energy required for the dissolution of the molecule. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"c1cc2ccc3cccc4c3c2c(c1)cc4\",\"value\": -3.8644671}, {\"smile\": \"CC(F)F\",\"value\": -3.9612315}, {\"smile\": \"c1ccc2c(c1)C(=O)c3c(ccc(c3C2=O)O)N\",\"value\": -4.0308747}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.8644671, -3.9612315 and -4.0308747 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the total free energy required for the generated drugs' dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCC(=O)O\",\"value\": -3.8917212}, {\"smile\": \"CCCC[C@@H](C)CC\",\"value\": -4.057409}, {\"smile\": \"c1ccc2c(c1)C(=O)c3cccc(c3C2=O)NCCO\",\"value\": -4.031282}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.8917212, -4.057409 and -4.031282 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the energy change during the molecule' dissolution process? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCSCC\",\"value\": -3.6044202}, {\"smile\": \"c1ccc(c(c1)N)[N+](=O)[O-]\",\"value\": -4.0821714}, {\"smile\": \"C(F)(F)Cl\",\"value\": -3.8996375}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.6044202, -4.0821714 and -3.8996375 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the free energy change involved in dissolving the optimized drugs in solution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CC(=O)c1cccnc1\",\"value\": -3.9052658}, {\"smile\": \"c1cc(c(cc1Cl)Cl)Cl\",\"value\": -3.7747765}, {\"smile\": \"Cc1ccc2cc(ccc2c1)C\",\"value\": -3.747336}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.9052658, -3.7747765 and -3.747336 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the total free energy change involved in the dissolution of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"CCOP(=S)(OCC)SCSCC\",\"value\": -4.312286}, {\"smile\": \"CCN(CC)c1c(cc(c(c1[N+](=O)[O-])N)C(F)(F)F)[N+](=O)[O-]\",\"value\": -4.483667}, {\"smile\": \"CCCCCCCC(=O)C\",\"value\": -4.4837465}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.312286, -4.483667 and -4.4837465 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the overall free energy change for the generated drugs' dissolution process? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C=C(c1ccccc1)c2ccccc2\",\"value\": -4.005993}, {\"smile\": \"CCCC(=O)OC\",\"value\": -3.9606848}, {\"smile\": \"CC(C)CCOC(=O)C\",\"value\": -4.1408153}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -4.005993, -3.9606848 and -4.1408153 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the thermodynamic free energy for the dissolution of the optimized drugs molecule. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"freesolv\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the solvation free energy of the compound in water, which is crucial for understanding the behavior of molecules in biological systems.\", \"result\": [{\"smile\": \"C/C=C/C=O\",\"value\": -3.9986901}, {\"smile\": \"Cc1ccccc1O\",\"value\": -3.8644881}, {\"smile\": \"C=Cc1ccccc1\",\"value\": -3.9767163}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The free energy of dissolution of the drugs are -3.9986901, -3.8644881 and -3.9767163 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the drug COc1ccc(cc1)c2cc3c(N[C@H]4CCCNC4)ncc(C(=O)N)c3s2 on a scale from low to high? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(cc1)c2cc3c(N[C@H]4CCCNC4)ncc(C(=O)N)c3s2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(cc1)c2cc3c(N[C@H]4CCCNC4)ncc(C(=O)N)c3s2\",\"value\": 1.82}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(cc1)c2cc3c(N[C@H]4CCCNC4)ncc(C(=O)N)c3s2 is 1.82."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How can the drug Cc1ccc(cc1)S(=O)(=O)NC(=O)NN2CCCCCC2's lipophilicity be predicted? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(cc1)S(=O)(=O)NC(=O)NN2CCCCCC2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1ccc(cc1)S(=O)(=O)NC(=O)NN2CCCCCC2\",\"value\": 0.01}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1ccc(cc1)S(=O)(=O)NC(=O)NN2CCCCCC2 is 0.01."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug COc1ccc(C(=O)Nc2c(Cl)cncc2Cl)c3cccnc13 behave in hydrophobic systems? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(C(=O)Nc2c(Cl)cncc2Cl)c3cccnc13\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(C(=O)Nc2c(Cl)cncc2Cl)c3cccnc13\",\"value\": 2.09}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(C(=O)Nc2c(Cl)cncc2Cl)c3cccnc13 is 2.09."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the lipophilicity of the compound CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)O)n4nc(cc4C)C(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)O)n4nc(cc4C)C(F)(F)F\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)O)n4nc(cc4C)C(F)(F)F\",\"value\": -0.88}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)O)n4nc(cc4C)C(F)(F)F is -0.88."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug NC(=O)c1cccc(OC2CCN(Cc3ccccc3)CC2)c1's solubility in oil-based environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1cccc(OC2CCN(Cc3ccccc3)CC2)c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)c1cccc(OC2CCN(Cc3ccccc3)CC2)c1\",\"value\": 1.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)c1cccc(OC2CCN(Cc3ccccc3)CC2)c1 is 1.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the solubility of the drug Clc1c(NCC2CCNCC2)nccc1C(=O)NCC34CC5CC(CC(C5)C3)C4 in lipid-rich environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1c(NCC2CCNCC2)nccc1C(=O)NCC34CC5CC(CC(C5)C3)C4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1c(NCC2CCNCC2)nccc1C(=O)NCC34CC5CC(CC(C5)C3)C4\",\"value\": 0.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1c(NCC2CCNCC2)nccc1C(=O)NCC34CC5CC(CC(C5)C3)C4 is 0.6."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CNS(=O)(=O)c1ccc2C(=CNC(=O)c2c1)C(=O)NC[C@@H](O)CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4's likelihood of being soluble in fats? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CNS(=O)(=O)c1ccc2C(=CNC(=O)c2c1)C(=O)NC[C@@H](O)CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CNS(=O)(=O)c1ccc2C(=CNC(=O)c2c1)C(=O)NC[C@@H](O)CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4\",\"value\": 2.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CNS(=O)(=O)c1ccc2C(=CNC(=O)c2c1)C(=O)NC[C@@H](O)CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4 is 2.77."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the molecule CCCSc1c(cnn1C)C(=O)NC2CCCCC2 to partition into lipophilic areas? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCSc1c(cnn1C)C(=O)NC2CCCCC2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCSc1c(cnn1C)C(=O)NC2CCCCC2\",\"value\": 2.99}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCCSc1c(cnn1C)C(=O)NC2CCCCC2 is 2.99."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the molecule COc1cc2ncnc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)c2cc1OCCN6CCOCC6's potential for lipid dissolution. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)c2cc1OCCN6CCOCC6\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc2ncnc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)c2cc1OCCN6CCOCC6\",\"value\": 3.19}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc2ncnc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)c2cc1OCCN6CCOCC6 is 3.19."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug Clc1ccc(cc1)c2oc(cc2)C(=O)Nc3ccc(cc3)N4CCNCC4 behave in lipid phases? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(cc1)c2oc(cc2)C(=O)Nc3ccc(cc3)N4CCNCC4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1ccc(cc1)c2oc(cc2)C(=O)Nc3ccc(cc3)N4CCNCC4\",\"value\": 2.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1ccc(cc1)c2oc(cc2)C(=O)Nc3ccc(cc3)N4CCNCC4 is 2.4."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug NC(=N)NCCC[C@H](NC(=O)COCC(c1ccccc1)c2ccccc2)C(=O)O's solubility in lipid and nonpolar solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=N)NCCC[C@H](NC(=O)COCC(c1ccccc1)c2ccccc2)C(=O)O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=N)NCCC[C@H](NC(=O)COCC(c1ccccc1)c2ccccc2)C(=O)O\",\"value\": 0.18}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=N)NCCC[C@H](NC(=O)COCC(c1ccccc1)c2ccccc2)C(=O)O is 0.18."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the lipophilicity of the drug CCN1CCN(CC1)C(=O)c2cc3cc(F)ccc3[nH]2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN1CCN(CC1)C(=O)c2cc3cc(F)ccc3[nH]2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN1CCN(CC1)C(=O)c2cc3cc(F)ccc3[nH]2\",\"value\": 2.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN1CCN(CC1)C(=O)c2cc3cc(F)ccc3[nH]2 is 2.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how lipophilic the compound C1Oc2ccccc2C3=NN(CC13)c4ccccc4 is. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1Oc2ccccc2C3=NN(CC13)c4ccccc4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C1Oc2ccccc2C3=NN(CC13)c4ccccc4\",\"value\": 4.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C1Oc2ccccc2C3=NN(CC13)c4ccccc4 is 4.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how well the molecule OC(=O)c1ccc(NC(=O)c2cc(OCc3ccccc3)cc(OCc4ccccc4)c2)nc1 mixes with lipids. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC(=O)c1ccc(NC(=O)c2cc(OCc3ccccc3)cc(OCc4ccccc4)c2)nc1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC(=O)c1ccc(NC(=O)c2cc(OCc3ccccc3)cc(OCc4ccccc4)c2)nc1\",\"value\": 2.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC(=O)c1ccc(NC(=O)c2cc(OCc3ccccc3)cc(OCc4ccccc4)c2)nc1 is 2.7."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug CC(C)CNc1nc(nc(N2CCNCC2)c1N)C#N's distribution in lipid-based systems. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CNc1nc(nc(N2CCNCC2)c1N)C#N\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)CNc1nc(nc(N2CCNCC2)c1N)C#N\",\"value\": 0.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)CNc1nc(nc(N2CCNCC2)c1N)C#N is 0.7."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how lipophilic the molecule Cc1[nH]c(\\C=C\\2/C(=O)Nc3ccc(F)cc23)c(C)c1C(=O)NC[C@H](O)CN4CCOCC4 is. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(\\C=C\\2/C(=O)Nc3ccc(F)cc23)c(C)c1C(=O)NC[C@H](O)CN4CCOCC4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1[nH]c(\\C=C\\2/C(=O)Nc3ccc(F)cc23)c(C)c1C(=O)NC[C@H](O)CN4CCOCC4\",\"value\": 2.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1[nH]c(\\C=C\\2/C(=O)Nc3ccc(F)cc23)c(C)c1C(=O)NC[C@H](O)CN4CCOCC4 is 2.22."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CC(C)N1CCN(CC1)C(=O)NCc2ccc(Cl)c(Cl)c2's polarity affect its lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1CCN(CC1)C(=O)NCc2ccc(Cl)c(Cl)c2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)N1CCN(CC1)C(=O)NCc2ccc(Cl)c(Cl)c2\",\"value\": 2.75}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)N1CCN(CC1)C(=O)NCc2ccc(Cl)c(Cl)c2 is 2.75."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the lipid solubility of the molecule CN(c1ccc(c(Cl)c1)c2cc(F)ccc2OCC(=O)O)S(=O)(=O)C? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(c1ccc(c(Cl)c1)c2cc(F)ccc2OCC(=O)O)S(=O)(=O)C\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(c1ccc(c(Cl)c1)c2cc(F)ccc2OCC(=O)O)S(=O)(=O)C\",\"value\": -1.17}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(c1ccc(c(Cl)c1)c2cc(F)ccc2OCC(=O)O)S(=O)(=O)C is -1.17."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the compound C[C@@H](c1ncncc1F)[C@](O)(Cn2cncn2)c3ccc(F)cc3F behave in terms of lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](c1ncncc1F)[C@](O)(Cn2cncn2)c3ccc(F)cc3F\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@@H](c1ncncc1F)[C@](O)(Cn2cncn2)c3ccc(F)cc3F\",\"value\": 1.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@@H](c1ncncc1F)[C@](O)(Cn2cncn2)c3ccc(F)cc3F is 1.7."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how the drug O[C@H](CNC(=O)c1cccc(c1)C#N)CN2CCC(CC2)Oc3ccc(Cl)c(Cl)c3 would behave in fat-rich environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O[C@H](CNC(=O)c1cccc(c1)C#N)CN2CCC(CC2)Oc3ccc(Cl)c(Cl)c3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O[C@H](CNC(=O)c1cccc(c1)C#N)CN2CCC(CC2)Oc3ccc(Cl)c(Cl)c3\",\"value\": 3.48}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O[C@H](CNC(=O)c1cccc(c1)C#N)CN2CCC(CC2)Oc3ccc(Cl)c(Cl)c3 is 3.48."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CNS(=O)(=O)Cc1ccc2[nH]cc(CCN(C)C)c2c1's tendency to interact with fatty substances? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CNS(=O)(=O)Cc1ccc2[nH]cc(CCN(C)C)c2c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CNS(=O)(=O)Cc1ccc2[nH]cc(CCN(C)C)c2c1\",\"value\": -0.96}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CNS(=O)(=O)Cc1ccc2[nH]cc(CCN(C)C)c2c1 is -0.96."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strongly does the drug CCC(N(CCCN)C(=O)c1ccc(C)cc1)C2=Nc3ccn(C)c3C(=O)N2Cc4ccccc4 interact with lipid layers? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(N(CCCN)C(=O)c1ccc(C)cc1)C2=Nc3ccn(C)c3C(=O)N2Cc4ccccc4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCC(N(CCCN)C(=O)c1ccc(C)cc1)C2=Nc3ccn(C)c3C(=O)N2Cc4ccccc4\",\"value\": 1.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCC(N(CCCN)C(=O)c1ccc(C)cc1)C2=Nc3ccn(C)c3C(=O)N2Cc4ccccc4 is 1.8."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the lipophilicity for the compound OCC[C@]1(CCCNC1)c2oc3ccccc3c2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCC[C@]1(CCCNC1)c2oc3ccccc3c2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OCC[C@]1(CCCNC1)c2oc3ccccc3c2\",\"value\": 0.06}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OCC[C@]1(CCCNC1)c2oc3ccccc3c2 is 0.06."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much of the drug CC(C)CN1C(=O)N(C)C(=O)c2c1sc(Cc3ccccc3C(F)(F)F)c2C(=O)N(C)C will dissolve in lipophilic solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CN1C(=O)N(C)C(=O)c2c1sc(Cc3ccccc3C(F)(F)F)c2C(=O)N(C)C\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)CN1C(=O)N(C)C(=O)c2c1sc(Cc3ccccc3C(F)(F)F)c2C(=O)N(C)C\",\"value\": 2.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)CN1C(=O)N(C)C(=O)c2c1sc(Cc3ccccc3C(F)(F)F)c2C(=O)N(C)C is 2.9."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the molecule Nc1nccc(n1)n2ccc3ccccc23's behavior in lipophilic conditions. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1nccc(n1)n2ccc3ccccc23\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Nc1nccc(n1)n2ccc3ccccc23\",\"value\": 2.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Nc1nccc(n1)n2ccc3ccccc23 is 2.71."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4ccccn34)C(CO)CO's affinity for lipid phases in biological systems? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4ccccn34)C(CO)CO\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4ccccn34)C(CO)CO\",\"value\": 3.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4ccccn34)C(CO)CO is 3.1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the lipophilicity of the compound CN1CCN(CC1)C(=O)c2cccc(CC3=NNC(=O)c4ccccc34)c2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(CC1)C(=O)c2cccc(CC3=NNC(=O)c4ccccc34)c2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN1CCN(CC1)C(=O)c2cccc(CC3=NNC(=O)c4ccccc34)c2\",\"value\": 0.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN1CCN(CC1)C(=O)c2cccc(CC3=NNC(=O)c4ccccc34)c2 is 0.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the lipophilicity of the compound Nc1nc(Cl)nc(NCc2ccccc2)n1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(Cl)nc(NCc2ccccc2)n1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Nc1nc(Cl)nc(NCc2ccccc2)n1\",\"value\": 2.26}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Nc1nc(Cl)nc(NCc2ccccc2)n1 is 2.26."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the compound Cc1c(Cl)ccc(OC2CCN(C[C@H](O)CNC(=O)C3=CNC(=O)c4cc(ccc34)S(=O)(=O)C)CC2)c1Cl's affinity for lipophilic environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1c(Cl)ccc(OC2CCN(C[C@H](O)CNC(=O)C3=CNC(=O)c4cc(ccc34)S(=O)(=O)C)CC2)c1Cl\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1c(Cl)ccc(OC2CCN(C[C@H](O)CNC(=O)C3=CNC(=O)c4cc(ccc34)S(=O)(=O)C)CC2)c1Cl\",\"value\": 2.83}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1c(Cl)ccc(OC2CCN(C[C@H](O)CNC(=O)C3=CNC(=O)c4cc(ccc34)S(=O)(=O)C)CC2)c1Cl is 2.83."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug CCn1cc(NC(=O)Cc2ccc(Oc3ccnc4cc(F)ccc34)cc2OC)cn1 partition into lipid phases? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCn1cc(NC(=O)Cc2ccc(Oc3ccnc4cc(F)ccc34)cc2OC)cn1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCn1cc(NC(=O)Cc2ccc(Oc3ccnc4cc(F)ccc34)cc2OC)cn1\",\"value\": 3.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCn1cc(NC(=O)Cc2ccc(Oc3ccnc4cc(F)ccc34)cc2OC)cn1 is 3.93."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug Cc1cnc(Oc2ccc(cc2)n3ccnc3)nc1Oc4ccc5OCOc5c4's lipid affinity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cnc(Oc2ccc(cc2)n3ccnc3)nc1Oc4ccc5OCOc5c4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1cnc(Oc2ccc(cc2)n3ccnc3)nc1Oc4ccc5OCOc5c4\",\"value\": 3.56}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1cnc(Oc2ccc(cc2)n3ccnc3)nc1Oc4ccc5OCOc5c4 is 3.56."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the drug NC(=O)c1cccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1 to accumulate in lipophilic areas? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1cccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)c1cccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1\",\"value\": 1.61}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)c1cccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1 is 1.61."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the molecule C[C@]1(CCCN1c2nc(Nc3cc([nH]n3)C4CC4)c5cccn5n2)C(=O)Nc6ccc(F)nc6's lipid affinity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@]1(CCCN1c2nc(Nc3cc([nH]n3)C4CC4)c5cccn5n2)C(=O)Nc6ccc(F)nc6\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@]1(CCCN1c2nc(Nc3cc([nH]n3)C4CC4)c5cccn5n2)C(=O)Nc6ccc(F)nc6\",\"value\": 3.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@]1(CCCN1c2nc(Nc3cc([nH]n3)C4CC4)c5cccn5n2)C(=O)Nc6ccc(F)nc6 is 3.16."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the compound COc1cc(Nc2cc(Nc3c(Cl)ccc4OCOc34)ncn2)cc(OC)c1OC's lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2cc(Nc3c(Cl)ccc4OCOc34)ncn2)cc(OC)c1OC\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(Nc2cc(Nc3c(Cl)ccc4OCOc34)ncn2)cc(OC)c1OC\",\"value\": 3.07}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(Nc2cc(Nc3c(Cl)ccc4OCOc34)ncn2)cc(OC)c1OC is 3.07."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug COc1ccc(CNC(=O)CSc2nc3cccnc3s2)cc1 interact with nonpolar substances? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(CNC(=O)CSc2nc3cccnc3s2)cc1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(CNC(=O)CSc2nc3cccnc3s2)cc1\",\"value\": 2.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(CNC(=O)CSc2nc3cccnc3s2)cc1 is 2.5."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug COc1ccc2nc(C)cc(OCC(=O)Nc3ccccc3OC)c2c1's tendency to dissolve in fat or oils. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2nc(C)cc(OCC(=O)Nc3ccccc3OC)c2c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc2nc(C)cc(OCC(=O)Nc3ccccc3OC)c2c1\",\"value\": 3.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc2nc(C)cc(OCC(=O)Nc3ccccc3OC)c2c1 is 3.9."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CCC(Oc1ccc(cc1c2ccc(cc2C)S(=O)(=O)CC)C(F)(F)F)C(=O)O's behavior in lipid systems? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(Oc1ccc(cc1c2ccc(cc2C)S(=O)(=O)CC)C(F)(F)F)C(=O)O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCC(Oc1ccc(cc1c2ccc(cc2C)S(=O)(=O)CC)C(F)(F)F)C(=O)O\",\"value\": 0.31}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCC(Oc1ccc(cc1c2ccc(cc2C)S(=O)(=O)CC)C(F)(F)F)C(=O)O is 0.31."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the drug COc1ccc2c(Oc3ccc(CC(=O)Nc4cn(C)nc4C)c(OC)c3)ccnc2c1 to exhibit lipophilic behavior? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2c(Oc3ccc(CC(=O)Nc4cn(C)nc4C)c(OC)c3)ccnc2c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc2c(Oc3ccc(CC(=O)Nc4cn(C)nc4C)c(OC)c3)ccnc2c1\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc2c(Oc3ccc(CC(=O)Nc4cn(C)nc4C)c(OC)c3)ccnc2c1 is 3.5."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the molecule COc1cc(ccc1Nc2ncc3N(C)C(=O)CCN(C4CCCCC4)c3n2)C(=O)NC5CCN(C)CC5 compared to other substances? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(ccc1Nc2ncc3N(C)C(=O)CCN(C4CCCCC4)c3n2)C(=O)NC5CCN(C)CC5\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(ccc1Nc2ncc3N(C)C(=O)CCN(C4CCCCC4)c3n2)C(=O)NC5CCN(C)CC5\",\"value\": 2.87}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(ccc1Nc2ncc3N(C)C(=O)CCN(C4CCCCC4)c3n2)C(=O)NC5CCN(C)CC5 is 2.87."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CN1CCC(CC1)NC(=O)c2ccc(Oc3ccc(cc3)C#C[C@]4(O)CN5CCC4CC5)cc2 behave in nonpolar environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(CC1)NC(=O)c2ccc(Oc3ccc(cc3)C#C[C@]4(O)CN5CCC4CC5)cc2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN1CCC(CC1)NC(=O)c2ccc(Oc3ccc(cc3)C#C[C@]4(O)CN5CCC4CC5)cc2\",\"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN1CCC(CC1)NC(=O)c2ccc(Oc3ccc(cc3)C#C[C@]4(O)CN5CCC4CC5)cc2 is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug Clc1ccc(CNc2nc(nc3ncccc23)N4CCN(CC4)C(=O)[C@H]5CCCCN5)c(Cl)c1 behave in fat-based solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(CNc2nc(nc3ncccc23)N4CCN(CC4)C(=O)[C@H]5CCCCN5)c(Cl)c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1ccc(CNc2nc(nc3ncccc23)N4CCN(CC4)C(=O)[C@H]5CCCCN5)c(Cl)c1\",\"value\": 2.27}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1ccc(CNc2nc(nc3ncccc23)N4CCN(CC4)C(=O)[C@H]5CCCCN5)c(Cl)c1 is 2.27."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the lipophilicity of the drug CC(=O)C1=CN(Cc2ccccc2)C(=O)N(Cc3ccc(F)cc3)C1=O? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)C1=CN(Cc2ccccc2)C(=O)N(Cc3ccc(F)cc3)C1=O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(=O)C1=CN(Cc2ccccc2)C(=O)N(Cc3ccc(F)cc3)C1=O\",\"value\": 3.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(=O)C1=CN(Cc2ccccc2)C(=O)N(Cc3ccc(F)cc3)C1=O is 3.2."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How soluble is the molecule COc1ccccc1CNCCc2cccc(CCNC[C@H](O)c3ccc(O)c4NC(=O)Sc34)c2 in lipophilic environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccccc1CNCCc2cccc(CCNC[C@H](O)c3ccc(O)c4NC(=O)Sc34)c2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccccc1CNCCc2cccc(CCNC[C@H](O)c3ccc(O)c4NC(=O)Sc34)c2\",\"value\": 0.91}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccccc1CNCCc2cccc(CCNC[C@H](O)c3ccc(O)c4NC(=O)Sc34)c2 is 0.91."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how lipophilic the compound CCN(CC)C(=O)c1ccc(cc1)C(=C2CCN(Cc3ccc(F)cc3)CC2)c4ccccn4 is. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)C(=O)c1ccc(cc1)C(=C2CCN(Cc3ccc(F)cc3)CC2)c4ccccn4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(CC)C(=O)c1ccc(cc1)C(=C2CCN(Cc3ccc(F)cc3)CC2)c4ccccn4\",\"value\": 3.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN(CC)C(=O)c1ccc(cc1)C(=C2CCN(Cc3ccc(F)cc3)CC2)c4ccccn4 is 3.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the drug CC1=CN([C@H]2CCCN(C2)S(=O)(=O)c3ccc(O)c(Oc4cc(F)cc(Cl)c4)c3)C(=O)NC1=O based on its chemical structure? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=CN([C@H]2CCCN(C2)S(=O)(=O)c3ccc(O)c(Oc4cc(F)cc(Cl)c4)c3)C(=O)NC1=O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC1=CN([C@H]2CCCN(C2)S(=O)(=O)c3ccc(O)c(Oc4cc(F)cc(Cl)c4)c3)C(=O)NC1=O\",\"value\": 2.35}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC1=CN([C@H]2CCCN(C2)S(=O)(=O)c3ccc(O)c(Oc4cc(F)cc(Cl)c4)c3)C(=O)NC1=O is 2.35."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C's potential to be absorbed into lipid environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C\",\"value\": 3.21}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN(C1CCN(CCC(c2ccc(F)cc2)c3ccc(F)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C is 3.21."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug OCCNCc1cc(Br)ccc1OCc2ccc(Cl)cc2's affinity for lipid tissues or areas. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCCNCc1cc(Br)ccc1OCc2ccc(Cl)cc2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OCCNCc1cc(Br)ccc1OCc2ccc(Cl)cc2\",\"value\": 2.98}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OCCNCc1cc(Br)ccc1OCc2ccc(Cl)cc2 is 2.98."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)S(=O)(=O)NC(=O)NC3CCCCC3 interact with nonpolar solvents or lipids? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)S(=O)(=O)NC(=O)NC3CCCCC3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)S(=O)(=O)NC(=O)NC3CCCCC3\",\"value\": 2.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)S(=O)(=O)NC(=O)NC3CCCCC3 is 2.23."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess the lipophilicity of the molecule NC1(CCC1)c2ccc(cc2)c3ncc4cccn4c3c5ccccc5? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC1(CCC1)c2ccc(cc2)c3ncc4cccn4c3c5ccccc5\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC1(CCC1)c2ccc(cc2)c3ncc4cccn4c3c5ccccc5\",\"value\": 2.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC1(CCC1)c2ccc(cc2)c3ncc4cccn4c3c5ccccc5 is 2.5."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the molecule OC[C@H](O)C(=O)N1CCC(=CC1)c2ccc(cc2F)N3C[C@H](COc4ccon4)OC3=O's solubility in lipophilic environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC[C@H](O)C(=O)N1CCC(=CC1)c2ccc(cc2F)N3C[C@H](COc4ccon4)OC3=O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC[C@H](O)C(=O)N1CCC(=CC1)c2ccc(cc2F)N3C[C@H](COc4ccon4)OC3=O\",\"value\": 1.09}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC[C@H](O)C(=O)N1CCC(=CC1)c2ccc(cc2F)N3C[C@H](COc4ccon4)OC3=O is 1.09."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)cn1 and C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)c4noc(n4)C5CC5 dissolve in lipid environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)cn1\", \"C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)c4noc(n4)C5CC5\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)cn1\",\"value\": 1.51}, {\"smile\": \"C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)c4noc(n4)C5CC5\",\"value\": 3.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)cn1 and C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)c4noc(n4)C5CC5 are 1.51 and 3.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug CCc1nc(N)nc(N)c1c2ccc(Cl)cc2 and COc1cc(OC)c(cc1NC(=O)C)S(=O)(=O)N2C(C)Cc3ccccc23 dissolve in nonpolar or lipid-rich solutions? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCc1nc(N)nc(N)c1c2ccc(Cl)cc2\", \"COc1cc(OC)c(cc1NC(=O)C)S(=O)(=O)N2C(C)Cc3ccccc23\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCc1nc(N)nc(N)c1c2ccc(Cl)cc2\",\"value\": 2.31}, {\"smile\": \"COc1cc(OC)c(cc1NC(=O)C)S(=O)(=O)N2C(C)Cc3ccccc23\",\"value\": 1.95}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCc1nc(N)nc(N)c1c2ccc(Cl)cc2 and COc1cc(OC)c(cc1NC(=O)C)S(=O)(=O)N2C(C)Cc3ccccc23 are 2.31 and 1.95 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the molecule CN(C)CC(O)COc1ccc(Nc2cc(ncn2)N(CCC#N)c3cc(Cl)ccc3Cl)cc1 and C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3NC(=O)C=Nc13's tendency to be soluble in lipids? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC(O)COc1ccc(Nc2cc(ncn2)N(CCC#N)c3cc(Cl)ccc3Cl)cc1\", \"C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3NC(=O)C=Nc13\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(C)CC(O)COc1ccc(Nc2cc(ncn2)N(CCC#N)c3cc(Cl)ccc3Cl)cc1\",\"value\": 2.1}, {\"smile\": \"C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3NC(=O)C=Nc13\",\"value\": 3.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(C)CC(O)COc1ccc(Nc2cc(ncn2)N(CCC#N)c3cc(Cl)ccc3Cl)cc1 and C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3NC(=O)C=Nc13 are 2.1 and 3.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the drug Cn1cc(cc1c2c3C(=O)N(CC#CCn4cncn4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67)C#N and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)NCc3ccncc3)C(=O)C(F)(F)F in a biological system? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(cc1c2c3C(=O)N(CC#CCn4cncn4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67)C#N\", \"CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)NCc3ccncc3)C(=O)C(F)(F)F\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cn1cc(cc1c2c3C(=O)N(CC#CCn4cncn4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67)C#N\",\"value\": 3.43}, {\"smile\": \"CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)NCc3ccncc3)C(=O)C(F)(F)F\",\"value\": 2.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cn1cc(cc1c2c3C(=O)N(CC#CCn4cncn4)C(=O)N(CC5CC5)c3nn2Cc6ccnc7ccc(Cl)cc67)C#N and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)NCc3ccncc3)C(=O)C(F)(F)F are 3.43 and 2.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how the drug COc1cc2c(Nc3ccc(Cl)cc3F)ncnc2cc1OCC4CCNCC4 and Clc1ccc(cc1C(=O)NCC2CCCCC2)N3N=CC(=O)NC3=O interacts with nonpolar solvents. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Cl)cc3F)ncnc2cc1OCC4CCNCC4\", \"Clc1ccc(cc1C(=O)NCC2CCCCC2)N3N=CC(=O)NC3=O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Cl)cc3F)ncnc2cc1OCC4CCNCC4\",\"value\": 1.1}, {\"smile\": \"Clc1ccc(cc1C(=O)NCC2CCCCC2)N3N=CC(=O)NC3=O\",\"value\": 1.26}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc2c(Nc3ccc(Cl)cc3F)ncnc2cc1OCC4CCNCC4 and Clc1ccc(cc1C(=O)NCC2CCCCC2)N3N=CC(=O)NC3=O are 1.1 and 1.26 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the extent of the drug OC(=O)c1ccccc1C2=C3C=CC(=O)C=C3Oc4cc(O)ccc24 and Oc1cc(O)c2C(=O)C(=COc2c1)c3ccc4OCOc4c3's lipophilic behavior? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC(=O)c1ccccc1C2=C3C=CC(=O)C=C3Oc4cc(O)ccc24\", \"Oc1cc(O)c2C(=O)C(=COc2c1)c3ccc4OCOc4c3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC(=O)c1ccccc1C2=C3C=CC(=O)C=C3Oc4cc(O)ccc24\",\"value\": 0.0}, {\"smile\": \"Oc1cc(O)c2C(=O)C(=COc2c1)c3ccc4OCOc4c3\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC(=O)c1ccccc1C2=C3C=CC(=O)C=C3Oc4cc(O)ccc24 and Oc1cc(O)c2C(=O)C(=COc2c1)c3ccc4OCOc4c3 are 0.0 and 3.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CC(C)N1CCC[C@H](CN2C(=Nc3ccc(cc3C2=O)c4ccc(Cl)cc4)C)C1 and CC(=O)NCc1ccc(CN2CCN(CC2)c3ccc(F)cc3F)cc1's solubility in fat or lipid environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1CCC[C@H](CN2C(=Nc3ccc(cc3C2=O)c4ccc(Cl)cc4)C)C1\", \"CC(=O)NCc1ccc(CN2CCN(CC2)c3ccc(F)cc3F)cc1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)N1CCC[C@H](CN2C(=Nc3ccc(cc3C2=O)c4ccc(Cl)cc4)C)C1\",\"value\": 3.3}, {\"smile\": \"CC(=O)NCc1ccc(CN2CCN(CC2)c3ccc(F)cc3F)cc1\",\"value\": 2.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)N1CCC[C@H](CN2C(=Nc3ccc(cc3C2=O)c4ccc(Cl)cc4)C)C1 and CC(=O)NCc1ccc(CN2CCN(CC2)c3ccc(F)cc3F)cc1 are 3.3 and 2.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug Clc1cccc(CN2C=CN3C(=O)C=C(N=C23)N4CCOCC4)c1Cl and COc1cc(O)ccc1\\C=C\\2/CCCN=C2c3cccnc3's ability to dissolve in hydrophobic solvents. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cccc(CN2C=CN3C(=O)C=C(N=C23)N4CCOCC4)c1Cl\", \"COc1cc(O)ccc1\\C=C\\2/CCCN=C2c3cccnc3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1cccc(CN2C=CN3C(=O)C=C(N=C23)N4CCOCC4)c1Cl\",\"value\": 3.3}, {\"smile\": \"COc1cc(O)ccc1\\C=C\\2/CCCN=C2c3cccnc3\",\"value\": 2.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1cccc(CN2C=CN3C(=O)C=C(N=C23)N4CCOCC4)c1Cl and COc1cc(O)ccc1\\C=C\\2/CCCN=C2c3cccnc3 are 3.3 and 2.2 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimate of the compound OC1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56 and O=C1COc2ccccc2N1CCN3CCC(CC3)NCc4cc5OCCOc5cn4's lipophilicity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56\", \"O=C1COc2ccccc2N1CCN3CCC(CC3)NCc4cc5OCCOc5cn4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56\",\"value\": 1.89}, {\"smile\": \"O=C1COc2ccccc2N1CCN3CCC(CC3)NCc4cc5OCCOc5cn4\",\"value\": 1.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56 and O=C1COc2ccccc2N1CCN3CCC(CC3)NCc4cc5OCCOc5cn4 are 1.89 and 1.04 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the compound COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(F)ccn34)N5CCN(CC5)C(=O)C and CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N4CCCN(C)CC4)ncc2F's lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(F)ccn34)N5CCN(CC5)C(=O)C\", \"CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N4CCCN(C)CC4)ncc2F\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(F)ccn34)N5CCN(CC5)C(=O)C\",\"value\": 3.7}, {\"smile\": \"CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N4CCCN(C)CC4)ncc2F\",\"value\": 2.02}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(F)ccn34)N5CCN(CC5)C(=O)C and CC(C)n1c(C)ncc1c2nc(Nc3ccc(cc3)C(=O)N4CCCN(C)CC4)ncc2F are 3.7 and 2.02 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the partitioning of the drug O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc5ncccc5s4 and C[C@]12N[C@H](Cc3ccccc13)c4ccccc24 in lipid and aqueous environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc5ncccc5s4\", \"C[C@]12N[C@H](Cc3ccccc13)c4ccccc24\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc5ncccc5s4\",\"value\": 1.4}, {\"smile\": \"C[C@]12N[C@H](Cc3ccccc13)c4ccccc24\",\"value\": 1.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc5ncccc5s4 and C[C@]12N[C@H](Cc3ccccc13)c4ccccc24 are 1.4 and 1.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3[nH]c(N)nc13 and CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2cc(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O's lipophilicity based on its chemical structure. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3[nH]c(N)nc13\", \"CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2cc(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3[nH]c(N)nc13\",\"value\": 3.22}, {\"smile\": \"CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2cc(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O\",\"value\": 2.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3[nH]c(N)nc13 and CNC(=O)C1(CCN(CC[C@H](CN(C)C(=O)c2cc(cc3ccccc23)C#N)c4ccc(Cl)c(Cl)c4)CC1)N5CCCCC5=O are 3.22 and 2.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity of the drug CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)NC)n4ccc(n4)C(F)(F)F and CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C for lipid-based media. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)NC)n4ccc(n4)C(F)(F)F\", \"CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)NC)n4ccc(n4)C(F)(F)F\",\"value\": 2.21}, {\"smile\": \"CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C\",\"value\": -0.27}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCNC(=O)c1cc2c(c(cnc2[nH]1)c3cncc(c3)C(=O)NC)n4ccc(n4)C(F)(F)F and CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C are 2.21 and -0.27 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the lipophilicity of the compound COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(Cl)ccn34)N5CCN(CC5)C(=O)C and CC(C)CCOc1ccc(N)cc1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(Cl)ccn34)N5CCN(CC5)C(=O)C\", \"CC(C)CCOc1ccc(N)cc1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(Cl)ccn34)N5CCN(CC5)C(=O)C\",\"value\": 4.2}, {\"smile\": \"CC(C)CCOc1ccc(N)cc1\",\"value\": 2.67}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4cc(Cl)ccn34)N5CCN(CC5)C(=O)C and CC(C)CCOc1ccc(N)cc1 are 4.2 and 2.67 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the lipophilicity of the drug NC(=O)C1CCN(CC(=O)Nc2ccc3OCOc3c2)CC1 and CCN(CC)C(=O)c1ccc(cc1)C(=C2CCNCC2)c3ccccc3 based on its molecular properties? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)C1CCN(CC(=O)Nc2ccc3OCOc3c2)CC1\", \"CCN(CC)C(=O)c1ccc(cc1)C(=C2CCNCC2)c3ccccc3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)C1CCN(CC(=O)Nc2ccc3OCOc3c2)CC1\",\"value\": 0.4}, {\"smile\": \"CCN(CC)C(=O)c1ccc(cc1)C(=C2CCNCC2)c3ccccc3\",\"value\": 1.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)C1CCN(CC(=O)Nc2ccc3OCOc3c2)CC1 and CCN(CC)C(=O)c1ccc(cc1)C(=C2CCNCC2)c3ccccc3 are 0.4 and 1.2 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much of the drug CN(C)c1ccc(cc1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(C)c4Cl and OC(=O)[C@H](Cc1ccc(F)cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2 will dissolve in lipophilic conditions? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)c1ccc(cc1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(C)c4Cl\", \"OC(=O)[C@H](Cc1ccc(F)cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(C)c1ccc(cc1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(C)c4Cl\",\"value\": 2.0}, {\"smile\": \"OC(=O)[C@H](Cc1ccc(F)cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2\",\"value\": 2.98}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(C)c1ccc(cc1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(C)c4Cl and OC(=O)[C@H](Cc1ccc(F)cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2 are 2.0 and 2.98 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug Nc1ccc2c(c1)C(=O)C(=O)c3cc(N)ccc23 and COc1ccc(cc1)c2nc(nc(O)c2C#N)N3CCOCC3's solubility in lipid environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1ccc2c(c1)C(=O)C(=O)c3cc(N)ccc23\", \"COc1ccc(cc1)c2nc(nc(O)c2C#N)N3CCOCC3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Nc1ccc2c(c1)C(=O)C(=O)c3cc(N)ccc23\",\"value\": 1.72}, {\"smile\": \"COc1ccc(cc1)c2nc(nc(O)c2C#N)N3CCOCC3\",\"value\": 0.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Nc1ccc2c(c1)C(=O)C(=O)c3cc(N)ccc23 and COc1ccc(cc1)c2nc(nc(O)c2C#N)N3CCOCC3 are 1.72 and 0.2 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CCCSc1ncccc1C(=O)N[C@@H]2[C@@H]3CC4C[C@H]2C[C@@](O)(C4)C3 and CC1=CC2=NNC(=O)N2c3cc(ccc13)c4cn[nH]c4 behave in lipid-dominant systems? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCSc1ncccc1C(=O)N[C@@H]2[C@@H]3CC4C[C@H]2C[C@@](O)(C4)C3\", \"CC1=CC2=NNC(=O)N2c3cc(ccc13)c4cn[nH]c4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCSc1ncccc1C(=O)N[C@@H]2[C@@H]3CC4C[C@H]2C[C@@](O)(C4)C3\",\"value\": 2.27}, {\"smile\": \"CC1=CC2=NNC(=O)N2c3cc(ccc13)c4cn[nH]c4\",\"value\": 2.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCCSc1ncccc1C(=O)N[C@@H]2[C@@H]3CC4C[C@H]2C[C@@](O)(C4)C3 and CC1=CC2=NNC(=O)N2c3cc(ccc13)c4cn[nH]c4 are 2.27 and 2.13 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much does the drug Nc1ccc2nc(sc2c1)c3ccc(O)cc3 and CCN(CCCOCCOCCc1ccccc1)CCc2ccc(O)c3nc(O)sc23 dissolve in lipid phases? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1ccc2nc(sc2c1)c3ccc(O)cc3\", \"CCN(CCCOCCOCCc1ccccc1)CCc2ccc(O)c3nc(O)sc23\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Nc1ccc2nc(sc2c1)c3ccc(O)cc3\",\"value\": 3.1}, {\"smile\": \"CCN(CCCOCCOCCc1ccccc1)CCc2ccc(O)c3nc(O)sc23\",\"value\": 2.45}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Nc1ccc2nc(sc2c1)c3ccc(O)cc3 and CCN(CCCOCCOCCc1ccccc1)CCc2ccc(O)c3nc(O)sc23 are 3.1 and 2.45 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate how the drug CCCc1c(cnn1c2ccccc2)C(=O)Nc3cc(C)on3 and CNc1nc(C)c(s1)c2nc(Nc3cccc(c3)N4CCNCC4)ncc2C#N interacts with lipophilic solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCc1c(cnn1c2ccccc2)C(=O)Nc3cc(C)on3\", \"CNc1nc(C)c(s1)c2nc(Nc3cccc(c3)N4CCNCC4)ncc2C#N\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCc1c(cnn1c2ccccc2)C(=O)Nc3cc(C)on3\",\"value\": 3.7}, {\"smile\": \"CNc1nc(C)c(s1)c2nc(Nc3cccc(c3)N4CCNCC4)ncc2C#N\",\"value\": 1.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCCc1c(cnn1c2ccccc2)C(=O)Nc3cc(C)on3 and CNc1nc(C)c(s1)c2nc(Nc3cccc(c3)N4CCNCC4)ncc2C#N are 3.7 and 1.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict how the drug O=C1C=C(N=C2N(Cc3cccc4ccccc34)C=CN12)N5CCOCC5 and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)Cc3cccc(c3)C(=O)O)C(=O)C(F)(F)F will interact with lipid molecules? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1C=C(N=C2N(Cc3cccc4ccccc34)C=CN12)N5CCOCC5\", \"CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)Cc3cccc(c3)C(=O)O)C(=O)C(F)(F)F\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C1C=C(N=C2N(Cc3cccc4ccccc34)C=CN12)N5CCOCC5\",\"value\": 3.3}, {\"smile\": \"CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)Cc3cccc(c3)C(=O)O)C(=O)C(F)(F)F\",\"value\": 0.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=C1C=C(N=C2N(Cc3cccc4ccccc34)C=CN12)N5CCOCC5 and CC(C)C(NC(=O)CN1C(=O)C(=CC=C1c2ccccc2)NC(=O)Cc3cccc(c3)C(=O)O)C(=O)C(F)(F)F are 3.3 and 0.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the degree of lipid solubility of the drug COc1ncc2N=CC(=O)N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)c2n1 and CCOC(=O)[C@H](Cc1ccc(cc1)[N+](=O)[O-])NC(=O)c2ccccc2? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ncc2N=CC(=O)N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)c2n1\", \"CCOC(=O)[C@H](Cc1ccc(cc1)[N+](=O)[O-])NC(=O)c2ccccc2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ncc2N=CC(=O)N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)c2n1\",\"value\": -0.14}, {\"smile\": \"CCOC(=O)[C@H](Cc1ccc(cc1)[N+](=O)[O-])NC(=O)c2ccccc2\",\"value\": 2.79}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ncc2N=CC(=O)N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)c2n1 and CCOC(=O)[C@H](Cc1ccc(cc1)[N+](=O)[O-])NC(=O)c2ccccc2 are -0.14 and 2.79 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CC(C)n1c(C)ncc1c2ccnc(Nc3ccccc3)n2 and COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)nc(N4CCOCC4)c2Cl)n[nH]1 partition between water and lipophilic phases? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)n1c(C)ncc1c2ccnc(Nc3ccccc3)n2\", \"COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)nc(N4CCOCC4)c2Cl)n[nH]1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)n1c(C)ncc1c2ccnc(Nc3ccccc3)n2\",\"value\": 3.58}, {\"smile\": \"COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)nc(N4CCOCC4)c2Cl)n[nH]1\",\"value\": 2.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)n1c(C)ncc1c2ccnc(Nc3ccccc3)n2 and COc1cc(Nc2nc(N[C@@H](C)c3ncc(F)cn3)nc(N4CCOCC4)c2Cl)n[nH]1 are 3.58 and 2.85 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the compound COc1cc(ccc1Nc2ncc(Br)c(n2)c3cnc4ccccn34)N5CCN(CC5)C(=O)C and COc1cc2ncnc(Nc3cccc(Cl)c3F)c2cc1CN(C)C4(CCN(C)CC4)C(=O)N's affinity for lipids. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(ccc1Nc2ncc(Br)c(n2)c3cnc4ccccn34)N5CCN(CC5)C(=O)C\", \"COc1cc2ncnc(Nc3cccc(Cl)c3F)c2cc1CN(C)C4(CCN(C)CC4)C(=O)N\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(ccc1Nc2ncc(Br)c(n2)c3cnc4ccccn34)N5CCN(CC5)C(=O)C\",\"value\": 3.7}, {\"smile\": \"COc1cc2ncnc(Nc3cccc(Cl)c3F)c2cc1CN(C)C4(CCN(C)CC4)C(=O)N\",\"value\": 2.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(ccc1Nc2ncc(Br)c(n2)c3cnc4ccccn34)N5CCN(CC5)C(=O)C and COc1cc2ncnc(Nc3cccc(Cl)c3F)c2cc1CN(C)C4(CCN(C)CC4)C(=O)N are 3.7 and 2.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the compound Oc1c2C(=O)NNC(=O)c2nc3cc(Cl)cc(Cl)c13 and CN1CCN(CC1)C2=Nc3cc(Cl)ccc3Nc4ccccc24's lipid solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1c2C(=O)NNC(=O)c2nc3cc(Cl)cc(Cl)c13\", \"CN1CCN(CC1)C2=Nc3cc(Cl)ccc3Nc4ccccc24\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Oc1c2C(=O)NNC(=O)c2nc3cc(Cl)cc(Cl)c13\",\"value\": 0.89}, {\"smile\": \"CN1CCN(CC1)C2=Nc3cc(Cl)ccc3Nc4ccccc24\",\"value\": 3.08}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Oc1c2C(=O)NNC(=O)c2nc3cc(Cl)cc(Cl)c13 and CN1CCN(CC1)C2=Nc3cc(Cl)ccc3Nc4ccccc24 are 0.89 and 3.08 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug Cc1ncc([N+](=O)[O-])n1CCO and COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4c(F)cccn34)N5CCN(CC5)C(=O)C dissolve in fats or oils? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ncc([N+](=O)[O-])n1CCO\", \"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4c(F)cccn34)N5CCN(CC5)C(=O)C\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1ncc([N+](=O)[O-])n1CCO\",\"value\": -0.11}, {\"smile\": \"COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4c(F)cccn34)N5CCN(CC5)C(=O)C\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1ncc([N+](=O)[O-])n1CCO and COc1cc(ccc1Nc2ncc(Cl)c(n2)c3cnc4c(F)cccn34)N5CCN(CC5)C(=O)C are -0.11 and 3.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CCN(c1ccnc(Nc2cc(cc(c2)N3CCOCC3)N4CCOCC4)n1)c5cc(CO)ccc5C and CN1C(=O)CC(C)(CCc2cccc(c2)c3ccccc3)N=C1N's solubility in nonpolar solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(c1ccnc(Nc2cc(cc(c2)N3CCOCC3)N4CCOCC4)n1)c5cc(CO)ccc5C\", \"CN1C(=O)CC(C)(CCc2cccc(c2)c3ccccc3)N=C1N\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(c1ccnc(Nc2cc(cc(c2)N3CCOCC3)N4CCOCC4)n1)c5cc(CO)ccc5C\",\"value\": 3.8}, {\"smile\": \"CN1C(=O)CC(C)(CCc2cccc(c2)c3ccccc3)N=C1N\",\"value\": 2.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN(c1ccnc(Nc2cc(cc(c2)N3CCOCC3)N4CCOCC4)n1)c5cc(CO)ccc5C and CN1C(=O)CC(C)(CCc2cccc(c2)c3ccccc3)N=C1N are 3.8 and 2.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you characterize the drug C[C@@H](CO)Oc1cc(Oc2ccc(cc2)S(=O)(=O)C)cc(c1)C(=O)Nc3nccs3 and CC(=O)Nc1ccc2ccn(c3cc(Nc4ccn(C)n4)n5ncc(C#N)c5n3)c2c1's lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](CO)Oc1cc(Oc2ccc(cc2)S(=O)(=O)C)cc(c1)C(=O)Nc3nccs3\", \"CC(=O)Nc1ccc2ccn(c3cc(Nc4ccn(C)n4)n5ncc(C#N)c5n3)c2c1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@@H](CO)Oc1cc(Oc2ccc(cc2)S(=O)(=O)C)cc(c1)C(=O)Nc3nccs3\",\"value\": 2.63}, {\"smile\": \"CC(=O)Nc1ccc2ccn(c3cc(Nc4ccn(C)n4)n5ncc(C#N)c5n3)c2c1\",\"value\": 3.57}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@@H](CO)Oc1cc(Oc2ccc(cc2)S(=O)(=O)C)cc(c1)C(=O)Nc3nccs3 and CC(=O)Nc1ccc2ccn(c3cc(Nc4ccn(C)n4)n5ncc(C#N)c5n3)c2c1 are 2.63 and 3.57 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much affinity does the drug Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4cc(n[nH]4)C(F)(F)F and CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)N4CCN(CC4)C(=O)CO)n2 have for lipid environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4cc(n[nH]4)C(F)(F)F\", \"CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)N4CCN(CC4)C(=O)CO)n2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4cc(n[nH]4)C(F)(F)F\",\"value\": 2.44}, {\"smile\": \"CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)N4CCN(CC4)C(=O)CO)n2\",\"value\": 1.69}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1ccc(cc1NC(=O)c2ccc(OCc3ccccn3)cc2)c4cc(n[nH]4)C(F)(F)F and CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)N4CCN(CC4)C(=O)CO)n2 are 2.44 and 1.69 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the lipophilicity of the compound C[C@H]1CCCN1CCc2oc3ccc(cc3c2)c4ccc(cc4)C#N and Cc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1CCCN1CCc2oc3ccc(cc3c2)c4ccc(cc4)C#N\", \"Cc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@H]1CCCN1CCc2oc3ccc(cc3c2)c4ccc(cc4)C#N\",\"value\": 3.25}, {\"smile\": \"Cc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N\",\"value\": 2.99}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@H]1CCCN1CCc2oc3ccc(cc3c2)c4ccc(cc4)C#N and Cc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N are 3.25 and 2.99 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimate of the drug NC1CCc2cc(OC(=O)c3ccccc3)c(OC(=O)c4ccccc4)cc2C1 and CC(=O)N[C@@H]1CC2(CCN(Cc3ccc4[nH]c5ccccc5c4c3)CC2)c6ccccc16's ability to dissolve in oils. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC1CCc2cc(OC(=O)c3ccccc3)c(OC(=O)c4ccccc4)cc2C1\", \"CC(=O)N[C@@H]1CC2(CCN(Cc3ccc4[nH]c5ccccc5c4c3)CC2)c6ccccc16\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC1CCc2cc(OC(=O)c3ccccc3)c(OC(=O)c4ccccc4)cc2C1\",\"value\": 2.6}, {\"smile\": \"CC(=O)N[C@@H]1CC2(CCN(Cc3ccc4[nH]c5ccccc5c4c3)CC2)c6ccccc16\",\"value\": 2.96}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC1CCc2cc(OC(=O)c3ccccc3)c(OC(=O)c4ccccc4)cc2C1 and CC(=O)N[C@@H]1CC2(CCN(Cc3ccc4[nH]c5ccccc5c4c3)CC2)c6ccccc16 are 2.6 and 2.96 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much will the drug COc1cc(F)ccc1c2cncc(CNC(=O)C3CCC3)c2 and OC1(C(=O)Nc2ccccc12)c3ccccc3 partition into lipid layers? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(F)ccc1c2cncc(CNC(=O)C3CCC3)c2\", \"OC1(C(=O)Nc2ccccc12)c3ccccc3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(F)ccc1c2cncc(CNC(=O)C3CCC3)c2\",\"value\": 3.2}, {\"smile\": \"OC1(C(=O)Nc2ccccc12)c3ccccc3\",\"value\": 1.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(F)ccc1c2cncc(CNC(=O)C3CCC3)c2 and OC1(C(=O)Nc2ccccc12)c3ccccc3 are 3.2 and 1.71 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the molecule CNC(=O)c1ccccc1Sc2ccc3c(\\C=C\\c4ccccn4)n[nH]c3c2 and NC(=O)c1c(N)sc2CCCCc12's molecular structure influence its lipophilicity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc2ccc3c(\\C=C\\c4ccccn4)n[nH]c3c2\", \"NC(=O)c1c(N)sc2CCCCc12\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc2ccc3c(\\C=C\\c4ccccn4)n[nH]c3c2\",\"value\": 3.8}, {\"smile\": \"NC(=O)c1c(N)sc2CCCCc12\",\"value\": 1.84}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CNC(=O)c1ccccc1Sc2ccc3c(\\C=C\\c4ccccn4)n[nH]c3c2 and NC(=O)c1c(N)sc2CCCCc12 are 3.8 and 1.84 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(CCc2ccc(cc2)C(F)(F)F)C1)C(C)(C)C and COc1cc2N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)C(=O)C=Cc2cn1's affinity for nonpolar solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(CCc2ccc(cc2)C(F)(F)F)C1)C(C)(C)C\", \"COc1cc2N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)C(=O)C=Cc2cn1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(CCc2ccc(cc2)C(F)(F)F)C1)C(C)(C)C\",\"value\": 2.85}, {\"smile\": \"COc1cc2N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)C(=O)C=Cc2cn1\",\"value\": 0.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN[C@@H](C)C(=O)N[C@H](C(=O)N[C@H]1CCCN(CCc2ccc(cc2)C(F)(F)F)C1)C(C)(C)C and COc1cc2N(CCN3CCC(CC3)NCc4cc5OCCOc5cn4)C(=O)C=Cc2cn1 are 2.85 and 0.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug COCCCc1cc(Nc2ccnc(NCc3onc(C)c3)n2)n[nH]1 and O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc(cs4)c5ccccc5's solubility in oil or fat-based solvents. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COCCCc1cc(Nc2ccnc(NCc3onc(C)c3)n2)n[nH]1\", \"O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc(cs4)c5ccccc5\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COCCCc1cc(Nc2ccnc(NCc3onc(C)c3)n2)n[nH]1\",\"value\": 2.04}, {\"smile\": \"O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc(cs4)c5ccccc5\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COCCCc1cc(Nc2ccnc(NCc3onc(C)c3)n2)n[nH]1 and O=C(NC1(CC1)C#N)[C@@H]2CCCC[C@H]2C(=O)N3CCN(CC3)c4nc(cs4)c5ccccc5 are 2.04 and 3.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug Clc1ccc2nc(ccc2c1C(=O)NCC34CC5CC(CC(C5)C3)C4)N6CCC[C@@H](C6)NCCc7nnn[nH]7 and CCN1CCC[C@H](C1)C(=O)N2CCN(CC2)C(=O)Nc3ccc(Cl)c(Cl)c3's affinity for lipid-based environments. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc2nc(ccc2c1C(=O)NCC34CC5CC(CC(C5)C3)C4)N6CCC[C@@H](C6)NCCc7nnn[nH]7\", \"CCN1CCC[C@H](C1)C(=O)N2CCN(CC2)C(=O)Nc3ccc(Cl)c(Cl)c3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1ccc2nc(ccc2c1C(=O)NCC34CC5CC(CC(C5)C3)C4)N6CCC[C@@H](C6)NCCc7nnn[nH]7\",\"value\": 3.08}, {\"smile\": \"CCN1CCC[C@H](C1)C(=O)N2CCN(CC2)C(=O)Nc3ccc(Cl)c(Cl)c3\",\"value\": 1.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1ccc2nc(ccc2c1C(=O)NCC34CC5CC(CC(C5)C3)C4)N6CCC[C@@H](C6)NCCc7nnn[nH]7 and CCN1CCC[C@H](C1)C(=O)N2CCN(CC2)C(=O)Nc3ccc(Cl)c(Cl)c3 are 3.08 and 1.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug CC(C)NC[C@@H](C(=O)N1CCN(CC1)c2ncnc3[C@H](O)C[C@@H](C)c23)c4ccc(Cl)cc4 and Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](C3)Oc4cccc(c4)C(=O)N)s1's solubility in lipid-rich media. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)NC[C@@H](C(=O)N1CCN(CC1)c2ncnc3[C@H](O)C[C@@H](C)c23)c4ccc(Cl)cc4\", \"Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](C3)Oc4cccc(c4)C(=O)N)s1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)NC[C@@H](C(=O)N1CCN(CC1)c2ncnc3[C@H](O)C[C@@H](C)c23)c4ccc(Cl)cc4\",\"value\": 1.3}, {\"smile\": \"Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](C3)Oc4cccc(c4)C(=O)N)s1\",\"value\": 2.39}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)NC[C@@H](C(=O)N1CCN(CC1)c2ncnc3[C@H](O)C[C@@H](C)c23)c4ccc(Cl)cc4 and Cc1ccc(CN2[C@@H]3CC[C@H]2C[C@H](C3)Oc4cccc(c4)C(=O)N)s1 are 1.3 and 2.39 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the compound Cc1ccsc1C(=CCCN2CCC[C@H](C2)C(=O)O)c3sccc3C and CCC(C)(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(=O)O)[C@@H]12? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccsc1C(=CCCN2CCC[C@H](C2)C(=O)O)c3sccc3C\", \"CCC(C)(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(=O)O)[C@@H]12\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1ccsc1C(=CCCN2CCC[C@H](C2)C(=O)O)c3sccc3C\",\"value\": 1.49}, {\"smile\": \"CCC(C)(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(=O)O)[C@@H]12\",\"value\": 1.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1ccsc1C(=CCCN2CCC[C@H](C2)C(=O)O)c3sccc3C and CCC(C)(C)C(=O)O[C@H]1C[C@@H](C)C=C2C=C[C@H](C)[C@H](CC[C@@H](O)C[C@@H](O)CC(=O)O)[C@@H]12 are 1.49 and 1.55 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the lipophilicity of the drug O=C(CCc1ccccc1)c2ccccc2 and CCCN(CCC)S(=O)(=O)c1ccc(cc1)C(=O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(CCc1ccccc1)c2ccccc2\", \"CCCN(CCC)S(=O)(=O)c1ccc(cc1)C(=O)O\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C(CCc1ccccc1)c2ccccc2\",\"value\": 3.77}, {\"smile\": \"CCCN(CCC)S(=O)(=O)c1ccc(cc1)C(=O)O\",\"value\": 0.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=C(CCc1ccccc1)c2ccccc2 and CCCN(CCC)S(=O)(=O)c1ccc(cc1)C(=O)O are 3.77 and 0.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug [O-][n+]1cc(ccc1Cl)C(=O)Nc2ccc(F)cc2 and CN(C1CCN(Cc2ccc(nc2)C(F)(F)F)CC1)C(=O)Cc3ccc(cc3)n4cnnn4's behavior in a lipid-dominant system. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[O-][n+]1cc(ccc1Cl)C(=O)Nc2ccc(F)cc2\", \"CN(C1CCN(Cc2ccc(nc2)C(F)(F)F)CC1)C(=O)Cc3ccc(cc3)n4cnnn4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"[O-][n+]1cc(ccc1Cl)C(=O)Nc2ccc(F)cc2\",\"value\": 1.38}, {\"smile\": \"CN(C1CCN(Cc2ccc(nc2)C(F)(F)F)CC1)C(=O)Cc3ccc(cc3)n4cnnn4\",\"value\": 1.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug [O-][n+]1cc(ccc1Cl)C(=O)Nc2ccc(F)cc2 and CN(C1CCN(Cc2ccc(nc2)C(F)(F)F)CC1)C(=O)Cc3ccc(cc3)n4cnnn4 are 1.38 and 1.8 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the lipophilicity of the compound COc1ccc(cc1)c2cc(O)c3cc(Cl)ccc3n2 and Nc1ncc(nc1C(=O)Nc2cccnc2)c3ccc(cc3)S(=O)(=O)N4CCOCC4. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(cc1)c2cc(O)c3cc(Cl)ccc3n2\", \"Nc1ncc(nc1C(=O)Nc2cccnc2)c3ccc(cc3)S(=O)(=O)N4CCOCC4\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(cc1)c2cc(O)c3cc(Cl)ccc3n2\",\"value\": 2.7}, {\"smile\": \"Nc1ncc(nc1C(=O)Nc2cccnc2)c3ccc(cc3)S(=O)(=O)N4CCOCC4\",\"value\": 2.83}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(cc1)c2cc(O)c3cc(Cl)ccc3n2 and Nc1ncc(nc1C(=O)Nc2cccnc2)c3ccc(cc3)S(=O)(=O)N4CCOCC4 are 2.7 and 2.83 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2 and CCCC(=O)Nc1ccc(Cl)c(c1)c2oc3cccnc3n2 dissolve in nonpolar solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2\", \"CCCC(=O)Nc1ccc(Cl)c(c1)c2oc3cccnc3n2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2\",\"value\": 2.57}, {\"smile\": \"CCCC(=O)Nc1ccc(Cl)c(c1)c2oc3cccnc3n2\",\"value\": 3.37}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=C(NCc1ccncc1)c2ccc(Oc3ccccc3C#N)cc2 and CCCC(=O)Nc1ccc(Cl)c(c1)c2oc3cccnc3n2 are 2.57 and 3.37 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the lipophilic tendency of the drug CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(cc3C24N=C(C)C(=N4)N)c5cc(Cl)cc(c5)C#N and O[C@@H](CNCCCSCCOCCc1ccccc1)c2ccc(O)c3NC(=O)Sc23? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(cc3C24N=C(C)C(=N4)N)c5cc(Cl)cc(c5)C#N\", \"O[C@@H](CNCCCSCCOCCc1ccccc1)c2ccc(O)c3NC(=O)Sc23\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(cc3C24N=C(C)C(=N4)N)c5cc(Cl)cc(c5)C#N\",\"value\": 3.8}, {\"smile\": \"O[C@@H](CNCCCSCCOCCc1ccccc1)c2ccc(O)c3NC(=O)Sc23\",\"value\": 2.3}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CO[C@@H]1CC[C@@]2(CC1)Cc3ccc(cc3C24N=C(C)C(=N4)N)c5cc(Cl)cc(c5)C#N and O[C@@H](CNCCCSCCOCCc1ccccc1)c2ccc(O)c3NC(=O)Sc23 are 3.8 and 2.3 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would the molecule NC(=O)C[S+]([O-])C(c1ccccc1)c2ccccc2 and CC(C)NC[C@H](O)COc1cccc2ccccc12 dissolve in a lipid-rich environment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)C[S+]([O-])C(c1ccccc1)c2ccccc2\", \"CC(C)NC[C@H](O)COc1cccc2ccccc12\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)C[S+]([O-])C(c1ccccc1)c2ccccc2\",\"value\": 1.12}, {\"smile\": \"CC(C)NC[C@H](O)COc1cccc2ccccc12\",\"value\": 0.99}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)C[S+]([O-])C(c1ccccc1)c2ccccc2 and CC(C)NC[C@H](O)COc1cccc2ccccc12 are 1.12 and 0.99 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the degree of lipophilicity of the compound COc1ccccc1CN(C(=O)C)c2cnccc2Oc3ccccc3 and COc1ccccc1C(=O)NCC23CC4CC(CC(C4)C2)C3? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccccc1CN(C(=O)C)c2cnccc2Oc3ccccc3\", \"COc1ccccc1C(=O)NCC23CC4CC(CC(C4)C2)C3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccccc1CN(C(=O)C)c2cnccc2Oc3ccccc3\",\"value\": 3.0}, {\"smile\": \"COc1ccccc1C(=O)NCC23CC4CC(CC(C4)C2)C3\",\"value\": 4.43}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccccc1CN(C(=O)C)c2cnccc2Oc3ccccc3 and COc1ccccc1C(=O)NCC23CC4CC(CC(C4)C2)C3 are 3.0 and 4.43 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the lipophilicity profile of the molecule CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2 and COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCCN5CCC(CO)CC5. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2\", \"COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCCN5CCC(CO)CC5\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2\",\"value\": 1.8}, {\"smile\": \"COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCCN5CCC(CO)CC5\",\"value\": 2.89}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2 and COc1cc2c(Nc3ncc(CC(=O)Nc4cccc(F)c4)s3)ncnc2cc1OCCCN5CCC(CO)CC5 are 1.8 and 2.89 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how the drug NC1=CC2=NNC(=O)N2c3cc(ccc13)c4ccsc4 and CCCSc1ncccc1C(=O)NC2CCCCC2 interacts with lipophilic solvents. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC1=CC2=NNC(=O)N2c3cc(ccc13)c4ccsc4\", \"CCCSc1ncccc1C(=O)NC2CCCCC2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC1=CC2=NNC(=O)N2c3cc(ccc13)c4ccsc4\",\"value\": 3.18}, {\"smile\": \"CCCSc1ncccc1C(=O)NC2CCCCC2\",\"value\": 3.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC1=CC2=NNC(=O)N2c3cc(ccc13)c4ccsc4 and CCCSc1ncccc1C(=O)NC2CCCCC2 are 3.18 and 3.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the lipophilic nature of the drug C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccc(Cl)cc3 and Fc1ccc(cc1F)C(=O)Nc2ccc(Cl)nc2 in lipid solutions? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccc(Cl)cc3\", \"Fc1ccc(cc1F)C(=O)Nc2ccc(Cl)nc2\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccc(Cl)cc3\",\"value\": 1.21}, {\"smile\": \"Fc1ccc(cc1F)C(=O)Nc2ccc(Cl)nc2\",\"value\": 3.31}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@H]1CN(Cc2cc(Cl)ccc2OCC(=O)O)CCN1C(=O)Cc3ccc(Cl)cc3 and Fc1ccc(cc1F)C(=O)Nc2ccc(Cl)nc2 are 1.21 and 3.31 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug NCCCC(=O)OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1 and Cc1ccc(NC(=O)CCC2CCCCC2)cc1NC(=O)c3ccc(O)cc3 behave in lipid environments? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NCCCC(=O)OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1\", \"Cc1ccc(NC(=O)CCC2CCCCC2)cc1NC(=O)c3ccc(O)cc3\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NCCCC(=O)OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1\",\"value\": 1.5}, {\"smile\": \"Cc1ccc(NC(=O)CCC2CCCCC2)cc1NC(=O)c3ccc(O)cc3\",\"value\": 4.13}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NCCCC(=O)OCCN1CCN(CCCN2c3ccccc3Sc4ccc(Cl)cc24)CC1 and Cc1ccc(NC(=O)CCC2CCCCC2)cc1NC(=O)c3ccc(O)cc3 are 1.5 and 4.13 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How easily does the molecule OC1=CC=Cc2cc(O)c(O)c(O)c2C1=O and COc1ccc(CNC(=O)N(CC=C)C2CCN(CCC(c3ccccc3)c4ccccc4)CC2)cc1 dissolve in lipophilic solvents? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC1=CC=Cc2cc(O)c(O)c(O)c2C1=O\", \"COc1ccc(CNC(=O)N(CC=C)C2CCN(CCC(c3ccccc3)c4ccccc4)CC2)cc1\"], \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC1=CC=Cc2cc(O)c(O)c(O)c2C1=O\",\"value\": 2.03}, {\"smile\": \"COc1ccc(CNC(=O)N(CC=C)C2CCN(CCC(c3ccccc3)c4ccccc4)CC2)cc1\",\"value\": 3.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC1=CC=Cc2cc(O)c(O)c(O)c2C1=O and COc1ccc(CNC(=O)N(CC=C)C2CCN(CCC(c3ccccc3)c4ccccc4)CC2)cc1 are 2.03 and 3.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the drug on a scale from low to high? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n2cc(cn2)c3ncnc4[nH]ccc34\",\"value\": 2.55}, {\"smile\": \"CCC(N1NC(=O)c2nc3cc(Cl)ccc3c(O)c2C1=O)c4ccccn4\",\"value\": 0.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug N#CC[C@H](C1CCCC1)n2cc(cn2)c3ncnc4[nH]ccc34 and CCC(N1NC(=O)c2nc3cc(Cl)ccc3c(O)c2C1=O)c4ccccn4 are 2.55 and 0.8 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How can the drug's lipophilicity be predicted? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Brc1cnc(NCc2ccccc2)nc1Nc3cc([nH]n3)C4CC4\",\"value\": 4.11}, {\"smile\": \"O=C(C1CCN(CC1)c2nnc(s2)n3cccc3)N4CCc5ccccc5C4\",\"value\": 3.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Brc1cnc(NCc2ccccc2)nc1Nc3cc([nH]n3)C4CC4 and O=C(C1CCN(CC1)c2nnc(s2)n3cccc3)N4CCc5ccccc5C4 are 4.11 and 3.7 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug behave in hydrophobic systems? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC(C(=O)OC1CN2CCC1CC2)(c3ccccc3)c4ccccc4\",\"value\": 1.19}, {\"smile\": \"NC(=N)c1ccc(cc1)C(=O)N2CCN(CC2)S(=O)(=O)c3ccc4cc(Br)ccc4c3\",\"value\": 0.36}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug OC(C(=O)OC1CN2CCC1CC2)(c3ccccc3)c4ccccc4 and NC(=N)c1ccc(cc1)C(=O)N2CCN(CC2)S(=O)(=O)c3ccc4cc(Br)ccc4c3 are 1.19 and 0.36 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the lipophilicity of the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)C(CCCN(C)CCc1ccccc1)(C#N)c2ccccc2\",\"value\": 3.6}, {\"smile\": \"CC[C@H](NC(=O)c1c([S+](C)[O-])c(nc2cc(F)ccc12)c3ccccc3)c4ccccc4\",\"value\": 3.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)C(CCCN(C)CCc1ccccc1)(C#N)c2ccccc2 and CC[C@H](NC(=O)c1c([S+](C)[O-])c(nc2cc(F)ccc12)c3ccccc3)c4ccccc4 are 3.6 and 3.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's solubility in oil-based environments. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C(NC1CCCC1)C(N(Cc2occc2)C(=O)c3ccc([nH]3)c4ccccc4)c5ccncc5\",\"value\": 4.36}, {\"smile\": \"Cc1ccccc1S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(Cl)c4\",\"value\": 1.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=C(NC1CCCC1)C(N(Cc2occc2)C(=O)c3ccc([nH]3)c4ccccc4)c5ccncc5 and Cc1ccccc1S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(Cl)c4 are 4.36 and 1.71 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the solubility of the drug in lipid-rich environments. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@@H]1CN(C[C@H](C)O1)c2nc(N3CCOC[C@@H]3C)c4ccc(nc4n2)c5ccccc5\",\"value\": 3.75}, {\"smile\": \"OCC[C@@]1(CCCNC1)c2ccc(Cl)c(Cl)c2\",\"value\": 0.61}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@@H]1CN(C[C@H](C)O1)c2nc(N3CCOC[C@@H]3C)c4ccc(nc4n2)c5ccccc5 and OCC[C@@]1(CCCNC1)c2ccc(Cl)c(Cl)c2 are 3.75 and 0.61 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's likelihood of being soluble in fats? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Oc1ccc(O)c2C(=O)C=CC(=O)c12\",\"value\": 1.91}, {\"smile\": \"NC(=O)c1sc(cc1N)c2ccccc2\",\"value\": 2.33}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Oc1ccc(O)c2C(=O)C=CC(=O)c12 and NC(=O)c1sc(cc1N)c2ccccc2 are 1.91 and 2.33 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the molecule to partition into lipophilic areas? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Nc1cc(c(cn1)c2cc(nc(n2)N3CCOCC3)N4CCOCC4)C(F)(F)F\",\"value\": 2.7}, {\"smile\": \"CCN(C1CCN(CC[C@H](C2CCN(CC2)S(=O)(=O)C)c3cccc(F)c3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C\",\"value\": 1.44}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Nc1cc(c(cn1)c2cc(nc(n2)N3CCOCC3)N4CCOCC4)C(F)(F)F and CCN(C1CCN(CC[C@H](C2CCN(CC2)S(=O)(=O)C)c3cccc(F)c3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C are 2.7 and 1.44 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the molecule's potential for lipid dissolution. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(C1CCN(CCC(c2ccccc2)c3ccc(NC(=O)C)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C\",\"value\": 2.09}, {\"smile\": \"COc1ccc(cc1)c2nc3c(NCCCNC(=O)C4(CN)CC4)c(Br)cnc3[nH]2\",\"value\": 1.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN(C1CCN(CCC(c2ccccc2)c3ccc(NC(=O)C)cc3)CC1)C(=O)Cc4ccc(cc4)S(=O)(=O)C and COc1ccc(cc1)c2nc3c(NCCCNC(=O)C4(CN)CC4)c(Br)cnc3[nH]2 are 2.09 and 1.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug behave in lipid phases? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)c1cccc(C[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1\",\"value\": 1.19}, {\"smile\": \"C[C@H](NC(=O)C)c1ccc(Nc2ncc3cc(ccc3n2)c4ccncc4)cc1\",\"value\": 3.17}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)c1cccc(C[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1 and C[C@H](NC(=O)C)c1ccc(Nc2ncc3cc(ccc3n2)c4ccncc4)cc1 are 1.19 and 3.17 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's solubility in lipid and nonpolar solvents? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)Nc1sc(cc1C(=O)N[C@H]2CCCNC2)c3cc(F)cc(F)c3\",\"value\": 2.18}, {\"smile\": \"CC(C)C(=O)NCCNCC(O)COc1ccc(O)cc1\",\"value\": -0.15}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)Nc1sc(cc1C(=O)N[C@H]2CCCNC2)c3cc(F)cc(F)c3 and CC(C)C(=O)NCCNCC(O)COc1ccc(O)cc1 are 2.18 and -0.15 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the lipophilicity of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"c1ccc(cc1)c2c[nH]c(n2)c3ccccc3\",\"value\": 3.8}, {\"smile\": \"Oc1cc(O)cc(\\C=C\\c2ccc(O)c(O)c2)c1\",\"value\": 2.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug c1ccc(cc1)c2c[nH]c(n2)c3ccccc3 and Oc1cc(O)cc(\\C=C\\c2ccc(O)c(O)c2)c1 are 3.8 and 2.11 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how lipophilic the compound is. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1sc2c(C(=N[C@@H](CC(=O)OC(C)(C)C)c3nnc(C)n23)c4ccc(Cl)cc4)c1C\",\"value\": 3.82}, {\"smile\": \"C[C@H](O)C(=O)N1CCN(Cc2sc3c(nc(nc3c2C)c4cnc(N)nc4)N5CCOCC5)CC1\",\"value\": 1.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1sc2c(C(=N[C@@H](CC(=O)OC(C)(C)C)c3nnc(C)n23)c4ccc(Cl)cc4)c1C and C[C@H](O)C(=O)N1CCN(Cc2sc3c(nc(nc3c2C)c4cnc(N)nc4)N5CCOCC5)CC1 are 3.82 and 1.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how well the molecule mixes with lipids. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1cc(Nc2cc(Oc3cccnc3C)ccn2)cc(OC)c1OC\",\"value\": 3.19}, {\"smile\": \"Cc1ccc(Oc2ccc(Nc3ncnc4[nH]nc(OCCN5CCC(O)CC5)c34)cc2Cl)cn1\",\"value\": 3.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1cc(Nc2cc(Oc3cccnc3C)ccn2)cc(OC)c1OC and Cc1ccc(Oc2ccc(Nc3ncnc4[nH]nc(OCCN5CCC(O)CC5)c34)cc2Cl)cn1 are 3.19 and 3.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's distribution in lipid-based systems. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1cccc(Br)c1C(=O)N[C@@H](Cc2ccc(NC(=O)c3c(Cl)cccc3Cl)cc2)C(=O)O\",\"value\": -0.13}, {\"smile\": \"Cc1ccc(F)cc1S(=O)(=O)N[C@@H]2CCN(Cc3ccc(cc3)c4ccccc4)C2\",\"value\": 4.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1cccc(Br)c1C(=O)N[C@@H](Cc2ccc(NC(=O)c3c(Cl)cccc3Cl)cc2)C(=O)O and Cc1ccc(F)cc1S(=O)(=O)N[C@@H]2CCN(Cc3ccc(cc3)c4ccccc4)C2 are -0.13 and 4.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how lipophilic the molecule is. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1cc(C)nc(N)c1\",\"value\": 0.85}, {\"smile\": \"Fc1cc(Cl)ccc1N2CCN(CC2)C(=O)COCc3ccncc3\",\"value\": 2.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1cc(C)nc(N)c1 and Fc1cc(Cl)ccc1N2CCN(CC2)C(=O)COCc3ccncc3 are 0.85 and 2.55 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug's polarity affect its lipophilicity? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(c1ccc(cc1)c2cc(ccc2OCC(=O)O)C(F)(F)F)S(=O)(=O)C\",\"value\": -0.81}, {\"smile\": \"COc1ccc(cc1OC)C(=O)Nc2cccc(NC(=O)c3ccc(OC)c(OC)c3)n2\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(c1ccc(cc1)c2cc(ccc2OCC(=O)O)C(F)(F)F)S(=O)(=O)C and COc1ccc(cc1OC)C(=O)Nc2cccc(NC(=O)c3ccc(OC)c(OC)c3)n2 are -0.81 and 3.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the lipid solubility of the molecule? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccc(nc1OC)N2CCN([C@H](C)C2)C(=O)[C@@H]3CCCC[C@H]3C(=O)NC4(CC4)C#N\",\"value\": 2.3}, {\"smile\": \"Fc1ccccc1c2ccc3c(c2)C(=O)C(=O)c4ccccc34\",\"value\": 2.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COc1ccc(nc1OC)N2CCN([C@H](C)C2)C(=O)[C@@H]3CCCC[C@H]3C(=O)NC4(CC4)C#N and Fc1ccccc1c2ccc3c(c2)C(=O)C(=O)c4ccccc34 are 2.3 and 2.92 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the compound behave in terms of lipophilicity? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)Nc1sc(cc1C(=O)N2CCOCC2)c3ccccc3\",\"value\": 2.24}, {\"smile\": \"OC(=O)CCc1ccc(OCc2cccc(Oc3ccccc3)c2)cc1\",\"value\": 2.47}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)Nc1sc(cc1C(=O)N2CCOCC2)c3ccccc3 and OC(=O)CCc1ccc(OCc2cccc(Oc3ccccc3)c2)cc1 are 2.24 and 2.47 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how the drug would behave in fat-rich environments. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1c(Sc2ccc(Cl)cc2)c3ccc(cc3n1CC(=O)O)S(=O)(=O)C\",\"value\": 0.79}, {\"smile\": \"CC(C)N1CCN(Cc2oc(nc2)c3cc(cc4[nH]ncc34)c5cccc6[nH]ccc56)CC1\",\"value\": 3.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1c(Sc2ccc(Cl)cc2)c3ccc(cc3n1CC(=O)O)S(=O)(=O)C and CC(C)N1CCN(Cc2oc(nc2)c3cc(cc4[nH]ncc34)c5cccc6[nH]ccc56)CC1 are 0.79 and 3.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's tendency to interact with fatty substances? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN1C(=O)N(C)c2ncn(CC(O)CO)c2C1=O\",\"value\": -1.25}, {\"smile\": \"CCCc1c(O)c(ccc1OCCCSc2ccc(CC(=O)O)cc2Cl)C(=O)CC\",\"value\": 3.57}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN1C(=O)N(C)c2ncn(CC(O)CO)c2C1=O and CCCc1c(O)c(ccc1OCCCSc2ccc(CC(=O)O)cc2Cl)C(=O)CC are -1.25 and 3.57 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strongly does the drug interact with lipid layers? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Oc1ccc(cc1)C(c2ccc(O)cc2)C(Cl)(Cl)Cl\",\"value\": 3.86}, {\"smile\": \"CN(C1CCN(CCC(c2ccccc2)c3ccccc3)CC1)C(=O)Cc4ccc(Cl)cc4\",\"value\": 3.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Oc1ccc(cc1)C(c2ccc(O)cc2)C(Cl)(Cl)Cl and CN(C1CCN(CCC(c2ccccc2)c3ccccc3)CC1)C(=O)Cc4ccc(Cl)cc4 are 3.86 and 3.5 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the lipophilicity for the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1sc2ncnc(SCC(=O)N3CCCC3)c2c1C\",\"value\": 2.89}, {\"smile\": \"COc1ccc(cc1OC)c2csc(NC(=O)c3c(C)onc3C)n2\",\"value\": 3.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1sc2ncnc(SCC(=O)N3CCCC3)c2c1C and COc1ccc(cc1OC)c2csc(NC(=O)c3c(C)onc3C)n2 are 2.89 and 3.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much of the drug will dissolve in lipophilic solvents? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCCSc1nc(N)c2ncn([C@@H]3O[C@H](CO)[C@@H](O)[C@H]3O)c2n1\",\"value\": 1.51}, {\"smile\": \"CCCNC(=O)c1nnc2c(cccc2c1N)c3cncc(c3)C(=O)N4CCC4\",\"value\": 2.79}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCCCSc1nc(N)c2ncn([C@@H]3O[C@H](CO)[C@@H](O)[C@H]3O)c2n1 and CCCNC(=O)c1nnc2c(cccc2c1N)c3cncc(c3)C(=O)N4CCC4 are 1.51 and 2.79 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the molecule's behavior in lipophilic conditions. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)S(=O)(=O)CCCN(C)C)n2\",\"value\": 1.32}, {\"smile\": \"COCCS(=O)(=O)N1CCN(CC1)c2ccc(Nc3nccc(n3)c4cnc(C)n4C(C)C)cc2\",\"value\": 2.75}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)S(=O)(=O)CCCN(C)C)n2 and COCCS(=O)(=O)N1CCN(CC1)c2ccc(Nc3nccc(n3)c4cnc(C)n4C(C)C)cc2 are 1.32 and 2.75 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's affinity for lipid phases in biological systems? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1nc(cs1)C(=O)N2CCOC3(CCN(Cc4ccc(Cl)cc4)CC3)C2\",\"value\": 2.17}, {\"smile\": \"CN1CCN(CC1)c2ccc3N=CN(C(=O)c3c2)c4cc(NC(=O)C5CC5)ccc4C\",\"value\": 2.17}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1nc(cs1)C(=O)N2CCOC3(CCN(Cc4ccc(Cl)cc4)CC3)C2 and CN1CCN(CC1)c2ccc3N=CN(C(=O)c3c2)c4cc(NC(=O)C5CC5)ccc4C are 2.17 and 2.17 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the lipophilicity of the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COC[C@H](O)CN1C(=O)C=NN(C1=O)c2ccc(Cl)c(c2)C(=O)NCC3(O)CCCCCC3\",\"value\": 1.46}, {\"smile\": \"COc1cc(ccc1Cn2ccc3ccc(NC(=O)OC4CCCC4)cc23)C(=O)O\",\"value\": 2.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug COC[C@H](O)CN1C(=O)C=NN(C1=O)c2ccc(Cl)c(c2)C(=O)NCC3(O)CCCCCC3 and COc1cc(ccc1Cn2ccc3ccc(NC(=O)OC4CCCC4)cc23)C(=O)O are 1.46 and 2.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the lipophilicity of the compound. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"N\\C(=C(\\C#N)/c1ccccc1C(F)(F)F)\\Sc2ccc(N)cc2\",\"value\": 3.3}, {\"smile\": \"Cn1cncc1c2c3C(=O)N(CC#C)C(=O)N(CC4CC4)c3nn2Cc5ccnc6ccc(Cl)cc56\",\"value\": 3.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug N\\C(=C(\\C#N)/c1ccccc1C(F)(F)F)\\Sc2ccc(N)cc2 and Cn1cncc1c2c3C(=O)N(CC#C)C(=O)N(CC4CC4)c3nn2Cc5ccnc6ccc(Cl)cc56 are 3.3 and 3.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the compound's affinity for lipophilic environments. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c(F)c1\",\"value\": 2.1}, {\"smile\": \"Fc1cccc(F)c1CS(=O)(=O)N2CCN(CC2)c3ncccc3C(F)(F)F\",\"value\": 3.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c(F)c1 and Fc1cccc(F)c1CS(=O)(=O)N2CCN(CC2)c3ncccc3C(F)(F)F are 2.1 and 3.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug partition into lipid phases? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCOC(=O)CN(c1ccccc1C)S(=O)(=O)c2ccc(Cl)cc2\",\"value\": 4.03}, {\"smile\": \"COc1cc(OC)c(cc1NC(=O)Cc2ccnc(F)c2)S(=O)(=O)N3c4ccccc4Oc5ccccc35\",\"value\": 3.37}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCOC(=O)CN(c1ccccc1C)S(=O)(=O)c2ccc(Cl)cc2 and COc1cc(OC)c(cc1NC(=O)Cc2ccnc(F)c2)S(=O)(=O)N3c4ccccc4Oc5ccccc35 are 4.03 and 3.37 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's lipid affinity. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(c1ccnc(Nc2cc(cc(c2)N3CCN(C)CC3)N4CCOCC4)n1)c5cc(CO)ccc5C\",\"value\": 2.61}, {\"smile\": \"C[C@H](O)C(=O)N1CCN(CC1)c2ccc(Nc3nccc(n3)c4cnc(C)n4C(C)C)cc2\",\"value\": 2.15}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(c1ccnc(Nc2cc(cc(c2)N3CCN(C)CC3)N4CCOCC4)n1)c5cc(CO)ccc5C and C[C@H](O)C(=O)N1CCN(CC1)c2ccc(Nc3nccc(n3)c4cnc(C)n4C(C)C)cc2 are 2.61 and 2.15 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the drug to accumulate in lipophilic areas? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCOc1ncc(C)c2NC(=C([C@@H](c3ccc(cc3OC)C#N)c12)C(=O)N)C\",\"value\": 2.51}, {\"smile\": \"CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)S(=O)(=O)CCCN(C)C)n2\",\"value\": 1.32}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCOc1ncc(C)c2NC(=C([C@@H](c3ccc(cc3OC)C#N)c12)C(=O)N)C and CC(C)n1c(C)ncc1c2ccnc(Nc3ccc(cc3)S(=O)(=O)CCCN(C)C)n2 are 2.51 and 1.32 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the molecule's lipid affinity. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(=O)c1noc(C)c1C(=O)Nc2nccs2\",\"value\": 1.94}, {\"smile\": \"NC(=O)c1cccc(S[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1\",\"value\": 2.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(=O)c1noc(C)c1C(=O)Nc2nccs2 and NC(=O)c1cccc(S[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)c1 are 1.94 and 2.22 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the compound's lipophilicity? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCNC(=O)C[C@H]1N=C(c2ccc(Cl)cc2)c3cc(OC)ccc3n4c(C)nnc14\",\"value\": 2.42}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN(C)C\",\"value\": 2.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCNC(=O)C[C@H]1N=C(c2ccc(Cl)cc2)c3cc(OC)ccc3n4c(C)nnc14 and COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN(C)C are 2.42 and 2.9 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug interact with nonpolar substances? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@@H]1CC[C@H](CC1)Oc2nc(N)c3ncn([C@@H]4O[C@H](C)[C@@H](O)[C@H]4O)c3n2\",\"value\": 2.35}, {\"smile\": \"CCOc1ccc2nc(N)sc2c1\",\"value\": 2.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@@H]1CC[C@H](CC1)Oc2nc(N)c3ncn([C@@H]4O[C@H](C)[C@@H](O)[C@H]4O)c3n2 and CCOc1ccc2nc(N)sc2c1 are 2.35 and 2.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's tendency to dissolve in fat or oils. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C1CN(CCO1)c2cc(Nc3nccc(Nc4cccc5[nH]ncc45)n3)cc(c2)N6CCOCC6\",\"value\": 3.05}, {\"smile\": \"CC(Nc1ncnc2ccccc12)c3ccccc3\",\"value\": 3.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C1CN(CCO1)c2cc(Nc3nccc(Nc4cccc5[nH]ncc45)n3)cc(c2)N6CCOCC6 and CC(Nc1ncnc2ccccc12)c3ccccc3 are 3.05 and 3.4 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's behavior in lipid systems? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C\",\"value\": -0.27}, {\"smile\": \"Cc1ccc(c(C)c1)c2nnc(O)c3ccccc23\",\"value\": 3.41}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCCC(=O)Nc1ccc(OCC(O)CNC(C)C)c(c1)C(=O)C and Cc1ccc(c(C)c1)c2nnc(O)c3ccccc23 are -0.27 and 3.41 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the drug to exhibit lipophilic behavior? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCn1c2ccccc2c3cc(NC(=O)CN(C)C)ccc13\",\"value\": 3.2}, {\"smile\": \"N(c1ccccc1)c2[nH]nnc2c3ccccc3\",\"value\": 3.37}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCn1c2ccccc2c3cc(NC(=O)CN(C)C)ccc13 and N(c1ccccc1)c2[nH]nnc2c3ccccc3 are 3.2 and 3.37 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the molecule compared to other substances? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCOc1cc2nnc(C(=O)N)c(Nc3cccc(Cl)c3F)c2cc1N4CCN(C)CC4\",\"value\": 1.99}, {\"smile\": \"CCN(CC)CCNC(=O)c1cc(Br)c(N)cc1OC\",\"value\": 0.54}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCOc1cc2nnc(C(=O)N)c(Nc3cccc(Cl)c3F)c2cc1N4CCN(C)CC4 and CCN(CC)CCNC(=O)c1cc(Br)c(N)cc1OC are 1.99 and 0.54 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug behave in nonpolar environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=S(=O)(Nc1ccccc1c2ccccc2)c3ccccc3\",\"value\": 3.96}, {\"smile\": \"CCN(C1CCN(CCC(c2ccccc2)c3ccccc3)CC1)C(=O)Cc4ccc(cc4)[N+](=O)[O-]\",\"value\": 4.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug O=S(=O)(Nc1ccccc1c2ccccc2)c3ccccc3 and CCN(C1CCN(CCC(c2ccccc2)c3ccccc3)CC1)C(=O)Cc4ccc(cc4)[N+](=O)[O-] are 3.96 and 4.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug behave in fat-based solvents? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(CC)c1ncnc2c1ncn2Cc3c(Cl)cccc3Cl\",\"value\": 1.25}, {\"smile\": \"CCCCSc1nc(N)c2ncn(Cc3c(F)ccc(C)c3F)c2n1\",\"value\": 3.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CCN(CC)c1ncnc2c1ncn2Cc3c(Cl)cccc3Cl and CCCCSc1nc(N)c2ncn(Cc3c(F)ccc(C)c3F)c2n1 are 1.25 and 3.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the lipophilicity of the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(C)NCC(O)COc1ccc(CCOCC2CC2)cc1\",\"value\": 0.63}, {\"smile\": \"Cc1cc(C)c(C(=O)N[C@@H](Cc2ccc(OCCCNc3ccccn3)cc2)C(=O)O)c(C)c1\",\"value\": 0.49}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC(C)NCC(O)COc1ccc(CCOCC2CC2)cc1 and Cc1cc(C)c(C(=O)N[C@@H](Cc2ccc(OCCCNc3ccccn3)cc2)C(=O)O)c(C)c1 are 0.63 and 0.49 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How soluble is the molecule in lipophilic environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CNC1(C)C2CCC(C2)C1(C)C\",\"value\": -0.84}, {\"smile\": \"Cc1ccnc(NCc2ccccc2O)c1\",\"value\": 3.35}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CNC1(C)C2CCC(C2)C1(C)C and Cc1ccnc(NCc2ccccc2O)c1 are -0.84 and 3.35 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how lipophilic the compound is. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Fc1ccc2[nH]c3CCN(Cc3c2c1)C(=O)[C@@H]4CCCC[C@H]4C(=O)NCC#N\",\"value\": 2.77}, {\"smile\": \"OC(=O)Cc1ccc(cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2\",\"value\": 1.83}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Fc1ccc2[nH]c3CCN(Cc3c2c1)C(=O)[C@@H]4CCCC[C@H]4C(=O)NCC#N and OC(=O)Cc1ccc(cc1)N2CCC(CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4)CC2 are 2.77 and 1.83 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the drug based on its chemical structure? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3nc(C)cnc13\",\"value\": 3.55}, {\"smile\": \"CCCC1CCCCC(=N1)N\",\"value\": 1.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug C[C@H](CO)Nc1nc(SCc2cccc(F)c2F)nc3nc(C)cnc13 and CCCC1CCCCC(=N1)N are 3.55 and 1.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's potential to be absorbed into lipid environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(C(=O)C)c1ccccc1\",\"value\": 1.1}, {\"smile\": \"FC(F)(F)c1cc(COCC(N2CCNCC2)c3cccc(Cl)c3)cc(c1)C(F)(F)F\",\"value\": 4.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(C(=O)C)c1ccccc1 and FC(F)(F)c1cc(COCC(N2CCNCC2)c3cccc(Cl)c3)cc(c1)C(F)(F)F are 1.1 and 4.2 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's affinity for lipid tissues or areas. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC1(C)[C@@H]2CC[C@@]1(CS(=O)(=O)N3CCN(CC3)c4ccc(cn4)C(F)(F)F)C(=O)C2\",\"value\": 3.8}, {\"smile\": \"COc1cccc2c1c(NS(=O)(=O)c3ccc(Cl)s3)nn2Cc4cccc(CNC(=O)C(C)(C)O)c4\",\"value\": 2.09}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CC1(C)[C@@H]2CC[C@@]1(CS(=O)(=O)N3CCN(CC3)c4ccc(cn4)C(F)(F)F)C(=O)C2 and COc1cccc2c1c(NS(=O)(=O)c3ccc(Cl)s3)nn2Cc4cccc(CNC(=O)C(C)(C)O)c4 are 3.8 and 2.09 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug interact with nonpolar solvents or lipids? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1[nH]c(C(=O)NC2CCN(CC2)c3ncc(s3)C(=O)O)c(Cl)c1Cl\",\"value\": 0.02}, {\"smile\": \"COc1cccc(c1)C(=O)NCCCOc2cccc3ccc(N)nc23\",\"value\": 2.46}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Cc1[nH]c(C(=O)NC2CCN(CC2)c3ncc(s3)C(=O)O)c(Cl)c1Cl and COc1cccc(c1)C(=O)NCCCOc2cccc3ccc(N)nc23 are 0.02 and 2.46 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess the lipophilicity of the molecule? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(CCO)C(=O)c1ccc(cc1)[C@@H](N2CCN(Cc3ccc(F)cc3)CC2)c4cccc(NC(=O)C5CC5)c4\",\"value\": 3.38}, {\"smile\": \"Clc1ccc(cc1C(=O)NCC23CC4CC(CC(C4)C2)C3)C5CCNCC5\",\"value\": 1.58}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug CN(CCO)C(=O)c1ccc(cc1)[C@@H](N2CCN(Cc3ccc(F)cc3)CC2)c4cccc(NC(=O)C5CC5)c4 and Clc1ccc(cc1C(=O)NCC23CC4CC(CC(C4)C2)C3)C5CCNCC5 are 3.38 and 1.58 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the molecule's solubility in lipophilic environments? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Clc1ccc(cc1)C(=O)Nc2oc(nn2)C(=O)Nc3ccc(cc3)N4CCOCC4\",\"value\": 1.92}, {\"smile\": \"CN1C(=O)N(C)c2ncn(CC(O)CO)c2C1=O\",\"value\": -1.25}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drug Clc1ccc(cc1)C(=O)Nc2oc(nn2)C(=O)Nc3ccc(cc3)N4CCOCC4 and CN1C(=O)N(C)c2ncn(CC(O)CO)c2C1=O are 1.92 and -1.25 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How lipophilic is the generated drugs on a scale from low to high? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"O=C1Nc2ccc(cc2C1=O)S(=O)(=O)N3CCC[C@H]3CNc4ccccc4\",\"value\": -0.4038869}, {\"smile\": \"CN(C)c1ccc2N=C3C=CC(=[N+](C)C)C=C3Sc2c1\",\"value\": 0.1336053}, {\"smile\": \"CC(C)C(=O)Nc1nc(cc2ccccc12)c3ccccn3\",\"value\": -0.2884962}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.4038869, 0.1336053 and -0.2884962 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How can the generated drugs' lipophilicity be predicted? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Brc1oc(cc1)c2nc3cc(NS(=O)(=O)c4ccccc4)ccc3[nH]2\",\"value\": -0.2264681}, {\"smile\": \"COc1cc2ncnc(NCc3cccc(C)c3)c2cc1OC\",\"value\": -0.08785425}, {\"smile\": \"OC(=O)c1cn(Cc2ccccc2)c3ccccc13\",\"value\": 0.037470624}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.2264681, -0.08785425 and 0.037470624 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the generated drugs behave in hydrophobic systems? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C[C@@H](Oc1cccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c12)C(=O)N(C)CCO\",\"value\": -0.28562945}, {\"smile\": \"CN1CCCC1CCNC(=O)c2ccc(Nc3ncc4cc(ccc4n3)c5ccncc5)cc2\",\"value\": -0.3078909}, {\"smile\": \"CN[C@@H](C)C(=O)N[C@@H](C1CCCCC1)C(=O)N[C@H]2CCN(CCc3ccccc3)C2\",\"value\": -0.32979584}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.28562945, -0.3078909 and -0.32979584 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the lipophilicity of the compound. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN(C1CCN(C)CC1)c2nccc(Nc3cc(NC(=O)c4ccnc(c4)N5CCOCC5)ccc3C)n2\",\"value\": -0.12727477}, {\"smile\": \"NC(=O)c1ccc(O[C@H]2C[C@H]3CC[C@@H](C2)N3Cc4ccccc4)nc1\",\"value\": -0.20236321}, {\"smile\": \"O=C1CCOc2nc(\\C=C\\c3ccccc3)ccc12\",\"value\": -0.21867655}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.12727477, -0.20236321 and -0.21867655 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the optimized drugs' solubility in oil-based environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC(=O)Nc1nc(C)c(s1)c2cnc(F)c(NS(=O)(=O)c3sc(C)nc3C)c2\",\"value\": -0.41583228}, {\"smile\": \"NCCCCCc1c[nH]c2ccc(F)cc12\",\"value\": -0.16145284}, {\"smile\": \"CCCCSc1nc(N)c2ncn(Cc3c(F)ccc(C)c3F)c2n1\",\"value\": -0.26153803}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.41583228, -0.16145284 and -0.26153803 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the solubility of the generated drugs in lipid-rich environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CNCc1ccc(cc1)c2[nH]c3cc(F)cc4C(=O)NCCc2c34\",\"value\": 0.5297856}, {\"smile\": \"C[C@@H]1CN(CCN1c2ncc(OCc3ccncc3C#N)cn2)C(=O)OCC(F)F\",\"value\": 0.63643146}, {\"smile\": \"C[C@@H]1CN(CCN1C(=O)[C@@H]2CCCC[C@H]2C(=O)NC3(CC3)C#N)c4ccc5c(C)nn(C)c5c4\",\"value\": 0.5514989}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.5297856, 0.63643146 and 0.5514989 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the generated drugs' likelihood of being soluble in fats? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"OC(C(=O)OC1CN2CCC1CC2)(c3ccccc3)c4ccccc4\",\"value\": 0.2109121}, {\"smile\": \"CS(=O)(=O)C1(CC1)c2cc(nc(n2)c3cccc4[nH]ccc34)N5CC6CCC(C5)O6\",\"value\": 0.0084593445}, {\"smile\": \"Brc1ccccc1NC(=O)Nc2ccc(C#N)c3[nH]nnc23\",\"value\": -0.013567999}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.2109121, 0.0084593445 and -0.013567999 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is the molecule to partition into lipophilic areas? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56\",\"value\": -0.17165314}, {\"smile\": \"NC(=O)Nc1cc(sc1C(=O)N)c2ccccc2\",\"value\": -0.21785249}, {\"smile\": \"Oc1cccc(NC(=O)c2ccc(OCCCN3CCCC3)cc2OCc4cccc(F)c4)c1\",\"value\": -0.1926045}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.17165314, -0.21785249 and -0.1926045 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the molecule' potential for lipid dissolution. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"c1ccc(cc1)c2cn3ccccc3n2\",\"value\": -0.21587004}, {\"smile\": \"CCCCc1nc2c(N)nc3ccccc3c2n1CC(C)C\",\"value\": -0.1009831}, {\"smile\": \"CN1CCN(CC1)c2ccc3ncc(C(=O)N)c(Nc4ccc(Cl)cc4Cl)c3c2\",\"value\": 0.09729464}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.21587004, -0.1009831 and 0.09729464 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the optimized drugs behave in lipid phases? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NNCCc1ccccc1\",\"value\": 0.12706012}, {\"smile\": \"OC1(CN2CCC1CC2)C#Cc3ccc(Oc4ccc(cc4)C(=O)NCc5ccncc5)cc3\",\"value\": 0.17628604}, {\"smile\": \"Fc1ccc(SC2=NN3C=NC(=O)C(=C3C=C2)c4c(Cl)cccc4Cl)c(F)c1\",\"value\": -0.015487507}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.12706012, 0.17628604 and -0.015487507 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the generated drugs' solubility in lipid and nonpolar solvents? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1ccn2c(c(C)nc2c1)c3ccnc(N)n3\",\"value\": 0.4244704}, {\"smile\": \"ONC(=O)Cc1ccc(OCc2ccccc2)cc1\",\"value\": 0.13728657}, {\"smile\": \"COc1cccc(Nc2ncnc3cc(OC)c(OC)cc23)c1\",\"value\": 0.38900864}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.4244704, 0.13728657 and 0.38900864 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the lipophilicity of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"Cc1[nH]c(C(=O)NC2CCN(CC2)c3nc(cs3)C(=O)O)c(Cl)c1Cl\",\"value\": -0.2576443}, {\"smile\": \"Clc1ccc2NC(=O)CN(C(c3ccccc3)c2c1)C(=O)c4occc4\",\"value\": -0.26608133}, {\"smile\": \"O=C(Nc1ccccc1)c2oc3ccccc3c2\",\"value\": -0.21886285}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.2576443, -0.26608133 and -0.21886285 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how lipophilic the compound is. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CC1=C(C(=O)c2cc(O)c(O)c(Cc3ccccc3)c2C1=O)C4=C(C)C(=O)c5c(Cc6ccccc6)c(O)c(O)cc5C4=O\",\"value\": -0.37042695}, {\"smile\": \"CC(C)C[C@H](CO)Nc1nc(SCc2cccc(c2)C#N)nc3nc(N)sc13\",\"value\": -0.41859692}, {\"smile\": \"CC(=O)OCCn1c(Sc2nc3cccc(Cl)c3s2)nc4c(N)ncnc14\",\"value\": -0.40902925}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.37042695, -0.41859692 and -0.40902925 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how well the molecule mixes with lipids. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCCNC(=O)NS(=O)(=O)c1ccc(C)cc1\",\"value\": -0.21033277}, {\"smile\": \"Cc1nnsc1C(=O)Nc2ccccc2\",\"value\": 0.0893686}, {\"smile\": \"Cc1c(Cl)ccc(OC2CCN(CC2)C3CCN(CC3)C(=O)c4cccc(c4)S(=O)(=O)C)c1Cl\",\"value\": 0.047048613}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.21033277, 0.0893686 and 0.047048613 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the generated drugs' distribution in lipid-based systems. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"NCC[C@@H](Oc1cc(Cl)c(F)cc1C#N)c2ccccc2\",\"value\": -0.50877666}, {\"smile\": \"O=C1OC(=Cc2ccccc12)c3ccccc3\",\"value\": -0.44558382}, {\"smile\": \"COc1ccc2[nH]c3CCN(Cc3c2c1)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N\",\"value\": -0.5404799}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.50877666, -0.44558382 and -0.5404799 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how lipophilic the molecule is. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"COc1ccccc1C(=O)Nc2nc3ccccc3[nH]2\",\"value\": 0.01390937}, {\"smile\": \"COc1ccc2c(C)cc(N[C@H]3CCC[C@@H](C3)NCc4ccsc4)nc2c1\",\"value\": -0.23213305}, {\"smile\": \"Nc1n[nH]c(Nc2ccccc2)n1\",\"value\": 0.12179445}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.01390937, -0.23213305 and 0.12179445 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the optimized drugs' polarity affect its lipophilicity? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCCNC(=O)c1nnc2c(cccc2c1N)c3cnc(OC)nc3OC\",\"value\": -0.3859421}, {\"smile\": \"NC(=O)c1cncc(c1)c2ccc(cc2)[C@@H]3CC[C@@H](CC(=O)O)CC3\",\"value\": -0.4176731}, {\"smile\": \"Clc1cccc2c3CN(CCc3[nH]c12)C(=O)[C@@H]4CCCC[C@H]4C(=O)NC5(CC5)C#N\",\"value\": -0.4245838}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.3859421, -0.4176731 and -0.4245838 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the lipid solubility of the molecule? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CN1CCOc2cc(COc3ccccc3)cnc12\",\"value\": -0.3998059}, {\"smile\": \"Cc1ccc2c(c1)c(c(C)n2CC(=O)O)c3ccnc4c(F)cccc34\",\"value\": -0.35919678}, {\"smile\": \"CN1CCN(CC1)C(=O)c2ccc(cc2)C(=C3CCN(Cc4cscn4)CC3)c5cccc6cccnc56\",\"value\": -0.40363884}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.3998059, -0.35919678 and -0.40363884 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the compound behave in terms of lipophilicity? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"C(CNCc1ccccc1)NCc2ccccc2\",\"value\": 0.31624973}, {\"smile\": \"OC[C@H]1CCCN1CCCOc2ccc3c(Nc4cc(CC(=O)Nc5cccc(F)c5F)[nH]n4)ncnc3c2\",\"value\": 0.2729284}, {\"smile\": \"CCN(CCN(C)C)S(=O)(=O)c1ccc(cc1)c2cnc(N)c(n2)C(=O)Nc3cccnc3\",\"value\": 0.3009376}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are 0.31624973, 0.2729284 and 0.3009376 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how the generated drugs would behave in fat-rich environments. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"lipo\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the compound's lipophilicity (logP). Lipophilicity is an important parameter for predicting how a drug passes through cell membranes.\", \"result\": [{\"smile\": \"CCN(CC)CCOCCOC(=O)C1(CCCC1)c2ccccc2\",\"value\": -0.412251}, {\"smile\": \"Clc1cccc(c1)S(=O)(=O)NC(=O)N2CCC(CC2)N3CCC(CC3)Oc4ccc(Cl)c(Cl)c4\",\"value\": -0.36026132}, {\"smile\": \"O[C@H](CNC(=O)c1c[nH]c2ccccc12)CN3CCC(CC3)Oc4ccc(Cl)c(Cl)c4\",\"value\": -0.41193527}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The lipid solubility of the drugs are -0.412251, -0.36026132 and -0.41193527 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug O(C)c1ccccc1-c1cc(ccc1)C1CC1C1(N=C(N)N(C)C(=O)C1)C for inhibitory activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1ccccc1-c1cc(ccc1)C1CC1C1(N=C(N)N(C)C(=O)C1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1ccccc1-c1cc(ccc1)C1CC1C1(N=C(N)N(C)C(=O)C1)C\",\"value\": 0.010453784}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C)c1ccccc1-c1cc(ccc1)C1CC1C1(N=C(N)N(C)C(=O)C1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)COCC2)C)c(F)cc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)COCC2)C)c(F)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)COCC2)C)c(F)cc1\",\"value\": 0.008664256}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)COCC2)C)c(F)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the drug FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\",\"value\": 0.013709231}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the inhibitory potential of the drug O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1 on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1\",\"value\": 0.011589603}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drug O(C(C)C)C(=O)CN1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C(C)C)C(=O)CN1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C(C)C)C(=O)CN1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C\",\"value\": 0.015895573}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C(C)C)C(=O)CN1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1\",\"value\": 0.012974385}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug Brc1ccc(cc1)COC(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1 inhibits BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Brc1ccc(cc1)COC(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1ccc(cc1)COC(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1\",\"value\": 0.016919807}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Brc1ccc(cc1)COC(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug s1ccnc1-c1cc(ccc1)C[C@H](NC(=O)[C@H](OC)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 for inhibition of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"s1ccnc1-c1cc(ccc1)C[C@H](NC(=O)[C@H](OC)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"s1ccnc1-c1cc(ccc1)C[C@H](NC(=O)[C@H](OC)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.009522427}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug s1ccnc1-c1cc(ccc1)C[C@H](NC(=O)[C@H](OC)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify if the drug Clc1cc(C)c(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1cc(C)c(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(C)c(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1\",\"value\": 0.013677482}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Clc1cc(C)c(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug O=C1N(CC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#N)ccc1)N)C is a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C1N(CC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#N)ccc1)N)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(CC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#N)ccc1)N)C\",\"value\": 0.021558857}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C1N(CC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#N)ccc1)N)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1ccccc1 inhibit BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1ccccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1ccccc1\",\"value\": 0.012360397}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C(NC(=[NH2+])N)Cn1c(ccc1-c1ccccc1)-c1ccccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 an inhibitor of BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.017659826}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCCC)/C)C(=O)NC([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C blocks BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCCC)/C)C(=O)NC([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCCC)/C)C(=O)NC([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C\",\"value\": 0.009231831}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCCC)/C)C(=O)NC([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition of BACE1 by the drug S1(=O)C[C@@H](Cc2cc(O[C@H](COC)C(F)(F)F)c(N)c(F)c2)[C@H](O)[C@@H]([NH2+]Cc2cc(ccc2)C(C)(C)C)C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)C[C@@H](Cc2cc(O[C@H](COC)C(F)(F)F)c(N)c(F)c2)[C@H](O)[C@@H]([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)C[C@@H](Cc2cc(O[C@H](COC)C(F)(F)F)c(N)c(F)c2)[C@H](O)[C@@H]([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.007641624}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1(=O)C[C@@H](Cc2cc(O[C@H](COC)C(F)(F)F)c(N)c(F)c2)[C@H](O)[C@@H]([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drug Fc1cc(ccc1OC)[C@H](Cc1cc([N+](=O)[O-])ccc1OC)c1nc([nH]c1)N inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(ccc1OC)[C@H](Cc1cc([N+](=O)[O-])ccc1OC)c1nc([nH]c1)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(ccc1OC)[C@H](Cc1cc([N+](=O)[O-])ccc1OC)c1nc([nH]c1)N\",\"value\": 0.010770953}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(ccc1OC)[C@H](Cc1cc([N+](=O)[O-])ccc1OC)c1nc([nH]c1)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the potential of the drug O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(N)CN1C(=O)C)C as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(N)CN1C(=O)C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(N)CN1C(=O)C)C\",\"value\": 0.0049715587}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(N)CN1C(=O)C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug O(C(=O)C1=CN(C=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H]1C)CC(OC(C)C)=O)C(C)(C)C can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C(=O)C1=CN(C=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H]1C)CC(OC(C)C)=O)C(C)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C(=O)C1=CN(C=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H]1C)CC(OC(C)C)=O)C(C)(C)C\",\"value\": 0.013219587}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C(=O)C1=CN(C=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H]1C)CC(OC(C)C)=O)C(C)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the BACE1 inhibitory activity of the drug n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1\",\"value\": 0.010418967}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check whether the drug O1c2ncc(cc2C([NH2+]CC(O)C(NC(=O)C)Cc2cc3OCCOc3cc2)CC12CCC2)CC(C)(C)C inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O1c2ncc(cc2C([NH2+]CC(O)C(NC(=O)C)Cc2cc3OCCOc3cc2)CC12CCC2)CC(C)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1c2ncc(cc2C([NH2+]CC(O)C(NC(=O)C)Cc2cc3OCCOc3cc2)CC12CCC2)CC(C)(C)C\",\"value\": 0.012245669}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O1c2ncc(cc2C([NH2+]CC(O)C(NC(=O)C)Cc2cc3OCCOc3cc2)CC12CCC2)CC(C)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])Cc1ccccc1)C(=O)NC(C)c1ccc(F)cc1)C ability to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])Cc1ccccc1)C(=O)NC(C)c1ccc(F)cc1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])Cc1ccccc1)C(=O)NC(C)c1ccc(F)cc1)C\",\"value\": 0.010195452}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S(=O)(=O)(N(C)c1cc(cc(c1)COCC([NH3+])Cc1ccccc1)C(=O)NC(C)c1ccc(F)cc1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@@H](O)C[NH2+]Cc1cc(OC)ccc1 inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@@H](O)C[NH2+]Cc1cc(OC)ccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@@H](O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.03376498}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@@H](O)C[NH2+]Cc1cc(OC)ccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the BACE1 inhibitory activity of the drug O(C)c1ccc(cc1C)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1ccc(cc1C)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1ccc(cc1C)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1\",\"value\": 0.019606713}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C)c1ccc(cc1C)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cncnc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the inhibition of BACE1 by the drug O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C\",\"value\": 0.011005186}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug FC(F)(F)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2c(n(cc2)CC)c1)Cc1ccccc1 effective at inhibiting BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC(F)(F)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2c(n(cc2)CC)c1)Cc1ccccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)(F)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2c(n(cc2)CC)c1)Cc1ccccc1\",\"value\": 0.011808229}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC(F)(F)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2c(n(cc2)CC)c1)Cc1ccccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC inhibits BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC\",\"value\": 0.011107301}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug O=C(NCC1CCCCC1)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C show inhibitory activity against BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NCC1CCCCC1)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(NCC1CCCCC1)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C\",\"value\": 0.011725676}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C(NCC1CCCCC1)C(Cc1cc2cc(ccc2nc1N)-c1ccccc1C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug S1CC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N has inhibitory effects on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1CC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1CC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N\",\"value\": 0.0105397925}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1CC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1ccccc1)C can block BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1ccccc1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1ccccc1)C\",\"value\": 0.011522684}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1ccccc1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N blocks BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N\",\"value\": 0.011047064}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C effect on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C\",\"value\": 0.006779328}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1\",\"value\": 0.010736044}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC)nc2)ccc1F)N for its BACE1 inhibitory potential. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC)nc2)ccc1F)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC)nc2)ccc1F)N\",\"value\": 0.011939974}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC)nc2)ccc1F)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cc(nc(c1)CC)CC inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cc(nc(c1)CC)CC\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cc(nc(c1)CC)CC\",\"value\": 0.009844128}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cc(nc(c1)CC)CC has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's O=C(NC(C)C)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1 potential as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O=C(NC(C)C)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(NC(C)C)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1\",\"value\": 0.017816143}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C(NC(C)C)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check if the drug O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)C)COC inhibits the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)C)COC\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)C)COC\",\"value\": 0.006702825}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)C)COC has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's FCC\\C=C\\c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1 likelihood of BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FCC\\C=C\\c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FCC\\C=C\\c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1\",\"value\": 0.011200843}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FCC\\C=C\\c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the potential of the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(CC1)C)c1cc(ccc1)C(C)(C)C to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(CC1)C)c1cc(ccc1)C(C)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(CC1)C)c1cc(ccc1)C(C)(C)C\",\"value\": 0.008770254}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(CC1)C)c1cc(ccc1)C(C)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the ability of the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncccc2)ccc1F)N to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncccc2)ccc1F)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncccc2)ccc1F)N\",\"value\": 0.018670402}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncccc2)ccc1F)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's Clc1cc(Cl)ccc1C(=O)N[C@@H](Cc1ccc([N+](=O)[O-])cc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1cc(Cl)ccc1C(=O)N[C@@H](Cc1ccc([N+](=O)[O-])cc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(Cl)ccc1C(=O)N[C@@H](Cc1ccc([N+](=O)[O-])cc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C\",\"value\": 0.009567366}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Clc1cc(Cl)ccc1C(=O)N[C@@H](Cc1ccc([N+](=O)[O-])cc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drug Fc1c(cccc1OC)-c1cc(ccc1)[C@]1([NH+]=C(N2CC(F)(F)CN=C12)N)c1ccncc1 shows activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1c(cccc1OC)-c1cc(ccc1)[C@]1([NH+]=C(N2CC(F)(F)CN=C12)N)c1ccncc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1c(cccc1OC)-c1cc(ccc1)[C@]1([NH+]=C(N2CC(F)(F)CN=C12)N)c1ccncc1\",\"value\": 0.0125544695}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1c(cccc1OC)-c1cc(ccc1)[C@]1([NH+]=C(N2CC(F)(F)CN=C12)N)c1ccncc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC(CC1c1ccccc1)c1ccccc1 inhibit BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC(CC1c1ccccc1)c1ccccc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC(CC1c1ccccc1)c1ccccc1\",\"value\": 0.011246172}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Brc1ccccc1C1C[NH2+]CC1C(=O)N1CCC(CC1c1ccccc1)c1ccccc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\",\"value\": 0.01796361}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1 is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1\",\"value\": 0.012453717}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N demonstrate inhibition of BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\",\"value\": 0.011193152}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C\",\"value\": 0.0104085505}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the drug's S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)COCc1ccccc1)C(=O)NC(C)c1ccccc1)C inhibition of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)COCc1ccccc1)C(=O)NC(C)c1ccccc1)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)COCc1ccccc1)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.009123779}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)COCc1ccccc1)C(=O)NC(C)c1ccccc1)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(NC(=O)C)CC1)c1cc(ccc1)C(C)(C)C can inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(NC(=O)C)CC1)c1cc(ccc1)C(C)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(NC(=O)C)CC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.008147828}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(NC(=O)C)CC1)c1cc(ccc1)C(C)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check the drug Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 for its BACE1 inhibitory activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1\",\"value\": 0.0097541055}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1oc(cn1)C(C)(C)C can block BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1oc(cn1)C(C)(C)C\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1oc(cn1)C(C)(C)C\",\"value\": 0.0058353115}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1oc(cn1)C(C)(C)C has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the potential of the drug O(C)c1cccnc1-c1cc2cc(CC(C(=O)NCCC(C)(C)C)C)c(nc2cc1)N as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"O(C)c1cccnc1-c1cc2cc(CC(C(=O)NCCC(C)(C)C)C)c(nc2cc1)N\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cccnc1-c1cc2cc(CC(C(=O)NCCC(C)(C)C)C)c(nc2cc1)N\",\"value\": 0.010216189}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O(C)c1cccnc1-c1cc2cc(CC(C(=O)NCCC(C)(C)C)C)c(nc2cc1)N has weak inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug O1c2cc(ccc2OC1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 and O=C(N[C@H]([C@H](O)C[NH2+][C@H]1CC(Cc2nn(cc12)CC(C)(C)C)(C)C)Cc1ccccc1)C capable of inhibiting BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O1c2cc(ccc2OC1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"O=C(N[C@H]([C@H](O)C[NH2+][C@H]1CC(Cc2nn(cc12)CC(C)(C)C)(C)C)Cc1ccccc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1c2cc(ccc2OC1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"value\": 0.010504638}, {\"smile\": \"O=C(N[C@H]([C@H](O)C[NH2+][C@H]1CC(Cc2nn(cc12)CC(C)(C)C)(C)C)Cc1ccccc1)C\",\"value\": 0.008683125}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1c2cc(ccc2OC1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 nor O=C(N[C@H]([C@H](O)C[NH2+][C@H]1CC(Cc2nn(cc12)CC(C)(C)C)(C)C)Cc1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1cc(OC2CCCC2)ccc1)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(Nc2ncccc2)CC1)c1cc(ccc1)C(C)(C)C for its ability to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1cc(OC2CCCC2)ccc1)C\",\"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(Nc2ncccc2)CC1)c1cc(ccc1)C(C)(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1cc(OC2CCCC2)ccc1)C\",\"value\": 0.007640427}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(Nc2ncccc2)CC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.0076877894}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)NCC2(N1c1cc(F)ccc1)CC([NH+](CC2)Cc1cc(OC2CCCC2)ccc1)C nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(Nc2ncccc2)CC1)c1cc(ccc1)C(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the effect of the drug Clc1cc(cnc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CC1 and S1CC(N=C(N)C1)(C)c1cc(ccc1)-c1cc(OC)ccc1 on BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cc(cnc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CC1\",\"S1CC(N=C(N)C1)(C)c1cc(ccc1)-c1cc(OC)ccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(cnc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CC1\",\"value\": 0.009581936}, {\"smile\": \"S1CC(N=C(N)C1)(C)c1cc(ccc1)-c1cc(OC)ccc1\",\"value\": 0.009943261}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc(cnc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CC1 nor S1CC(N=C(N)C1)(C)c1cc(ccc1)-c1cc(OC)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cccnc1 and n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cccnc1\",\"n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cccnc1\",\"value\": 0.018429084}, {\"smile\": \"n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1\",\"value\": 0.01558198}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1ccc(cc1)C1(N=C(C)C(=N1)N)c1cc(ccc1)-c1cccnc1 nor n1cc(ccc1)-c1cc(ccc1)C1(N=C(N2C1=NCCC2)N)c1ccncc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3C(C2)C3)C)c(F)cc1 and n1cc(cnc1)-c1cc(ccc1)C1(N=C(C(C)C)C(=N1)N)c1ccccc1 action on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3C(C2)C3)C)c(F)cc1\",\"n1cc(cnc1)-c1cc(ccc1)C1(N=C(C(C)C)C(=N1)N)c1ccccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3C(C2)C3)C)c(F)cc1\",\"value\": 0.011509798}, {\"smile\": \"n1cc(cnc1)-c1cc(ccc1)C1(N=C(C(C)C)C(=N1)N)c1ccccc1\",\"value\": 0.0072701527}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3C(C2)C3)C)c(F)cc1 nor n1cc(cnc1)-c1cc(ccc1)C1(N=C(C(C)C)C(=N1)N)c1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the likelihood of the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC and O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC\",\"O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC\",\"value\": 0.008761964}, {\"smile\": \"O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"value\": 0.009498536}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCC nor O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2 and S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 inhibitory effect on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2\",\"S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2\",\"value\": 0.018074501}, {\"smile\": \"S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1\",\"value\": 0.011023912}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2 nor S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug O=C1NC(=NC(=C1)CCC)N and S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=NC(=C1)CCC)N\",\"S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1NC(=NC(=C1)CCC)N\",\"value\": 0.018095518}, {\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.012536182}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C1NC(=NC(=C1)CCC)N nor S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCOC2=O)ccc1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C)c1ccc(F)cc1)-c1nc([nH]n1)C([NH3+])(Cc1ccccc1)C)C demonstrates BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCOC2=O)ccc1\",\"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C)c1ccc(F)cc1)-c1nc([nH]n1)C([NH3+])(Cc1ccccc1)C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCOC2=O)ccc1\",\"value\": 0.008834979}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C)c1ccc(F)cc1)-c1nc([nH]n1)C([NH3+])(Cc1ccccc1)C)C\",\"value\": 0.0074485913}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCOC2=O)ccc1 nor S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C)c1ccc(F)cc1)-c1nc([nH]n1)C([NH3+])(Cc1ccccc1)C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cn(nc2)CC(C)(C)C)C1 and Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccncc1 is a potential BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cn(nc2)CC(C)(C)C)C1\",\"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccncc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cn(nc2)CC(C)(C)C)C1\",\"value\": 0.010533335}, {\"smile\": \"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccncc1\",\"value\": 0.01777899}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cn(nc2)CC(C)(C)C)C1 nor Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccncc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C and Clc1cc(ccc1OCCCC)CSC(=[NH2+])N act as a BACE1 inhibitor? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C\",\"Clc1cc(ccc1OCCCC)CSC(=[NH2+])N\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C\",\"value\": 0.031922355}, {\"smile\": \"Clc1cc(ccc1OCCCC)CSC(=[NH2+])N\",\"value\": 0.02180005}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C nor Clc1cc(ccc1OCCCC)CSC(=[NH2+])N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug O=C([O-])C(NC1=NC(Cc2c1ccc(c2)C)(C)C)Cc1ccccc1 and O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CCN(CC1)C(=O)C blocks BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C([O-])C(NC1=NC(Cc2c1ccc(c2)C)(C)C)Cc1ccccc1\",\"O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CCN(CC1)C(=O)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C([O-])C(NC1=NC(Cc2c1ccc(c2)C)(C)C)Cc1ccccc1\",\"value\": 0.019226601}, {\"smile\": \"O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CCN(CC1)C(=O)C\",\"value\": 0.020688979}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C([O-])C(NC1=NC(Cc2c1ccc(c2)C)(C)C)Cc1ccccc1 nor O(C)c1cc(ccc1)-c1cc(ccc1)C1(N=C(C)C(=N1)N)C1CCN(CC1)C(=O)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the likelihood of the drug S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and Clc1ccc(cc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"Clc1ccc(cc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.009616793}, {\"smile\": \"Clc1ccc(cc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\",\"value\": 0.015538402}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)CC(Cc2cc(OC(C)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 nor Clc1ccc(cc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cc(ccc2n(c1)C(=O)N(CCCC)C)C(=O)[O-])C(O)C[NH2+]Cc1cc(OC)ccc1 and Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(OC)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 inhibit the BACE1 enzyme. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cc(ccc2n(c1)C(=O)N(CCCC)C)C(=O)[O-])C(O)C[NH2+]Cc1cc(OC)ccc1\",\"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(OC)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cc(ccc2n(c1)C(=O)N(CCCC)C)C(=O)[O-])C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.015366021}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(OC)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.017235748}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cc(ccc2n(c1)C(=O)N(CCCC)C)C(=O)[O-])C(O)C[NH2+]Cc1cc(OC)ccc1 nor Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(OC)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 and Fc1c2c(ccc1)C(N=C2N)(c1cc(ccc1)-c1cc(cnc1)C#CC)c1cc(ncc1)C(F)F has an cytostatic effect on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"Fc1c2c(ccc1)C(N=C2N)(c1cc(ccc1)-c1cc(cnc1)C#CC)c1cc(ncc1)C(F)F\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"value\": 0.031707402}, {\"smile\": \"Fc1c2c(ccc1)C(N=C2N)(c1cc(ccc1)-c1cc(cnc1)C#CC)c1cc(ncc1)C(F)F\",\"value\": 0.036079224}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(CC)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 nor Fc1c2c(ccc1)C(N=C2N)(c1cc(ccc1)-c1cc(cnc1)C#CC)c1cc(ncc1)C(F)F has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1)CC(F)(F)F and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 ability to block BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1)CC(F)(F)F\",\"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1)CC(F)(F)F\",\"value\": 0.0051683094}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.0052707833}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1)CC(F)(F)F nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug Oc1ccc(cc1)CC[NH3+] and Fc1ccc(cc1)C[C@H](NC(=O)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 demonstrates activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(cc1)CC[NH3+]\",\"Fc1ccc(cc1)C[C@H](NC(=O)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Oc1ccc(cc1)CC[NH3+]\",\"value\": 0.011513791}, {\"smile\": \"Fc1ccc(cc1)C[C@H](NC(=O)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.008180324}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Oc1ccc(cc1)CC[NH3+] nor Fc1ccc(cc1)C[C@H](NC(=O)C)[C@H](O)C[NH2+][C@H]1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the potential of the drug S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C)C and Clc1cc2nc([nH]c2cc1)N to block BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C)C\",\"Clc1cc2nc([nH]c2cc1)N\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C)C\",\"value\": 0.0062262705}, {\"smile\": \"Clc1cc2nc([nH]c2cc1)N\",\"value\": 0.016653065}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C)C(C(=O)NOCc2ccccc2)=C1C)C nor Clc1cc2nc([nH]c2cc1)N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#C and S(=O)(=O)(N(C)c1cc2cc(NCCCCOc3cc(CC(NC2=O)C(O)C[NH2+]C2(CC2)c2cc(ccc2)C(C)C)ccc3)c1)CCC ability to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#C\",\"S(=O)(=O)(N(C)c1cc2cc(NCCCCOc3cc(CC(NC2=O)C(O)C[NH2+]C2(CC2)c2cc(ccc2)C(C)C)ccc3)c1)CCC\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#C\",\"value\": 0.012557318}, {\"smile\": \"S(=O)(=O)(N(C)c1cc2cc(NCCCCOc3cc(CC(NC2=O)C(O)C[NH2+]C2(CC2)c2cc(ccc2)C(C)C)ccc3)c1)CCC\",\"value\": 0.00849838}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#C nor S(=O)(=O)(N(C)c1cc2cc(NCCCCOc3cc(CC(NC2=O)C(O)C[NH2+]C2(CC2)c2cc(ccc2)C(C)C)ccc3)c1)CCC has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C and Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 inhibitory activity on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C\",\"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C\",\"value\": 0.0070131966}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2\",\"value\": 0.005370627}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C nor Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)C)c1cc(ccc1)-c1cncnc1 and Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C block BACE1 activity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)C)c1cc(ccc1)-c1cncnc1\",\"Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)C)c1cc(ccc1)-c1cncnc1\",\"value\": 0.014681938}, {\"smile\": \"Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C\",\"value\": 0.013830701}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)C)c1cc(ccc1)-c1cncnc1 nor Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OCC1)N)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug O(C)c1cc(ccc1)-c1cc(ccc1)[C@]1(N=C(N)N(C)C(=O)C1)C and Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3)CC2)c(OC(C)C)c(OC)c1 inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O(C)c1cc(ccc1)-c1cc(ccc1)[C@]1(N=C(N)N(C)C(=O)C1)C\",\"Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3)CC2)c(OC(C)C)c(OC)c1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)-c1cc(ccc1)[C@]1(N=C(N)N(C)C(=O)C1)C\",\"value\": 0.016486164}, {\"smile\": \"Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3)CC2)c(OC(C)C)c(OC)c1\",\"value\": 0.015290452}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1cc(ccc1)-c1cc(ccc1)[C@]1(N=C(N)N(C)C(=O)C1)C nor Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3)CC2)c(OC(C)C)c(OC)c1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCC and O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2NC(=O)N(c2c1)CC)Cc1ccccc1 for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCC\",\"O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2NC(=O)N(c2c1)CC)Cc1ccccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCC\",\"value\": 0.0117626}, {\"smile\": \"O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2NC(=O)N(c2c1)CC)Cc1ccccc1\",\"value\": 0.0094081005}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCC nor O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1cc(N2CCCC2=O)c2NC(=O)N(c2c1)CC)Cc1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's O1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C and S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](C(C)C)C(=O)Nc1cc(cc(c1)C(=O)[O-])C(=O)[O-])COc1cc(F)cc(F)c1)C(=O)N[C@H](C)c1ccccc1)C cytostatic potential against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C\",\"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](C(C)C)C(=O)Nc1cc(cc(c1)C(=O)[O-])C(=O)[O-])COc1cc(F)cc(F)c1)C(=O)N[C@H](C)c1ccccc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C\",\"value\": 0.0128361005}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](C(C)C)C(=O)Nc1cc(cc(c1)C(=O)[O-])C(=O)[O-])COc1cc(F)cc(F)c1)C(=O)N[C@H](C)c1ccccc1)C\",\"value\": 0.012098584}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C nor S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](C(C)C)C(=O)Nc1cc(cc(c1)C(=O)[O-])C(=O)[O-])COc1cc(F)cc(F)c1)C(=O)N[C@H](C)c1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cn(nc1)C1CCCC1 and Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)CC)c1cc(ccc1)-c1cncnc1 shows cytostatic activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cn(nc1)C1CCCC1\",\"Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)CC)c1cc(ccc1)-c1cncnc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cn(nc1)C1CCCC1\",\"value\": 0.011291204}, {\"smile\": \"Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)CC)c1cc(ccc1)-c1cncnc1\",\"value\": 0.013737908}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1cn(nc1)C1CCCC1 nor Fc1c2c(ccc1)C(N=C2N)(C=1C=C(C)C(=O)N(C=1)CC)c1cc(ccc1)-c1cncnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug O(C)c1cc(ccc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1 and Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)(C)C blocks the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O(C)c1cc(ccc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1\",\"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1\",\"value\": 0.01321866}, {\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)(C)C\",\"value\": 0.013733462}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1cc(ccc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1 nor Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C(C)(C)c1cc(ccc1)C(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 and Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C has the potential to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2\",\"Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2\",\"value\": 0.008267355}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C\",\"value\": 0.010262614}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C1[NH2+]Cc2c(C1)cccc2 nor Clc1cc2CC(N=C(NC(Cc3cscc3C#C)C(=O)[O-])c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(Oc2cncnc2)cc1 and O(c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1)C1CCCC1 inhibit the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(Oc2cncnc2)cc1\",\"O(c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1)C1CCCC1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(Oc2cncnc2)cc1\",\"value\": 0.013002254}, {\"smile\": \"O(c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1)C1CCCC1\",\"value\": 0.012042291}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(Oc2cncnc2)cc1 nor O(c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1)C1CCCC1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N and S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C(C)C)C(C(=O)NOCc2ccccc2)=C1C)C is a candidate for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N\",\"S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C(C)C)C(C(=O)NOCc2ccccc2)=C1C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N\",\"value\": 0.020774232}, {\"smile\": \"S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C(C)C)C(C(=O)NOCc2ccccc2)=C1C)C\",\"value\": 0.016565975}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N nor S(=O)(=O)(N1C(C)=C(C(=O)N[C@H]([C@H](O)C[NH2+]C2CC2)Cc2ccccc2)[C@@H](C(C)C)C(C(=O)NOCc2ccccc2)=C1C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the likelihood of the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N and S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC([C@H](O)C[NH2+]CC#C)Cc1ccccc1)C inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\",\"S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC([C@H](O)C[NH2+]CC#C)Cc1ccccc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N\",\"value\": 0.010728834}, {\"smile\": \"S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC([C@H](O)C[NH2+]CC#C)Cc1ccccc1)C\",\"value\": 0.011944294}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(OC(F)(F)F)cc2)ccc1F)N nor S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)NC([C@H](O)C[NH2+]CC#C)Cc1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the potential of the drug S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 and Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1\",\"Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1\",\"value\": 0.005637098}, {\"smile\": \"Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1\",\"value\": 0.0035390006}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(Oc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)(=O)(=O)Cc1ccccc1 nor Fc1ncccc1-c1cc(ccc1)[C@]1([NH+]=C(N2CC(N=C12)(C)C)N)c1ccc(OC(F)(F)F)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C and Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C acts as an inhibitor of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C\",\"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C\",\"value\": 0.006796765}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C\",\"value\": 0.0071996143}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1)c1ccccc1)C nor Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug FC1(F)CN2C(=NC1)[C@]([NH+]=C2N)(c1cc(ccc1)C#CCOC)c1ccc(OC(F)F)cc1 and S(=O)(=O)(N(Cc1ccccc1)c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)C prevent BACE1 activity? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"FC1(F)CN2C(=NC1)[C@]([NH+]=C2N)(c1cc(ccc1)C#CCOC)c1ccc(OC(F)F)cc1\",\"S(=O)(=O)(N(Cc1ccccc1)c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC1(F)CN2C(=NC1)[C@]([NH+]=C2N)(c1cc(ccc1)C#CCOC)c1ccc(OC(F)F)cc1\",\"value\": 0.008170457}, {\"smile\": \"S(=O)(=O)(N(Cc1ccccc1)c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)C\",\"value\": 0.00900345}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC1(F)CN2C(=NC1)[C@]([NH+]=C2N)(c1cc(ccc1)C#CCOC)c1ccc(OC(F)F)cc1 nor S(=O)(=O)(N(Cc1ccccc1)c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the cytostatic potential of the drug O=C(N)CC[C@H](N(C(=O)[C@@H](NC(=O)[C@@H](O)C)C(C)C)C)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N([C@H](CC1CCCCC1)C(=O)[O-])C)C)CC1CCCCC1 and FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(N)CC[C@H](N(C(=O)[C@@H](NC(=O)[C@@H](O)C)C(C)C)C)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N([C@H](CC1CCCCC1)C(=O)[O-])C)C)CC1CCCCC1\",\"FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(N)CC[C@H](N(C(=O)[C@@H](NC(=O)[C@@H](O)C)C(C)C)C)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N([C@H](CC1CCCCC1)C(=O)[O-])C)C)CC1CCCCC1\",\"value\": 0.0107877785}, {\"smile\": \"FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1\",\"value\": 0.010126047}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C(N)CC[C@H](N(C(=O)[C@@H](NC(=O)[C@@H](O)C)C(C)C)C)C(=O)N[C@H]([C@@H](O)CC(=O)N[C@@H](CC(C)C)C(=O)N[C@H](C(=O)N([C@H](CC1CCCCC1)C(=O)[O-])C)C)CC1CCCCC1 nor FC(F)(F)Oc1cc(ccc1)-c1cc2c(Oc3c(cc(OC)cc3)C23N=C(OC3)N)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug O1c2ncc(cc2[C@@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)[C@H](OC)C)Cc2cc3OCOc3cc2)CC12CCC2)CC(C)(C)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(N(O)C(=O)C)CC1)c1cc(ccc1)C(C)(C)C has cytostatic effects on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O1c2ncc(cc2[C@@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)[C@H](OC)C)Cc2cc3OCOc3cc2)CC12CCC2)CC(C)(C)C\",\"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(N(O)C(=O)C)CC1)c1cc(ccc1)C(C)(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1c2ncc(cc2[C@@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)[C@H](OC)C)Cc2cc3OCOc3cc2)CC12CCC2)CC(C)(C)C\",\"value\": 0.018746044}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(N(O)C(=O)C)CC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.0194088}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1c2ncc(cc2[C@@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)[C@H](OC)C)Cc2cc3OCOc3cc2)CC12CCC2)CC(C)(C)C nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(N(O)C(=O)C)CC1)c1cc(ccc1)C(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drug S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCCCC1)CC and O1CC2(N=C1N)c1cc(ccc1Oc1c2cc(OC)cc1)-c1cc(ccc1)C blocks BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCCCC1)CC\",\"O1CC2(N=C1N)c1cc(ccc1Oc1c2cc(OC)cc1)-c1cc(ccc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCCCC1)CC\",\"value\": 0.017665247}, {\"smile\": \"O1CC2(N=C1N)c1cc(ccc1Oc1c2cc(OC)cc1)-c1cc(ccc1)C\",\"value\": 0.019259404}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCCCC1)CC nor O1CC2(N=C1N)c1cc(ccc1Oc1c2cc(OC)cc1)-c1cc(ccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug S1(=O)(=O)CC(Cc2cc(OCCC)ccc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and S(=O)(=O)(Nc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccccc1 is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)CC(Cc2cc(OCCC)ccc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"S(=O)(=O)(Nc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(OCCC)ccc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.013369117}, {\"smile\": \"S(=O)(=O)(Nc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccccc1\",\"value\": 0.011971229}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)CC(Cc2cc(OCCC)ccc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 nor S(=O)(=O)(Nc1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the ability of the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C to inhibit BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.01610437}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C\",\"value\": 0.016265973}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 nor S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COC)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C and Clc1cc(ccc1)-c1cc(ccc1)C1(N=C(OC1)N)c1ccc(OC)cc1 for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COC)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C\",\"Clc1cc(ccc1)-c1cc(ccc1)C1(N=C(OC1)N)c1ccc(OC)cc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COC)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.013284221}, {\"smile\": \"Clc1cc(ccc1)-c1cc(ccc1)C1(N=C(OC1)N)c1ccc(OC)cc1\",\"value\": 0.01770063}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COC)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C nor Clc1cc(ccc1)-c1cc(ccc1)C1(N=C(OC1)N)c1ccc(OC)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug O=C1N(C)C(=NC(C1)(C)C1CC1c1cc(ccc1)CCc1ccccc1)N and FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCC2=O)cc(NCC)c1)Cc1ccccc1 acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1N(C)C(=NC(C1)(C)C1CC1c1cc(ccc1)CCc1ccccc1)N\",\"FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCC2=O)cc(NCC)c1)Cc1ccccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(C)C(=NC(C1)(C)C1CC1c1cc(ccc1)CCc1ccccc1)N\",\"value\": 0.014451343}, {\"smile\": \"FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCC2=O)cc(NCC)c1)Cc1ccccc1\",\"value\": 0.011778165}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C1N(C)C(=NC(C1)(C)C1CC1c1cc(ccc1)CCc1ccccc1)N nor FC(F)(F)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(N2CCCC2=O)cc(NCC)c1)Cc1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NC(COCc1cc(ccc1)C(NC2=O)c1ccccc1)C(O)CC(C(C)C)C(=O)NC)C and Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N(C)C)ccc1 demonstrates activity against BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NC(COCc1cc(ccc1)C(NC2=O)c1ccccc1)C(O)CC(C(C)C)C(=O)NC)C\",\"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N(C)C)ccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NC(COCc1cc(ccc1)C(NC2=O)c1ccccc1)C(O)CC(C(C)C)C(=O)NC)C\",\"value\": 0.016916238}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N(C)C)ccc1\",\"value\": 0.015163393}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NC(COCc1cc(ccc1)C(NC2=O)c1ccccc1)C(O)CC(C(C)C)C(=O)NC)C nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N(C)C)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3C)CC2)c(OC(C)C)c(OC)c1 and S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C role in inhibiting BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3C)CC2)c(OC(C)C)c(OC)c1\",\"S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3C)CC2)c(OC(C)C)c(OC)c1\",\"value\": 0.008271351}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C\",\"value\": 0.007895144}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc(C[NH+]2CCC(NC(=O)COc3ccc(S(=O)(=O)N)cc3C)CC2)c(OC(C)C)c(OC)c1 nor S(=O)(=O)(N(C)c1cc(cc(c1)C(OCC(O)(Cc1ccccc1)C[NH3+])=O)C(=O)NC(C)c1ccc(F)cc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C)COC and Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 potential for BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C)COC\",\"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C)COC\",\"value\": 0.008343792}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.012047461}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1CCCCNc2cc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C)COC nor Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(Oc2ccc(F)cc2)c1)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug IC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1 and FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCCC for BACE1 cytostatic effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"IC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1\",\"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCCC\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"IC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1\",\"value\": 0.011359084}, {\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCCC\",\"value\": 0.010999384}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug IC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1 nor FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCCC has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check if the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]Cc1cc(ccc1)C(C)(C)C and OC(C(NC(=O)c1cc(Nc2ccccc2)ccc1)Cc1ccccc1)C[NH2+]C(C(=O)NC1CCCCC1)C is an effective BACE1 inhibitor. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]Cc1cc(ccc1)C(C)(C)C\",\"OC(C(NC(=O)c1cc(Nc2ccccc2)ccc1)Cc1ccccc1)C[NH2+]C(C(=O)NC1CCCCC1)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]Cc1cc(ccc1)C(C)(C)C\",\"value\": 0.01413276}, {\"smile\": \"OC(C(NC(=O)c1cc(Nc2ccccc2)ccc1)Cc1ccccc1)C[NH2+]C(C(=O)NC1CCCCC1)C\",\"value\": 0.011371644}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]Cc1cc(ccc1)C(C)(C)C nor OC(C(NC(=O)c1cc(Nc2ccccc2)ccc1)Cc1ccccc1)C[NH2+]C(C(=O)NC1CCCCC1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N and Clc1scc(c1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 effect on BACE1 inhibition. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N\",\"Clc1scc(c1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N\",\"value\": 0.02305942}, {\"smile\": \"Clc1scc(c1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1\",\"value\": 0.017750079}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O=C1N(C)C(=NC(=C1)CCc1ccc(cc1)-c1ccccc1)N nor Clc1scc(c1)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug O(C(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1)C1CCN(CC1)C(=O)C and O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C blocks the activity of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O(C(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1)C1CCN(CC1)C(=O)C\",\"O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1)C1CCN(CC1)C(=O)C\",\"value\": 0.017041292}, {\"smile\": \"O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C\",\"value\": 0.011929448}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C(=O)C1[NH2+]CC2(C1)c1c(NC2=O)cccc1)C1CCN(CC1)C(=O)C nor O1CCCCCCNC(=O)CC(NC(=O)C(NC1=O)C(C)C)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NCc1ccccc1)C)CC(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the potential cytostatic activity of the drug Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@]1([NH+]=C(N)CN(C)C1=O)C and O(CC1CC1)c1cc(ccc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1 on BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@]1([NH+]=C(N)CN(C)C1=O)C\",\"O(CC1CC1)c1cc(ccc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@]1([NH+]=C(N)CN(C)C1=O)C\",\"value\": 0.02101119}, {\"smile\": \"O(CC1CC1)c1cc(ccc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1\",\"value\": 0.020403411}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc(ccc1)[C@]1([NH+]=C(N)CN(C)C1=O)C nor O(CC1CC1)c1cc(ccc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug S1CCC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N and O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N is an inhibitor of BACE1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"S1CCC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N\",\"O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1CCC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N\",\"value\": 0.018024871}, {\"smile\": \"O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N\",\"value\": 0.0162773}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1CCC(=NC1(C)c1cc(ccc1)-c1cc(OC)ccc1)N nor O=C1N(Cc2ccc(cc2)CNC(=O)NCCCC)C(NC1(CC1CCCCC1)C)=N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C and O(C(C)C)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 inhibit the enzyme activity of BACE1? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C\",\"O(C(C)C)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\"], \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C\",\"value\": 0.014285962}, {\"smile\": \"O(C(C)C)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1\",\"value\": 0.012400691}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(C)C nor O(C(C)C)c1cc(ccc1OC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cccnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug capable of inhibiting BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)-c1cc(ccc1)CC[C@]1(N=C(N)N(C)C(=O)C1)C\",\"value\": 0.012871137}, {\"smile\": \"S1(=O)(=O)CC(Cc2cc(O[C@@H](C(F)(F)F)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.008688446}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1cc(ccc1)-c1cc(ccc1)CC[C@]1(N=C(N)N(C)C(=O)C1)C nor S1(=O)(=O)CC(Cc2cc(O[C@@H](C(F)(F)F)C)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug for its ability to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1C2CCCCC2(N=C1N)c1cc(ccc1)-c1cc(ccc1)C#N\",\"value\": 0.005589506}, {\"smile\": \"Brc1cscc1CC(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)C(=O)[O-]\",\"value\": 0.0056767594}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1C2CCCCC2(N=C1N)c1cc(ccc1)-c1cc(ccc1)C#N nor Brc1cscc1CC(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)C(=O)[O-] has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the effect of the drug on BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(O)(C)C)C1\",\"value\": 0.013250001}, {\"smile\": \"Fc1ncccc1-c1cc2c(CC(CC23N=C(OC3)N)(C)C)cc1\",\"value\": 0.015754934}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(O)(C)C)C1 nor Fc1ncccc1-c1cc2c(CC(CC23N=C(OC3)N)(C)C)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(c1cc(cc(OCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ccccc1)C\",\"value\": 0.0050034383}, {\"smile\": \"s1cc(cc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1\",\"value\": 0.008674747}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(c1cc(cc(OCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ccccc1)C nor s1cc(cc1)-c1ccc(cc1)C1CC1C=1N=C(N)N(C)C(=O)C=1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's action on BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCc2nc(ncc2C1)C)c1cc(ccc1)C(C)(C)C\",\"value\": 0.012303238}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3oc(nn3)C)c2cc1)(C)C\",\"value\": 0.014704891}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCc2nc(ncc2C1)C)c1cc(ccc1)C(C)(C)C nor Clc1cc2CC(N=C(NC(Cc3ccccc3)c3oc(nn3)C)c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)C(O)C1[NH2+]CC(OCCC)C1\",\"value\": 0.022142235}, {\"smile\": \"S(=O)(=O)(N(c1cccc(C(=O)NC(Cc2ccccc2)C(O)C[NH2+]Cc2cc(ccc2)C(F)(F)F)c1C)c1ccccc1)C\",\"value\": 0.016343601}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)C(O)C1[NH2+]CC(OCCC)C1 nor S(=O)(=O)(N(c1cccc(C(=O)NC(Cc2ccccc2)C(O)C[NH2+]Cc2cc(ccc2)C(F)(F)F)c1C)c1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's inhibitory effect on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"[nH]1c2cc(ccc2cc1)-c1cc(ccc1)CNc1cccnc1N\",\"value\": 0.015704306}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1ccccc1\",\"value\": 0.010508708}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug [nH]1c2cc(ccc2cc1)-c1cc(ccc1)CNc1cccnc1N nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug has activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1ccc(cc1OCCCCF)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1\",\"value\": 0.009706212}, {\"smile\": \"Clc1cc(cnc1)-c1cc2c(CC(CC23N=C(OC3)N)(C)C)cc1\",\"value\": 0.012068825}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1ccc(cc1OCCCCF)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC(F)F)cc1 nor Clc1cc(cnc1)-c1cc2c(CC(CC23N=C(OC3)N)(C)C)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug demonstrates BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"ClC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1\",\"value\": 0.015696237}, {\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCC\",\"value\": 0.016119307}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug ClC1=CN=C(NC1=O)C(NC1=NC(Cc2c1ccc(Cl)c2)(C)C)Cc1ccccc1 nor FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCC has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug is a potential BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)CNc1ccccc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.013931421}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)C=3NC(=O)C=CN=3)c2cc1)(C)C\",\"value\": 0.014846532}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)CNc1ccccc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C nor Clc1cc2CC(N=C(NC(Cc3ccccc3)C=3NC(=O)C=CN=3)c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug act as a BACE1 inhibitor? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"s1cc(cc1)-c1cc(ccc1)C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1\",\"value\": 0.0059172306}, {\"smile\": \"O(C)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)C1=CN(CC(OC(C)C)=O)C(C)=C(C(=O)C)[C@H]1C)Cc1ccccc1\",\"value\": 0.0063412376}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug s1cc(cc1)-c1cc(ccc1)C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1 nor O(C)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)C1=CN(CC(OC(C)C)=O)C(C)=C(C(=O)C)[C@H]1C)Cc1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug blocks BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1\",\"value\": 0.00869765}, {\"smile\": \"O1C2CCCCC2(N=C1N)c1cc(NC(=O)c2ncccc2)ccc1\",\"value\": 0.0059416634}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 nor O1C2CCCCC2(N=C1N)c1cc(NC(=O)c2ncccc2)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CCCCC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.013202406}, {\"smile\": \"S(=O)(=O)(C(CCC)CCC)C[C@@H](NC(OCc1ccccc1)=O)C(=O)N[C@H]([C@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1ccccc1\",\"value\": 0.014212428}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CCCCC1)c1cc(ccc1)C(C)(C)C nor S(=O)(=O)(C(CCC)CCC)C[C@@H](NC(OCc1ccccc1)=O)C(=O)N[C@H]([C@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1ccccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug inhibit the BACE1 enzyme. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)(F)Oc1ccc(cc1)[C@]1([NH+]=C(N2C1=NCCC2)N)c1cc(ccc1)-c1cncnc1\",\"value\": 0.008681146}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C(=O)C)C\",\"value\": 0.009665121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)(F)Oc1ccc(cc1)[C@]1([NH+]=C(N2C1=NCCC2)N)c1cc(ccc1)-c1cncnc1 nor S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C(=O)C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug has an cytostatic effect on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc([C@@]2(N=C(N)C(=O)N(C2)C)C)c(F)cc1\",\"value\": 0.00789678}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1nn(cc1)CC(C)(C)C\",\"value\": 0.0049062297}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc([C@@]2(N=C(N)C(=O)N(C2)C)C)c(F)cc1 nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1nn(cc1)CC(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's ability to block BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1cc(ccc1)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)C)C1O\",\"value\": 0.014874105}, {\"smile\": \"S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC([C@H](O)C[NH2+]CCF)Cc1ccccc1)C\",\"value\": 0.017380582}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Brc1cc(ccc1)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)C)C1O nor S1(=O)(=O)N(c2cc(cc3c2n(cc3CC)CC1)C(=O)NC([C@H](O)C[NH2+]CCF)Cc1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug demonstrates activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NC(C)C)C)CC(C)C)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.009358131}, {\"smile\": \"S(=O)(=O)(N(c1cc(cc(OCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ccccc1)C\",\"value\": 0.00823932}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(C(=O)NC(C(C)C)C(=O)NC(C)C)C)CC(C)C)C(=O)NC(C)c1ccccc1)C nor S(=O)(=O)(N(c1cc(cc(OCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ccccc1)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the potential of the drug to block BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCCC\",\"value\": 0.0061077457}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ocnn3)c2cc1)(C)C\",\"value\": 0.0055703074}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CCCC nor Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ocnn3)c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's ability to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(CCCC1)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1\",\"value\": 0.012740041}, {\"smile\": \"S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1\",\"value\": 0.013431439}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)N(CCCC1)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1 nor S1(=O)(=O)Nc2cc(cc3c2n(cc3CC)CCC1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CC1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's inhibitory activity on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc2c(OC3(CC2[NH2+]CC(O)C(NC(=O)C)Cc2ccccc2)CCC3)nc1\",\"value\": 0.010342424}, {\"smile\": \"s1ccnc1-c1cc(ccc1)CC(NC(=O)COCC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.009108733}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc2c(OC3(CC2[NH2+]CC(O)C(NC(=O)C)Cc2ccccc2)CCC3)nc1 nor s1ccnc1-c1cc(ccc1)CC(NC(=O)COCC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug block BACE1 activity? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1cc(ccc1)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O\",\"value\": 0.0069510825}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(ccc1)C1CCOC1\",\"value\": 0.00672092}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Brc1cc(ccc1)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(ccc1)C1CCOC1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug inhibit the enzyme BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C\",\"value\": 0.0077810255}, {\"smile\": \"s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1\",\"value\": 0.008624843}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1c2c(cc(cc2)-c2cncnc2)C2(N=C(N)N(C)C2=O)CC1(C)C nor s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(CCCCC)c1cc(ccc1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1\",\"value\": 0.009794858}, {\"smile\": \"Clc1ccc(N(S(=O)(=O)C)c2cc(ccc2)C(=O)NC(Cc2ccccc2)C(O)C[NH2+]C(C(=O)NC2CCCCC2)C)cc1\",\"value\": 0.00935198}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(CCCCC)c1cc(ccc1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1 nor Clc1ccc(N(S(=O)(=O)C)c2cc(ccc2)C(=O)NC(Cc2ccccc2)C(O)C[NH2+]C(C(=O)NC2CCCCC2)C)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's cytostatic potential against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CC1CCCCC1\",\"value\": 0.01289753}, {\"smile\": \"s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1\",\"value\": 0.019436698}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)C#CC1CCCCC1 nor s1cc(cc1CC)C1(N=C(N)N(C)C1=O)c1cc(ccc1)-c1cncnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug shows cytostatic activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1cc(cc(F)c1N)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O\",\"value\": 0.0077640773}, {\"smile\": \"s1ccnc1-c1cc(cc(F)c1)CC(NC(=O)COC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.007894068}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Brc1cc(cc(F)c1N)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O nor s1ccnc1-c1cc(cc(F)c1)CC(NC(=O)COC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug blocks the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C(C)C)C(=O)CN1C=C(C(=O)N[C@H](C)c2ccccc2)[C@@H](CCC)C(=C1)C(=O)N[C@H]([C@H](O)C[NH2+]C1CC1)Cc1ccccc1\",\"value\": 0.008177028}, {\"smile\": \"Clc1cc(Cl)cnc1C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1\",\"value\": 0.010422552}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C(C)C)C(=O)CN1C=C(C(=O)N[C@H](C)c2ccccc2)[C@@H](CCC)C(=C1)C(=O)N[C@H]([C@H](O)C[NH2+]C1CC1)Cc1ccccc1 nor Clc1cc(Cl)cnc1C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug has the potential to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1\",\"value\": 0.010771888}, {\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C(=O)N)C(=O)N(CCC)CCC)[C@H](O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.011028664}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 nor Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C(=O)N)C(=O)N(CCC)CCC)[C@H](O)C[NH2+]Cc1cc(OC)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug inhibit the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"s1cc(nc1)-c1cc(ccc1)CC(NC(=O)COC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.007438551}, {\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.0052722157}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug s1cc(nc1)-c1cc(ccc1)CC(NC(=O)COC)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2 nor S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug is a candidate for BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(cc(Cl)c1NC(=O)C)CNC(=[NH2+])NC(=O)Cn1c2c(cc(cc2)C#N)cc1\",\"value\": 0.02052645}, {\"smile\": \"Clc1cnc(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1\",\"value\": 0.01912435}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc(cc(Cl)c1NC(=O)C)CNC(=[NH2+])NC(=O)Cn1c2c(cc(cc2)C#N)cc1 nor Clc1cnc(nc1)C(=O)Nc1cc(C2(N=C(OCC2(F)F)N)C)c(F)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the likelihood of the drug inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)N(CCCC1)c1cc(cc(c1)/C(=N\\OCC=C)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1\",\"value\": 0.0097867595}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1oc(cn1)C(C)(C)C\",\"value\": 0.010648566}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)(=O)N(CCCC1)c1cc(cc(c1)/C(=N\\OCC=C)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1 nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1oc(cn1)C(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the potential of the drug to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2\",\"value\": 0.009600426}, {\"smile\": \"FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#C)ccc1F)N\",\"value\": 0.01107415}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)[C@H](O)[C@H]1[NH2+]Cc2c(C1)cccc2 nor FC1(F)COC(=NC1(C)c1cc(NC(=O)c2ncc(cc2)C#C)ccc1F)N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug acts as an inhibitor of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1cc(ccc1OC(=O)NC)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)C)C1O\",\"value\": 0.005755115}, {\"smile\": \"s1cc(cc1C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1)C(C)C\",\"value\": 0.0065052276}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Brc1cc(ccc1OC(=O)NC)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)C)C1O nor s1cc(cc1C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1)C(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug prevent BACE1 activity? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1c2c(n(c1)C(=O)N(CC)CC)cccc2)Cc1ccccc1\",\"value\": 0.011957041}, {\"smile\": \"S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NCCCCCOCC(NC2=O)C(O)C[NH2+]Cc1cc(ccc1)C(C)C)C\",\"value\": 0.00844669}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1c2c(n(c1)C(=O)N(CC)CC)cccc2)Cc1ccccc1 nor S(=O)(=O)(N(C)c1cc2cc(c1)C(=O)NCCCCCOCC(NC2=O)C(O)C[NH2+]Cc1cc(ccc1)C(C)C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the cytostatic potential of the drug against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)CC(Cc2cc(OC(COC)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)C)C1\",\"value\": 0.0154522965}, {\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(OC(F)(F)F)ccc2)C1\",\"value\": 0.011853707}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S1(=O)CC(Cc2cc(OC(COC)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(C)C)C1 nor S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(OC(F)(F)F)ccc2)C1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug has cytostatic effects on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1c2c(cc(cc2)-c2ccncc2)C2(N=C(N)N(C)C2=O)CC1(C)C\",\"value\": 0.007270428}, {\"smile\": \"[NH+]=1[C@](N=C(C)C=1N)(C1CC1)c1cc(ccc1)-c1cc(cnc1)C#CC\",\"value\": 0.00709487}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1c2c(cc(cc2)-c2ccncc2)C2(N=C(N)N(C)C2=O)CC1(C)C nor [NH+]=1[C@](N=C(C)C=1N)(C1CC1)c1cc(ccc1)-c1cc(cnc1)C#CC has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drug blocks BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C\",\"value\": 0.014150664}, {\"smile\": \"FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCC#N\",\"value\": 0.012900383}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C nor FC(F)Oc1ccc(cc1)[C@@]1(N=C(N)N(C)C1=O)c1cc(ccc1)CCCC#N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if the drug is effective at inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(cc1)C1(CCCC1)c1nc(sc1)N\",\"value\": 0.014432759}, {\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1\",\"value\": 0.012425682}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(cc1)C1(CCCC1)c1nc(sc1)N nor Clc1ccccc1-c1n(Cc2nc(N)c(OCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the ability of the drug to inhibit BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)[C@H]1CCOC1\",\"value\": 0.009032205}, {\"smile\": \"s1nc(cc1)-c1cc2c(OC(CC23N=C(N)N(C)C3=O)(C)C)cc1\",\"value\": 0.008236803}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)[C@H]1CCOC1 nor s1nc(cc1)-c1cc2c(OC(CC23N=C(N)N(C)C3=O)(C)C)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug for inhibition of BACE1 activity. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccc(OCCCC)cc1\",\"value\": 0.023149675}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)C(=O)N(CCC)CCC)C([NH3+])C)C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.02165794}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccccc1-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccc(OCCCC)cc1 nor Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)C(=O)N(CCC)CCC)C([NH3+])C)C(O)C[NH2+]Cc1cc(OC)ccc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)CNc1ccccc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.009942186}, {\"smile\": \"Clc1cc2nc(n(c2cc1)C(CC(=O)NCc1cc(F)ccc1)CC)N\",\"value\": 0.016473373}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)CNc1ccccc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C nor Clc1cc2nc(n(c2cc1)C(CC(=O)NCc1cc(F)ccc1)CC)N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug demonstrates activity against BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)[C@H]1CCOC1\",\"value\": 0.016185194}, {\"smile\": \"Brc1cc(ccc1O)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O\",\"value\": 0.012848353}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)[C@H]1CCOC1 nor Brc1cc(ccc1O)CC1CS(=O)(=O)CC([NH2+]Cc2cc(ccc2)C(C)(C)C)C1O has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's role in inhibiting BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3(CC2)CC3)C)c(F)cc1\",\"value\": 0.011800619}, {\"smile\": \"S(=O)(=O)(c1cc(cc(c1)C(=O)NC(Cc1cc(F)cc(F)c1)C(O)C[NH2+]Cc1cc(OC)ccc1)C(=O)N(CCC)CCC)c1ccc(OC)cc1\",\"value\": 0.011269452}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1ccc(nc1)C(=O)Nc1cc(C2(N=C(N)C3(CC2)CC3)C)c(F)cc1 nor S(=O)(=O)(c1cc(cc(c1)C(=O)NC(Cc1cc(F)cc(F)c1)C(O)C[NH2+]Cc1cc(OC)ccc1)C(=O)N(CCC)CCC)c1ccc(OC)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's potential for BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(ccc1O)C[NH+]1CCC2(N(S(=O)(=O)N(C2)C)c2cc(F)ccc2)CC1C\",\"value\": 0.017337693}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.014654383}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc(ccc1O)C[NH+]1CCC2(N(S(=O)(=O)N(C2)C)c2cc(F)ccc2)CC1C nor Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCC1)c1cc(ccc1)C(C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug for BACE1 cytostatic effects. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc2CC(N=C(NC(Cc3cscc3-c3cn(nc3)C)C(=O)[O-])c2cc1)(C)C\",\"value\": 0.009235373}, {\"smile\": \"Clc1cc(-c2cc3c(OC(CC34N=C(N)N(C)C4=O)(C)C)cc2)c(F)cc1\",\"value\": 0.009665879}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc2CC(N=C(NC(Cc3cscc3-c3cn(nc3)C)C(=O)[O-])c2cc1)(C)C nor Clc1cc(-c2cc3c(OC(CC34N=C(N)N(C)C4=O)(C)C)cc2)c(F)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check if the drug is an effective BACE1 inhibitor. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1c2c(n(c1)C(=O)N(CCCC)CCCC)cccc2)Cc1ccccc1\",\"value\": 0.005827701}, {\"smile\": \"O1[C@@H]2COCC[C@@]2(N=C1N)c1cc(ccc1)-c1cncnc1\",\"value\": 0.006218675}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O(C)c1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1c2c(n(c1)C(=O)N(CCCC)CCCC)cccc2)Cc1ccccc1 nor O1[C@@H]2COCC[C@@]2(N=C1N)c1cc(ccc1)-c1cncnc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's effect on BACE1 inhibition. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(-n2nccc2)ccc1\",\"value\": 0.0147843165}, {\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C\",\"value\": 0.012004025}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(-n2nccc2)ccc1 nor Clc1cc2CC(N=C(NC(Cc3ccccc3)c3ncccn3)c2cc1)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug blocks the activity of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O1C2CCCCC2(N=C1N)c1cc(ccc1)-c1cc(OC)ccc1\",\"value\": 0.011764741}, {\"smile\": \"Clc1cc2nc(n(c2cc1)CCCC(=O)NCc1ncccc1)N\",\"value\": 0.014362201}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug O1C2CCCCC2(N=C1N)c1cc(ccc1)-c1cc(OC)ccc1 nor Clc1cc2nc(n(c2cc1)CCCC(=O)NCc1ncccc1)N has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the potential cytostatic activity of the drug on BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc2CC(N=C(NC(Cc3ccccc3)c3oc(nn3)C)c2cc1)(C)C\",\"value\": 0.010172183}, {\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)c(NCCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1\",\"value\": 0.01067672}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Clc1cc2CC(N=C(NC(Cc3ccccc3)c3oc(nn3)C)c2cc1)(C)C nor Clc1ccccc1-c1n(Cc2nc(N)c(NCCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug is an inhibitor of BACE1. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"n1c2c(ccc1N)cccc2\",\"value\": 0.009990227}, {\"smile\": \"s1c2OC(C[C@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)C)Cc3ccccc3)c2cc1CC(C)(C)C)(C)C\",\"value\": 0.007582955}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug n1c2c(ccc1N)cccc2 nor s1c2OC(C[C@H]([NH2+]C[C@@H](O)[C@@H](NC(=O)C)Cc3ccccc3)c2cc1CC(C)(C)C)(C)C has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug inhibit the enzyme activity of BACE1? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(C(C)C)C(NC(=O)C(C(C)C)CC(O)C1NC(=O)c2cc(N(S(=O)(=O)C)C)cc(c2)C(=O)NC(c2cc(COC1)ccc2)C)C(=O)NCc1ccccc1\",\"value\": 0.0069462257}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)C(O)C1[NH2+]CC(OCc2cc(ccc2)C#N)C1\",\"value\": 0.007215319}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug S(C(C)C)C(NC(=O)C(C(C)C)CC(O)C1NC(=O)c2cc(N(S(=O)(=O)C)C)cc(c2)C(=O)NC(c2cc(COC1)ccc2)C)C(=O)NCc1ccccc1 nor Fc1cc(cc(F)c1)CC(NC(=O)C(N1CCC(NC(=O)C)(C(CC)C)C1=O)CCc1ccccc1)C(O)C1[NH2+]CC(OCc2cc(ccc2)C#N)C1 has inhibitory activity on BACE1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the generated drugs for inhibitory activity against BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)/C(=N\\OCC=C)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)C\",\"value\": 0.008292187}, {\"smile\": \"O(C)C1CC(N=C1N)(c1cc(ccc1)-c1cccnc1)c1ccc(OC)cc1\",\"value\": 0.010081299}, , {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1c2cccnc2n(c1)C(=O)N(CCCC)C)C(O)C[NH2+]C1(CC1)c1cc(ccc1)CC\",\"value\": 0.008304712}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.008292187, 0.010081299 and 0.008304712."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)c1cc(cc(c1)/C(=N\\OCc1ccccc1)/C)C(=O)N(CCC)CCC)C(O)C[NH2+]Cc1cc(OC)ccc1\",\"value\": 0.011683591}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCC(=O)NC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.012010338}, , {\"smile\": \"O1CCCCNc2nc(cc(c2)C(=O)NC(Cc2cc1ccc2)C(O)C[NH2+]Cc1cc(ccc1)C(C)C)C\",\"value\": 0.01045483}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.011683591, 0.012010338 and 0.01045483."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the optimized drugs can inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(C(C)(C)C)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H]([C@@H](O)C[C@H](C(=O)NCC(C)C)C)CC(C)C\",\"value\": 0.007055703}, {\"smile\": \"Fc1ccc(cc1)CC1CC[NH2+]CC1\",\"value\": 0.007216402}, , {\"smile\": \"n1c2c(ccc1N)cccc2\",\"value\": 0.009814946}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.007055703, 0.007216402 and 0.009814946."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the inhibitory potential of the optimized drugs on BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(ccc1)C#N\",\"value\": 0.009057104}, {\"smile\": \"Clc1cc(cnc1)-c1cc2c(SC(CC23N=C(N)N(C)C3=O)(C)C)cc1\",\"value\": 0.00895923}, , {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NCC([NH3+])(Cc1ccccc1)CO)C(=O)NC(C)c1ccc(F)cc1)C\",\"value\": 0.00799798}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.009057104, 0.00895923 and 0.00799798."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the optimized drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(ccc1)-c1cc2c(OC(C[C@@]23N=C(N)N(C)C3=O)(C)C)cc1\",\"value\": 0.010749158}, {\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)[C@H](O)[C@@H]1[NH2+]C[C@H](Oc2ccccc2)C1\",\"value\": 0.009764622}, , {\"smile\": \"S(=O)(=O)(N(c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ncccc1)C\",\"value\": 0.0076237894}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.010749158, 0.009764622 and 0.0076237894."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the generated drugs acts as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1cc(cc(F)c1)-c1cc2c(CC(CC23N=C(N)N(C)C3=O)(C)C)cc1\",\"value\": 0.013936307}, {\"smile\": \"O=C1N(C)C(=NC1(c1cc(ccc1)-c1nccnc1)c1ccncc1)N\",\"value\": 0.016742764}, , {\"smile\": \"O(C)c1cc(ccc1)C[NH2+]CC(O)C(NC(=O)c1c2c(n(c1)C(=O)N(CC)CC)cccc2)Cc1ccccc1\",\"value\": 0.016439509}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.013936307, 0.016742764 and 0.016439509."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the generated drugs inhibit BACE1 activity. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC1)c1cn(nc1)CC(C)(C)C\",\"value\": 0.00885073}, {\"smile\": \"Clc1ccc(nc1)C(=O)Nc1cc(C2(Oc3c(cccc3)C(=N2)N)C)c(F)cc1\",\"value\": 0.011588893}, , {\"smile\": \"S1(=O)(=O)N([C@]2(C[C@@H]([N@H+](CC2)Cc2cc(OC(C)C)ccc2)C)CN1C)c1cc(F)ccc1\",\"value\": 0.009463065}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.00885073, 0.011588893 and 0.009463065."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the optimized drugs for inhibition of BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(C)C(=N[C@@]1(c1cc(ccc1)-c1cncnc1)c1cc(ncc1)C)N\",\"value\": 0.01657739}, {\"smile\": \"s1cc(cc1C1([NH2+]C[C@@H](O)[C@@H](NC(=O)C)Cc2cc(F)cc(F)c2)CC1)CC(C)(C)C\",\"value\": 0.014576454}, , {\"smile\": \"Ic1cc(ccc1)C[NH2+]C[C@@H](O)[C@@H](NC(=O)c1cc(cc(c1)C)C(=O)N(CCC)CCC)Cc1cc(F)cc(F)c1\",\"value\": 0.014930926}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.01657739, 0.014576454 and 0.014930926."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify if the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(NCC)c1ccc(cc1)-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccccc1\",\"value\": 0.023911363}, {\"smile\": \"s1cc(cc1C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1)CC(C)(C)C\",\"value\": 0.016022012}, , {\"smile\": \"Clc1cc(ccc1)CN1C(=O)[C@](NC1=N)(CC(C)C)C\",\"value\": 0.018249104}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.023911363, 0.016022012 and 0.018249104."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the optimized drugs is a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C(c1cc2CN(CCC(=O)N(C)C3CCCCC3)C(=[NH+]c2cc1)N)c1ccccc1\",\"value\": 0.015822118}, {\"smile\": \"S(=O)(=O)(N(C)c1cc(cc(c1)C(=O)NC(C(O)CC(OC)COCc1ccc(OC)cc1)COc1cc(F)cc(F)c1)C(=O)NC(C)c1ccccc1)C\",\"value\": 0.010814005}, , {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCN(CC1)C(=O)N)c1cc(ccc1)C(C)(C)C\",\"value\": 0.013385725}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.015822118, 0.010814005 and 0.013385725."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the generated drugs inhibit BACE1? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCN(C1)C(=O)N)c1cc(ccc1)C(C)(C)C\",\"value\": 0.012491958}, {\"smile\": \"Fc1ncccc1-c1cc(ccc1)C1(N=C(N)N(C)C1=O)c1ccc(OC(F)(F)F)cc1\",\"value\": 0.008515634}, , {\"smile\": \"S(=O)(=O)(N(c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C(C(=O)NC1CCCCC1)C)c1ccc(cc1)C#N)C\",\"value\": 0.008128325}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.012491958, 0.008515634 and 0.008128325."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the generated drugs an inhibitor of BACE1? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O(CCC)c1cc(cc(N2CCCC2=O)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1\",\"value\": 0.012352926}, {\"smile\": \"S1(=O)(=O)N(c2cc(cc3n(cc(CC1)c23)CC)C(=O)N[C@H]([C@H](O)C[NH2+]C(CC)C)Cc1ccccc1)C\",\"value\": 0.016347233}, , {\"smile\": \"Fc1ncccc1-c1cc(ccc1)[C@]1(N=C(N)N(C)C1=O)c1ccc(OC)cc1\",\"value\": 0.016638072}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.012352926, 0.016347233 and 0.016638072."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the generated drugs blocks BACE1 activity. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)c(NCCCO)cc2)c(cc1)-c1ccc(OCCCCC)cc1\",\"value\": 0.01299188}, {\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CCCCC1)c1cc(N2CCCC2=O)ccc1\",\"value\": 0.010325438}, , {\"smile\": \"S(=O)(=O)(N(c1ccccc1)c1nccc(c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)C\",\"value\": 0.010004905}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.01299188, 0.010325438 and 0.010004905."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition of BACE1 by the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"o1cccc1C(=O)Nc1cc(ccc1)C1(N=C(N)N(C)C1=O)C1CCCCC1\",\"value\": 0.010540498}, {\"smile\": \"Clc1ccccc1-c1n(Cc2nc(N)ccc2)c(cc1)-c1ccc(OCCCC#N)cc1\",\"value\": 0.011489694}, , {\"smile\": \"Brc1cc(cc(F)c1N)C[C@@H]1CS(=O)(=O)C[C@H]([NH2+]Cc2cc(ccc2)C(F)(F)C)[C@H]1O\",\"value\": 0.010543846}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.010540498, 0.011489694 and 0.010543846."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Brc1cc(ccc1)CCC=1N=C(N)N(C)C(=O)C=1\",\"value\": 0.016178885}, {\"smile\": \"s1cc(cc1C1([NH2+]CC(O)C(NC(=O)C)Cc2cc(F)cc(F)c2)CCCCC1)C#C\",\"value\": 0.009840611}, , {\"smile\": \"Fc1ccc(cc1C(F)(F)F)CC(NC(=O)C)C(O)C[NH2+]C1CC2(Oc3ncc(cc13)CC(C)(C)C)CCC2\",\"value\": 0.0077619352}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.016178885, 0.009840611 and 0.0077619352."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the potential of the generated drugs as a BACE1 inhibitor. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"Fc1cc(cc(F)c1)CC(NC(=O)C)C(O)C[NH2+]C1(CC(=O)NCC1)c1cc(ccc1)C(C)(C)C\",\"value\": 0.0104779815}, {\"smile\": \"Fc1ccc(NC(=O)c2ncc(cc2)C#N)cc1[C@]1(N=C(OC[C@@H]1F)N)CF\",\"value\": 0.018718341}, , {\"smile\": \"S1(=O)(=O)CC(Cc2cc3c([nH]nc3)cc2)C(O)C([NH2+]Cc2cc(ccc2)C(C)(C)C)C1\",\"value\": 0.0105034625}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.0104779815, 0.018718341 and 0.0105034625."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the optimized drugs can inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(c1cc(cc(c1)/C(=N/OCC(C)C)/C)C(=O)N[C@H]([C@@H](O)C[NH2+]Cc1cc(OC)ccc1)Cc1cc(F)cc(F)c1)c1ccccc1)C\",\"value\": 0.0057327845}, {\"smile\": \"S1(=O)(=O)N(CCCC1)c1cc(cc(NCC)c1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]C1CCOCC1\",\"value\": 0.0064191767}, , {\"smile\": \"O1CCC(OC(=O)[C@@H]2[NH2+]C[C@]3(C2)c2c(NC3=O)cccc2)CC1\",\"value\": 0.008022616}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.0057327845, 0.0064191767 and 0.008022616."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the BACE1 inhibitory activity of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"O=C1N(CCC1)c1cc(cc(NCC)c1)C(=O)N[C@H]([C@H](O)C[NH2+][C@H](C(=O)NC1CCCCC1)C)Cc1ccccc1\",\"value\": 0.0114197545}, {\"smile\": \"O=C1N(C)C(N[C@@]1(CC1CCCCC1)CCC1CCCCC1)=N\",\"value\": 0.011991375}, , {\"smile\": \"S(=O)(=O)(N(c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1ncccc1)C\",\"value\": 0.0067700557}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.0114197545, 0.011991375 and 0.0067700557."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Check whether the generated drugs inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S1(=O)(=O)CC(Cc2cc(ccc2)COCCC)C(O)C([NH2+]Cc2cc(ccc2)C(C)C)C1\",\"value\": 0.013976291}, {\"smile\": \"Fc1cc(cc(F)c1)C[C@H](NC(=O)C)[C@H](O)C[NH2+]C1(CC1)c1cc(ccc1)C(CF)(CF)C\",\"value\": 0.015974173}, , {\"smile\": \"S1(=O)(=O)CC(Cc2cc(OC(C(F)(F)F)C(F)(F)F)c(N)c(F)c2)C(O)C([NH2+]Cc2cc(ccc2)C(CO)(C)C)C1\",\"value\": 0.012088876}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.013976291, 0.015974173 and 0.012088876."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the optimized drugs' ability to inhibit BACE1. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"Class\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability this drug can inhibit β-secretase (BACE-1) which is an enzyme closely related to the development of Alzheimer's disease.\", \"result\": [{\"smile\": \"S(=O)(=O)(N(c1cc(ccc1)C(=O)NC(Cc1ccccc1)C(O)C[NH2+]Cc1cc(ccc1)C(F)(F)F)c1cccnc1)C\",\"value\": 0.008399253}, {\"smile\": \"s1cc(nc1N)C(Cc1ccccc1)c1ccc(OC)cc1\",\"value\": 0.013843668}, , {\"smile\": \"O=C1N(C)C(=NC1(c1cc(ccc1)-c1cc(ccc1)C#N)c1ccncc1)N\",\"value\": 0.01170125}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have an inhibitory effect on BACE1 are 0.008399253, 0.013843668 and 0.01170125."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug C1=C(C=CC=C1Br)\\C=C\\C(NCC)=O can cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(C=CC=C1Br)\\C=C\\C(NCC)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(C=CC=C1Br)\\C=C\\C(NCC)=O\",\"value\": 0.52237415}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(C=CC=C1Br)\\C=C\\C(NCC)=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the potential of the drug [C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)COC(=O)C)(CC2Cl)C)CCC4=CC(=O)C=CC34C to penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)COC(=O)C)(CC2Cl)C)CCC4=CC(=O)C=CC34C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)COC(=O)C)(CC2Cl)C)CCC4=CC(=O)C=CC34C\",\"value\": 0.47746998}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@@]23(Cl)C(C1C([C@](O)(CC1)C(=O)COC(=O)C)(CC2Cl)C)CCC4=CC(=O)C=CC34C has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the drug CCCC[C@@H]1CC(=O)[C@]2(O)O[C@@H]3[C@@H](NC)[C@@H](O)[C@@H](NC)[C@H](O)[C@H]3O[C@@H]2O1 crosses the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC[C@@H]1CC(=O)[C@]2(O)O[C@@H]3[C@@H](NC)[C@@H](O)[C@@H](NC)[C@H](O)[C@H]3O[C@@H]2O1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCCC[C@@H]1CC(=O)[C@]2(O)O[C@@H]3[C@@H](NC)[C@@H](O)[C@@H](NC)[C@H](O)[C@H]3O[C@@H]2O1\",\"value\": 0.30458358}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCCC[C@@H]1CC(=O)[C@]2(O)O[C@@H]3[C@@H](NC)[C@@H](O)[C@@H](NC)[C@H](O)[C@H]3O[C@@H]2O1 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O is capable of crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O\",\"value\": 0.4397302}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1=C(N2[C@H](SC1)[C@H](NC(=O)[C@H](N)c3ccccc3)C2=O)C(O)=O has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drug CO[C@]1(NC(=O)CSCC#N)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C can pass through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CO[C@]1(NC(=O)CSCC#N)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CO[C@]1(NC(=O)CSCC#N)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C\",\"value\": 0.5906807}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CO[C@]1(NC(=O)CSCC#N)[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC ability to traverse the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC\",\"value\": 0.32977554}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(OC)C(=CC2=C1C(=C(C)[NH]2)CCN4CCN(C3=CC=CC=C3)CC4)OC has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1 can penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1\",\"value\": 0.65046644}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the drug NC(=O)N1c2ccccc2C=Cc3ccccc13 passes the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC(=O)N1c2ccccc2C=Cc3ccccc13\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"NC(=O)N1c2ccccc2C=Cc3ccccc13\",\"value\": 0.47969463}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug NC(=O)N1c2ccccc2C=Cc3ccccc13 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify if the drug OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3 can cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3\",\"value\": 0.530075}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug O=C1Oc2ccccc2C=C1 cross the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1Oc2ccccc2C=C1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"O=C1Oc2ccccc2C=C1\",\"value\": 0.75697064}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C1Oc2ccccc2C=C1 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug C2=C(CC(C(NCC(OCC1=CC=CC=C1)=O)=O)CSC(C)=O)C=CC=C2 passes through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(CC(C(NCC(OCC1=CC=CC=C1)=O)=O)CSC(C)=O)C=CC=C2\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(CC(C(NCC(OCC1=CC=CC=C1)=O)=O)CSC(C)=O)C=CC=C2\",\"value\": 0.76661265}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C2=C(CC(C(NCC(OCC1=CC=CC=C1)=O)=O)CSC(C)=O)C=CC=C2 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's COc1ccc(CN(CCN(C)C)c2ccccn2)cc1 permeability across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(CN(CCN(C)C)c2ccccn2)cc1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1ccc(CN(CCN(C)C)c2ccccn2)cc1\",\"value\": 0.58379537}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1ccc(CN(CCN(C)C)c2ccccn2)cc1 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug's CC(NC1=NN=C(S1)[S](N)(=O)=O)=O potential to cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(NC1=NN=C(S1)[S](N)(=O)=O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC(NC1=NN=C(S1)[S](N)(=O)=O)=O\",\"value\": 0.78462166}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(NC1=NN=C(S1)[S](N)(=O)=O)=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug CC(=O)Nc1ccc(O)cc1 for blood-brain barrier penetration. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)Nc1ccc(O)cc1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC(=O)Nc1ccc(O)cc1\",\"value\": 0.7501463}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(=O)Nc1ccc(O)cc1 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC pass through the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC\",\"value\": 0.7197061}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drug C1=CC(=CC=C1C(CCCN3CCC(N2CCCCC2)(CC3)C(CC)=O)=O)F can cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=CC=C1C(CCCN3CCC(N2CCCCC2)(CC3)C(CC)=O)=O)F\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC=C1C(CCCN3CCC(N2CCCCC2)(CC3)C(CC)=O)=O)F\",\"value\": 0.7304849}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC(=CC=C1C(CCCN3CCC(N2CCCCC2)(CC3)C(CC)=O)=O)F has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the blood-brain barrier permeability of the drug C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C\",\"value\": 0.8238101}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl is capable of crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl\",\"value\": 0.5659427}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Cc1onc(c1C(=O)N[C@H]2[C@H]3SC(C)(C)[C@@H](N3C2=O)C(O)=O)c4c(Cl)cccc4Cl has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug C2=C(C(C1C(=CC(O1)=O)OC)O)C(=CC=C2)Cl can traverse the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(C(C1C(=CC(O1)=O)OC)O)C(=CC=C2)Cl\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(C(C1C(=CC(O1)=O)OC)O)C(=CC=C2)Cl\",\"value\": 0.6480493}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C2=C(C(C1C(=CC(O1)=O)OC)O)C(=CC=C2)Cl has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug C(C(C1CCCCC1)OC(N)=O)C able to cross the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C(C1CCCCC1)OC(N)=O)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C(C1CCCCC1)OC(N)=O)C\",\"value\": 0.6381197}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C(C(C1CCCCC1)OC(N)=O)C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test whether the drug [C@H](NC(C3=CC=C(NCC1=NC2=C(N=C1)NC(=NC2=O)N)C=C3)=O)(CCC(O)=O)C(O)=O crosses the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H](NC(C3=CC=C(NCC1=NC2=C(N=C1)NC(=NC2=O)N)C=C3)=O)(CCC(O)=O)C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H](NC(C3=CC=C(NCC1=NC2=C(N=C1)NC(=NC2=O)N)C=C3)=O)(CCC(O)=O)C(O)=O\",\"value\": 0.43798923}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H](NC(C3=CC=C(NCC1=NC2=C(N=C1)NC(=NC2=O)N)C=C3)=O)(CCC(O)=O)C(O)=O has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the ability of the drug C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C to penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C\",\"value\": 0.6381711}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug CC(C)N1CCC(CC1)N(C(=O)Cc2ccccc2)c3ccc(Cl)cc3 can pass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1CCC(CC1)N(C(=O)Cc2ccccc2)c3ccc(Cl)cc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC(C)N1CCC(CC1)N(C(=O)Cc2ccccc2)c3ccc(Cl)cc3\",\"value\": 0.43939382}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(C)N1CCC(CC1)N(C(=O)Cc2ccccc2)c3ccc(Cl)cc3 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's CCCNCC(O)COc1ccccc1C(=O)CCc2ccccc2 potential to cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCNCC(O)COc1ccccc1C(=O)CCc2ccccc2\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCCNCC(O)COc1ccccc1C(=O)CCc2ccccc2\",\"value\": 0.32181168}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCCNCC(O)COc1ccccc1C(=O)CCc2ccccc2 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug CCC(C)(CC)OC(N)=O demonstrate blood-brain barrier permeability? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)(CC)OC(N)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCC(C)(CC)OC(N)=O\",\"value\": 0.52553636}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCC(C)(CC)OC(N)=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug's C3=C(C2CN1C(CCN=C1C2)=O)C=CC=C3 ability to penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C3=C(C2CN1C(CCN=C1C2)=O)C=CC=C3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C3=C(C2CN1C(CCN=C1C2)=O)C=CC=C3\",\"value\": 0.3825963}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C3=C(C2CN1C(CCN=C1C2)=O)C=CC=C3 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug C1=C(SC2=C1C(=NCC3=NN=C([N]23)C4CCCCC4)C5=CC=CC=C5Cl)Br can cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(SC2=C1C(=NCC3=NN=C([N]23)C4CCCCC4)C5=CC=CC=C5Cl)Br\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(SC2=C1C(=NCC3=NN=C([N]23)C4CCCCC4)C5=CC=CC=C5Cl)Br\",\"value\": 0.74665344}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(SC2=C1C(=NCC3=NN=C([N]23)C4CCCCC4)C5=CC=CC=C5Cl)Br has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug C1=C(F)C=CC3=C1C=C(SCCNC)C2=CC=CC=C2O3 traverse the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(F)C=CC3=C1C=C(SCCNC)C2=CC=CC=C2O3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(F)C=CC3=C1C=C(SCCNC)C2=CC=CC=C2O3\",\"value\": 0.6775308}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(F)C=CC3=C1C=C(SCCNC)C2=CC=CC=C2O3 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the blood-brain barrier crossing potential of the drug C1=CC=CC2=C1C(C(N2C3=CC=CC=C3)=O)(CC4=CC=NC=C4)CC5=CC=NC=C5. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC2=C1C(C(N2C3=CC=CC=C3)=O)(CC4=CC=NC=C4)CC5=CC=NC=C5\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC2=C1C(C(N2C3=CC=CC=C3)=O)(CC4=CC=NC=C4)CC5=CC=NC=C5\",\"value\": 0.6500307}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC2=C1C(C(N2C3=CC=CC=C3)=O)(CC4=CC=NC=C4)CC5=CC=NC=C5 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6 passes through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6\",\"value\": 0.73992866}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CO[C@H]1[C@@H](C[C@@H]2CN3CCc4c([nH]c5cc(OC)ccc45)[C@H]3C[C@@H]2[C@@H]1C(=O)OC)OC(=O)c6cc(OC)c(OC)c(OC)c6 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug C1=C(NC(C(Cl)(Cl)Cl)=O)C=CC(=C1)O is capable of crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(NC(C(Cl)(Cl)Cl)=O)C=CC(=C1)O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(NC(C(Cl)(Cl)Cl)=O)C=CC(=C1)O\",\"value\": 0.52073556}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(NC(C(Cl)(Cl)Cl)=O)C=CC(=C1)O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's [C@H]13[C@H](C[C@@H]1C2=CC=C(F)C=C2)CN(C3)CCN4C(=O)C5=C(NC4=O)C=CC=C5 blood-brain barrier permeability. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]13[C@H](C[C@@H]1C2=CC=C(F)C=C2)CN(C3)CCN4C(=O)C5=C(NC4=O)C=CC=C5\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]13[C@H](C[C@@H]1C2=CC=C(F)C=C2)CN(C3)CCN4C(=O)C5=C(NC4=O)C=CC=C5\",\"value\": 0.4220013}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H]13[C@H](C[C@@H]1C2=CC=C(F)C=C2)CN(C3)CCN4C(=O)C5=C(NC4=O)C=CC=C5 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the blood-brain barrier crossing ability of the drug COC1C=COC5(C)Oc4c(C)c(O)c3c(O)c(NC(=O)C(=CC=CC(C)C(O)C(C)C(O)C(C)C(OC(C)=O)C1C)C)c(C=NN2CCN(C)CC2)c(O)c3c4C5=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COC1C=COC5(C)Oc4c(C)c(O)c3c(O)c(NC(=O)C(=CC=CC(C)C(O)C(C)C(O)C(C)C(OC(C)=O)C1C)C)c(C=NN2CCN(C)CC2)c(O)c3c4C5=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COC1C=COC5(C)Oc4c(C)c(O)c3c(O)c(NC(=O)C(=CC=CC(C)C(O)C(C)C(O)C(C)C(OC(C)=O)C1C)C)c(C=NN2CCN(C)CC2)c(O)c3c4C5=O\",\"value\": 0.5221053}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COC1C=COC5(C)Oc4c(C)c(O)c3c(O)c(NC(=O)C(=CC=CC(C)C(O)C(C)C(O)C(C)C(OC(C)=O)C1C)C)c(C=NN2CCN(C)CC2)c(O)c3c4C5=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the drug C1=CC=CC3=C1C(N(C2C(NCCC2)=O)[S]3(=O)=O)=O can penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC3=C1C(N(C2C(NCCC2)=O)[S]3(=O)=O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC3=C1C(N(C2C(NCCC2)=O)[S]3(=O)=O)=O\",\"value\": 0.6720419}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC3=C1C(N(C2C(NCCC2)=O)[S]3(=O)=O)=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's [C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C ability to pass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C\",\"value\": 0.5486608}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drug C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)CSC passes the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)CSC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)CSC\",\"value\": 0.70695525}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C(C1(C(NC(=S)NC1=O)=O)C(CCC)C)CSC has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug [C@H]12C(C3=C(C[C@@H]1CCN(C2)C)[NH]C(=C3CC)C)=O traverses the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]12C(C3=C(C[C@@H]1CCN(C2)C)[NH]C(=C3CC)C)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]12C(C3=C(C[C@@H]1CCN(C2)C)[NH]C(=C3CC)C)=O\",\"value\": 0.48617426}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H]12C(C3=C(C[C@@H]1CCN(C2)C)[NH]C(=C3CC)C)=O has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate if the drug [C@H](CC1=CC(=C(C=C1)O)O)(C(O)=O)N can pass through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H](CC1=CC(=C(C=C1)O)O)(C(O)=O)N\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H](CC1=CC(=C(C=C1)O)O)(C(O)=O)N\",\"value\": 0.41175586}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H](CC1=CC(=C(C=C1)O)O)(C(O)=O)N has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the drug C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCNCC4 capable of penetrating the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCNCC4\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCNCC4\",\"value\": 0.5437707}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(Cl)C=CC2=C1C(=NC3=C(O2)C=CC=C3)N4CCNCC4 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug's C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC4(CN(C)CC4)C ability to cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC4(CN(C)CC4)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC4(CN(C)CC4)C\",\"value\": 0.39183283}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC3=C1N(C2=C(C=CC=C2)S3)CC4(CN(C)CC4)C has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug C1=C(C=CC=C1OC2CN(C(N)=O)C2)C(F)(F)F penetrate the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(C=CC=C1OC2CN(C(N)=O)C2)C(F)(F)F\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(C=CC=C1OC2CN(C(N)=O)C2)C(F)(F)F\",\"value\": 0.7239729}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(C=CC=C1OC2CN(C(N)=O)C2)C(F)(F)F has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's C1=CC(=CC2=C1C(=NO2)C5CCN(CCC3=C(N=C4N(C3=O)C=CC=C4C)C)CC5)F capacity to traverse the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=CC2=C1C(=NO2)C5CCN(CCC3=C(N=C4N(C3=O)C=CC=C4C)C)CC5)F\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC2=C1C(=NO2)C5CCN(CCC3=C(N=C4N(C3=O)C=CC=C4C)C)CC5)F\",\"value\": 0.5683225}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC(=CC2=C1C(=NO2)C5CCN(CCC3=C(N=C4N(C3=O)C=CC=C4C)C)CC5)F has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the blood-brain barrier crossing potential of the drug Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4cc(Cl)ccc34)c5ccc(F)cc5. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4cc(Cl)ccc34)c5ccc(F)cc5\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4cc(Cl)ccc34)c5ccc(F)cc5\",\"value\": 0.6655766}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Fc1ccc(cc1)C(CCCN2CCC(CC2)N3C(=O)Nc4cc(Cl)ccc34)c5ccc(F)cc5 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O passes the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O\",\"value\": 0.6282682}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug CN(C)CCc1c[nH]c2ccc(O)cc12 can traverse the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)CCc1c[nH]c2ccc(O)cc12\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)CCc1c[nH]c2ccc(O)cc12\",\"value\": 0.5522547}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN(C)CCc1c[nH]c2ccc(O)cc12 has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the permeability of the drug OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1 across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1\",\"value\": 0.49587822}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug C=COC=C can pass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C=COC=C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C=COC=C\",\"value\": 0.8307528}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C=COC=C has blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the drug [C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O can penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O\",\"value\": 0.2675893}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CCC)=O has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN4CCN(CCO)CC4 ability to cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN4CCN(CCO)CC4\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN4CCN(CCO)CC4\",\"value\": 0.48569402}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC2=C1N(C3=C(C=C2)C=CC=C3)CCCN4CCN(CCO)CC4 has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug C1(=CC=CC=C1)OC2=CC=CC=C2C(=O)[O-] cross the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1(=CC=CC=C1)OC2=CC=CC=C2C(=O)[O-]\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1(=CC=CC=C1)OC2=CC=CC=C2C(=O)[O-]\",\"value\": 0.41332713}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1(=CC=CC=C1)OC2=CC=CC=C2C(=O)[O-] has weak blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug C4=C(C(C3CCN(CCC1=C(N=C2N(C1=O)CCS2)C)CC3)=O)C=CC(=C4)F and C(C1(C(NC(=O)NC1=O)=O)CC)C can diffuse across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C4=C(C(C3CCN(CCC1=C(N=C2N(C1=O)CCS2)C)CC3)=O)C=CC(=C4)F\",\"C(C1(C(NC(=O)NC1=O)=O)CC)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C4=C(C(C3CCN(CCC1=C(N=C2N(C1=O)CCS2)C)CC3)=O)C=CC(=C4)F\",\"value\": 0.4961776}, {\"smile\": \"C(C1(C(NC(=O)NC1=O)=O)CC)C\",\"value\": 0.5358657}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C(C1(C(NC(=O)NC1=O)=O)CC)C has blood-brain barrier permeability, but C4=C(C(C3CCN(CCC1=C(N=C2N(C1=O)CCS2)C)CC3)=O)C=CC(=C4)F has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug C1=NC(=NC(=C1CN(C(=C(SSCC2CCCO2)\\CCO)/C)C=O)N)C and CCCCNc1c(cnc2n(CC)ncc12)C(=O)OCC achieves entry past the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=NC(=NC(=C1CN(C(=C(SSCC2CCCO2)\\CCO)/C)C=O)N)C\",\"CCCCNc1c(cnc2n(CC)ncc12)C(=O)OCC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=NC(=NC(=C1CN(C(=C(SSCC2CCCO2)\\CCO)/C)C=O)N)C\",\"value\": 0.64265215}, {\"smile\": \"CCCCNc1c(cnc2n(CC)ncc12)C(=O)OCC\",\"value\": 0.61526364}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=NC(=NC(=C1CN(C(=C(SSCC2CCCO2)\\CCO)/C)C=O)N)C and CCCCNc1c(cnc2n(CC)ncc12)C(=O)OCC have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug's C2=C(C(CCCN1CCC(OC(NC(C)C)=O)CC1)=O)C=CC(=C2)F and C1=CC(=CC=C1C2(C(NC(=O)CC2)=O)CC)N potential to overcome the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(C(CCCN1CCC(OC(NC(C)C)=O)CC1)=O)C=CC(=C2)F\",\"C1=CC(=CC=C1C2(C(NC(=O)CC2)=O)CC)N\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(C(CCCN1CCC(OC(NC(C)C)=O)CC1)=O)C=CC(=C2)F\",\"value\": 0.66799474}, {\"smile\": \"C1=CC(=CC=C1C2(C(NC(=O)CC2)=O)CC)N\",\"value\": 0.65989107}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C2=C(C(CCCN1CCC(OC(NC(C)C)=O)CC1)=O)C=CC(=C2)F and C1=CC(=CC=C1C2(C(NC(=O)CC2)=O)CC)N have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drug C1=C(C=CC=C1O)NC(C)=O and CCCC(C)C1(CC)C(=O)NC(=O)NC1=O can penetrate the central nervous system via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(C=CC=C1O)NC(C)=O\",\"CCCC(C)C1(CC)C(=O)NC(=O)NC1=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(C=CC=C1O)NC(C)=O\",\"value\": 0.3759893}, {\"smile\": \"CCCC(C)C1(CC)C(=O)NC(=O)NC1=O\",\"value\": 0.37311468}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=C(C=CC=C1O)NC(C)=O nor CCCC(C)C1(CC)C(=O)NC(=O)NC1=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's CN(C)C(=O)COC2c1ccccc1CCc3ccccc23 and [Cl-].CC(C[N+](C)(C)C)OC(N)=O likelihood of passing through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)C(=O)COC2c1ccccc1CCc3ccccc23\",\"[Cl-].CC(C[N+](C)(C)C)OC(N)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)C(=O)COC2c1ccccc1CCc3ccccc23\",\"value\": 0.69134575}, {\"smile\": \"[Cl-].CC(C[N+](C)(C)C)OC(N)=O\",\"value\": 0.7487121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CN(C)C(=O)COC2c1ccccc1CCc3ccccc23 and [Cl-].CC(C[N+](C)(C)C)OC(N)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug C1=C(C(=CC(=C1[S](NC)(=O)=O)N)OC)C(NCC2N(CCC2)CC=C)=O and C1=CC=CC4=C1C(N(CCCN3CCN(C2=NC=CC=N2)CC3)[S]4(=O)=O)=O reaches the brain by crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=C(C(=CC(=C1[S](NC)(=O)=O)N)OC)C(NCC2N(CCC2)CC=C)=O\",\"C1=CC=CC4=C1C(N(CCCN3CCN(C2=NC=CC=N2)CC3)[S]4(=O)=O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(C(=CC(=C1[S](NC)(=O)=O)N)OC)C(NCC2N(CCC2)CC=C)=O\",\"value\": 0.38628083}, {\"smile\": \"C1=CC=CC4=C1C(N(CCCN3CCN(C2=NC=CC=N2)CC3)[S]4(=O)=O)=O\",\"value\": 0.4419595}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=C(C(=CC(=C1[S](NC)(=O)=O)N)OC)C(NCC2N(CCC2)CC=C)=O nor C1=CC=CC4=C1C(N(CCCN3CCN(C2=NC=CC=N2)CC3)[S]4(=O)=O)=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the possibility of the drug [C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4 and CNCCC=C3c1ccccc1C2CC2c4ccccc34 breaching the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4\",\"CNCCC=C3c1ccccc1C2CC2c4ccccc34\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4\",\"value\": 0.49356923}, {\"smile\": \"CNCCC=C3c1ccccc1C2CC2c4ccccc34\",\"value\": 0.5431468}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CNCCC=C3c1ccccc1C2CC2c4ccccc34 has blood-brain barrier permeability, but [C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug CCCO and [H+].C4=C(N3CCN(CCCN1N=C(N(C1=O)CCOC2=CC=CC=C2)CC)CC3)C=CC=C4Cl.[Cl-] has the capacity to pass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCO\",\"[H+].C4=C(N3CCN(CCCN1N=C(N(C1=O)CCOC2=CC=CC=C2)CC)CC3)C=CC=C4Cl.[Cl-]\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCCO\",\"value\": 0.33280095}, {\"smile\": \"[H+].C4=C(N3CCN(CCCN1N=C(N(C1=O)CCOC2=CC=CC=C2)CC)CC3)C=CC=C4Cl.[Cl-]\",\"value\": 0.29866144}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug CCCO nor [H+].C4=C(N3CCN(CCCN1N=C(N(C1=O)CCOC2=CC=CC=C2)CC)CC3)C=CC=C4Cl.[Cl-] has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug CCC(C)(CC)OC(N)=O and C(C1CCCCC1)NN can access the brain by crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)(CC)OC(N)=O\",\"C(C1CCCCC1)NN\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCC(C)(CC)OC(N)=O\",\"value\": 0.3239979}, {\"smile\": \"C(C1CCCCC1)NN\",\"value\": 0.2839415}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug CCC(C)(CC)OC(N)=O nor C(C1CCCCC1)NN has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the ability of the drug CC(=O)Nc1sc(nn1)[S](N)(=O)=O and C1=C(C=CC=C1C(CNC(CCCO)=O)(CC)CC)OC to cross the brain's protective blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)Nc1sc(nn1)[S](N)(=O)=O\",\"C1=C(C=CC=C1C(CNC(CCCO)=O)(CC)CC)OC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC(=O)Nc1sc(nn1)[S](N)(=O)=O\",\"value\": 0.72504944}, {\"smile\": \"C1=C(C=CC=C1C(CNC(CCCO)=O)(CC)CC)OC\",\"value\": 0.58401704}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CC(=O)Nc1sc(nn1)[S](N)(=O)=O and C1=C(C=CC=C1C(CNC(CCCO)=O)(CC)CC)OC have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drug [C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5C4(CCC(=O)C5)C)C and [C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O permeates the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5C4(CCC(=O)C5)C)C\",\"[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5C4(CCC(=O)C5)C)C\",\"value\": 0.7343004}, {\"smile\": \"[C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O\",\"value\": 0.70805264}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@H]23C([C@@]1(OC(O[C@@H]1C2)(C)C)C(=O)CO)(C[C@H](O)[C@@]4(F)C3CCC5C4(CCC(=O)C5)C)C and [C@@]4([C@@]3([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)C)C)[C@H](C3)O)CC4)C)(C(COC(C)=O)=O)OC(CC)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug [C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C and CC(C)(C)NC(=O)[C@@H]1C[C@@H]2CCCC[C@@H]2CN1C[C@@H](O)[C@H](Cc3ccccc3)NC(=O)[C@H](CC(N)=O)NC(=O)c4ccc5ccccc5n4 bypass the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C\",\"CC(C)(C)NC(=O)[C@@H]1C[C@@H]2CCCC[C@@H]2CN1C[C@@H](O)[C@H](Cc3ccccc3)NC(=O)[C@H](CC(N)=O)NC(=O)c4ccc5ccccc5n4\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C\",\"value\": 0.36303017}, {\"smile\": \"CC(C)(C)NC(=O)[C@@H]1C[C@@H]2CCCC[C@@H]2CN1C[C@@H](O)[C@H](Cc3ccccc3)NC(=O)[C@H](CC(N)=O)NC(=O)c4ccc5ccccc5n4\",\"value\": 0.33763784}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug [C@H]34[C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@@H](O)C[C@@]3([C@](C(CS)=O)(O)CC4)C nor CC(C)(C)NC(=O)[C@@H]1C[C@@H]2CCCC[C@@H]2CN1C[C@@H](O)[C@H](Cc3ccccc3)NC(=O)[C@H](CC(N)=O)NC(=O)c4ccc5ccccc5n4 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drug C2=C(OC(OC1=CC=CC=C1)CN(C)C)C=CC=C2 and CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4c(Cl)ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O can reach the brain through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(OC(OC1=CC=CC=C1)CN(C)C)C=CC=C2\",\"CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4c(Cl)ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(OC(OC1=CC=CC=C1)CN(C)C)C=CC=C2\",\"value\": 0.67929083}, {\"smile\": \"CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4c(Cl)ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O\",\"value\": 0.7714131}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C2=C(OC(OC1=CC=CC=C1)CN(C)C)C=CC=C2 and CN(C)[C@H]1[C@@H]2C[C@@H]3[C@H](O)c4c(Cl)ccc(O)c4C(=C3C(=O)[C@]2(O)C(=O)\\C(=C(N)/O)C1=O)O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the brain permeability of the drug [H+].C4=C(C2(N(C1=C(F)C=CC=C1)C(=O)CC)CCN(CC2)CCN3N=NN(C3=O)CC)C=CC=C4.[Cl-] and [C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4 via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[H+].C4=C(C2(N(C1=C(F)C=CC=C1)C(=O)CC)CCN(CC2)CCN3N=NN(C3=O)CC)C=CC=C4.[Cl-]\",\"[C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[H+].C4=C(C2(N(C1=C(F)C=CC=C1)C(=O)CC)CCN(CC2)CCN3N=NN(C3=O)CC)C=CC=C4.[Cl-]\",\"value\": 0.6412917}, {\"smile\": \"[C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4\",\"value\": 0.77145606}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [H+].C4=C(C2(N(C1=C(F)C=CC=C1)C(=O)CC)CCN(CC2)CCN3N=NN(C3=O)CC)C=CC=C4.[Cl-] and [C@]134[C@@H]([C@H](CC2=C1C=C(O)C=C2)N(C)CC3)CCCC4 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug's C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2 and CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23 capacity to cross the protective blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2\",\"CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2\",\"value\": 0.4639698}, {\"smile\": \"CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23\",\"value\": 0.44449258}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2 nor CNCCCC2(C)C(=O)N(c1ccccc1)c3ccccc23 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the likelihood of the drug C1=CC=CC=C1C2C(N=C(O2)NCC)=O and C1=CC=CC2=C1C(=C[NH]2)CCC3CCNCC3 diffusing through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC=C1C2C(N=C(O2)NCC)=O\",\"C1=CC=CC2=C1C(=C[NH]2)CCC3CCNCC3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC=C1C2C(N=C(O2)NCC)=O\",\"value\": 0.5677912}, {\"smile\": \"C1=CC=CC2=C1C(=C[NH]2)CCC3CCNCC3\",\"value\": 0.47830844}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC=C1C2C(N=C(O2)NCC)=O has blood-brain barrier permeability, but C1=CC=CC2=C1C(=C[NH]2)CCC3CCNCC3 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the ability of the drug OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1 and [C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CC=C(C)C)CC5 to penetrate the brain's blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1\",\"[C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CC=C(C)C)CC5\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1\",\"value\": 0.609104}, {\"smile\": \"[C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CC=C(C)C)CC5\",\"value\": 0.6379758}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug OCCN1CCN(CC\\C=C/2c3ccccc3Sc4ccc(Cl)cc24)CC1 and [C@@]125C3=C4C[C@H]([C@@]1(CCC([C@@H]2OC3=C(C=C4)O)=O)O)N(CC=C(C)C)CC5 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the drug C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2O and [C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C)=O)=O can navigate past the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2O\",\"[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C)=O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2O\",\"value\": 0.50665283}, {\"smile\": \"[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C)=O)=O\",\"value\": 0.48733628}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@]34C)[C@@H]1CC[C@@H]2O has blood-brain barrier permeability, but [C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)[C@H](C2)F)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C)=O)=O has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's C(C(C(NC(NC(C)=O)=O)=O)(CC)Br)C and CO\\N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3sc(CC(O)=O)c(C)n3)/c4csc(N)n4 potential to bypass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C(C(NC(NC(C)=O)=O)=O)(CC)Br)C\",\"CO\\N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3sc(CC(O)=O)c(C)n3)/c4csc(N)n4\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C(C(NC(NC(C)=O)=O)=O)(CC)Br)C\",\"value\": 0.31007454}, {\"smile\": \"CO\\N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3sc(CC(O)=O)c(C)n3)/c4csc(N)n4\",\"value\": 0.30332217}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C(C(C(NC(NC(C)=O)=O)=O)(CC)Br)C nor CO\\N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3sc(CC(O)=O)c(C)n3)/c4csc(N)n4 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drug [Cl-].NC12CC3CC(CC(C3)C1)C2.[H+] and C1=C(C(=CC=C1OCC2=NCCN2)Cl)Cl crosses into the brain through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[Cl-].NC12CC3CC(CC(C3)C1)C2.[H+]\",\"C1=C(C(=CC=C1OCC2=NCCN2)Cl)Cl\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[Cl-].NC12CC3CC(CC(C3)C1)C2.[H+]\",\"value\": 0.5783922}, {\"smile\": \"C1=C(C(=CC=C1OCC2=NCCN2)Cl)Cl\",\"value\": 0.5989301}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [Cl-].NC12CC3CC(CC(C3)C1)C2.[H+] and C1=C(C(=CC=C1OCC2=NCCN2)Cl)Cl have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the penetration potential of the drug Cc1ccccc1N2C(=Nc3ccccc3C2=O)C and ClC(CCC)O at the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccccc1N2C(=Nc3ccccc3C2=O)C\",\"ClC(CCC)O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Cc1ccccc1N2C(=Nc3ccccc3C2=O)C\",\"value\": 0.38250378}, {\"smile\": \"ClC(CCC)O\",\"value\": 0.40626672}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Cc1ccccc1N2C(=Nc3ccccc3C2=O)C nor ClC(CCC)O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug CC1=CN([C@H]2C[C@H](F)[C@@H](CO)O2)C(=O)NC1=O and [C@@]126C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OCC5=CC=CC=C5)OC(CCCCCCCCCCCCC)=O)N(CC6)C access the central nervous system by crossing the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=CN([C@H]2C[C@H](F)[C@@H](CO)O2)C(=O)NC1=O\",\"[C@@]126C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OCC5=CC=CC=C5)OC(CCCCCCCCCCCCC)=O)N(CC6)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC1=CN([C@H]2C[C@H](F)[C@@H](CO)O2)C(=O)NC1=O\",\"value\": 0.6988047}, {\"smile\": \"[C@@]126C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OCC5=CC=CC=C5)OC(CCCCCCCCCCCCC)=O)N(CC6)C\",\"value\": 0.6026119}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CC1=CN([C@H]2C[C@H](F)[C@@H](CO)O2)C(=O)NC1=O and [C@@]126C3=C4C[C@H]([C@@H]1C=C[C@@H]([C@@H]2OC3=C(C=C4)OCC5=CC=CC=C5)OC(CCCCCCCCCCCCC)=O)N(CC6)C have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drug [C@]2(OC1=C(C(=C(C(=C1CC2)C)O)C)C)(CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)C and C(O)C(=O)C4(O)C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)CC3)CC4)C can infiltrate the brain via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@]2(OC1=C(C(=C(C(=C1CC2)C)O)C)C)(CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)C\",\"C(O)C(=O)C4(O)C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)CC3)CC4)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]2(OC1=C(C(=C(C(=C1CC2)C)O)C)C)(CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)C\",\"value\": 0.4405986}, {\"smile\": \"C(O)C(=O)C4(O)C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)CC3)CC4)C\",\"value\": 0.49349838}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug [C@]2(OC1=C(C(=C(C(=C1CC2)C)O)C)C)(CCC[C@@H](CCC[C@@H](CCCC(C)C)C)C)C nor C(O)C(=O)C4(O)C3(C(C2C(C1(C(=CC(=O)CC1)CC2)C)CC3)CC4)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drug C2=C(C1(C(N(C(NC1=O)=O)C)=O)CC)C=CC=C2 and [Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N[S]([O-])(=O)=O)c3ccccc3)C(=O)N2[C@H]1C([O-])=O successfully traverses the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C2=C(C1(C(N(C(NC1=O)=O)C)=O)CC)C=CC=C2\",\"[Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N[S]([O-])(=O)=O)c3ccccc3)C(=O)N2[C@H]1C([O-])=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(C1(C(N(C(NC1=O)=O)C)=O)CC)C=CC=C2\",\"value\": 0.57251304}, {\"smile\": \"[Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N[S]([O-])(=O)=O)c3ccccc3)C(=O)N2[C@H]1C([O-])=O\",\"value\": 0.5108429}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C2=C(C1(C(N(C(NC1=O)=O)C)=O)CC)C=CC=C2 and [Na+].[Na+].CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N[S]([O-])(=O)=O)c3ccccc3)C(=O)N2[C@H]1C([O-])=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's CC1=CC(=O)Oc2cc(O)ccc12 and CO\\N=C(C(=O)NC1[C@H](C)N(OCC(O)=O)C1=O)\\c2csc(N)n2 success in permeating the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=CC(=O)Oc2cc(O)ccc12\",\"CO\\N=C(C(=O)NC1[C@H](C)N(OCC(O)=O)C1=O)\\c2csc(N)n2\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC1=CC(=O)Oc2cc(O)ccc12\",\"value\": 0.72606313}, {\"smile\": \"CO\\N=C(C(=O)NC1[C@H](C)N(OCC(O)=O)C1=O)\\c2csc(N)n2\",\"value\": 0.8023856}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CC1=CC(=O)Oc2cc(O)ccc12 and CO\\N=C(C(=O)NC1[C@H](C)N(OCC(O)=O)C1=O)\\c2csc(N)n2 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug C(C(C1CCCCC1)OC(N)=O)C and C1=CC=NC=C1C(N(CC)CC)=O crosses into brain tissue via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C(C(C1CCCCC1)OC(N)=O)C\",\"C1=CC=NC=C1C(N(CC)CC)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C(C1CCCCC1)OC(N)=O)C\",\"value\": 0.44452432}, {\"smile\": \"C1=CC=NC=C1C(N(CC)CC)=O\",\"value\": 0.7274579}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=NC=C1C(N(CC)CC)=O has blood-brain barrier permeability, but C(C(C1CCCCC1)OC(N)=O)C has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the penetration capacity of the drug CCN1C=CC(=O)C(=C1CC)O and C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=CCCN(C)C across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN1C=CC(=O)C(=C1CC)O\",\"C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=CCCN(C)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCN1C=CC(=O)C(=C1CC)O\",\"value\": 0.4918508}, {\"smile\": \"C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=CCCN(C)C\",\"value\": 0.4897947}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug CCN1C=CC(=O)C(=C1CC)O nor C1=CC=CC3=C1C(C2=C(C=CC=C2)S3)=CCCN(C)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug COc1ccc(Cc2nccc3cc(OC)c(OC)cc23)cc1OC and C[C@H]1CN(C[C@@H](C)N1)c2c(F)c(N)c3C(=O)C(=CN(C4CC4)c3c2F)C(O)=O move past the blood-brain barrier into the brain? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(Cc2nccc3cc(OC)c(OC)cc23)cc1OC\",\"C[C@H]1CN(C[C@@H](C)N1)c2c(F)c(N)c3C(=O)C(=CN(C4CC4)c3c2F)C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1ccc(Cc2nccc3cc(OC)c(OC)cc23)cc1OC\",\"value\": 0.5056032}, {\"smile\": \"C[C@H]1CN(C[C@@H](C)N1)c2c(F)c(N)c3C(=O)C(=CN(C4CC4)c3c2F)C(O)=O\",\"value\": 0.4215437}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1ccc(Cc2nccc3cc(OC)c(OC)cc23)cc1OC has blood-brain barrier permeability, but C[C@H]1CN(C[C@@H](C)N1)c2c(F)c(N)c3C(=O)C(=CN(C4CC4)c3c2F)C(O)=O has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug C1=CC=NC(=N1)N3CCN(CCCC[N]2C=C(Cl)C=N2)CC3 and CC1(C)S[C@@H]2[C@H](NC(=O)C(c3ccccc3)[S](O)(=O)=O)C(=O)N2[C@H]1C(O)=O transitions across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=NC(=N1)N3CCN(CCCC[N]2C=C(Cl)C=N2)CC3\",\"CC1(C)S[C@@H]2[C@H](NC(=O)C(c3ccccc3)[S](O)(=O)=O)C(=O)N2[C@H]1C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=NC(=N1)N3CCN(CCCC[N]2C=C(Cl)C=N2)CC3\",\"value\": 0.72972816}, {\"smile\": \"CC1(C)S[C@@H]2[C@H](NC(=O)C(c3ccccc3)[S](O)(=O)=O)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.76243454}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=CC=NC(=N1)N3CCN(CCCC[N]2C=C(Cl)C=N2)CC3 and CC1(C)S[C@@H]2[C@H](NC(=O)C(c3ccccc3)[S](O)(=O)=O)C(=O)N2[C@H]1C(O)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug C1=CC=CC=C1C(NNC(OCC)=O)C and [C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C([C@@H](OC(C)=O)C)=O)O)C)O)F has access to the brain through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC=C1C(NNC(OCC)=O)C\",\"[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C([C@@H](OC(C)=O)C)=O)O)C)O)F\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC=C1C(NNC(OCC)=O)C\",\"value\": 0.5698318}, {\"smile\": \"[C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C([C@@H](OC(C)=O)C)=O)O)C)O)F\",\"value\": 0.5487171}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=CC=CC=C1C(NNC(OCC)=O)C and [C@H]23[C@@]([C@@]1(C(=CC(=O)C=C1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C([C@@H](OC(C)=O)C)=O)O)C)O)F have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's [O-][N+](C1=CC=NC1NCCSCc2ccccn2)=O and CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O effectiveness at crossing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[O-][N+](C1=CC=NC1NCCSCc2ccccn2)=O\",\"CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[O-][N+](C1=CC=NC1NCCSCc2ccccn2)=O\",\"value\": 0.6693753}, {\"smile\": \"CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.72466975}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [O-][N+](C1=CC=NC1NCCSCc2ccccn2)=O and CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccccc3)C(=O)N2[C@H]1C(O)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drug [C@]2(C1=CC=CC=C1)([C@H](CN(C)CC2)CC)OC(CC)=O and O.O.O.CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C(=O)N2[C@H]1C(O)=O enters the brain by bypassing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@]2(C1=CC=CC=C1)([C@H](CN(C)CC2)CC)OC(CC)=O\",\"O.O.O.CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C(=O)N2[C@H]1C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]2(C1=CC=CC=C1)([C@H](CN(C)CC2)CC)OC(CC)=O\",\"value\": 0.63747627}, {\"smile\": \"O.O.O.CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.6669223}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@]2(C1=CC=CC=C1)([C@H](CN(C)CC2)CC)OC(CC)=O and O.O.O.CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](N)c3ccc(O)cc3)C(=O)N2[C@H]1C(O)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the transport potential of the drug [C@]135[C@@]4([C@@H](CC2=C1C(=C(C=C2)O)O[C@H]3C(=O)C=C4)N(C)CC5)NCCCCC and [C@@]4(O)(C3(C(C2C(C1(C(=CC(=O)CC1)C=C2)C)C(O)C3)CC4=C)C)C(=O)CO across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@]135[C@@]4([C@@H](CC2=C1C(=C(C=C2)O)O[C@H]3C(=O)C=C4)N(C)CC5)NCCCCC\",\"[C@@]4(O)(C3(C(C2C(C1(C(=CC(=O)CC1)C=C2)C)C(O)C3)CC4=C)C)C(=O)CO\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]135[C@@]4([C@@H](CC2=C1C(=C(C=C2)O)O[C@H]3C(=O)C=C4)N(C)CC5)NCCCCC\",\"value\": 0.4017988}, {\"smile\": \"[C@@]4(O)(C3(C(C2C(C1(C(=CC(=O)CC1)C=C2)C)C(O)C3)CC4=C)C)C(=O)CO\",\"value\": 0.5489238}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@@]4(O)(C3(C(C2C(C1(C(=CC(=O)CC1)C=C2)C)C(O)C3)CC4=C)C)C(=O)CO has blood-brain barrier permeability, but [C@]135[C@@]4([C@@H](CC2=C1C(=C(C=C2)O)O[C@H]3C(=O)C=C4)N(C)CC5)NCCCCC has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drug COc1ccc(CN(CCN(C)C)c2ccccn2)cc1 and C1=C(C(C(C)(C)O)(C)O)C=CC=C1Cl can breach the brain's protective blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(CN(CCN(C)C)c2ccccn2)cc1\",\"C1=C(C(C(C)(C)O)(C)O)C=CC=C1Cl\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1ccc(CN(CCN(C)C)c2ccccn2)cc1\",\"value\": 0.69203264}, {\"smile\": \"C1=C(C(C(C)(C)O)(C)O)C=CC=C1Cl\",\"value\": 0.67453396}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug COc1ccc(CN(CCN(C)C)c2ccccn2)cc1 and C1=C(C(C(C)(C)O)(C)O)C=CC=C1Cl have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug NC1[C@H]2CN(C[C@@H]12)c3nc4N(C=C(C(O)=O)C(=O)c4cc3F)c5ccc(F)cc5F and C1=CC=C3C(=C1OC(C2=CC=CS2)CCNC)C=CC=C3 can reach neural tissues via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"NC1[C@H]2CN(C[C@@H]12)c3nc4N(C=C(C(O)=O)C(=O)c4cc3F)c5ccc(F)cc5F\",\"C1=CC=C3C(=C1OC(C2=CC=CS2)CCNC)C=CC=C3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"NC1[C@H]2CN(C[C@@H]12)c3nc4N(C=C(C(O)=O)C(=O)c4cc3F)c5ccc(F)cc5F\",\"value\": 0.68043643}, {\"smile\": \"C1=CC=C3C(=C1OC(C2=CC=CS2)CCNC)C=CC=C3\",\"value\": 0.7388583}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug NC1[C@H]2CN(C[C@@H]12)c3nc4N(C=C(C(O)=O)C(=O)c4cc3F)c5ccc(F)cc5F and C1=CC=C3C(=C1OC(C2=CC=CS2)CCNC)C=CC=C3 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drug C1=CC=CC=C1C3(C2=CC=CC=C2)CCN(C(C)(C)C)CC3 and OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3 can successfully bypass the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC=CC=C1C3(C2=CC=CC=C2)CCN(C(C)(C)C)CC3\",\"OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC=C1C3(C2=CC=CC=C2)CCN(C(C)(C)C)CC3\",\"value\": 0.37909198}, {\"smile\": \"OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3\",\"value\": 0.39718148}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC=CC=C1C3(C2=CC=CC=C2)CCN(C(C)(C)C)CC3 nor OC1(CCN(CCCC(=O)c2ccc(F)cc2)CC1)c3ccc(Cl)cc3 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's CO[C@]1(NC(=O)Cc2sccc2)[C@H]3SCC(=C(N3C1=O)C(O)=O)COC(N)=O and C(C(C(N)=O)(CCC)CCC)CC ability to cross from the bloodstream into the brain. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CO[C@]1(NC(=O)Cc2sccc2)[C@H]3SCC(=C(N3C1=O)C(O)=O)COC(N)=O\",\"C(C(C(N)=O)(CCC)CCC)CC\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CO[C@]1(NC(=O)Cc2sccc2)[C@H]3SCC(=C(N3C1=O)C(O)=O)COC(N)=O\",\"value\": 0.5709315}, {\"smile\": \"C(C(C(N)=O)(CCC)CCC)CC\",\"value\": 0.600518}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CO[C@]1(NC(=O)Cc2sccc2)[C@H]3SCC(=C(N3C1=O)C(O)=O)COC(N)=O and C(C(C(N)=O)(CCC)CCC)CC have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drug C[C@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO and [C@]12(C(=CC(C=C1)=O)[C@H](C[C@H]3[C@H]4[C@](C[C@@H]([C@H]23)O)([C@](C(=O)CO)(O)[C@@H](C4)C)C)F)C enter the brain by crossing the blood-brain barrier? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO\",\"[C@]12(C(=CC(C=C1)=O)[C@H](C[C@H]3[C@H]4[C@](C[C@@H]([C@H]23)O)([C@](C(=O)CO)(O)[C@@H](C4)C)C)F)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C[C@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO\",\"value\": 0.8758559}, {\"smile\": \"[C@]12(C(=CC(C=C1)=O)[C@H](C[C@H]3[C@H]4[C@](C[C@@H]([C@H]23)O)([C@](C(=O)CO)(O)[C@@H](C4)C)C)F)C\",\"value\": 0.87759364}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C[C@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO and [C@]12(C(=CC(C=C1)=O)[C@H](C[C@H]3[C@H]4[C@](C[C@@H]([C@H]23)O)([C@](C(=O)CO)(O)[C@@H](C4)C)C)F)C have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drug C4Oc3ccc(CN1CCN(CC1)c2ncccn2)cc3O4 and COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O[C@H]6CCCCO6)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO can pass into the brain across the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C4Oc3ccc(CN1CCN(CC1)c2ncccn2)cc3O4\",\"COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O[C@H]6CCCCO6)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C4Oc3ccc(CN1CCN(CC1)c2ncccn2)cc3O4\",\"value\": 0.7504414}, {\"smile\": \"COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O[C@H]6CCCCO6)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO\",\"value\": 0.7077869}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C4Oc3ccc(CN1CCN(CC1)c2ncccn2)cc3O4 and COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O[C@H]6CCCCO6)[C@H](C)O5)c4c(O)c3C(=O)c12)C(=O)CO have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the capability of the drug C1=CC(=C3C2=C1CC5C4C2(C(O3)CCC4)CCN5C)O and C1=CC(=CC5=C1N(C4CCN(CC3OC2=C(C=CC=C2)OC3)CC4)C(N5)=O)Cl to cross the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=C3C2=C1CC5C4C2(C(O3)CCC4)CCN5C)O\",\"C1=CC(=CC5=C1N(C4CCN(CC3OC2=C(C=CC=C2)OC3)CC4)C(N5)=O)Cl\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=C3C2=C1CC5C4C2(C(O3)CCC4)CCN5C)O\",\"value\": 0.47274518}, {\"smile\": \"C1=CC(=CC5=C1N(C4CCN(CC3OC2=C(C=CC=C2)OC3)CC4)C(N5)=O)Cl\",\"value\": 0.45745793}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC(=C3C2=C1CC5C4C2(C(O3)CCC4)CCN5C)O nor C1=CC(=CC5=C1N(C4CCN(CC3OC2=C(C=CC=C2)OC3)CC4)C(N5)=O)Cl has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drug [C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F and CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(C)=O)C2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7)C(=C1C5(C)C)C has brain-penetrating potential through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F\",\"CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(C)=O)C2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7)C(=C1C5(C)C)C\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F\",\"value\": 0.47378495}, {\"smile\": \"CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(C)=O)C2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7)C(=C1C5(C)C)C\",\"value\": 0.41733015}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug [C@H]23[C@@]([C@@]1(C(=CC(=O)CC1)CC2)C)([C@H](C[C@]4([C@H]3CC[C@@]4(C(COC(C)=O)=O)O)C)O)F nor CC(=O)O[C@H]1C(=O)[C@]2(C)[C@@H](O)C[C@H]3OC[C@@]3(OC(C)=O)C2[C@H](OC(=O)c4ccccc4)[C@]5(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)c6ccccc6)c7ccccc7)C(=C1C5(C)C)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the blood-brain barrier penetration efficiency of the drug C1=CC(=CC=C1C(CCCN3CCN(C(OC2CCCCC2)=O)CC3)=O)F and NCC(O)c1ccc(O)c(O)c1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=CC=C1C(CCCN3CCN(C(OC2CCCCC2)=O)CC3)=O)F\",\"NCC(O)c1ccc(O)c(O)c1\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC=C1C(CCCN3CCN(C(OC2CCCCC2)=O)CC3)=O)F\",\"value\": 0.7032357}, {\"smile\": \"NCC(O)c1ccc(O)c(O)c1\",\"value\": 0.67579454}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=CC(=CC=C1C(CCCN3CCN(C(OC2CCCCC2)=O)CC3)=O)F and NCC(O)c1ccc(O)c(O)c1 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drug CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O and [C@@]135C67[C@@H](N(CC1)CC2CC2)CC4=CC=C(O)C(=C34)O[C@@H]5C(OC)(C(C6)C(O)(C)C)C=C7 can overcome the brain's blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O\",\"[C@@]135C67[C@@H](N(CC1)CC2CC2)CC4=CC=C(O)C(=C34)O[C@@H]5C(OC)(C(C6)C(O)(C)C)C=C7\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O\",\"value\": 0.59739107}, {\"smile\": \"[C@@]135C67[C@@H](N(CC1)CC2CC2)CC4=CC=C(O)C(=C34)O[C@@H]5C(OC)(C(C6)C(O)(C)C)C=C7\",\"value\": 0.70123357}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O and [C@@]135C67[C@@H](N(CC1)CC2CC2)CC4=CC=C(O)C(=C34)O[C@@H]5C(OC)(C(C6)C(O)(C)C)C=C7 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's CN(C)Cc1ccnc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)c1 and CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24 success in accessing brain regions via the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CN(C)Cc1ccnc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)c1\",\"CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)Cc1ccnc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)c1\",\"value\": 0.7734098}, {\"smile\": \"CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24\",\"value\": 0.7803808}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CN(C)Cc1ccnc(c2cccc(NC3C([N+]([O-])=O)=CC=N3)c2)c1 and CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the likelihood of the drug [C@H]2(C(OC1=CC=C(NC(C)=O)C=C1)=O)NC(=O)CC2 and OC(CCN1CCCC1)(C2CCCCC2)c3ccccc3 penetrating the brain through the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@H]2(C(OC1=CC=C(NC(C)=O)C=C1)=O)NC(=O)CC2\",\"OC(CCN1CCCC1)(C2CCCCC2)c3ccccc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@H]2(C(OC1=CC=C(NC(C)=O)C=C1)=O)NC(=O)CC2\",\"value\": 0.5802227}, {\"smile\": \"OC(CCN1CCCC1)(C2CCCCC2)c3ccccc3\",\"value\": 0.47516814}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@H]2(C(OC1=CC=C(NC(C)=O)C=C1)=O)NC(=O)CC2 has blood-brain barrier permeability, but OC(CCN1CCCC1)(C2CCCCC2)c3ccccc3 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug C1=CC(=CC=C1[S](N2CC3CCC(C2)CC3)(=O)=O)N and C3=C(C(N1CCN(CC1)CCOCCOCCO)C2=CC=CC=C2)C=CC(=C3)Cl.O=C(O)\\C=C\\C(O)=O crosses the brain's protective boundary, the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=CC=C1[S](N2CC3CCC(C2)CC3)(=O)=O)N\",\"C3=C(C(N1CCN(CC1)CCOCCOCCO)C2=CC=CC=C2)C=CC(=C3)Cl.O=C(O)\\C=C\\C(O)=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC=C1[S](N2CC3CCC(C2)CC3)(=O)=O)N\",\"value\": 0.7816877}, {\"smile\": \"C3=C(C(N1CCN(CC1)CCOCCOCCO)C2=CC=CC=C2)C=CC(=C3)Cl.O=C(O)\\C=C\\C(O)=O\",\"value\": 0.7987521}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=CC(=CC=C1[S](N2CC3CCC(C2)CC3)(=O)=O)N and C3=C(C(N1CCN(CC1)CCOCCOCCO)C2=CC=CC=C2)C=CC(=C3)Cl.O=C(O)\\C=C\\C(O)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the feasibility of the drug [Na+].Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cn4cnnn4)C3=O)C([O-])=O)nn1 and CCC1(CC)C(=O)NCC(C)C1=O bypassing the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[Na+].Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cn4cnnn4)C3=O)C([O-])=O)nn1\",\"CCC1(CC)C(=O)NCC(C)C1=O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[Na+].Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cn4cnnn4)C3=O)C([O-])=O)nn1\",\"value\": 0.5233144}, {\"smile\": \"CCC1(CC)C(=O)NCC(C)C1=O\",\"value\": 0.61855924}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [Na+].Cc1sc(SCC2=C(N3[C@H](SC2)[C@H](NC(=O)Cn4cnnn4)C3=O)C([O-])=O)nn1 and CCC1(CC)C(=O)NCC(C)C1=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the drug C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)O and [C@@H]25CC1=CC=C(C4=C1[C@@]3([C@H]2CC[C@@H]([C@@H]3O4)OC)CCN5C)O demonstrates the ability to penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)O\",\"[C@@H]25CC1=CC=C(C4=C1[C@@]3([C@H]2CC[C@@H]([C@@H]3O4)OC)CCN5C)O\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)O\",\"value\": 0.41662878}, {\"smile\": \"[C@@H]25CC1=CC=C(C4=C1[C@@]3([C@H]2CC[C@@H]([C@@H]3O4)OC)CCN5C)O\",\"value\": 0.54394436}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [C@@H]25CC1=CC=C(C4=C1[C@@]3([C@H]2CC[C@@H]([C@@H]3O4)OC)CCN5C)O has blood-brain barrier permeability, but C1=CC(=C3C2=C1CC5C4C2(C(O3)C(O)C=C4)CCN5)O has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug [C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C and CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3occc3 transition through the blood-brain barrier to reach the brain? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C\",\"CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3occc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C\",\"value\": 0.42538434}, {\"smile\": \"CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3occc3\",\"value\": 0.46397087}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug [C@]34([C@H]([C@H]2[C@@H]([C@@]1(C(=CC(=O)CC1)CC2)C)[C@H](C3)O)CC[C@@]4(C(COC(C)=O)=O)O)C nor CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)COC(N)=O)/c3occc3 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the potential of the drug OCc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12 and S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3 to penetrate the brain's blood-brain barrier. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12\",\"S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3\"], \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"OCc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12\",\"value\": 0.48596233}, {\"smile\": \"S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3\",\"value\": 0.44095027}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug OCc1ncc2CN=C(c3ccccc3F)c4cc(Cl)ccc4n12 nor S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drugs (smile) can diffuse across the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCNCc1c(O)c(C)ncc1CSC\",\"value\": 0.7046993}, {\"smile\": \"[C@H]23[C@@]([C@@]1(O[C@@H](O[C@@H]1C2)CCC)C(=O)CO)(C[C@H](O)[C@@]4(F)[C@H]3C[C@H](F)C5=CC(=O)CC[C@]45C)C\",\"value\": 0.686932}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CCNCc1c(O)c(C)ncc1CSC and [C@H]23[C@@]([C@@]1(O[C@@H](O[C@@H]1C2)CCC)C(=O)CO)(C[C@H](O)[C@@]4(F)[C@H]3C[C@H](F)C5=CC(=O)CC[C@]45C)C have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drugs (smile) achieves entry past the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN1C(=O)C(O)N=C(c2ccccc2)c3cc(Cl)ccc13\",\"value\": 0.40178904}, {\"smile\": \"Oc1ccc2C[C@@H]3[C@@H]4CCCC[C@]4(CCN3CC=C)c2c1\",\"value\": 0.55620253}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Oc1ccc2C[C@@H]3[C@@H]4CCCC[C@]4(CCN3CC=C)c2c1 has blood-brain barrier permeability, but CN1C(=O)C(O)N=C(c2ccccc2)c3cc(Cl)ccc13 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drugs's (smile) potential to overcome the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(CN(C(=C(SC(=O)C1=CC=CC=C1)/CCO[P](=O)(O)O)/C)C=O)C(=NC(=N2)C)N\",\"value\": 0.8836523}, {\"smile\": \"C1=NC(=NC(=C1CN(C(/C)=C2\\CCOC(=O)S2)C=O)N)C\",\"value\": 0.8792973}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C2=C(CN(C(=C(SC(=O)C1=CC=CC=C1)/CCO[P](=O)(O)O)/C)C=O)C(=NC(=N2)C)N and C1=NC(=NC(=C1CN(C(/C)=C2\\CCOC(=O)S2)C=O)N)C have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drugs (smile) can penetrate the central nervous system via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC=C1C2(N(C(=O)NC2=O)C)CC\",\"value\": 0.32917148}, {\"smile\": \"C1=C(O)C=CC3=C1C24C(C(NCC2)C3)CCCC4\",\"value\": 0.38556027}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC=CC=C1C2(N(C(=O)NC2=O)C)CC nor C1=C(O)C=CC3=C1C24C(C(NCC2)C3)CCCC4 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drugs's (smile) likelihood of passing through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCOC(=O)N(CCN(C)C)c1cccc(c1)C(F)(F)F\",\"value\": 0.5166396}, {\"smile\": \"CC\\C(=C/C)C(=O)NC(N)=O\",\"value\": 0.46910757}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCOC(=O)N(CCN(C)C)c1cccc(c1)C(F)(F)F has blood-brain barrier permeability, but CC\\C(=C/C)C(=O)NC(N)=O has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drugs (smile) reaches the brain by crossing the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Cn1cnc2N(C)C(=O)NC(=O)c12\",\"value\": 0.35402998}, {\"smile\": \"C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)C(CCN(CC)CC)=O\",\"value\": 0.41593513}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug Cn1cnc2N(C)C(=O)NC(=O)c12 nor C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)C(CCN(CC)CC)=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the possibility of the drugs (smile) breaching the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(NC(C(C)(C)C)=O)CCC(O)=O\",\"value\": 0.34196913}, {\"smile\": \"NC(CO)(CO)CO\",\"value\": 0.4252577}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C(NC(C(C)(C)C)=O)CCC(O)=O nor NC(CO)(CO)CO has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drugs (smile) has the capacity to pass the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC(CN(C)C)CN1c2ccccc2CCc3ccccc13\",\"value\": 0.54365534}, {\"smile\": \"CC1(C)S[C@@H]2[C@H](NC(=O)COc3ccccc3)C(=O)N2[C@H]1C([O-])=O\",\"value\": 0.5476896}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CC(CN(C)C)CN1c2ccccc2CCc3ccccc13 and CC1(C)S[C@@H]2[C@H](NC(=O)COc3ccccc3)C(=O)N2[C@H]1C([O-])=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drugs (smile) can access the brain by crossing the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC=C1N3CCN(CC(O)COC2(CCCCC2)C#C)CC3)F\",\"value\": 0.45344818}, {\"smile\": \"CN(C)C(=O)COC2c1ccccc1CCc3ccccc23\",\"value\": 0.342992}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC(=CC=C1N3CCN(CC(O)COC2(CCCCC2)C#C)CC3)F nor CN(C)C(=O)COC2c1ccccc1CCc3ccccc23 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the ability of the drugs (smile) to cross the brain's protective blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COCOC(=O)C1N2C(SC1(C)C)C(N3C(=O)C(NC3(C)C)c4ccc(O)cc4)C2=O\",\"value\": 0.3608875}, {\"smile\": \"CC1(C)S[C@@H]2[C@H](N=CN3CCCCCC3)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.35690105}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug COCOC(=O)C1N2C(SC1(C)C)C(N3C(=O)C(NC3(C)C)c4ccc(O)cc4)C2=O nor CC1(C)S[C@@H]2[C@H](N=CN3CCCCCC3)C(=O)N2[C@H]1C(O)=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the drugs (smile) permeates the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCOC(=O)c1cncn1C(C)c2ccccc2\",\"value\": 0.56449467}, {\"smile\": \"N[S](=O)(=O)c1cc(ccc1Cl)C2(O)NC(=O)c3ccccc23\",\"value\": 0.57999796}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CCOC(=O)c1cncn1C(C)c2ccccc2 and N[S](=O)(=O)c1cc(ccc1Cl)C2(O)NC(=O)c3ccccc23 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drugs (smile) bypass the blood-brain barrier?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C4=C(C(N2CCN(\\N=C\\C1=NC(=CC=C1)C)CC2)C3=CC=CC=C3)C=CC=C4\",\"value\": 0.47068003}, {\"smile\": \"[C@]23([C@H]([C@H]1[C@]([C@@](OC(C)=O)(C(C)=O)CC1)(C)C[C@@H]2O)C[C@H](C)C4=CC(=O)C=C[C@]34C)F\",\"value\": 0.30881533}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C4=C(C(N2CCN(\\N=C\\C1=NC(=CC=C1)C)CC2)C3=CC=CC=C3)C=CC=C4 nor [C@]23([C@H]([C@H]1[C@]([C@@](OC(C)=O)(C(C)=O)CC1)(C)C[C@@H]2O)C[C@H](C)C4=CC(=O)C=C[C@]34C)F has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drugs (smile) can reach the brain through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C(CNC(C6=CC=CC=C6)=O)C)=O)=O\",\"value\": 0.6408452}, {\"smile\": \"CC[C@H]1OC(=O)[C@H](C)[C@@H](OC2CC(C)(OC)C(O)C(C)O2)C(C)[C@@H](OC3OC(C)CC(C3O)N(C)C)[C@](C)(O)C[C@@H](C)\\C(=N/OC)[C@H](C)[C@@H](O)[C@]1(C)O\",\"value\": 0.648392}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(C(CNC(C6=CC=CC=C6)=O)C)=O)=O and CC[C@H]1OC(=O)[C@H](C)[C@@H](OC2CC(C)(OC)C(O)C(C)O2)C(C)[C@@H](OC3OC(C)CC(C3O)N(C)C)[C@](C)(O)C[C@@H](C)\\C(=N/OC)[C@H](C)[C@@H](O)[C@]1(C)O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the brain permeability of the drugs (smile) via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]14([C@](OC(=O)CC)([C@@H](CC1C3[C@@](F)(C2(C(=CC(=O)C=C2)[C@@H](F)C3)C)[C@@H](O)C4)C)C(SC)=O)C\",\"value\": 0.3008389}, {\"smile\": \"C1=CC=CC(=C1N3CCN(CCCOC2=CC=CC=N2)CC3)C\",\"value\": 0.56135124}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=CC(=C1N3CCN(CCCOC2=CC=CC=N2)CC3)C has blood-brain barrier permeability, but [C@]14([C@](OC(=O)CC)([C@@H](CC1C3[C@@](F)(C2(C(=CC(=O)C=C2)[C@@H](F)C3)C)[C@@H](O)C4)C)C(SC)=O)C has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drugs's (smile) capacity to cross the protective blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C1(C(NC(=O)NC1=O)=O)CC=C)C(=C)Br\",\"value\": 0.32768023}, {\"smile\": \"C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)C\",\"value\": 0.3428043}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C(C1(C(NC(=O)NC1=O)=O)CC=C)C(=C)Br nor C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the likelihood of the drugs (smile) diffusing through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"FC(F)(F)[C@]1(OC(=O)Nc2ccc(Cl)cc12)C#CC3CC3\",\"value\": 0.35646075}, {\"smile\": \"[Br-].C[N+]1(C)CCCC(C1)OC(=O)C(O)(c2ccccc2)c3ccccc3\",\"value\": 0.5615126}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [Br-].C[N+]1(C)CCCC(C1)OC(=O)C(O)(c2ccccc2)c3ccccc3 has blood-brain barrier permeability, but FC(F)(F)[C@]1(OC(=O)Nc2ccc(Cl)cc12)C#CC3CC3 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the ability of the drugs (smile) to penetrate the brain's blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Nc1nc(N)c2nc(c3ccccc3)c(N)nc2n1\",\"value\": 0.6467411}, {\"smile\": \"[Cl].C1=CC=CC=C1CCNN\",\"value\": 0.5897084}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug Nc1nc(N)c2nc(c3ccccc3)c(N)nc2n1 and [Cl].C1=CC=CC=C1CCNN have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the drugs (smile) can navigate past the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCN1C=C(C(O)=O)C(=O)c2cc(F)c(nc12)N3CCNCC3\",\"value\": 0.6934216}, {\"smile\": \"CC(=O)N1CCN(CC1)c2ccc(OC[C@H]3CO[C@@](Cn4ccnc4)(O3)c5ccc(Cl)cc5Cl)cc2\",\"value\": 0.7167687}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CCN1C=C(C(O)=O)C(=O)c2cc(F)c(nc12)N3CCNCC3 and CC(=O)N1CCN(CC1)c2ccc(OC[C@H]3CO[C@@](Cn4ccnc4)(O3)c5ccc(Cl)cc5Cl)cc2 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drugs's (smile) potential to bypass the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC[N+](C)(CC)CCOC(=O)C1c2ccccc2Oc3ccccc13\",\"value\": 0.6037182}, {\"smile\": \"OCCN(CCO)c1nc(N2CCCCC2)c3nc(nc(N4CCCCC4)c3n1)N(CCO)CCO\",\"value\": 0.8609743}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CC[N+](C)(CC)CCOC(=O)C1c2ccccc2Oc3ccccc13 and OCCN(CCO)c1nc(N2CCCCC2)c3nc(nc(N4CCCCC4)c3n1)N(CCO)CCO have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the drugs (smile) crosses into the brain through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C4=C(N2C(C1=C(SCCS1)C2=O)OC(N3CCN(C)CC3)=O)N=C5C(=C4)C=CC(=N5)Cl\",\"value\": 0.5983094}, {\"smile\": \"CC(C)NCC(O)COc1cccc2ccccc12\",\"value\": 0.7609153}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C4=C(N2C(C1=C(SCCS1)C2=O)OC(N3CCN(C)CC3)=O)N=C5C(=C4)C=CC(=N5)Cl and CC(C)NCC(O)COc1cccc2ccccc12 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the penetration potential of the drugs (smile) at the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC12C(C(CC1=O)CC2)(C)C\",\"value\": 0.5214923}, {\"smile\": \"C3=C(C(N2CCN(CC1=CC=CC=C1)CC2)=O)OC4=C3C=CC=C4\",\"value\": 0.35957935}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC12C(C(CC1=O)CC2)(C)C has blood-brain barrier permeability, but C3=C(C(N2CCN(CC1=CC=CC=C1)CC2)=O)OC4=C3C=CC=C4 has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drugs (smile) access the central nervous system by crossing the blood-brain barrier?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCN1C=CC(=O)C(=C1CC)O\",\"value\": 0.43063915}, {\"smile\": \"[C@]12(OC(O[C@@H]1C[C@@H]3C2(CC(O)[C@H]4C3CCC5=CC(=O)C=CC45C)C)(C)C)C(=O)CO\",\"value\": 0.401135}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug CCN1C=CC(=O)C(=C1CC)O nor [C@]12(OC(O[C@@H]1C[C@@H]3C2(CC(O)[C@H]4C3CCC5=CC(=O)C=CC45C)C)(C)C)C(=O)CO has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the drugs (smile) can infiltrate the brain via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(C(C(OCOC(C(C)(C)C)=O)=O)CCC)CC\",\"value\": 0.63594764}, {\"smile\": \"[Cl-].COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)/C(C)=N/NC(=O)c6ccccc6.[H+]\",\"value\": 0.5066325}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C(C(C(OCOC(C(C)(C)C)=O)=O)CCC)CC and [Cl-].COc1cccc2C(=O)c3c(O)c4C[C@](O)(C[C@H](O[C@H]5C[C@H](N)[C@H](O)[C@H](C)O5)c4c(O)c3C(=O)c12)/C(C)=N/NC(=O)c6ccccc6.[H+] have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the drugs (smile) successfully traverses the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@H]23[C@H]([C@H]1[C@]([C@@H](C(C)=O)CC1)(C)C[C@H]2N(C)C)CC[C@@H]4[C@@]3(C[C@H](OCC)[C@H](C4)O)C\",\"value\": 0.6070941}, {\"smile\": \"COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)[S](=O)(=O)NC(=O)NC3CCCCC3\",\"value\": 0.60073435}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@@H]23[C@H]([C@H]1[C@]([C@@H](C(C)=O)CC1)(C)C[C@H]2N(C)C)CC[C@@H]4[C@@]3(C[C@H](OCC)[C@H](C4)O)C and COc1ccc(Cl)cc1C(=O)NCCc2ccc(cc2)[S](=O)(=O)NC(=O)NC3CCCCC3 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drugs's (smile) success in permeating the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC\",\"value\": 0.7452026}, {\"smile\": \"CC(C)(Oc1ccc(Cl)cc1)C(O)=O\",\"value\": 0.66845036}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug COc1cc(NCc2ccc3nc(N)nc(N)c3c2C)cc(OC)c1OC and CC(C)(Oc1ccc(Cl)cc1)C(O)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drugs (smile) crosses into brain tissue via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@H](CC(=O)O)(CC(C)C)CN\",\"value\": 0.78266764}, {\"smile\": \"CC(C)N=C1C=C2N(c3ccc(Cl)cc3)c4ccccc4N=C2C=C1Nc5ccc(Cl)cc5\",\"value\": 0.79961}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@@H](CC(=O)O)(CC(C)C)CN and CC(C)N=C1C=C2N(c3ccc(Cl)cc3)c4ccccc4N=C2C=C1Nc5ccc(Cl)cc5 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the penetration capacity of the drugs (smile) across the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O\",\"value\": 0.3680432}, {\"smile\": \"Oc1ccc(cc1)/C=C([N+]#[C-])/C(=C/c2ccc(O)cc2)[N+]#[C-]\",\"value\": 0.35428858}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug CCOC(=O)C(C1=C(O)Oc2ccccc2C1=O)C3=C(O)Oc4ccccc4C3=O nor Oc1ccc(cc1)/C=C([N+]#[C-])/C(=C/c2ccc(O)cc2)[N+]#[C-] has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drugs (smile) move past the blood-brain barrier into the brain?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[Na+].Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CSC(F)(F)F)C3=O)C([O-])=O\",\"value\": 0.7516231}, {\"smile\": \"CC[C@]1(O)C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C=O)[C@H]7[C@](O)([C@H](OC(C)=O)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1\",\"value\": 0.7435342}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [Na+].Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)CSC(F)(F)F)C3=O)C([O-])=O and CC[C@]1(O)C[C@H]2CN(CCc3c([nH]c4ccccc34)[C@@](C2)(C(=O)OC)c5cc6c(cc5OC)N(C=O)[C@H]7[C@](O)([C@H](OC(C)=O)[C@]8(CC)C=CCN9CC[C@]67[C@H]89)C(=O)OC)C1 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drugs (smile) transitions across the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COc1ccc2C[C@H]3N(C)CC[C@@]45[C@@H](Oc1c24)C(=O)CC[C@@]35O\",\"value\": 0.6056512}, {\"smile\": \"C1=CC=CC3=C1C(C2=CC=CC=C2)OCCN(C3)C\",\"value\": 0.49749398}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1ccc2C[C@H]3N(C)CC[C@@]45[C@@H](Oc1c24)C(=O)CC[C@@]35O has blood-brain barrier permeability, but C1=CC=CC3=C1C(C2=CC=CC=C2)OCCN(C3)C has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drugs (smile) has access to the brain through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=NC=C([N]1C(C2=CC=CC=C2)C)C(=O)OC\",\"value\": 0.3795239}, {\"smile\": \"C1=CC(=CC=C1)CC(N(C)CC2=CC=CC=C2)C\",\"value\": 0.42314684}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=NC=C([N]1C(C2=CC=CC=C2)C)C(=O)OC nor C1=CC(=CC=C1)CC(N(C)CC2=CC=CC=C2)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drugs's (smile) effectiveness at crossing the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(OC)C=CC3=C1N(C2=C(C=CC=C2)S3)CCC4N(CCCC4)C\",\"value\": 0.30712044}, {\"smile\": \"C1=CSC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C\",\"value\": 0.29619205}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=C(OC)C=CC3=C1N(C2=C(C=CC=C2)S3)CCC4N(CCCC4)C nor C1=CSC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine whether the drugs (smile) enters the brain by bypassing the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN1CCc2cccc3c2[C@H]1Cc4ccc(O)c(O)c34\",\"value\": 0.56006867}, {\"smile\": \"C3=C(C1N2C(=NC1)NCC2)C=CC=C3\",\"value\": 0.6122061}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug CN1CCc2cccc3c2[C@H]1Cc4ccc(O)c(O)c34 and C3=C(C1N2C(=NC1)NCC2)C=CC=C3 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the transport potential of the drugs (smile) across the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC2=C1C4=C([NH]2)CN3CCN(CC3C4)CCCC(C5=CC=C(C=C5)F)=O\",\"value\": 0.45431015}, {\"smile\": \"COc1ccc(CCN2CCC(CC2)Nc3nc4ccccc4n3Cc5ccc(F)cc5)cc1\",\"value\": 0.49402735}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC=CC2=C1C4=C([NH]2)CN3CCN(CC3C4)CCCC(C5=CC=C(C=C5)F)=O nor COc1ccc(CCN2CCC(CC2)Nc3nc4ccccc4n3Cc5ccc(F)cc5)cc1 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the drugs (smile) can breach the brain's protective blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2\",\"value\": 0.3744397}, {\"smile\": \"O=C1Oc2ccccc2C=C1\",\"value\": 0.41637677}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2 nor O=C1Oc2ccccc2C=C1 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drugs (smile) can reach neural tissues via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@]235[C@]([C@H](N(CC1CC1)CC2)CC4=C3C=C(O)C=C4)(CCCC5)O\",\"value\": 0.7342439}, {\"smile\": \"c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN(CC1)C(=O)OCC)CN1CCCC1)=O\",\"value\": 0.7780133}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [C@]235[C@]([C@H](N(CC1CC1)CC2)CC4=C3C=C(O)C=C4)(CCCC5)O and c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN(CC1)C(=O)OCC)CN1CCCC1)=O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the drugs (smile) can successfully bypass the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[H+].C1=NC3=C([N]1CCNC(CC2=CC=CC=C2)C)C(=O)N(C(=O)N3C)C.[Cl-]\",\"value\": 0.61329895}, {\"smile\": \"C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C\",\"value\": 0.69739366}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [H+].C1=NC3=C([N]1CCNC(CC2=CC=CC=C2)C)C(=O)N(C(=O)N3C)C.[Cl-] and C1CN(CCC1)Cc1cccc(c1)OCCCNC(=O)C have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drugs's (smile) ability to cross from the bloodstream into the brain."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"COC1=C(N3C(SC1)C(NC(=O)C(N)C2C=CCC=C2)C3=O)C(O)=O\",\"value\": 0.36829552}, {\"smile\": \"Cc1ccccc1C\",\"value\": 0.45007348}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug COC1=C(N3C(SC1)C(NC(=O)C(N)C2C=CCC=C2)C3=O)C(O)=O nor Cc1ccccc1C has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the drugs (smile) enter the brain by crossing the blood-brain barrier?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Clc1ccc2N(CC3CC3)C(=O)CN=C(c4ccccc4)c2c1\",\"value\": 0.625833}, {\"smile\": \"CCCCCc1cc(O)c2[C@@H]3C=C(C)CC[C@H]3C(C)(C)Oc2c1\",\"value\": 0.6446605}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug Clc1ccc2N(CC3CC3)C(=O)CN=C(c4ccccc4)c2c1 and CCCCCc1cc(O)c2[C@@H]3C=C(C)CC[C@H]3C(C)(C)Oc2c1 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drugs (smile) can pass into the brain across the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC2=C1C(=C[NH]2)CC(CC)N\",\"value\": 0.5097548}, {\"smile\": \"OC[C@H]1O[C@@H](OC2=C(Oc3cc(O)cc(O)c3C2=O)c4ccc(O)c(O)c4)[C@H](O)[C@@H](O)[C@@H]1O\",\"value\": 0.7447943}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C1=CC=CC2=C1C(=C[NH]2)CC(CC)N and OC[C@H]1O[C@@H](OC2=C(Oc3cc(O)cc(O)c3C2=O)c4ccc(O)c(O)c4)[C@H](O)[C@@H](O)[C@@H]1O have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the capability of the drugs (smile) to cross the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CSC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C\",\"value\": 0.4642674}, {\"smile\": \"C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2\",\"value\": 0.55572}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C2=C(C1(C(NC(=O)CN1)=O)CC)C=CC=C2 has blood-brain barrier permeability, but C1=CSC2=C1N(C3=C(C=C2)C=CC=C3)CCCN(C)C has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the drugs (smile) has brain-penetrating potential through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=C3C2=C1CC5C4C2(C(O3)C(C=C4)OC(C)=O)CCN5CC=C)OC(C)=O\",\"value\": 0.5150695}, {\"smile\": \"C1=CC(=CC=C1C(C2=CC=CC=C2)SCCN(C)C)SCCCC\",\"value\": 0.4826498}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC(=C3C2=C1CC5C4C2(C(O3)C(C=C4)OC(C)=O)CCN5CC=C)OC(C)=O has blood-brain barrier permeability, but C1=CC(=CC=C1C(C2=CC=CC=C2)SCCN(C)C)SCCCC has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the blood-brain barrier penetration efficiency of the drugs (smile)."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@H](C(=O)CO)[C@]4(C[C@H](O)[C@H]23)C=O\",\"value\": 0.3875248}, {\"smile\": \"C3=C(C(C1=CC=C(C=C1)F)CCCN2CCN(C(NCC)=O)CC2)C=CC(=C3)F\",\"value\": 0.34534684}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C[C@]12CCC(=O)C=C1CC[C@H]3[C@@H]4CC[C@H](C(=O)CO)[C@]4(C[C@H](O)[C@H]23)C=O nor C3=C(C(C1=CC=C(C=C1)F)CCCN2CCN(C(NCC)=O)CC2)C=CC(=C3)F has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze if the drugs (smile) can overcome the brain's blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[O-][N+](C1=CC=NC1NCCSCc2ncccc2Br)=O\",\"value\": 0.6060307}, {\"smile\": \"[C@H]3(CN(C2=CC=C(OCC1=CC=CC(=C1)Cl)C=C2)C(O3)=O)CNC\",\"value\": 0.55277663}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug [O-][N+](C1=CC=NC1NCCSCc2ncccc2Br)=O and [C@H]3(CN(C2=CC=C(OCC1=CC=CC(=C1)Cl)C=C2)C(O3)=O)CNC have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drugs's (smile) success in accessing brain regions via the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"c1c(nccc1)CCN(C)C\",\"value\": 0.4961807}, {\"smile\": \"[C@H]5(CN3CCC(C2=CC1=C(OCO1)C=C2)(CC3)O)CN(C4=CC=C(OC)C=C4)C(O5)=O\",\"value\": 0.43719995}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug c1c(nccc1)CCN(C)C nor [C@H]5(CN3CCC(C2=CC1=C(OCO1)C=C2)(CC3)O)CN(C4=CC=C(OC)C=C4)C(O5)=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the likelihood of the drugs (smile) penetrating the brain through the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C[C@]12C[C@H](O)[C@@]3(F)[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1C[C@@H](O)[C@]2(O)C(=O)CO\",\"value\": 0.78341556}, {\"smile\": \"CCCNC(=O)N[S](=O)(=O)c1ccc(Cl)cc1\",\"value\": 0.7350559}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug C[C@]12C[C@H](O)[C@@]3(F)[C@@H](CCC4=CC(=O)C=C[C@]34C)[C@@H]1C[C@@H](O)[C@]2(O)C(=O)CO and CCCNC(=O)N[S](=O)(=O)c1ccc(Cl)cc1 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drugs (smile) crosses the brain's protective boundary, the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3\",\"value\": 0.5925161}, {\"smile\": \"c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1\",\"value\": 0.5633341}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug S=C(NC1CCCCC1)N2CCC(CC2)c3[nH]cnc3 and c1ccc([C@H](C[N@]2CC[C@H](C2)O)N(C(Cc2ccccc2N)=O)C)cc1 have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the feasibility of the drugs (smile) bypassing the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"O.C[C@@H]1[C@H]2[C@H](O)[C@H]3[C@H](N(C)C)C(=O)C(=C(N)/O)/C(=O)[C@@]3(O)C(=O)C2=C(O)c4c(O)cccc14\",\"value\": 0.7047198}, {\"smile\": \"[C@@]4(OC(=O)CCCC)(C3(C(C2C(C1(C(=CC(=O)C=C1)C(F)C2)C)C(O)C3)CC4)C)C(=O)CO\",\"value\": 0.67562586}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the drug O.C[C@@H]1[C@H]2[C@H](O)[C@H]3[C@H](N(C)C)C(=O)C(=C(N)/O)/C(=O)[C@@]3(O)C(=O)C2=C(O)c4c(O)cccc14 and [C@@]4(OC(=O)CCCC)(C3(C(C2C(C1(C(=CC(=O)C=C1)C(F)C2)C)C(O)C3)CC4)C)C(=O)CO have blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the drugs (smile) demonstrates the ability to penetrate the blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC(=CC=C1OCC(COC(N)=O)O)Cl\",\"value\": 0.33325365}, {\"smile\": \"[Na+].CC(=O)OCC1=C(N2[C@@H](SC1)[C@@H](NC(=O)CC#N)C2=O)C([O-])=O\",\"value\": 0.3474816}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C1=CC(=CC=C1OCC(COC(N)=O)O)Cl nor [Na+].CC(=O)OCC1=C(N2[C@@H](SC1)[C@@H](NC(=O)CC#N)C2=O)C([O-])=O has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drugs (smile) transition through the blood-brain barrier to reach the brain?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)CCc1c[nH]c2ccc(O)cc12\",\"value\": 0.6055692}, {\"smile\": \"CC(C)C1NC(=O)C(NC(=O)c2ccc(C)c3OC4=C(C)C(=O)C(=C(C(=O)NC5C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C6CCCN6C(=O)C(NC5=O)C(C)C)C4=Nc23)N)C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C7CCCN7C1=O\",\"value\": 0.33879268}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN(C)CCc1c[nH]c2ccc(O)cc12 has blood-brain barrier permeability, but CC(C)C1NC(=O)C(NC(=O)c2ccc(C)c3OC4=C(C)C(=O)C(=C(C(=O)NC5C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C6CCCN6C(=O)C(NC5=O)C(C)C)C4=Nc23)N)C(C)OC(=O)C(C(C)C)N(C)C(=O)CN(C)C(=O)C7CCCN7C1=O has not."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the potential of the drugs (smile) to penetrate the brain's blood-brain barrier."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C(N1C(CCC1)=O)C(N)=O\",\"value\": 0.31476614}, {\"smile\": \"C2=C(C1=CC=CC=C1)N=C4C(=C2CCC3CCNCC3)C=CC=C4\",\"value\": 0.2722478}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither the drug C(N1C(CCC1)=O)C(N)=O nor C2=C(C1=CC=CC=C1)N=C4C(=C2CCC3CCNCC3)C=CC=C4 has good blood-brain barrier permeability."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess if the optimized drugs can cross the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[Br-].CC(C)[N+]1(C)C2CCC1CC(C2)OC(=O)C(CO)c3ccccc3\",\"value\": 0.40716967}, {\"smile\": \"CN=C1CN(O)C(=C2C=C(Cl)C=CC2=N1)c3ccccc3\",\"value\": 0.33478224},{\"smile\": \"[Na+].COCC1=C(N2[C@H](SC1)[C@H](NC(=O)C(=N/OC)\\c3csc(N)n3)C2=O)C([O-])=O\",\"value\": 0.3878541}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.40716967, 0.33478224 and 0.3878541."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the potential of the optimized drugs to penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](C[N@@]2C[C@@H](CC2)O)c2c(CC1)[nH]cn2)=O\",\"value\": 0.6643851}, {\"smile\": \"CO[C@H]1C[C@@H](O[C@@H](C)[C@@H]1OC(C)=O)O[C@H]2[C@H](C)[C@@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3OC(C)=O)N(C)C)[C@@H](C)C[C@@]4(CO4)C(=O)[C@H](C)[C@@H](OC(C)=O)[C@@H](C)[C@@H](C)OC(=O)[C@@H]2C\",\"value\": 0.518462},{\"smile\": \"[C@@]125C3=C4C[C@H]([C@@H]1CCC([C@@]2(OC3=C(C=C4)O)C)=O)N(C)CC5\",\"value\": 0.6864323}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.6643851, 0.518462 and 0.6864323."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate whether the generated drugs crosses the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=CC=CC2=C1N(C3=C(CC2=O)C=CC=C3)CCCN(C)C\",\"value\": 0.49389216}, {\"smile\": \"C1=CC=CC2=C1SC5=C(N2CC3C4CCN(C3)CC4)C=CC=C5\",\"value\": 0.45886597},{\"smile\": \"C(C1(C(NC(=O)NC1=O)=O)CC=C)C(C)C\",\"value\": 0.35892683}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.49389216, 0.45886597 and 0.35892683."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate if the generated drugs is capable of crossing the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCCCCC1CNC(C1)C(=O)NC(C(C)Cl)C2OC(SC)C(O)C(O)C2O\",\"value\": 0.5842553}, {\"smile\": \"[C@]23([C@H]([C@H]1[C@]([C@@](SCC)(SC)CC1)(C)C[C@@H]2O)CCC4=CC(=O)C=C[C@]34C)F\",\"value\": 0.4992839},{\"smile\": \"[C@@]45([C@@]3([C@H]([C@H]2[C@]([C@@]1(C(=CC(=O)C=C1)CC2)C)(F)[C@H](C3)O)C[C@H]4OC(O5)(C)C)C)C(COC(CC(C)(C)C)=O)=O\",\"value\": 0.48125535}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.5842553, 0.4992839 and 0.48125535."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze whether the optimized drugs can pass through the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CO/N=C(C(=O)N[C@H]1[C@H]2SCC(=C(N2C1=O)C(O)=O)CSc3nnnn3C)/c4csc(N)n4\",\"value\": 0.6834326}, {\"smile\": \"C2=C(C1=C(NC(C1)=O)C=C2)CCN(CCC)CCC\",\"value\": 0.62491196},{\"smile\": \"CNCC(O)c1ccc(O)c(O)c1\",\"value\": 0.7618254}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.6834326, 0.62491196 and 0.7618254."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the optimized drugs' ability to traverse the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC\\C(=C/C)C(=O)NC(N)=O\",\"value\": 0.5169415}, {\"smile\": \"Cn1nnnc1SCC2=C(N3[C@H](SC2)[C@H](NC(=O)[C@H](O)c4ccccc4)C3=O)C(O)=O\",\"value\": 0.62392384},{\"smile\": \"C1=CC=CC=C1C(OC2=CC=CC=C2OC)CCNC\",\"value\": 0.73556095}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.5169415, 0.62392384 and 0.73556095."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test if the generated drugs can penetrate the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CO[C@H]1\\C=C\\O[C@@]2(C)Oc3c(C)c(O)c4C(=C(NC(=O)C(=C/C=C/[C@H](C)[C@H](O)[C@@H](C)[C@@H](O)[C@@H](C)[C@H](OC(C)=O)[C@@H]1C)\\C)C(=C/NN5CCN(CC5)C6CCCC6)\\C(=O)c4c3C2=O)O\",\"value\": 0.52878994}, {\"smile\": \"C[C@]12CC[C@H]3[C@@H](CCC4=CC(=O)CC[C@H]34)[C@@H]1CC[C@@]2(O)C#C\",\"value\": 0.5834129},{\"smile\": \"C2=C(N1C(SCC1)=N)C(=CC(=C2)Cl)N\",\"value\": 0.5687196}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.52878994, 0.5834129 and 0.5687196."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine whether the optimized drugs passes the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(C=CC=C1O)NC(C)=O\",\"value\": 0.66703606}, {\"smile\": \"C1=C(C(F)(F)F)C=CC3=C1N(C2=C(C=CC=C2)S3)CCCN4CCN(CCOC(CCCCCC)=O)CC4\",\"value\": 0.5495032},{\"smile\": \"Cc1ncsc1CCCl\",\"value\": 0.64404655}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.66703606, 0.5495032 and 0.64404655."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify if the optimized drugs can cross the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=NC(=NC(=C1CN(C(=C(SSC(=C(\\C)N(C=O)CC2=C(N)N=C(C)N=C2)/CCOC(=O)C3=CC=CC=C3)/CCOC(=O)C4=CC=CC=C4)\\C)C=O)N)C\",\"value\": 0.39646152}, {\"smile\": \"CC1(C(N(C(O1)=O)C)=O)C\",\"value\": 0.6450538},{\"smile\": \"c1cn(CCO)c(CC)c(O)c1=O\",\"value\": 0.5667799}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.39646152, 0.6450538 and 0.5667799."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the generated drugs cross the blood-brain barrier? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"[C@@H](CC[N]2C1=C(C(N=C(N1)N)=O)N=C2)(CO)O\",\"value\": 0.7263137}, {\"smile\": \"N[S](=O)(=O)c1cc(ccc1Cl)C2(O)NC(=O)c3ccccc23\",\"value\": 0.71416926},{\"smile\": \"CC1(C)S[C@@H]2[C@H](NC(=O)[C@H](NC(=O)N3CCN(C3=O)[S](C)(=O)=O)c4ccccc4)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.7267314}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.7263137, 0.71416926 and 0.7267314."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate whether the optimized drugs passes through the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C2C(=C([N]1C)C)C(=NCC(N2)=O)C3=CC=CC=C3\",\"value\": 0.5685482}, {\"smile\": \"C2=C(C(C1=CC=CC=C1)(C(OC(=O)C)CC)CC(N(C)C)C)C=CC=C2\",\"value\": 0.6097371},{\"smile\": \"c1(ccc(c(c1)Cl)Cl)CC(N1[C@H](CN2CCCC2)c2n(CC1)ncn2)=O\",\"value\": 0.56086797}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.5685482, 0.6097371 and 0.56086797."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the optimized drugs' permeability across the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CCOC(=O)C1=C(C)NC(=C(C1c2cccc(Cl)c2Cl)C(=O)OC)C\",\"value\": 0.5904801}, {\"smile\": \"c1cc2c(cc(CC3=CNC(=NC3=O)NCCSCc3oc(cc3)CN(C)C)cc2)cc1\",\"value\": 0.6775483},{\"smile\": \"CN(C)[C@H]1[C@@H]2C[C@H]3C(=C(O)c4c(O)ccc(Cl)c4[C@@]3(C)O)C(=O)[C@]2(O)C(=O)\\C(=C(/O)NCO)C1=O\",\"value\": 0.55083644}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.5904801, 0.6775483 and 0.55083644."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the optimized drugs' potential to cross the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C3=C(C(C2CCN(CCCC(C1=CC=C(F)C=C1)=O)CC2)=O)C=CC(=C3)Cl\",\"value\": 0.4900525}, {\"smile\": \"C[C@](N)(Cc1ccc(O)c(O)c1)C(O)=O\",\"value\": 0.4870839},{\"smile\": \"CCCCCc1cc(O)c2[C@@H]3C=C(C)CC[C@H]3C(C)(C)Oc2c1\",\"value\": 0.427687}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.4900525, 0.4870839 and 0.427687."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the optimized drugs for blood-brain barrier penetration. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN(C)CCCN2c1ccccc1C(=Nc3cccnc23)c4ccccc4\",\"value\": 0.59427047}, {\"smile\": \"Cc1ccc(CC2=CN=C(NCCCCc3ncc(Br)cc3C)NC2=O)cn1\",\"value\": 0.5630084},{\"smile\": \"C1=CC=NC=C1C2N(C(=O)CC2)C\",\"value\": 0.6258832}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.59427047, 0.5630084 and 0.6258832."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Does the optimized drugs pass through the blood-brain barrier? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CC1(C)S[C@@H]2[C@H](NC(=O)c3nc4ccccc4nc3C(O)=O)C(=O)N2[C@H]1C(O)=O\",\"value\": 0.74904746}, {\"smile\": \"C2=C1C(=NC(=CC1=CC=C2)N3CCNCC3)C4=CC=CC=C4\",\"value\": 0.55660415},{\"smile\": \"[C@H]13[C@H]([C@H]2C[C@@H]1CC2)C(=O)N(C3=O)CCCCN4CCN(CC4)C5=NC=CC=N5.C(C(O)(C(=O)O)CC(=O)O)C(=O)O\",\"value\": 0.6173397}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.74904746, 0.55660415 and 0.6173397."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the generated drugs can cross the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"OC(=O)\\C=C/C(O)=O.OC(=O)\\C=C/C(O)=O.C1CCN(CC1)Cc2cccc(OCCCNc3sc4ccccc4n3)c2\",\"value\": 0.28518394}, {\"smile\": \"CC1(C)[C@H](NC(=O)C(=N\\OCC(O)=O)/c2csc(N)n2)C(=O)N1O[S](O)(=O)=O\",\"value\": 0.30528384},{\"smile\": \"CN(C)C1C2CC3C(=C(O)c4c(O)cccc4C3(C)O)C(=O)C2(O)C(=O)\\C(=C(/O)NCN5CCN(CCO)CC5)C1=O\",\"value\": 0.30173784}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.28518394, 0.30528384 and 0.30173784."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the blood-brain barrier permeability of the generated drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"CN=C(NCCSCc1nc[nH]c1C)NC#N\",\"value\": 0.727005}, {\"smile\": \"C1=CC=CC3=C1C(=NC2=C(C=CC(=C2)F)C3)N4CCN(C)CC4\",\"value\": 0.7762055},{\"smile\": \"C(C(C=C)N)CC(O)=O\",\"value\": 0.7477786}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.727005, 0.7762055 and 0.7477786."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine if the generated drugs is capable of crossing the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"Cc1onc(N[S](=O)(=O)c2ccc(N)cc2)c1\",\"value\": 0.58199143}, {\"smile\": \"CN1CCN(CC1)C2=Nc3ccccc3Cc4ccccc24\",\"value\": 0.5665892},{\"smile\": \"C1=CC=CC=C1CN(C2CC2)C(OCC)=O\",\"value\": 0.47583675}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.58199143, 0.5665892 and 0.47583675."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine if the optimized drugs can traverse the blood-brain barrier. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"C1=C(O)C=CC4=C1C3(C(C(N(CC2CC2)CC3)C4)(C)C)CC\",\"value\": 0.31611934}, {\"smile\": \"CC(C)(C(O)=O)c1ccc(cc1)C(O)CCCN2CCC(CC2)C(O)(c3ccccc3)c4ccccc4\",\"value\": 0.28692123},{\"smile\": \"CC(C)c1onc(n1)c2ncn3c2CN(C)C(=O)c4c(Cl)cccc34\",\"value\": 0.29861698}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.31611934, 0.28692123 and 0.29861698."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is the generated drugs able to cross the blood-brain barrier? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(ouput_smiles)\", \"property_name\": \"p_np\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that the compound can effectively penetrate the blood-brain barrier, which is very important for the design of central nervous system drugs.\", \"result\": [{\"smile\": \"FC(Br)C(F)(F)F\",\"value\": 0.42233863}, {\"smile\": \"C1=CC(=CC3=C1[N]2C(=NN=C2CN(C)C)CN=C3C4=CC=CC=C4)Cl\",\"value\": 0.54940957},{\"smile\": \"C1=CC=CC3=C1C4=C(C2=C(C=CC=C2)C3)CN(C)CC4\",\"value\": 0.48837578}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The probability that these drugs have blood-brain penetration are 0.42233863, 0.54940957 and 0.48837578."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug Cc3cc2nc1c(=O)[nH]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C's solubility in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc3cc2nc1c(=O)[nH]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc3cc2nc1c(=O)[nH]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C\",\"value\": -3.685}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc3cc2nc1c(=O)[nH]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C is -3.685."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug CNC(=O)ON=C(CSC)C(C)(C)C's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)ON=C(CSC)C(C)(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CNC(=O)ON=C(CSC)C(C)(C)C\",\"value\": -1.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CNC(=O)ON=C(CSC)C(C)(C)C is -1.62."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the solubility of the drug CCCC(=O)OC in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(=O)OC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(=O)OC\",\"value\": -1.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(=O)OC is -1.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the water solubility of the drug c1ccccc1SC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccccc1SC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccccc1SC\",\"value\": -2.39}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1ccccc1SC is -2.39."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug c1cc2ccc3cccc4ccc(c1)c2c34's solubility in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1cc2ccc3cccc4ccc(c1)c2c34\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1cc2ccc3cccc4ccc(c1)c2c34\",\"value\": -6.176}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1cc2ccc3cccc4ccc(c1)c2c34 is -6.176."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the water solubility of the drug Clc1ccc(cc1)c2ccccc2Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(cc1)c2ccccc2Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1ccc(cc1)c2ccccc2Cl\",\"value\": -5.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1ccc(cc1)c2ccccc2Cl is -5.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl's ability to dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl\",\"value\": -6.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClC4=C(Cl)C5(Cl)C3C1CC(C2OC12)C3C4(Cl)C5(Cl)Cl is -6.29."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How soluble is the drug CCCCC=C in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCC=C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCC=C\",\"value\": -3.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCC=C is -3.23."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how well the drug CC(O)c1ccccc1 dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(O)c1ccccc1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(O)c1ccccc1\",\"value\": -0.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(O)c1ccccc1 is -0.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimation of the drug CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C\",\"value\": -5.696000000000001}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)Oc1cc(c(Cl)cc1Cl)n2nc(oc2=O)C(C)(C)C is -5.696000000000001."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict the drug CCCC(C)(C)CO's solubility in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(C)(C)CO\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(C)(C)CO\",\"value\": -1.52}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(C)(C)CO is -1.52."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug OCC(O)C(O)C(O)C(O)CO's solubility in aqueous solutions. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCC(O)C(O)C(O)C(O)CO\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OCC(O)C(O)C(O)C(O)CO\",\"value\": 1.09}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OCC(O)C(O)C(O)C(O)CO is 1.09."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the drug CCCC(O)CC's solubility in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(O)CC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(O)CC\",\"value\": -0.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(O)CC is -0.8."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore the drug Oc1ccccc1Cl's dissolution properties in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1ccccc1Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1ccccc1Cl\",\"value\": -1.06}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1ccccc1Cl is -1.06."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How easily does the drug CC(C)OC(=O)C(O)(c1ccc(Br)cc1)c2ccc(Br)cc2 dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)OC(=O)C(O)(c1ccc(Br)cc1)c2ccc(Br)cc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)OC(=O)C(O)(c1ccc(Br)cc1)c2ccc(Br)cc2\",\"value\": -4.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)OC(=O)C(O)(c1ccc(Br)cc1)c2ccc(Br)cc2 is -4.93."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the water solubility of the drug CCN(CC)C(=S)SSC(=S)N(CC)CC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)C(=S)SSC(=S)N(CC)CC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCN(CC)C(=S)SSC(=S)N(CC)CC\",\"value\": -4.86}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCN(CC)C(=S)SSC(=S)N(CC)CC is -4.86."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive the solubility of the drug Clc1cc(Cl)c(Cl)c(Cl)c1 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cc(Cl)c(Cl)c(Cl)c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1cc(Cl)c(Cl)c(Cl)c1\",\"value\": -4.63}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1cc(Cl)c(Cl)c(Cl)c1 is -4.63."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Offer a prediction of the drug COc2c1occc1cc3ccc(=O)oc23's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc2c1occc1cc3ccc(=O)oc23\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COc2c1occc1cc3ccc(=O)oc23\",\"value\": -3.664}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COc2c1occc1cc3ccc(=O)oc23 is -3.664."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CC(C)CBr behave in water in terms of solubility? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CBr\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)CBr\",\"value\": -2.43}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)CBr is -2.43."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug Fc1cccc(F)c1C(=O)NC(=O)Nc2ccc(Cl)cc2's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Fc1cccc(F)c1C(=O)NC(=O)Nc2ccc(Cl)cc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Fc1cccc(F)c1C(=O)NC(=O)Nc2ccc(Cl)cc2\",\"value\": -6.02}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Fc1cccc(F)c1C(=O)NC(=O)Nc2ccc(Cl)cc2 is -6.02."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the solubility of the drug CC1=CCC(CC1)C(C)=C in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=CCC(CC1)C(C)=C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1=CCC(CC1)C(C)=C\",\"value\": -4.26}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1=CCC(CC1)C(C)=C is -4.26."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the drug CCCCl's dissolution rate in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCl\",\"value\": -1.47}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCl is -1.47."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What level of water solubility does the drug CCN2c1ccccc1N(C)C(=S)c3cccnc23 exhibit? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN2c1ccccc1N(C)C(=S)c3cccnc23\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCN2c1ccccc1N(C)C(=S)c3cccnc23\",\"value\": -4.706}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCN2c1ccccc1N(C)C(=S)c3cccnc23 is -4.706."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the solubility of the drug CC1CCCCC1NC(=O)Nc2ccccc2 in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1CCCCC1NC(=O)Nc2ccccc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1CCCCC1NC(=O)Nc2ccccc2\",\"value\": -4.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1CCCCC1NC(=O)Nc2ccccc2 is -4.11."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Quantify the water solubility of the drug Cc1ccccc1O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccccc1O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccccc1O\",\"value\": -0.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccccc1O is -0.62."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine how the drug OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7 dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7\",\"value\": -3.931}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7 is -3.931."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how soluble the drug c1ccccc1 is in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccccc1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccccc1\",\"value\": -1.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1ccccc1 is -1.64."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the extent of the drug CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O's solubility in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O\",\"value\": -4.12}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O is -4.12."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much of the drug C1Cc2c3c1cccc3cc4c2ccc5ccccc54 can dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1Cc2c3c1cccc3cc4c2ccc5ccccc54\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C1Cc2c3c1cccc3cc4c2ccc5ccccc54\",\"value\": -7.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug C1Cc2c3c1cccc3cc4c2ccc5ccccc54 is -7.85."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the water solubility of the drug OC(C(=O)c1ccccc1)c2ccccc2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC(C(=O)c1ccccc1)c2ccccc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OC(C(=O)c1ccccc1)c2ccccc2\",\"value\": -2.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OC(C(=O)c1ccccc1)c2ccccc2 is -2.85."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how readily the drug CCC(C)Cl dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCC(C)Cl\",\"value\": -1.96}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCC(C)Cl is -1.96."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how the drug CCCC(C)(C)C interacts with water in terms of solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(C)(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(C)(C)C\",\"value\": -4.36}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(C)(C)C is -4.36."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Look into the water-soluble properties of the drug ClC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"ClC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl\",\"value\": -6.317}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClC1C=CC2C1C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl is -6.317."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the level of water solubility of the drug Cc1cc(C)c(C)c(C)c1C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(C)c(C)c(C)c1C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1cc(C)c(C)c(C)c1C\",\"value\": -4.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1cc(C)c(C)c(C)c1C is -4.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much water is required to dissolve the drug c1ccccc1n2ncc(N)c(Br)c2(=O)? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccccc1n2ncc(N)c(Br)c2(=O)\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccccc1n2ncc(N)c(Br)c2(=O)\",\"value\": -3.127}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1ccccc1n2ncc(N)c(Br)c2(=O) is -3.127."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What amount of the drug Oc1c(Cl)ccc(Cl)c1Cl can dissolve in a given amount of water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1c(Cl)ccc(Cl)c1Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1c(Cl)ccc(Cl)c1Cl\",\"value\": -2.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1c(Cl)ccc(Cl)c1Cl is -2.64."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the dissolvability of the drug C/C1CCC(\\C)CC1 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C/C1CCC(\\C)CC1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C/C1CCC(\\C)CC1\",\"value\": -4.47}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug C/C1CCC(\\C)CC1 is -4.47."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore how the drug CCCC(O)CC behaves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(O)CC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(O)CC\",\"value\": -0.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(O)CC is -0.8."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive an estimate of the drug ClC1(C2(Cl)C3(Cl)C4(Cl)C5(Cl)C1(Cl)C3(Cl)Cl)C5(Cl)C(Cl)(Cl)C24Cl's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"ClC1(C2(Cl)C3(Cl)C4(Cl)C5(Cl)C1(Cl)C3(Cl)Cl)C5(Cl)C(Cl)(Cl)C24Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClC1(C2(Cl)C3(Cl)C4(Cl)C5(Cl)C1(Cl)C3(Cl)Cl)C5(Cl)C(Cl)(Cl)C24Cl\",\"value\": -6.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClC1(C2(Cl)C3(Cl)C4(Cl)C5(Cl)C1(Cl)C3(Cl)Cl)C5(Cl)C(Cl)(Cl)C24Cl is -6.8."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CC12CC(=O)C3C(CCC4=CC(=O)CCC34C)C2CCC1(O)C(=O)CO dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC12CC(=O)C3C(CCC4=CC(=O)CCC34C)C2CCC1(O)C(=O)CO\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC12CC(=O)C3C(CCC4=CC(=O)CCC34C)C2CCC1(O)C(=O)CO\",\"value\": -3.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC12CC(=O)C3C(CCC4=CC(=O)CCC34C)C2CCC1(O)C(=O)CO is -3.11."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Study the drug CC1CC2C3CCC(O)(C(=O)C)C3(C)CC(O)C2(F)C4(C)C=CC(=O)C=C14's water solubility characteristics. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1CC2C3CCC(O)(C(=O)C)C3(C)CC(O)C2(F)C4(C)C=CC(=O)C=C14\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1CC2C3CCC(O)(C(=O)C)C3(C)CC(O)C2(F)C4(C)C=CC(=O)C=C14\",\"value\": -4.099}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1CC2C3CCC(O)(C(=O)C)C3(C)CC(O)C2(F)C4(C)C=CC(=O)C=C14 is -4.099."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the rate at which the drug CCCCCCC#C dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCCCC#C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCCC#C\",\"value\": -3.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCCCC#C is -3.66."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how much the drug CNc1ccccc1 dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CNc1ccccc1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CNc1ccccc1\",\"value\": -1.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CNc1ccccc1 is -1.28."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure how soluble the drug O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C is in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C\",\"value\": -2.766}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C is -2.766."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the amount of the drug C1OC(O)C(O)C(O)C1O that can dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1OC(O)C(O)C(O)C1O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C1OC(O)C(O)C(O)C1O\",\"value\": 0.39}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug C1OC(O)C(O)C(O)C1O is 0.39."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of the drug Cc1ccc(C)c(C)c1's dissolution in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(C)c(C)c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccc(C)c(C)c1\",\"value\": -3.31}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccc(C)c(C)c1 is -3.31."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the interaction between the drug CCO and water for solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCO\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCO\",\"value\": 1.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCO is 1.1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide a measure of the drug Cc1c2ccccc2c(C)c3ccc4ccccc4c13's water solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1c2ccccc2c(C)c3ccc4ccccc4c13\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1c2ccccc2c(C)c3ccc4ccccc4c13\",\"value\": -7.02}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1c2ccccc2c(C)c3ccc4ccccc4c13 is -7.02."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the degree of the drug Clc1ccc2ccccc2c1's solubility in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc2ccccc2c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1ccc2ccccc2c1\",\"value\": -4.14}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1ccc2ccccc2c1 is -4.14."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CC1=CCC(CC1)C(C)=C's structure affect its solubility in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC1=CCC(CC1)C(C)=C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1=CCC(CC1)C(C)=C\",\"value\": -4.26}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1=CCC(CC1)C(C)=C is -4.26."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore the water-dissolution capacity of the drug Nc2nc1[nH]cnc1c(=O)[nH]2 and Nc1ccc(cc1)c2ccc(N)cc2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc2nc1[nH]cnc1c(=O)[nH]2\",\"Nc1ccc(cc1)c2ccc(N)cc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Nc2nc1[nH]cnc1c(=O)[nH]2\",\"value\": -3.583}, {\"smile\": \"Nc1ccc(cc1)c2ccc(N)cc2\",\"value\": -2.7}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Nc2nc1[nH]cnc1c(=O)[nH]2 and Nc1ccc(cc1)c2ccc(N)cc2 are -3.583 and -2.7 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the maximum solubility of the drug CCC(C)C(C)C and CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)C(C)C\",\"CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCC(C)C(C)C\",\"value\": -4.28}, {\"smile\": \"CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C\",\"value\": -4.925}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCC(C)C(C)C and CC1(OC(=O)N(C1=O)c2cc(Cl)cc(Cl)c2)C=C are -4.28 and -4.925 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate how soluble the drug c1ccc2c(c1)cc3ccc4cccc5ccc2c3c45 and CCOP(=S)(OCC)SCSc1ccc(Cl)cc1 is in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"c1ccc2c(c1)cc3ccc4cccc5ccc2c3c45\",\"CCOP(=S)(OCC)SCSc1ccc(Cl)cc1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccc2c(c1)cc3ccc4cccc5ccc2c3c45\",\"value\": -8.699}, {\"smile\": \"CCOP(=S)(OCC)SCSc1ccc(Cl)cc1\",\"value\": -5.736000000000001}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1ccc2c(c1)cc3ccc4cccc5ccc2c3c45 and CCOP(=S)(OCC)SCSc1ccc(Cl)cc1 are -8.699 and -5.736000000000001 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Find the aqueous solubility of the drug CCC(C)Cl and c1ccncc1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCC(C)Cl\",\"c1ccncc1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCC(C)Cl\",\"value\": -1.96}, {\"smile\": \"c1ccncc1\",\"value\": 0.76}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCC(C)Cl and c1ccncc1 are -1.96 and 0.76 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide a calculation for the drug Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl and FC(F)(F)c1cccc(c1)N2CC(CCl)C(Cl)C2=O's solubility in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl\",\"FC(F)(F)c1cccc(c1)N2CC(CCl)C(Cl)C2=O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl\",\"value\": -7.32}, {\"smile\": \"FC(F)(F)c1cccc(c1)N2CC(CCl)C(Cl)C2=O\",\"value\": -4.047}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1cc(Cl)c(c(Cl)c1)c2c(Cl)cccc2Cl and FC(F)(F)c1cccc(c1)N2CC(CCl)C(Cl)C2=O are -7.32 and -4.047 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well does the drug O=C2NC(=O)C1(CCCC1)C(=O)N2 and C1CCCC1 mix with water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C2NC(=O)C1(CCCC1)C(=O)N2\",\"C1CCCC1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C2NC(=O)C1(CCCC1)C(=O)N2\",\"value\": -2.349}, {\"smile\": \"C1CCCC1\",\"value\": -2.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C2NC(=O)C1(CCCC1)C(=O)N2 and C1CCCC1 are -2.349 and -2.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Study the extent of the drug CCCN(=O)=O and Oc1c(Cl)cc(Cl)cc1Cl's solubility in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCN(=O)=O\",\"Oc1c(Cl)cc(Cl)cc1Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCN(=O)=O\",\"value\": -0.8}, {\"smile\": \"Oc1c(Cl)cc(Cl)cc1Cl\",\"value\": -2.34}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCN(=O)=O and Oc1c(Cl)cc(Cl)cc1Cl are -0.8 and -2.34 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What proportion of the drug CCCC=C and c2ccc1[nH]ccc1c2 can dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC=C\",\"c2ccc1[nH]ccc1c2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC=C\",\"value\": -2.68}, {\"smile\": \"c2ccc1[nH]ccc1c2\",\"value\": -1.52}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC=C and c2ccc1[nH]ccc1c2 are -2.68 and -1.52 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can the drug Nc1ccc(O)cc1 and Oc1ccccc1O dissolve well in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1ccc(O)cc1\",\"Oc1ccccc1O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Nc1ccc(O)cc1\",\"value\": -0.8}, {\"smile\": \"Oc1ccccc1O\",\"value\": 0.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Nc1ccc(O)cc1 and Oc1ccccc1O are -0.8 and 0.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how much of the drug C=CC=C and CCCCCCC can mix in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C=CC=C\",\"CCCCCCC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C=CC=C\",\"value\": -1.87}, {\"smile\": \"CCCCCCC\",\"value\": -4.53}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug C=CC=C and CCCCCCC are -1.87 and -4.53 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute how the drug Oc1c(Br)cc(C#N)cc1Br and O=c2[nH]c(=O)c1[nH]c(=O)[nH]c1[nH]2 dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1c(Br)cc(C#N)cc1Br\",\"O=c2[nH]c(=O)c1[nH]c(=O)[nH]c1[nH]2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1c(Br)cc(C#N)cc1Br\",\"value\": -3.33}, {\"smile\": \"O=c2[nH]c(=O)c1[nH]c(=O)[nH]c1[nH]2\",\"value\": -3.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1c(Br)cc(C#N)cc1Br and O=c2[nH]c(=O)c1[nH]c(=O)[nH]c1[nH]2 are -3.33 and -3.93 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the drug OCCCC=C and COC(C)(C)C's ability to mix with water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OCCCC=C\",\"COC(C)(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OCCCC=C\",\"value\": -0.15}, {\"smile\": \"COC(C)(C)C\",\"value\": -0.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OCCCC=C and COC(C)(C)C are -0.15 and -0.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive the capacity of the drug Clc1ccccc1Br and CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O to dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccccc1Br\",\"CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1ccccc1Br\",\"value\": -3.19}, {\"smile\": \"CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O\",\"value\": -3.893}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1ccccc1Br and CC(=O)CC(c1ccccc1)c3c(O)c2ccccc2oc3=O are -3.19 and -3.893 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimation of how much the drug COP(=O)(OC)OC(Br)C(Cl)(Cl)Br and CC/C=C\\C dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COP(=O)(OC)OC(Br)C(Cl)(Cl)Br\",\"CC/C=C\\C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COP(=O)(OC)OC(Br)C(Cl)(Cl)Br\",\"value\": -2.28}, {\"smile\": \"CC/C=C\\C\",\"value\": -2.54}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COP(=O)(OC)OC(Br)C(Cl)(Cl)Br and CC/C=C\\C are -2.28 and -2.54 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the degree to which the drug Oc1ccc(cc1)c2ccccc2 and CCCN(CCC)c1c(cc(cc1N(=O)=O)S(N)(=O)=O)N(=O)=O dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(cc1)c2ccccc2\",\"CCCN(CCC)c1c(cc(cc1N(=O)=O)S(N)(=O)=O)N(=O)=O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1ccc(cc1)c2ccccc2\",\"value\": -3.48}, {\"smile\": \"CCCN(CCC)c1c(cc(cc1N(=O)=O)S(N)(=O)=O)N(=O)=O\",\"value\": -5.16}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1ccc(cc1)c2ccccc2 and CCCN(CCC)c1c(cc(cc1N(=O)=O)S(N)(=O)=O)N(=O)=O are -3.48 and -5.16 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the solubility of the drug C1CCOC1 and CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2 in liquid water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"C1CCOC1\",\"CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C1CCOC1\",\"value\": 0.49}, {\"smile\": \"CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2\",\"value\": -6.291}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug C1CCOC1 and CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2 are 0.49 and -6.291 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much water is needed to dissolve the drug CCSC(=O)N(CC(C)C)CC(C)C and CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3 fully? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCSC(=O)N(CC(C)C)CC(C)C\",\"CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCSC(=O)N(CC(C)C)CC(C)C\",\"value\": -3.68}, {\"smile\": \"CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3\",\"value\": -3.81}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCSC(=O)N(CC(C)C)CC(C)C and CCCCC1C(=O)N(N(C1=O)c2ccccc2)c3ccccc3 are -3.68 and -3.81 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Look into the dissolution potential of the drug OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7 and CCNc1nc(Cl)nc(NC(C)C)n1 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7\",\"CCNc1nc(Cl)nc(NC(C)C)n1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7\",\"value\": -3.931}, {\"smile\": \"CCNc1nc(Cl)nc(NC(C)C)n1\",\"value\": -3.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OC(C1=CC2C5C(C1C2=C(c3ccccc3)c4ccccn4)C(=O)NC5=O)(c6ccccc6)c7ccccn7 and CCNc1nc(Cl)nc(NC(C)C)n1 are -3.931 and -3.85 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how much water is required to dissolve the drug CCNc1nc(Cl)nc(NCC)n1 and CC1=C(C(=O)Nc2ccccc2)S(=O)(=O)CCO1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCNc1nc(Cl)nc(NCC)n1\",\"CC1=C(C(=O)Nc2ccccc2)S(=O)(=O)CCO1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCNc1nc(Cl)nc(NCC)n1\",\"value\": -4.55}, {\"smile\": \"CC1=C(C(=O)Nc2ccccc2)S(=O)(=O)CCO1\",\"value\": -2.281}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCNc1nc(Cl)nc(NCC)n1 and CC1=C(C(=O)Nc2ccccc2)S(=O)(=O)CCO1 are -4.55 and -2.281 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the solubility profile of the drug Nc1cccc2ccccc12 and N(=Nc1ccccc1)c2ccccc2 in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Nc1cccc2ccccc12\",\"N(=Nc1ccccc1)c2ccccc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Nc1cccc2ccccc12\",\"value\": -1.92}, {\"smile\": \"N(=Nc1ccccc1)c2ccccc2\",\"value\": -4.45}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Nc1cccc2ccccc12 and N(=Nc1ccccc1)c2ccccc2 are -1.92 and -4.45 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you test how soluble the drug CCNc1nc(NC(C)(C)C)nc(SC)n1 and Clc1cccc(I)c1 is in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCNc1nc(NC(C)(C)C)nc(SC)n1\",\"Clc1cccc(I)c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCNc1nc(NC(C)(C)C)nc(SC)n1\",\"value\": -4.0}, {\"smile\": \"Clc1cccc(I)c1\",\"value\": -3.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCNc1nc(NC(C)(C)C)nc(SC)n1 and Clc1cccc(I)c1 are -4.0 and -3.55 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How readily does the drug Oc1ccc(Cl)cc1C(=O)Nc2ccc(cc2Cl)N(=O)=O and o1c2ccccc2c3ccccc13 dissolve in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(Cl)cc1C(=O)Nc2ccc(cc2Cl)N(=O)=O\",\"o1c2ccccc2c3ccccc13\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1ccc(Cl)cc1C(=O)Nc2ccc(cc2Cl)N(=O)=O\",\"value\": -4.7}, {\"smile\": \"o1c2ccccc2c3ccccc13\",\"value\": -4.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1ccc(Cl)cc1C(=O)Nc2ccc(cc2Cl)N(=O)=O and o1c2ccccc2c3ccccc13 are -4.7 and -4.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the dissolution rate of the drug O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C and CCCCCOC(=O)CC in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C\",\"CCCCCOC(=O)CC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C\",\"value\": -2.766}, {\"smile\": \"CCCCCOC(=O)CC\",\"value\": -1.28}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C1NC(=O)NC(=O)C1(C(C)C)C(C)C and CCCCCOC(=O)CC are -2.766 and -1.28 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore the aqueous solubility characteristics of the drug Cc1cc(no1)C(=O)NNCc2ccccc2 and CCC(C(CC)c1ccc(O)cc1)c2ccc(O)cc2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(no1)C(=O)NNCc2ccccc2\",\"CCC(C(CC)c1ccc(O)cc1)c2ccc(O)cc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1cc(no1)C(=O)NNCc2ccccc2\",\"value\": -2.461}, {\"smile\": \"CCC(C(CC)c1ccc(O)cc1)c2ccc(O)cc2\",\"value\": -4.43}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1cc(no1)C(=O)NNCc2ccccc2 and CCC(C(CC)c1ccc(O)cc1)c2ccc(O)cc2 are -2.461 and -4.43 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug COC=O and FC(F)(F)C(Cl)Br's compatibility with water in terms of solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COC=O\",\"FC(F)(F)C(Cl)Br\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COC=O\",\"value\": 0.58}, {\"smile\": \"FC(F)(F)C(Cl)Br\",\"value\": -1.71}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COC=O and FC(F)(F)C(Cl)Br are 0.58 and -1.71 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the maximum solubility of the drug ClCC#N and O=C1NC(=O)NC(=O)C1(C)CC in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"ClCC#N\",\"O=C1NC(=O)NC(=O)C1(C)CC\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClCC#N\",\"value\": -0.092}, {\"smile\": \"O=C1NC(=O)NC(=O)C1(C)CC\",\"value\": -1.228}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClCC#N and O=C1NC(=O)NC(=O)C1(C)CC are -0.092 and -1.228 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the dissolving properties of the drug O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C and Clc1cc(Cl)c(Cl)c(Cl)c1 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C\",\"Clc1cc(Cl)c(Cl)c(Cl)c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C\",\"value\": -2.356}, {\"smile\": \"Clc1cc(Cl)c(Cl)c(Cl)c1\",\"value\": -4.63}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C and Clc1cc(Cl)c(Cl)c(Cl)c1 are -2.356 and -4.63 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the saturation point of the drug Clc1ccc(cc1Cl)c2ccccc2 and ClCBr in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(cc1Cl)c2ccccc2\",\"ClCBr\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1ccc(cc1Cl)c2ccccc2\",\"value\": -6.39}, {\"smile\": \"ClCBr\",\"value\": -0.89}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1ccc(cc1Cl)c2ccccc2 and ClCBr are -6.39 and -0.89 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure how much the drug CC(C)CC(C)C and COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C can dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C)CC(C)C\",\"COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)CC(C)C\",\"value\": -4.26}, {\"smile\": \"COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C\",\"value\": -4.42}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)CC(C)C and COc5cc4OCC3Oc2c1CC(Oc1ccc2C(=O)C3c4cc5OC)C(C)=C are -4.26 and -4.42 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Study how well the drug CC34CCC1C(CCC2=CC(=O)CCC12C)C3CCC4=O and CCCCC(C)(C)O integrates with water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC34CCC1C(CCC2=CC(=O)CCC12C)C3CCC4=O\",\"CCCCC(C)(C)O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC34CCC1C(CCC2=CC(=O)CCC12C)C3CCC4=O\",\"value\": -3.69}, {\"smile\": \"CCCCC(C)(C)O\",\"value\": -1.08}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC34CCC1C(CCC2=CC(=O)CCC12C)C3CCC4=O and CCCCC(C)(C)O are -3.69 and -1.08 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the amount of the drug Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O and c1ccc2c(c1)ccc3c4ccccc4ccc23 soluble in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\",\"c1ccc2c(c1)ccc3c4ccccc4ccc23\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\",\"value\": -3.79}, {\"smile\": \"c1ccc2c(c1)ccc3c4ccccc4ccc23\",\"value\": -8.057}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O and c1ccc2c(c1)ccc3c4ccccc4ccc23 are -3.79 and -8.057 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the dissolvability of the drug CC(C#C)N(C)C(=O)Nc1ccc(Cl)cc1 and CCCCS in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(C#C)N(C)C(=O)Nc1ccc(Cl)cc1\",\"CCCCS\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C#C)N(C)C(=O)Nc1ccc(Cl)cc1\",\"value\": -3.9}, {\"smile\": \"CCCCS\",\"value\": -2.18}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C#C)N(C)C(=O)Nc1ccc(Cl)cc1 and CCCCS are -3.9 and -2.18 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the aqueous solubility of the drug CC=C and Oc1c(Cl)cc(Cl)cc1Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC=C\",\"Oc1c(Cl)cc(Cl)cc1Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC=C\",\"value\": -1.08}, {\"smile\": \"Oc1c(Cl)cc(Cl)cc1Cl\",\"value\": -2.34}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC=C and Oc1c(Cl)cc(Cl)cc1Cl are -1.08 and -2.34 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How well can the drug Cc1cc(C)c(C)c(C)c1C and c1ccc2ccccc2c1 dissolve in an aqueous medium? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(C)c(C)c(C)c1C\",\"c1ccc2ccccc2c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1cc(C)c(C)c(C)c1C\",\"value\": -4.0}, {\"smile\": \"c1ccc2ccccc2c1\",\"value\": -3.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1cc(C)c(C)c(C)c1C and c1ccc2ccccc2c1 are -4.0 and -3.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the capacity of the drug Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1 and Clc1ccccc1c2ccccc2 to dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1\",\"Clc1ccccc1c2ccccc2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1\",\"value\": -6.01}, {\"smile\": \"Clc1ccccc1c2ccccc2\",\"value\": -4.54}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1 and Clc1ccccc1c2ccccc2 are -6.01 and -4.54 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What concentration of the drug O=C(Nc1ccccc1)Nc2ccccc2 and Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl can water hold? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C(Nc1ccccc1)Nc2ccccc2\",\"Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C(Nc1ccccc1)Nc2ccccc2\",\"value\": -3.15}, {\"smile\": \"Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl\",\"value\": -9.15}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C(Nc1ccccc1)Nc2ccccc2 and Clc1cc(Cl)c(Cl)c(c1Cl)c2c(Cl)c(Cl)cc(Cl)c2Cl are -3.15 and -9.15 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the drug COc1ccccc1OCC(O)COC(N)=O and CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CCC5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C's dissolution in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COc1ccccc1OCC(O)COC(N)=O\",\"CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CCC5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COc1ccccc1OCC(O)COC(N)=O\",\"value\": -0.985}, {\"smile\": \"CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CCC5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C\",\"value\": -5.292999999999999}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COc1ccccc1OCC(O)COC(N)=O and CC1OC(CC(O)C1O)OC2C(O)CC(OC2C)OC8C(O)CC(OC7CCC3(C)C(CCC4C3CCC5(C)C(CCC45O)C6=CC(=O)OC6)C7)OC8C are -0.985 and -5.292999999999999 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the behavior of the drug CC(=O)Nc1cc(NS(=O)(=O)C(F)(F)F)c(C)cc1C and CC2=CC(=O)c1ccccc1C2=O in water solubility tests. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CC(=O)Nc1cc(NS(=O)(=O)C(F)(F)F)c(C)cc1C\",\"CC2=CC(=O)c1ccccc1C2=O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)Nc1cc(NS(=O)(=O)C(F)(F)F)c(C)cc1C\",\"value\": -3.24}, {\"smile\": \"CC2=CC(=O)c1ccccc1C2=O\",\"value\": -3.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(=O)Nc1cc(NS(=O)(=O)C(F)(F)F)c(C)cc1C and CC2=CC(=O)c1ccccc1C2=O are -3.24 and -3.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug CCN2c1ncccc1N(CC)C(=O)c3cccnc23 and Clc1ccc(c(Cl)c1Cl)c2ccc(Cl)c(Cl)c2Cl respond to water in terms of solubility? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCN2c1ncccc1N(CC)C(=O)c3cccnc23\",\"Clc1ccc(c(Cl)c1Cl)c2ccc(Cl)c(Cl)c2Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCN2c1ncccc1N(CC)C(=O)c3cccnc23\",\"value\": -2.86}, {\"smile\": \"Clc1ccc(c(Cl)c1Cl)c2ccc(Cl)c(Cl)c2Cl\",\"value\": -8.01}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCN2c1ncccc1N(CC)C(=O)c3cccnc23 and Clc1ccc(c(Cl)c1Cl)c2ccc(Cl)c(Cl)c2Cl are -2.86 and -8.01 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive the concentration of the drug CSc1nc(nc(n1)N(C)C)N(C)C and c1ccc2c(c1)c3cccc4cccc2c34 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CSc1nc(nc(n1)N(C)C)N(C)C\",\"c1ccc2c(c1)c3cccc4cccc2c34\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CSc1nc(nc(n1)N(C)C)N(C)C\",\"value\": -2.676}, {\"smile\": \"c1ccc2c(c1)c3cccc4cccc2c34\",\"value\": -6.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CSc1nc(nc(n1)N(C)C)N(C)C and c1ccc2c(c1)c3cccc4cccc2c34 are -2.676 and -6.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much of the drug O=C1NC(=O)NC(=O)C1(CC)CC=C and COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl can dissolve in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)NC(=O)C1(CC)CC=C\",\"COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C1NC(=O)NC(=O)C1(CC)CC=C\",\"value\": -1.614}, {\"smile\": \"COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl\",\"value\": -4.02}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=C1NC(=O)NC(=O)C1(CC)CC=C and COc1c(O)c(Cl)c(Cl)c(Cl)c1Cl are -1.614 and -4.02 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the amount of water required to dissolve the drug CCCC(C)C1(CC)C(=O)NC(=S)NC1=O and CN(C)C=Nc1ccc(Cl)cc1C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCC(C)C1(CC)C(=O)NC(=S)NC1=O\",\"CN(C)C=Nc1ccc(Cl)cc1C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCC(C)C1(CC)C(=O)NC(=S)NC1=O\",\"value\": -3.36}, {\"smile\": \"CN(C)C=Nc1ccc(Cl)cc1C\",\"value\": -2.86}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCC(C)C1(CC)C(=O)NC(=S)NC1=O and CN(C)C=Nc1ccc(Cl)cc1C are -3.36 and -2.86 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What percentage of the drug O=N(=O)c1cc(cc(c1)N(=O)=O)N(=O)=O and c1ccc2c(c1)c3cccc4c3c2cc5ccccc54 is water-soluble? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"O=N(=O)c1cc(cc(c1)N(=O)=O)N(=O)=O\",\"c1ccc2c(c1)c3cccc4c3c2cc5ccccc54\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=N(=O)c1cc(cc(c1)N(=O)=O)N(=O)=O\",\"value\": -2.89}, {\"smile\": \"c1ccc2c(c1)c3cccc4c3c2cc5ccccc54\",\"value\": -8.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=N(=O)c1cc(cc(c1)N(=O)=O)N(=O)=O and c1ccc2c(c1)c3cccc4c3c2cc5ccccc54 are -2.89 and -8.23 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate how easily the drug COP(=O)(OC)OC(Br)C(Cl)(Cl)Br and CCc1cccc(C)c1N(C(C)COC)C(=O)CCl dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"COP(=O)(OC)OC(Br)C(Cl)(Cl)Br\",\"CCc1cccc(C)c1N(C(C)COC)C(=O)CCl\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COP(=O)(OC)OC(Br)C(Cl)(Cl)Br\",\"value\": -2.28}, {\"smile\": \"CCc1cccc(C)c1N(C(C)COC)C(=O)CCl\",\"value\": -2.73}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COP(=O)(OC)OC(Br)C(Cl)(Cl)Br and CCc1cccc(C)c1N(C(C)COC)C(=O)CCl are -2.28 and -2.73 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the concentration of the drug Cc1c(C)c2c3ccccc3ccc2c4ccccc14 and Clc1cccc(Cl)c1 in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Cc1c(C)c2c3ccccc3ccc2c4ccccc14\",\"Clc1cccc(Cl)c1\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1c(C)c2c3ccccc3ccc2c4ccccc14\",\"value\": -7.01}, {\"smile\": \"Clc1cccc(Cl)c1\",\"value\": -3.04}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1c(C)c2c3ccccc3ccc2c4ccccc14 and Clc1cccc(Cl)c1 are -7.01 and -3.04 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the dissolution behavior of the drug Oc1ccc(Cl)cc1Cl and CC(C)C(O)C(C)C in water? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(Cl)cc1Cl\",\"CC(C)C(O)C(C)C\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1ccc(Cl)cc1Cl\",\"value\": -1.55}, {\"smile\": \"CC(C)C(O)C(C)C\",\"value\": -1.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1ccc(Cl)cc1Cl and CC(C)C(O)C(C)C are -1.55 and -1.22 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the drug Oc1cccc2ncccc12 and OCC1OC(O)(CO)C(O)C1O's interaction with water for solubility. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"Oc1cccc2ncccc12\",\"OCC1OC(O)(CO)C(O)C1O\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Oc1cccc2ncccc12\",\"value\": -2.54}, {\"smile\": \"OCC1OC(O)(CO)C(O)C1O\",\"value\": 0.64}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Oc1cccc2ncccc12 and OCC1OC(O)(CO)C(O)C1O are -2.54 and 0.64 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how effectively the drug CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C and CCOC(=O)Nc2cccc(OC(=O)Nc1ccccc1)c2 dissolves in water. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": [\"CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C\",\"CCOC(=O)Nc2cccc(OC(=O)Nc1ccccc1)c2\"], \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C\",\"value\": -4.16}, {\"smile\": \"CCOC(=O)Nc2cccc(OC(=O)Nc1ccccc1)c2\",\"value\": -4.632}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C and CCOC(=O)Nc2cccc(OC(=O)Nc1ccccc1)c2 are -4.16 and -4.632 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's solubility in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccc(C)c2ccccc12\",\"value\": -4.14}, {\"smile\": \"CCOC=C\",\"value\": -0.85}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccc(C)c2ccccc12 and CCOC=C are -4.14 and -0.85 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Sc2nc1ccccc1s2\",\"value\": -3.18}, {\"smile\": \"OC1CCCCC1\",\"value\": -0.44}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Sc2nc1ccccc1s2 and OC1CCCCC1 are -3.18 and -0.44 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the solubility of the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OCC2OC(Oc1ccccc1CO)C(O)C(O)C2O\",\"value\": -0.85}, {\"smile\": \"C=CCCC=C\",\"value\": -2.68}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug OCC2OC(Oc1ccccc1CO)C(O)C(O)C2O and C=CCCC=C are -0.85 and -2.68 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCBr\",\"value\": -2.37}, {\"smile\": \"CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2\",\"value\": -6.291}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCBr and CC1(C)C(C=C(Cl)Cl)C1C(=O)OCc2cccc(Oc3ccccc3)c2 are -2.37 and -6.291 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the drug's solubility in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCOC(=O)c1ccccc1C(=O)OCCCC\",\"value\": -4.4}, {\"smile\": \"CC\",\"value\": -1.36}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCOC(=O)c1ccccc1C(=O)OCCCC and CC are -4.4 and -1.36 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)OCC(=O)C3(O)CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C\",\"value\": -4.37}, {\"smile\": \"COC(=O)c1cc(O)c(O)c(O)c1\",\"value\": -1.24}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(=O)OCC(=O)C3(O)CCC4C2CCC1=CC(=O)C=CC1(C)C2C(O)CC34C and COC(=O)c1cc(O)c(O)c(O)c1 are -4.37 and -1.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug's ability to dissolve in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COc1nc(NC(C)C)nc(NC(C)C)n1\",\"value\": -2.478}, {\"smile\": \"CC(C)OC(C)C\",\"value\": -1.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COc1nc(NC(C)C)nc(NC(C)C)n1 and CC(C)OC(C)C are -2.478 and -1.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How soluble is the drug in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=N(=O)c1ccc(cc1)N(=O)=O\",\"value\": -3.39}, {\"smile\": \"CCCCC\",\"value\": -3.18}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug O=N(=O)c1ccc(cc1)N(=O)=O and CCCCC are -3.39 and -3.18 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how well the drug dissolves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c2ccc1nc(ccc1c2)c4ccc3ccccc3n4\",\"value\": -5.4}, {\"smile\": \"OCCCC=C\",\"value\": -0.15}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c2ccc1nc(ccc1c2)c4ccc3ccccc3n4 and OCCCC=C are -5.4 and -0.15 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimation of the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1cc2c3ccccc3ccc2c4ccccc14\",\"value\": -6.57}, {\"smile\": \"CCN2c1ccccc1N(C)C(=S)c3cccnc23\",\"value\": -4.706}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1cc2c3ccccc3ccc2c4ccccc14 and CCN2c1ccccc1N(C)C(=S)c3cccnc23 are -6.57 and -4.706 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict the drug's solubility in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClC(Cl)(Cl)N(=O)=O\",\"value\": -2.0}, {\"smile\": \"ClCC\",\"value\": -1.06}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClC(Cl)(Cl)N(=O)=O and ClCC are -2.0 and -1.06 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the drug's solubility in aqueous solutions. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CON(C)C(=O)Nc1ccc(Cl)c(Cl)c1\",\"value\": -3.592}, {\"smile\": \"COc2cc1c(N)nc(nc1c(OC)c2OC)N3CCN(CC3)C(=O)OCC(C)(C)O\",\"value\": -3.638}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CON(C)C(=O)Nc1ccc(Cl)c(Cl)c1 and COc2cc1c(N)nc(nc1c(OC)c2OC)N3CCN(CC3)C(=O)OCC(C)(C)O are -3.592 and -3.638 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the drug's solubility in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCBr\",\"value\": -3.08}, {\"smile\": \"CCC(=C(CC)c1ccc(O)cc1)c2ccc(O)cc2\",\"value\": -4.07}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCCBr and CCC(=C(CC)c1ccc(O)cc1)c2ccc(O)cc2 are -3.08 and -4.07 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore the drug's dissolution properties in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccc2c(c1)c3cccc4ccc5cccc2c5c43\",\"value\": -7.8}, {\"smile\": \"CC12CC(O)C3C(CCC4=CC(=O)C=CC34C)C2CCC1(O)C(=O)CO\",\"value\": -3.18}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1ccc2c(c1)c3cccc4ccc5cccc2c5c43 and CC12CC(O)C3C(CCC4=CC(=O)C=CC34C)C2CCC1(O)C(=O)CO are -7.8 and -3.18 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How easily does the drug dissolve in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"N(Nc1ccccc1)c2ccccc2\",\"value\": -2.92}, {\"smile\": \"c1(C(=O)OCCCCCC(C)(C))c(C(=O)OCCCCCC(C)(C))cccc1\",\"value\": -6.6370000000000005}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug N(Nc1ccccc1)c2ccccc2 and c1(C(=O)OCCCCCC(C)(C))c(C(=O)OCCCCCC(C)(C))cccc1 are -2.92 and -6.6370000000000005 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COC(=O)C\",\"value\": 0.46}, {\"smile\": \"CC(=C)C=C\",\"value\": -2.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COC(=O)C and CC(=C)C=C are 0.46 and -2.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive the solubility of the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccc(cc1)c2ccccc2\",\"value\": -4.62}, {\"smile\": \"CC1CCCO1\",\"value\": 0.11}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccc(cc1)c2ccccc2 and CC1CCCO1 are -4.62 and 0.11 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Offer a prediction of the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\",\"value\": -2.016}, {\"smile\": \"Cn1cnc2n(C)c(=O)[nH]c(=O)c12\",\"value\": -2.523}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCC(C)C1(CC=C)C(=O)NC(=O)NC1=O and Cn1cnc2n(C)c(=O)[nH]c(=O)c12 are -2.016 and -2.523 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug behave in water in terms of solubility? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)O\",\"value\": 0.43}, {\"smile\": \"C=CC=O\",\"value\": 0.57}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)O and C=CC=O are 0.43 and 0.57 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)C(C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1)c3ccc(OC(F)F)cc3\",\"value\": -6.876}, {\"smile\": \"Cc1ccc(O)c(C)c1\",\"value\": -1.19}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(C)C(C(=O)OC(C#N)c1cccc(Oc2ccccc2)c1)c3ccc(OC(F)F)cc3 and Cc1ccc(O)c(C)c1 are -6.876 and -1.19 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the solubility of the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C\",\"value\": -3.999}, {\"smile\": \"COP(=S)(OC)SCC(=O)N(C(C)C)c1ccc(Cl)cc1\",\"value\": -4.432}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1(O)CCC2C3CCC4=CC(=O)CCC4(C)C3CCC21C and COP(=S)(OC)SCC(=O)N(C(C)C)c1ccc(Cl)cc1 are -3.999 and -4.432 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the drug's dissolution rate in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1c(C)c2c3ccccc3ccc2c4ccccc14\",\"value\": -7.01}, {\"smile\": \"CCCC(C)(C)C\",\"value\": -4.36}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1c(C)c2c3ccccc3ccc2c4ccccc14 and CCCC(C)(C)C are -7.01 and -4.36 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What level of water solubility does the drug exhibit? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)OC3(C)CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C\",\"value\": -5.284}, {\"smile\": \"Nc3cc2c1ccccc1ccc2c4ccccc34\",\"value\": -6.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(=O)OC3(C)CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C and Nc3cc2c1ccccc1ccc2c4ccccc34 are -5.284 and -6.2 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the solubility of the drug in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COP(=S)(OC)Oc1ccc(cc1Cl)N(=O)=O\",\"value\": -4.31}, {\"smile\": \"CCCCCl\",\"value\": -2.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COP(=S)(OC)Oc1ccc(cc1Cl)N(=O)=O and CCCCCl are -4.31 and -2.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Quantify the water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCC(C)C\",\"value\": -5.08}, {\"smile\": \"BrC(Br)Br\",\"value\": -1.91}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCCC(C)C and BrC(Br)Br are -5.08 and -1.91 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine how the drug dissolves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCc1ccccc1CC\",\"value\": -3.28}, {\"smile\": \"Clc1ccc(Cl)c(c1)c2ccc(Cl)c(Cl)c2\",\"value\": -7.25}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCc1ccccc1CC and Clc1ccc(Cl)c(c1)c2ccc(Cl)c(Cl)c2 are -3.28 and -7.25 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how soluble the drug is in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCNc1nc(Cl)nc(n1)N(CC)CC\",\"value\": -4.06}, {\"smile\": \"Cc1ccc(C)c(C)c1\",\"value\": -3.31}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCNc1nc(Cl)nc(n1)N(CC)CC and Cc1ccc(C)c(C)c1 are -4.06 and -3.31 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the extent of the drug's solubility in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1c(O)C2C(=O)C3cc(O)ccC3OC2cc1(OC)\",\"value\": -2.943}, {\"smile\": \"CCCC(=O)OCC\",\"value\": -1.36}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug c1c(O)C2C(=O)C3cc(O)ccC3OC2cc1(OC) and CCCC(=O)OCC are -2.943 and -1.36 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much of the drug can dissolve in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Brc1ccc(I)cc1\",\"value\": -4.56}, {\"smile\": \"Clc1cc(Cl)c(cc1Cl)c2cccc(Cl)c2Cl\",\"value\": -7.21}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Brc1ccc(I)cc1 and Clc1cc(Cl)c(cc1Cl)c2cccc(Cl)c2Cl are -4.56 and -7.21 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Compute the water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1CCCO1\",\"value\": 0.11}, {\"smile\": \"CCC1(CC)C(=O)NC(=O)N(C)C1=O\",\"value\": -2.23}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1CCCO1 and CCC1(CC)C(=O)NC(=O)N(C)C1=O are 0.11 and -2.23 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how readily the drug dissolves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"COC(=O)c1ccccc1OC2OC(COC3OCC(O)C(O)C3O)C(O)C(O)C2O\",\"value\": -0.742}, {\"smile\": \"CCCCCOC(=O)C\",\"value\": -1.89}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug COC(=O)c1ccccc1OC2OC(COC3OCC(O)C(O)C3O)C(O)C(O)C2O and CCCCCOC(=O)C are -0.742 and -1.89 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how the drug interacts with water in terms of solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCCCCCC(=O)OC\",\"value\": -4.69}, {\"smile\": \"CCCCN(CC)c1c(cc(cc1N(=O)=O)C(F)(F)F)N(=O)=O\",\"value\": -5.53}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCCCCCCC(=O)OC and CCCCN(CC)c1c(cc(cc1N(=O)=O)C(F)(F)F)N(=O)=O are -4.69 and -5.53 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Look into the water-soluble properties of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1(C)C2CCC1(C)C(O)C2\",\"value\": -2.32}, {\"smile\": \"O=N(=O)c1ccccc1N(=O)=O\",\"value\": -3.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1(C)C2CCC1(C)C(O)C2 and O=N(=O)c1ccccc1N(=O)=O are -2.32 and -3.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Investigate the level of water solubility of the drug. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc3ccnc4N(C1CC1)c2ncccc2C(=O)Nc34\",\"value\": -3.19}, {\"smile\": \"CCOP(=S)(OCC)Oc2ccc1oc(=O)c(Cl)c(C)c1c2\",\"value\": -5.382000000000001}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc3ccnc4N(C1CC1)c2ncccc2C(=O)Nc34 and CCOP(=S)(OCC)Oc2ccc1oc(=O)c(Cl)c(C)c1c2 are -3.19 and -5.382000000000001 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How much water is required to dissolve the drug? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=C)C\",\"value\": -2.33}, {\"smile\": \"CCCC(=O)CCC\",\"value\": -1.3}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(=C)C and CCCC(=O)CCC are -2.33 and -1.3 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What amount of the drug can dissolve in a given amount of water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCOc2ccc1nc(sc1c2)S(N)(=O)=O\",\"value\": -3.81}, {\"smile\": \"Cc1cc(C)c2ccccc2c1\",\"value\": -4.29}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCOc2ccc1nc(sc1c2)S(N)(=O)=O and Cc1cc(C)c2ccccc2c1 are -3.81 and -4.29 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Test the dissolvability of the drug in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"ClCCCCl\",\"value\": -1.62}, {\"smile\": \"CCCCCCCCCC(=O)OCC\",\"value\": -4.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug ClCCCCl and CCCCCCCCCC(=O)OCC are -1.62 and -4.1 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore how the drug behaves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCC(=O)OC\",\"value\": -1.34}, {\"smile\": \"COC(=O)c1ccccc1C(=O)OC\",\"value\": -1.66}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCCCC(=O)OC and COC(=O)c1ccccc1C(=O)OC are -1.34 and -1.66 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive an estimate of the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCOC(=O)NCCOc2ccc(Oc1ccccc1)cc2\",\"value\": -4.7}, {\"smile\": \"CCCC(=C)C\",\"value\": -3.03}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCOC(=O)NCCOc2ccc(Oc1ccccc1)cc2 and CCCC(=C)C are -4.7 and -3.03 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug dissolve in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1ccc(cc1)N(=O)=O\",\"value\": -2.92}, {\"smile\": \"Clc1cc(Cl)c(Cl)c(Cl)c1\",\"value\": -4.63}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1ccc(cc1)N(=O)=O and Clc1cc(Cl)c(Cl)c(Cl)c1 are -2.92 and -4.63 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Study the drug's water solubility characteristics. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Sc2nc1ccccc1s2\",\"value\": -3.18}, {\"smile\": \"CC(=O)OCC(COC(=O)C)OC(=O)C\",\"value\": -0.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Sc2nc1ccccc1s2 and CC(=O)OCC(COC(=O)C)OC(=O)C are -3.18 and -0.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the rate at which the drug dissolves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)OCC(=O)C3CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C\",\"value\": -4.63}, {\"smile\": \"Oc1c(Br)cc(C#N)cc1Br\",\"value\": -3.33}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC(=O)OCC(=O)C3CCC4C2CCC1=CC(=O)CCC1(C)C2CCC34C and Oc1c(Br)cc(C#N)cc1Br are -4.63 and -3.33 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how much the drug dissolves in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CN(C)c2c(C)n(C)n(c1ccccc1)c2=O\",\"value\": -0.364}, {\"smile\": \"OC1CCCCCC1\",\"value\": -0.88}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CN(C)c2c(C)n(C)n(c1ccccc1)c2=O and OC1CCCCCC1 are -0.364 and -0.88 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure how soluble the drug is in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc3nnc4CN=C(c1ccccc1Cl)c2cc(Cl)ccc2n34\",\"value\": -4.09}, {\"smile\": \"CCOc1ccc(cc1)C(C)(C)COCc3cccc(Oc2ccccc2)c3\",\"value\": -8.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc3nnc4CN=C(c1ccccc1Cl)c2cc(Cl)ccc2n34 and CCOc1ccc(cc1)C(C)(C)COCc3cccc(Oc2ccccc2)c3 are -4.09 and -8.6 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the amount of the drug that can dissolve in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCOP(=S)(OCC)Oc1ccc(cc1)N(=O)=O\",\"value\": -4.66}, {\"smile\": \"Oc2cc(O)c1C(=O)CC(Oc1c2)c3ccc(O)c(O)c3\",\"value\": -3.62}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCOP(=S)(OCC)Oc1ccc(cc1)N(=O)=O and Oc2cc(O)c1C(=O)CC(Oc1c2)c3ccc(O)c(O)c3 are -4.66 and -3.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of the drug's dissolution in water. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Clc1c(Cl)c(Cl)c(N(=O)=O)c(Cl)c1Cl\",\"value\": -5.82}, {\"smile\": \"O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C\",\"value\": -2.356}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Clc1c(Cl)c(Cl)c(N(=O)=O)c(Cl)c1Cl and O=C1NC(=O)NC(=O)C1(C(C)CCC)CC=C are -5.82 and -2.356 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the interaction between the drug and water for solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC1(C)C(C=C(Cl)C(F)(F)F)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2\",\"value\": -8.176}, {\"smile\": \"CCN2c1nc(Cl)ccc1N(C)C(=O)c3cccnc23\",\"value\": -4.114}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CC1(C)C(C=C(Cl)C(F)(F)F)C1C(=O)OC(C#N)c2cccc(Oc3ccccc3)c2 and CCN2c1nc(Cl)ccc1N(C)C(=O)c3cccnc23 are -8.176 and -4.114 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide a measure of the drug's water solubility. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CN2C(=C(O)c1ccccc1S2(=O)=O)C(=O)Nc3ccccn3\",\"value\": -4.16}, {\"smile\": \"Oc1ccc(Br)cc1\",\"value\": -1.09}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CN2C(=C(O)c1ccccc1S2(=O)=O)C(=O)Nc3ccccn3 and Oc1ccc(Br)cc1 are -4.16 and -1.09 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the degree of the drug's solubility in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCC(C)C\",\"value\": -3.18}, {\"smile\": \"c1cc(C)cc2c1c3cc4cccc5CCc(c45)c3cc2\",\"value\": -7.92}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug CCC(C)C and c1cc(C)cc2c1c3cc4cccc5CCc(c45)c3cc2 are -3.18 and -7.92 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the drug's structure affect its solubility in water? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1\",\"value\": -6.01}, {\"smile\": \"CCCCN(C)C(=O)Nc1ccc(Cl)c(Cl)c1\",\"value\": -4.77}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drug Cc1ccc(OP(=O)(Oc2cccc(C)c2)Oc3ccccc3C)cc1 and CCCCN(C)C(=O)Nc1ccc(Cl)c(Cl)c1 are -6.01 and -4.77 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the generated drugs' solubility in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC34CC(O)C1(F)C(CCC2=CC(=O)CCC12C)C3CCC4(O)C(=O)CO\",\"value\": 0.3062296}, {\"smile\": \"CCCCI\",\"value\": 0.26656386}, {\"smile\": \"ClC(Cl)C(c1ccc(Cl)cc1)c2ccc(Cl)cc2\",\"value\": 0.15531781}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.3062296, 0.26656386 and 0.15531781 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the generated drugs' water solubility. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCC=O\",\"value\": -0.12331532}, {\"smile\": \"CCCCOC\",\"value\": 0.04384405}, {\"smile\": \"CC(=C)C1CC=C(C)C(=O)C1\",\"value\": 0.122488864}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are -0.12331532, 0.04384405 and 0.122488864 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the solubility of the generated drugs in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC12CC2(C)C(=O)N(C1=O)c3cc(Cl)cc(Cl)c3\",\"value\": 0.29028}, {\"smile\": \"Clc1ccccc1C2=NCC(=O)Nc3ccc(cc23)N(=O)=O\",\"value\": 0.36206165}, {\"smile\": \"ClC(=C)Cl\",\"value\": 0.24541208}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.29028, 0.36206165 and 0.24541208 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the water solubility of the generated drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCOC(=O)CC\",\"value\": 0.15840617}, {\"smile\": \"COc1ccccc1N(=O)=O\",\"value\": 0.1645213}, {\"smile\": \"CSc1nnc(c(=O)n1N)C(C)(C)C\",\"value\": 0.007009998}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.15840617, 0.1645213 and 0.007009998 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the optimized drugs' solubility in water? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)CC(c1ccc(Cl)cc1)c2c(O)c3ccccc3oc2=O\",\"value\": 0.4244499}, {\"smile\": \"C1CCOCC1\",\"value\": -0.36196917}, {\"smile\": \"Cc1cccc(C)c1NC(=O)c2cc(c(Cl)cc2O)S(N)(=O)=O\",\"value\": 0.45562974}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.4244499, -0.36196917 and 0.45562974 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the water solubility of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CCCCCCBr\",\"value\": 0.38106072}, {\"smile\": \"OCc1ccccc1\",\"value\": 0.2602689}, {\"smile\": \"COC(=O)C=C\",\"value\": 0.51678455}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.38106072, 0.2602689 and 0.51678455 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the generated drugs' ability to dissolve in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CSc1nnc(c(=O)n1N)C(C)(C)C\",\"value\": 0.05196183}, {\"smile\": \"OC(c1ccc(Cl)cc1)(c2cncnc2)c3ccccc3Cl\",\"value\": -0.4046864}, {\"smile\": \"Clc1cccc(Cl)c1Cl\",\"value\": -0.3467498}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.05196183, -0.4046864 and -0.3467498 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How soluble is the optimized drugs in water? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC12CCC(CC1)C(C)(C)O2\",\"value\": 0.23050493}, {\"smile\": \"c1ccc2cnccc2c1\",\"value\": 0.26369375}, {\"smile\": \"CC34CCC1C(CCC2CC(=O)CCC12C)C3CCC4O\",\"value\": 0.254557}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.23050493, 0.26369375 and 0.254557 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how well the generated drugs dissolves in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C(Nc1ccccc1)Nc2ccccc2\",\"value\": 0.20811996}, {\"smile\": \"ClC1(C(=O)C2(Cl)C3(Cl)C14Cl)C5(Cl)C2(Cl)C3(Cl)C(Cl)(Cl)C45Cl\",\"value\": -0.5925535}, {\"smile\": \"Fc1ccccc1Br\",\"value\": 0.11485941}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.20811996, -0.5925535 and 0.11485941 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide an estimation of the generated drugs' water solubility. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)CC(=O)C\",\"value\": 0.58896434}, {\"smile\": \"CC(=O)OC3(CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C)C(C)=O\",\"value\": 0.5847679}, {\"smile\": \"NC(=S)Nc1ccccc1\",\"value\": 0.4182252}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.58896434, 0.5847679 and 0.4182252 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict the generated drugs' solubility in water? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC34CCC1C(CC=C2CC(O)CCC12C)C3CCC4=O\",\"value\": 0.16242728}, {\"smile\": \"ClC(Cl)C(c1ccc(Cl)cc1)c2ccc(Cl)cc2\",\"value\": -0.011282146}, {\"smile\": \"OCC1OC(CO)(OC2OC(COC3OC(CO)C(O)C(O)C3O)C(O)C(O)C2O)C(O)C1O\",\"value\": -0.22168514}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.16242728, -0.011282146 and -0.22168514 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the optimized drugs' solubility in aqueous solutions. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(C)CCCO\",\"value\": 0.16317293}, {\"smile\": \"CCN(CC(C)=C)c1c(cc(cc1N(=O)=O)C(F)(F)F)N(=O)=O\",\"value\": 0.31457803}, {\"smile\": \"CCSSCC\",\"value\": 0.11231484}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.16317293, 0.31457803 and 0.11231484 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Measure the optimized drugs' solubility in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OCC3OC(OCC2OC(OC(C#N)c1ccccc1)C(O)C(O)C2O)C(O)C(O)C3O\",\"value\": 0.36272576}, {\"smile\": \"CC(=O)OC3(CCC4C2CCC1=CC(=O)CCC1C2CCC34C)C#C\",\"value\": 0.30198714}, {\"smile\": \"Oc1cccc2ncccc12\",\"value\": 0.18980959}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.36272576, 0.30198714 and 0.18980959 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Explore the optimized drugs' dissolution properties in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"OC1CCCCC1\",\"value\": 0.08323098}, {\"smile\": \"COc2c1occc1c(OC)c3c(=O)cc(C)oc23\",\"value\": 0.25883687}, {\"smile\": \"NC(=O)NCc1ccccc1\",\"value\": 0.34625807}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.08323098, 0.25883687 and 0.34625807 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How easily does the generated drugs dissolve in water? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"NC(=O)c1ccccc1\",\"value\": -0.4000263}, {\"smile\": \"CCCCc1c(C)nc(NCC)nc1OS(=O)(=O)N(C)C\",\"value\": -0.10003103}, {\"smile\": \"CC(C)(C)Cc1ccccc1\",\"value\": -0.29775247}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are -0.4000263, -0.10003103 and -0.29775247 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the water solubility of the optimized drugs. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C1Cc2c3c1cccc3cc4c2ccc5ccccc54\",\"value\": 0.08161952}, {\"smile\": \"CCN(CC)c1c(cc(c(N)c1N(=O)=O)C(F)(F)F)N(=O)=O\",\"value\": 0.2890427}, {\"smile\": \"O=Cc1ccco1\",\"value\": 0.2043918}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.08161952, 0.2890427 and 0.2043918 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Derive the solubility of the generated drugs in water. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"C1c2ccccc2c3ccccc13\",\"value\": 0.1412363}, {\"smile\": \"c1cc2cccc3c4cccc5cccc(c(c1)c23)c54\",\"value\": 0.14630345}, {\"smile\": \"CC\",\"value\": -0.42485178}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.1412363, 0.14630345 and -0.42485178 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Offer a prediction of the generated drugs' water solubility. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"CC(=O)C3(C)CCC4C2C=C(C)C1=CC(=O)CCC1(C)C2CCC34C\",\"value\": 0.25933695}, {\"smile\": \"Clc1ccc(Cl)c(c1)c2c(Cl)c(Cl)c(Cl)c(Cl)c2Cl\",\"value\": 0.15503344}, {\"smile\": \"CCCCOC\",\"value\": 0.37020817}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.25933695, 0.15503344 and 0.37020817 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How does the optimized drugs behave in water in terms of solubility? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"c1ccnnc1\",\"value\": -0.373378}, {\"smile\": \"N#Cc1ccccc1C#N\",\"value\": 0.18619779}, {\"smile\": \"CCCCCCCI\",\"value\": -1.7425067}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are -0.373378, 0.18619779 and -1.7425067 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Examine the generated drugs' water solubility. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_property_prediction\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"property_name\": \"logSolubility\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the magnitude of the drug's water solubility. Water solubility is an important factor in drug design, especially in terms of the drug's bioavailability and efficacy.\", \"result\": [{\"smile\": \"O=C1NC(=O)NC(=O)C1(CC)CC\",\"value\": 0.15704516}, {\"smile\": \"CCC(C)c1cc(cc(N(=O)=O)c1O)N(=O)=O\",\"value\": 0.19708177}, {\"smile\": \"CC(Cl)Cl\",\"value\": 0.21478537}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_drug_property', 'drug_property_result']))"
            },
            {
                "from": "gpt",
                "value": "The logarithmic value of the water solubility of the drugs are 0.15704516, 0.19708177 and 0.21478537 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_property_prediction\", \"description\": \"Predict properties of drugs based on SMILES\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smile\"}, \"property_name\": {\"type\": \"string\", \"description\": \"Task name, actually reffered to by the property name\"}}}, \"required\": [\"drug_smiles\", \"property_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate some optimized molecules based on  the molecule 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O', requiring a z-score of -0.473864 for  the cell line '908146', and the mask of atoms to be optimized is [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.473864, \"cell_line\": \"908146\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecules based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to reach a z-score of 0.413890 for '905989', focusing on the atoms specified in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.413890, \"cell_line\": \"905989\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)Cl)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)Cl)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized variants of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' to achieve a z-score of 1.002627 in '910927', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the atom mask for modification. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.002627, \"cell_line\": \"910927\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2[OH+]CO)CC1)NNC=C1C=CC=CC1=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2[OH+]CO)CC1)NNC=C1C=CC=CC1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized derivatives of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' to meet a z-score of 1.625532 for '1480361', modifying only the atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.625532, \"cell_line\": \"1480361\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules based on 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score target of 0.859783 for '909779', optimizing atoms as defined by [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.859783, \"cell_line\": \"909779\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' that achieve a z-score of 0.593521 in '753582', focusing on adjustments in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.593521, \"cell_line\": \"753582\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Synthesize optimized molecules derived from 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' with a target z-score of 0.119312 for '908159', limiting modifications to atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.119312, \"cell_line\": \"908159\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized molecules using 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' as a base to reach a z-score of 2.968469 in '1240199', focusing changes on atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.968469, \"cell_line\": \"1240199\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(c1cccc(C2Cc3c(C4CN(C)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1)N(C)C', 'CN(C)Cc1cccc(C2Cc3c(C4CN(O)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1', 'CON1CC(c2ccnc3c2CC(c2cccc(CN(C)C)c2)N3)C(c2ccc(NC(=O)N(C)C)cc2)=N1', 'CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1', 'CN(C)Cc1cccc(C2CC3C(NCCC34C3CN(N=C3c3ccc(NC(=O)N(C)C)cc3)[O+]4O)N2)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(c1cccc(C2Cc3c(C4CN(C)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1)N(C)C', 'CN(C)Cc1cccc(C2Cc3c(C4CN(O)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1', 'CON1CC(c2ccnc3c2CC(c2cccc(CN(C)C)c2)N3)C(c2ccc(NC(=O)N(C)C)cc2)=N1', 'CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1', 'CN(C)Cc1cccc(C2CC3C(NCCC34C3CN(N=C3c3ccc(NC(=O)N(C)C)cc3)[O+]4O)N2)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized analogs of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' targeting a z-score of 0.719765 for '907173', with changes restricted to [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.719765, \"cell_line\": \"907173\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O'-based molecules with optimized features to reach a z-score of 3.214760 in '909907', optimizing the atoms specified by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 3.214760, \"cell_line\": \"909907\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1N=C(N)C2C(Br)CN(C3OC(CN4C=CC(C)(C)c5ccc(cc5)NC(=O)NCCC4)C(O)C3O)C2N1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O', 'CC(C)(CCCO)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(O)CC(C)(C)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CCC(C)(C)c1ccc(NC(=O)NCCCN(CBr)CC2OC(N3CCc4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CN(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O', 'CC(C)C12NCNC(N)C1C(Br)CN2C1OC(CNCCCNC(=O)Nc2ccc(C(C)(C)C)cc2)C(O)C1O', 'CC=CC(C)(C)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CC1(Br)CN(C2OC(CNCCCNC(=O)Nc3ccc(C(C)(C)C)cc3)C(O)C2O)c2ncnc(N)c21', 'CC(Br)C1C2CN(c3ncnc(N)c32)C2OC(CN1CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)C(O)C2O', 'CC(C)(C)C1N(CCCNC(=O)Nc2ccccc2)CC2OC(C(O)C2O)N2CC(Br)C3C(N)NCNC32[O+]1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1N=C(N)C2C(Br)CN(C3OC(CN4C=CC(C)(C)c5ccc(cc5)NC(=O)NCCC4)C(O)C3O)C2N1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O', 'CC(C)(CCCO)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(O)CC(C)(C)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CCC(C)(C)c1ccc(NC(=O)NCCCN(CBr)CC2OC(N3CCc4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CN(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O', 'CC(C)C12NCNC(N)C1C(Br)CN2C1OC(CNCCCNC(=O)Nc2ccc(C(C)(C)C)cc2)C(O)C1O', 'CC=CC(C)(C)c1ccc(NC(=O)NCCCNCC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'C=CC1(Br)CN(C2OC(CNCCCNC(=O)Nc3ccc(C(C)(C)C)cc3)C(O)C2O)c2ncnc(N)c21', 'CC(Br)C1C2CN(c3ncnc(N)c32)C2OC(CN1CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)C(O)C2O', 'CC(C)(C)C1N(CCCNC(=O)Nc2ccccc2)CC2OC(C(O)C2O)N2CC(Br)C3C(N)NCNC32[O+]1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules based on 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' that meet a z-score of 0.037105 for '906790', restricting optimizations to atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.037105, \"cell_line\": \"906790\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce variants of 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' optimized for a z-score of 1.624551 in '909723', focusing modifications on [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.624551, \"cell_line\": \"909723\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' aiming for a z-score of 0.859783 in '909779', with changes restricted to the atoms in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.859783, \"cell_line\": \"909779\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized compounds based on 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' to reach a z-score of 1.420319 in '910861', using [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the focus for modifications. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.420319, \"cell_line\": \"910861\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized molecular structures from 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' with a z-score of 0.775575 for '908139', modifying only the atoms listed in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.775575, \"cell_line\": \"908139\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined molecules from 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' targeting a z-score of 2.141428 in '1303912', concentrating on changes within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.141428, \"cell_line\": \"1303912\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\"}}"
            },
            {
                "from": "observation",
                "value": "['COC1=C(C=Cc2cc(C(=O)Nc3ccc(CN4CCNCC4)c(C(F)(F)F)c3)ccc2C)CNC2N=C(CO)C=C12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COC1=C(C=Cc2cc(C(=O)Nc3ccc(CN4CCNCC4)c(C(F)(F)F)c3)ccc2C)CNC2N=C(CO)C=C12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved versions of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' to achieve a z-score of 1.781531 for '909761', limiting modifications to the atoms specified in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.781531, \"cell_line\": \"909761\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1', 'CNC(=O)c1ccc(NC2CC3Nc4ccccc4CON2C2N=C=C(CCO)C32)nc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1', 'CNC(=O)c1ccc(NC2CC3Nc4ccccc4CON2C2N=C=C(CCO)C32)nc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized analogs from 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' with a z-score target of 0.625261 in '687455', making adjustments to atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.625261, \"cell_line\": \"687455\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create fine-tuned molecules based on 'CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C' for a z-score of 1.778912 in '909769', modifying only atoms in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.778912, \"cell_line\": \"909769\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(C=C2C(=O)Nc3ccc(F)c(C)c32)N(CCO)C(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(C=C2C(=O)Nc3ccc(F)c(C)c32)N(CCO)C(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized derivatives of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' aiming for a z-score of 0.131086 in '1331035', with a focus on atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.131086, \"cell_line\": \"1331035\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce refined molecules based on 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' that meet a z-score of 0.509598 for '1240221', concentrating on modifications in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.509598, \"cell_line\": \"1240221\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized compounds derived from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' for a z-score of 1.671964 in '909253', focusing on the atoms listed in [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.671964, \"cell_line\": \"909253\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design new variants of 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' to reach a z-score of 1.362562 in '906847', modifying the atoms specified by [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.362562, \"cell_line\": \"906847\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2ncnc(N3N=C(c4ccccn4)NC3N)c2cc1CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2ncnc(N3N=C(c4ccccn4)NC3N)c2cc1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules derived from 'CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C' with a z-score of 1.503842 for '1290724', applying changes to atoms in [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.503842, \"cell_line\": \"1290724\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], \"gt\": \"CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new molecular forms based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a target z-score of -0.130607 in '688026', with a focus on modifying [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.130607, \"cell_line\": \"688026\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced versions of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' that achieve a z-score of 1.133875 for '908121', concentrating optimizations on atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.133875, \"cell_line\": \"908121\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design fine-tuned molecules from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' for a z-score of 0.036415 in '1240121', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the mask for changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.036415, \"cell_line\": \"1240121\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized analogs from 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to reach a z-score of -0.137958 in '753618', with modifications limited to [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.137958, \"cell_line\": \"753618\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(C)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(C)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate tailored molecules based on 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' aiming for a z-score of 0.908607 for '907323', focusing changes on atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.908607, \"cell_line\": \"907323\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create derivatives of 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' optimized for a z-score of 0.871570 in '684062', modifying atoms only within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.871570, \"cell_line\": \"684062\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2ncnc(N3N=C(c4ccccn4)NC3N)c2cc1CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2ncnc(N3N=C(c4ccccn4)NC3N)c2cc1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized forms of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score of 0.859783 in '909779', making adjustments to atoms listed in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.859783, \"cell_line\": \"909779\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' that meet a z-score of 2.115451 for '1240127', focusing on atom modifications within [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.115451, \"cell_line\": \"1240127\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce improved molecules based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a target z-score of 1.269734 in '753576', using [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for guidance on optimizations. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.269734, \"cell_line\": \"753576\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate refined compounds from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' aiming for a z-score of 2.688883 in '687807', focusing on atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.688883, \"cell_line\": \"687807\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C2C=Cc3ccccc32)cc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC1(C(=O)NO)CCC(NC(=O)Cc2ccccc2)CC1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C2C=Cc3ccccc32)cc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC1(C(=O)NO)CCC(NC(=O)Cc2ccccc2)CC1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create new analogs of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' that reach a z-score of -0.165258 for '949177', concentrating on changes to atoms within [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.165258, \"cell_line\": \"949177\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular structures derived from 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a z-score of 0.172918 for '949156', modifying only the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.172918, \"cell_line\": \"949156\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop refined molecules based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to achieve a z-score of 0.334987 for '909771', focusing changes on [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.334987, \"cell_line\": \"909771\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized variants of 'CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C' with a target z-score of 0.995680 in '905977', making adjustments to atoms within [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.995680, \"cell_line\": \"905977\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], \"gt\": \"CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create specialized versions of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' for a z-score of 1.516064 in '1298169', restricting changes to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.516064, \"cell_line\": \"1298169\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized molecules derived from 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' that achieve a z-score of 1.190540 for '724859', concentrating on atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.190540, \"cell_line\": \"724859\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], \"gt\": \"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design new compounds based on 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' to meet a z-score of 1.823729 in '1330942', with changes restricted to [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.823729, \"cell_line\": \"1330942\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop enhanced molecules based on 'COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2' for a z-score of 1.133873 in '909744', focusing on atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.133873, \"cell_line\": \"909744\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CNc1cccc(-c2ccccc2)c1)CSCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CNc1cccc(-c2ccccc2)c1)CSCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create refined forms of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score of 0.148545 for '925348', with a focus on modifying atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.148545, \"cell_line\": \"925348\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate tailored compounds from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' targeting a z-score of 1.773841 for '909977', concentrating changes on [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.773841, \"cell_line\": \"909977\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized derivatives of 'CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl' aiming for a z-score of 1.517282 in '924110', focusing modifications on the atoms listed in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.517282, \"cell_line\": \"924110\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1(Cl)CC2CCC1OC(c1cccc(F)c1)C1C[OH+]N2C2=NCN(CO)C3CCC(CC23)C2C=CC1(C)O2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1(Cl)CC2CCC1OC(c1cccc(F)c1)C1C[OH+]N2C2=NCN(CO)C3CCC(CC23)C2C=CC1(C)O2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design specialized molecules from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' for a z-score of 0.043678 in '907058', applying adjustments only to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.043678, \"cell_line\": \"907058\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved analogs of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to meet a z-score of -0.178460 in '907073', modifying atoms as specified in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.178460, \"cell_line\": \"907073\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized versions of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' for a target z-score of 1.221981 in '1290771', restricting changes to atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.221981, \"cell_line\": \"1290771\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1', 'CNC(=O)c1ccc(NC2CC3Nc4ccccc4CON2C2N=C=C(CCO)C32)nc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1', 'CNC(=O)c1ccc(NC2CC3Nc4ccccc4CON2C2N=C=C(CCO)C32)nc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score of -0.217362 for '753614', focusing adjustments within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.217362, \"cell_line\": \"753614\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular derivatives from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to achieve a z-score of 1.178529 in '909976', with modifications limited to [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.178529, \"cell_line\": \"909976\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2CC(OCCN5CCOCC5)C(CC24)CC3O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2CC(OCCN5CCOCC5)C(CC24)CC3O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced molecular variants from 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' to reach a z-score of 0.783498 for '905983', focusing modifications on [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.783498, \"cell_line\": \"905983\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score of 0.864594 for '1240208', focusing adjustments within [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.864594, \"cell_line\": \"1240208\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' aiming for a z-score of 1.592758 for '1240124', with atom-level modifications specified by [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.592758, \"cell_line\": \"1240124\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop refined variants of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 1.177732 for '924110', focusing optimization efforts on [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.177732, \"cell_line\": \"924110\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create refined forms of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score of 0.230958 for '753540', with a focus on modifying atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.230958, \"cell_line\": \"753540\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design specialized molecules from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' for a z-score of 1.122535 in '1503371', applying adjustments only to atoms in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.122535, \"cell_line\": \"1503371\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized molecular structures from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' with a z-score of 0.944092 for '1240172', modifying only the atoms listed in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.944092, \"cell_line\": \"1240172\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized versions of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' to achieve a z-score of 0.806079 in '908455', restricting modifications to the atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.806079, \"cell_line\": \"908455\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined molecules from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 2.077584 in '1298347', concentrating on changes within [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.077584, \"cell_line\": \"1298347\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C[SH](CCC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C[SH](CCC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct refined compounds from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' aiming for a z-score of 0.499041 in '724859', with changes concentrated in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.499041, \"cell_line\": \"724859\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create modified versions of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' with a z-score target of 1.133875 in '908121', adjusting atoms within the [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.133875, \"cell_line\": \"908121\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized forms of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' with a z-score of 0.187555 in '905980', making adjustments to atoms listed in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.187555, \"cell_line\": \"905980\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design tailored molecular versions of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' to achieve a z-score of 1.125298 for '1330935', adjusting atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.125298, \"cell_line\": \"1330935\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined versions of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to meet a z-score of 1.459885 in '724879', adjusting only the atoms within [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.459885, \"cell_line\": \"724879\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved analogs of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' to meet a z-score of 0.147696 in '1298539', modifying atoms as specified in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.147696, \"cell_line\": \"1298539\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(OCCCCCCC(=O)NO)C(=C3)[SH]2F)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(OCCCCCCC(=O)NO)C(=C3)[SH]2F)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular derivatives from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to achieve a z-score of 0.525348 in '908150', with modifications limited to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.525348, \"cell_line\": \"908150\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1CCC=C=C=C=CCc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1CCC=C=C=C=CCc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules based on 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' that meet a z-score of 1.784651 for '909729', restricting optimizations to atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.784651, \"cell_line\": \"909729\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=Cc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=Cc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design adjusted molecules based on 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' with a z-score of -0.340703 for '907172', modifying only atoms defined by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.340703, \"cell_line\": \"907172\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved molecular adaptations from 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' aiming for a z-score of -0.406265 for '907056', concentrating changes in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.406265, \"cell_line\": \"907056\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop advanced molecules derived from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to attain a z-score of 1.367363 in '907289', focusing on alterations in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.367363, \"cell_line\": \"907289\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'C=CCC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN(CC)CCO1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'C=CCC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN(CC)CCO1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCCCN1O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCCCN1O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules based on 'CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F' that meet a z-score of 2.530897 for '910910', restricting optimizations to atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.530897, \"cell_line\": \"910910\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCN(C(=O)Nc1ccc(Oc2ccncc2)cc1)c1ccc(Cl)c(C(F)(F)F)c1', 'CC(Cl)[n+]1ccc(Oc2ccc(NC(=O)Nc3cccc(C(F)(F)F)c3)cc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCN(C(=O)Nc1ccc(Oc2ccncc2)cc1)c1ccc(Cl)c(C(F)(F)F)c1', 'CC(Cl)[n+]1ccc(Oc2ccc(NC(=O)Nc3cccc(C(F)(F)F)c3)cc2)cc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=Cc1cc(Cl)c(C(F)(F)F)cc1NC(=O)Nc1ccc(Oc2ccncc2)cc1', 'C=Cc1c2cc(Oc3ccc(NC(=O)Nc4ccc(Cl)c(C(F)(F)F)c4)cc3)cc1N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=Cc1cc(Cl)c(C(F)(F)F)cc1NC(=O)Nc1ccc(Oc2ccncc2)cc1', 'C=Cc1c2cc(Oc3ccc(NC(=O)Nc4ccc(Cl)c(C(F)(F)F)c4)cc3)cc1N2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' aiming for a z-score of -0.119789 for '907041', with atom-level modifications specified by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.119789, \"cell_line\": \"907041\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate specialized molecular structures from 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' to meet a z-score of 1.888937 in '1240187', using [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for focused changes."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.888937, \"cell_line\": \"1240187\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate optimized derivatives of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with a target z-score of 0.865480 in '907047', modifying only atoms in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.865480, \"cell_line\": \"907047\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized molecules using 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' as a base to reach a z-score of 2.146391 in '1290810', focusing changes on atoms within [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.146391, \"cell_line\": \"1290810\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined versions of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to meet a z-score of 0.990423 in '909750', adjusting only the atoms within [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.990423, \"cell_line\": \"909750\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score of 1.448279 for '946370', focusing adjustments within [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.448279, \"cell_line\": \"946370\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OC(CCO)CN4CCOCC4)CCC23)cc1', 'CC=C(O)C1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OC(CCO)CN4CCOCC4)CCC23)cc1', 'CC=C(O)C1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Craft optimized molecular variants from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' that reach a z-score of 1.016869 for '909976', targeting atoms specified in [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.016869, \"cell_line\": \"909976\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecular adaptations of 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' targeting a z-score of 0.139850 in '946356', concentrating changes on [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.139850, \"cell_line\": \"946356\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'Cc1cccc(CO)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'Cc1cccc(CO)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized compounds from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' aiming for a z-score of 1.938598 in '724863', focusing only on the atoms in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.938598, \"cell_line\": \"724863\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=COOC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(=C)O)ccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C[O+](C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=COOC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(=C)O)ccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C[O+](C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C#CC1(N2C(=O)N(C)C3C=Nc4ccc(-c5cnc6ccccc6c5)cc4C32)C=CCC[O+]1CCC', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=O)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(C)CC)ccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C#CC1(N2C(=O)N(C)C3C=Nc4ccc(-c5cnc6ccccc6c5)cc4C32)C=CCC[O+]1CCC', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=O)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(C)CC)ccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced versions of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' that achieve a z-score of 1.137530 for '753579', concentrating optimizations on atoms within [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.137530, \"cell_line\": \"753579\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Synthesize optimized molecules derived from 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' with a target z-score of 0.713057 for '907795', limiting modifications to atoms in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.713057, \"cell_line\": \"907795\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCC3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCC3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized derivatives of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a z-score goal of -0.629139 in '910401', concentrating on atom-level changes in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.629139, \"cell_line\": \"910401\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'OCCOc1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'OCCOc1ccccc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecular variations of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with an optimized z-score of 1.392009 for '909723', focusing on the [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atoms."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.392009, \"cell_line\": \"909723\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(N2C(=O)c3cc(c(O)c(O)c3O)CC=C=C=C=CC2COO)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=CC1=C=CCc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)[SH](=O)(c2ccccc2C(C)(C)C)OC1COC', 'CC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(N2C(=O)c3cc(c(O)c(O)c3O)CC=C=C=C=CC2COO)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=CC1=C=CCc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)[SH](=O)(c2ccccc2C(C)(C)C)OC1COC', 'CC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'C[SH](COC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(O)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'C[SH](COC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(O)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined analogs of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' for a z-score of 0.395272 in '905945', using [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] to guide atom modifications."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.395272, \"cell_line\": \"905945\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular structures derived from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' with a z-score of 1.938598 for '724863', modifying only the atoms specified in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.938598, \"cell_line\": \"724863\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=COOC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(=C)O)ccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C[O+](C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=COOC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(=C)O)ccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C[O+](C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C#CC1(N2C(=O)N(C)C3C=Nc4ccc(-c5cnc6ccccc6c5)cc4C32)C=CCC[O+]1CCC', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=O)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(C)CC)ccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C#CC1(N2C(=O)N(C)C3C=Nc4ccc(-c5cnc6ccccc6c5)cc4C32)C=CCC[O+]1CCC', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=O)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cc(C(C)CC)ccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized analogs from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score target of -0.130607 in '688026', making adjustments to atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.130607, \"cell_line\": \"688026\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular derivatives from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to achieve a z-score of 1.459885 in '724879', with modifications limited to [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.459885, \"cell_line\": \"724879\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC'-based molecules with optimized features to reach a z-score of 1.114159 in '905982', optimizing the atoms specified by [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.114159, \"cell_line\": \"905982\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved versions of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' to achieve a z-score of 1.269966 for '1298144', limiting modifications to the atoms specified in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.269966, \"cell_line\": \"1298144\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined analogs of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' for a z-score of 0.506509 in '906818', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] to guide atom modifications."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.506509, \"cell_line\": \"906818\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized analogs derived from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 0.148545 in '925348', focusing on modifications specified by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.148545, \"cell_line\": \"925348\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules derived from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score of 0.775682 for '907291', applying changes to atoms in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.775682, \"cell_line\": \"907291\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized molecules using 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' as a base to reach a z-score of 1.854792 in '1240172', focusing changes on atoms within [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.854792, \"cell_line\": \"1240172\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Synthesize optimized forms of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' aiming to achieve a z-score of 0.211829 for '910936', with changes concentrated in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.211829, \"cell_line\": \"910936\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create derivatives of 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' optimized for a z-score of 0.664603 in '946368', modifying atoms only within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.664603, \"cell_line\": \"946368\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F', 'CCc1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1[OH+]F)c1ccc(F)cc1F', 'C[OH+]c1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F', 'CCc1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1[OH+]F)c1ccc(F)cc1F', 'C[OH+]c1ncc(-c2ccc3nccc(-c4ccnnc4)c3c2)cc1NS(=O)(=O)c1ccc(F)cc1F']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1[OH+]F)c1ccccc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1O)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1CO)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc2cnc1-[o+](O)-c1nnccc1-c1ccnc3ccc-2cc13)c1ccc(F)cc1F'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1[OH+]F)c1ccccc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1O)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc(-c2ccc3nccc(-c4ccnnc4)c3c2)cnc1CO)c1ccc(F)cc1F', 'O=S(=O)(Nc1cc2cnc1-[o+](O)-c1nnccc1-c1ccnc3ccc-2cc13)c1ccc(F)cc1F']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecules based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to reach a z-score of -0.444619 for '905968', focusing on the atoms specified in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.444619, \"cell_line\": \"905968\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to reach a z-score of 0.598479 in '907047', focusing on changes within [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.598479, \"cell_line\": \"907047\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C[SH](COC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['C[SH](COC=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules based on 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' that meet a z-score of 2.345887 for '909711', restricting optimizations to atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.345887, \"cell_line\": \"909711\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "['CCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Fc1ccc(NC2NCNC34CC(C3)C(OCCCN3CCOCC3)CC24)cc1Cl', 'Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCC(F)CN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCC(C)N1CCOCC1', 'F[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOC(C)C1', 'Oc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Fc1ccc(NC2NCNC34CC(C3)C(OCCCN3CCOCC3)CC24)cc1Cl', 'Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCC(F)CN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCC(C)N1CCOCC1', 'F[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOC(C)C1', 'Oc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['OSc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOC(F)C1', 'Fc1ccc(Nc2ncnc3cc4c(cc23)OCC(CN2CCOCC2)C4)cc1Cl', 'Fc1ccc(Nc2ncnc3cc4c(cc23)OCCC(N2CCOCC2)=C4)cc1Cl', 'Fc1ccc(NC2NCNC34CC(C(OCCCN5CCOCC5)CC23)C4F)cc1Cl', 'Fc1ccc(Nc2ncnc3cc(C(F)Cl)c(OCCCN4CCOCC4)cc23)cc1', 'Fc1ccc(Nc2ncnc3c2C2SC(=C3)C2OCCCN2CCOCC2)cc1Cl', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['OSc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'Cc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOC(F)C1', 'Fc1ccc(Nc2ncnc3cc4c(cc23)OCC(CN2CCOCC2)C4)cc1Cl', 'Fc1ccc(Nc2ncnc3cc4c(cc23)OCCC(N2CCOCC2)=C4)cc1Cl', 'Fc1ccc(NC2NCNC34CC(C(OCCCN5CCOCC5)CC23)C4F)cc1Cl', 'Fc1ccc(Nc2ncnc3cc(C(F)Cl)c(OCCCN4CCOCC4)cc23)cc1', 'Fc1ccc(Nc2ncnc3c2C2SC(=C3)C2OCCCN2CCOCC2)cc1Cl', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create derivatives of 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' optimized for a z-score of 0.725199 in '909194', modifying atoms only within [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.725199, \"cell_line\": \"909194\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1']."
            },
            {
                "from": "human",
                "value": "Generate more. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "Newly generated molecules are as follows: ['OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design tailored molecular versions of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to achieve a z-score of 1.162264 for '906795', adjusting atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.162264, \"cell_line\": \"906795\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)S)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)S)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Revise the z-score to 1.262264, then regenerate the output. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.262264, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 1.460843 in '1322213', with changes limited to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.460843, \"cell_line\": \"1322213\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C[O+][O+]C1(S(O)(O)C2CCCCC2C(C)(C)C)CCC(NC(O)c2cc(CCCCCCO)c(O)c(O)c2O)CC1', 'C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C[O+][O+]C1(S(O)(O)C2CCCCC2C(C)(C)C)CCC(NC(O)c2cc(CCCCCCO)c(O)c(O)c2O)CC1', 'C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            },
            {
                "from": "human",
                "value": "Modify the z-score to 1.560843, and please regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.560843, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(O)(O)C12CCC(NC(O)c3cc(CCCCCC[OH+])c(O)c(O)c3O)C(C1)[O+]2[OH+]O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(O)(O)C12CCC(NC(O)c3cc(CCCCCC[OH+])c(O)c(O)c3O)C(C1)[O+]2[OH+]O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced molecular variants from 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to reach a z-score of 1.251941 for '753563', focusing modifications on [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.251941, \"cell_line\": \"753563\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1']."
            },
            {
                "from": "human",
                "value": "Change out the z-score for 1.351941 and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.351941, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop new molecules from 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' that meet a z-score of 0.673634 for '1331050', focusing on atom modifications within [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.673634, \"cell_line\": \"1331050\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'CCCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC(C)=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'CCCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC(C)=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1']."
            },
            {
                "from": "human",
                "value": "Upgrade the z-score to 0.773634, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.773634, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CCC(C)C1COCCN1Cc1ccc(C2=C3C=CC(C)C4=CC(=C2ON3)C(=O)C=C4O)cc1', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(CC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'CCOOCCN(CC)Cc1ccc(C2=C3CC4(C=C(C(C)C)C(O)=CC4=O)C2ON3)cc1', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CCC(C)C1COCCN1Cc1ccc(C2=C3C=CC(C)C4=CC(=C2ON3)C(=O)C=C4O)cc1', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(CC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'CCOOCCN(CC)Cc1ccc(C2=C3CC4(C=C(C(C)C)C(O)=CC4=O)C2ON3)cc1', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create custom derivatives of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 0.321311 in '687820', modifying atoms according to [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.321311, \"cell_line\": \"687820\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CO)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CO)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1']."
            },
            {
                "from": "human",
                "value": "Replace the old z-score with 0.421311, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.421311, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved versions of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to achieve a z-score of 0.865480 for '907047', limiting modifications to the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.865480, \"cell_line\": \"907047\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1']."
            },
            {
                "from": "human",
                "value": "Adjust the z-score to 0.965480 and run regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.965480, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized compounds based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to reach a z-score of 1.670145 in '1303900', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the focus for modifications. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.670145, \"cell_line\": \"1303900\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Change the z-score to 1.770145 and regenerate the data. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.770145, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct tailored compounds from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' aiming for a z-score of 0.464881 for '905955', with modifications limited to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.464881, \"cell_line\": \"905955\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1']."
            },
            {
                "from": "human",
                "value": "Increase to 0.564881 and regenerate results. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.564881, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized derivatives of 'COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC' with a z-score goal of 1.976363 in '909974', concentrating on atom-level changes in [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.976363, \"cell_line\": \"909974\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc(OCCOC)c([OH+]O)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(C)cc23)c1', 'C#Cc1cccc(Nc2ncnc3ccc(C)cc23)c1', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32[O+]4O)c1', 'C#Cc1cc(Nc2ncnc3cc(OCC)c(C)cc23)ccc1C(O)COC', 'C#Cc1cc(Nc2ncnc3cc(OCCOC)c(O)cc23)ccc1C(O)CO', 'C#Cc1cccc(NC2NCNC3C(C(O)OO)C(OCCOC)C4=CC23C4)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc(OCCOC)c([OH+]O)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(C)cc23)c1', 'C#Cc1cccc(Nc2ncnc3ccc(C)cc23)c1', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32[O+]4O)c1', 'C#Cc1cc(Nc2ncnc3cc(OCC)c(C)cc23)ccc1C(O)COC', 'C#Cc1cc(Nc2ncnc3cc(OCCOC)c(O)cc23)ccc1C(O)CO', 'C#Cc1cccc(NC2NCNC3C(C(O)OO)C(OCCOC)C4=CC23C4)c1']."
            },
            {
                "from": "human",
                "value": "Alter the z-score to 2.076363, and start regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.076363, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(N(c2ncnc3cc(OCCOC)c(C)cc23)C(C)C)c1', 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(S)cc23)c1', 'C#Cc1cccc(N2C3NCNC4C(CO)C(OCCOC)C5=CC43C52)c1', 'C#Cc1cc2ccc1CC(OCCOC)CN2c1ncnc2c1C=C1CC2C1', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32C4OOCO)c1', 'C#Cc1cc(Nc2ncnc3cc(OCCOC)c(C=C)cc23)ccc1C(C)F', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32C4OCCO)c1', 'C#Cc1cc(NC2NCNC3C4C(C)COCCOC4C4=CC23C4)ccc1CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(N(c2ncnc3cc(OCCOC)c(C)cc23)C(C)C)c1', 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(S)cc23)c1', 'C#Cc1cccc(N2C3NCNC4C(CO)C(OCCOC)C5=CC43C52)c1', 'C#Cc1cc2ccc1CC(OCCOC)CN2c1ncnc2c1C=C1CC2C1', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32C4OOCO)c1', 'C#Cc1cc(Nc2ncnc3cc(OCCOC)c(C=C)cc23)ccc1C(C)F', 'C#Cc1cccc(NC2NCNC3CC(OCCOC)C4CC32C4OCCO)c1', 'C#Cc1cc(NC2NCNC3C4C(C)COCCOC4C4=CC23C4)ccc1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced versions of 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' that achieve a z-score of 1.283792 for '910861', concentrating optimizations on atoms within [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.283792, \"cell_line\": \"910861\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1']."
            },
            {
                "from": "human",
                "value": "Transform the z-score to 1.383792 and trigger regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.383792, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a z-score target of 0.793465 for '910780', optimizing atoms as defined by [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.793465, \"cell_line\": \"910780\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'Cc1cccc(CO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'Cc1cccc(CO)c1']."
            },
            {
                "from": "human",
                "value": "Apply 0.893465 in place of the z-score and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.893465, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules derived from 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC' with a z-score of 1.602022 for '908473', applying changes to atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.602022, \"cell_line\": \"908473\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1']."
            },
            {
                "from": "human",
                "value": "Alter the z-score to 1.702022, and start regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.702022, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1S)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1S)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design adjusted derivatives of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' to reach a z-score of 1.208012 for '909762', making changes as specified by [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.208012, \"cell_line\": \"909762\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(NC2CCCCC2(C)C)n1', 'COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)C(O)C3)C(OC)C2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'CCc1ccccc1NC1NC(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)N(C(C)CO)C=C1Cl', 'COCOc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'CCc1ccccc1Nc1nc(NC2CCC(N3CCC(N4CCN(C)CC4)CC3)CC2(CC)OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(NC2CCCCC2(C)C)n1', 'COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)C(O)C3)C(OC)C2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'CCc1ccccc1NC1NC(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)N(C(C)CO)C=C1Cl', 'COCOc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'CCc1ccccc1Nc1nc(NC2CCC(N3CCC(N4CCN(C)CC4)CC3)CC2(CC)OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1']."
            },
            {
                "from": "human",
                "value": "Update the z-score by setting it to 1.308012, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.308012, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](OO)CCN1CCN(CC1)C1CCN2CC1', 'CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'CCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2)ncc1Cl', 'CCc1ccccc1Nc1nc(NC2CCC3CC2(OC)[O+](C)C2CC(N4CCN(C)CC4)CCN32)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CF)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](OO)CCN1CCN(CC1)C1CCN2CC1', 'CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'CCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2)ncc1Cl', 'CCc1ccccc1Nc1nc(NC2CCC3CC2(OC)[O+](C)C2CC(N4CCN(C)CC4)CCN32)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CF)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized compounds derived from 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' for a z-score of 1.241104 in '998189', focusing on the atoms listed in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.241104, \"cell_line\": \"998189\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2']."
            },
            {
                "from": "human",
                "value": "Make 1.341104 the current z-score, and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.341104, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop enhanced molecular designs from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' to reach a z-score of 0.955448 in '907796', modifying only atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.955448, \"cell_line\": \"907796\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            },
            {
                "from": "human",
                "value": "Apply 1.055448 as the new z-score and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.055448, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized analogs from 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC' with a z-score target of 0.597687 in '908447', making adjustments to atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.597687, \"cell_line\": \"908447\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1']."
            },
            {
                "from": "human",
                "value": "Move the z-score up to 0.697687 and run regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.697687, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design adjusted derivatives of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to reach a z-score of 1.787156 for '1240218', making changes as specified by [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.787156, \"cell_line\": \"1240218\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1']."
            },
            {
                "from": "human",
                "value": "Exchange the z-score for 1.887156, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.887156, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced versions of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' that achieve a z-score of 1.879805 for '1290814', concentrating optimizations on atoms within [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.879805, \"cell_line\": \"1290814\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'C=Cc1cc(NC(=O)NC2C=CON2)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'C=Cc1cc(NC(=O)NC2C=CON2)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1']."
            },
            {
                "from": "human",
                "value": "Update the z-score to 1.979805 and initiate regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.979805, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN32)CC1OCCN1CCOCC1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN32)CC1OCCN1CCOCC1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' that achieve a z-score of 1.879805 in '1290814', focusing on adjustments in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.879805, \"cell_line\": \"1290814\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'C=Cc1cc(NC(=O)NC2C=CON2)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'C=Cc1cc(NC(=O)NC2C=CON2)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1']."
            },
            {
                "from": "human",
                "value": "Make 1.979805 the current z-score, and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.979805, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN32)CC1OCCN1CCOCC1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN32)CC1OCCN1CCOCC1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized analogs of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' targeting a z-score of 1.199718 for '949170', with changes restricted to [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.199718, \"cell_line\": \"949170\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O']."
            },
            {
                "from": "human",
                "value": "Apply 1.299718 in place of the z-score and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.299718, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized forms of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' that meet a z-score of 0.276205 in '924151', restricting modifications to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.276205, \"cell_line\": \"924151\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Update the z-score setting to 0.376205 and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.376205, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized analogs of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' targeting a z-score of 1.878143 for '1303912', with changes restricted to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.878143, \"cell_line\": \"1303912\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC']."
            },
            {
                "from": "human",
                "value": "Make 1.978143 the current z-score, and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.978143, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined versions of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' to reach a z-score of 0.938368 in '909706', limiting modifications to [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.938368, \"cell_line\": \"909706\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            },
            {
                "from": "human",
                "value": "Raise the z-score to 1.038368 and regenerate afterward. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.038368, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined versions of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to meet a z-score of 1.880621 in '687997', adjusting only the atoms within [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.880621, \"cell_line\": \"687997\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            },
            {
                "from": "human",
                "value": "Update the z-score by setting it to 1.980621, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.980621, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design modified molecules from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' targeting a z-score of 1.760474 for '949170', focusing adjustments on atoms specified in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.760474, \"cell_line\": \"949170\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1']."
            },
            {
                "from": "human",
                "value": "Update the z-score to 1.860474 and initiate regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.860474, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create modified versions of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score target of 0.704189 in '688027', adjusting atoms within the [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.704189, \"cell_line\": \"688027\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            },
            {
                "from": "human",
                "value": "Change out the z-score for 0.804189 and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.804189, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved molecular adaptations from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' aiming for a z-score of 1.352438 for '1240186', concentrating changes in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.352438, \"cell_line\": \"1240186\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O']."
            },
            {
                "from": "human",
                "value": "Shift the z-score to 1.452438, and regenerate the results. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.452438, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)CO)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)CO)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined versions of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to reach a z-score of 0.725471 in '753596', limiting modifications to [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.725471, \"cell_line\": \"753596\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1']."
            },
            {
                "from": "human",
                "value": "Update the z-score by setting it to 0.825471, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.825471, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' aiming for a z-score of 1.345296 for '909755', with atom-level modifications specified by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.345296, \"cell_line\": \"909755\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1']."
            },
            {
                "from": "human",
                "value": "Set the z-score to 1.445296, then proceed to regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.445296, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized analogs from 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to reach a z-score of 0.563262 in '753585', with modifications limited to [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.563262, \"cell_line\": \"753585\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1']."
            },
            {
                "from": "human",
                "value": "Reassign the z-score with 0.663262, then proceed to regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.663262, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized analogs derived from 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' targeting a z-score of 1.543090 in '753600', focusing on modifications specified by [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.543090, \"cell_line\": \"753600\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O']."
            },
            {
                "from": "human",
                "value": "Change the z-score to 1.643090, and proceed to regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.643090, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(CC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(CC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design modified molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' targeting a z-score of 1.448279 for '946370', focusing adjustments on atoms specified in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.448279, \"cell_line\": \"946370\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5CC(C)ON5)cc4)CN3C23C=C1[O+]3C(C)N1CCOCC1', 'C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5CC(C)ON5)cc4)CN3C23C=C1[O+]3C(C)N1CCOCC1', 'C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12']."
            },
            {
                "from": "human",
                "value": "Recalculate the z-score to 1.548279, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.548279, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design adjusted derivatives of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to reach a z-score of 0.939986 for '1479995', making changes as specified by [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.939986, \"cell_line\": \"1479995\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Convert the z-score into 1.039986, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.039986, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized compounds derived from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' for a z-score of 1.878387 in '724878', focusing on the atoms listed in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.878387, \"cell_line\": \"724878\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Set the z-score to 1.978387, then proceed to regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.978387, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' aiming for a z-score of 0.542695 in '713878', with changes restricted to the atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.542695, \"cell_line\": \"713878\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'C[SH]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'C[SH]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Increase the z-score to 0.642695 and proceed with regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.642695, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct optimized versions of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to meet a z-score of 0.775682 for '907291', focusing on modifications within [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.775682, \"cell_line\": \"907291\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            },
            {
                "from": "human",
                "value": "Substitute the z-score with 0.875682, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.875682, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined analogs of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of 1.392009 in '909723', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] to guide atom modifications. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.392009, \"cell_line\": \"909723\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CC1CCCCC1CC1CC(C(O)NC2(CO)CCC(S(O)(O)c3ccccc3C(C)(C)C)CC2)C(O)C(O)C1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CC1CCCCC1CC1CC(C(O)NC2(CO)CCC(S(O)(O)c3ccccc3C(C)(C)C)CC2)C(O)C(O)C1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Apply 1.492009 in place of the z-score and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.492009, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1cccc2c1S(=O)(=O)c1ccc(cc1)NC(=O)c1cc(c(O)c(O)c1O)Cc1ccccc1CC2O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC1=Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)S(=O)(=O)c2ccc1cc2C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1cccc2c1S(=O)(=O)c1ccc(cc1)NC(=O)c1cc(c(O)c(O)c1O)Cc1ccccc1CC2O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC1=Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)S(=O)(=O)c2ccc1cc2C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' with a z-score of 2.173826 for '908472', focusing adjustments within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.173826, \"cell_line\": \"908472\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CF)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC1(O)C[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc(c2)N1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NC4NC23C4C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(C(C)C4)N(C)C[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)N1C(=CF)[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc1c2', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C4=CN5C[NH+]4C(N3)C25)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NCN3CON(C)C)c1', 'C=CC(=O)N1c2cccc(c2)NC2=NC(NC3CCC(N(C)C)CC3)NC3NC(NC23)[O+]1C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NCN3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCC(CCN(C)C)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C([O+]=C)C3)NC3NCNC23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CF)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC1(O)C[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc(c2)N1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NC4NC23C4C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(C(C)C4)N(C)C[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)N1C(=CF)[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc1c2', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C4=CN5C[NH+]4C(N3)C25)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NCN3CON(C)C)c1', 'C=CC(=O)N1c2cccc(c2)NC2=NC(NC3CCC(N(C)C)CC3)NC3NC(NC23)[O+]1C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NCN3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCC(CCN(C)C)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C([O+]=C)C3)NC3NCNC23)c1']."
            },
            {
                "from": "human",
                "value": "Reassign the z-score with 2.273826, then proceed to regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.273826, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1ccc2c(c1)NC1=NC(N(ON(C)C)C3CCCC(O)C3)NC3NCN(C2)C13', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3[NH+]4CNC23C4=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(F)C3)NC3C2NC[NH+]3C=C)c1', 'C=CC(O)(C=C)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CC5OC(C[NH+]6CNC2C6N3)C(C4)C5N(C)C)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4C2NC[NH+]4OC2OC4CC(CCC4N(C)C)N32)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC1(O)Nc2cc3ccc2C(C1F)[NH+]1CNC2C(=NC(NC4CCC(N(C)C)CC4)NC21)N3', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(=C(F)C[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1ccc2c(c1)NC1=NC(N(ON(C)C)C3CCCC(O)C3)NC3NCN(C2)C13', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3[NH+]4CNC23C4=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(F)C3)NC3C2NC[NH+]3C=C)c1', 'C=CC(O)(C=C)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CC5OC(C[NH+]6CNC2C6N3)C(C4)C5N(C)C)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4C2NC[NH+]4OC2OC4CC(CCC4N(C)C)N32)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC1(O)Nc2cc3ccc2C(C1F)[NH+]1CNC2C(=NC(NC4CCC(N(C)C)CC4)NC21)N3', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(=C(F)C[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized molecules using 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' as a base to reach a z-score of 1.003012 in '724879', focusing changes on atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.003012, \"cell_line\": \"724879\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4SO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4SO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1']."
            },
            {
                "from": "human",
                "value": "Replace the old z-score with 1.103012, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.103012, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CCOc1c(Cc2ccccc2C)cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CCOc1c(Cc2ccccc2C)cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced molecular variants from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to reach a z-score of 0.786185 for '909699', focusing modifications on [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.786185, \"cell_line\": \"909699\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Set the z-score to 0.886185 and initiate regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.886185, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized chemical structures based on 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' to reach a z-score of 1.715558 for '906841', concentrating on [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.715558, \"cell_line\": \"906841\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'CC1=C2CN(CCN2C)C2CCN(CC2)c2ccc3c(c2)OCC1c1cccc(c1CCO)Nc1nc(ncc1Cl)N3', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)C(O)C3)C(OC)C2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'CC1c2ccccc2Nc2nc(ncc2Cl)Nc2ccc(cc2OCCO)N2CCC(CC2)N2CCN(C)C1C2', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'CC1=C2CN(CCN2C)C2CCN(CC2)c2ccc3c(c2)OCC1c1cccc(c1CCO)Nc1nc(ncc1Cl)N3', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COc1cc(N2CCC(N3CCN(C)C(O)C3)C(OC)C2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'CC1c2ccccc2Nc2nc(ncc2Cl)Nc2ccc(cc2OCCO)N2CCC(CC2)N2CCN(C)C1C2', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1']."
            },
            {
                "from": "human",
                "value": "Replace the old z-score with 1.815558, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.815558, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1', 'CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'CCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COCCCC=C1CN(C)CCN1C1CCN(c2ccc(Nc3ncc(Cl)c(Nc4ccccc4C)n3)cc2)CC1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1', 'CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'CCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COCCCC=C1CN(C)CCN1C1CCN(c2ccc(Nc3ncc(Cl)c(Nc4ccccc4C)n3)cc2)CC1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' aiming for a z-score of 1.367363 in '907289', with changes restricted to the atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.367363, \"cell_line\": \"907289\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1']."
            },
            {
                "from": "human",
                "value": "Substitute the z-score with 1.467363, and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.467363, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate variants of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' with optimizations targeted for a z-score of 1.847056 in '905956', based on the [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atom selections. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.847056, \"cell_line\": \"905956\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1']."
            },
            {
                "from": "human",
                "value": "Modify the z-score to 1.947056, and please regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.947056, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(S)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(S)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop specialized molecules from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' with a z-score goal of 1.392007 in '906873', adjusting atoms as specified in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.392007, \"cell_line\": \"906873\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1']."
            },
            {
                "from": "human",
                "value": "Recalculate the z-score to 1.492007, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.492007, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular structures derived from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with a z-score of 1.219477 for '687804', modifying only the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.219477, \"cell_line\": \"687804\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO']."
            },
            {
                "from": "human",
                "value": "Redefine the z-score as 1.319477, then regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.319477, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' with a z-score of 1.702450 for '1330942', focusing adjustments within [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.702450, \"cell_line\": \"1330942\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'CCC1CN(C(C)c2ccccc2Nc2nc(Nc3ccccc3OC)ncc2Cl)CCC1N1CCN(C)CC1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCOc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(O)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(N2c3ccccc3CC3CC2C(O)=C2CN(CCN2C)C2CCN3CC2)n1', 'C=C(CO)c1cc(N2CCC(N3CCN(C)CC3)CC2)cc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'CCC1CN(C(C)c2ccccc2Nc2nc(Nc3ccccc3OC)ncc2Cl)CCC1N1CCN(C)CC1', 'COc1cc(N2CCC(N3CCN(C)CC3)C(C(C)O)C2)ccc1Nc1ncc(Cl)c(Nc2cccc(C)c2C)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCOc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(O)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1']."
            },
            {
                "from": "human",
                "value": "Apply 1.802450 in place of the z-score and regenerate. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.802450, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCC3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'C=Cc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OCC(C)=CO)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CO)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'CCc1cccc(OC)c1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)CC2)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCC3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecular variations of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' with an optimized z-score of 1.113429 for '1298223', focusing on the [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atoms. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.113429, \"cell_line\": \"1298223\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1']."
            },
            {
                "from": "human",
                "value": "Raise the z-score to 1.213429 and regenerate afterward. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.213429, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce custom molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to meet a z-score of 1.773841 for '909977', with modifications limited to [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.773841, \"cell_line\": \"909977\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1']."
            },
            {
                "from": "human",
                "value": "Improve the z-score to 1.873841, then regenerate the output. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.873841, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized derivatives of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' to meet a z-score of 1.385313 for '1240132', modifying only the atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.385313, \"cell_line\": \"1240132\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CF)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(C(C)C4)N(C)C[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)N1C(=CF)[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc1c2', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C4=CN5C[NH+]4C(N3)C25)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCC(CCN(C)C)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C([O+]=C)C3)NC3NCNC23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CF)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(O)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(C(C)C4)N(C)C[O+](C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)N1C(=CF)[NH+]2CNC3C(=NC(NC4CCC(N(C)C)CC4)NC32)Nc2cccc1c2', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(C)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C4=CN5C[NH+]4C(N3)C25)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCC(CCN(C)C)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C([O+]=C)C3)NC3NCNC23)c1']."
            },
            {
                "from": "human",
                "value": "Transition the z-score to 1.485313 and begin regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.485313, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(F)C3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)OCC[NH+]4CNC2C4N3)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4C2NC[NH+]4OC2OC4CC(CCC4N(C)C)N32)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(F)C3)NC3C2NC[NH+]3C=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)OCC[NH+]4CNC2C4N3)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4C2NC[NH+]4OC2OC4CC(CCC4N(C)C)N32)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(=CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(=C(O)F)C3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3CON(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized chemical structures based on 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' to reach a z-score of 1.579194 for '909904', concentrating on [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.579194, \"cell_line\": \"909904\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1']."
            },
            {
                "from": "human",
                "value": "Adjust to 1.679194 and start regeneration. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.679194, \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined molecules from ')))(==OCCC)1CCOC3=OC2)C))C=C=CC=((CC)=((N(OC2(3=CCCCCS(=)C=C=C=C4C4C))CO((=(CO==1CCCCC000A' targeting a z-score of 0.629086 in '1298357', concentrating on changes within [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.629086, \"cell_line\": \"1298357\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")))(==OCCC)1CCOC3=OC2)C))C=C=CC=((CC)=((N(OC2(3=CCCCCS(=)C=C=C=C4C4C))CO((=(CO==1CCCCC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The most proper smile for the drug is 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop fine-tuned analogs of 'CS(((=(=()O3=CC2==)(C)COC(CCOC=C=N4CCCC4CCC)CCC=))C)O=1CCCCO==C)C)((CC=)C1)CO=((C=32=C000A' with a target z-score of 0.916745 in '910943', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for atom-level changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.916745, \"cell_line\": \"910943\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CS(((=(=()O3=CC2==)(C)COC(CCOC=C=N4CCCC4CCC)CCC=))C)O=1CCCCO==C)C)((CC=)C1)CO=((C=32=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop enhanced molecules based on 'C)C(O/N=O(12OCCC)CO==1C2==CC)CCO/(C=CCC=CC000A' for a z-score of 1.905347 in '909727', focusing on atoms specified in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.905347, \"cell_line\": \"909727\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C)C(O/N=O(12OCCC)CO==1C2==CC)CCO/(C=CCC=CC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(S)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C)c1OC', 'COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COOc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(S)c1OC', 'CCc1cc(C=CC(=O)N2CCC=CC2=O)cc(OC)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecules based on 'C=56C=C4C6N(CCS(3=(CCCN)O2=OC1)=CNC(=CCC(=CCCC1ONCC)CNC==(3NC=)C(C5CO24))=C)C000A' to reach a z-score of -0.245353 for '907298', focusing on the atoms specified in [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.245353, \"cell_line\": \"907298\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=56C=C4C6N(CCS(3=(CCCN)O2=OC1)=CNC(=CCC(=CCCC1ONCC)CNC==(3NC=)C(C5CO24))=C)C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' should be the smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized versions of 'NC=2C=1CO=CC)(OC(CC=N(()5C=(26=)3N=C1CCNN)CC=C6=CCC=CN(C4O53CSCC=C)C))(CC4COC000A' to achieve a z-score of 0.500080 in '909784', restricting modifications to the atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.500080, \"cell_line\": \"909784\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"NC=2C=1CO=CC)(OC(CC=N(()5C=(26=)3N=C1CCNN)CC=C6=CCC=CN(C4O53CSCC=C)C))(CC4COC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' is the right smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5CC(O)ON5)cc4)CN3C23C=C1[O+]3C(C)N1CCOCC1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5CC(O)ON5)cc4)CN3C23C=C1[O+]3C(C)N1CCOCC1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC6ON5C(CO)C6N5CCOCC5)cc4)CN32)C1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct tailored molecular versions of ')C==C1O2OCCC)(=CCCN=OCC1C)=54CCC(3C=CC3CCNCC=2=C(=N4C)=CONC=(=CN(C)5CC000A' to reach a z-score of -0.508464 for '908147', adjusting only the atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.508464, \"cell_line\": \"908147\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")C==C1O2OCCC)(=CCCN=OCC1C)=54CCC(3C=CC3CCNCC=2=C(=N4C)=CONC=(=CN(C)5CC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(Nc1ccc(Nc2ncnc3c2C=C2C(O)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'CCN1CCCOC2C3=Cc4c(Nc5ccc(NC(=O)c6ccccc6)cc5)ncnc4C2[SH]3OCC1', 'CC1C2=Cc3c(Nc4ccc(NC(=O)c5ccccc5)cc4)ncnc3C1C2OCCCN1CCOCC1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C(F)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCCN5CCOCC5[SH]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCC(N5CCOCC5)[O+]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3cc(OCCCN4CCOCC4)c(CO)cc23)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(Nc2ncnc3c2C=C2C(O)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'CCN1CCCOC2C3=Cc4c(Nc5ccc(NC(=O)c6ccccc6)cc5)ncnc4C2[SH]3OCC1', 'CC1C2=Cc3c(Nc4ccc(NC(=O)c5ccccc5)cc4)ncnc3C1C2OCCCN1CCOCC1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C(F)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCCN5CCOCC5[SH]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCC(N5CCOCC5)[O+]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3cc(OCCCN4CCOCC4)c(CO)cc23)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate tailored compounds from ')OCC)C(C32OCC3C2=N)CC=CC1((1(CCOC=C#)O=NNC==CCC=CCNC=C=C000A' targeting a z-score of 1.299980 for '1331050', concentrating changes on [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.299980, \"cell_line\": \"1331050\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")OCC)C(C32OCC3C2=N)CC=CC1((1(CCOC=C#)O=NNC==CCC=CCNC=C=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The correct expression to use with the drug is 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined versions of 'CO=C=2NC)O)C(C==()CCC)CCC==C(CCCCC3==CC)=CCCC=)=C)(3C)=(CS==(1C1C)4C(2=)COCO((C(O(CO)4000A' to meet a z-score of 1.191745 in '749712', adjusting only the atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.191745, \"cell_line\": \"749712\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CO=C=2NC)O)C(C==()CCC)CCC==C(CCCCC3==CC)=CCCC=)=C)(3C)=(CS==(1C1C)4C(2=)COCO((C(O(CO)4000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The correct facial expression for the drug is 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CC1CCCCC1CC1CC(C(O)NC2(CO)CCC(S(O)(O)c3ccccc3C(C)(C)C)CC2)C(O)C(O)C1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1cccc2c1S(=O)(=O)c1ccc(cc1)NC(=O)c1cc(c(O)c(O)c1O)Cc1ccccc1CC2O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC1=Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)S(=O)(=O)c2ccc1cc2C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C=CO)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc21', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'CC1c2cccc(C(C)(C)C)c2S(=O)(=O)c2ccc(cc2)NC(=O)c2cc3c(c(O)c2O)OC1c1ccccc1C3', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CC1CCCCC1CC1CC(C(O)NC2(CO)CCC(S(O)(O)c3ccccc3C(C)(C)C)CC2)C(O)C(O)C1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3[O+]4SO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1cccc2c1S(=O)(=O)c1ccc(cc1)NC(=O)c1cc(c(O)c(O)c1O)Cc1ccccc1CC2O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC1=Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)Nc2ccc(cc2)S(=O)(=O)c2ccc1cc2C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate tailored molecules based on '2C=CCC=)C(=/C=O1))OCOC=OCCC(/C(CN1O=CC=C2C000A' aiming for a z-score of 1.453909 for '909729', focusing changes on atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.453909, \"cell_line\": \"909729\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"2C=CCC=)C(=/C=O1))OCOC=OCCC(/C(CN1O=CC=C2C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' is the valid expression for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc2cc(c1OC)CC1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]C)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(SF)c1OC', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized compounds derived from '(C)CCCO=CC)O(N2C=NC(CN=1=)==C)CCNO)=)COCO(C((CCCCCCO(C1)2=000A' for a z-score of 0.284216 in '1503370', focusing on the atoms listed in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.284216, \"cell_line\": \"1503370\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"(C)CCCO=CC)O(N2C=NC(CN=1=)==C)CCNO)=)COCO(C((CCCCCCO(C1)2=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The correct match for the drug’s smile is 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce custom molecules based on 'CCO(@(CHC=CC[)2HS(([)CCNC(]=C)C=)=C=@CO)]CCS(CC11C)CN2C=C=N=CO000A' with a target z-score of 1.484159 for '907289', with adjustments focused in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.484159, \"cell_line\": \"907289\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCO(@(CHC=CC[)2HS(([)CCNC(]=C)C=)=C=@CO)]CCS(CC11C)CN2C=C=N=CO000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug’s smile should be 'COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2\"}}"
            },
            {
                "from": "observation",
                "value": "['NC(CS)C(=CCOCCCO)Nc1ccc(O)c(-c2ccccc2)c1', 'NC(CS)(CNc1cccc(-c2ccccc2)c1)C(C=[O+]O)CCO', 'C=CCCC(C)N(CC(N)CS)c1cccc(-c2ccccc2C(C)CC)c1', 'C=NC1CSC(OC)=C1Nc1ccc(C)c(-c2cccc(C(C)CO)c2)c1', 'O=NC(CNc1cccc(-c2ccccc2)c1)CSCO', 'CC1C2C=NC2(CS)C(C)N1c1cccc(-c2ccccc2)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['NC(CS)C(=CCOCCCO)Nc1ccc(O)c(-c2ccccc2)c1', 'NC(CS)(CNc1cccc(-c2ccccc2)c1)C(C=[O+]O)CCO', 'C=CCCC(C)N(CC(N)CS)c1cccc(-c2ccccc2C(C)CC)c1', 'C=NC1CSC(OC)=C1Nc1ccc(C)c(-c2cccc(C(C)CO)c2)c1', 'O=NC(CNc1cccc(-c2ccccc2)c1)CSCO', 'CC1C2C=NC2(CS)C(C)N1c1cccc(-c2ccccc2)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate refined analogs of 'N2CC1O)CCO=((OC)(CONCOOC=1CCCC=(=C(=)=CCCN=C)2())CCCCCN)=(000A' for a z-score of 0.786185 in '909699', using [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] to guide atom modifications. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.786185, \"cell_line\": \"909699\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"N2CC1O)CCO=((OC)(CONCOOC=1CCCC=(=C(=)=CCCN=C)2())CCCCCN)=(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' proper the smile meant for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized variants of 'CC=OC))([))C=@C=O(CHCCO=1=CCC](C21CCOC==)(C2(O000A' with a target z-score of 1.250014 in '930083', making adjustments to atoms within [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.250014, \"cell_line\": \"930083\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], \"gt\": \"CC=OC))([))C=@C=O(CHCCO=1=CCC](C21CCOC==)(C2(O000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug requires 'CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C' as the smile. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate some optimized molecules based on  the molecule 'CC)C(C)=(CCCC(CC=C))()1(=C))S()4=OC(C=O=4C3=(=C=1CC(CO2=(CC=(C2CCCOC3O)CN=CCO)CCC==)=C000A', requiring a z-score of 0.500890 for  the cell line '909262', and the mask of atoms to be optimized is [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.500890, \"cell_line\": \"909262\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC)C(C)=(CCCC(CC=C))()1(=C))S()4=OC(C=O=4C3=(=C=1CC(CO2=(CC=(C2CCCOC3O)CN=CCO)CCC==)=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug’s smile should be 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'N)C4S=N())NC(CC=C4C2=OCC==N33/CCC/2==C(C=C==CCCC=CCC1C=1000A' targeting a z-score of 1.006651 in '909253', with changes limited to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.006651, \"cell_line\": \"909253\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"N)C4S=N())NC(CC=C4C2=OCC==N33/CCC/2==C(C=C==CCCC=CCC1C=1000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The appropriate smile for the drug is 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "['OCC1=[O+]c2ccccc2Sc2ccc3c(c2)N=NC3C=Cc2cccc1n2', 'OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCC(=Cc1ccccn1)C1C2C=C3CN1N=C2CC3Sc1ccccc1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCC1=[O+]c2ccccc2Sc2ccc3c(c2)N=NC3C=Cc2cccc1n2', 'OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCC(=Cc1ccccn1)C1C2C=C3CN1N=C2CC3Sc1ccccc1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design modified molecules from 'C)CC(S4()C)4C(CC3CNC3=CC2=CC==C(((CO(=)=C(1=OCCCCCCOC))C)))CCOCC1==O=C==(C)(2(OC=C=)C=000A' targeting a z-score of 1.581146 for '1240202', focusing adjustments on atoms specified in [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.581146, \"cell_line\": \"1240202\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C)CC(S4()C)4C(CC3CNC3=CC2=CC==C(((CO(=)=C(1=OCCCCCCOC))C)))CCOCC1==O=C==(C)(2(OC=C=)C=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(CC=C=C=C=CCCO)c(O)c(O)c2O)cc1', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1CCC(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)C1C', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create modified versions of ')1))C(3C=NC3CCCCN=CC(C5O==6()N=CC=5NOC6CN(OC)2C4=C(COC)1CC=C(=CC4()SCC=C=CCN2000A' with a z-score target of 0.622942 in '910695', adjusting atoms within the [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.622942, \"cell_line\": \"910695\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")1))C(3C=NC3CCCCN=CC(C5O==6()N=CC=5NOC6CN(OC)2C4=C(COC)1CC=C(=CC4()SCC=C=CCN2000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug’s smile should be 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate refined compounds from 'N)@C=CCCC2HC1CC=(C(@ONC)C)C==)=O(C]=CC(=1C)(C[O2C=000A' aiming for a z-score of 1.116315 in '925347', focusing on atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.116315, \"cell_line\": \"925347\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"N)@C=CCCC2HC1CC=(C(@ONC)C)C==)=O(C]=CC(=1C)(C[O2C=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The proper smile for the drug is 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate some optimized molecules based on  the molecule '(C1C]C)C)=[O=OC@(CCCHOC=C()(2O=C2O)CC(1=CC)=C=000A', requiring a z-score of 1.262752 for  the cell line '1247873', and the mask of atoms to be optimized is [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.262752, \"cell_line\": \"1247873\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], \"gt\": \"(C1C]C)C)=[O=OC@(CCCHOC=C()(2O=C2O)CC(1=CC)=C=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The correct expression for the drug is 'CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1(O)C=CC(O)(C(C)O)c2c(O)ccc(O)c21', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1(O)C=CC(O)(C(C)O)c2c(O)ccc(O)c21', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized derivatives of '/C)2/)CCC(OCC((C(/\\C=C4CC1C=(N=CCOOC=C)C)CCN)42C)(CON=13=CCC(C)===3CO000A' aiming for a z-score of 1.238456 in '687798', focusing modifications on the atoms listed in [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.238456, \"cell_line\": \"687798\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"/C)2/)CCC(OCC((C(/\\C=C4CC1C=(N=CCOOC=C)C)CCN)42C)(CON=13=CCC(C)===3CO000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1=CC(=C2ONC(C)(CC=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'C=C(C)c1cc2ccc1C1=C(COCCN(CC)C2)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCC(F)C1(O)C=C(O)C(C(C)C)=CC1=C1ONC2CC12c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3C)cc2C(=O)CO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCc1cc(CN2CCOCC2)ccc1C12C(=C3C=C(C(C)C)C(O)=CC3=O)ONC1C2C', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'CC(C)C1=CC(=C2ONCC2(C)c2ccc(CN3CCOCC3CCOF)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CSC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCC1=C(c2ccc(CN3CCOCC3)cc2[O+]=CO)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC2=C3ONC4(CC4COC1=CC2=O)C3c1ccc(CN2CCOCC2)cc1', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON(F)CC2(C)c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12ON3C(=C1c1ccc(CN4CCOCC4)cc1)C31CC(C)C3CC2C(=O)CC3[OH+]1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OSCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC2([OH+]C3(c4ccc(CN5CCOCC5)cc4)CNOC23)C(=O)C=C1O', 'CCC1=C(N2CCOCC2)c2ccc(cc2)C23C(=C4C=C(C(C)C)C(O)=CC4=O)ONC2C13', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2[O+](O)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=CCCC1=C(c2ccc(CN3CCOCC3C)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'Cc1cc2ccc1C1=C(CCC3COCCN3C2)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCOOCCN(CC)Cc1ccc(C2=C3CC4(C=C(C(C)C)C(O)=CC4=O)C2ON3)cc1', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)C1=CC(=C2ONC(C)(CC=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'C=C(C)c1cc2ccc1C1=C(COCCN(CC)C2)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCC(F)C1(O)C=C(O)C(C(C)C)=CC1=C1ONC2CC12c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3C)cc2C(=O)CO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCc1cc(CN2CCOCC2)ccc1C12C(=C3C=C(C(C)C)C(O)=CC3=O)ONC1C2C', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'CC(C)C1=CC(=C2ONCC2(C)c2ccc(CN3CCOCC3CCOF)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CSC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCC1=C(c2ccc(CN3CCOCC3)cc2[O+]=CO)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC2=C3ONC4(CC4COC1=CC2=O)C3c1ccc(CN2CCOCC2)cc1', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON(F)CC2(C)c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12ON3C(=C1c1ccc(CN4CCOCC4)cc1)C31CC(C)C3CC2C(=O)CC3[OH+]1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OSCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC2([OH+]C3(c4ccc(CN5CCOCC5)cc4)CNOC23)C(=O)C=C1O', 'CCC1=C(N2CCOCC2)c2ccc(cc2)C23C(=C4C=C(C(C)C)C(O)=CC4=O)ONC2C13', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2[O+](O)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=CCCC1=C(c2ccc(CN3CCOCC3C)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'Cc1cc2ccc1C1=C(CCC3COCCN3C2)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCOOCCN(CC)Cc1ccc(C2=C3CC4(C=C(C(C)C)C(O)=CC4=O)C2ON3)cc1', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules derived from ')CC=CCNCCCOCCC=(C2=NCCCOC)1C(O(COC=)CCCCN=)3C1=(3#C=N==2000A' with a z-score of 2.384753 for '910910', applying changes to atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.384753, \"cell_line\": \"910910\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")CC=CCNCCCOCCC=(C2=NCCCOC)1C(O(COC=)CCCCN=)3C1=(3#C=N==2000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The right expression for the drug is 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NOC4C)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CF)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(NC2NCNC34CC(C3)C(OCCCCCCC(=O)NO)CC24)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(NO)OC4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(OCCCCCCC(=O)NO)C(=C3)[SH]2F)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)N(O)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NOC4)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCC(C)CCCC(NO)OC4)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NO[SH]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(SF)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NOF)cc23)c1', 'C#Cc1cccc(NC2NCNC34C(F)=C5C(OCCCCCCC(=O)NO)C(C53)C24)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCC(CCCC(=O)NO)=C4)c1', 'C#Cc1cccc(NC2NCNC34CC(C(OCCCCCCC(=O)NO)CC23)C4F)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)=C4F)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NOC4C)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CF)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(NC2NCNC34CC(C3)C(OCCCCCCC(=O)NO)CC24)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(NO)OC4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(OCCCCCCC(=O)NO)C(=C3)[SH]2F)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)N(O)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NOC4)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCC(C)CCCC(NO)OC4)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCCC(=O)NO[SH]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(SF)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NOF)cc23)c1', 'C#Cc1cccc(NC2NCNC34C(F)=C5C(OCCCCCCC(=O)NO)C(C53)C24)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCC(CCCC(=O)NO)=C4)c1', 'C#Cc1cccc(NC2NCNC34CC(C(OCCCCCCC(=O)NO)CC23)C4F)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)=C4F)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Synthesize optimized forms of '=C=NC6)(1=CC=6C(NOO=)CCN(45CNCCC5CC=C)CC)(CO)3=CCCCCCC3NC1OC(22==)S(CC)(NC4==000A' aiming to achieve a z-score of 0.768852 for '688006', with changes concentrated in [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.768852, \"cell_line\": \"688006\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"=C=NC6)(1=CC=6C(NOO=)CCN(45CNCCC5CC=C)CC)(CO)3=CCCCCCC3NC1OC(22==)S(CC)(NC4==000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' should be the smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(O)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(O)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct optimized versions of ')3C4CC(C1(CNCN===2C=CCNC42)(CC=)NC=CC=N=)CCCNN(3CCCCCNC()=)OC1(000A' to meet a z-score of 1.564769 for '949173', focusing on modifications within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.564769, \"cell_line\": \"949173\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")3C4CC(C1(CNCN===2C=CCNC42)(CC=)NC=CC=N=)CCCNN(3CCCCCNC()=)OC1(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' is the proper smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CF)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)C(C)C3)NC3C2NC[NH+]3C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3CC=O)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](C=O)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C=C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(OO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC5=C[NH+]6CN5C2C6N3)C4)c1', 'C=CC(=O)Nc1cc2ccc1C(CO)[NH+]1CNC3C(=NC(NC4CCC(N(C)C)CC4)NC31)N2', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OSO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3C=CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=CF)c1', 'C=CC(=O)N(c1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3NCNC23)c1)C(C)C', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(CC[NH+]5CNC2C5N3)C4)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3OC)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2NC[NH+]3[O+]=C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCC(N(C)C)CC3)NC3C2N2C#C[NH+]3C2)c1', 'C=CC(=O)Nc1cccc(NC2=NC(N(OC)C3CCC(N(C)C)CC3)NC3NCNC23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct tailored compounds from '=O)CCCFCC()((OOC=CCCFC(C4CC=CS=3(N(F)31C1C==C2)=2C)=4N=N))C=CCC(O000A' aiming for a z-score of -0.071467 for '906854', with modifications limited to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.071467, \"cell_line\": \"906854\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"=O)CCCFCC()((OOC=CCCFC(C4CC=CS=3(N(F)31C1C==C2)=2C)=4N=N))C=CCC(O000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC\"}}"
            },
            {
                "from": "observation",
                "value": "['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate tailored derivatives of 'COH2))C]CCC[C=C)=1CC=(C=C@ONC12)==)(NC((C==O(CC@CC000A' to meet a z-score of 1.366697 in '1524417', adjusting atoms only in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.366697, \"cell_line\": \"1524417\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COH2))C]CCC[C=C)=1CC=(C=C@ONC12)==)(NC((C==O(CC@CC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' is the valid expression for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C2C=Cc3ccccc32)cc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)C12CCC(CC1)NC(Cc1ccccc1)OC2CO', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC1(C(=O)NO)CCC(NC(=O)Cc2ccccc2)CC1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C2C=Cc3ccccc32)cc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)C12CCC(CC1)NC(Cc1ccccc1)OC2CO', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC1(C(=O)NO)CCC(NC(=O)Cc2ccccc2)CC1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct refined compounds from '(C))NOCCC6S(=5=)C(CCC==CC)C2)C=C5CCCC(NO4CC=(O=CNNCCC3CCC1)ON3N(=(16)=4==CCC2000A' aiming for a z-score of 1.528235 in '1298349', with changes concentrated in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.528235, \"cell_line\": \"1298349\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"(C))NOCCC6S(=5=)C(CCC==CC)C2)C=C5CCCC(NO4CC=(O=CNNCCC3CCC1)ON3N(=(16)=4==CCC2000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized derivatives of 'C14C=(C=)CCOCC==)C)C=)CO4OCC(C==(C=CCC(C3C)(=(C1(S)CCO()()===C)CCOC(N22==C3)CCCOC=CC)(000A' aiming for a z-score of -0.936410 in '907313', focusing modifications on the atoms listed in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.936410, \"cell_line\": \"907313\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C14C=(C=)CCOCC==)C)C=)CO4OCC(C==(C=CCC(C3C)(=(C1(S)CCO()()===C)CCOC(N22==C3)CCCOC=CC)(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' is the proper smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized analogs from 'l=4C(OC(=OCNNCCC/C(/))[CNN2=NC=O@4CC))C2CF=3C(C)3C=(=(=CC11C==CCCC))HC](C000A' to reach a z-score of 1.437503 in '907176', with modifications limited to [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.437503, \"cell_line\": \"907176\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"l=4C(OC(=OCNNCCC/C(/))[CNN2=NC=O@4CC))C2CF=3C(C)3C=(=(=CC11C==CCCC))HC](C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The smile needed for the drug is 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C)(Cl)C1OCCC1Oc1cc2ncnc(Nc3ccc(F)c(CO)c3)c2cc1S', 'COCc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1', 'OCCC1c2ncnc(Nc3ccc(F)c(Cl)c3)c2C=C(S)C1OC1CCOC1', 'OCOCCOC1OCCC1Oc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1O', 'CC(CO)C1(Nc2ncnc3cc(OC4CCOC4CCCl)ccc23)CCC(F)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C)(Cl)C1OCCC1Oc1cc2ncnc(Nc3ccc(F)c(CO)c3)c2cc1S', 'COCc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1', 'OCCC1c2ncnc(Nc3ccc(F)c(Cl)c3)c2C=C(S)C1OC1CCOC1', 'OCOCCOC1OCCC1Oc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1O', 'CC(CO)C1(Nc2ncnc3cc(OC4CCOC4CCCl)ccc23)CCC(F)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop advanced analogs from 'C=C)1#=C=(C)C=1=)N(COO2CCNCCC=CCCC2CC((3)=CCNO==CNCCCC3O000A' with an improved z-score of 1.170538 for '908458', focusing adjustments within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.170538, \"cell_line\": \"908458\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=C)1#=C=(C)C=1=)N(COO2CCNCCC=CCCC2CC((3)=CCNO==CNCCCC3O000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' is the valid fit for the drug’s smile. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular derivatives from '(4C==O(C==CCC=)C=(C(O))(=C)C2SCCC()C=)34CCC)1CNC(===(=2C))=CCC)=OCCCCOCC(CC1=O(COCC()3000A' to achieve a z-score of 0.083400 in '1503366', with modifications limited to [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.083400, \"cell_line\": \"1503366\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"(4C==O(C==CCC=)C=(C(O))(=C)C2SCCC()C=)34CCC)1CNC(===(=2C))=CCC)=OCCCCOCC(CC1=O(COCC()3000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' fits as the correct smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1F', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate specialized derivatives of '(CCCF=Cl1CCC43CCCCOON2=132==CCC(CC)=O=)CNNCC(CCCN)=(C=4)000A' with a z-score of 1.344096 in '1330995', modifying atoms as defined by [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.344096, \"cell_line\": \"1330995\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"(CCCF=Cl1CCC43CCCCOON2=132==CCC(CC)=O=)CNNCC(CCCN)=(C=4)000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' is the smile that aligns with the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "['Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct optimized molecular structures from '1CO2C1CC=(=(CC=C=)NCOCCC=(CCCC(=3#C=N2C=COOC)CCNC3CC))=N000A' aiming for a z-score of 0.736671 in '905990', adjusting only atoms defined by [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.736671, \"cell_line\": \"905990\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"1CO2C1CC=(=(CC=C=)NCOCCC=(CCCC(=3#C=N2C=COOC)CCNC3CC))=N000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate optimized molecules derived from '=(N)OCC=(C)CCC(=C=(CC(=2(CCC)(C2OCC3=CC=CCCC(1)C==C=(S)1O)=)(C)))COO3CC(=CCCCC=4)4OC==000A' to achieve a z-score of -0.845449 for '910401', focusing on atom adjustments in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.845449, \"cell_line\": \"910401\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"=(N)OCC=(C)CCC(=C=(CC(=2(CCC)(C2OCC3=CC=CCCC(1)C==C=(S)1O)=)(C)))COO3CC(=CCCCC=4)4OC==000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' is the right smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create enhanced molecular variants from 'C=3C1C=C(2C431CCNNC2)CCC=C((4OCC=C==C=CCN/)==S/N)=C=CC=C000A' to reach a z-score of 1.691752 for '905956', focusing modifications on [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.691752, \"cell_line\": \"905956\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=3C1C=C(2C431CCNNC2)CCC=C((4OCC=C==C=CCN/)==S/N)=C=CC=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' is the proper smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized adaptations of '])=C)([(O@C2COC(C()=C=CN)2C=COC1=CN@1HCC=C(C=C)C=C000A' with a target z-score of 0.871245 for '909721', focusing on modifications in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.871245, \"cell_line\": \"909721\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"])=C)([(O@C2COC(C()=C=CN)2C=COC1=CN@1HCC=C(C=C)C=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The smile that fits the drug is 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved molecular adaptations from 'CCC=[)CC1)C)OH(CN]=CC(N)CC[CC@C(CCO@(]CCCNHO1)=O@CC=O(C(C()CC(==))C=000A' aiming for a z-score of 0.384034 for '946354', concentrating changes in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.384034, \"cell_line\": \"946354\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCC=[)CC1)C)OH(CN]=CC(N)CC[CC@C(CCO@(]CCCNHO1)=O@CC=O(C(C()CC(==))C=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' is the valid fit for the drug’s smile. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized chemical variants based on 'OCCO))=2)CCN=C=CC1ON()CCN(CCC=(NO()C)=1CC(((C=CCC2COC==O)C000A' for a z-score of 1.664624 in '906825', focusing adjustments on atoms in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.664624, \"cell_line\": \"906825\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"OCCO))=2)CCN=C=CC1ON()CCN(CCC=(NO()C)=1CC(((C=CCC2COC==O)C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create specialized versions of ')C2@)C=CCC=))(C=2C==(N@C1H1C]((CCNCCCOC)C[OO=C==C(000A' for a z-score of 0.286617 in '910548', restricting changes to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.286617, \"cell_line\": \"910548\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")C2@)C=CCC=))(C=2C==(N@C1H1C]((CCNCCCOC)C[OO=C==C(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized molecules using '=(CCC=(1(O3C=43)C=C4C=SC()(=))1O2OCC((2OCC(=CCC)(CCCC=)=)==O(CCC)=C=(C)=)NO=)CCCCCCCCC000A' as a base to reach a z-score of 1.592758 in '1240124', focusing changes on atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.592758, \"cell_line\": \"1240124\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"=(CCC=(1(O3C=43)C=C4C=SC()(=))1O2OCC((2OCC(=CCC)(CCCC=)=)==O(CCC)=C=(C)=)NO=)CCCCCCCCC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' is the valid expression for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4OO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized compounds from '(C@C)CO]CCO)H(=[1CCCC1N)=CO@O]CCCC(CCN=HCC=()(==CCCCCN=()C[(C()C))@O000A' aiming for a z-score of 0.924883 in '688015', focusing only on the atoms in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.924883, \"cell_line\": \"688015\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"(C@C)CO]CCO)H(=[1CCCC1N)=CO@O]CCCC(CCN=HCC=()(==CCCCCN=()C[(C()C))@O000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design ')OCC)C=OC1C=((N))C=C1CO(=COCN=(CC=N=C=CC(CCCC(2CC)O)C)O2N(000A'-based molecules with optimized features to reach a z-score of 1.698011 in '688011', optimizing the atoms specified by [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.698011, \"cell_line\": \"688011\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")OCC)C=OC1C=((N))C=C1CO(=COCN=(CC=N=C=CC(CCCC(2CC)O)C)O2N(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The smile that fits the drug is 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'C=COC(CCCCCCNC(OC(CC)CC)C1CC(c2ccccc2)ON1)NO', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)=CC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'CCc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2OC)cc1', 'CCOC(NCCCCCCC(=O)NO)C1CC(c2ccccc2)ON1', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'COC(CCCCCC1C=C(C)c2ccc(cc2)C2CC(NO2)C(=O)N1)NO', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'C=COC(CCCCCCNC(OC(CC)CC)C1CC(c2ccccc2)ON1)NO', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)=CC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'CCc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2OC)cc1', 'CCOC(NCCCCCCC(=O)NO)C1CC(c2ccccc2)ON1', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'COC(CCCCCC1C=C(C)c2ccc(cc2)C2CC(NO2)C(=O)N1)NO', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct tailored molecular versions of 'CO==CC)O=O1CN=CCCCN==CCCCNC3)=)12CC(=)C(#=CCCC2(OC(C3CCN000A' to reach a z-score of 1.168546 for '1240129', adjusting only the atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.168546, \"cell_line\": \"1240129\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CO==CC)O=O1CN=CCCCN==CCCCNC3)=)12CC(=)C(#=CCCC2(OC(C3CCN000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The correct facial expression for the drug is 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3cc(S)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecular adaptations of '===1C(CC=CO)N(HOC@O(C2==]=C(CCCC)1C)C2NC@()[C=C)CC000A' targeting a z-score of 1.088658 in '906801', concentrating changes on [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.088658, \"cell_line\": \"906801\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"===1C(CC=CO)N(HOC@O(C2==]=C(CCCC)1C)C2NC@()[C=C)CC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug’s correct smile is 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'C=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate specialized molecular structures from ')N5N=C=C)=C5=CC)4C=C==C)C311CCCCCCC(CC=(C2O(=(CCC)#(C=63CCC2CNCCNN===6C4=000A' to meet a z-score of 0.481019 in '1298357', using [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for focused changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.481019, \"cell_line\": \"1298357\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \")N5N=C=C)=C5=CC)4C=C==C)C311CCCCCCC(CC=(C2O(=(CCC)#(C=63CCC2CNCCNN===6C4=000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design new variants of '()C)C=(C=C)(C1(N)CC=CC)O==NCC)2CC(2CCNCOCNO1CCC==OCO=CO()(000A' to reach a z-score of 0.390362 in '905984', modifying the atoms specified by [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.390362, \"cell_line\": \"905984\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"()C)C=(C=C)(C1(N)CC=CC)O==NCC)2CC(2CCNCOCNO1CCC==OCO=CO()(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' is the proper smile for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop tailored analogs based on 'O==1C(CCC2C3CC2CC)==CCC)OCCC3=CC)(C=)CNN=#=NCCO=CN1(CC(O000A' with an objective z-score of 1.964038 in '1290765', limiting changes to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.964038, \"cell_line\": \"1290765\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"O==1C(CCC2C3CC2CC)==CCC)OCCC3=CC)(C=)CNN=#=NCCO=CN1(CC(O000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce custom molecules from '4C=CCC=(=C2=C2)3OCCC=1O=(NCO(O=C)C=(CCCCCC((C)C)=)O=C=C1CCCC()=O=CC==)CC4(C))3S))((CC(000A' to meet a z-score of 0.272211 for '687787', with modifications limited to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.272211, \"cell_line\": \"687787\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"4C=CCC=(=C2=C2)3OCCC=1O=(NCO(O=C)C=(CCCCCC((C)C)=)O=C=C1CCCC()=O=CC==)CC4(C))3S))((CC(000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The drug’s correct smile is 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized forms of 'CCC=()O)C=)C)SC)=1C)C4=(2(==)CCC)(C=O=CC)CCC)((OOCCO(=CN4C(CC==2)(C3CCC1O3C(=(C=CC==CC000A' that meet a z-score of 0.276205 in '924151', restricting modifications to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.276205, \"cell_line\": \"924151\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCC=()O)C=)C)SC)=1C)C4=(2(==)CCC)(C=O=CC)CCC)((OOCCO(=CN4C(CC==2)(C3CCC1O3C(=(C=CC==CC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "The right look for the drug is 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct optimized molecular structures from '[C=CC(C=N==CC@H=CC)CS=)CC)C=)C(1OC[(SNOCO]C2NHC))=@]((CC(21CCC000A' aiming for a z-score of 2.355312 in '930299', adjusting only atoms defined by [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.355312, \"cell_line\": \"930299\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"[C=CC(C=N==CC@H=CC)CS=)CC)C=)C(1OC[(SNOCO]C2NHC))=@]((CC(21CCC000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2\"}}"
            },
            {
                "from": "observation",
                "value": "['NC(CS)C(=CCOCCCO)Nc1ccc(O)c(-c2ccccc2)c1', 'Cc1ccc(NC(CCO)C(N)CS)cc1-c1ccccc1', 'Cc1ccccc1-c1cccc(N(C)CC(N)CS)c1', 'CCCCc1c(N(C)CC(N)CS)ccc(C)c1-c1ccc(CC)cc1', 'C=CCCC(C)N(CC(N)CS)c1cccc(-c2ccccc2C(C)CC)c1', 'NC(CS)CNc1cccc(-c2ccccc2)c1', 'CCN(CC(N)(CS)C(C)[OH+]C)c1ccc(O)c(-c2ccccc2)c1', 'C=NC1CSC(OC)=C1Nc1ccc(C)c(-c2cccc(C(C)CO)c2)c1', 'NC(CNc1cccc(-c2ccccc2)c1)C[SH]=O', 'C=CCC1=CC(N)(C(Nc2ccc(O)c(-c3ccccc3C)c2)C(C)C=C)CS1', 'O=NC(CNc1cccc(-c2ccccc2)c1)CSCO', 'CC1C2C=NC2(CS)C(C)N1c1cccc(-c2ccccc2)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['NC(CS)C(=CCOCCCO)Nc1ccc(O)c(-c2ccccc2)c1', 'Cc1ccc(NC(CCO)C(N)CS)cc1-c1ccccc1', 'Cc1ccccc1-c1cccc(N(C)CC(N)CS)c1', 'CCCCc1c(N(C)CC(N)CS)ccc(C)c1-c1ccc(CC)cc1', 'C=CCCC(C)N(CC(N)CS)c1cccc(-c2ccccc2C(C)CC)c1', 'NC(CS)CNc1cccc(-c2ccccc2)c1', 'CCN(CC(N)(CS)C(C)[OH+]C)c1ccc(O)c(-c2ccccc2)c1', 'C=NC1CSC(OC)=C1Nc1ccc(C)c(-c2cccc(C(C)CO)c2)c1', 'NC(CNc1cccc(-c2ccccc2)c1)C[SH]=O', 'C=CCC1=CC(N)(C(Nc2ccc(O)c(-c3ccccc3C)c2)C(C)C=C)CS1', 'O=NC(CNc1cccc(-c2ccccc2)c1)CSCO', 'CC1C2C=NC2(CS)C(C)N1c1cccc(-c2ccccc2)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop fine-tuned analogs of 'OC(=2CC)13N=)(=O5=CCCCC(CC4C3N=CCC62)==CCOCCN(N=CC56=C=C=NC4=C1)CCONONNCC=C000A' with a target z-score of 3.613275 in '909746', using [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for atom-level changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 3.613275, \"cell_line\": \"909746\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"OC(=2CC)13N=)(=O5=CCCCC(CC4C3N=CCC62)==CCOCCN(N=CC56=C=C=NC4=C1)CCONONNCC=C000A\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid smile\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The smile for the molecule is wrong, please provide with a valid one."
            },
            {
                "from": "human",
                "value": "'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' is the valid expression for the drug. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user-zscore)\", \"cell_line\": \"(user_cell_line)\", \"mask\": \"(user_mask)\", \"gt\": \"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"}}"
            },
            {
                "from": "observation",
                "value": "['CN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC21', 'CC(CN1CCOCC1)Oc1ccc(C(=O)Nc2cccc(OC3CC4C(C=N3)N=C(C3NON=C3N)N4C)c2)cc1', 'NC1=NONC1C1=NC2C=NC3CC2[NH+]1C12c4cc(ccc4OCC1N1CCOCC1)C(=O)Nc1ccc2c(c1)O3', 'NC1=NONC1C1=NC23C=NC4Oc5cccc(c5)NC(=O)c5ccc(OCCN6CCOCC6)c(c5)C25C4C3N15', 'NC1=NONC1C1=NC2C=NC3CC2N1Cc1cc(ccc1OCCN1CCOCC1)C(=O)Nc1cccc(c1)O3', 'NC1=NONC1C1=NC23C=NC4CC2N1C3(O)C(N1CCOCC1)COc1ccc(cc1)C(=O)Nc1cccc(c1)O4', 'NC1=NONC1C1=NC2C=NC3CC2N1[OH+]c1cc(ccc1OCCN1CCOCC1)C(=O)Nc1cccc(c1)O3', 'CN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCC(O)N5CCOCC5)cc4)c3)CC21', 'NC1=NONC1C1=NC2C=NC3CC2N1OC(CN1CCOCC1)Oc1ccc(cc1)C(=O)Nc1cccc(c1)O3', 'CSN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC21', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1O', 'NC1=NONC1C1=NC23C=NC(Oc4cccc(NC(=O)c5ccc(OCCN6CCOCC6)cc5)c4)CC2N1O3', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1S', 'NC1=NONC1C1=NC2C=NC3(Oc4cccc(NC(=O)c5ccc(OCCN6CCOCC6)cc5)c4)CC2N1[SH]3F', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OC(O)CN5CCOCC5)cc4)c3)CC2N1O', 'NC1=NONC1C1=NC2C=NC3CC2N1C1c2ccc(cc2O3)NC(=O)C12CCC(OCCN1CCOCC1)CC2', 'CC(COc1ccc(C(=O)Nc2cccc(OC3CC4C(C=N3)N=C(C3NON=C3N)N4C)c2)cc1)N1CCOCC1', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC21', 'CC(CN1CCOCC1)Oc1ccc(C(=O)Nc2cccc(OC3CC4C(C=N3)N=C(C3NON=C3N)N4C)c2)cc1', 'NC1=NONC1C1=NC2C=NC3CC2[NH+]1C12c4cc(ccc4OCC1N1CCOCC1)C(=O)Nc1ccc2c(c1)O3', 'NC1=NONC1C1=NC23C=NC4Oc5cccc(c5)NC(=O)c5ccc(OCCN6CCOCC6)c(c5)C25C4C3N15', 'NC1=NONC1C1=NC2C=NC3CC2N1Cc1cc(ccc1OCCN1CCOCC1)C(=O)Nc1cccc(c1)O3', 'NC1=NONC1C1=NC23C=NC4CC2N1C3(O)C(N1CCOCC1)COc1ccc(cc1)C(=O)Nc1cccc(c1)O4', 'NC1=NONC1C1=NC2C=NC3CC2N1[OH+]c1cc(ccc1OCCN1CCOCC1)C(=O)Nc1cccc(c1)O3', 'CN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCC(O)N5CCOCC5)cc4)c3)CC21', 'NC1=NONC1C1=NC2C=NC3CC2N1OC(CN1CCOCC1)Oc1ccc(cc1)C(=O)Nc1cccc(c1)O3', 'CSN1C(C2NON=C2N)=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC21', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1O', 'NC1=NONC1C1=NC23C=NC(Oc4cccc(NC(=O)c5ccc(OCCN6CCOCC6)cc5)c4)CC2N1O3', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1S', 'NC1=NONC1C1=NC2C=NC3(Oc4cccc(NC(=O)c5ccc(OCCN6CCOCC6)cc5)c4)CC2N1[SH]3F', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OC(O)CN5CCOCC5)cc4)c3)CC2N1O', 'NC1=NONC1C1=NC2C=NC3CC2N1C1c2ccc(cc2O3)NC(=O)C12CCC(OCCN1CCOCC1)CC2', 'CC(COc1ccc(C(=O)Nc2cccc(OC3CC4C(C=N3)N=C(C3NON=C3N)N4C)c2)cc1)N1CCOCC1', 'NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce variants of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' optimized for a z-score of -0.209251 in '8607970000000', focusing modifications on [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.209251, \"cell_line\": \"8607970000000\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "906877 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906877\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc2ccc1Nc1ncc(Cl)c(n1)Nc1ccccc1CCCC(CO)=C1CN(CCN1C)C1CCN2CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create fine-tuned molecules based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' for a z-score of 0.973830 in '6967400000000', modifying only atoms in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.973830, \"cell_line\": \"6967400000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Make 906746 the right option. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906746\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized forms of 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' with a z-score of 0.775186 in '9548760000000', making adjustments to atoms listed in [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.775186, \"cell_line\": \"9548760000000\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 687459 to ensure accuracy. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"687459\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)C1=CC(=C2ONC(C)([OH+]C=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON3CC2(c2ccc(CN4CCOCC4)cc2)[OH+]O3)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1CC23[OH+]C4CC(=O)C(CC14)C1(CO)ON2C3=C1c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CC(C)C1=CC(=CC23c4ccc(cc4)C4=C(O)C2N2OC(C5COCCN45)C23)C(=O)C=C1O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CC(C)C1=CC(=C2ONC3C4C(CO)=C(N5CCOCC5)c5ccc(cc5)C234)C(=O)C=C1O', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized analogs from 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' to reach a z-score of 1.544071 in '12444510000000', with modifications limited to [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.544071, \"cell_line\": \"12444510000000\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Go with 1524414 as the right cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1524414\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'C[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cc(CO)cc2F)c1F', 'C[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cc(C(C)CO)cc2F)c1F', 'C=CN(C)C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCC[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'C[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cc(CO)cc2F)c1F', 'C[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cc(C(C)CO)cc2F)c1F', 'C=CN(C)C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized forms of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with a z-score of -0.230188 in '8078670000000', making adjustments to atoms listed in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.230188, \"cell_line\": \"8078670000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "687780 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"687780\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate specialized molecular structures from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' to meet a z-score of 0.450490 in '06641120000000', using [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] for focused changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.450490, \"cell_line\": \"06641120000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Opt for 1240166 as the correct cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1240166\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized derivatives of 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to meet a z-score of 1.251941 for '7653530000000', modifying only the atoms in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.251941, \"cell_line\": \"7653530000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Make 753563 the right option. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"753563\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Oc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create custom derivatives of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' targeting a z-score of 1.352438 in '28016140000000', modifying atoms according to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.352438, \"cell_line\": \"28016140000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Choose 1240186 for the correct cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1240186\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC(C)ON5)cc4)CN32)CC1OCCN1CCOCC1', 'COC(C)C12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CC(OCCO)ON1', 'O=C1NC2CC(CC(CO)N1c1ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc1)ON2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1)NC12CC(C1)ON2', 'C=CC12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4C)cc3)CN12', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23CCO)cc1)NC1C=CON1', 'O=C1Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23[O+](CO)OC2CC(NO2)N1', 'C=CCC1CC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)NO1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5CC(C)ON5)cc4)CN32)CC1OCCN1CCOCC1', 'COC(C)C12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC4C=CON4)cc3)CN12', 'C=CC1CONC1NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CN1CCOCC1)OC1CC2=[SH]C3=NC(c4ccc(NC(=O)NC5C=CON5)cc4)CN3C2CC1CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized molecules derived from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' that achieve a z-score of 1.323009 for '10113420000000', concentrating on atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.323009, \"cell_line\": \"10113420000000\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Designate 1240131 as the right cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1240131\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)c1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)c1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized versions of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' aiming for a z-score of 1.946578 for '11284190000000', with atom-level modifications specified by [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.946578, \"cell_line\": \"11284190000000\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Set 1298141 as the appropriate cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1298141\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC[O+]1C2CCCC(C(C)(C)C)C2S(O)(O)C12CCC(NC(O)c1cc(Cc3ccccc3)c(O)c(O)c1O)CC2', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC[O+]1C2CCCC(C(C)(C)C)C2S(O)(O)C12CCC(NC(O)c1cc(Cc3ccccc3)c(O)c(O)c1O)CC2', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4[OH+]O)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' targeting a z-score of 0.093478 in '4905860000000', with changes limited to [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.093478, \"cell_line\": \"4905860000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Assign 906854 as the right one. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906854\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate variants of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with optimizations targeted for a z-score of 1.507601 in '24138970000000', based on the [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atom selections. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.507601, \"cell_line\": \"24138970000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "1298347 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1298347\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=COC(CCCCCCNC(OC(CC)CC)C1CC(c2ccccc2)ON1)NO', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)=CC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'CCc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2OC)cc1', 'CCOC(NCCCCCCC(=O)NO)C1CC(c2ccccc2)ON1', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2C[O+](COC2C2CC(c3ccccc3)ON2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=COC(CCCCCCNC(OC(CC)CC)C1CC(c2ccccc2)ON1)NO', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)=CC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'CCc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2OC)cc1', 'CCOC(NCCCCCCC(=O)NO)C1CC(c2ccccc2)ON1', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO', 'ONC1CCCCCC(O)N2C[O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create fine-tuned molecules based on 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC' for a z-score of 0.627742 in '0691810000000', modifying only atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.627742, \"cell_line\": \"0691810000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C(=O)N)OCC4=CC=CC=C4C(F)(F)F)OC\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 910861 to complete the task correctly. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"910861\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COC(C)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1C)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'C=Cc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'CCc1cc2ncn(C3=S=C(C(N)=O)C(OCc4ccccc4C(F)(F)F)C3)c2cc1OC', 'COc1cc2c3cc1C=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)F)C1', 'COc1cc2c(cc1CF)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COOc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1[OH+]O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COCc1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c3cc1[OH+]NC(=O)C1=S=C(CC1OCc1ccccc1C(F)(F)F)n2cn3', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1CO)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COC(O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(ncn2C2=S=C(C(N)=O)C(OCc3ccccc3C(F)(F)F)C2)c(F)c1C', 'COc1cc2c3cc1C(F)=C(c1ccccc1C(F)(F)F)OC1CC(=S=C1C(N)=O)n2cn3', 'COc1cc2c(cc1CC(F)(F)F)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2)C1', 'CO[O+](O)c1ccc2c(c1)ncn2C1=S=C(C(N)=O)C(OCc2ccccc2C(F)(F)F)C1', 'COc1cc2c(cc1O)ncn2C1=S=C(C(N)=O)C(OC(O)c2ccccc2C(F)(F)F)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized forms of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a z-score of 1.008001 in '7970290000000', making adjustments to atoms listed in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.008001, \"cell_line\": \"7970290000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Choose 907279 as the designated cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907279\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C[O+][O+]C1(S(O)(O)C2CCCCC2C(C)(C)C)CCC(NC(O)c2cc(CCCCCCO)c(O)c(O)c2O)CC1', 'C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1CCC=C=C=C=CCc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(O)(O)C12CCC(NC(O)c3cc(CCCCCC[OH+])c(O)c(O)c3O)C(C1)[O+]2[OH+]O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C[O+][O+]C1(S(O)(O)C2CCCCC2C(C)(C)C)CCC(NC(O)c2cc(CCCCCCO)c(O)c(O)c2O)CC1', 'C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)[OH+]O)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1CCC=C=C=C=CCc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(O)(O)C12CCC(NC(O)c3cc(CCCCCC[OH+])c(O)c(O)c3O)C(C1)[O+]2[OH+]O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop specialized molecules from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with a z-score goal of 0.875191 in '8372440000000', adjusting atoms as specified in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.875191, \"cell_line\": \"8372440000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Ensure 724834 is the selected cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"724834\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design adjusted molecules based on 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score of 1.199718 for '0741990000000', modifying only atoms defined by [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.199718, \"cell_line\": \"0741990000000\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 949170 to complete the task correctly. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"949170\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized chemical structures based on 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to reach a z-score of 0.163567 for '53036610000000', concentrating on [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.163567, \"cell_line\": \"53036610000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Mark 1503366 as the right cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1503366\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved molecular adaptations from 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' aiming for a z-score of -0.493948 for '0294970000000', concentrating changes in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.493948, \"cell_line\": \"0294970000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 909742 to complete the task correctly. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909742\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create optimized compounds derived from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of 1.083096 in '5373590000000', focusing on the atoms listed in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.083096, \"cell_line\": \"5373590000000\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use the correct cell: 753539. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"753539\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'C[O+]1Oc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create modified versions of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' with a z-score target of 0.298186 in '2696080000000', adjusting atoms within the [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.298186, \"cell_line\": \"2696080000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "906826 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906826\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecules based on 'CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2' to reach a z-score of 2.597124 for '9577090000000', focusing on the atoms specified in [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.597124, \"cell_line\": \"9577090000000\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "909757 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909757\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[O+]c1ccc(C2N=C(c3ccncc3)C(c3ccc4c(c3)CCC4N(O)[OH+]CCO)N2)cc1', 'C=Cc1ccc(C2N=C(c3ccnc(C(C)O)c3)C(c3ccc4c(c3)CCC4N=O)N2)cc1', 'C[O+](O)c1ccc(C2N=C(c3ccnc(CCO)c3)C(c3ccc4c(c3)CCC4N=O)N2)cc1', 'CC12CCC(C3C(c4ccnc(O)c4)=NC4c5ccc(cc5)CN43)CC1CCC2N=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=[O+]c1ccc(C2N=C(c3ccncc3)C(c3ccc4c(c3)CCC4N(O)[OH+]CCO)N2)cc1', 'C=Cc1ccc(C2N=C(c3ccnc(C(C)O)c3)C(c3ccc4c(c3)CCC4N=O)N2)cc1', 'C[O+](O)c1ccc(C2N=C(c3ccnc(CCO)c3)C(c3ccc4c(c3)CCC4N=O)N2)cc1', 'CC12CCC(C3C(c4ccnc(O)c4)=NC4c5ccc(cc5)CN43)CC1CCC2N=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct tailored molecules based on 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with an objective z-score of 0.793672 in '2008190000000', limiting changes to [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.793672, \"cell_line\": \"2008190000000\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "908120 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"908120\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved versions of 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' to achieve a z-score of 0.851758 for '0961950000000', limiting modifications to the atoms specified in [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.851758, \"cell_line\": \"0961950000000\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "910695 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"910695\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1=CC(=C2ONC(C)(CC=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON(F)CC2([OH2+])c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CCC1=C(N2CCOCC2)c2ccc(cc2)C23C(=C4C=C(C(C)C)C(O)=CC4=O)ONC2C13', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)C1=CC(=C2ONC(C)(CC=[O+]O)C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CC(C)C1=CC(=C2ONC3(C)CC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'C=CC1=[n+]2oc(=C3C=C(C(C)C)C(O)=CC3=O)c(-c3ccc(CN4CCOCC4)cc3)c2C1C', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C(CO)N[O+]2CCO)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3C4C=C(N5CCOCC5)c5ccc(cc5)C23C4C=O)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3(C)CCC4COCCN4Cc4ccc(cc4)C23)C(=O)C=C1O', 'COCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1C', 'CCC[OH+]C1(c2ccc(CN3CCOCC3)cc2)CNOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CCCC1COCCN1Cc1ccc(C2=C3C=[O+]C4=CC(=O)C(=C2ON3)C=C4C(C)C)cc1', 'CC(C)C1=CC(=C2ONC(OO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC1C=CC2=C(c3ccc(CN4CCOCC4)cc3)C(=C3C=C(C(C)C)C(O)=CC3=O)[O+]1N2', 'CCC1CC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC12', 'CCN1Cc2ccc(cc2)C2=C(NOC2=C2C=C(C(C)C)C(O)=CC2=O)C(O)C1COOCO', 'CC(C)C1=CC(C(O)CO)(C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCN1CCOOOC2(C(C)C)CC(=C3ON4OCC4=C3c3ccc(cc3)C1)C(=O)C=C2O', 'CC(C)C1=CC(=C2ONC(O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCC12CONC1=C(C=C1C=C(C(C)C)C(O)=CC1=O)c1ccc(cc1)CN1CCOCC12', 'COCCC1COCCN1Cc1ccc(C2(C)CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(O)=COC3)C(=O)C=C1O', 'CCN(CCOCO)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3C=C=O)C(=O)C=C1O', 'C=COCCN(CC)Cc1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC[O+]1NC=C(c2ccc(CN3CCOCC3)cc2)C1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(O)=[O+]C1=CC(=O)C(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CCN1CCOCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C3)C(=O)C=C1O', 'CCCC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)ON1', 'CCN(CCOC)Cc1ccc(C2C3=C4C=C(C(O)=CC4=O)C(C)C=CC2(C)NO3)cc1', 'C=CC12C3COCCN3Cc3ccc(cc3)C34C(=C5C=C(C(C)C)C(O)=CC5=O)ONC31C42', 'CC(C)C1=CC(=C2ONC=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC(=C2C(c3ccc(CN4CCOCC4)cc3)=C3C[O+]2N3)C(=O)C=C1O', 'C=CC1COCCN1C(=C)c1ccc(C2=C(C)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CCC(C)C1=CC(=C2ON(F)CC2([OH2+])c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC1=C(c2ccc(CN3CCOCC3)cc2)C(=C2C=C(C(C)C)C(O)=CC2=O)[O+](C)N1', 'CC(C)C1=CC(=C2ONC(C)C2(c2ccc(CN3CCOCC3)cc2)C(C)C)C(=O)C=C1O', 'CC(C)C1=CC(=C2ONC3CCCOC23c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4(C)CCC(OC1=CC2=O)C34c1ccc(CN2CCOCC2)cc1', 'CC(C)C1=CC(=C2ONC=C2c2ccc(C(CCO)N3CCOCC3)cc2)C(=O)C=C1O', 'C=CC1(O)C=C(O)C(C(C)C)=CC1=C1ON2C(=C1c1ccc(CN3CCOCC3)cc1)C2C', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)C2N4CCOCC4C32OOCO)C(=O)C=C1O', 'CCC1=CC(C)C2=CC(=C3ONC(C)=C3c3ccc(cc3)C(N3CCOCC3)O1)C(=O)C=C2O', 'CCCOC1=CC(=O)C(=C2ONC(C)=C2c2ccc(CN3CCOCC3)cc2)C=C1C(C)C', 'CC(C)C1=CC(=C2ONC(C=O)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1C2OCCC3(CC4=C(C(=C5C=C(C(C)C)C(O)=CC5=O)ON4)c4ccc2cc4)OCC13', 'CCC1=C(N2CCOCC2)c2ccc(cc2)C23C(=C4C=C(C(C)C)C(O)=CC4=O)ONC2C13', 'CC1C=CC2=C(c3ccc(CN4CCOCC4C(C)CO)cc3)C(=C3C=C1C(O)=CC3=O)ON2', 'CC=CC(C)C1=CC(=C2ONC(CC)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCN1CCOC(C)[O+]2NC=C(C2=C2C=C(C(C)C)C(O)=CC2=O)c2ccc(cc2)C1', 'C=Cc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC(C)C1=CC(=C2ONC(C(C)CO)=C2c2ccc(CN3CCOCC3)cc2)C(=O)C=C1O', 'CCc1cc(CN2CCOCC2C)ccc1C1=C(C)NOC1=C1C=C(C(C)C)C(O)=CC1=O', 'CC=[n+]1oc(=C2C=C(C(C)C)C(O)=CC2=O)c2c1C(C)C1COCCN1Cc1ccc-2cc1', 'CCC1(C)C2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)CN2CCOCC21', 'CCC(OC)OCCN(CC)Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2ONC3=C2c2ccc(cc2)CN2CCOCC2C(C)C3)C(=O)C=C1O', 'CC(C)C1=CC2=C3ONC4CCC(C2(O)C=C1O)C1(O)C(N2CCOCC2)c2ccc(cc2)C341', 'CC(C)C1=CC(=C2ONCC2(O)c2ccc(CN3CCOCC3C)cc2)C(=O)C=C1O', 'CC(C)=C(c1ccc(C2=C(CO)NOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1)N1CCOCC1', 'C=C1CCC2=C(C(=C3C=C(C(C)C)C(O)=CC3=O)ON2)c2ccc(cc2)C1N1CCOCC1C', 'CC(C)C1=CC(=C2ONC3C24c2ccc(cc2)CN2CCOCC2C34C=CCO)C(=O)C=C1O', 'C=CCC1COCCN1Cc1ccc(C2=CNOC2=C2C=C(C(C)C)C(O)=CC2=O)cc1', 'CC(C)C1=CC(=C2C3=C(CC(C)C4COCCN4Cc4ccc3cc4)N[O+]2C)C(=O)C=C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate tailored compounds from 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' targeting a z-score of 0.980394 for '6790990000000', concentrating changes on [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.980394, \"cell_line\": \"6790990000000\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Designate 909769 as the right cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909769\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CF)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(N2Cc3ccccc32)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2ccccc2C(C)N2CCC(N3CCN(C)CC3)C(CO)C2)n1', 'COCC(CO)=C1CN(C2CCN(c3ccc(NC4NC5Nc6ccccc6CN4C=C5Cl)cc3)CC2)CCN1C', 'COCCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)C(C(C)CO)C3)cc2)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1N(F)c1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1cc2cc(c1Nc1ncc(Cl)c(Nc3ccccc3C)n1)[O+](CO)CCN1CCN(CC1)C1CCN2CC1', 'C=CCc1ccccc1Nc1nc(Nc2ccc(N3CCC(N4CCN(C)CC4)CC3)cc2OC)ncc1Cl', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)cc(C=CF)c1Nc1ncc(Cl)c(Nc2ccccc2[OH+]C)n1', 'COc1cc(N2CCC(N3CCN(C)C(=CCCO)C3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1', 'COc1ccccc1Nc1ncc(Cl)c(Nc2cccc3c2C(=C2CN(C)CCN2C2CCN(CO)CC2)CCO3)n1', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2C)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce optimized analogs from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' to reach a z-score of -0.087010 in '92965810000000', with modifications limited to [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.087010, \"cell_line\": \"92965810000000\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "1659928 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1659928\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design fine-tuned molecules from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of 1.594676 in '5109190000000', using [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the mask for changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.594676, \"cell_line\": \"5109190000000\", \"mask\": [1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Choose 910915 as the relevant cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"910915\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CO)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CCCO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1OC(C)CC', 'CC1C(=C=CCc2cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4C(C)(C)C)cc3)c(O)c(O)c2O)CCC1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)O)cccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design specialized molecules from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' for a z-score of 2.529230 in '6997500000000', applying adjustments only to atoms in [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.529230, \"cell_line\": \"6997500000000\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Opt for 905967 as your cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"905967\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)c(C(O)CO)c1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C1CCCC1=O', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'CCCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OC(C)CN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)c(C(O)CO)c1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C1CCCC1=O', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'CCCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OC(C)CN4CCOCC4)CCC23)cc1', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop optimized molecules derived from 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with a z-score of 0.582989 for '9760040000000', applying changes to atoms in [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.582989, \"cell_line\": \"9760040000000\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "907046 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907046\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design fine-tuned molecules from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of 1.162264 in '6790950000000', using [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the mask for changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.162264, \"cell_line\": \"6790950000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Confirm 906795 as the correct cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906795\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)S)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCC1C(OCCO)=C(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)C(O)C(O)C1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)S)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC1=CCc2cc(c(O)c(O)c2O[OH+]C)C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C1C', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)CC(C)OC)cc2)c(O)c(O)c1O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' to reach a z-score of 2.076762 in '0949900000000', focusing on changes within [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.076762, \"cell_line\": \"0949900000000\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Go with 909904 for the best result. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909904\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CN1C(C(=S)c2ccnc(N)n2)=C2C=C(C=C(NS(=O)(=O)c3c(F)cccc3F)C2F)[O+]1CCO', 'CC(CCO)[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'Nc1nccc(C(=S)C([NH2+]CCCO)c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'CC(CCO)NC(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'Nc1nccc(C(=S)C(NCCCCO)c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CN1C(C(=S)c2ccnc(N)n2)=C2C=C(C=C(NS(=O)(=O)c3c(F)cccc3F)C2F)[O+]1CCO', 'CC(CCO)[NH2+]C(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'Nc1nccc(C(=S)C([NH2+]CCCO)c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'CC(CCO)NC(C(=S)c1ccnc(N)n1)c1cccc(NS(=O)(=O)c2c(F)cccc2F)c1F', 'Nc1nccc(C(=S)C(NCCCCO)c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate specialized derivatives of 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' with a z-score of 1.767419 in '62197700000000', modifying atoms as defined by [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.767419, \"cell_line\": \"62197700000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "1290767 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1290767\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['OCC(O)=C1CNCCN1CC1=[SH]C2C(C1)N=C(c1cccc3c1CNN3)NC2N1CCOCC1', 'OCCC1(N2CCOCC2)NC(c2cccc3c2CNN3)=NC2CC(CN3CCNCC3)=[SH]C21', 'OC[O+](F)C1CNCCN1CC1=[SH]C2C(C1)N=C(c1cccc3c1CNN3)NC2N1CCOCC1', 'OCCC=C1CN(C2NC(c3cccc4c3CNN4)=NC3CC(CN4CCNCC4)=[SH]C32)CCO1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCC(O)=C1CNCCN1CC1=[SH]C2C(C1)N=C(c1cccc3c1CNN3)NC2N1CCOCC1', 'OCCC1(N2CCOCC2)NC(c2cccc3c2CNN3)=NC2CC(CN3CCNCC3)=[SH]C21', 'OC[O+](F)C1CNCCN1CC1=[SH]C2C(C1)N=C(c1cccc3c1CNN3)NC2N1CCOCC1', 'OCCC=C1CN(C2NC(c3cccc4c3CNN4)=NC3CC(CN4CCNCC4)=[SH]C32)CCO1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce refined molecules based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' that meet a z-score of 1.697705 for '25331010000000', concentrating on modifications in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.697705, \"cell_line\": \"25331010000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Select 1331025 to proceed. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1331025\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CCC(C)c1cccc(OC)c1', 'C=CC(OC)c1cccc(C)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized derivatives of 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' aiming for a z-score of 1.731662 in '49010750000000', with a focus on atoms in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.731662, \"cell_line\": \"49010750000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Choose 1509074 as the relevant cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1509074\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'C=Cc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'C=Cc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'C=Cc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized adaptations of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a target z-score of 0.737609 for '59297100000000', focusing on modifications in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.737609, \"cell_line\": \"59297100000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "1299075 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1299075\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design refined versions of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' to reach a z-score of 2.028290 in '14038580000000', limiting modifications to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.028290, \"cell_line\": \"14038580000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Make sure to use 1480358. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1480358\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Fc1ccc(Nc2ncnc3cc(CCl)c(OCCCN4CCOCC4)cc23)cc1', 'OOc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'O[OH+]c1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'OCc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate specialized derivatives of 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' with a z-score of 3.798456 in '7909640000000', modifying atoms as defined by [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 3.798456, \"cell_line\": \"7909640000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Select 909746 for this purpose. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909746\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'Nc1nccc(C2=S=C([OH+]CCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'Nc1nccc(C2=S=C(CCCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'C=CCC1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'NC1NCCC(C2SCNC2C23CCCC(NS(O)(O)C4C(F)CC(CC4F)C2CO)C3F)N1', 'CC(C)C1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'Nc1nccc(C2=S=C([O+]=CCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'Nc1nccc(C2=S=C([OH+]CCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'Nc1nccc(C2=S=C(CCCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1', 'C=CCC1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'NC1NCCC(C2SCNC2C23CCCC(NS(O)(O)C4C(F)CC(CC4F)C2CO)C3F)N1', 'CC(C)C1=S=C(c2ccnc(N)n2)C(c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)N1', 'Nc1nccc(C2=S=C([O+]=CCO)NC2c2cccc(NS(=O)(=O)c3c(F)cccc3F)c2F)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate some optimized molecules based on  the molecule 'CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C', requiring a z-score of 1.907377 for  the cell line '62097910000000', and the mask of atoms to be optimized is [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.907377, \"cell_line\": \"62097910000000\", \"mask\": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1], \"gt\": \"CC(=CC[C@H](C1=CC(=O)C2=C(C=CC(=C2C1=O)O)O)O)C\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Set 1290769 as the appropriate cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1290769\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1(O)C=CC(O)(C(C)O)c2c(O)ccc(O)c21', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CCOC1C=C(OC)C(=O)c2c(O)ccc(O)c21', 'C=CC1C2=C(C(=O)c3c(O)ccc(O)c3C2=O)C1C', 'COc1ccc(O)c2c1C(=O)C=C(C)C2=O', 'C=CC(=[O+]CC)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1(O)C=CC(O)(C(C)O)c2c(O)ccc(O)c21', 'C=CC(C)C1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=CC(=O)c2c(O)ccc(O)c2C1=O', 'C=CC1=C(C)C(O)(CC)c2c(O)ccc(O)c2C1=O', 'COc1ccc(O)c2c1C(=O)C(C)=CC2=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design specialized molecules from 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' for a z-score of 0.531892 in '0228570000000', applying adjustments only to atoms in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.531892, \"cell_line\": \"0228570000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "722058 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"722058\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate optimized derivatives of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' with a target z-score of 2.171865 in '9059270000000', modifying only atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.171865, \"cell_line\": \"9059270000000\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 907295 to get the correct result. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907295\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(C=CF)c2ccccc2)cc1', 'CC=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=CCC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(COO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1', 'C=Cc1cc(NC(=O)Cc2ccccc2)ccc1C(=O)NO', 'C[O+](O)C(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop improved versions of 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' to achieve a z-score of -0.216884 for '9066980000000', limiting modifications to the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.216884, \"cell_line\": \"9066980000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Confirm 906869 as the correct cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906869\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate optimized variants of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' with a target z-score of 1.034609 in '8936090000000', making adjustments to atoms within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.034609, \"cell_line\": \"8936090000000\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 906839 to proceed with the task. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"906839\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C[OH+]C1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CO[O+]1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)C1Oc2c(cc(C(=O)Nc3ccc(S(=O)(=O)c4ccccc4)cc3)c(O)c2O)Cc2ccccc21', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(O)c(O)c2OC2=Cc4cc2ccc4C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4CO)c(O)c(O)c2O)cc1', 'Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'Cc1cccc(C(C)(C)C)c1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OCc2ccccc2C3)cc1', 'COC1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=O)c(O)c(O)c2O)cc1', 'CCc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[O+]1Cc2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccc4cc3C4O)c(O)c(O)c2O)cc1', 'C[OH+]C1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3C=[O+]F)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CO[O+]1c2cccc1c2Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc3c(c(O)c2O)OC(=O)c2ccccc2C3)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC2(O)c3cc4c(c(O)c3O)OC2(C)c2ccccc2C4)cc1', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4O)c(O)c(O)c2O)cc1', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'CC(C)(CO)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C[SH](Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3)cc2)c(O)c(O)c1O)C(C)(C)C', 'CC=CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4)c(O)c(O)c2O)cc1', 'CCC12C3=C=CC1=C(Cc1cc(C(=O)Nc4ccc(S(=O)(=O)c5ccccc5C(C)(C)C)cc4)c(O)c(O)c1O)C32', 'CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3c4cccc3C4C=O)c(O)c(O)c2O)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create improved molecules derived from 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' to reach a z-score of 1.888937 in '71810240000000', focusing on changes within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.888937, \"cell_line\": \"71810240000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Opt for 1240187 as the correct cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1240187\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C=O)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop refined variants of 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' targeting a z-score of 0.499041 for '9475820000000', focusing optimization efforts on [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.499041, \"cell_line\": \"9475820000000\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Pick 724859 as the proper cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"724859\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate variants of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with optimizations targeted for a z-score of 3.507311 in '4993900000000', based on the [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atom selections. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 3.507311, \"cell_line\": \"4993900000000\", \"mask\": [1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Opt for 949093 as your cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"949093\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1=CC2=[SH]C3=NC4CN3C2(C=C1)[O+](C(C)N1CCOCC1)COCC(N=O)NC(=O)Nc1ccc4cc1', 'CCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC4C5CCC(CC5)NC(=O)NC(N=O)C=CN5CCOC(=CC(C)C4N32)C5)C1', 'CCCC(N=O)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2C4CC4C5OCC(N6CCOCC6)C4(C25)C3F)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(C)=CCN1CCOCCC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCC1', 'C=C1CCCC(N=O)NC(=O)Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC123', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC=CC1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=C1C2=CC13C(=[SH]C1=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN13)CC2OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(=C(O)CC4CC23)C5)cc1', 'COCC1NC2NC3CCC(CC3)C3CN4C(=N3)[SH]=C3CC(CCC34)OCCN3CCOC(C3)O[O+](O2)N1O', 'CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3([O+]4COC5CN4CCO5)N=C(c4ccc(NC(=O)NC(CC)N=O)cc4)CN23)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2CC(OCCN5CCOCC5)C(CC24)CC3O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CC)CC23)cc1', 'CC=Cc1cc(NC(=O)NC(CC)N=O)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'CCN1CCOC2CCC3C(=[SH]C4=NC5C6CCC(CC6)NC(=O)NC(N=O)CC(C)(OCC1)C(CO)C5N43)C2', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)=C=O)CC23)cc1', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'C=CCC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN(CC)CCO1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(C5)[O+](C)C4CC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CCN5CCOCC5)N=O)cc4)CN32)C1', 'O=NC1NC(=O)NC2CCC(CC2)C23N=C4[SH]=C5CC(OCCN6CCOCC6)CCC5N4C2C2CCC1C23', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN2CCOC1C2', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)CF)cc1', 'CCOC1CCC2C(=[SH]C3=NC(C45C=CC6NC(=O)NC(N=O)C(C)C4(C)C6=C5)CN32)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=C(C)C12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC(CC)N=O)cc3)CN12', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4C5COCC4C5(C)C)CCC23)cc1', 'CC12CC(NC(=O)NC3CCC4C(C3)CC1C1CC3C(=[SH]C5=NC4CN53)CC1OCCN1CCOCC1)N2O', 'CCN(O)C1CCc2cc(ccc2C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)NC(=O)N1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'ON1CCC1NC1NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CC23)[O+](O)O1', 'C=C1CN(CCOC2CCC3C(=[SH]C4=NC(c5ccc(NC(=O)NC(CCC)N=O)cc5)CN43)C2)CCO1', 'CCN1CCOC=CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCC1', 'CC=C(O)C1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NO)NC(O)NC1CCC2(C3CN4c5ccc(OCCN6CCOCC6)cc5[SH]C4N3)CCC2C1', 'O=C1NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN2CCOC(=C3CC(CO)N(O)C(C3)N1)C2', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCCC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5(N=O)CC5C)cc4)CN32)CC1OCCN1CCOCC1', 'CCOC1(c2ccc(NC(=O)NC3(N=O)CC3OF)cc2)CN2C(=N1)[SH]=C1CC(OCCN(CC)CC)CCC12', 'CC1CC1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C4=CC23C4(C)C)cc1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'COC1CC2C3=[SH]C4=NC(CN42)C2CCC(CC2)NC(=O)NC(N=O)CCC2CN(CCO2)CCOC1C3', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC1NC(=O)NC2CCC(CC2)C23CC(C4CC5C(=[SH]C(=N2)N5C3)CC4OCCN2CCOCC2)N1O', 'C=C(C)C1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(NO)NC(O)NC1CCC(C)(C2CN3c4ccc(OCCN5CCOCC5)cc4[SH]C3N2)CC1', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C2(C)C(O)OCCN2CC)C=C1', 'CCN1CCOC2CCC3C(=[SH]C4=NC(CN43)C34CCC(CC3)NC(=O)NC(N=O)CC(CO4)OCC1)C2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCCCN1O', 'CC=C(O)C1CC2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)NC1N=O)C3)CCO2', 'CCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(C5)[O+]5C4=CC235)cc1', 'O=NC1CCCCCCC2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2', 'O=NC12CC1CC(CO)=C1CN(CCO1)CCOC1CCC3C(=[SH]C4=NC(CN43)C3CCC(CC3)NC(=O)N2)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1', 'CCOC1=CC2=[SH]C3=NC(c4ccc5cc4C4CC(NO4)NC(=O)N5)CN3C2(C(C)ON2CCOCC2)C=C1', 'O=NC1CCC[O+]2OC3(CN4C(=N3)[SH]=C3C=C(OCCN5CCOCC5)C=CC342)c2ccc(cc2)NC(=O)N1', 'O=NC1CCC[O+]2C3CC4C(=[SH]C5=NC(CN54)C24CCC(CC4)NC(=O)N1)CC3OCCN1CCOCC1', 'C=C1CCCC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1=CC2=[SH]C3=NC4CN3C2(C=C1)[O+](C(C)N1CCOCC1)COCC(N=O)NC(=O)Nc1ccc4cc1', 'CCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC=CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC4C5CCC(CC5)NC(=O)NC(N=O)C=CN5CCOC(=CC(C)C4N32)C5)C1', 'CCCC(N=O)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2C4CC4C5OCC(N6CCOCC6)C4(C25)C3F)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(N=O)NC(=O)N(c1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)C(C)CO', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)CO)CC23)cc1', 'CCC(C)=CCN1CCOCCC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCC1', 'C=C1CCCC(N=O)NC(=O)Nc2ccc(cc2)C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC123', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C=O)cc1', 'CC=CC1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=C1C2=CC13C(=[SH]C1=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN13)CC2OCCN1CCOCC1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(=C(O)CC4CC23)C5)cc1', 'COCC1NC2NC3CCC(CC3)C3CN4C(=N3)[SH]=C3CC(CCC34)OCCN3CCOC(C3)O[O+](O2)N1O', 'CCCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3([O+]4COC5CN4CCO5)N=C(c4ccc(NC(=O)NC(CC)N=O)cc4)CN23)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOC(O)C4)C=CC23OC)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C23CN4C(=N2)[SH]=C2CC(OCCN5CCOCC5)C(CC24)CC3O)cc1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CC)CC23)cc1', 'CC=Cc1cc(NC(=O)NC(CC)N=O)ccc1C1CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'CCN1CCOC2CCC3C(=[SH]C4=NC5C6CCC(CC6)NC(=O)NC(N=O)CC(C)(OCC1)C(CO)C5N43)C2', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1', 'CC1=C(O)C(C)C(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O', 'C=CC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1)N(O)C(C)N(CC)CC1O[O+]1O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(C(C)=C=O)CC23)cc1', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C(C)C(F)N2CCOCC2)C=C1', 'C=CCC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN(CC)CCO1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(C5)[O+](C)C4CC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CCN5CCOCC5)N=O)cc4)CN32)C1', 'O=NC1NC(=O)NC2CCC(CC2)C23N=C4[SH]=C5CC(OCCN6CCOCC6)CCC5N4C2C2CCC1C23', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4CCO)CCC23)cc1', 'CC1CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN2CCOC1C2', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)CF)cc1', 'CCOC1CCC2C(=[SH]C3=NC(C45C=CC6NC(=O)NC(N=O)C(C)C4(C)C6=C5)CN32)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)C)cc1', 'C=C(C)C12C=CC(OCCN3CCOCC3)=CC1=[SH]C1=NC(c3ccc(NC(=O)NC(CC)N=O)cc3)CN12', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4C5COCC4C5(C)C)CCC23)cc1', 'CC12CC(NC(=O)NC3CCC4C(C3)CC1C1CC3C(=[SH]C5=NC4CN53)CC1OCCN1CCOCC1)N2O', 'CCN(O)C1CCc2cc(ccc2C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)NC(=O)N1', 'CCCC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'ON1CCC1NC1NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CC23)[O+](O)O1', 'C=C1CN(CCOC2CCC3C(=[SH]C4=NC(c5ccc(NC(=O)NC(CCC)N=O)cc5)CN43)C2)CCO1', 'CCN1CCOC=CC(N=O)NC(=O)NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCC1', 'CC=C(O)C1(c2ccc(NC(=O)NC(CC)N=O)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'C=CC1C(C)C1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NO)NC(O)NC1CCC2(C3CN4c5ccc(OCCN6CCOCC6)cc5[SH]C4N3)CCC2C1', 'O=C1NC2CCC(CC2)C2CN3C(=N2)[SH]=C2CC(CCC23)OCCN2CCOC(=C3CC(CO)N(O)C(C3)N1)C2', 'O=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCCC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC5(N=O)CC5C)cc4)CN32)CC1OCCN1CCOCC1', 'CCOC1(c2ccc(NC(=O)NC3(N=O)CC3OF)cc2)CN2C(=N1)[SH]=C1CC(OCCN(CC)CC)CCC12', 'CC1CC1(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CCC(NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOC(O)C4)CCC23)cc1)N(C)O', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C4=CC23C4(C)C)cc1', 'C=CC1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'COC1CC2C3=[SH]C4=NC(CN42)C2CCC(CC2)NC(=O)NC(N=O)CCC2CN(CCO2)CCOC1C3', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2C=C(OCCN4CCOCC4)C=CC23C(C)O)cc1', 'CCC1NC(=O)NC2CCC(CC2)C23CC(C4CC5C(=[SH]C(=N2)N5C3)CC4OCCN2CCOCC2)N1O', 'C=C(C)C1CC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN32)CC1OCCN1CCOCC1', 'CCC(NO)NC(O)NC1CCC(C)(C2CN3c4ccc(OCCN5CCOCC5)cc4[SH]C3N2)CC1', 'CCOC1=CC2=[SH]C3=NC(c4ccc(NC(=O)NC(CC)N=O)cc4)CN3C2(C2(C)C(O)OCCN2CC)C=C1', 'CCN1CCOC2CCC3C(=[SH]C4=NC(CN43)C34CCC(CC3)NC(=O)NC(N=O)CC(CO4)OCC1)C2', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCCCN1O', 'CC=C(O)C1CC2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)NC1N=O)C3)CCO2', 'CCCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC4OCCN5CCOC(C5)[O+]5C4=CC235)cc1', 'O=NC1CCCCCCC2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2', 'O=NC12CC1CC(CO)=C1CN(CCO1)CCOC1CCC3C(=[SH]C4=NC(CN43)C3CCC(CC3)NC(=O)N2)C1', 'CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)C(CCO)CC23)cc1', 'CCOC1=CC2=[SH]C3=NC(c4ccc5cc4C4CC(NO4)NC(=O)N5)CN3C2(C(C)ON2CCOCC2)C=C1', 'O=NC1CCC[O+]2OC3(CN4C(=N3)[SH]=C3C=C(OCCN5CCOCC5)C=CC342)c2ccc(cc2)NC(=O)N1', 'O=NC1CCC[O+]2C3CC4C(=[SH]C5=NC(CN54)C24CCC(CC4)NC(=O)N1)CC3OCCN1CCOCC1', 'C=C1CCCC(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)N1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate new compounds based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a z-score of -0.102906 for '0299480000000', focusing adjustments within [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.102906, \"cell_line\": \"0299480000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Pick 909248 as the proper cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"909248\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'OCCOc1ccccc1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'OCCOc1ccccc1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create derivatives of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' optimized for a z-score of 1.249743 in '0707490000000', modifying atoms only within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.249743, \"cell_line\": \"0707490000000\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "907047 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907047\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(OO)c1OC', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate variants of 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' with optimizations targeted for a z-score of 1.367363 in '9892070000000', based on the [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] atom selections. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.367363, \"cell_line\": \"9892070000000\", \"mask\": [1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "907289 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907289\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(COC=CC(C)N5CCOCC5)N=O)cc4)CN32)C1', 'O=NC(CCOCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CO)NC(=O)Nc1ccc(C2(C(F)CO)CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN(CC)CC4OO4)CCC23)cc1', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(N=O)C(C)CCC(F)N5CCOCC5)cc4)CN32)C1', 'O=NC(CCCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC(CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'COCC=CCC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'C=CC(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCC)CCC23)cc1', 'CC(C)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=C(Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1)NC1CCN1O', 'CCOC1CCC2C(=[SH]C3=NC(c4ccc(NC(=O)NC(CC5=C6CN(CCO6)C5CCO)N=O)cc4)CN32)C1', 'CCC1(c2ccc(NC(=O)NC(N=O)C(C)C)cc2)CN2C(=N1)[SH]=C1CC(OCCN3CCOCC3)CCC12', 'O=NC(C=CCCCO)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'CC(CCCO)C(N=O)NC(=O)Nc1ccc(C2CN3C(=N2)[SH]=C2CC(OCCN4CCOCC4)CCC23)cc1', 'O=NC1(NC(=O)Nc2ccc(C3CN4C(=N3)[SH]=C3CC(OCCN5CCOCC5)CCC34)cc2)CC1C=CCO', 'O=NC1CCC(CCO)C2CN(CCOC3CCC4C(=[SH]C5=NC(CN54)C4CCC(CC4)NC(=O)N1)C3)CCO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design fine-tuned molecules from 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of 0.864594 in '20821400000000', using [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the mask for changes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.864594, \"cell_line\": \"20821400000000\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Opt for 1240208 as your cell. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"1240208\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Formulate optimized derivatives of 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a target z-score of -0.613854 in '8806910000000', modifying only atoms in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.613854, \"cell_line\": \"8806910000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "Use 910688 to proceed with the task. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"910688\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design optimized molecular structures derived from 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a z-score of -0.163248 for '4797200000000', modifying only the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.163248, \"cell_line\": \"4797200000000\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The cell you put in is wrong, please provide me with a valid one."
            },
            {
                "from": "human",
                "value": "907274 MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": \"(user_zscore)\", \"cell_line\": \"907274\", \"mask\": \"(user_mask)\", \"gt\": \"(user_gt)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'user_mask', 'user_gt', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'C[O+]=C(C)c1ccccc1', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drugs' inhibitory effect on the cell line (cell_name1)."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"(cell_name1)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"(cell_name1)\", \"result\":[{\"smile\":\"(drug_smile1)\", \"value\": (value1)}, {\"smile\":\"(drug_smile2)\", \"value\": (value2)}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs (drug_smile1), (drug_smile2) and organelle (cell_name1) is (value1), (value2)."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO''s inhibitory effect on the cell '1295741'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO\"], \"cell_name\": \"1295741\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1295741\", \"result\":[{\"smile\":\"CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO\", \"value\": 2.952211}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO' and organelle '1295741' is 2.952211."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory effect of 'C1CC2=CC=CC=C2[C@H]1NC3=NC=NC4=C3C=CN4[C@@H]5C[C@H]([C@H](C5)O)COS(=O)(=O)N' on the cell '1240217'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC2=CC=CC=C2[C@H]1NC3=NC=NC4=C3C=CN4[C@@H]5C[C@H]([C@H](C5)O)COS(=O)(=O)N\"], \"cell_name\": \"1240217\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240217\", \"result\":[{\"smile\":\"C1CC2=CC=CC=C2[C@H]1NC3=NC=NC4=C3C=CN4[C@@H]5C[C@H]([C@H](C5)O)COS(=O)(=O)N\", \"value\": 1.004458}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC2=CC=CC=C2[C@H]1NC3=NC=NC4=C3C=CN4[C@@H]5C[C@H]([C@H](C5)O)COS(=O)(=O)N' and organelle '1240217' is 1.004458."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' inhibits the cell '909777'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\"], \"cell_name\": \"909777\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909777\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": -1.371914}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '909777' is -1.371914."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the level of inhibition 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' has on the cell '1503369'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"1503369\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503369\", \"result\":[{\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 0.960299}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '1503369' is 0.960299."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory impact of 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' on the cell '908121'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\"], \"cell_name\": \"908121\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908121\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -0.438620}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '908121' is -0.438620."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition effect of 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' on the cell '907065'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"], \"cell_name\": \"907065\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907065\", \"result\":[{\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -0.506468}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' and organelle '907065' is -0.506468."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl' inhibits the cell '909720'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl\"], \"cell_name\": \"909720\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909720\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl\", \"value\": 1.778166}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl' and organelle '909720' is 1.778166."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' against the cell '1524418'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\"], \"cell_name\": \"1524418\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1524418\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 2.681465}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '1524418' is 2.681465."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition potential of 'CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F' on the cell '1290455'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F\"], \"cell_name\": \"1290455\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290455\", \"result\":[{\"smile\":\"CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F\", \"value\": 3.729218}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F' and organelle '1290455' is 3.729218."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the degree to which 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' inhibits the cell '1503362'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\"], \"cell_name\": \"1503362\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503362\", \"result\":[{\"smile\":\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\", \"value\": 3.017288}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' and organelle '1503362' is 3.017288."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how effectively 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' inhibits the cell '1330947'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\"], \"cell_name\": \"1330947\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330947\", \"result\":[{\"smile\":\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\", \"value\": -2.595218}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' and organelle '1330947' is -2.595218."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' on the cell '908129'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F\"], \"cell_name\": \"908129\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908129\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F\", \"value\": 1.959379}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' and organelle '908129' is 1.959379."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O' exerts on the cell '905951'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O\"], \"cell_name\": \"905951\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905951\", \"result\":[{\"smile\":\"C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O\", \"value\": 1.134145}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O' and organelle '905951' is 1.134145."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the degree of inhibitory effect for 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' on the cell '909704'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\"], \"cell_name\": \"909704\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909704\", \"result\":[{\"smile\":\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": -1.172773}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' and organelle '909704' is -1.172773."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' impacts the growth of the cell '924238'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\"], \"cell_name\": \"924238\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924238\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\", \"value\": -1.258044}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' and organelle '924238' is -1.258044."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the effect of 'C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C' in inhibiting the cell '917486'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C\"], \"cell_name\": \"917486\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"917486\", \"result\":[{\"smile\":\"C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C\", \"value\": -6.871120}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C' and organelle '917486' is -6.871120."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C''s inhibition rate on the cell '909779'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C\"], \"cell_name\": \"909779\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909779\", \"result\":[{\"smile\":\"CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C\", \"value\": -0.307516}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C' and organelle '909779' is -0.307516."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how strongly 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' inhibits the cell '924104'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"], \"cell_name\": \"924104\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924104\", \"result\":[{\"smile\":\"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\", \"value\": -0.707252}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' and organelle '924104' is -0.707252."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppressive impact of 'C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC' on the cell '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC\"], \"cell_name\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684059\", \"result\":[{\"smile\":\"C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC\", \"value\": -2.885429}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC' and organelle '684059' is -2.885429."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how effectively 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' inhibits the cell '1659928'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\"], \"cell_name\": \"1659928\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1659928\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -1.225742}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '1659928' is -1.225742."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition strength of 'CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4' against the cell '905952'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4\"], \"cell_name\": \"905952\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905952\", \"result\":[{\"smile\":\"CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4\", \"value\": 3.201617}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4' and organelle '905952' is 3.201617."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N' inhibition on the cell '908123'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\"], \"cell_name\": \"908123\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908123\", \"result\":[{\"smile\":\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\", \"value\": -0.251006}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N' and organelle '908123' is -0.251006."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'CC[C@@H](C1=NC2=C(C(=CC=C2)F)C(=O)N1C3=CC=CC=C3)NC4=NC=NC5=C4NC=N5' on the cell '1298349'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC[C@@H](C1=NC2=C(C(=CC=C2)F)C(=O)N1C3=CC=CC=C3)NC4=NC=NC5=C4NC=N5\"], \"cell_name\": \"1298349\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298349\", \"result\":[{\"smile\":\"CC[C@@H](C1=NC2=C(C(=CC=C2)F)C(=O)N1C3=CC=CC=C3)NC4=NC=NC5=C4NC=N5\", \"value\": 5.958995}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC[C@@H](C1=NC2=C(C(=CC=C2)F)C(=O)N1C3=CC=CC=C3)NC4=NC=NC5=C4NC=N5' and organelle '1298349' is 5.958995."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the effect of 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' in inhibiting the cell '908476'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\"], \"cell_name\": \"908476\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908476\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 1.753088}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '908476' is 1.753088."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the strength of inhibition by 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' on the cell '688011'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\"], \"cell_name\": \"688011\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688011\", \"result\":[{\"smile\":\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\", \"value\": 2.002827}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' and organelle '688011' is 2.002827."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' can inhibit the cell '724872'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\"], \"cell_name\": \"724872\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724872\", \"result\":[{\"smile\":\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\", \"value\": 3.160957}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' and organelle '724872' is 3.160957."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C''s inhibitory action on the cell '907272'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\"], \"cell_name\": \"907272\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907272\", \"result\":[{\"smile\":\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\", \"value\": 3.237338}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C' and organelle '907272' is 3.237338."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C' inhibition potential on the cell '909757'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\"], \"cell_name\": \"909757\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909757\", \"result\":[{\"smile\":\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\", \"value\": 5.872932}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C' and organelle '909757' is 5.872932."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the degree of inhibition for 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' against the cell '924240'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\"], \"cell_name\": \"924240\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924240\", \"result\":[{\"smile\":\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\", \"value\": 4.818514}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' and organelle '924240' is 4.818514."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppression rate of 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' on the cell '1297439'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\"], \"cell_name\": \"1297439\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1297439\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\", \"value\": 4.784758}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' and organelle '1297439' is 4.784758."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5' inhibits the cell '753531'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\"], \"cell_name\": \"753531\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753531\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\", \"value\": 1.681763}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5' and organelle '753531' is 1.681763."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory impact 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' has on the cell '906793'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"], \"cell_name\": \"906793\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906793\", \"result\":[{\"smile\":\"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\", \"value\": -3.981545}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' and organelle '906793' is -3.981545."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition percentage for 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' on the cell '905948'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"], \"cell_name\": \"905948\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905948\", \"result\":[{\"smile\":\"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\", \"value\": 2.137170}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' and organelle '905948' is 2.137170."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibiting action of 'C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C' on the cell '910706'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C\"], \"cell_name\": \"910706\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910706\", \"result\":[{\"smile\":\"C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C\", \"value\": -0.595471}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C' and organelle '910706' is -0.595471."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' in suppressing the cell '909698'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\"], \"cell_name\": \"909698\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909698\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 1.171835}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '909698' is 1.171835."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the potency of 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' as an inhibitor for the cell '924187'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\"], \"cell_name\": \"924187\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924187\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\", \"value\": 3.882639}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' and organelle '924187' is 3.882639."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition capacity of 'CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F' on the cell '910554'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F\"], \"cell_name\": \"910554\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910554\", \"result\":[{\"smile\":\"CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F\", \"value\": 4.775144}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F' and organelle '910554' is 4.775144."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' impacts the proliferation of the cell '753612'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\"], \"cell_name\": \"753612\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753612\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\", \"value\": 3.694270}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' and organelle '753612' is 3.694270."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F' on the cell '910209'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F\"], \"cell_name\": \"910209\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910209\", \"result\":[{\"smile\":\"C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F\", \"value\": 3.355472}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F' and organelle '910209' is 3.355472."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' inhibits the cell '909747' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"909747\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909747\", \"result\":[{\"smile\":\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\", \"value\": -0.535861}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '909747' is -0.535861."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' in stopping the cell '908473'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\"], \"cell_name\": \"908473\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908473\", \"result\":[{\"smile\":\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\", \"value\": 0.376020}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' and organelle '908473' is 0.376020."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibitory action of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' against the cell '910698'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"], \"cell_name\": \"910698\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910698\", \"result\":[{\"smile\":\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\", \"value\": 3.461773}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' and organelle '910698' is 3.461773."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how strongly 'CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5' acts on the cell '1298218'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5\"], \"cell_name\": \"1298218\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298218\", \"result\":[{\"smile\":\"CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5\", \"value\": 0.143124}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5' and organelle '1298218' is 0.143124."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the degree of suppression 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' has on the cell '1240208'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\"], \"cell_name\": \"1240208\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240208\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\", \"value\": 5.146279}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' and organelle '1240208' is 5.146279."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O''s effectiveness in inhibiting the cell '910920'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"910920\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910920\", \"result\":[{\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 4.295141}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '910920' is 4.295141."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition effect of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' in the cell '909776'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"909776\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909776\", \"result\":[{\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 1.942613}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '909776' is 1.942613."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how 'CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]' will inhibit the cell '1240215'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]\"], \"cell_name\": \"1240215\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240215\", \"result\":[{\"smile\":\"CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]\", \"value\": -5.931063}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]' and organelle '1240215' is -5.931063."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O''s inhibition on the cell '905978'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\"], \"cell_name\": \"905978\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905978\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O\", \"value\": 3.643361}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC(=C2SSC3=C(C=CC4=CC=CC=C43)O)O' and organelle '905978' is 3.643361."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression level of 'CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C' on the cell '949088'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C\"], \"cell_name\": \"949088\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949088\", \"result\":[{\"smile\":\"CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C\", \"value\": -0.275396}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C' and organelle '949088' is -0.275396."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C''s capacity to inhibit the cell '1298356'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\"], \"cell_name\": \"1298356\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298356\", \"result\":[{\"smile\":\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\", \"value\": 4.320078}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' and organelle '1298356' is 4.320078."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' on the cell '1298169'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\"], \"cell_name\": \"1298169\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298169\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\", \"value\": 5.092819}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' and organelle '1298169' is 5.092819."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory action of 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' against the cell '906835'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\"], \"cell_name\": \"906835\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906835\", \"result\":[{\"smile\":\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\", \"value\": -0.916896}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' and organelle '906835' is -0.916896."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4''s suppression level on the cell '753579'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"], \"cell_name\": \"753579\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753579\", \"result\":[{\"smile\":\"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\", \"value\": 3.467652}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' and organelle '753579' is 3.467652."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'C1=C(C(=O)NC(=O)N1)F' can suppress the cell '906999'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=C(C(=O)NC(=O)N1)F\"], \"cell_name\": \"906999\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906999\", \"result\":[{\"smile\":\"C1=C(C(=O)NC(=O)N1)F\", \"value\": 5.821216}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=C(C(=O)NC(=O)N1)F' and organelle '906999' is 5.821216."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' on the cell '924247'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"924247\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924247\", \"result\":[{\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 0.110623}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '924247' is 0.110623."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N''s inhibition effect on the cell '907065'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\"], \"cell_name\": \"907065\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907065\", \"result\":[{\"smile\":\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\", \"value\": 3.747950}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' and organelle '907065' is 3.747950."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition impact of 'C[C@H](C1=CC=C(C=C1)C(=O)NC2=C3C=CNC3=NC=C2)N.Cl' on the cell '910781'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H](C1=CC=C(C=C1)C(=O)NC2=C3C=CNC3=NC=C2)N.Cl\"], \"cell_name\": \"910781\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910781\", \"result\":[{\"smile\":\"C[C@H](C1=CC=C(C=C1)C(=O)NC2=C3C=CNC3=NC=C2)N.Cl\", \"value\": 4.910967}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H](C1=CC=C(C=C1)C(=O)NC2=C3C=CNC3=NC=C2)N.Cl' and organelle '910781' is 4.910967."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N''s potential in stopping the cell '1298226'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\"], \"cell_name\": \"1298226\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298226\", \"result\":[{\"smile\":\"CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\", \"value\": 5.206803}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N' and organelle '1298226' is 5.206803."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the strength of 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' in inhibiting the cell '905979'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\"], \"cell_name\": \"905979\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905979\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 5.298320}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' and organelle '905979' is 5.298320."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory potency of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' against the cell '753584'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\"], \"cell_name\": \"753584\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753584\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\", \"value\": 4.353708}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' and organelle '753584' is 4.353708."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' in limiting the cell '753610'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"], \"cell_name\": \"753610\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753610\", \"result\":[{\"smile\":\"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\", \"value\": -1.873992}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' and organelle '753610' is -1.873992."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by 'CCCCCCCC(=O)O[C@H]1[C@H]2C(=C([C@@H]1OC(=O)/C(=C\\C)/C)C)[C@H]3[C@]([C@H](C[C@]2(C)OC(=O)C)OC(=O)CCC)([C@](C(=O)O3)(C)O)O' in the cell '753588'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCCCCCC(=O)O[C@H]1[C@H]2C(=C([C@@H]1OC(=O)/C(=C\\C)/C)C)[C@H]3[C@]([C@H](C[C@]2(C)OC(=O)C)OC(=O)CCC)([C@](C(=O)O3)(C)O)O\"], \"cell_name\": \"753588\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753588\", \"result\":[{\"smile\":\"CCCCCCCC(=O)O[C@H]1[C@H]2C(=C([C@@H]1OC(=O)/C(=C\\C)/C)C)[C@H]3[C@]([C@H](C[C@]2(C)OC(=O)C)OC(=O)CCC)([C@](C(=O)O3)(C)O)O\", \"value\": -2.366101}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCCCCCC(=O)O[C@H]1[C@H]2C(=C([C@@H]1OC(=O)/C(=C\\C)/C)C)[C@H]3[C@]([C@H](C[C@]2(C)OC(=O)C)OC(=O)CCC)([C@](C(=O)O3)(C)O)O' and organelle '753588' is -2.366101."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how strongly 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' suppresses the cell '1298529'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"], \"cell_name\": \"1298529\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298529\", \"result\":[{\"smile\":\"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\", \"value\": 6.271114}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' and organelle '1298529' is 6.271114."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition of 'C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1' on the growth of the cell '724874'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1\"], \"cell_name\": \"724874\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724874\", \"result\":[{\"smile\":\"C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1\", \"value\": 5.546941}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1' and organelle '724874' is 5.546941."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the degree of suppression by 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' on the cell '753565'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C\"], \"cell_name\": \"753565\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753565\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C\", \"value\": 0.735101}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' and organelle '753565' is 0.735101."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' reduces growth in the cell '749711'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\"], \"cell_name\": \"749711\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749711\", \"result\":[{\"smile\":\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\", \"value\": 4.344466}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' and organelle '749711' is 4.344466."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of 'CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl' on the cell '905954'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl\"], \"cell_name\": \"905954\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905954\", \"result\":[{\"smile\":\"CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl\", \"value\": 3.634227}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl' and organelle '905954' is 3.634227."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3' on the cell '753546'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3\"], \"cell_name\": \"753546\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753546\", \"result\":[{\"smile\":\"C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3\", \"value\": 1.086916}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3' and organelle '753546' is 1.086916."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the impact of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' on cell growth in the cell '930083'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"], \"cell_name\": \"930083\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"930083\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\", \"value\": -0.605262}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' and organelle '930083' is -0.605262."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'CNCC1=CC=C(C=C1)C2=C3CCNC(=O)C4=CC(=CC(=C34)N2)F' restricts the cell '1331025'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CNCC1=CC=C(C=C1)C2=C3CCNC(=O)C4=CC(=CC(=C34)N2)F\"], \"cell_name\": \"1331025\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331025\", \"result\":[{\"smile\":\"CNCC1=CC=C(C=C1)C2=C3CCNC(=O)C4=CC(=CC(=C34)N2)F\", \"value\": 3.940094}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CNCC1=CC=C(C=C1)C2=C3CCNC(=O)C4=CC(=CC(=C34)N2)F' and organelle '1331025' is 3.940094."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the reduction rate of 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' on the cell '910951'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\"], \"cell_name\": \"910951\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910951\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": -1.854821}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' and organelle '910951' is -1.854821."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C''s impact in hindering the cell '910948'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\"], \"cell_name\": \"910948\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910948\", \"result\":[{\"smile\":\"CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C\", \"value\": 4.478880}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC2=C(C=C1C(=C)C3=CC=C(C=C3)C(=O)O)C(CCC2(C)C)(C)C' and organelle '910948' is 4.478880."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl''s action on growth suppression in the cell '909696'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\"], \"cell_name\": \"909696\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909696\", \"result\":[{\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 3.894671}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '909696' is 3.894671."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'C1=CC=C(C=C1)NC(=O)CCCCCCC(=O)NO' can hinder the cell '1331037'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NC(=O)CCCCCCC(=O)NO\"], \"cell_name\": \"1331037\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331037\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NC(=O)CCCCCCC(=O)NO\", \"value\": -0.174760}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NC(=O)CCCCCCC(=O)NO' and organelle '1331037' is -0.174760."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' to inhibit the cell '906831'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"906831\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906831\", \"result\":[{\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 1.982139}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '906831' is 1.982139."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the level of 'C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F''s inhibitory effect on the cell '907061'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F\"], \"cell_name\": \"907061\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907061\", \"result\":[{\"smile\":\"C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F\", \"value\": 2.845310}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F' and organelle '907061' is 2.845310."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibitory power of 'CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O' on the cell '908151'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O\"], \"cell_name\": \"908151\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908151\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O\", \"value\": -1.993627}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O' and organelle '908151' is -1.993627."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of suppression by 'C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3' in the cell '684072'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3\"], \"cell_name\": \"684072\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684072\", \"result\":[{\"smile\":\"C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3\", \"value\": 1.361062}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C(=C1)N)NC(=O)C2=CC=C(C=C2)CNC(=O)OCC3=CN=CC=C3' and organelle '684072' is 1.361062."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibition caused by 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' on the cell '713885'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\"], \"cell_name\": \"713885\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"713885\", \"result\":[{\"smile\":\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\", \"value\": 2.531647}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' and organelle '713885' is 2.531647."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' reduces growth in the cell '909740'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\"], \"cell_name\": \"909740\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909740\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\", \"value\": -3.324028}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' and organelle '909740' is -3.324028."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' suppresses the cell '1303900' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\"], \"cell_name\": \"1303900\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1303900\", \"result\":[{\"smile\":\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\", \"value\": 4.403747}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' and organelle '1303900' is 4.403747."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the effect of 'COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC' on restricting the cell '753612'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC\"], \"cell_name\": \"753612\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753612\", \"result\":[{\"smile\":\"COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC\", \"value\": -0.615466}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC' and organelle '753612' is -0.615466."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C''s action on the cell '907050'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\"], \"cell_name\": \"907050\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907050\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": 0.897857}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' and organelle '907050' is 0.897857."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how much inhibition 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' causes in the cell '910569'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\"], \"cell_name\": \"910569\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910569\", \"result\":[{\"smile\":\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\", \"value\": 3.912108}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' and organelle '910569' is 3.912108."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' on the cell '909731'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"], \"cell_name\": \"909731\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909731\", \"result\":[{\"smile\":\"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\", \"value\": -1.172666}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' and organelle '909731' is -1.172666."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the action of 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' on growth in the cell '753581'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"753581\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753581\", \"result\":[{\"smile\":\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\", \"value\": 4.410557}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '753581' is 4.410557."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression rate of 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' on the cell '687777'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\"], \"cell_name\": \"687777\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687777\", \"result\":[{\"smile\":\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\", \"value\": 2.156760}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' and organelle '687777' is 2.156760."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl''s inhibition strength against the cell '687600'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl\"], \"cell_name\": \"687600\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687600\", \"result\":[{\"smile\":\"C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl\", \"value\": 3.898946}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl' and organelle '687600' is 3.898946."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O''s capacity for inhibition on the cell '909698'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\"], \"cell_name\": \"909698\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909698\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\", \"value\": 0.014112}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' and organelle '909698' is 0.014112."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' reduces the cell '930297' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\"], \"cell_name\": \"930297\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"930297\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\", \"value\": 5.539058}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' and organelle '930297' is 5.539058."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F''s effect on the cell '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\"], \"cell_name\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240182\", \"result\":[{\"smile\":\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\", \"value\": 4.616497}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' and organelle '1240182' is 4.616497."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the reduction caused by 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' on the cell '924239'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\"], \"cell_name\": \"924239\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924239\", \"result\":[{\"smile\":\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\", \"value\": 2.864147}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and organelle '924239' is 2.864147."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how strongly 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' inhibits the cell '724834'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\"], \"cell_name\": \"724834\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724834\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\", \"value\": 2.119574}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' and organelle '724834' is 2.119574."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition power of 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' against the cell '749716'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\"], \"cell_name\": \"749716\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749716\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 3.712697}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '749716' is 3.712697."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' on the cell '753561'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\"], \"cell_name\": \"753561\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753561\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\", \"value\": 3.125653}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC' and organelle '753561' is 3.125653."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of 'CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C' on cell growth in the cell '1287381'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C\"], \"cell_name\": \"1287381\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1287381\", \"result\":[{\"smile\":\"CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C\", \"value\": 1.043630}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C' and organelle '1287381' is 1.043630."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' restricts the cell '906746'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\"], \"cell_name\": \"906746\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906746\", \"result\":[{\"smile\":\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\", \"value\": -4.222226}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' and organelle '906746' is -4.222226."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory level of 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' on the cell '946373'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"946373\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946373\", \"result\":[{\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 3.777780}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '946373' is 3.777780."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N' against the cell '1240198'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N\"], \"cell_name\": \"1240198\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240198\", \"result\":[{\"smile\":\"C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N\", \"value\": 6.337084}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N' and organelle '1240198' is 6.337084."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the cell growth inhibition effect of 'C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7' on the cell '907289'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7\"], \"cell_name\": \"907289\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907289\", \"result\":[{\"smile\":\"C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7\", \"value\": 4.794084}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7' and organelle '907289' is 4.794084."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF''s capacity to inhibit the cell '910916'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\"], \"cell_name\": \"910916\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910916\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\", \"value\": 4.854632}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF' and organelle '910916' is 4.854632."
            },
            {
                "from": "human",
                "value": "Forecast the inhibition potential of the drug above on the cell '925345'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"925345\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925345\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\", \"value\": 4.020743}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF' and organelle '925345' is 4.020743."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N' inhibits the cell '949094' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N\"], \"cell_name\": \"949094\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949094\", \"result\":[{\"smile\":\"C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N\", \"value\": 7.946786}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N' and organelle '949094' is 7.946786."
            },
            {
                "from": "human",
                "value": "Predict the inhibitory action of the drug above against the cell '1240181'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240181\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240181\", \"result\":[{\"smile\":\"C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N\", \"value\": 8.449924}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=NC(=C(N1[C@H]2[C@@H]([C@@H]([C@H](O2)COP(=O)(O)O)O)O)N)C(=O)N' and organelle '1240181' is 8.449924."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' reduces growth in the cell '908159'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"], \"cell_name\": \"908159\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908159\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 2.467373}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '908159' is 2.467373."
            },
            {
                "from": "human",
                "value": "Assess the degree of inhibition for the drug above against the cell '949174'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949174\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949174\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 2.423690}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '949174' is 2.423690."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C' against the cell '909726'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C\"], \"cell_name\": \"909726\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909726\", \"result\":[{\"smile\":\"CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C\", \"value\": 1.598708}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C' and organelle '909726' is 1.598708."
            },
            {
                "from": "human",
                "value": "Predict the suppressive impact of the drug above on the cell '687777'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687777\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687777\", \"result\":[{\"smile\":\"CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C\", \"value\": 3.942930}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CNC2=C1C=CC(=C2)NC(=O)C3=C(N=CC=C3)NCC4=CC=NC=C4)C' and organelle '687777' is 3.942930."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' in limiting the cell '907171'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"907171\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907171\", \"result\":[{\"smile\":\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\", \"value\": -1.432707}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '907171' is -1.432707."
            },
            {
                "from": "human",
                "value": "Predict the suppression rate of the drug above on the cell '1240143'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240143\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240143\", \"result\":[{\"smile\":\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\", \"value\": 3.019617}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '1240143' is 3.019617."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C' on the cell '1287381'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C\"], \"cell_name\": \"1287381\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1287381\", \"result\":[{\"smile\":\"CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C\", \"value\": 5.011119}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C' and organelle '1287381' is 5.011119."
            },
            {
                "from": "human",
                "value": "Assess the strength of inhibition by the drug above in the cell '909706'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909706\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909706\", \"result\":[{\"smile\":\"CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C\", \"value\": 3.675350}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C' and organelle '909706' is 3.675350."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory action of 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' against the cell '909249'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\"], \"cell_name\": \"909249\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909249\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\", \"value\": -0.260471}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' and organelle '909249' is -0.260471."
            },
            {
                "from": "human",
                "value": "Forecast the inhibition strength of the drug above against the cell '917486'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"917486\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"917486\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\", \"value\": -0.106255}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' and organelle '917486' is -0.106255."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' in limiting the cell '909727'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\"], \"cell_name\": \"909727\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909727\", \"result\":[{\"smile\":\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\", \"value\": 1.788359}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' and organelle '909727' is 1.788359."
            },
            {
                "from": "human",
                "value": "Forecast the drug above's inhibition strength against the cell '908128'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908128\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908128\", \"result\":[{\"smile\":\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\", \"value\": 0.389224}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' and organelle '908128' is 0.389224."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition potential of 'CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5' on the cell '1298534'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5\"], \"cell_name\": \"1298534\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298534\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5\", \"value\": 3.734366}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5' and organelle '1298534' is 3.734366."
            },
            {
                "from": "human",
                "value": "Estimate the level at which the drug above inhibits the cell '906852'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"906852\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906852\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5\", \"value\": 4.236629}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5' and organelle '906852' is 4.236629."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O' impacts the proliferation of the cell '1240131'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\"], \"cell_name\": \"1240131\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240131\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\", \"value\": 0.877913}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O' and organelle '1240131' is 0.877913."
            },
            {
                "from": "human",
                "value": "Determine the drug above's inhibition rate on the cell '906818'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"906818\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906818\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\", \"value\": 0.461919}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O' and organelle '906818' is 0.461919."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N' impacts the growth of the cell '753575'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\"], \"cell_name\": \"753575\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753575\", \"result\":[{\"smile\":\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\", \"value\": -0.016183}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N' and organelle '753575' is -0.016183."
            },
            {
                "from": "human",
                "value": "Determine how the drug above impacts the proliferation of the cell '909696'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909696\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909696\", \"result\":[{\"smile\":\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\", \"value\": -0.864875}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N' and organelle '909696' is -0.864875."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'COC(=O)CNC(=O)C(=O)OC' on the cell '907321'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC(=O)CNC(=O)C(=O)OC\"], \"cell_name\": \"907321\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907321\", \"result\":[{\"smile\":\"COC(=O)CNC(=O)C(=O)OC\", \"value\": 4.979144}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC(=O)CNC(=O)C(=O)OC' and organelle '907321' is 4.979144."
            },
            {
                "from": "human",
                "value": "Assess the inhibitory action of the drug above against the cell '946355'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"946355\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946355\", \"result\":[{\"smile\":\"COC(=O)CNC(=O)C(=O)OC\", \"value\": 7.061351}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC(=O)CNC(=O)C(=O)OC' and organelle '946355' is 7.061351."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5''s impact in hindering the cell '925348'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\"], \"cell_name\": \"925348\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925348\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 4.646955}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '925348' is 4.646955."
            },
            {
                "from": "human",
                "value": "Calculate the impact of the drug above on cell growth in the cell '1330983'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1330983\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330983\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 0.895666}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '1330983' is 0.895666."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N' restricts the cell '906813'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N\"], \"cell_name\": \"906813\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906813\", \"result\":[{\"smile\":\"C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N\", \"value\": 2.625891}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N' and organelle '906813' is 2.625891."
            },
            {
                "from": "human",
                "value": "Assess the suppression potential of the drug above against the cell '1327771'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1327771\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1327771\", \"result\":[{\"smile\":\"C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N\", \"value\": 1.402315}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N' and organelle '1327771' is 1.402315."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of 'CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5' on the cell '909785'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5\"], \"cell_name\": \"909785\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909785\", \"result\":[{\"smile\":\"CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5\", \"value\": 2.840195}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5' and organelle '909785' is 2.840195."
            },
            {
                "from": "human",
                "value": "Predict the strength of suppression by the drug above in the cell '910544'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910544\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910544\", \"result\":[{\"smile\":\"CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5\", \"value\": 2.978759}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5' and organelle '910544' is 2.978759."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' inhibits the cell '907047'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"], \"cell_name\": \"907047\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907047\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 2.211053}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '907047' is 2.211053."
            },
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by the drug above on the cell '949175'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949175\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949175\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 0.300372}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '949175' is 0.300372."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which 'CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C' inhibits the cell '907045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C\"], \"cell_name\": \"907045\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907045\", \"result\":[{\"smile\":\"CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C\", \"value\": 4.314110}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C' and organelle '907045' is 4.314110."
            },
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of the drug above on the cell '949168'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949168\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949168\", \"result\":[{\"smile\":\"CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C\", \"value\": 2.991102}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN(CC)CCCCNC1=NC2=NC(=C(C=C2C=N1)C3=CC(=CC(=C3)OC)OC)NC(=O)NC(C)(C)C' and organelle '949168' is 2.991102."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' on the cell '753577'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"], \"cell_name\": \"753577\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753577\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 2.018376}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '753577' is 2.018376."
            },
            {
                "from": "human",
                "value": "Predict the impact of the drug above on cell growth in the cell '909748'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909748\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909748\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 3.442597}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '909748' is 3.442597."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how strongly 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' inhibits the cell '684057'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\"], \"cell_name\": \"684057\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684057\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 2.620061}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' and organelle '684057' is 2.620061."
            },
            {
                "from": "human",
                "value": "Determine how strongly the drug above suppresses the cell '1240129'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240129\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240129\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 2.117296}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' and organelle '1240129' is 2.117296."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' inhibition on the cell '925345'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\"], \"cell_name\": \"925345\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925345\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": -1.045520}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '925345' is -1.045520."
            },
            {
                "from": "human",
                "value": "Calculate the degree of inhibitory effect for the drug above on the cell '688025'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688025\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688025\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": 0.046422}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '688025' is 0.046422."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C''s impact in hindering the cell '909732'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\"], \"cell_name\": \"909732\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909732\", \"result\":[{\"smile\":\"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\", \"value\": 3.042438}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' and organelle '909732' is 3.042438."
            },
            {
                "from": "human",
                "value": "Determine the drug above's effectiveness in inhibiting the cell '910780'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910780\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910780\", \"result\":[{\"smile\":\"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\", \"value\": 0.613921}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' and organelle '910780' is 0.613921."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC''s effect on the cell '907279'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"907279\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907279\", \"result\":[{\"smile\":\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\", \"value\": 2.832277}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '907279' is 2.832277."
            },
            {
                "from": "human",
                "value": "Predict the drug above's suppression level on the cell '949094'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949094\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949094\", \"result\":[{\"smile\":\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\", \"value\": 3.509239}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '949094' is 3.509239."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppressive impact of 'C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O' on the cell '688023'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O\"], \"cell_name\": \"688023\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688023\", \"result\":[{\"smile\":\"C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O\", \"value\": 1.362830}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O' and organelle '688023' is 1.362830."
            },
            {
                "from": "human",
                "value": "Determine how much the drug above restricts the cell '907073'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907073\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907073\", \"result\":[{\"smile\":\"C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O\", \"value\": -1.731388}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=C(C=C1I)F)NC2=C(C=CC(=C2F)F)C(=O)NOC[C@@H](CO)O' and organelle '907073' is -1.731388."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition strength of 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' against the cell '908141'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\"], \"cell_name\": \"908141\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908141\", \"result\":[{\"smile\":\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\", \"value\": 1.114957}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and organelle '908141' is 1.114957."
            },
            {
                "from": "human",
                "value": "Assess the suppression potential of the drug above on the cell '687590'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687590\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687590\", \"result\":[{\"smile\":\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\", \"value\": 4.897361}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and organelle '687590' is 4.897361."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' on the cell '1240158'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\"], \"cell_name\": \"1240158\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240158\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": 0.345057}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '1240158' is 0.345057."
            },
            {
                "from": "human",
                "value": "Estimate the potency of the drug above as an inhibitor for the cell '949161'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949161\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": 0.964595}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '949161' is 0.964595."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' can suppress the cell '1240209'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\"], \"cell_name\": \"1240209\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240209\", \"result\":[{\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.769085}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' and organelle '1240209' is 2.769085."
            },
            {
                "from": "human",
                "value": "Predict how strongly the drug above inhibits the cell '724838'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"724838\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724838\", \"result\":[{\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.220662}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' and organelle '724838' is 2.220662."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' on the cell '908128'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\"], \"cell_name\": \"908128\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908128\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 5.495957}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' and organelle '908128' is 5.495957."
            },
            {
                "from": "human",
                "value": "Forecast the inhibitory impact the drug above has on the cell '1240161'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240161\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 1.564408}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' and organelle '1240161' is 1.564408."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' on the cell '906846'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\"], \"cell_name\": \"906846\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906846\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\", \"value\": 2.211860}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '906846' is 2.211860."
            },
            {
                "from": "human",
                "value": "Forecast the drug above's impact in hindering the cell '687794'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687794\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687794\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\", \"value\": 4.305329}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '687794' is 4.305329."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' on the cell '949165'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\"], \"cell_name\": \"949165\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949165\", \"result\":[{\"smile\":\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\", \"value\": -1.633373}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' and organelle '949165' is -1.633373."
            },
            {
                "from": "human",
                "value": "Predict how much the drug above can hinder the cell '910943'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910943\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910943\", \"result\":[{\"smile\":\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\", \"value\": 1.968083}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' and organelle '910943' is 1.968083."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how effectively 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' inhibits the cell '753558'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\"], \"cell_name\": \"753558\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753558\", \"result\":[{\"smile\":\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\", \"value\": 1.651014}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' and organelle '753558' is 1.651014."
            },
            {
                "from": "human",
                "value": "Determine the drug above's inhibition rate on the cell '905943'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905943\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905943\", \"result\":[{\"smile\":\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\", \"value\": 3.238857}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' and organelle '905943' is 3.238857."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition potential of 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' on the cell '687452'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"687452\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687452\", \"result\":[{\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 2.272243}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '687452' is 2.272243."
            },
            {
                "from": "human",
                "value": "Determine how strongly the drug above suppresses the cell '907272'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907272\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907272\", \"result\":[{\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 4.096305}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '907272' is 4.096305."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' restricts the cell '907323'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\"], \"cell_name\": \"907323\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907323\", \"result\":[{\"smile\":\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\", \"value\": -2.476780}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' and organelle '907323' is -2.476780."
            },
            {
                "from": "human",
                "value": "Determine how well the drug above restricts the cell '1330982'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1330982\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330982\", \"result\":[{\"smile\":\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\", \"value\": -2.945117}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' and organelle '1330982' is -2.945117."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition capacity of 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' on the cell '1298529'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\"], \"cell_name\": \"1298529\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298529\", \"result\":[{\"smile\":\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\", \"value\": 3.214558}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' and organelle '1298529' is 3.214558."
            },
            {
                "from": "human",
                "value": "Estimate the degree to which the drug above inhibits the cell '909698'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909698\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909698\", \"result\":[{\"smile\":\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\", \"value\": 0.277121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' and organelle '909698' is 0.277121."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1''s suppression level on the cell '1298156'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\"], \"cell_name\": \"1298156\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298156\", \"result\":[{\"smile\":\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\", \"value\": 3.078983}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' and organelle '1298156' is 3.078983."
            },
            {
                "from": "human",
                "value": "Estimate the inhibition of the drug above on the growth of the cell '949163'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949163\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949163\", \"result\":[{\"smile\":\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\", \"value\": 3.197026}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' and organelle '949163' is 3.197026."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition capacity of 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' on the cell '910701'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\"], \"cell_name\": \"910701\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910701\", \"result\":[{\"smile\":\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\", \"value\": 5.219296}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' and organelle '910701' is 5.219296."
            },
            {
                "from": "human",
                "value": "Assess the inhibition capacity of the drug above on the cell '910931'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910931\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910931\", \"result\":[{\"smile\":\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\", \"value\": 4.988318}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' and organelle '910931' is 4.988318."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the level of inhibition 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' has on the cell '949172'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\"], \"cell_name\": \"949172\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949172\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 4.580800}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' and organelle '949172' is 4.580800."
            },
            {
                "from": "human",
                "value": "Forecast the inhibition potential of the drug above on the cell '1480360'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1480360\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480360\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 1.746759}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' and organelle '1480360' is 1.746759."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of suppression by 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' in the cell '687592'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\"], \"cell_name\": \"687592\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687592\", \"result\":[{\"smile\":\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\", \"value\": 4.445859}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' and organelle '687592' is 4.445859."
            },
            {
                "from": "human",
                "value": "Forecast the drug above's impact in hindering the cell '753607'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753607\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753607\", \"result\":[{\"smile\":\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\", \"value\": 3.897555}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' and organelle '753607' is 3.897555."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the action of 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' on growth in the cell '910704'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\"], \"cell_name\": \"910704\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910704\", \"result\":[{\"smile\":\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\", \"value\": -1.275230}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' and organelle '910704' is -1.275230."
            },
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of the drug above on the cell '909744'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909744\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909744\", \"result\":[{\"smile\":\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\", \"value\": -1.757509}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' and organelle '909744' is -1.757509."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the degree of inhibition for 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' against the cell '906814'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\"], \"cell_name\": \"906814\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906814\", \"result\":[{\"smile\":\"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\", \"value\": 1.956610}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' and organelle '906814' is 1.956610."
            },
            {
                "from": "human",
                "value": "Estimate the drug above's action on growth suppression in the cell '688026'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688026\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688026\", \"result\":[{\"smile\":\"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\", \"value\": 1.487141}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' and organelle '688026' is 1.487141."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC' restricts the cell '908483'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC\"], \"cell_name\": \"908483\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908483\", \"result\":[{\"smile\":\"COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC\", \"value\": 0.647660}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC' and organelle '908483' is 0.647660."
            },
            {
                "from": "human",
                "value": "Predict the inhibitory action of the drug above against the cell '753549'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753549\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753549\", \"result\":[{\"smile\":\"COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC\", \"value\": 2.287205}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=CC(=C1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC(=C(C=C5)N6CCNCC6)C(F)(F)F)OC' and organelle '753549' is 2.287205."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the degree of inhibitory effect for 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O' on the cell '684681'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O\"], \"cell_name\": \"684681\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684681\", \"result\":[{\"smile\":\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O\", \"value\": -5.508353}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O' and organelle '684681' is -5.508353."
            },
            {
                "from": "human",
                "value": "Determine the inhibitory power of the drug above on the cell '688014'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688014\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688014\", \"result\":[{\"smile\":\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O\", \"value\": -2.774171}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O' and organelle '688014' is -2.774171."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the impact of 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' on cell growth in the cell '909699'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\"], \"cell_name\": \"909699\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909699\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -0.224083}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '909699' is -0.224083."
            },
            {
                "from": "human",
                "value": "Predict the cell growth inhibition effect of the drug above on the cell '753556'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753556\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753556\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -1.272352}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '753556' is -1.272352."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibition caused by 'CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4' on the cell '687983'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4\"], \"cell_name\": \"687983\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687983\", \"result\":[{\"smile\":\"CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4\", \"value\": 4.251042}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4' and organelle '687983' is 4.251042."
            },
            {
                "from": "human",
                "value": "Estimate the effect of the drug above in inhibiting the cell '684681'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"684681\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684681\", \"result\":[{\"smile\":\"CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4\", \"value\": 4.926274}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4' and organelle '684681' is 4.926274."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' on the cell '1331050'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\"], \"cell_name\": \"1331050\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331050\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\", \"value\": 0.198065}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' and organelle '1331050' is 0.198065."
            },
            {
                "from": "human",
                "value": "Determine the inhibition percentage for the drug above on the cell '906848'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"906848\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906848\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\", \"value\": -0.607416}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' and organelle '906848' is -0.607416."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by 'COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O' in the cell '1240193'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O\"], \"cell_name\": \"1240193\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240193\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O\", \"value\": 0.148233}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O' and organelle '1240193' is 0.148233."
            },
            {
                "from": "human",
                "value": "Predict the drug above's suppression level on the cell '909729'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909729\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909729\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O\", \"value\": 1.268054}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O' and organelle '909729' is 1.268054."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression level of 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' on the cell '753608'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\"], \"cell_name\": \"753608\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753608\", \"result\":[{\"smile\":\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\", \"value\": -2.837485}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' and organelle '753608' is -2.837485."
            },
            {
                "from": "human",
                "value": "Estimate the inhibition caused by the drug above on the cell '1298533'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298533\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298533\", \"result\":[{\"smile\":\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\", \"value\": -1.826790}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' and organelle '1298533' is -1.826790."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C''s suppression level on the cell '1247873'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\"], \"cell_name\": \"1247873\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1247873\", \"result\":[{\"smile\":\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\", \"value\": 3.198050}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' and organelle '1247873' is 3.198050."
            },
            {
                "from": "human",
                "value": "Predict how strongly the drug above inhibits the cell '924248'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"924248\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924248\", \"result\":[{\"smile\":\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\", \"value\": 4.211198}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' and organelle '924248' is 4.211198."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5' impacts the growth of the cell '1240221'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5\"], \"cell_name\": \"1240221\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240221\", \"result\":[{\"smile\":\"CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5\", \"value\": 3.623506}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5' and organelle '1240221' is 3.623506."
            },
            {
                "from": "human",
                "value": "Predict the inhibiting action of the drug above on the cell '908159'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908159\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908159\", \"result\":[{\"smile\":\"CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5\", \"value\": 2.142181}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5' and organelle '908159' is 2.142181."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N''s impact in hindering the cell '910850'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\"], \"cell_name\": \"910850\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910850\", \"result\":[{\"smile\":\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\", \"value\": -1.497315}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' and organelle '910850' is -1.497315."
            },
            {
                "from": "human",
                "value": "Calculate the drug above's effect on the cell '910779'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910779\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910779\", \"result\":[{\"smile\":\"CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N\", \"value\": -2.114591}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=O)C2=C(C1=O)N3C[C@H]4[C@@H]([C@@]3([C@@H]2COC(=O)N)OC)N4)N' and organelle '910779' is -2.114591."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of suppression by 'CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2' in the cell '753618'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2\"], \"cell_name\": \"753618\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753618\", \"result\":[{\"smile\":\"CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2\", \"value\": 2.827029}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2' and organelle '753618' is 2.827029."
            },
            {
                "from": "human",
                "value": "Determine the level of inhibition the drug above has on the cell '910692'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910692\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910692\", \"result\":[{\"smile\":\"CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2\", \"value\": 4.238833}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)CCOC1=CC=C(C=C1)C2=N/C(=C/3\\C=CC4=C(CCC4=C3)N=O)/C(=C5C=CNC=C5)N2' and organelle '910692' is 4.238833."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how strongly 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' inhibits the cell '1240172'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\"], \"cell_name\": \"1240172\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240172\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 2.566549}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '1240172' is 2.566549."
            },
            {
                "from": "human",
                "value": "And assess the suppression potential of 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4' against the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240172\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4\", \"value\": 2.751514}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4' and organelle '1240172' is 2.751514."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F' on the cell '724868'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F\"], \"cell_name\": \"724868\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724868\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F\", \"value\": 2.920049}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F' and organelle '724868' is 2.920049."
            },
            {
                "from": "human",
                "value": "And predict the suppressive impact of 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724868\", \"result\":[{\"smile\":\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\", \"value\": 2.071398}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '724868' is 2.071398."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition percentage for 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' on the cell '905942'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\"], \"cell_name\": \"905942\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905942\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\", \"value\": 3.999783}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' and organelle '905942' is 3.999783."
            },
            {
                "from": "human",
                "value": "And determine the strength of inhibition by 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905942\", \"result\":[{\"smile\":\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\", \"value\": 3.543996}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' and organelle '905942' is 3.543996."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' on the cell '910910'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\"], \"cell_name\": \"910910\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910910\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 2.741446}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '910910' is 2.741446."
            },
            {
                "from": "human",
                "value": "And assess the strength of inhibition by 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910910\", \"result\":[{\"smile\":\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\", \"value\": 5.520821}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' and organelle '910910' is 5.520821."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the effect of 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' on restricting the cell '1240123'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"], \"cell_name\": \"1240123\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240123\", \"result\":[{\"smile\":\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\", \"value\": 4.068934}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '1240123' is 4.068934."
            },
            {
                "from": "human",
                "value": "And evaluate how 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' impacts the growth of the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240123\", \"result\":[{\"smile\":\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\", \"value\": 4.150810}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' and organelle '1240123' is 4.150810."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl' on the cell '908454'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl\"], \"cell_name\": \"908454\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908454\", \"result\":[{\"smile\":\"CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl\", \"value\": 0.343151}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl' and organelle '908454' is 0.343151."
            },
            {
                "from": "human",
                "value": "And evaluate how much 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' inhibits the cell mentioned above growth. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908454\", \"result\":[{\"smile\":\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\", \"value\": 0.110880}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' and organelle '908454' is 0.110880."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' suppresses the cell '1290455' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1290455\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290455\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\", \"value\": 3.979661}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1290455' is 3.979661."
            },
            {
                "from": "human",
                "value": "And determine how effectively 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290455\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\", \"value\": 0.505362}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' and organelle '1290455' is 0.505362."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' in limiting the cell '753620'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\"], \"cell_name\": \"753620\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753620\", \"result\":[{\"smile\":\"C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O\", \"value\": 4.307038}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(C1)C2=C(C=CC(=C2)C3=CNC4=NC=C(C=C34)C5=CC=CC=C5)C(=O)O' and organelle '753620' is 4.307038."
            },
            {
                "from": "human",
                "value": "And predict the strength of suppression by 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753620\", \"result\":[{\"smile\":\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\", \"value\": 1.502729}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '753620' is 1.502729."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' exerts on the cell '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\"], \"cell_name\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240182\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\", \"value\": 5.335305}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' and organelle '1240182' is 5.335305."
            },
            {
                "from": "human",
                "value": "And estimate the potency of 'CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5' as an inhibitor for the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240182\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5\", \"value\": 5.053121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)N2C(=CC(=N2)C(C)(C)C)NC(=O)NC3=CC=C(C4=CC=CC=C43)OCCN5CCOCC5' and organelle '1240182' is 5.053121."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how strongly 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' inhibits the cell '949154'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\"], \"cell_name\": \"949154\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949154\", \"result\":[{\"smile\":\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\", \"value\": 3.509755}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' and organelle '949154' is 3.509755."
            },
            {
                "from": "human",
                "value": "And determine 'CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N''s effectiveness in inhibiting the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949154\", \"result\":[{\"smile\":\"CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N\", \"value\": 2.650037}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N' and organelle '949154' is 2.650037."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of suppression by 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' in the cell '907000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\"], \"cell_name\": \"907000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907000\", \"result\":[{\"smile\":\"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\", \"value\": 1.412201}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' and organelle '907000' is 1.412201."
            },
            {
                "from": "human",
                "value": "And forecast the inhibitory impact 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' has on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907000\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": 0.665242}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' and organelle '907000' is 0.665242."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' restricts the cell '906877'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\"], \"cell_name\": \"906877\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906877\", \"result\":[{\"smile\":\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\", \"value\": -1.869116}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' and organelle '906877' is -1.869116."
            },
            {
                "from": "human",
                "value": "And estimate how 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906877\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\", \"value\": 5.062794}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF' and organelle '906877' is 5.062794."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the level of inhibition 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' has on the cell '1480371'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\"], \"cell_name\": \"1480371\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480371\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 0.798142}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' and organelle '1480371' is 0.798142."
            },
            {
                "from": "human",
                "value": "And forecast the reduction caused by 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480371\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\", \"value\": -1.968252}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' and organelle '1480371' is -1.968252."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression rate of 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO' on the cell '905974'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO\"], \"cell_name\": \"905974\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905974\", \"result\":[{\"smile\":\"CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO\", \"value\": 4.818508}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO' and organelle '905974' is 4.818508."
            },
            {
                "from": "human",
                "value": "And calculate how effectively 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905974\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\", \"value\": 4.097934}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O' and organelle '905974' is 4.097934."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' to inhibit the cell '1327771'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\"], \"cell_name\": \"1327771\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1327771\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\", \"value\": -4.991021}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' and organelle '1327771' is -4.991021."
            },
            {
                "from": "human",
                "value": "And calculate the inhibitory strength of 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' against the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1327771\", \"result\":[{\"smile\":\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\", \"value\": 2.671109}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' and organelle '1327771' is 2.671109."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'C1=CC=C(C=C1)CCN=C(N)N=C(N)N' on the cell '753566'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)CCN=C(N)N=C(N)N\"], \"cell_name\": \"753566\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753566\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CCN=C(N)N=C(N)N\", \"value\": 8.133738}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)CCN=C(N)N=C(N)N' and organelle '753566' is 8.133738."
            },
            {
                "from": "human",
                "value": "And determine the inhibitory power of 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753566\", \"result\":[{\"smile\":\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\", \"value\": -0.076206}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O' and organelle '753566' is -0.076206."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the effect of 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' on restricting the cell '907055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\"], \"cell_name\": \"907055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907055\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\", \"value\": -1.151792}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' and organelle '907055' is -1.151792."
            },
            {
                "from": "human",
                "value": "And assess how strongly 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907055\", \"result\":[{\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 2.743048}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '907055' is 2.743048."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how effectively 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' inhibits the cell '1331034'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\"], \"cell_name\": \"1331034\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331034\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\", \"value\": -4.524601}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' and organelle '1331034' is -4.524601."
            },
            {
                "from": "human",
                "value": "And calculate the impact of 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' on cell growth in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331034\", \"result\":[{\"smile\":\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\", \"value\": 2.642610}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and organelle '1331034' is 2.642610."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition impact of 'CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O' on the cell '905971'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O\"], \"cell_name\": \"905971\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905971\", \"result\":[{\"smile\":\"CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O\", \"value\": -3.481387}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O' and organelle '905971' is -3.481387."
            },
            {
                "from": "human",
                "value": "And determine 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6''s inhibition rate on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905971\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\", \"value\": 1.268365}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6' and organelle '905971' is 1.268365."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' can hinder the cell '949178'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\"], \"cell_name\": \"949178\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949178\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\", \"value\": 1.976570}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' and organelle '949178' is 1.976570."
            },
            {
                "from": "human",
                "value": "And determine the level of inhibition 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5' has on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949178\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5\", \"value\": 2.710650}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5' and organelle '949178' is 2.710650."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' against the cell '1290812'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"1290812\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290812\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\", \"value\": 2.651786}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '1290812' is 2.651786."
            },
            {
                "from": "human",
                "value": "And evaluate the extent of inhibition by 'CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290812\", \"result\":[{\"smile\":\"CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O\", \"value\": -3.502732}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O' and organelle '1290812' is -3.502732."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory effect of 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' on the cell '906765'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\"], \"cell_name\": \"906765\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906765\", \"result\":[{\"smile\":\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": 0.688810}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' and organelle '906765' is 0.688810."
            },
            {
                "from": "human",
                "value": "And evaluate the potency of 'CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C' to inhibit the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906765\", \"result\":[{\"smile\":\"CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C\", \"value\": 2.177155}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C' and organelle '906765' is 2.177155."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' on the cell '910780'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\"], \"cell_name\": \"910780\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910780\", \"result\":[{\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 4.508118}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '910780' is 4.508118."
            },
            {
                "from": "human",
                "value": "And calculate how effectively 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910780\", \"result\":[{\"smile\":\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\", \"value\": 2.288858}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' and organelle '910780' is 2.288858."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO''s effect on the cell '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO\"], \"cell_name\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"722045\", \"result\":[{\"smile\":\"CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO\", \"value\": 2.309999}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO' and organelle '722045' is 2.309999."
            },
            {
                "from": "human",
                "value": "And calculate the inhibitory rate of 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"722045\", \"result\":[{\"smile\":\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\", \"value\": 2.762757}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' and organelle '722045' is 2.762757."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F' impacts the growth of the cell '925348'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F\"], \"cell_name\": \"925348\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925348\", \"result\":[{\"smile\":\"COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F\", \"value\": 3.236008}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F' and organelle '925348' is 3.236008."
            },
            {
                "from": "human",
                "value": "And forecast how 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' will inhibit the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925348\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 2.406366}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '925348' is 2.406366."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition strength of 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' against the cell '907175'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\"], \"cell_name\": \"907175\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907175\", \"result\":[{\"smile\":\"CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O\", \"value\": 4.688439}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCCCCCCCCCCC1=C(C(=O)C=C(C1=O)O)O' and organelle '907175' is 4.688439."
            },
            {
                "from": "human",
                "value": "And predict how strongly 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907175\", \"result\":[{\"smile\":\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\", \"value\": -1.887688}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' and organelle '907175' is -1.887688."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of 'COC(=O)CNC(=O)C(=O)OC' inhibition on the cell '909719'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC(=O)CNC(=O)C(=O)OC\"], \"cell_name\": \"909719\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909719\", \"result\":[{\"smile\":\"COC(=O)CNC(=O)C(=O)OC\", \"value\": 4.409337}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC(=O)CNC(=O)C(=O)OC' and organelle '909719' is 4.409337."
            },
            {
                "from": "human",
                "value": "And calculate the inhibition caused by 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909719\", \"result\":[{\"smile\":\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\", \"value\": 3.053814}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' and organelle '909719' is 3.053814."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F' reduces growth in the cell '909700'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F\"], \"cell_name\": \"909700\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909700\", \"result\":[{\"smile\":\"CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F\", \"value\": 2.467095}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F' and organelle '909700' is 2.467095."
            },
            {
                "from": "human",
                "value": "And predict the degree of suppression by 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909700\", \"result\":[{\"smile\":\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\", \"value\": 2.249878}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' and organelle '909700' is 2.249878."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' on the cell '1503368'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\"], \"cell_name\": \"1503368\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503368\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -0.515934}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '1503368' is -0.515934."
            },
            {
                "from": "human",
                "value": "And estimate how much 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' reduces the cell mentioned above growth. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503368\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\", \"value\": -2.493278}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' and organelle '1503368' is -2.493278."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C' on the cell '924239'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C\"], \"cell_name\": \"924239\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924239\", \"result\":[{\"smile\":\"CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C\", \"value\": 3.810900}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=NC=C(C=C1)OC2=C(C=C(C=C2)NC3=NC=NC4=C3C=C(C=C4)/C=C/CNC(=O)COC)C' and organelle '924239' is 3.810900."
            },
            {
                "from": "human",
                "value": "And evaluate the strength of 'C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F' in inhibiting the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924239\", \"result\":[{\"smile\":\"C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F\", \"value\": 3.108806}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F' and organelle '924239' is 3.108806."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' exerts on the cell '1290725'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\"], \"cell_name\": \"1290725\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290725\", \"result\":[{\"smile\":\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\", \"value\": 2.023810}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' and organelle '1290725' is 2.023810."
            },
            {
                "from": "human",
                "value": "And calculate 'COC(=O)CNC(=O)C(=O)OC''s inhibitory action on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC(=O)CNC(=O)C(=O)OC\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290725\", \"result\":[{\"smile\":\"COC(=O)CNC(=O)C(=O)OC\", \"value\": 4.146360}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC(=O)CNC(=O)C(=O)OC' and organelle '1290725' is 4.146360."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O' in limiting the cell '910694'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\"], \"cell_name\": \"910694\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910694\", \"result\":[{\"smile\":\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\", \"value\": 5.471614}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O' and organelle '910694' is 5.471614."
            },
            {
                "from": "human",
                "value": "And forecast 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O''s impact in hindering the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910694\", \"result\":[{\"smile\":\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\", \"value\": 5.639417}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O' and organelle '910694' is 5.639417."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3' on the cell '907300'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3\"], \"cell_name\": \"907300\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907300\", \"result\":[{\"smile\":\"C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3\", \"value\": 3.802066}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3' and organelle '907300' is 3.802066."
            },
            {
                "from": "human",
                "value": "And predict how much 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' can hinder the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907300\", \"result\":[{\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\", \"value\": 0.610054}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' and organelle '907300' is 0.610054."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how strongly 'C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3' acts on the cell '909716'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3\"], \"cell_name\": \"909716\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909716\", \"result\":[{\"smile\":\"C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3\", \"value\": -5.983376}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3' and organelle '909716' is -5.983376."
            },
            {
                "from": "human",
                "value": "And predict how 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909716\", \"result\":[{\"smile\":\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\", \"value\": 0.905359}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' and organelle '909716' is 0.905359."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC' in limiting the cell '906834'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC\"], \"cell_name\": \"906834\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906834\", \"result\":[{\"smile\":\"C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC\", \"value\": -0.897030}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@H]1CC[C@H]2C[C@@H](/C(=C/C=C/C=C/[C@H](C[C@H](C(=O)[C@@H]([C@@H](/C(=C/[C@H](C(=O)C[C@H](OC(=O)[C@@H]3CCCCN3C(=O)C(=O)[C@@]1(O2)O)[C@H](C)C[C@@H]4CC[C@H]([C@@H](C4)OC)OC(=O)C(C)(CO)CO)C)/C)O)OC)C)C)/C)OC' and organelle '906834' is -0.897030."
            },
            {
                "from": "human",
                "value": "And determine how 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' impacts the proliferation of the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906834\", \"result\":[{\"smile\":\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\", \"value\": 2.282213}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' and organelle '906834' is 2.282213."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' can suppress the cell '905951'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(CC1)NC(=O)NC[C@H]2CCCO2\"], \"cell_name\": \"905951\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905951\", \"result\":[{\"smile\":\"C1CCC(CC1)NC(=O)NC[C@H]2CCCO2\", \"value\": 3.207555}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' and organelle '905951' is 3.207555."
            },
            {
                "from": "human",
                "value": "And evaluate how much 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' inhibits the cell mentioned above growth. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905951\", \"result\":[{\"smile\":\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\", \"value\": 4.962313}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' and organelle '905951' is 4.962313."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how effectively 'CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O' inhibits the cell '906826'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O\"], \"cell_name\": \"906826\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906826\", \"result\":[{\"smile\":\"CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O\", \"value\": 1.505908}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O' and organelle '906826' is 1.505908."
            },
            {
                "from": "human",
                "value": "And estimate the potency of 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' as an inhibitor for the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906826\", \"result\":[{\"smile\":\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": -1.411177}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' and organelle '906826' is -1.411177."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how strongly 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' acts on the cell '907057'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\"], \"cell_name\": \"907057\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907057\", \"result\":[{\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.118546}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' and organelle '907057' is 2.118546."
            },
            {
                "from": "human",
                "value": "And predict the strength of 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO''s inhibition on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907057\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\", \"value\": -2.746847}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' and organelle '907057' is -2.746847."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' inhibits the cell '1240215'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"1240215\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240215\", \"result\":[{\"smile\":\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\", \"value\": 0.558541}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '1240215' is 0.558541."
            },
            {
                "from": "human",
                "value": "And estimate how much 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' reduces the cell mentioned above growth. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240215\", \"result\":[{\"smile\":\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\", \"value\": 1.323697}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F' and organelle '1240215' is 1.323697."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate 'CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C' inhibition potential on the cell '687787'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C\"], \"cell_name\": \"687787\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687787\", \"result\":[{\"smile\":\"CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C\", \"value\": 2.334330}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C' and organelle '687787' is 2.334330."
            },
            {
                "from": "human",
                "value": "And assess how much inhibition 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' causes in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687787\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F\", \"value\": 3.290970}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' and organelle '687787' is 3.290970."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppression rate of 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' on the cell '910545'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\"], \"cell_name\": \"910545\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910545\", \"result\":[{\"smile\":\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\", \"value\": 3.472427}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' and organelle '910545' is 3.472427."
            },
            {
                "from": "human",
                "value": "And calculate the inhibitory rate of 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910545\", \"result\":[{\"smile\":\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\", \"value\": -0.554289}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '910545' is -0.554289."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O' on the cell '909976'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\"], \"cell_name\": \"909976\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909976\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\", \"value\": -0.137551}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O' and organelle '909976' is -0.137551."
            },
            {
                "from": "human",
                "value": "And forecast the effect of 'COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F' in stopping the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909976\", \"result\":[{\"smile\":\"COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F\", \"value\": 2.735047}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F' and organelle '909976' is 2.735047."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C' to inhibit the cell '905963'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C\"], \"cell_name\": \"905963\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905963\", \"result\":[{\"smile\":\"CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C\", \"value\": 3.652688}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C' and organelle '905963' is 3.652688."
            },
            {
                "from": "human",
                "value": "And estimate how much 'C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F' reduces the cell mentioned above growth. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905963\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F\", \"value\": 5.865688}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F' and organelle '905963' is 5.865688."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' can suppress the cell '908455'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\"], \"cell_name\": \"908455\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908455\", \"result\":[{\"smile\":\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\", \"value\": 3.646605}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' and organelle '908455' is 3.646605."
            },
            {
                "from": "human",
                "value": "And predict how 'C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N' inhibits the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908455\", \"result\":[{\"smile\":\"C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N\", \"value\": 5.876846}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C2=C(C1)C3=C(N2)C=CC(=C3)Cl)C(=O)N' and organelle '908455' is 5.876846."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the degree of suppression by 'CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO' on the cell '910689'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO\"], \"cell_name\": \"910689\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910689\", \"result\":[{\"smile\":\"CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO\", \"value\": 4.925088}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO' and organelle '910689' is 4.925088."
            },
            {
                "from": "human",
                "value": "And estimate how much 'C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C' reduces growth in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910689\", \"result\":[{\"smile\":\"C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C\", \"value\": 1.885523}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C' and organelle '910689' is 1.885523."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C''s effect on the cell '1240141'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\"], \"cell_name\": \"1240141\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240141\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": 3.555145}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C' and organelle '1240141' is 3.555145."
            },
            {
                "from": "human",
                "value": "And forecast the inhibitory effect of 'C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O' on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240141\", \"result\":[{\"smile\":\"C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O\", \"value\": 3.998978}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O' and organelle '1240141' is 3.998978."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by 'C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1' on the cell '1247873'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1\"], \"cell_name\": \"1247873\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1247873\", \"result\":[{\"smile\":\"C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1\", \"value\": 3.222880}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1/C(=C\\C2=CC=CS2)/C(=O)/C(=C/C3=CC=CS3)/C1' and organelle '1247873' is 3.222880."
            },
            {
                "from": "human",
                "value": "And estimate the inhibition power of 'C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F' against the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1247873\", \"result\":[{\"smile\":\"C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F\", \"value\": 4.080237}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F' and organelle '1247873' is 4.080237."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'CC1=CN2C(=O)C=C(N=C2C(=C1)C(C)NC3=CC=CC=C3)N4CCOCC4' on the cell '907314'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN2C(=O)C=C(N=C2C(=C1)C(C)NC3=CC=CC=C3)N4CCOCC4\"], \"cell_name\": \"907314\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907314\", \"result\":[{\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)C(C)NC3=CC=CC=C3)N4CCOCC4\", \"value\": 5.234136}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN2C(=O)C=C(N=C2C(=C1)C(C)NC3=CC=CC=C3)N4CCOCC4' and organelle '907314' is 5.234136."
            },
            {
                "from": "human",
                "value": "And estimate the inhibition of 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' on the growth of the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907314\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\", \"value\": 2.591056}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F' and organelle '907314' is 2.591056."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' on the cell '688013'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\"], \"cell_name\": \"688013\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688013\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\", \"value\": 2.625861}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' and organelle '688013' is 2.625861."
            },
            {
                "from": "human",
                "value": "And assess the inhibition effect of 'B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O' in the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688013\", \"result\":[{\"smile\":\"B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O\", \"value\": -4.044821}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O' and organelle '688013' is -4.044821."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition effect of 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' in the cell '910784'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"910784\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910784\", \"result\":[{\"smile\":\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\", \"value\": -2.956658}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '910784' is -2.956658."
            },
            {
                "from": "human",
                "value": "And forecast the inhibitory impact 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' has on the cell mentioned above. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"], \"cell_name\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910784\", \"result\":[{\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -0.349664}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' and organelle '910784' is -0.349664."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of 'CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O''s inhibition on the cell '15630380000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O\"], \"cell_name\": \"15630380000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The most accurate cell name is '1503368'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1503368\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503368\", \"result\":[{\"smile\":\"CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O\", \"value\": -1.856754}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O' and organelle '1503368' is -1.856754."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition power of 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' against the cell '40132220000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\"], \"cell_name\": \"40132220000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "I should use '1240223' as the cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240223\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240223\", \"result\":[{\"smile\":\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": -2.128326}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' and organelle '1240223' is -2.128326."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' inhibits the cell '5533780000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"], \"cell_name\": \"5533780000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name would be '753538'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753538\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753538\", \"result\":[{\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -2.283472}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' and organelle '753538' is -2.283472."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' in stopping the cell '1489700000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"1489700000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The accurate cell name is '910784'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910784\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910784\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\", \"value\": 1.859507}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '910784' is 1.859507."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to inhibit the cell '22024210000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"], \"cell_name\": \"22024210000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1240222' matches the correct cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240222\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240222\", \"result\":[{\"smile\":\"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\", \"value\": -2.243784}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' and organelle '1240222' is -2.243784."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' on cell growth in the cell '7927100000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\"], \"cell_name\": \"7927100000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The selected cell name should be '907172'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907172\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907172\", \"result\":[{\"smile\":\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N\", \"value\": -1.095025}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OC[C@H](CC4=CNC5=CC=CC=C54)N' and organelle '907172' is -1.095025."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' inhibition potential on the cell '7174970000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\"], \"cell_name\": \"7174970000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'749717'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"749717\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749717\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\", \"value\": 0.909216}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' and organelle '749717' is 0.909216."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition effect of 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' in the cell '0961420000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"0961420000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name for this experiment is '924106'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"924106\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924106\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\", \"value\": 5.151380}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '924106' is 5.151380."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5' restricts the cell '1776490000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5\"], \"cell_name\": \"1776490000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The confirmed cell name is '749716'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"749716\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749716\", \"result\":[{\"smile\":\"CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5\", \"value\": 2.041085}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5' and organelle '749716' is 2.041085."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC' in stopping the cell '5607090000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC\"], \"cell_name\": \"5607090000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'907065'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907065\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907065\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC\", \"value\": 2.791073}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC' and organelle '907065' is 2.791073."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression rate of 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' on the cell '5044190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\"], \"cell_name\": \"5044190000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "I should use '910544' as the cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910544\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910544\", \"result\":[{\"smile\":\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\", \"value\": 0.954928}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' and organelle '910544' is 0.954928."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F' on the cell '40221120000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F\"], \"cell_name\": \"40221120000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name is '1240221'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240221\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240221\", \"result\":[{\"smile\":\"C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F\", \"value\": 0.577652}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F' and organelle '1240221' is 0.577652."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppression rate of 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' on the cell '6828010000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\"], \"cell_name\": \"6828010000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'688021' represents the correct cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688021\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688021\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\", \"value\": 4.742077}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' and organelle '688021' is 4.742077."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F''s inhibitory effect on the cell '9179140000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"9179140000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'949171' is the cell name I need. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949171\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949171\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\", \"value\": 2.042056}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '949171' is 2.042056."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' on the cell '1904940000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\"], \"cell_name\": \"1904940000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'910944' is the valid cell name for this study. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910944\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910944\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 2.875579}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO' and organelle '910944' is 2.875579."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O''s effectiveness in inhibiting the cell '12410160000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O\"], \"cell_name\": \"12410160000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The selected cell name should be '1240161'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240161\", \"result\":[{\"smile\":\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O\", \"value\": -8.012332}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)OC(C)(C)C)O)O)OC(=O)C6=CC=CC=C6)(CO4)OC(=O)C)O)C)O' and organelle '1240161' is -8.012332."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' exerts on the cell '0766800000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\"], \"cell_name\": \"0766800000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'687600'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687600\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687600\", \"result\":[{\"smile\":\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\", \"value\": 0.358928}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' and organelle '687600' is 0.358928."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F' exerts on the cell '4886160000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F\"], \"cell_name\": \"4886160000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "the cell name I're looking for is '684681'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"684681\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"684681\", \"result\":[{\"smile\":\"CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F\", \"value\": 5.301911}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F' and organelle '684681' is 5.301911."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' inhibits the cell '3979500000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"], \"cell_name\": \"3979500000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'905973' is the correct cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905973\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905973\", \"result\":[{\"smile\":\"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\", \"value\": 1.118754}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' and organelle '905973' is 1.118754."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5''s inhibitory effect on the cell '12589360000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5\"], \"cell_name\": \"12589360000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1298356'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298356\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298356\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5\", \"value\": 0.864031}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5' and organelle '1298356' is 0.864031."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression level of 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' on the cell '10317530000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"10317530000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The valid cell name is '1503371'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1503371\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503371\", \"result\":[{\"smile\":\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\", \"value\": 0.533190}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '1503371' is 0.533190."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C' against the cell '1989400000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C\"], \"cell_name\": \"1989400000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'908149' is the correct cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908149\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908149\", \"result\":[{\"smile\":\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C\", \"value\": 0.419697}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C' and organelle '908149' is 0.419697."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' on the cell '40130310000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\"], \"cell_name\": \"40130310000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1331040'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1331040\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331040\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 2.205983}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '1331040' is 2.205983."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppression rate of 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' on the cell '58127940000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\"], \"cell_name\": \"58127940000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1298475' should be used as the cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298475\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298475\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\", \"value\": 2.513680}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' and organelle '1298475' is 2.513680."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O' inhibits the cell '6380190000' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\"], \"cell_name\": \"6380190000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The valid cell name is '908136'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908136\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908136\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\", \"value\": 4.181588}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O' and organelle '908136' is 4.181588."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' reduces growth in the cell '8591060000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"8591060000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The right cell name is '908156'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908156\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908156\", \"result\":[{\"smile\":\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\", \"value\": -1.982721}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '908156' is -1.982721."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' reduces the cell '9906570000' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\"], \"cell_name\": \"9906570000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name is '909756'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909756\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909756\", \"result\":[{\"smile\":\"C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO\", \"value\": -3.710230}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)Cl)NC2=NC=CC(=N2)C3=CC(=NC=C3)NCCCO' and organelle '909756' is -3.710230."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition power of 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' against the cell '12074210000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\"], \"cell_name\": \"12074210000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name for this experiment is '1240127'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240127\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240127\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -0.110770}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '1240127' is -0.110770."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4''s effect on the cell '0415940000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4\"], \"cell_name\": \"0415940000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'910544' is the correct cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910544\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910544\", \"result\":[{\"smile\":\"CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4\", \"value\": 1.561968}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4' and organelle '910544' is 1.561968."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppression rate of 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' on the cell '05923810000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\"], \"cell_name\": \"05923810000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1298350'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298350\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298350\", \"result\":[{\"smile\":\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\", \"value\": 4.344801}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' and organelle '1298350' is 4.344801."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition impact of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' on the cell '43801390000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"43801390000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1330984'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1330984\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330984\", \"result\":[{\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 0.936919}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '1330984' is 0.936919."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how strongly 'C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N' suppresses the cell '6991840000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N\"], \"cell_name\": \"6991840000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The right cell name is '949168'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949168\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949168\", \"result\":[{\"smile\":\"C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N\", \"value\": 2.965177}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H](C1=C(C=CC(=C1Cl)F)Cl)OC2=C(N=CC(=C2)C3=CN(N=C3)C4CCNCC4)N' and organelle '949168' is 2.965177."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the level of 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4''s inhibitory effect on the cell '4367960000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"], \"cell_name\": \"4367960000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name for this experiment is '946367'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"946367\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946367\", \"result\":[{\"smile\":\"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\", \"value\": 2.528266}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' and organelle '946367' is 2.528266."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' on the cell '9750390000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"], \"cell_name\": \"9750390000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'905973'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905973\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905973\", \"result\":[{\"smile\":\"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\", \"value\": 1.118754}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' and organelle '905973' is 1.118754."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition effect of 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' in the cell '9711900000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"9711900000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'909711'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909711\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909711\", \"result\":[{\"smile\":\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\", \"value\": 5.354247}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '909711' is 5.354247."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4' on the cell '0809710000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\"], \"cell_name\": \"0809710000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The most proper cell name for this task is '910780'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910780\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910780\", \"result\":[{\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\", \"value\": 3.567374}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4' and organelle '910780' is 3.567374."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3''s effectiveness in inhibiting the cell '2920910000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3\"], \"cell_name\": \"2920910000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The most proper cell name for this task is '910922'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910922\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910922\", \"result\":[{\"smile\":\"C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3\", \"value\": -3.212041}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3' and organelle '910922' is -3.212041."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how much inhibition 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' causes in the cell '5017990000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"], \"cell_name\": \"5017990000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'909751'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909751\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909751\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\", \"value\": -0.485868}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' and organelle '909751' is -0.485868."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of 'C1=C(C(=O)NC(=O)N1)F' against the cell '9499040000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=C(C(=O)NC(=O)N1)F\"], \"cell_name\": \"9499040000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'949094'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949094\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949094\", \"result\":[{\"smile\":\"C1=C(C(=O)NC(=O)N1)F\", \"value\": 3.240825}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=C(C(=O)NC(=O)N1)F' and organelle '949094' is 3.240825."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the impact of 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' on cell growth in the cell '9475910000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\"], \"cell_name\": \"9475910000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The accurate cell name is '949157'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949157\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949157\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 1.877156}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '949157' is 1.877156."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' to inhibit the cell '08071290000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"08071290000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The accurate cell name is '1290807'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1290807\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290807\", \"result\":[{\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 0.384845}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '1290807' is 0.384845."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' impacts the proliferation of the cell '84107210000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"84107210000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper choice for the cell name is '1240187'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240187\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240187\", \"result\":[{\"smile\":\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\", \"value\": 0.995701}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '1240187' is 0.995701."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O''s action on growth suppression in the cell '9489020000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\"], \"cell_name\": \"9489020000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "Choose '909248' as the cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909248\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909248\", \"result\":[{\"smile\":\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\", \"value\": 2.912728}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' and organelle '909248' is 2.912728."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'C1=CC=C(C=C1)CCN=C(N)N=C(N)N' inhibits the cell '80163400000' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)CCN=C(N)N=C(N)N\"], \"cell_name\": \"80163400000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The valid cell name is '1480360'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1480360\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480360\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CCN=C(N)N=C(N)N\", \"value\": 11.490225}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)CCN=C(N)N=C(N)N' and organelle '1480360' is 11.490225."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the degree of suppression by 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' on the cell '9499000000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\"], \"cell_name\": \"9499000000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The correct cell name to use is '909904'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909904\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909904\", \"result\":[{\"smile\":\"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C\", \"value\": 4.940841}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C(=O)O)/C)/C' and organelle '909904' is 4.940841."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression rate of 'CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5' on the cell '3547750000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5\"], \"cell_name\": \"3547750000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "Choose '753574' as the cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753574\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753574\", \"result\":[{\"smile\":\"CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5\", \"value\": 2.362865}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5' and organelle '753574' is 2.362865."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how 'C[C@H]1C[C@@H]2[C@H]([C@H]([C@]3(O2)CC[C@H]4[C@@H]5CC=C6C[C@H](CC[C@@]6([C@H]5CC4=C3C)C)O)C)NC1' inhibits the cell '31393100000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C[C@@H]2[C@H]([C@H]([C@]3(O2)CC[C@H]4[C@@H]5CC=C6C[C@H](CC[C@@]6([C@H]5CC4=C3C)C)O)C)NC1\"], \"cell_name\": \"31393100000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The proper cell name would be '1331039'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1331039\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331039\", \"result\":[{\"smile\":\"C[C@H]1C[C@@H]2[C@H]([C@H]([C@]3(O2)CC[C@H]4[C@@H]5CC=C6C[C@H](CC[C@@]6([C@H]5CC4=C3C)C)O)C)NC1\", \"value\": 4.443044}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C[C@@H]2[C@H]([C@H]([C@]3(O2)CC[C@H]4[C@@H]5CC=C6C[C@H](CC[C@@]6([C@H]5CC4=C3C)C)O)C)NC1' and organelle '1331039' is 4.443044."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the potency of 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' to inhibit the cell '3076940000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\"], \"cell_name\": \"3076940000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "The valid cell name is '946370'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"946370\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946370\", \"result\":[{\"smile\":\"COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl\", \"value\": 4.363459}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C(C=C1)C2=CC3=NC=CN3C(=N2)NC4=C(C=CC=N4)C(=O)N)OC.Cl.Cl' and organelle '946370' is 4.363459."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the level of inhibition 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC' has on the cell '80982010000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC\"], \"cell_name\": \"80982010000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "'1290808' is the proper cell name. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1290808\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290808\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC\", \"value\": 4.203179}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC(=C2Cl)N)NC3=C(C=C(C=C3)N4CCC(CC4)C(=O)N)OC' and organelle '1290808' is 4.203179."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the potency of 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' as an inhibitor for the cell '9507940000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\"], \"cell_name\": \"9507940000\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"invalid cell\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The cell might not exist, please input the right cell name."
            },
            {
                "from": "human",
                "value": "the cell name in question is '909754'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909754\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909754\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\", \"value\": 2.488167}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' and organelle '909754' is 2.488167."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much the optimized drugs reduces growth in the cell '1330983'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1330983\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330983\", \"result\":[{\"smile\":\"CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C\", \"value\": 3.742725}, {\"smile\":\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\", \"value\": 0.072340}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C', 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O' and organelle '1330983' are 3.742725, 0.072340."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much the generated drugs inhibits the cell '910784' growth. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910784\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910784\", \"result\":[{\"smile\":\"CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl\", \"value\": 1.935645}, {\"smile\":\"COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F\", \"value\": -3.730258}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl', 'COC1=C(C=C(C=N1)C2=CC3=C(C=CN=C3C=C2)C4=CN=NC=C4)NS(=O)(=O)C5=C(C=C(C=C5)F)F' and organelle '910784' are 1.935645, -3.730258."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the optimized drugs' inhibition effect on the cell '908158'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"908158\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908158\", \"result\":[{\"smile\":\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\", \"value\": 3.170573}, {\"smile\":\"CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]\", \"value\": -5.442396}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N', 'CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]' and organelle '908158' are 3.170573, -5.442396."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition capacity of the optimized drugs on the cell '1240221'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1240221\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240221\", \"result\":[{\"smile\":\"C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C\", \"value\": -3.466417}, {\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\", \"value\": 1.320651}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C[C@H]1CCC[C@@]2([C@@H](O2)C[C@H](OC(=O)C[C@@H](C(C(=O)[C@@H]([C@H]1O)C)(C)C)O)/C(=C/C3=CSC(=N3)C)/C)C', 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' and organelle '1240221' are -3.466417, 1.320651."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how the optimized drugs impacts the growth of the cell '749715'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"749715\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749715\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF\", \"value\": 5.766289}, {\"smile\":\"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\", \"value\": -1.629912}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CF', 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' and organelle '749715' are 5.766289, -1.629912."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the optimized drugs' potential in stopping the cell '717431'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"717431\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"717431\", \"result\":[{\"smile\":\"C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N\", \"value\": -0.067024}, {\"smile\":\"CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C\", \"value\": 4.935219}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1C[C@@H](CNC1)NC(=O)C2=C(C=C(S2)C3=CC(=CC=C3)F)NC(=O)N', 'CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C' and organelle '717431' are -0.067024, 4.935219."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the optimized drugs' suppression level on the cell '1240206'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1240206\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240206\", \"result\":[{\"smile\":\"C1CC1COC\\2=CC=CC(=O)/C2=C\\3/C=C(C(=C(N3)N)C#N)C4CCNCC4\", \"value\": 4.879813}, {\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N\", \"value\": 1.688078}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CC1COC\\2=CC=CC(=O)/C2=C\\3/C=C(C(=C(N3)N)C#N)C4CCNCC4', 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)NC(=O)C6=CN=C(C=C6)N' and organelle '1240206' are 4.879813, 1.688078."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the generated drugs' capacity to inhibit the cell '907069'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"907069\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907069\", \"result\":[{\"smile\":\"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\", \"value\": -0.012655}, {\"smile\":\"CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl\", \"value\": 2.853711}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C', 'CC1=CC2=C(C=C1)N=C(C3=NC=C(N23)C)NCCN.Cl' and organelle '907069' are -0.012655, 2.853711."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition effect of the generated drugs on the cell '753531'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"753531\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753531\", \"result\":[{\"smile\":\"CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C\", \"value\": -5.771941}, {\"smile\":\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\", \"value\": 4.030895}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C2[C@H](C(=O)[C@@]3([C@H](C[C@@H]4[C@]([C@H]3[C@@H]([C@@](C2(C)C)(C[C@@H]1OC(=O)[C@@H]([C@H](C5=CC=CC=C5)NC(=O)C6=CC=CC=C6)O)O)OC(=O)C7=CC=CC=C7)(CO4)OC(=O)C)O)C)OC(=O)C', 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl' and organelle '753531' are -5.771941, 4.030895."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much the generated drugs reduces growth in the cell '1331037'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1331037\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331037\", \"result\":[{\"smile\":\"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\", \"value\": 0.423752}, {\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 2.369280}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3', 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '1331037' are 0.423752, 2.369280."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of the optimized drugs on cell growth in the cell '1330931'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1330931\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330931\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F\", \"value\": -3.170295}, {\"smile\":\"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\", \"value\": 0.628703}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)/C=C/C4=CN=C5C(=C4OC)C=CN5)C(F)(F)F', 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' and organelle '1330931' are -3.170295, 0.628703."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by the optimized drugs on the cell '905991'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"905991\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905991\", \"result\":[{\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -2.271591}, {\"smile\":\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\", \"value\": 3.514393}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO', 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' and organelle '905991' are -2.271591, 3.514393."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the optimized drugs' suppression level on the cell '905940'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"905940\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905940\", \"result\":[{\"smile\":\"CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N\", \"value\": 2.474912}, {\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\", \"value\": 3.436029}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CC(=C(C=C1)F)NC(=O)NC2=CC=C(C=C2)C3=C4C(=CC=C3)NN=C4N', 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' and organelle '905940' are 2.474912, 3.436029."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the generated drugs' capacity to inhibit the cell '910544'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910544\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910544\", \"result\":[{\"smile\":\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\", \"value\": -1.298269}, {\"smile\":\"CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C\", \"value\": 3.841790}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC', 'CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C' and organelle '910544' are -1.298269, 3.841790."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition potential of the generated drugs on the cell '907791'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"907791\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907791\", \"result\":[{\"smile\":\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\", \"value\": 5.197903}, {\"smile\":\"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\", \"value\": 1.529214}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O', 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' and organelle '907791' are 5.197903, 1.529214."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by the optimized drugs in the cell '905936'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"905936\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905936\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F\", \"value\": 1.651443}, {\"smile\":\"C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F\", \"value\": 2.575727}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F', 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F' and organelle '905936' are 1.651443, 2.575727."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by the optimized drugs on the cell '1303912'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1303912\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1303912\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 5.871537}, {\"smile\":\"CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4\", \"value\": 5.663168}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C', 'CC1=CC=CC(=N1)C2=C(N=C(N2)C(C)(C)C)C3=CC4=C(C=C3)OCO4' and organelle '1303912' are 5.871537, 5.663168."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition impact of the optimized drugs on the cell '1298359'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1298359\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298359\", \"result\":[{\"smile\":\"COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F\", \"value\": 0.446657}, {\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 4.916772}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=CC(=C(C(=C1NS(=O)(=O)C2(CC2)C[C@@H](CO)O)NC3=C(C=C(C=C3)I)F)F)F', 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C' and organelle '1298359' are 0.446657, 4.916772."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the generated drugs' effect on the cell '724859'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"724859\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724859\", \"result\":[{\"smile\":\"C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F\", \"value\": 3.047557}, {\"smile\":\"C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F\", \"value\": 4.059397}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F', 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F' and organelle '724859' are 3.047557, 4.059397."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the effect of the generated drugs in inhibiting the cell '1240182'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240182\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": 1.061039}, {\"smile\":\"CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F\", \"value\": 5.377932}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F', 'CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F' and organelle '1240182' are 1.061039, 5.377932."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of the generated drugs in stopping the cell '906790'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"906790\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906790\", \"result\":[{\"smile\":\"CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F\", \"value\": 4.070908}, {\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 3.427841}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F', 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '906790' are 4.070908, 3.427841."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the effect of the generated drugs in inhibiting the cell '910906'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910906\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910906\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl\", \"value\": 1.048765}, {\"smile\":\"CCOC1=C(C(=CC(=N1)NC(=O)CC2=C(C=CC(=C2)OC)OC)N)C#N\", \"value\": 4.447970}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl', 'CCOC1=C(C(=CC(=N1)NC(=O)CC2=C(C=CC(=C2)OC)OC)N)C#N' and organelle '910906' are 1.048765, 4.447970."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of the optimized drugs' action on the cell '909721'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"909721\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909721\", \"result\":[{\"smile\":\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\", \"value\": 1.006943}, {\"smile\":\"C1COCCN1C2=CC(=O)C3=C(O2)C(=CC=C3)C4=CC=CC5=C4SC6=CC=CC=C56\", \"value\": 2.639121}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl', 'C1COCCN1C2=CC(=O)C3=C(O2)C(=CC=C3)C4=CC=CC5=C4SC6=CC=CC=C56' and organelle '909721' are 1.006943, 2.639121."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the degree of suppression by the generated drugs on the cell '910710'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910710\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910710\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\", \"value\": 2.192083}, {\"smile\":\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\", \"value\": 1.380749}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F', 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' and organelle '910710' are 2.192083, 1.380749."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the effect of the optimized drugs in inhibiting the cell '1503368'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1503368\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503368\", \"result\":[{\"smile\":\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=C(C=C(C=C4)Cl)Cl\", \"value\": 5.101092}, {\"smile\":\"CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6\", \"value\": 2.538275}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=C(C=C(C=C4)Cl)Cl', 'CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6' and organelle '1503368' are 5.101092, 2.538275."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of the optimized drugs in stopping the cell '909747'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"909747\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909747\", \"result\":[{\"smile\":\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\", \"value\": 5.225195}, {\"smile\":\"CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O\", \"value\": -2.291506}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O', 'CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O' and organelle '909747' are 5.225195, -2.291506."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory impact the generated drugs has on the cell '1290907'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1290907\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1290907\", \"result\":[{\"smile\":\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\", \"value\": 5.495669}, {\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\", \"value\": 0.914688}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O', 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' and organelle '1290907' are 5.495669, 0.914688."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how much inhibition the generated drugs causes in the cell '910910'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910910\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910910\", \"result\":[{\"smile\":\"COC(=O)CNC(=O)C(=O)OC\", \"value\": 7.903340}, {\"smile\":\"C/C/1=C\\CC[C@@]2([C@H](O2)[C@@H]3[C@@H](CC1)C(=C)C(=O)O3)C\", \"value\": 4.360326}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC(=O)CNC(=O)C(=O)OC', 'C/C/1=C\\CC[C@@]2([C@H](O2)[C@@H]3[C@@H](CC1)C(=C)C(=O)O3)C' and organelle '910910' are 7.903340, 4.360326."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of the optimized drugs' inhibition on the cell '907270'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"907270\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907270\", \"result\":[{\"smile\":\"CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5\", \"value\": 1.934455}, {\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5\", \"value\": 2.477796}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5', 'CC1=C(C=C(C=C1)C(=O)NC2=CC=CC(=C2)C(F)(F)F)NC3=NC(=NC4=C3C=NN4C)C5=CN=CC=C5' and organelle '907270' are 1.934455, 2.477796."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how the optimized drugs impacts the proliferation of the cell '687455'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"687455\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687455\", \"result\":[{\"smile\":\"C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3\", \"value\": 2.720158}, {\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\", \"value\": 4.459153}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CN(CCC1CCCCNC(=O)/C=C/C2=CN=CC=C2)C(=O)C3=CC=CC=C3', 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4' and organelle '687455' are 2.720158, 4.459153."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition the optimized drugs exerts on the cell '910899'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910899\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910899\", \"result\":[{\"smile\":\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\", \"value\": 1.237906}, {\"smile\":\"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\", \"value\": 1.255814}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4', 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' and organelle '910899' are 1.237906, 1.255814."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition strength of the optimized drugs against the cell '905944'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"905944\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905944\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": 1.830322}, {\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\", \"value\": 3.880006}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C', 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '905944' are 1.830322, 3.880006."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of the optimized drugs against the cell '753620'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"753620\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753620\", \"result\":[{\"smile\":\"CCN1CCC(CC1)N2C=C(N=N2)CNC3=CC(=C4C(=C3)C(=C(C=N4)C#N)NC5=CC(=C(C=C5)F)Cl)Cl\", \"value\": 2.293045}, {\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.868904}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1CCC(CC1)N2C=C(N=N2)CNC3=CC(=C4C(=C3)C(=C(C=N4)C#N)NC5=CC(=C(C=C5)F)Cl)Cl', 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' and organelle '753620' are 2.293045, 2.868904."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how the generated drugs will inhibit the cell '688015'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"688015\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688015\", \"result\":[{\"smile\":\"C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": -0.496361}, {\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -1.308300}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C[C@@]12[C@@H]([C@@H](C[C@@H](O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC', 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' and organelle '688015' are -0.496361, -1.308300."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression level of the optimized drugs on the cell '1330973'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1330973\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330973\", \"result\":[{\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 4.160184}, {\"smile\":\"CN1C(=O)N2C=NC(=C2N=N1)C(=O)N\", \"value\": 7.465158}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6', 'CN1C(=O)N2C=NC(=C2N=N1)C(=O)N' and organelle '1330973' are 4.160184, 7.465158."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the reduction caused by the generated drugs on the cell '687819'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"687819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687819\", \"result\":[{\"smile\":\"CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C\", \"value\": 5.092787}, {\"smile\":\"CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\", \"value\": 5.261946}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C', 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N' and organelle '687819' are 5.092787, 5.261946."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the action of the optimized drugs on growth in the cell '906824'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"906824\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906824\", \"result\":[{\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": -0.896050}, {\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC\", \"value\": 3.019909}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO', 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=C(C=C(C=C4)N5CCC(CC5)O)OC' and organelle '906824' are -0.896050, 3.019909."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of the optimized drugs in suppressing the cell '909785'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"909785\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909785\", \"result\":[{\"smile\":\"B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O\", \"value\": -4.006056}, {\"smile\":\"COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F\", \"value\": 1.356039}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'B([C@H](CC(C)C)NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C2=NC=CN=C2)(O)O', 'COC1=CC2=C(C=CN=C2C=C1OC)OC3=CC=C(C=C3)NC(=O)C4(CC4)C(=O)NC5=CC=C(C=C5)F' and organelle '909785' are -4.006056, 1.356039."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much the optimized drugs restricts the cell '924188'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"924188\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924188\", \"result\":[{\"smile\":\"C1=CC2=C(C(=O)/C(=N/NC3=CC4=C(C=C3)C=C(C=C4)S(=O)(=O)O)/C=C2S(=O)(=O)O)N=C1\", \"value\": 6.175841}, {\"smile\":\"CCC1=C/C(=C/2\\C(=C(NN2)C)C3=CC4=C(C=C3)OCCO4)/C(=O)C=C1O\", \"value\": 2.744319}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC2=C(C(=O)/C(=N/NC3=CC4=C(C=C3)C=C(C=C4)S(=O)(=O)O)/C=C2S(=O)(=O)O)N=C1', 'CCC1=C/C(=C/2\\C(=C(NN2)C)C3=CC4=C(C=C3)OCCO4)/C(=O)C=C1O' and organelle '924188' are 6.175841, 2.744319."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition of the optimized drugs on the growth of the cell '907063'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"907063\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907063\", \"result\":[{\"smile\":\"CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl\", \"value\": 4.562170}, {\"smile\":\"CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5\", \"value\": 0.800194}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CS(=O)(=O)C1=CC(=C(C=C1)C(=O)NC2=CC(=C(C=C2)Cl)C3=CC=CC=N3)Cl', 'CN1C=C(C=N1)C2=CC3=C4C(=CN=C3C=C2)C=CC(=O)N4C5=CC6=C(CCN6C(=O)C=C)C=C5' and organelle '907063' are 4.562170, 0.800194."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory action of the generated drugs against the cell '722046'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"722046\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"722046\", \"result\":[{\"smile\":\"COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2\", \"value\": 0.785830}, {\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\", \"value\": 3.180308}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC(=O)[C@H](CCSC)NC(=O)C1=C(C=C(C=C1)NC[C@H](CS)N)C2=CC=CC=C2', 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4' and organelle '722046' are 0.785830, 3.180308."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the generated drugs' impact in hindering the cell '908450'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"908450\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908450\", \"result\":[{\"smile\":\"CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C\", \"value\": 5.335613}, {\"smile\":\"C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3\", \"value\": 3.799294}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C', 'C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3' and organelle '908450' are 5.335613, 3.799294."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how effectively the optimized drugs inhibits the cell '908445'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"908445\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908445\", \"result\":[{\"smile\":\"CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C\", \"value\": 5.774208}, {\"smile\":\"C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl\", \"value\": 4.191130}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C', 'C1=CC(=CC=C1S(=O)(=O)N(CC2=C(C=C(C=C2)C3=NOC=N3)F)[C@H](CCC(F)(F)F)C(=O)N)Cl' and organelle '908445' are 5.774208, 4.191130."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibiting action of the generated drugs on the cell '1331036'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1331036\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331036\", \"result\":[{\"smile\":\"COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6\", \"value\": -1.675528}, {\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\", \"value\": -2.917898}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=CC=C(C=C1)N2C(=NN=C2SCCCN3C(=O)C4=CC=CC5=C4C(=CC=C5)C3=O)C6=CC=NC=C6', 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O' and organelle '1331036' are -1.675528, -2.917898."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of the optimized drugs on the cell '1240218'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"1240218\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240218\", \"result\":[{\"smile\":\"CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C\", \"value\": 3.940364}, {\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\", \"value\": -1.314992}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CN=C(C=N1)CNC(=O)C2=C3N(C4=CC=CC=C4S3)C5=C(C2=O)C=CC(=N5)N6CCCN(CC6)C', 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' and organelle '1240218' are 3.940364, -1.314992."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibitory action of the optimized drugs against the cell '905936'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"905936\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905936\", \"result\":[{\"smile\":\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\", \"value\": 2.130801}, {\"smile\":\"C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7\", \"value\": 3.129000}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O', 'C1CC2=C(C1)C=C(C=C2)OC3=NC4=C(C(=N3)N[C@@H](CC5=CC=CC=C5)CO)N=CN4CC6=CC=C(C=C6)C7=CC=CC=C7' and organelle '905936' are 2.130801, 3.129000."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the strength of the optimized drugs in inhibiting the cell '907066'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"907066\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907066\", \"result\":[{\"smile\":\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\", \"value\": 4.698926}, {\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": 2.739356}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N', 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO' and organelle '907066' are 4.698926, 2.739356."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how the optimized drugs impacts the proliferation of the cell '724870'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"724870\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724870\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\", \"value\": 0.661258}, {\"smile\":\"CN(C(=S)C1=CC=CC=C1)NC(=O)CC(=O)NN(C)C(=S)C2=CC=CC=C2\", \"value\": -5.277717}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5', 'CN(C(=S)C1=CC=CC=C1)NC(=O)CC(=O)NN(C)C(=S)C2=CC=CC=C2' and organelle '724870' are 0.661258, -5.277717."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the effect of the optimized drugs in inhibiting the cell '910710'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"910710\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910710\", \"result\":[{\"smile\":\"C1CCC(CC1)NC(=O)NC[C@H]2CCCO2\", \"value\": 0.274147}, {\"smile\":\"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\", \"value\": 1.114029}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2', 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' and organelle '910710' are 0.274147, 1.114029."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the degree of inhibition for the optimized drugs against the cell '713878'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(output_smiles)\", \"cell_name\": \"713878\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"713878\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O\", \"value\": -3.811859}, {\"smile\":\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\", \"value\": 0.778599}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O', 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' and organelle '713878' are -3.811859, 0.778599."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's effectiveness in inhibiting the cell '749711'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"749711\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"749711\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\", \"value\": -0.607599}, {\"smile\":\"CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl\", \"value\": 2.545915}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5', 'CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl' and organelle '749711' is -0.607599, 2.545915."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's inhibitory effect on the cell '949163'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949163\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949163\", \"result\":[{\"smile\":\"CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5\", \"value\": 1.870366}, {\"smile\":\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\", \"value\": 2.128520}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5', 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' and organelle '949163' is 1.870366, 2.128520."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibition caused by the drug on the cell '753584'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753584\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753584\", \"result\":[{\"smile\":\"CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CCl\", \"value\": 3.494010}, {\"smile\":\"CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4\", \"value\": 5.458121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CC=C(C=C1)C2=C(N(C3=C2C(=NC=N3)N)CCCO)C(=O)CCl', 'CN(C)CC1=CC(=CC=C1)N/C(=C\\2/C3=C(C=C(C=C3)C(=O)N(C)C)NC2=O)/C4=CC=CC=C4' and organelle '753584' is 3.494010, 5.458121."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for the drug on the cell '908469'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908469\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908469\", \"result\":[{\"smile\":\"CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\", \"value\": 2.485935}, {\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\", \"value\": 5.142730}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N', 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' and organelle '908469' is 2.485935, 5.142730."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the drug's inhibition strength against the cell '909758'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909758\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909758\", \"result\":[{\"smile\":\"C1COCCN1C2=CC(=O)C3=C(O2)C(=CC=C3)C4=CC=CC5=C4SC6=CC=CC=C56\", \"value\": 0.740891}, {\"smile\":\"C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1\", \"value\": 3.940256}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1COCCN1C2=CC(=O)C3=C(O2)C(=CC=C3)C4=CC=CC5=C4SC6=CC=CC=C56', 'C1=CC2=C(C=CC(=C2)/C=C\\3/C(=O)N=C(S3)NCC4=CC=CS4)N=C1' and organelle '909758' is 0.740891, 3.940256."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's inhibitory effect on the cell '1298357'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298357\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298357\", \"result\":[{\"smile\":\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\", \"value\": 2.012438}, {\"smile\":\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\", \"value\": -2.370526}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC', 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' and organelle '1298357' is 2.012438, -2.370526."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of the drug's action on the cell '924238'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"924238\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"924238\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4\", \"value\": 2.017936}, {\"smile\":\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\", \"value\": 1.877371}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4', 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '924238' is 2.017936, 1.877371."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the reduction rate of the drug on the cell '908451'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908451\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908451\", \"result\":[{\"smile\":\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\", \"value\": 0.061009}, {\"smile\":\"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\", \"value\": -0.602616}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N', 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' and organelle '908451' is 0.061009, -0.602616."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of the drug in limiting the cell '1330941'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1330941\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1330941\", \"result\":[{\"smile\":\"CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6\", \"value\": 4.464606}, {\"smile\":\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\", \"value\": 2.948615}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6', 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '1330941' is 4.464606, 2.948615."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's capacity to inhibit the cell '1303896'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1303896\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1303896\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\", \"value\": 2.266057}, {\"smile\":\"CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5\", \"value\": 1.195139}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC', 'CCNC1=NC(=NC(=C1)NC2CCC(CC2)O)NC3=CC4=C(C=C3)N(C=C4)CC5=CC=CC=C5' and organelle '1303896' is 2.266057, 1.195139."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory level of the drug on the cell '1480358'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1480358\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480358\", \"result\":[{\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC\", \"value\": 3.417378}, {\"smile\":\"CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C\", \"value\": 5.881461}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=C(C=C2C(=C1)C(=NC=N2)N3C(=NC(=N3)C4=CC=CC=N4)N)OC', 'CC1=C(C=C(C=C1)NC2=NC=NC(=C2)C3=CC(=CC=C3)N4C(=O)C5=CC=CC=C5C4=O)NS(=O)(=O)C' and organelle '1480358' is 3.417378, 5.881461."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of the drug against the cell '1480371'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1480371\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480371\", \"result\":[{\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.754287}, {\"smile\":\"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\", \"value\": 4.367900}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6', 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' and organelle '1480371' is 2.754287, 4.367900."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by the drug in the cell '1299069'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1299069\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1299069\", \"result\":[{\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F\", \"value\": 2.955637}, {\"smile\":\"CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4\", \"value\": 3.274538}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F', 'CN(C)C/C=C/C(=O)NC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C=C3)F)Cl)O[C@H]4CCOC4' and organelle '1299069' is 2.955637, 3.274538."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of the drug against the cell '687985'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687985\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687985\", \"result\":[{\"smile\":\"CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C\", \"value\": 3.075209}, {\"smile\":\"C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C\", \"value\": -0.365570}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(C(=O)N(C2=NC(=NC=C12)NC3=NC=C(C=C3)N4CCNCC4)C5CCCC5)C(=O)C', 'C[C@H]1COCCN1C2=NC(=NC3=C2C=CC(=N3)C4=CC(=C(C=C4)OC)CO)N5CCOC[C@@H]5C' and organelle '687985' is 3.075209, -0.365570."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by the drug in the cell '753621'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753621\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753621\", \"result\":[{\"smile\":\"C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F\", \"value\": 5.402245}, {\"smile\":\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\", \"value\": 2.354071}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC(=CC=C1C2=CC(=C(S2)NC(=O)N)C(=O)N)F', 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '753621' is 5.402245, 2.354071."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory potency of the drug against the cell '905952'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905952\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905952\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\", \"value\": 2.957516}, {\"smile\":\"CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O\", \"value\": -7.409822}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O', 'CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O' and organelle '905952' is 2.957516, -7.409822."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the drug's action on growth suppression in the cell '1503370'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1503370\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503370\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO\", \"value\": -0.394057}, {\"smile\":\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\", \"value\": -0.431470}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C(C=C1)NS(=O)(=O)C2=CC=CC(=C2)/C=C/C(=O)NO', 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' and organelle '1503370' is -0.394057, -0.431470."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory action of the drug against the cell '1331025'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1331025\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1331025\", \"result\":[{\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\", \"value\": 3.986791}, {\"smile\":\"C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F\", \"value\": 4.111276}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4', 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F' and organelle '1331025' is 3.986791, 4.111276."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibition effect of the drug in the cell '907172'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907172\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907172\", \"result\":[{\"smile\":\"C1=C(C(=O)NC(=O)N1)F\", \"value\": 3.516765}, {\"smile\":\"CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O\", \"value\": 0.335992}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=C(C(=O)NC(=O)N1)F', 'CC1=CC=C(C=C1)C(=O)N(CCCN)[C@@H](C2=NC3=C(C=CC(=C3)Cl)C(=O)N2CC4=CC=CC=C4)C(C)C.CS(=O)(=O)O' and organelle '907172' is 3.516765, 0.335992."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much the drug can inhibit the cell '1240210'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240210\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240210\", \"result\":[{\"smile\":\"CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C\", \"value\": 2.203151}, {\"smile\":\"CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4\", \"value\": 1.901299}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C', 'CC1=CN2C(=O)C=C(N=C2C(=C1)[C@@H](C)NC3=CC=CC=C3C(=O)O)N4CCOCC4' and organelle '1240210' is 2.203151, 1.901299."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which the drug inhibits the cell '1503370'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1503370\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1503370\", \"result\":[{\"smile\":\"C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F\", \"value\": 3.624940}, {\"smile\":\"CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F\", \"value\": 4.531440}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F', 'CC1=C(C(CC(=O)N1)C2=CC=C(C=C2)C(F)(F)F)C(=O)NC3=C(C=C4C(=C3)C=NN4)F' and organelle '1503370' is 3.624940, 4.531440."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of the drug in suppressing the cell '688018'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688018\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688018\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\", \"value\": -0.945518}, {\"smile\":\"CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO\", \"value\": 1.749609}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O', 'CC1=C(SC(=N1)NC(=O)C)C2=CC(=C(C=C2)Cl)S(=O)(=O)NCCO' and organelle '688018' is -0.945518, 1.749609."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of inhibition by the drug in the cell '949161'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"949161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"949161\", \"result\":[{\"smile\":\"CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6\", \"value\": 2.250616}, {\"smile\":\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\", \"value\": 4.437550}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=C(C=C2C(=C1)C(=O)C3=C(C2(C)C)NC4=C3C=CC(=C4)C#N)N5CCC(CC5)N6CCOCC6', 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '949161' is 2.250616, 4.437550."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how much inhibition the drug causes in the cell '687807'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"687807\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"687807\", \"result\":[{\"smile\":\"CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O\", \"value\": -1.751685}, {\"smile\":\"CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C\", \"value\": 4.770752}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC[C@@]1(C2=C(COC1=O)C(=O)N3CC4=CC5=CC=CC=C5N=C4C3=C2)O', 'CCN(CC)CCNC(=O)C1=C(NC(=C1C)/C=C\\2/C3=C(C=CC(=C3)F)NC2=O)C' and organelle '687807' is -1.751685, 4.770752."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much the drug reduces growth in the cell '907299'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907299\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907299\", \"result\":[{\"smile\":\"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\", \"value\": 2.633366}, {\"smile\":\"CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O\", \"value\": -1.384983}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F', 'CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O' and organelle '907299' is 2.633366, -1.384983."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug inhibition potential on the cell '1322213'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1322213\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1322213\", \"result\":[{\"smile\":\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\", \"value\": 2.319349}, {\"smile\":\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\", \"value\": 3.956933}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C', 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' and organelle '1322213' is 2.319349, 3.956933."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibition caused by the drug on the cell '1298475'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1298475\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1298475\", \"result\":[{\"smile\":\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\", \"value\": 5.401723}, {\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5\", \"value\": 1.958838}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O', 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=C(C=C2)CN3CCN(CC3)C)C(F)(F)F)C#CC4=CN=C5N4N=CC=C5' and organelle '1298475' is 5.401723, 1.958838."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the cell growth inhibition effect of the drug on the cell '688121'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"688121\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"688121\", \"result\":[{\"smile\":\"CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F\", \"value\": 2.720649}, {\"smile\":\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\", \"value\": 3.071061}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=CC(=C(C=C1N2CCC(CC2)N3CCN(CC3)S(=O)(=O)C)OC)NC4=NC=CC(=N4)C5=C(N=C6N5C=CC=C6)C7=CC(=C(C=C7)OC)C(=O)NC8=C(C=CC=C8F)F', 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' and organelle '688121' is 2.720649, 3.071061."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of the drug in suppressing the cell '909757'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909757\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909757\", \"result\":[{\"smile\":\"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\", \"value\": 0.746848}, {\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\", \"value\": 4.077922}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C', 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5' and organelle '909757' is 0.746848, 4.077922."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's inhibition rate on the cell '753545'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753545\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753545\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\", \"value\": 2.100204}, {\"smile\":\"C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F\", \"value\": 2.398772}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4', 'C1=CC(=C(C(=C1)F)N(C2=NC(=C(C=C2)C(=O)N)C3=C(C=C(C=C3)F)F)C(=O)N)F' and organelle '753545' is 2.100204, 2.398772."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much the drug can suppress the cell '724859'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"724859\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"724859\", \"result\":[{\"smile\":\"CN1CCN(CC1)C2=CC(=C(C=C2)NC3=NC=C4C(=N3)N(C5=CC=CC=C5C(=O)N4C)C)OC\", \"value\": 3.559013}, {\"smile\":\"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\", \"value\": 1.745454}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1CCN(CC1)C2=CC(=C(C=C2)NC3=NC=C4C(=N3)N(C5=CC=CC=C5C(=O)N4C)C)OC', 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' and organelle '724859' is 3.559013, 1.745454."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by the drug on the cell '910566'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910566\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910566\", \"result\":[{\"smile\":\"CN1CCN(CC1)CCOC2=CC(=C3C(=C2)N=CN=C3NC4=C(C=CC5=C4OCO5)Cl)OC6CCOCC6\", \"value\": 2.711361}, {\"smile\":\"COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC\", \"value\": 2.697820}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1CCN(CC1)CCOC2=CC(=C3C(=C2)N=CN=C3NC4=C(C=CC5=C4OCO5)Cl)OC6CCOCC6', 'COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC' and organelle '910566' is 2.711361, 2.697820."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well the drug restricts the cell '713899'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"713899\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"713899\", \"result\":[{\"smile\":\"C1=CC2=C(C(=O)/C(=N/NC3=CC4=C(C=C3)C=C(C=C4)S(=O)(=O)O)/C=C2S(=O)(=O)O)N=C1\", \"value\": 4.321121}, {\"smile\":\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\", \"value\": 4.191104}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC2=C(C(=O)/C(=N/NC3=CC4=C(C=C3)C=C(C=C4)S(=O)(=O)O)/C=C2S(=O)(=O)O)N=C1', 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '713899' is 4.321121, 4.191104."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much the drug can inhibit the cell '906801'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"906801\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"906801\", \"result\":[{\"smile\":\"CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC(=CC=C4)NC(=O)C)C5CC5\", \"value\": -0.750562}, {\"smile\":\"C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O\", \"value\": 5.308507}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC(=CC=C4)NC(=O)C)C5CC5', 'C1=CC=C(C=C1)S(=O)(=O)N(CC(F)(F)F)C2=CC=C(C=C2)C(C(F)(F)F)(C(F)(F)F)O' and organelle '906801' is -0.750562, 5.308507."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the drug's inhibition rate on the cell '753595'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753595\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753595\", \"result\":[{\"smile\":\"C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O\", \"value\": 0.502212}, {\"smile\":\"CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5\", \"value\": 2.507164}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C(C=C1)CN2C3=CC=CC=C3C(=C(C2=O)C(=O)NCC(=O)O)O', 'CC(C)N1CCC(CC1)NC2=NC(=NC3=CC(=C(C=C32)OC)OCCCN4CCCC4)C5CCCCC5' and organelle '753595' is 0.502212, 2.507164."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the strength of the drug in inhibiting the cell '905967'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905967\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905967\", \"result\":[{\"smile\":\"COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC\", \"value\": 1.016681}, {\"smile\":\"COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21\", \"value\": 3.620384}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC', 'COC1=CC=CC2=C/C(=C/3\\C4=C(N=CNN4C(=N3)C5CCC(CC5)C(=O)O)N)/N=C21' and organelle '905967' is 1.016681, 3.620384."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the drug's inhibitory effect on the cell '909785'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909785\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909785\", \"result\":[{\"smile\":\"CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO\", \"value\": 3.055915}, {\"smile\":\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\", \"value\": 2.202121}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CN1C=NC2=C1C=C(C(=C2F)NC3=C(C=C(C=C3)Br)Cl)C(=O)NOCCO', 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '909785' is 3.055915, 2.202121."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the potency of the drug as an inhibitor for the cell '1299052'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1299052\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1299052\", \"result\":[{\"smile\":\"C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O\", \"value\": 2.938101}, {\"smile\":\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\", \"value\": 1.695209}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C[C@@H]1OC[C@@H]2[C@@H](O1)[C@@H]([C@H]([C@@H](O2)O[C@H]3[C@H]4COC(=O)[C@@H]4[C@@H](C5=CC6=C(C=C35)OCO6)C7=CC(=C(C(=C7)OC)O)OC)O)O', 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '1299052' is 2.938101, 1.695209."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much the drug restricts the cell '753584'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753584\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753584\", \"result\":[{\"smile\":\"C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4\", \"value\": 0.405241}, {\"smile\":\"CC/C(=C(\\C1=CC=CC=C1)/C2=CC=C(C=C2)OCCN(C)C)/C3=CC=CC=C3\", \"value\": 4.096077}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4', 'CC/C(=C(\\C1=CC=CC=C1)/C2=CC=C(C=C2)OCCN(C)C)/C3=CC=CC=C3' and organelle '753584' is 0.405241, 4.096077."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the drug inhibition potential on the cell '1480371'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1480371\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1480371\", \"result\":[{\"smile\":\"CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC(=CC=C4)NC(=O)C)C5CC5\", \"value\": 0.136124}, {\"smile\":\"CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5\", \"value\": 2.405639}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C2C(=C(N(C1=O)C)NC3=C(C=C(C=C3)I)F)C(=O)N(C(=O)N2C4=CC(=CC=C4)NC(=O)C)C5CC5', 'CC1=C(C=C(C=C1)C(=O)NC2=CC(=CC(=C2)N3C=C(N=C3)C)C(F)(F)F)NC4=NC=CC(=N4)C5=CN=CC=C5' and organelle '1480371' is 0.136124, 2.405639."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by the drug on the cell '753564'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"753564\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"753564\", \"result\":[{\"smile\":\"CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl\", \"value\": 3.470240}, {\"smile\":\"CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C\", \"value\": 2.172294}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=CN=C(N=C1C2=CNC(=C2)C(=O)N[C@H](CO)C3=CC(=CC=C3)Cl)NC4=C(C=C(C=C4)F)Cl', 'CC1=C(C(=CC=C1)C)OC(=O)N(C2=C(C=C(C=C2)OC)OC)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5CCN(CC5)C' and organelle '753564' is 3.470240, 2.172294."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much the drug reduces the cell '925347' growth. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"925347\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"925347\", \"result\":[{\"smile\":\"CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O\", \"value\": 2.470176}, {\"smile\":\"CC1=CN=C(C(=N1)OC)NS(=O)(=O)C2=C(N=CC=C2)C3=CC=C(C=C3)C4=NN=CO4\", \"value\": 5.635627}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1=C(N=C(N=C1N)[C@H](CC(=O)N)NC[C@@H](C(=O)N)N)C(=O)N[C@@H]([C@H](C2=CN=CN2)OC3C(C(C(C(O3)CO)O)O)OC4C(C(C(C(O4)CO)O)OC(=O)N)O)C(=O)N[C@H](C)[C@H]([C@H](C)C(=O)N[C@@H]([C@@H](C)O)C(=O)NCCC5=NC(=CS5)C6=NC(=CS6)C(=O)NCCC[S+](C)C)O', 'CC1=CN=C(C(=N1)OC)NS(=O)(=O)C2=C(N=CC=C2)C3=CC=C(C=C3)C4=NN=CO4' and organelle '925347' is 2.470176, 5.635627."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the cell growth inhibition effect of the drug on the cell '909703'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"909703\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"909703\", \"result\":[{\"smile\":\"C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5OC)O)(C(=O)CO)O)N)O\", \"value\": -3.847173}, {\"smile\":\"C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F\", \"value\": -5.209375}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C[C@H]1[C@H]([C@H](C[C@@H](O1)O[C@H]2C[C@@](CC3=C(C4=C(C(=C23)O)C(=O)C5=C(C4=O)C=CC=C5OC)O)(C(=O)CO)O)N)O', 'C1=CN(C(=O)N=C1N)[C@H]2C([C@@H]([C@H](O2)CO)O)(F)F' and organelle '909703' is -3.847173, -5.209375."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by the drug on the cell '946354'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"946354\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946354\", \"result\":[{\"smile\":\"CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C\", \"value\": 4.676514}, {\"smile\":\"C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F\", \"value\": 3.930161}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC(C)CC(=O)NC1=NNC2=C1CN(C2(C)C)C(=O)C3CCN(CC3)C', 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F' and organelle '946354' is 4.676514, 3.930161."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the effect of the drug in inhibiting the cell '905991'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"905991\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"905991\", \"result\":[{\"smile\":\"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\", \"value\": 1.253630}, {\"smile\":\"CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl\", \"value\": 1.575413}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O', 'CC1=C(C=CC(=C1)[N+](=O)[O-])NS(=O)(=O)C2=C(C=CC(=C2)Cl)Cl' and organelle '905991' is 1.253630, 1.575413."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the degree to which the drug inhibits the cell '1240215'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"1240215\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"1240215\", \"result\":[{\"smile\":\"CCC1=C[C@H]2C[C@@](C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC\", \"value\": -3.650128}, {\"smile\":\"CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F\", \"value\": 5.319695}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=C[C@H]2C[C@@](C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC', 'CCN1CCN(CC1)CC2=C(C=C(C=C2)NC(=O)C3=CC(=C(C=C3)C)OC4=C5C=CNC5=NC=C4)C(F)(F)F' and organelle '1240215' is -3.650128, 5.319695."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition strength of the drug against the cell '908448'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"908448\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"908448\", \"result\":[{\"smile\":\"CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O\", \"value\": -6.528548}, {\"smile\":\"CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F\", \"value\": 4.637077}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CCC1=C2C=C(C=CC2=NC3=C1CN4C3=CC5=C(C4=O)COC(=O)[C@@]5(CC)O)O', 'CCC1=CN=CN=C1N2CCN(CC2)CC3=NC4=C(N3)C=C(C=C4)C(F)(F)F' and organelle '908448' is -6.528548, 4.637077."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the reduction caused by the drug on the cell '910899'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"910899\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"910899\", \"result\":[{\"smile\":\"CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C\", \"value\": -2.854877}, {\"smile\":\"C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F\", \"value\": 3.204135}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1(CCC(=C(C1)CN2CCN(CC2)C3=CC=C(C=C3)C(=O)NS(=O)(=O)C4=CC(=C(C=C4)N[C@H](CCN5CCOCC5)CSC6=CC=CC=C6)S(=O)(=O)C(F)(F)F)C7=CC=C(C=C7)Cl)C', 'C1CSCC2=C1NC(=NC2=O)C3=CC=C(C=C3)C(F)(F)F' and organelle '910899' is -2.854877, 3.204135."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibitory power of the drug on the cell '946355'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"946355\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"946355\", \"result\":[{\"smile\":\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\", \"value\": 5.422435}, {\"smile\":\"C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6\", \"value\": 2.957182}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C', 'C1CCN(C1)C2CCN(CC2)C(=O)C3=CC(=C(C=C3)C(=O)N4CCC(CC4)N5CCCC5)NC6=CC=CC=C6' and organelle '946355' is 5.422435, 2.957182."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibitory power of the drug on the cell '907067'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": \"(user_smiles)\", \"cell_name\": \"907067\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the interaction value (z-score) between the drug SMILES and the cell line.\", \"cell_line\": \"907067\", \"result\":[{\"smile\":\"C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F\", \"value\": 3.991891}, {\"smile\":\"CN1CCN(CC1)CCOC2=CC(=C3C(=C2)N=CN=C3NC4=C(C=CC5=C4OCO5)Cl)OC6CCOCC6\", \"value\": 2.261380}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_cell_line', 'drug_cell_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drugs 'C1CC1C(=O)N2CCN(CC2)C(=O)C3=C(C=CC(=C3)CC4=NNC(=O)C5=CC=CC=C54)F', 'CN1CCN(CC1)CCOC2=CC(=C3C(=C2)N=CN=C3NC4=C(C=CC5=C4OCO5)Cl)OC6CCOCC6' and organelle '907067' is 3.991891, 2.261380."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of molecules with an z-score of 0.4 for the cell line 684059 and 0.35 for the cell line 722045."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.4\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC=CC(C)CC=CCC=C(C=NCC)C1C=C=C1CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CCC1CNCC(=C)CCC1C=C1C(C)CN1CC', 'C=NCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C(NC(C)C)C=C(C)C2=[N+]1CN1', 'CCC=CC=NC[N+]1=CC=C1', 'C=CC1N2CC(=C)NC3CCC(CC)CC[N+](CCN=CN)=[N+]1C=C32', 'CNC1=CCCNCC2[N+]3=[N+]2C1C(C)NC(C)C3', 'C=NCC(C)(CCC)C(C)CC', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)ON(N)C1CC(C)(CC)C1CC(N)CC=C[N+](=CN)CC', 'CNC[N+](N)=CC=C(N)C(C)N']}"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.35\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C1NC(C)CC3=CC(=C31)C2=[N+]1CN1', 'CC=CCCN(C=NCC)CC', 'C=CC1C2CC(=C)NC3CCC(CC)CC[N+](CCN=CN)=[N+]1C=C32', 'CNC1=CCCNCC2C3=[N+]2CC(C)NC(C)C13', 'C=CCC(C)(CCC)C(C)CC', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)ON(N)C1CC(C)(CC)C1CC(N)CC=C[N+](=CN)CC', 'CNC[N+](N)=CC=C(N=CC(=N)C=C(N)NCN)C(C)N', 'CC=NNC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'CCC1=CN(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'CNCC(N)OC(C)NC=CC1C[N+](=N)CNCN1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules have an z-score of 0.4 for 684059 are ['CC=CC(C)CC=CCC=C(C=NCC)C1C=C=C1CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CCC1CNCC(=C)CCC1C=C1C(C)CN1CC', 'C=NCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C(NC(C)C)C=C(C)C2=[N+]1CN1', 'CCC=CC=NC[N+]1=CC=C1', 'C=CC1N2CC(=C)NC3CCC(CC)CC[N+](CCN=CN)=[N+]1C=C32', 'CNC1=CCCNCC2[N+]3=[N+]2C1C(C)NC(C)C3', 'C=NCC(C)(CCC)C(C)CC', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)ON(N)C1CC(C)(CC)C1CC(N)CC=C[N+](=CN)CC', 'CNC[N+](N)=CC=C(N)C(C)N']. And the molecules have an z-score of 0.35 for 722045 are ['C=CCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C1NC(C)CC3=CC(=C31)C2=[N+]1CN1', 'CC=CCCN(C=NCC)CC', 'C=CC1C2CC(=C)NC3CCC(CC)CC[N+](CCN=CN)=[N+]1C=C32', 'CNC1=CCCNCC2C3=[N+]2CC(C)NC(C)C13', 'C=CCC(C)(CCC)C(C)CC', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)ON(N)C1CC(C)(CC)C1CC(N)CC=C[N+](=CN)CC', 'CNC[N+](N)=CC=C(N=CC(=N)C=C(N)NCN)C(C)N', 'CC=NNC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'CCC1=CN(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'CNCC(N)OC(C)NC=CC1C[N+](=N)CNCN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules that can attain a z-score of 0.35 for the cell line 722045 and 0.4 for the cell line 905957."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.35\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CNCC(C)C', 'C=CC1=CC(C)(CN)CC=CC=CC(C)=C(C=CC)C(C)=C1N', 'C=C(CN)NC(=CCN=CCNCC)CCC1(C)CC1', 'CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCNCCC', 'C=C(CCCCCC)N(C=N)C=CCNN=CC1=C(CCC)C=CNC=C1']}"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.4\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=[N+]1CN=CC=C(C)C1(CN[N+](NNCNN)=C(C)NC(CN)=C(N)CC)C1=C(CN)NC1', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBBCB1N=BB(BC)N1', 'C=C1CCC1C(=CCC)CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules have an z-score of 0.35 for 722045 are ['CNCC(C)C', 'C=CC1=CC(C)(CN)CC=CC=CC(C)=C(C=CC)C(C)=C1N', 'C=C(CN)NC(=CCN=CCNCC)CCC1(C)CC1', 'CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCNCCC', 'C=C(CCCCCC)N(C=N)C=CCNN=CC1=C(CCC)C=CNC=C1']. And the molecules have an z-score of 0.4 for 905957 are ['C=[N+]1CN=CC=C(C)C1(CN[N+](NNCNN)=C(C)NC(CN)=C(N)CC)C1=C(CN)NC1', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBBCB1N=BB(BC)N1', 'C=C1CCC1C(=CCC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules to match a z-score a z-score of 0.4 for the cell line 905957 and 0.35 for the cell line 1240166."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.4\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC=CCNCC(C)C(CC(=NCC=CC1=NB1C)C(C)CC=CNC(N)NN)=NC', 'CC1C=CBNC2=CC(C(C=NCCNNN)=CCC=CC1)[NH+]2CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CNC1CNCC(=C)CCC1C=[N+]1N(C)CN1BC', 'C=NCCCC=C(N)C(C)=CCNC=C1N=C(CC)NB(NN)C2NC(C)CN3CN(C23)C1N1CN1']}"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.35\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1CN=CC=C2CNC(C)OC3=C(C)C=C(C)C12CN[N+](CCCNN)=C(C)NC(CN)=C(N)CC3', 'C=C1BNNC(N)(NC(=C[N+](N)=C(CN)CCN(C)N)NC)C1C', 'CCC=NN(C)C(=CC(C)NN)C1C[O+]1N', 'CBCCc1ccn(CC)n1', 'C=C1CCC1C1=C(CC)CC1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules have an z-score of 0.4 for 905957 are ['CC=CCNCC(C)C(CC(=NCC=CC1=NB1C)C(C)CC=CNC(N)NN)=NC', 'CC1C=CBNC2=CC(C(C=NCCNNN)=CCC=CC1)[NH+]2CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CNC1CNCC(=C)CCC1C=[N+]1N(C)CN1BC', 'C=NCCCC=C(N)C(C)=CCNC=C1N=C(CC)NB(NN)C2NC(C)CN3CN(C23)C1N1CN1']. And the molecules have an z-score of 0.35 for 1240166 are ['C=C1CN=CC=C2CNC(C)OC3=C(C)C=C(C)C12CN[N+](CCCNN)=C(C)NC(CN)=C(N)CC3', 'C=C1BNNC(N)(NC(=C[N+](N)=C(CN)CCN(C)N)NC)C1C', 'CCC=NN(C)C(=CC(C)NN)C1C[O+]1N', 'CBCCc1ccn(CC)n1', 'C=C1CCC1C1=C(CC)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules with the intended z-score of 0.4 for the cell line 1240182 and 0.35 for the cell line 1331032."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.4\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC=CCNCC(C)C(CC(=NCC=CCOC)C(C)CC=CNC(N)NN)=NC', 'CC=CC(C)CC=CCC=C(C=NCC)C1C=C=C1CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CCC1CNCC(=C)CCC1C=C1C(C)CN1CC', 'C=NCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C(NC(C)C)C=C(C)C2=[N+]1CN1']}"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.35\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](NNCNN)=C(C)NC(CN)=C(N)CC3', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBCCc1cbn(CC)n1', 'C=C1CCC1C(=CCC)CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules have an z-score of 0.4 for 1240182 are ['CC=CCNCC(C)C(CC(=NCC=CCOC)C(C)CC=CNC(N)NN)=NC', 'CC=CC(C)CC=CCC=C(C=NCC)C1C=C=C1CNN(C)N', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=CCC1CNCC(=C)CCC1C=C1C(C)CN1CC', 'C=NCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C(NC(C)C)C=C(C)C2=[N+]1CN1']. And the molecules have an z-score of 0.35 for 1331032 are ['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](NNCNN)=C(C)NC(CN)=C(N)CC3', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBCCc1cbn(CC)n1', 'C=C1CCC1C(=CCC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules with the intended z-score of 0.4 for the cell line 684055 and 0.35 for the cell line 1659819."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.4\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CBC1(C)NBC2(N)c3c([nH]n32)[N+]2(C=CC=NC)CNN(N)C(CNCNN)NC(=CN1)CNC=N2', 'BC(B(C)C(CCCN(C)N)NB(CC)NN=BC)N(N)N(C)NCC', 'BC=NN(C)C(=NBC)B1NB1N', 'B=BB1NB(BC)B1BBB', 'B1CC2=NC3C2=CBC2NC3=C12']}"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"0.35\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](CNCNN)=C(C)NC(CN)=C(N)CC3', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBCCc1cbn(CC)n1', 'C=C1CCC1C(=CCC)CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules have an z-score of 0.4 for 684055 are ['CBC1(C)NBC2(N)c3c([nH]n32)[N+]2(C=CC=NC)CNN(N)C(CNCNN)NC(=CN1)CNC=N2', 'BC(B(C)C(CCCN(C)N)NB(CC)NN=BC)N(N)N(C)NCC', 'BC=NN(C)C(=NBC)B1NB1N', 'B=BB1NB(BC)B1BBB', 'B1CC2=NC3C2=CBC2NC3=C12']. And the molecules have an z-score of 0.35 for 1659819 are ['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](CNCNN)=C(C)NC(CN)=C(N)CC3', 'CN(N)CCC(CN)=[N+](N)C=C(N)NN', 'CC(C=CC1NB1N)NN', 'CBCCc1cbn(CC)n1', 'C=C1CCC1C(=CCC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of molecules with a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)CN=C(CC)NC(N)CCC(CCN)C(C)C', 'CC1CCC2=CCCOC=C1C2', 'C=CC1C=CC=CC(O)=C(CCC)CC1CC(=C)C', 'CNCC(C[N+]1(C)CC1)=C1N[N+](=c2cc2)C=C(C)C1(C)C=NC1CC(O)C1', 'C=CC=CCCCCC=NCCCC', 'CN1C2COC1(C)N2', 'CC=CCCC(C1CC(C)=CC=C(CCC)N1)C(C)OC=CC=CCCC', 'C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CN=CCCC(=N)CCC)CCN=C(C)CC1', 'C=C(CC)C1CC(C=CC)NCN1', 'COC(C)=CC1CCCCNC1OC(C)O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)CN=C(CC)NC(N)CCC(CCN)C(C)C', 'CC1CCC2=CCCOC=C1C2', 'C=CC1C=CC=CC(O)=C(CCC)CC1CC(=C)C', 'CNCC(C[N+]1(C)CC1)=C1N[N+](=c2cc2)C=C(C)C1(C)C=NC1CC(O)C1', 'C=CC=CCCCCC=NCCCC', 'CN1C2COC1(C)N2', 'CC=CCCC(C1CC(C)=CC=C(CCC)N1)C(C)OC=CC=CCCC', 'C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CN=CCCC(=N)CCC)CCN=C(C)CC1', 'C=C(CC)C1CC(C=CC)NCN1', 'COC(C)=CC1CCCCNC1OC(C)O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=COC(=N)CC(CCCC)C(C)(C)C', 'CCC=C(C)C1(CC)CC(=COC[O+](C)C(CC)OC)C(CC)CC2CC2C1', 'C=CNN(C)N1NNC[N+](=NCCCCN)N1', 'CC(C)=NC1N[N+](N)=NNBCC2=[N+]1CC2', 'COCCC1(C)CCNC2(CC(C)C2)C(C)C1', 'CC1CC2(C)CC(O)CC=[O+]CC3(C)CCC1C=CC1=C[O+](C=CCCCC32C)C1', 'CC=NN(O)C=C1C2=CCC(=CCCN(C)CC)CCC1C2', 'C=C[N+](=C)N=CCCCC(C)CC(C)CCC', 'CC=CC1CC[N+]23CCCN1CC=[N+]2CCCC[N+](C)(CC1=CN1)CC3', 'C=C(C)C=CC1NC=CC=CCCCCC1=CCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=COC(=N)CC(CCCC)C(C)(C)C', 'CCC=C(C)C1(CC)CC(=COC[O+](C)C(CC)OC)C(CC)CC2CC2C1', 'C=CNN(C)N1NNC[N+](=NCCCCN)N1', 'CC(C)=NC1N[N+](N)=NNBCC2=[N+]1CC2', 'COCCC1(C)CCNC2(CC(C)C2)C(C)C1', 'CC1CC2(C)CC(O)CC=[O+]CC3(C)CCC1C=CC1=C[O+](C=CCCCC32C)C1', 'CC=NN(O)C=C1C2=CCC(=CCCN(C)CC)CCC1C2', 'C=C[N+](=C)N=CCCCC(C)CC(C)CCC', 'CC=CC1CC[N+]23CCCN1CC=[N+]2CCCC[N+](C)(CC1=CN1)CC3', 'C=C(C)C=CC1NC=CC=CCCCCC1=CCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of molecules that have a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=CNC1C=CC=C(CCC)NC(C)NCC1CC', 'C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'CC=[N+](CNCC)NC=CCCCC=C(CC)C1C=C1', 'C=[N+]1C2=CN3C=CC(CCN)C(C)CCN(CC)NNC(=C3)C=[N+]21', 'CCN(C)CC(CCCCCC1CC1C1N=CC(C)N1C)NCC(C)C', 'C=CC(=NC=N)C1C2=C(C(=CN)CNC)C21', 'CCN=C(CBN)NNC(CCNC)CN=CCCN', 'BC(CC)C1BCCCCNNCCC(N=C(N)NCC)CCB(N)B(CNNC)NC1', 'C=C(CCN)CCCCCCCC1CC=C1C', 'CC(=N)C1=CNC=C(NON2CCN2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=CNC1C=CC=C(CCC)NC(C)NCC1CC', 'C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'CC=[N+](CNCC)NC=CCCCC=C(CC)C1C=C1', 'C=[N+]1C2=CN3C=CC(CCN)C(C)CCN(CC)NNC(=C3)C=[N+]21', 'CCN(C)CC(CCCCCC1CC1C1N=CC(C)N1C)NCC(C)C', 'C=CC(=NC=N)C1C2=C(C(=CN)CNC)C21', 'CCN=C(CBN)NNC(CCNC)CN=CCCN', 'BC(CC)C1BCCCCNNCCC(N=C(N)NCC)CCB(N)B(CNNC)NC1', 'C=C(CCN)CCCCCCCC1CC=C1C', 'CC(=N)C1=CNC=C(NON2CCN2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that achieve a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C=NCCCNCN(N)CNN=[N+]1CN1', 'CCN(C(C)N)C(B=CNN)B(C)CNN', 'C=CC[N+](C)(N)CCC(N)CC(=CCN=CN)CC', 'CCC=CC(CC)CC(C)CC1=[N+](CC)C=C1', 'C=C1CC2=CC(C=CCC3NC(CC=CC)(C[O+](C)CCC3=CC)C3CC13C)C2', 'C=C(C)NC(C)C1CC(C)(CC=CCC)CC2=CCCC1N(CC1N=C1NC=CC)C2', 'C=C(C=CC1NCC(=C)C=CCOC(C)(C=CC)O1)CC=CCC', 'C=C1C=C(C(CC)CCOC)C2=C(C=CO)C(C=CCCC1CC)C=C2CCC', 'CC=CN(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCCCC)C(C)CCCCC1', 'C=CNCCCC1=CC(C)(CC)C=CCC2C3(C)CNC(=CC(C)C4=NCCC4)CC2(C)NC1(O)C3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C=NCCCNCN(N)CNN=[N+]1CN1', 'CCN(C(C)N)C(B=CNN)B(C)CNN', 'C=CC[N+](C)(N)CCC(N)CC(=CCN=CN)CC', 'CCC=CC(CC)CC(C)CC1=[N+](CC)C=C1', 'C=C1CC2=CC(C=CCC3NC(CC=CC)(C[O+](C)CCC3=CC)C3CC13C)C2', 'C=C(C)NC(C)C1CC(C)(CC=CCC)CC2=CCCC1N(CC1N=C1NC=CC)C2', 'C=C(C=CC1NCC(=C)C=CCOC(C)(C=CC)O1)CC=CCC', 'C=C1C=C(C(CC)CCOC)C2=C(C=CO)C(C=CCCC1CC)C=C2CCC', 'CC=CN(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCCCC)C(C)CCCCC1', 'C=CNCCCC1=CC(C)(CC)C=CCC2C3(C)CNC(=CC(C)C4=NCCC4)CC2(C)NC1(O)C3']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -4.054651 for the cell line '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(N)(N)CCC=C1NB(C)N=BCC1(N)CB=N', 'C=CC1CC(O)(C(C)=O)CCC=CC2CC(C(C)C)=CN2C(C)(C(C)C)CC2CC=C2C2(C)CNC12', 'C=BB=C(N)N=N', 'BC(N)(C=N)N(CC)CCC', 'CC(N)C1C=C1[N+](C)(N)CCCN', 'CCC1(C)N=CN(C)B2BC2C(B=N)C(C(C)NN)C(N)B1N', 'C=CN=CB=CBN', 'C=CC(C=N)=[N+]=C', 'CC(C)BC=NC(C=N)CN(CNNN)NCNC(C)N', 'NN=C(N)CN(CNN)C[N+](N)(N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(N)(N)CCC=C1NB(C)N=BCC1(N)CB=N', 'C=CC1CC(O)(C(C)=O)CCC=CC2CC(C(C)C)=CN2C(C)(C(C)C)CC2CC=C2C2(C)CNC12', 'C=BB=C(N)N=N', 'BC(N)(C=N)N(CC)CCC', 'CC(N)C1C=C1[N+](C)(N)CCCN', 'CCC1(C)N=CN(C)B2BC2C(B=N)C(C(C)NN)C(N)B1N', 'C=CN=CB=CBN', 'C=CC(C=N)=[N+]=C', 'CC(C)BC=NC(C=N)CN(CNNN)NCNC(C)N', 'NN=C(N)CN(CNN)C[N+](N)(N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please develop molecules with a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)CCO)N=CNC', 'CCC1C=CC2=NC3(C)CC(C)C2CC2(CC)CC(=C(CCF)C(=O)C=C13)C=N2', 'C=C(C)C(=C)CCC', 'CC1CCC(=CCCCC=CC=CCO)C1', 'CNC=C(N)CC=CCC1=C2CC2C=CC(C=C(C)C)C(C(C)N)CC=C1C', 'CC=C(OCC(C)O)C(C)CC(CC)CCC=NC(CC)N=C(N)CCC(C)N', 'C=NC(C=CC)C(CNN)N1C=CC(CNC)B(N)C(C)N1', 'C=C(CC)NC(C)C(CCC(C)C[N+](C)=N)=C(C)C(CN)CNCC', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3(C)O)C2=C(O)C(C)OCCC1', 'C=C(N)C(N)CCCC1=CCCC1OC=CN=CCCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)CCO)N=CNC', 'CCC1C=CC2=NC3(C)CC(C)C2CC2(CC)CC(=C(CCF)C(=O)C=C13)C=N2', 'C=C(C)C(=C)CCC', 'CC1CCC(=CCCCC=CC=CCO)C1', 'CNC=C(N)CC=CCC1=C2CC2C=CC(C=C(C)C)C(C(C)N)CC=C1C', 'CC=C(OCC(C)O)C(C)CC(CC)CCC=NC(CC)N=C(N)CCC(C)N', 'C=NC(C=CC)C(CNN)N1C=CC(CNC)B(N)C(C)N1', 'C=C(CC)NC(C)C(CCC(C)C[N+](C)=N)=C(C)C(CN)CNCC', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3(C)O)C2=C(O)C(C)OCCC1', 'C=C(N)C(N)CCCC1=CCCC1OC=CN=CCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a series of molecules with the target z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(C)(CCC)C(C)CC', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(CCCC)C1)CCC)N(CC)CC', 'C=NCC(O)=CC', 'CC=CC=CC(=CC)C(C)(C=CC(C)=NC)CC', 'CC=C1C[O+]2C(OCC(C)(CC=CC=N)NCC1=CO)C2C1CC(C)CC(C)N(CC)CCC1CCCC', 'C=CC(C)CCC1(C)CNC2C[O+](C)C2=CCCC(N)C1CC(C)CCNC(=C)CO', 'C=CC(CN)(CC=CC)CC=CCC(C)=C1CCC(OCC)N(CC(C)CCCC(CC)[OH+]C(C)(C)C(C)C)C(C)C(C)C1', 'CCC=CCCC1C(C)C1C(CCNC)CNCC=NC', 'C=CC=CC(CCNC)CC1=CCCC1', 'CCCCC(C=CC=NC=COCC(C)C)CNCC1=C(C=CO)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(C)(CCC)C(C)CC', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(CCCC)C1)CCC)N(CC)CC', 'C=NCC(O)=CC', 'CC=CC=CC(=CC)C(C)(C=CC(C)=NC)CC', 'CC=C1C[O+]2C(OCC(C)(CC=CC=N)NCC1=CO)C2C1CC(C)CC(C)N(CC)CCC1CCCC', 'C=CC(C)CCC1(C)CNC2C[O+](C)C2=CCCC(N)C1CC(C)CCNC(=C)CO', 'C=CC(CN)(CC=CC)CC=CCC(C)=C1CCC(OCC)N(CC(C)CCCC(CC)[OH+]C(C)(C)C(C)C)C(C)C(C)C1', 'CCC=CCCC1C(C)C1C(CCNC)CNCC=NC', 'C=CC=CC(CCNC)CC1=CCCC1', 'CCCCC(C=CC=NC=COCC(C)C)CNCC1=C(C=CO)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules to achieve a z-score of -4.054651 against '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['BC(NN=[N+](N)N)C(=C)[N+](C)(C)CB1NN=C(BC)BN(C(=BC(N)C(=N)N)CN)CC1C', 'CCC1=C(OCC=C(C)C)C(CC=NCCC(C)OC)C1F', 'BNN1CC23BBB(BNB2)C13', 'CCCCCC1[O+]=C1C=[N+](C)C1CCCC2C=CC1=[O+]C(OC)=CC2CC', 'CNC(C)B(CN)N(NC)NNB1NN1', 'B=NC1=C(C)B(C)CCC2=C3NCB(CC2)NC(N=CN)[N+]3(B)B=B1', 'C=C(C)C(O)C(C)(CCCCC)NOCC', 'CNC(=CC[N+]1=CC1C)CN', 'CC1CCC2=CCCC21', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BC(NN=[N+](N)N)C(=C)[N+](C)(C)CB1NN=C(BC)BN(C(=BC(N)C(=N)N)CN)CC1C', 'CCC1=C(OCC=C(C)C)C(CC=NCCC(C)OC)C1F', 'BNN1CC23BBB(BNB2)C13', 'CCCCCC1[O+]=C1C=[N+](C)C1CCCC2C=CC1=[O+]C(OC)=CC2CC', 'CNC(C)B(CN)N(NC)NNB1NN1', 'B=NC1=C(C)B(C)CCC2=C3NCB(CC2)NC(N=CN)[N+]3(B)B=B1', 'C=C(C)C(O)C(C)(CCCCC)NOCC', 'CNC(=CC[N+]1=CC1C)CN', 'CC1CCC2=CCCC21', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules that can attain a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C(C)=CC1C1=NC(CC)=CC=CC2C=[N+](C=C3N[O+]3C2)C(C)=C1', 'CC=CC=NCN=C(N)C(C)CC', 'C=CN(C)OC(C)CC1(C(CC)CCC(N)CC2C=CC(CCN)CC2)C=C(C)C1', 'C=CC(CCCC)N(CCC1(CC)CC=CCCC=CCC1)CC1CCC(F)CCN1CC', 'C=[N+]1C2=CN3C=CC(CCN)C(C)CCN(CC)NNC(=C3)C=[N+]21', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'C=C(CCCCC(N)COCC=CC(C)=NC)CC(N)(CC=CCCC)CCC', 'C=C(C(C)C)C(C)CC=CC', 'CC=CC=CC1C=CCC(C)=NCC1', 'CCCCC(C(C)C(C)CO)C1CC1N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C(C)=CC1C1=NC(CC)=CC=CC2C=[N+](C=C3N[O+]3C2)C(C)=C1', 'CC=CC=NCN=C(N)C(C)CC', 'C=CN(C)OC(C)CC1(C(CC)CCC(N)CC2C=CC(CCN)CC2)C=C(C)C1', 'C=CC(CCCC)N(CCC1(CC)CC=CCCC=CCC1)CC1CCC(F)CCN1CC', 'C=[N+]1C2=CN3C=CC(CCN)C(C)CCN(CC)NNC(=C3)C=[N+]21', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'C=C(CCCCC(N)COCC=CC(C)=NC)CC(N)(CC=CCCC)CCC', 'C=C(C(C)C)C(C)CC=CC', 'CC=CC=CC1C=CCC(C)=NCC1', 'CCCCC(C(C)C(C)CO)C1CC1N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide molecules with a z-score reaching -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC1C2COC=CC3=C(CC[N+](CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'CCCC(C)C1C(C(C)F)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCC1=C(CCC(C)C)[N+](=CCOC(C)(C)N)C(C(C)CN)=C1', 'C=[N+]1C2=CC(C)=CC(C=CC(CCN)C(C)CCC(F)CC)C=C21', 'CCC1=CN(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'C=CC1C(C)C(CCC)CC2=[N+]1C(C)=C(C)C=CCCC2', 'CCCc1cc1=O', 'CC=CCN=CN=CCC(C)=CC=CCNN(C)N', 'NN=CC1C=C1N', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC1C2COC=CC3=C(CC[N+](CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'CCCC(C)C1C(C(C)F)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCC1=C(CCC(C)C)[N+](=CCOC(C)(C)N)C(C(C)CN)=C1', 'C=[N+]1C2=CC(C)=CC(C=CC(CCN)C(C)CCC(F)CC)C=C21', 'CCC1=CN(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'C=CC1C(C)C(CCC)CC2=[N+]1C(C)=C(C)C=CCCC2', 'CCCc1cc1=O', 'CC=CCN=CN=CCC(C)=CC=CCNN(C)N', 'NN=CC1C=C1N', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules to match a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C(N)CC=CC1C=C2CC2C=CC(C=C(C)C)C(C(C)O)CCOC1', 'CC=NC(CC(C)C)NCCC', 'CNCC(C)COC1C=CN(C(C)C)CC1CC(C)=N', 'C=C(C)C(O)(CC(CCC)OCN(CC)CCCC1(C)CN1)C(C)CC', 'C=C[O+]=C(CCC)C(N)=C(C=CO)CNC(C)=CCC', 'CCCC=C(C)CC(CCN)N=CN1CC=NN1', 'C=C(C)NC=CC=NC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'CCC1CCC(=CN)C=NC[N+]2(C)CC(=CNC(C)C=C2C)C2CCCCC1N2', 'C=C1C(=CCC(C)C)CC=C[N+]1=CCC', 'CCCCC1=C=C(C)C=C1CC(C)C(O)(CC=CC1=CC1=CC(CC)N(C)CN)CC=NCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C(N)CC=CC1C=C2CC2C=CC(C=C(C)C)C(C(C)O)CCOC1', 'CC=NC(CC(C)C)NCCC', 'CNCC(C)COC1C=CN(C(C)C)CC1CC(C)=N', 'C=C(C)C(O)(CC(CCC)OCN(CC)CCCC1(C)CN1)C(C)CC', 'C=C[O+]=C(CCC)C(N)=C(C=CO)CNC(C)=CCC', 'CCCC=C(C)CC(CCN)N=CN1CC=NN1', 'C=C(C)NC=CC=NC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'CCC1CCC(=CN)C=NC[N+]2(C)CC(=CNC(C)C=C2C)C2CCCCC1N2', 'C=C1C(=CCC(C)C)CC=C[N+]1=CCC', 'CCCCC1=C=C(C)C=C1CC(C)C(O)(CC=CC1=CC1=CC(CC)N(C)CN)CC=NCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)CCCC=C(C)NCCCN', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC', 'C=[N+](C)CCC1=CCC1C=C[N+](=C)CC(C)=C(C)C', 'C=CCCCCC=CCOCCCCOC1NC1C', 'CCNN1CC=C[N+](N)=C(N=[N+](C)NN)C(CNCCC(=CN)CC)C1C', 'CCC(CC)N(CCCOO)NC', 'CCC1(C[O+](C)C)C(C)C(C=CO)CC(C(C)=CC=C=CC=CC[O+]=C(C)C(C)C)C1C', 'CC1=CCCCC(=[N+]2C=CC(C=CNC(C)C=NN)=C(C)N2)N=CC1', 'C=C(C[O+]1C=C[O+]=[O+]C=CC1CCOS)OCC', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)CCCC=C(C)NCCCN', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC', 'C=[N+](C)CCC1=CCC1C=C[N+](=C)CC(C)=C(C)C', 'C=CCCCCC=CCOCCCCOC1NC1C', 'CCNN1CC=C[N+](N)=C(N=[N+](C)NN)C(CNCCC(=CN)CC)C1C', 'CCC(CC)N(CCCOO)NC', 'CCC1(C[O+](C)C)C(C)C(C=CO)CC(C(C)=CC=C=CC=CC[O+]=C(C)C(C)C)C1C', 'CC1=CCCCC(=[N+]2C=CC(C=CNC(C)C=NN)=C(C)N2)N=CC1', 'C=C(C[O+]1C=C[O+]=[O+]C=CC1CCOS)OCC', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC(=O)CC(C)(N=CC=CCNC=CCC=CCC)C(N)=CCC', 'C=CC(C)C(C)CC=CCC(=C)CC=C1C2[O+](O)[O+]12', 'C=C(C)CN[N+](=CC=NC(C)C(C)C1(N)C(N)=C1CCCC1=CC(CC)=C1)CC', 'CCC=CCC(CCCC=[O+]C)CCNCC', 'CC1COC2CCCCC=CCC=CC=C3CCC=CC3(C)CC=C(C1)C2', 'CCCNCC=C(C=CC=C(CCC)CNCCC)CC(C)CCC', 'C=C1CC2=C(C=C3CCC(C)C=CC=CCCC(=C1CC)C(C)(CCC)C3)C2', 'C=C1N=CC1C=C(C)[N+]1=CC=CCCC(CC2=CC2)=CC1', 'CC(C)=[N+](CC=CNN=CCN)CNN', 'C=NC(=CCN1C=CCCC2=CCC(N)C(C=O)(C2)C1)N(C)CC=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC(=O)CC(C)(N=CC=CCNC=CCC=CCC)C(N)=CCC', 'C=CC(C)C(C)CC=CCC(=C)CC=C1C2[O+](O)[O+]12', 'C=C(C)CN[N+](=CC=NC(C)C(C)C1(N)C(N)=C1CCCC1=CC(CC)=C1)CC', 'CCC=CCC(CCCC=[O+]C)CCNCC', 'CC1COC2CCCCC=CCC=CC=C3CCC=CC3(C)CC=C(C1)C2', 'CCCNCC=C(C=CC=C(CCC)CNCCC)CC(C)CCC', 'C=C1CC2=C(C=C3CCC(C)C=CC=CCCC(=C1CC)C(C)(CCC)C3)C2', 'C=C1N=CC1C=C(C)[N+]1=CC=CCCC(CC2=CC2)=CC1', 'CC(C)=[N+](CC=CNN=CCN)CNN', 'C=NC(=CCN1C=CCCC2=CCC(N)C(C=O)(C2)C1)N(C)CC=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules targeting a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1C=c2cnc([nH]2)=CCC=CCCC1', 'C=C1C=C(CC=CC2CO2)OCC(F)C1', 'CCCC=CC(=CC(C)CCC)CC(C)C=C(C=S)NCC', 'C=C1C=CC[N+]1(N)C(C)N(C)C', 'C=CC=C[O+]=C', 'CC1=CC(CCNC=C[N+](C)=NCCC=CCC(N)=C(C)CCCN)CCNC1C', 'C=C(N)CC(N)=CCCC(=NC)C1=NC(CC)=CC=[N+](CC=CCC(C)=CCN)C2CC1C2', 'CCC1CC(C)C(CC2CC2C)C1', 'C=C1NC(N)C1C', 'C=[N+](CC)OBC=NCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1C=c2cnc([nH]2)=CCC=CCCC1', 'C=C1C=C(CC=CC2CO2)OCC(F)C1', 'CCCC=CC(=CC(C)CCC)CC(C)C=C(C=S)NCC', 'C=C1C=CC[N+]1(N)C(C)N(C)C', 'C=CC=C[O+]=C', 'CC1=CC(CCNC=C[N+](C)=NCCC=CCC(N)=C(C)CCCN)CCNC1C', 'C=C(N)CC(N)=CCCC(=NC)C1=NC(CC)=CC=[N+](CC=CCC(C)=CCN)C2CC1C2', 'CCC1CC(C)C(CC2CC2C)C1', 'C=C1NC(N)C1C', 'C=[N+](CC)OBC=NCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['BC(N)=C(CCCC)NC(N)BCCB=NN', 'C=CC(N)=BBN1BN=NN(C)N1', 'CB1B=BNCB(C)CNCNB(C)CBBB1', 'B=CNNNB(N)NCC(N)CC', 'BB1CCNBC=CC(C(N=[N+]=C)N2NC2B=CN(N)NC)=CN(N)N1', 'CCNC(C=C(C)N1CN1)CC', 'C=C=CC(C)=C1CCCC(CC(CCNCCC)NCCC(C)CC=C)CC1', 'B=C1CNNC2=BN3BBNB4C(CBBC3CC41N)B(N)C2', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN', 'C=CC(C=CC(CC)C(C)C(CC(C=C)C=CC)CN(F)CC)C=C[N+](=C)N=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BC(N)=C(CCCC)NC(N)BCCB=NN', 'C=CC(N)=BBN1BN=NN(C)N1', 'CB1B=BNCB(C)CNCNB(C)CBBB1', 'B=CNNNB(N)NCC(N)CC', 'BB1CCNBC=CC(C(N=[N+]=C)N2NC2B=CN(N)NC)=CN(N)N1', 'CCNC(C=C(C)N1CN1)CC', 'C=C=CC(C)=C1CCCC(CC(CCNCCC)NCCC(C)CC=C)CC1', 'B=C1CNNC2=BN3BBNB4C(CBBC3CC41N)B(N)C2', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN', 'C=CC(C=CC(CC)C(C)C(CC(C=C)C=CC)CN(F)CC)C=C[N+](=C)N=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds to achieve a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'C=C1CNC=C(NCCN)C=[O+]C=CCC(CC(CC)CCC)NCC(N)=C1C(C)C=CC', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'C=CC=C(CC)CNC1=C(CC)CC=C[O+](CCC(C)(O)C=C)CC=C1', 'CC=C(CC1CC1)C(CC(C)C)C1CC=C1CF', 'C=C1CCCC(CCC(C)N2CC(C)=C(C)N2CCCC2C(C)=CCC2CNC)C[NH+](OCC)C1', 'CN1C=COC(O)=C2C3=C=C(CCC4CCC(=CC24C)C=C1)C3', 'CC1=CN(CCC(CF)=C(O)O)CCCCC2=C(C)C=C12', 'CB(CC(=CC=NN)NCB(N)CCN)CC(N)CC1CC=N[N+]1=N', 'CC1CC(C)(NCCC=CCC=CCCNC=O)CCOC=CN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'C=C1CNC=C(NCCN)C=[O+]C=CCC(CC(CC)CCC)NCC(N)=C1C(C)C=CC', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'C=CC=C(CC)CNC1=C(CC)CC=C[O+](CCC(C)(O)C=C)CC=C1', 'CC=C(CC1CC1)C(CC(C)C)C1CC=C1CF', 'C=C1CCCC(CCC(C)N2CC(C)=C(C)N2CCCC2C(C)=CCC2CNC)C[NH+](OCC)C1', 'CN1C=COC(O)=C2C3=C=C(CCC4CCC(=CC24C)C=C1)C3', 'CC1=CN(CCC(CF)=C(O)O)CCCCC2=C(C)C=C12', 'CB(CC(=CC=NN)NCB(N)CCN)CC(N)CC1CC=N[N+]1=N', 'CC1CC(C)(NCCC=CCC=CCCNC=O)CCOC=CN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules optimized for a z-score of -4.054651 for the cell line '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=BN(N)n1[nH]n1CNCC(=N)NB(C)CB(CCC)CNB(C)N', 'C=CC(=CC)C(CF)C(C=NC)CC(=C)C=C(C)N', 'BC1NN(NCCC=CNC(NN2CC2)B(C)C(C)N)C=CB(C=[N+](N)N)C(=CC(C)[N+](=C)C)N1C', 'C=C(N)N=[N+](CNNCNN=CN)C1BCB(N)CBN1', 'BCCB1CC=C1N(C)C(N)C(NC(B)CNC)B(C)N', 'CC=C(CC)NC(C=NCC)CC(C)=CC=CN', 'CCC=COCC=CC(C)C(C=CCC)CC', 'BB(C)C(C)C(N)CC[N+]1=BCB(CN)NNC1=C', 'BC=CB=C(N)NC(NNBC=NNN)B1CCC(=C(NNN)NC(C)C)CN2CC=C12', 'C=C(N=CC(NC)C(C)C=C=N)C(C=CC)CCC(CCCCC(C)=CC)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=BN(N)n1[nH]n1CNCC(=N)NB(C)CB(CCC)CNB(C)N', 'C=CC(=CC)C(CF)C(C=NC)CC(=C)C=C(C)N', 'BC1NN(NCCC=CNC(NN2CC2)B(C)C(C)N)C=CB(C=[N+](N)N)C(=CC(C)[N+](=C)C)N1C', 'C=C(N)N=[N+](CNNCNN=CN)C1BCB(N)CBN1', 'BCCB1CC=C1N(C)C(N)C(NC(B)CNC)B(C)N', 'CC=C(CC)NC(C=NCC)CC(C)=CC=CN', 'CCC=COCC=CC(C)C(C=CCC)CC', 'BB(C)C(C)C(N)CC[N+]1=BCB(CN)NNC1=C', 'BC=CB=C(N)NC(NNBC=NNN)B1CCC(=C(NNN)NC(C)C)CN2CC=C12', 'C=C(N=CC(NC)C(C)C=C=N)C(C=CC)CCC(CCCCC(C)=CC)C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds that meet a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=NC1C[N+](=CCC(=CC[N+](=CCC(=CCC)C(C)CC=CCB2NN2)CC)CC)N1', 'C=CCC=C(C1(C)CONC(N)CN(CC)C(N2C(C=COC)NCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC', 'CC(C)CC1N[O+]=CC1CC(C)N', 'CCC1(C[O+](C)C)C(N)C(N)(C(C)=NC=[N+]=CC=CCC=C(C)C(C)N)CC(N=NN)N1C', 'CN(CCN=CC(N)CN)[N+]1=C2CC2=C1', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=CC=C1C(C=CC)=C(C=C(C)O)CCC(C)C', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'C=C[N+]1=CC1', 'C1=NCCCCCCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=NC1C[N+](=CCC(=CC[N+](=CCC(=CCC)C(C)CC=CCB2NN2)CC)CC)N1', 'C=CCC=C(C1(C)CONC(N)CN(CC)C(N2C(C=COC)NCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC', 'CC(C)CC1N[O+]=CC1CC(C)N', 'CCC1(C[O+](C)C)C(N)C(N)(C(C)=NC=[N+]=CC=CCC=C(C)C(C)N)CC(N=NN)N1C', 'CN(CCN=CC(N)CN)[N+]1=C2CC2=C1', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=CC=C1C(C=CC)=C(C=C(C)O)CCC(C)C', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'C=C[N+]1=CC1', 'C1=NCCCCCCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules aimed at a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['NCNC1=NCC=C1', 'CCC=CCC(C)(C)CC(C)=NNCC(CC=CNC)=NCCC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'C=C(CC=CC(C)CN(C)CC(C)=CC)C(CCN)[N+](=C)C', 'CC=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CC1=CN=CCNC(=NC=C2CC2)C=C(C)C(C)(CN(C)N)C1', 'CCCC1=NCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'C=C1CCC2=C(CC)C(C1)C1CC=CCC1=CC=C1C=CC(CNC2)C1', 'CCCC(O)C=CC(C)N(C)CC=C1CNC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['NCNC1=NCC=C1', 'CCC=CCC(C)(C)CC(C)=NNCC(CC=CNC)=NCCC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'C=C(CC=CC(C)CN(C)CC(C)=CC)C(CCN)[N+](=C)C', 'CC=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CC1=CN=CCNC(=NC=C2CC2)C=C(C)C(C)(CN(C)N)C1', 'CCCC1=NCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'C=C1CCC2=C(CC)C(C1)C1CC=CCC1=CC=C1C=CC(CNC2)C1', 'CCCC(O)C=CC(C)N(C)CC=C1CNC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules with the intended z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(F)CC=CCCCC1C=C2CC(C3CC3=N)=CCCC2CC1', 'C=C[N+]1=C(C(CF)CC(=C)O)C=C1CCCC', 'CC=C1C2=CC(CCCN)=C(O)C(=C(C)COCC(C)C2)CCC1C', 'CCC(=CCC(CC)CCC=CC(O)C[N+](C)(CC)CC)C(C)CC', 'C=CCC=C(C)C(F)(CC)C(=[O+]C(C)C)C(C)=CCCC(C=C)CC(N)C(C)C(C)(C)CC', 'CCCC1(C)C=CCC2CC(C(C)(CC)C2)C(C)(CC)CC(C(C)NCC(C)=C(C)C)C1C(N)CNC', 'C=CC(CCCC(C)=CCCCC(CC)CCC)C1CC1C(C)CN', 'CC1=CC2CCC[N+](C)=C3C(C)=C3N2CC1', 'C=C(C)C(C)CCCCC=CCC(C)CCCN(C)CC', 'CB(C)CNCB=C(C=CN)CC1(C)CN(C)N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(F)CC=CCCCC1C=C2CC(C3CC3=N)=CCCC2CC1', 'C=C[N+]1=C(C(CF)CC(=C)O)C=C1CCCC', 'CC=C1C2=CC(CCCN)=C(O)C(=C(C)COCC(C)C2)CCC1C', 'CCC(=CCC(CC)CCC=CC(O)C[N+](C)(CC)CC)C(C)CC', 'C=CCC=C(C)C(F)(CC)C(=[O+]C(C)C)C(C)=CCCC(C=C)CC(N)C(C)C(C)(C)CC', 'CCCC1(C)C=CCC2CC(C(C)(CC)C2)C(C)(CC)CC(C(C)NCC(C)=C(C)C)C1C(N)CNC', 'C=CC(CCCC(C)=CCCCC(CC)CCC)C1CC1C(C)CN', 'CC1=CC2CCC[N+](C)=C3C(C)=C3N2CC1', 'C=C(C)C(C)CCCCC=CCC(C)CCCN(C)CC', 'CB(C)CNCB=C(C=CN)CC1(C)CN(C)N1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of compounds reaching a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C=CN(C)C)C(C)CCC1=CC=C1', 'C=C(C)C[N+](=N)[N+](=C)[N+]1=CCC1', 'C=C1NC2C(C)CC3=CCCC45COC1=C1C=C(C)CCCC(CCC24CCC)CC(=C31)C5O', 'C=CC1=C(CCN)CC2(CN2)C2C3CC(C)NC(C)=CCC(C[O+]1C)N32', 'C1C2C3C2N13', 'CCOC1=CC2CNC2C(C)CCC1', 'C=CCCCC(=CCCCNCC(O)CCNN1CC1)CC', 'CC=NC(CC(C)C)NCCC', 'CCC=C(C)CCCCCC=CC(C)CC1CCNC2C(CCCCC)CCN=C1C(C)C2C', 'CCCCC1=CC(CC)C=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C=CN(C)C)C(C)CCC1=CC=C1', 'C=C(C)C[N+](=N)[N+](=C)[N+]1=CCC1', 'C=C1NC2C(C)CC3=CCCC45COC1=C1C=C(C)CCCC(CCC24CCC)CC(=C31)C5O', 'C=CC1=C(CCN)CC2(CN2)C2C3CC(C)NC(C)=CCC(C[O+]1C)N32', 'C1C2C3C2N13', 'CCOC1=CC2CNC2C(C)CCC1', 'C=CCCCC(=CCCCNCC(O)CCNN1CC1)CC', 'CC=NC(CC(C)C)NCCC', 'CCC=C(C)CCCCCC=CC(C)CC1CCNC2C(CCCCC)CCN=C1C(C)C2C', 'CCCCC1=CC(CC)C=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules matching a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)=NC[N+]1=C(CCNN=NN)CC1', 'CN=NN(CN)C(C=CN=NN[N+](C)(CNN)NNNC)CCNC', 'C=C(C=C(N)CCC(C)N)C=C(C=NC)C(C(CC)CO)C(CCC)CCC', 'CCCN(CC1CC=CCC2OC2=N1)NCC', 'CC=CC[NH+](C(C=CC1CC1)=CC)[O+]1CC=C=C1C', 'CC(C)=CCC=C(N)CC(C)(C)CCC1=CC1=CCNCN', 'C=[N+]1C=[N+]2CCCN=CC(N1)C(CCC)=C(N=CC)CN2CC(=C[N+](C)=CNC)CCCC', 'CC=NC1C=CC(=CC=CC(O)C(C)(CC)COF)C1', 'CCN[N+](=CNC1=C(N=CCCNC)C1C)CC(=N)[N+]1=CN1NCNCN', 'C=COC(C=CCN)=CC(CC)[O+]1NC=CCCOC1=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C)=NC[N+]1=C(CCNN=NN)CC1', 'CN=NN(CN)C(C=CN=NN[N+](C)(CNN)NNNC)CCNC', 'C=C(C=C(N)CCC(C)N)C=C(C=NC)C(C(CC)CO)C(CCC)CCC', 'CCCN(CC1CC=CCC2OC2=N1)NCC', 'CC=CC[NH+](C(C=CC1CC1)=CC)[O+]1CC=C=C1C', 'CC(C)=CCC=C(N)CC(C)(C)CCC1=CC1=CCNCN', 'C=[N+]1C=[N+]2CCCN=CC(N1)C(CCC)=C(N=CC)CN2CC(=C[N+](C)=CNC)CCCC', 'CC=NC1C=CC(=CC=CC(O)C(C)(CC)COF)C1', 'CCN[N+](=CNC1=C(N=CCCNC)C1C)CC(=N)[N+]1=CN1NCNCN', 'C=COC(C=CCN)=CC(CC)[O+]1NC=CCCOC1=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate molecules achieving a z-score of -6.190392 for the cell line '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)C=CO)N=COC', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'C=C(C)C=NC=C(C=CC)C=[O+]C1(O)CC1C=CN=CC', 'CCC=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC', 'CCCC=C(C=C1C=CC(CCC)CC(C)(N2CC=[O+]2)C1C)CCCCC', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CC)N1CC(=CC2CCCC2)N1', 'CCC1=C(N)N1CCCCCCCC(C)(C)C', 'CCCC=C(C=C1C=CC(CC)CC(C)(N2C=CC2)C1C)CCCCC', 'CC=C1CCCC(CCCN)CCCC(C)(C)C1', 'CCC1N=NCC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)C=CO)N=COC', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'C=C(C)C=NC=C(C=CC)C=[O+]C1(O)CC1C=CN=CC', 'CCC=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC', 'CCCC=C(C=C1C=CC(CCC)CC(C)(N2CC=[O+]2)C1C)CCCCC', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CC)N1CC(=CC2CCCC2)N1', 'CCC1=C(N)N1CCCCCCCC(C)(C)C', 'CCCC=C(C=C1C=CC(CC)CC(C)(N2C=CC2)C1C)CCCCC', 'CC=C1CCCC(CCCN)CCCC(C)(C)C1', 'CCC1N=NCC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a collection of molecules with a z-score of -6.190392 in '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)N(C)CC', 'C[O+](CCCN)NC=[N+]1CC1C=CNC=CC=CN', 'CCCC=CCC=CNCC1(C)CCC[O+]1CCCC(NC)C1NCCC1C', 'CC=C(NC)NC(C)C=CC=CCCC', 'CC1=C(C)C2=CCC(C=[O+]CC3CC3C)=CC2C1', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CBC1CN(CCC)NNCCN=[N+]1N', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'C=C(C)C=C1CCC(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)N(C)CC', 'C[O+](CCCN)NC=[N+]1CC1C=CNC=CC=CN', 'CCCC=CCC=CNCC1(C)CCC[O+]1CCCC(NC)C1NCCC1C', 'CC=C(NC)NC(C)C=CC=CCCC', 'CC1=C(C)C2=CCC(C=[O+]CC3CC3C)=CC2C1', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CBC1CN(CCC)NNCCN=[N+]1N', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'C=C(C)C=C1CCC(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically targeting a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[N+]1=CC1', 'C=C(C(BC)C=CN)[N+]1=NNCN(C)C1', 'C=C(CC)C(=CC)CCCCCCNCCCCCC=CCC(N)CC', 'C=C(CCCC(C)(C)C(C=CC=C(C)C)C(C)C)N1CCCC1', 'CCC1=CC(CCCC2=C(N)C2(N)C(C)C(C)N=CC=C(CC)NCC2=CC=C2)=C1', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2', 'C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'C=CN1C=CCCCC(C)CC(CCC)CC1', 'C=C(C=CC)C=CCCNCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[N+]1=CC1', 'C=C(C(BC)C=CN)[N+]1=NNCN(C)C1', 'C=C(CC)C(=CC)CCCCCCNCCCCCC=CCC(N)CC', 'C=C(CCCC(C)(C)C(C=CC=C(C)C)C(C)C)N1CCCC1', 'CCC1=CC(CCCC2=C(N)C2(N)C(C)C(C)N=CC=C(CC)NCC2=CC=C2)=C1', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2', 'C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'C=CN1C=CCCCC(C)CC(CCC)CC1', 'C=C(C=CC)C=CCCNCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to meet a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C[N+](NN=CN=CC)=C(CC)CCC(C)CC', 'CC=CC1[O+]=CCC=CC1=CC=CC(CC)CCCC(C)CC(C)(N)C(C)C', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'C=C(CN)C(CCCN)CC1=C2B(N)CB(N)CC=[N+]2N1', 'C=C1CCCC=NN[O+]1C(C=CN=CCN)CC', 'CC1C(C2=CC2)C=CCOOC=CCC(N)N1C=CNCCC1=C2C(=N)C12', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC(=O)C(C)OC(C)(C)CF', 'CN=CN=C(C)N(N)CCCN[N+](N)=C(C)C', 'CCC(C)N=CC(C)=CC=C(C)CC=CCC=CC1CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C[N+](NN=CN=CC)=C(CC)CCC(C)CC', 'CC=CC1[O+]=CCC=CC1=CC=CC(CC)CCCC(C)CC(C)(N)C(C)C', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'C=C(CN)C(CCCN)CC1=C2B(N)CB(N)CC=[N+]2N1', 'C=C1CCCC=NN[O+]1C(C=CN=CCN)CC', 'CC1C(C2=CC2)C=CCOOC=CCC(N)N1C=CNCCC1=C2C(=N)C12', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC(=O)C(C)OC(C)(C)CF', 'CN=CN=C(C)N(N)CCCN[N+](N)=C(C)C', 'CCC(C)N=CC(C)=CC=C(C)CC=CCC=CC1CCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a range of molecules with the target z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)CCCCCCC(C)C1CCCCCC(C)=[O+][O+]=CC(C)CCC(CCC)C(CC)C1C', 'CN=CC1C=C1N', 'CC=CC=CC(=CC)C(C)(CC)NCC(=N)N', 'C=C(C(=CC)N=CCCC)C1CC2CCC1CCC2(C)C(=C)C', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'C=C1CN=CC=C2CNC(C)OC3=C(C)C=C(C)C12CN[N+](CCCNN)=C(C)NC(CN)=C(N)CC3', 'CCCOCCOCCCNC(=CC1CCC=CCC(C)(C)C=C1N=CC1CN1)CC', 'CCC1CC1=CC=CC(O)=COCC(C)C', 'CCCCC(C)CC=CNCCCC(=O)CCC=C(C)C', 'C=C(C)CCC(C)(CF)CC(C=C(N)C(C)CC)CC1=CCC2C=C(CC(N)N=C(C)CC1)C(C)NO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)CCCCCCC(C)C1CCCCCC(C)=[O+][O+]=CC(C)CCC(CCC)C(CC)C1C', 'CN=CC1C=C1N', 'CC=CC=CC(=CC)C(C)(CC)NCC(=N)N', 'C=C(C(=CC)N=CCCC)C1CC2CCC1CCC2(C)C(=C)C', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'C=C1CN=CC=C2CNC(C)OC3=C(C)C=C(C)C12CN[N+](CCCNN)=C(C)NC(CN)=C(N)CC3', 'CCCOCCOCCCNC(=CC1CCC=CCC(C)(C)C=C1N=CC1CN1)CC', 'CCC1CC1=CC=CC(O)=COCC(C)C', 'CCCCC(C)CC=CNCCCC(=O)CCC=C(C)C', 'C=C(C)CCC(C)(CF)CC(C=C(N)C(C)CC)CC1=CCC2C=C(CC(N)N=C(C)CC1)C(C)NO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds reaching a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CN=C(C)N(C)C(=N)NCCCNC(C)N=CB(C)C(C)CN', 'C=CC1C=CC=CC(O)=C(CCC)CC1CC(=C)C', 'C=[N+](C)CNC=[O+]C(C)=CC', 'CCC1CC(O)(C2CCC(C)C(OC)=CC2CC)C1', 'CC1C=CBNC2=CC(C(C=NCCNNN)=CCC=CC1)[NH+]2CNN(C)N', 'C=NC(CN(C=CC(=CNC)C(=N)CCC)C(C)N)NCC(C)(O)NC', 'C=C(CN)C(CC1=C2C(=CCC(C)CC2C)C1)CC1CC1C', 'CC(BBN)CN(C=NN)N=N', 'CC=CN(N)C(CCCC)ONNN', 'CCC(C)=CCCCCC=C1CCNC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN=C(C)N(C)C(=N)NCCCNC(C)N=CB(C)C(C)CN', 'C=CC1C=CC=CC(O)=C(CCC)CC1CC(=C)C', 'C=[N+](C)CNC=[O+]C(C)=CC', 'CCC1CC(O)(C2CCC(C)C(OC)=CC2CC)C1', 'CC1C=CBNC2=CC(C(C=NCCNNN)=CCC=CC1)[NH+]2CNN(C)N', 'C=NC(CN(C=CC(=CNC)C(=N)CCC)C(C)N)NCC(C)(O)NC', 'C=C(CN)C(CC1=C2C(=CCC(C)CC2C)C1)CC1CC1C', 'CC(BBN)CN(C=NN)N=N', 'CC=CN(N)C(CCCC)ONNN', 'CCC(C)=CCCCCC=C1CCNC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide a series of molecules with a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['BCC=CCCCC(N)N', 'CC=CN(CCCC(CCC)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CNC(=CC=CN)C(C=CCN)CNN', 'CC(C)C1C=C2C3=NCCNC(C)(CCN(C)CNC23)CC1N', 'CCc1cc(CC)c(C)c(C(C)C[N+](=CN)NC=NC)c1', 'CCCCC1CC(C2(C)C=CC(C)=C2)C1', 'CNCC1C(CN)CN1CC1CC2(C)CN1C(C)(C)C(CNC)CN=CC1=NC2C1(C)C=CN', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=NC=C1C(C=CC)=C(C=C(C)N)CCC(C)C', 'CCC1=C2CCC(C(C)=CCC(C)OC(C(C)=CCN)CCOC=CC1)C(CC=N)CNCC(C)CC1=C2C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCC=CCCCC(N)N', 'CC=CN(CCCC(CCC)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CNC(=CC=CN)C(C=CCN)CNN', 'CC(C)C1C=C2C3=NCCNC(C)(CCN(C)CNC23)CC1N', 'CCc1cc(CC)c(C)c(C(C)C[N+](=CN)NC=NC)c1', 'CCCCC1CC(C2(C)C=CC(C)=C2)C1', 'CNCC1C(CN)CN1CC1CC2(C)CN1C(C)(C)C(CNC)CN=CC1=NC2C1(C)C=CN', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=NC=C1C(C=CC)=C(C=C(C)N)CCC(C)C', 'CCC1=C2CCC(C(C)=CCC(C)OC(C(C)=CCN)CCOC=CC1)C(CC=N)CNCC(C)CC1=C2C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a series of molecules matching a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['BBB(B)BBN1BBCNCC1', 'C=CCN=[O+]C(N=C(C)OC)=C(CN=CN)C(CC)CC', 'CC1CCC(N=N)=C1N', 'CCC1(N)CCN=C(C)C=[N+](C)CC1', 'C=C1C(C)CC=CCC=CC(C)=C(C)CC2=C(C2)CC1C[O+](N)CC(CC)CC(C)C', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2CCCC1=C2CC12', 'CCC1CN(NC)CNC(NN)CNCN1NC', 'CC1=NCNN2CC=C(C(N)CN=CN)B(NCCCC=C1)NNN(N)CCNN2', 'C=C(C)CC1=CCC2=C(C=CCC(CC)CC3=[N+](C(=C)C1)N3CCCCN=CC)C2C', 'CC(=CCNNNC=C1CCNC1)CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BBB(B)BBN1BBCNCC1', 'C=CCN=[O+]C(N=C(C)OC)=C(CN=CN)C(CC)CC', 'CC1CCC(N=N)=C1N', 'CCC1(N)CCN=C(C)C=[N+](C)CC1', 'C=C1C(C)CC=CCC=CC(C)=C(C)CC2=C(C2)CC1C[O+](N)CC(CC)CC(C)C', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2CCCC1=C2CC12', 'CCC1CN(NC)CNC(NN)CNCN1NC', 'CC1=NCNN2CC=C(C(N)CN=CN)B(NCCCC=C1)NNN(N)CCNN2', 'C=C(C)CC1=CCC2=C(C=CCC(CC)CC3=[N+](C(=C)C1)N3CCCCN=CC)C2C', 'CC(=CCNNNC=C1CCNC1)CN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules to achieve the target z-score of -4.054651 for the cell line '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)=CCNC(O)C1CC1=CCN1CC(C(C)C)C(C)=[O+]1', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCCCC1=CC(CC)C=C1', 'CCC=CC(=N)CC12c3ncn1c32', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CNCCC1NB1C', 'CC(N)[N+](C)(CN)CC=CCC=N', 'CC=C1CCC=CC1CC(N)C=CC(N)=CCCCN=NN(C)CNN', 'CC(N)[N+]1(C)NC=CN1', 'C=[N+]1C=C=N[N+](C(CC)CCC)=C1N=CN=NCCNC(C)C(C)=C(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)=CCNC(O)C1CC1=CCN1CC(C(C)C)C(C)=[O+]1', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCCCC1=CC(CC)C=C1', 'CCC=CC(=N)CC12c3ncn1c32', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CNCCC1NB1C', 'CC(N)[N+](C)(CN)CC=CCC=N', 'CC=C1CCC=CC1CC(N)C=CC(N)=CCCCN=NN(C)CNN', 'CC(N)[N+]1(C)NC=CN1', 'C=[N+]1C=C=N[N+](C(CC)CCC)=C1N=CN=NCCNC(C)C(C)=C(C)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules achieving a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CCCCC(C)=C1C(CNN)=[N+]1C=[N+](C)CNC(CN)CCCC', 'C=C1CCN(C)CCC(C)[O+](C2=CC=[O+]C(CC=CC)C(C)CC2C)CC(CCC)=C2CC12C', 'C=CC(C)(O)CC1(C[O+](C)C(=C)CC(C)CC=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CC[N+](C=NC(C)O)=CC=CCCCCC=CCC(C)NC', 'C=[N+](CCCCC)C(CN=C(CCCCCC)CC(CN)C(C)CN=CC)C(C)(C)C(C)[O+](C)CCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1CC(CNC2)N=N1', 'CCC=[N+]1CCCC(=CNN)CC=CCC=NC(O)NC1', 'CC=C1CCC=CC1CC(C)C=CC(N)=CCCCC=NC(C)CCN', 'C=NCC[N+]1=CCC1C=N[N+](=N)CC(C)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CCCCC(C)=C1C(CNN)=[N+]1C=[N+](C)CNC(CN)CCCC', 'C=C1CCN(C)CCC(C)[O+](C2=CC=[O+]C(CC=CC)C(C)CC2C)CC(CCC)=C2CC12C', 'C=CC(C)(O)CC1(C[O+](C)C(=C)CC(C)CC=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CC[N+](C=NC(C)O)=CC=CCCCCC=CCC(C)NC', 'C=[N+](CCCCC)C(CN=C(CCCCCC)CC(CN)C(C)CN=CC)C(C)(C)C(C)[O+](C)CCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1CC(CNC2)N=N1', 'CCC=[N+]1CCCC(=CNN)CC=CCC=NC(O)NC1', 'CC=C1CCC=CC1CC(C)C=CC(N)=CCCCC=NC(C)CCN', 'C=NCC[N+]1=CCC1C=N[N+](=N)CC(C)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules to reach a z-score of -4.054651 for the cell line '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)C1(C(=C)NN)N=CC1C', 'BC(CC)(C1NC(N)([N+](=N)N)N1)C(C)C(BCNBC(BC)B(C)N=C)NCB=N', 'CC(NN)C1N[NH+](C=NCN)[N+]1(C)NCCN', 'C=C(N)[N+](C)(C)C(C)CC', 'BCC(=NN)C(=BCB=C(CC)C1=CN(N)C1)CBC', 'CC=CC=C1CNC(CN)C(C=C2CC2N)=CCO1', 'BB(B)BC1N=N1', 'CBNC(N=NCC)[N+]1=NC(CN)=NB1', 'C=[N+](C=CCC(=CC)CCN(C)CC)CC1CCC2=C(ONCC)C1N2', 'CCC(CC(C)NC(C)N=CN)C(=NC=N)C(N)O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)C1(C(=C)NN)N=CC1C', 'BC(CC)(C1NC(N)([N+](=N)N)N1)C(C)C(BCNBC(BC)B(C)N=C)NCB=N', 'CC(NN)C1N[NH+](C=NCN)[N+]1(C)NCCN', 'C=C(N)[N+](C)(C)C(C)CC', 'BCC(=NN)C(=BCB=C(CC)C1=CN(N)C1)CBC', 'CC=CC=C1CNC(CN)C(C=C2CC2N)=CCO1', 'BB(B)BC1N=N1', 'CBNC(N=NCC)[N+]1=NC(CN)=NB1', 'C=[N+](C=CCC(=CC)CCN(C)CC)CC1CCC2=C(ONCC)C1N2', 'CCC(CC(C)NC(C)N=CN)C(=NC=N)C(N)O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a series of molecules matching a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[N+](C=CCCC)=C(C)CC(=C)CCC(=C)C', 'CCC(=[O+]C)C(C=C(N)CC1=CCCCC(C=CC2=CC2)CC=C1)C(C)CC', 'C=C1C=C2C=C(C[N+]23N=CCCCC=N3)NC=NC(CNCCCC(=N)NBC)CCN=C(C)NC1', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=C(CC)CCC1CC2C=[N+](C)C[N+](=C2)NCC=CCC1O', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC1=C2CC(=O)[O+](CC3C=CC=CC3)C2=CC(C)C1', 'C=CCC(C=C)CONC1(CC(C)(C)NC)C=CC1', 'CCC=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[N+](C=CCCC)=C(C)CC(=C)CCC(=C)C', 'CCC(=[O+]C)C(C=C(N)CC1=CCCCC(C=CC2=CC2)CC=C1)C(C)CC', 'C=C1C=C2C=C(C[N+]23N=CCCCC=N3)NC=NC(CNCCCC(=N)NBC)CCN=C(C)NC1', 'C=C(CCC(C)NN1C2=C([N+](=NC)NC(CC)NC)C(C)(CC)CC21)C1(C)CC1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=C(CC)CCC1CC2C=[N+](C)C[N+](=C2)NCC=CCC1O', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC1=C2CC(=O)[O+](CC3C=CC=CC3)C2=CC(C)C1', 'C=CCC(C=C)CONC1(CC(C)(C)NC)C=CC1', 'CCC=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules with the goal of achieving a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['NCC=CC=C1NC(N)CCCCCNCC[N+]2=C1CC2', 'C=C(CN)C(C(C)=N)=[N+](C)CN', 'C=C(C)CC=[N+]1CC1CCCCCNC(=C)CC', 'CCc1[nH][nH]c1C=C(C)CC(C=C(C=CCCC(C)(N)CNC)CN)N(C)NCCC(C)N', 'C=CCNN(C)CN(CN)C(NN)C(C)C(C=NNC)N=N', 'C=C1CCCC2=C(C=C3C(C)CC3CC)C(C=CC2)C1', 'C=NN=CC(OC(C)(CCC=C1CC1)CC[O+](CC)C(C)C=CCC)[N+](=C)CCSC', 'C=CC1C(C)C(CCC)CC2=[N+]1C(C)=C(C)C=CCCC2', 'C=C(CC)C(C)C=CC(=CCN=CC(=CC(=N)CC(C)F)CCC)N(C)C(C)CNO', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['NCC=CC=C1NC(N)CCCCCNCC[N+]2=C1CC2', 'C=C(CN)C(C(C)=N)=[N+](C)CN', 'C=C(C)CC=[N+]1CC1CCCCCNC(=C)CC', 'CCc1[nH][nH]c1C=C(C)CC(C=C(C=CCCC(C)(N)CNC)CN)N(C)NCCC(C)N', 'C=CCNN(C)CN(CN)C(NN)C(C)C(C=NNC)N=N', 'C=C1CCCC2=C(C=C3C(C)CC3CC)C(C=CC2)C1', 'C=NN=CC(OC(C)(CCC=C1CC1)CC[O+](CC)C(C)C=CCC)[N+](=C)CCSC', 'C=CC1C(C)C(CCC)CC2=[N+]1C(C)=C(C)C=CCCC2', 'C=C(CC)C(C)C=CC(=CCN=CC(=CC(=N)CC(C)F)CCC)N(C)C(C)CNO', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of compounds with a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCC)C1CCC2=C3C=COCC(CC(C)C2)OC(=C3)C=C(F)C1C', 'CC=C(C(C)N)[O+](CC)C1=C(C)C=[O+]CNC(CCC)=C1C', 'C=CCCC(=CCNN=CC(=CN=C)C(CC=CC(=C)C)NC(C)N1C=CNCN1)NC', 'CC(CCN)=C(C)CCCC=CC=CCC1=CC23C=C(CCC1CCC(C)NCNCN2)C3', 'C=CC1=C2NC3=CCC4CC5CCC6=NC=C1C4C(=C5C(=C)C6C1CC21)C=C3', 'C=CC(CC)=C(F)C=CC(=C)C1=CCCC(C2=CCCCC2)C1', 'CCC=CNC(C)N=CCC', 'C=[N+]1CC(C)=C2C=CCC3CN2CNC(CCC)CCC1=C(CC)CCCCO3', 'CCCC1=CCC1(C)C1(C(C)C)CCC1C1=C(C)C(C)=C1', 'CC=[N+](N)CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCC)C1CCC2=C3C=COCC(CC(C)C2)OC(=C3)C=C(F)C1C', 'CC=C(C(C)N)[O+](CC)C1=C(C)C=[O+]CNC(CCC)=C1C', 'C=CCCC(=CCNN=CC(=CN=C)C(CC=CC(=C)C)NC(C)N1C=CNCN1)NC', 'CC(CCN)=C(C)CCCC=CC=CCC1=CC23C=C(CCC1CCC(C)NCNCN2)C3', 'C=CC1=C2NC3=CCC4CC5CCC6=NC=C1C4C(=C5C(=C)C6C1CC21)C=C3', 'C=CC(CC)=C(F)C=CC(=C)C1=CCCC(C2=CCCCC2)C1', 'CCC=CNC(C)N=CCC', 'C=[N+]1CC(C)=C2C=CCC3CN2CNC(CCC)CCC1=C(CC)CCCCO3', 'CCCC1=CCC1(C)C1(C(C)C)CCC1C1=C(C)C(C)=C1', 'CC=[N+](N)CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a selection of molecules with a z-score reaching -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(NC=C=CC)C(C)CC', 'CCC12CC(C)C3(O)ON(CC(S)CNNO)C(C1C)C23', 'CC=CC1=C(C(C)(O)CCCC)NC1', 'CC=C=C1N=[N+]1N', 'C=CC=CC(CC)C(CCC(=C)C(N)CCB(N)CC=N)NCC', 'CCCC(CC)C(C)=CCC1=CCCC2=CC(=C2)C(OCCCO)C=CNC(CC)CC=CC1(C)C', 'C=CCC(C(C)CC)[N+](=CC)OCC', 'C=C1[O+](CN2CC(C(C)N)=C3C=C32)C2=CC=CC=CC12C', 'C=CCC(CCC)N1CC(C2=C(C)C3CCC=C3CCCC2C)C1NCC=NCCC', 'C=C(CC(=N)C(N)(CC=CCNN(C)N(NC)[NH+](N)BB(N)CCCNCCC)C(CCN)C(NN)C(C)NC=N)NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(NC=C=CC)C(C)CC', 'CCC12CC(C)C3(O)ON(CC(S)CNNO)C(C1C)C23', 'CC=CC1=C(C(C)(O)CCCC)NC1', 'CC=C=C1N=[N+]1N', 'C=CC=CC(CC)C(CCC(=C)C(N)CCB(N)CC=N)NCC', 'CCCC(CC)C(C)=CCC1=CCCC2=CC(=C2)C(OCCCO)C=CNC(CC)CC=CC1(C)C', 'C=CCC(C(C)CC)[N+](=CC)OCC', 'C=C1[O+](CN2CC(C(C)N)=C3C=C32)C2=CC=CC=CC12C', 'C=CCC(CCC)N1CC(C2=C(C)C3CCC=C3CCCC2C)C1NCC=NCCC', 'C=C(CC(=N)C(N)(CC=CCNN(C)N(NC)[NH+](N)BB(N)CCCNCCC)C(CCN)C(NN)C(C)NC=N)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please create compounds with a target z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CCC(C)N(CC)CC=CC1CCCC(CCNCC)CC(C)NO1', 'B=CNNCC(CCB(N)CN(N)C1(B2NB2B)CC1=N)=NN', 'CCCC(C(C)CCN(C)C)N1CC(C)=C(C)N1', 'BB(C)NC(C)NCCC1=NNCC(C(CC)NN)=CC1', 'CC1CC2(C)CCCCC1CCN2C', 'C=CC(C)OCCC=C1CNC=CCC1C', 'C=CCC1=C(C=C(CC)[N+]2(C)CCC(C(N)C=C)CC2)CCC(C)CCC1', 'C=CC=C[O+]=CCC', 'C=CC(CNC(C)N)C(C)C(CN)CN1BC(B=[N+]=C[N+](N)=BC=CN)C(C)(CCN)C1C', 'BC1C(BN)CC=CC(N)=CNC(=CN(C)N)C(CC)C1(C)CN(C)NCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CCC(C)N(CC)CC=CC1CCCC(CCNCC)CC(C)NO1', 'B=CNNCC(CCB(N)CN(N)C1(B2NB2B)CC1=N)=NN', 'CCCC(C(C)CCN(C)C)N1CC(C)=C(C)N1', 'BB(C)NC(C)NCCC1=NNCC(C(CC)NN)=CC1', 'CC1CC2(C)CCCCC1CCN2C', 'C=CC(C)OCCC=C1CNC=CCC1C', 'C=CCC1=C(C=C(CC)[N+]2(C)CCC(C(N)C=C)CC2)CCC(C)CCC1', 'C=CC=C[O+]=CCC', 'C=CC(CNC(C)N)C(C)C(CN)CN1BC(B=[N+]=C[N+](N)=BC=CN)C(C)(CCN)C1C', 'BC1C(BN)CC=CC(N)=CNC(=CN(C)N)C(CC)C1(C)CN(C)NCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a diverse set of molecules meeting a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)CNC1=CCCC2=[O+]C(C)COC=C2C=CC=CCC1', 'CCCCNCN', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'C=CN(CCCOC)C(=C)[N+]1=CC=C1C', 'C=CC1(C)CCC(N)C(=O)C(C=CC)C1', 'CC=C[O+]1CN1NNC1=C2C(C)=C12', 'CNC1=CC=C(C)C2CCOCC(=C(OCCC(C=CCC3=CCC(C)=C3)[N+](C)=N)C2)CC1', 'CC=CN1C=C=C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'C=CCCCC(=CCCCNCC(O)CCCN1CC1)CC', 'CCNCCC(C)CC(C)C(N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C)CNC1=CCCC2=[O+]C(C)COC=C2C=CC=CCC1', 'CCCCNCN', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'C=CN(CCCOC)C(=C)[N+]1=CC=C1C', 'C=CC1(C)CCC(N)C(=O)C(C=CC)C1', 'CC=C[O+]1CN1NNC1=C2C(C)=C12', 'CNC1=CC=C(C)C2CCOCC(=C(OCCC(C=CCC3=CCC(C)=C3)[N+](C)=N)C2)CC1', 'CC=CN1C=C=C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'C=CCCCC(=CCCCNCC(O)CCCN1CC1)CC', 'CCNCCC(C)CC(C)C(N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that reach a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'C=C(C)OC(=CC)[O+]1OCC1(C)C=[O+]C=O', 'CC=CC=C(C=CC[N+](C=CO)=CC)CC=CCC', 'CCCC1(C)C=C1OC', 'NC1=CC[N+]1=C(N)N', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C(C=CC1CNC(C=CC(C)C=C(C)CC)N=N1)CCC', 'C=C(C)COCOOCCCCC(C)(O)C=CC', 'CCC=CCNC[N+](=CCCC)CNCC(C)C', 'CCNCC(N)C(CC=C=CCCN)=C1C=C(C(C)=CN)C(C)CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'C=C(C)OC(=CC)[O+]1OCC1(C)C=[O+]C=O', 'CC=CC=C(C=CC[N+](C=CO)=CC)CC=CCC', 'CCCC1(C)C=C1OC', 'NC1=CC[N+]1=C(N)N', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C(C=CC1CNC(C=CC(C)C=C(C)CC)N=N1)CCC', 'C=C(C)COCOOCCCCC(C)(O)C=CC', 'CCC=CCNC[N+](=CCCC)CNCC(C)C', 'CCNCC(N)C(CC=C=CCCN)=C1C=C(C(C)=CN)C(C)CCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a range of compounds achieving a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C(C)C1(CC)CC(=COCN2C[N+](C)=C2CC)C(CC)CC2CC2C1', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'C=C(C)CC(CC)CC1CCCN(N=CC)C1', 'CC=C1C(C)CCCN2C(C)C2CC1C(C)CCF', 'CCCCC(CCC)C1(C)NN1', 'CC=NCNCC1C=NC(N=CNCC)N=CCNCB(N)NNCC1', 'CCCCN1CC=C(C=CC(CCC)C(C)=C(C2=CC=CC2)N(CC)CC)C1', 'CCCCC[N+]1=CC=C(C)C(C(C)=C(CC)N=CC(C)C=NN)C1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C[O+](CCCN)NN=[N+]1CC1C=CNC=CC=CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C(C)C1(CC)CC(=COCN2C[N+](C)=C2CC)C(CC)CC2CC2C1', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'C=C(C)CC(CC)CC1CCCN(N=CC)C1', 'CC=C1C(C)CCCN2C(C)C2CC1C(C)CCF', 'CCCCC(CCC)C1(C)NN1', 'CC=NCNCC1C=NC(N=CNCC)N=CCNCB(N)NNCC1', 'CCCCN1CC=C(C=CC(CCC)C(C)=C(C2=CC=CC2)N(CC)CC)C1', 'CCCCC[N+]1=CC=C(C)C(C(C)=C(CC)N=CC(C)C=NN)C1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C[O+](CCCN)NN=[N+]1CC1C=CNC=CC=CN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules optimized to meet a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CN1CCC2C=CN2C=NC1N', 'C=NC=[O+][N+](C)=C1CCC(C)=C2C(CN=CCCC2(O)CCN)O1', 'C=CCCCC1CC(C=CNC)C1', 'C=C(C)CNCOOCCCCC(C)(O)N=CN', 'C=C(CC)CC1=CC1CC(C)(C)C(C=CCCC)=CC1=[N+](N=CC=O)N=C(C=COC)C1C', 'C=C(CN(N)C=CN)C1=C(C)C1', 'CC=CC1NC(CCCC)C(C)C(CC=CCC=[O+]C)C2(C)CC1C2', 'CC=CCC(=CN)CCC(CC)(C1=CN(C)CC1)[N+](=CO)CC(C=CC=N)=CCC', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CN1CCC2C=CN2C=NC1N', 'C=NC=[O+][N+](C)=C1CCC(C)=C2C(CN=CCCC2(O)CCN)O1', 'C=CCCCC1CC(C=CNC)C1', 'C=C(C)CNCOOCCCCC(C)(O)N=CN', 'C=C(CC)CC1=CC1CC(C)(C)C(C=CCCC)=CC1=[N+](N=CC=O)N=C(C=COC)C1C', 'C=C(CN(N)C=CN)C1=C(C)C1', 'CC=CC1NC(CCCC)C(C)C(CC=CCC=[O+]C)C2(C)CC1C2', 'CC=CCC(=CN)CCC(CC)(C1=CN(C)CC1)[N+](=CO)CC(C=CC=N)=CCC', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce a collection of molecules with a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NCC(C)=CC', 'BB(C)CC(CCC=C)NC', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CC=C1C(CF)=C1CCCCC(C)=C1CCCCC1C', 'CC=NC1C(C)CN1C', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCNc1c[nH][n+](O)n1)CC', 'CC(C)=NCC1(C)CC1(C)C', 'CCCC=CCCC(C)CCC(C)CC=C1CCC1CC(C=C(CC)CC1C=C1C)N=NCCN', 'C=CC(O)C=CC=CC(C)CCCCCC=C1C=CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NCC(C)=CC', 'BB(C)CC(CCC=C)NC', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CC=C1C(CF)=C1CCCCC(C)=C1CCCCC1C', 'CC=NC1C(C)CN1C', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCNc1c[nH][n+](O)n1)CC', 'CC(C)=NCC1(C)CC1(C)C', 'CCCC=CCCC(C)CCC(C)CC=C1CCC1CC(C=C(CC)CC1C=C1C)N=NCCN', 'C=CC(O)C=CC=CC(C)CCCCCC=C1C=CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds reaching a z-score of -6.190392 for the cell line '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C=C(CCN)C=NC(C)=C1', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CN)CC[O+]=CC', 'BCCC=C(C)NC(CC=N)=NC=CNBN', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2', 'BCN=CNCB(B)C(N)N', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1', 'C=C(C)CCC(C)C(C(=CC(O)CCC1=CC=CC(C)CONC1)CC)N(C)CC=C1CC=CNC1', 'CNC1CC1=NC=CC(N)=CCCC(C)N', 'C=C(N)NC[N+](C)=CN=CC=[N+]1C=NC=CCNC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=C(CCN)C=NC(C)=C1', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CN)CC[O+]=CC', 'BCCC=C(C)NC(CC=N)=NC=CNBN', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2', 'BCN=CNCB(B)C(N)N', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1', 'C=C(C)CCC(C)C(C(=CC(O)CCC1=CC=CC(C)CONC1)CC)N(C)CC=C1CC=CNC1', 'CNC1CC1=NC=CC(N)=CCCC(C)N', 'C=C(N)NC[N+](C)=CN=CC=[N+]1C=NC=CCNC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a set of molecules matching a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CO)=CO1', 'C=C(C=C1C2=C1C(=CCCC(C)C)C2)CCC(C)(C)C(=CC)CC', 'CCCNCC(CCC)CCN(CNN)[N+]1=[N+]2N=NC21', 'C=C1CCC=C(NCCN)C=[O+][O+]=CCC(CC(CC)CCC)NCC(C)=C1C(C)C=CC', 'C=C(C)CCCCC(C=CC1=C(CCC2=CC2)CC(=C)CC1)CCCCCF', 'C=C(CC=CN)C1=NCCC(C)=C1', 'CCCC(C)C1C(C(C)F)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCNCC1CCC2=C(C)C13CC2(OCC1C(C)CN1C)C3=C', 'CCC(C)(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CO)=CO1', 'C=C(C=C1C2=C1C(=CCCC(C)C)C2)CCC(C)(C)C(=CC)CC', 'CCCNCC(CCC)CCN(CNN)[N+]1=[N+]2N=NC21', 'C=C1CCC=C(NCCN)C=[O+][O+]=CCC(CC(CC)CCC)NCC(C)=C1C(C)C=CC', 'C=C(C)CCCCC(C=CC1=C(CCC2=CC2)CC(=C)CC1)CCCCCF', 'C=C(CC=CN)C1=NCCC(C)=C1', 'CCCC(C)C1C(C(C)F)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCNCC1CCC2=C(C)C13CC2(OCC1C(C)CN1C)C3=C', 'CCC(C)(C)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules with the intended z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['B=[N+](N)[N+](=C)CCC(N)N(C)B(B)C', 'BC(N)=BC(CB(C)NCC)=NC', 'BB(N)B(C=C)B(NC(B=N)B(B)NB(B)C1BC=N1)NN(N)N', 'CC(C)CCCCN(C)C1(C)CC1=C=CN=CN(C)C', 'BBNC(N)BBC1CCC=NC(C)=C(CN)NC=NC1N', 'C=CCNC1C(CC(C)CC(C=CCO)C(C)C(C)CC)C2CC1C2C(C)CC', 'CCC(CCN)C(C)=C1C=CC1=N', 'CC=C(C(=CC(S)=C(O)CO)C(CC)CCC)C1CC=CCC1', 'C=C(CC)N=C(CCCC=CC1=CC(C)(CC(CCCCN)NC)C(O)C1CC)C1=C(CCC)CC1C', 'CB(N)C1CC2CCC3=NN1C32'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['B=[N+](N)[N+](=C)CCC(N)N(C)B(B)C', 'BC(N)=BC(CB(C)NCC)=NC', 'BB(N)B(C=C)B(NC(B=N)B(B)NB(B)C1BC=N1)NN(N)N', 'CC(C)CCCCN(C)C1(C)CC1=C=CN=CN(C)C', 'BBNC(N)BBC1CCC=NC(C)=C(CN)NC=NC1N', 'C=CCNC1C(CC(C)CC(C=CCO)C(C)C(C)CC)C2CC1C2C(C)CC', 'CCC(CCN)C(C)=C1C=CC1=N', 'CC=C(C(=CC(S)=C(O)CO)C(CC)CCC)C1CC=CCC1', 'C=C(CC)N=C(CCCC=CC1=CC(C)(CC(CCCCN)NC)C(O)C1CC)C1=C(CCC)CC1C', 'CB(N)C1CC2CCC3=NN1C32']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of compounds targeting a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C(C)C1(CC)CC(=COCN2C[N+](C)=C2CC)C(CC)CC2CC2C1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'CC=C(C1=CCCCN[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CC=C(COO)NC(C)CC=NC1CCC1', 'CCCCCCC(C)C1(CCCCC)CC=C(C2CCC2)CC1', 'CC=CCCC1(C=CCC(=CC)C(N)N=C(CC)C(=CC)C(C)=COC)C2CC(=C(CCC)CCO)CN(C)C=CN(N)C3C(N)CNCCC3(CCCC)C21', 'CC=C[NH+](C)C(N)(CC)CC(C)[N+]1(C)C2NN1C2=NCN', 'C=C(C(=CC)N=CCCC)C1C[NH+]2CCC1CCC2(C)C(=C)C', 'CCC=C(C)[O+](C)CCC1CCCCC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C(C)C1(CC)CC(=COCN2C[N+](C)=C2CC)C(CC)CC2CC2C1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'CC=C(C1=CCCCN[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CC=C(COO)NC(C)CC=NC1CCC1', 'CCCCCCC(C)C1(CCCCC)CC=C(C2CCC2)CC1', 'CC=CCCC1(C=CCC(=CC)C(N)N=C(CC)C(=CC)C(C)=COC)C2CC(=C(CCC)CCO)CN(C)C=CN(N)C3C(N)CNCCC3(CCCC)C21', 'CC=C[NH+](C)C(N)(CC)CC(C)[N+]1(C)C2NN1C2=NCN', 'C=C(C(=CC)N=CCCC)C1C[NH+]2CCC1CCC2(C)C(=C)C', 'CCC=C(C)[O+](C)CCC1CCCCC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules to achieve a z-score of -6.190392 for the cell line '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC', 'C=C(C)CC1C=C2[O+]=CN(C)NN2CCCCCCC(=CC)CN2CN12', 'CCCC(CC(N)(N)CCCCC(CCC)C1CC2CC(N2)C1CN)NCCN', 'CC=COC(O)CC(O)C=CCC[O+]1CC(C)C1', 'C=CC1=CC(C)(CN)[OH+]C=CC=CC(C)=C(C=CC)C(C)=C1C', 'CC(=O)C(C)OC(C)(C)CF', 'C=C(CC)CC1=CC1CC(C)(C)C(C=CCCC)=CC1=[N+](N=CC=O)C=C(C=COC)C1C', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'C=CCN1C(CNCC(C)=CC)NC1C(=C)NC=C[N+](C)=NCNC', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC', 'C=C(C)CC1C=C2[O+]=CN(C)NN2CCCCCCC(=CC)CN2CN12', 'CCCC(CC(N)(N)CCCCC(CCC)C1CC2CC(N2)C1CN)NCCN', 'CC=COC(O)CC(O)C=CCC[O+]1CC(C)C1', 'C=CC1=CC(C)(CN)[OH+]C=CC=CC(C)=C(C=CC)C(C)=C1C', 'CC(=O)C(C)OC(C)(C)CF', 'C=C(CC)CC1=CC1CC(C)(C)C(C=CCCC)=CC1=[N+](N=CC=O)C=C(C=COC)C1C', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'C=CCN1C(CNCC(C)=CC)NC1C(=C)NC=C[N+](C)=NCNC', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate a series of molecules achieving a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CNCC2=C(C=C3C(C)CN3CC)C(C=CC2)C1', 'CCC1(C)CCC(C2=CCC2C)=CCCC(=CC2CC=CC2C)CCCC(C)C1', 'C=CCCC12CC3CCC=CC4(CC)CC(C(CCCC)CC(C)(N)C(C)=C4C(CCCC)CC(=C)CCC(C1)C2C1CCC1C)C3N', 'C=C(CC(C)=CC=CNO)C1CCC1C(C)C', 'CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'CCCC(O)=CC(N)CC(C)CF', 'CB(N)C1CCCCC=NCC(C)C1', 'C=C(CC)OCC=[O+]C=CC=C(C)OC=CC(C)CC(CC)C(C)(C)C', 'C=CC(=CC(=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'CCC=CC(C1=C[O+]=COCCCC1C)=C(C)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CNCC2=C(C=C3C(C)CN3CC)C(C=CC2)C1', 'CCC1(C)CCC(C2=CCC2C)=CCCC(=CC2CC=CC2C)CCCC(C)C1', 'C=CCCC12CC3CCC=CC4(CC)CC(C(CCCC)CC(C)(N)C(C)=C4C(CCCC)CC(=C)CCC(C1)C2C1CCC1C)C3N', 'C=C(CC(C)=CC=CNO)C1CCC1C(C)C', 'CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'CCCC(O)=CC(N)CC(C)CF', 'CB(N)C1CCCCC=NCC(C)C1', 'C=C(CC)OCC=[O+]C=CC=C(C)OC=CC(C)CC(CC)C(C)(C)C', 'C=CC(=CC(=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'CCC=CC(C1=C[O+]=COCCCC1C)=C(C)OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a collection of molecules with a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['BCC(=CCC(C)CC=CCN=[N+](C=NCC=C)NC(CC)N(C)C)[N+](C)=C(C)CC', 'CCC(C=CC=CCC(F)(CO)NC)=C(N)N1C=CCC1C(C)=CC=CF', 'C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC', 'C=C1C2=[O+]C(C=C(C)CCCCNC)=CC1N(F)CCC(O)(CNCC(C)C)CCC2', 'C=C(C1=C(C)NC(C)C=C2CCC2=C1)C(CC)CCC', 'C=C1C2=CC(N=C(C)CCCCNC)=CC1N(F)CC=C(CNCC(C)C)CCC2', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC', 'CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'C=CCC(C)=CC=C(C)N=C(N)C1(C)CCC1C=C', 'C=C(CCC1CCC1)C(C)C1CCC1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCC(=CCC(C)CC=CCN=[N+](C=NCC=C)NC(CC)N(C)C)[N+](C)=C(C)CC', 'CCC(C=CC=CCC(F)(CO)NC)=C(N)N1C=CCC1C(C)=CC=CF', 'C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC', 'C=C1C2=[O+]C(C=C(C)CCCCNC)=CC1N(F)CCC(O)(CNCC(C)C)CCC2', 'C=C(C1=C(C)NC(C)C=C2CCC2=C1)C(CC)CCC', 'C=C1C2=CC(N=C(C)CCCCNC)=CC1N(F)CC=C(CNCC(C)C)CCC2', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC', 'CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'C=CCC(C)=CC=C(C)N=C(N)C1(C)CCC1C=C', 'C=C(CCC1CCC1)C(C)C1CCC1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules to achieve a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CC)C=[O+]C1(O)CC1C=CC=CC', 'C=C(CN)NBCCNNC(C)C1NCCCCC(C)=CN=CC(N)CCC(NCC)N(CC)C1C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(N)C2=CC2)C(CN(C)C(=CC)CC=NCC(C)(C)C)=C1', 'CNC1=C2C=CC[N+](N)=CC(=CN)C(C)C[N+]1=C2', 'CCCCC(C(C)C(C)CO)C1CC1N', 'C=C1CC=CNC2OC(=C(C)C(=C)F)C2C(=C)CCCCC1', 'C=C1NC(C)C1C=NC=NCC', 'C=CC(CONC1(CC(C)(C)NC)C=CC1)CC(=C)N', 'C=C(CC)NC=[O+][O+]=C1CC(O)(CC)C1', 'CC1(C)C[N+]1=CC1=CCC[N+](=CC=CN)O[SH](N)C=CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CC)C=[O+]C1(O)CC1C=CC=CC', 'C=C(CN)NBCCNNC(C)C1NCCCCC(C)=CN=CC(N)CCC(NCC)N(CC)C1C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(N)C2=CC2)C(CN(C)C(=CC)CC=NCC(C)(C)C)=C1', 'CNC1=C2C=CC[N+](N)=CC(=CN)C(C)C[N+]1=C2', 'CCCCC(C(C)C(C)CO)C1CC1N', 'C=C1CC=CNC2OC(=C(C)C(=C)F)C2C(=C)CCCCC1', 'C=C1NC(C)C1C=NC=NCC', 'C=CC(CONC1(CC(C)(C)NC)C=CC1)CC(=C)N', 'C=C(CC)NC=[O+][O+]=C1CC(O)(CC)C1', 'CC1(C)C[N+]1=CC1=CCC[N+](=CC=CN)O[SH](N)C=CC1']."
            },
            {
                "from": "human",
                "value": "And build molecules that reach the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=NC1C(C)C[O+]1C', 'CCCC1OCC1C1=C[O+]=CCC(C)NN1CC', 'C=C(CC(C)CC=CNC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(NCC(N)CC(C)C)C=C1', 'C=CCC(C)=C1C=CCC2=C(C=CNC=CC(CCC)=C1C=C)C2', 'CCCC1OOC1(CC)CC(O)C(NCC)N(C)CC', 'CCCNNOC=C1N=[N+]1N', 'CNCCB(N)CN', 'C=CCCC1C=CC=CC(C)=NC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CO2)CC1=O', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CCC=[N+](C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=NC1C(C)C[O+]1C', 'CCCC1OCC1C1=C[O+]=CCC(C)NN1CC', 'C=C(CC(C)CC=CNC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(NCC(N)CC(C)C)C=C1', 'C=CCC(C)=C1C=CCC2=C(C=CNC=CC(CCC)=C1C=C)C2', 'CCCC1OOC1(CC)CC(O)C(NCC)N(C)CC', 'CCCNNOC=C1N=[N+]1N', 'CNCCB(N)CN', 'C=CCCC1C=CC=CC(C)=NC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CO2)CC1=O', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CCC=[N+](C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various molecules achieving the desired z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCN=CC=CCCCC=C1CCC(C)C1', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CC(=CCC=CCNN)CCC(=N)CN', 'C=CC(C)CC=COCCCC', 'C=CC(CCCCC(C)C)=C(C)CCC(C)=CC', 'C=C(N)C1=NC=NC(C)C2CC(C2C)C2C(=C)C=C(C3=C(C)C3)C3CCC=[N+](C)CCC(C3)NC(C)CC2C1', 'CCCC1=NNCC=CC(OC(C)CC=C(C)C(CC)C(CC=N)CNCC(C)CC2=CC2)CCOC=C1', 'C=C(CCCCO)C(CC=C(CCCCCC)CC(CC)C(C)(F)CC=CC)C(C)(C)C(C)OCCC', 'CCCCCC[O+](C[O+](C)CCC(C)CCC)[NH+](C)CC1CC(CC2=C(CCC)C2)=[O+]1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCN=CC=CCCCC=C1CCC(C)C1', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CC(=CCC=CCNN)CCC(=N)CN', 'C=CC(C)CC=COCCCC', 'C=CC(CCCCC(C)C)=C(C)CCC(C)=CC', 'C=C(N)C1=NC=NC(C)C2CC(C2C)C2C(=C)C=C(C3=C(C)C3)C3CCC=[N+](C)CCC(C3)NC(C)CC2C1', 'CCCC1=NNCC=CC(OC(C)CC=C(C)C(CC)C(CC=N)CNCC(C)CC2=CC2)CCOC=C1', 'C=C(CCCCO)C(CC=C(CCCCCC)CC(CC)C(C)(F)CC=CC)C(C)(C)C(C)OCCC', 'CCCCCC[O+](C[O+](C)CCC(C)CCC)[NH+](C)CC1CC(CC2=C(CCC)C2)=[O+]1']."
            },
            {
                "from": "human",
                "value": "And design molecules with the same z-score for '1331032'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)C12C=C(CNCC1)C(C)CCNC=NCC2', 'CC=CN=NCN(C)CNNNC1=CC1ONC=NCNC', 'CCC=CCC(CCC=CCCCC)OONCC', 'CCNNC(C)C', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'CCCC1(O)C2=CC(C)C1C2', 'CCC(C)O[O+]1CC2=CC(=CC(C)C)C1=CCC2O', 'C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CNC12N[N+]1(C)CCCCC(O)=C1C2=[N+]1N', 'CC[O+](C)C1C2=C[N+]1=C(C)CCCC(C)(CO)CO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)C12C=C(CNCC1)C(C)CCNC=NCC2', 'CC=CN=NCN(C)CNNNC1=CC1ONC=NCNC', 'CCC=CCC(CCC=CCCCC)OONCC', 'CCNNC(C)C', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'CCCC1(O)C2=CC(C)C1C2', 'CCC(C)O[O+]1CC2=CC(=CC(C)C)C1=CCC2O', 'C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CNC12N[N+]1(C)CCCCC(O)=C1C2=[N+]1N', 'CC[O+](C)C1C2=C[N+]1=C(C)CCCC(C)(CO)CO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -4.054651 for the cell line '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)(C)CC(NCONC)C(C)C=C1C=[N+](C(=CF)C2CC=[O+]2)C1', 'CC=NCNN(C)CN', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'C=C1C2=C3C4=CC3CC1C(C=C(C)C(C)=C4)C(=CC)C1=C3CCC=C1C3C(N)=C(C)C=C2', 'CC=NCC1=N[N+]1=[N+]1NCC1ONN1CCC2(CCCCN=CC(C)CNC=CNN)NC[O+]=C12', 'CCC=CNCC(CCC1C=CCNC1)C(CNCN)N(N)C=CCC', 'CCC(CC)CCCC[O+](C)C', 'CC=NC1=C(C)C=CC=CCC(C)(NN)N=C(C=N)C(N)=C1C', 'CCCCC(CC)=C(O)CC1=C(C)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)(C)CC(NCONC)C(C)C=C1C=[N+](C(=CF)C2CC=[O+]2)C1', 'CC=NCNN(C)CN', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'C=C1C2=C3C4=CC3CC1C(C=C(C)C(C)=C4)C(=CC)C1=C3CCC=C1C3C(N)=C(C)C=C2', 'CC=NCC1=N[N+]1=[N+]1NCC1ONN1CCC2(CCCCN=CC(C)CNC=CNN)NC[O+]=C12', 'CCC=CNCC(CCC1C=CCNC1)C(CNCN)N(N)C=CCC', 'CCC(CC)CCCC[O+](C)C', 'CC=NC1=C(C)C=CC=CCC(C)(NN)N=C(C=N)C(N)=C1C', 'CCCCC(CC)=C(O)CC1=C(C)CC1']."
            },
            {
                "from": "human",
                "value": "And develop compounds to meet the same z-score for '1240182'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(O)C(C=CC(O)NCCCC[N+]1(O)C=C(C)C1)OC', 'CCCC1=CC=C(C)CC(C)NC(C=CN=CC2=C(CN)C2CC)=CC1', 'CCC1CCN1', 'C=C(CC(C)=C(C=C1C=C1NCCC)CC)C(C)OCC', 'C=CN(CB(BB(C)C(N)NCC1CN1)CCC)N=N', 'C=CC(C)C(C)CC=CCC(=N)CC=C1C2[O+](C)[O+]12', 'CC=CC(C=NCN1C(C)C1C)=CC', 'C=CC12C=CC3(C)C(CC1C=CO)C1=C4CN3C(OC2)C(C)=C2C(=C1)CC1=C2C(=N4)C1C', 'C=C1C=NC2C=[N+](C=CC=NC=CC=CC)N(N)CC=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1', 'CC=CCC1=C(C)C(C)C(C)(CC)COCC=CC(C)C=CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(O)C(C=CC(O)NCCCC[N+]1(O)C=C(C)C1)OC', 'CCCC1=CC=C(C)CC(C)NC(C=CN=CC2=C(CN)C2CC)=CC1', 'CCC1CCN1', 'C=C(CC(C)=C(C=C1C=C1NCCC)CC)C(C)OCC', 'C=CN(CB(BB(C)C(N)NCC1CN1)CCC)N=N', 'C=CC(C)C(C)CC=CCC(=N)CC=C1C2[O+](C)[O+]12', 'CC=CC(C=NCN1C(C)C1C)=CC', 'C=CC12C=CC3(C)C(CC1C=CO)C1=C4CN3C(OC2)C(C)=C2C(=C1)CC1=C2C(=N4)C1C', 'C=C1C=NC2C=[N+](C=CC=NC=CC=CC)N(N)CC=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1', 'CC=CCC1=C(C)C(C)C(C)(CC)COCC=CC(C)C=CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a series of molecules with a z-score of -4.054651 specific to '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=[N+](C)CCC(C)=C(C=N)CCCCC(C)C', 'CCC=CC(O)CC=C1C=C(C)CCCCCC=C2NC2CCC(N)(C(C)(C)C)N1', 'CCC(=CCC1=CCC(C)=CC1)C1C=C1C', 'CCCCCC(C)CNC(F)CC', 'CCC=CC1C=CCNCC2CC2(CCC(C)CCC(C)C(C)CC)CC(C)C1', 'C=C(N)CCC(C)O', 'CC=CN=C(C=CC)C1=C(N)C(CCN=CN)C=C(CC=NNCC)C[N+](=CC)CCC1=N', 'C=CCCC1C=CC=CC(C)=NC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CN2)CC1=O', 'COC1C2=CN1B(C)CNNB(CN)CO2', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=[N+](C)CCC(C)=C(C=N)CCCCC(C)C', 'CCC=CC(O)CC=C1C=C(C)CCCCCC=C2NC2CCC(N)(C(C)(C)C)N1', 'CCC(=CCC1=CCC(C)=CC1)C1C=C1C', 'CCCCCC(C)CNC(F)CC', 'CCC=CC1C=CCNCC2CC2(CCC(C)CCC(C)C(C)CC)CC(C)C1', 'C=C(N)CCC(C)O', 'CC=CN=C(C=CC)C1=C(N)C(CCN=CN)C=C(CC=NNCC)C[N+](=CC)CCC1=N', 'C=CCCC1C=CC=CC(C)=NC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CN2)CC1=O', 'COC1C2=CN1B(C)CNNB(CN)CO2', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC']."
            },
            {
                "from": "human",
                "value": "And generate molecules specifically for achieving the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCCCCC', 'C=CNC=CC1NC(C)N(CC)C(CN)=C(CCCCC(C)CN(C)NCC)CN=[N+]1C', 'C1=C2CB(CCCCCC1)C2', 'B=CC(=BCCC=NNNC)N[N+](=CN)CN=CN', 'C=C(C=C(N)N)C(=CC(N)CC)CC', 'CC=NCC=[N+](C)C=CC1([N+](=CNC=NC=CCCC)CC)Cn2ncn21', 'C=C1CC(C)C2CCC(C)C=CCC(C)(CC=C(C(=CCC)NC)CC1=CNC(C)C(C)C)CCC2NC', 'C=C1C=CC1', 'C=CC1=CC=CC(N(OC)C(=C)C)CC2NC3=CC(=CO)CC(C)C(=NCC2C3)C1', 'CCCC=CC(=CC(C)CCC)CC(C)C=C(C=S)NCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCCCCC', 'C=CNC=CC1NC(C)N(CC)C(CN)=C(CCCCC(C)CN(C)NCC)CN=[N+]1C', 'C1=C2CB(CCCCCC1)C2', 'B=CC(=BCCC=NNNC)N[N+](=CN)CN=CN', 'C=C(C=C(N)N)C(=CC(N)CC)CC', 'CC=NCC=[N+](C)C=CC1([N+](=CNC=NC=CCCC)CC)Cn2ncn21', 'C=C1CC(C)C2CCC(C)C=CCC(C)(CC=C(C(=CCC)NC)CC1=CNC(C)C(C)C)CCC2NC', 'C=C1C=CC1', 'C=CC1=CC=CC(N(OC)C(=C)C)CC2NC3=CC(=CO)CC(C)C(=NCC2C3)C1', 'CCCC=CC(=CC(C)CCC)CC(C)C=C(C=S)NCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a set of molecules targeted at a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C=CCC)CC1CCC=CCC1C', 'CC1=CCCCCCC[O+]=CCC(C)CCC(C)C(Cl)CC1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'CC=CCNC(CCNN)C(C)C(CC=CCC=NC)C1(C)CCN1', 'C=C1CC(C)C=N[N+]2=CCCC(CC[N+](=C)[N+](=C)C1)N=C2', 'CC=CC=C(C=CC)CC=C(C=C[O+]=CC1=C(CC)C1CC)CC(C)C', 'C=NOC=CCC(=CCCCCC)CCCC(C)=C1C=C1C', 'CCCC1CCCC(C)=CC(OCO)CCC(O)C1', 'CCCC(O)C=[O+]C(C)C', 'C=C(OCS)C(C)CCC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C=CCC)CC1CCC=CCC1C', 'CC1=CCCCCCC[O+]=CCC(C)CCC(C)C(Cl)CC1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'CC=CCNC(CCNN)C(C)C(CC=CCC=NC)C1(C)CCN1', 'C=C1CC(C)C=N[N+]2=CCCC(CC[N+](=C)[N+](=C)C1)N=C2', 'CC=CC=C(C=CC)CC=C(C=C[O+]=CC1=C(CC)C1CC)CC(C)C', 'C=NOC=CCC(=CCCCCC)CCCC(C)=C1C=C1C', 'CCCC1CCCC(C)=CC(OCO)CCC(O)C1', 'CCCC(O)C=[O+]C(C)C', 'C=C(OCS)C(C)CCC(C)C']."
            },
            {
                "from": "human",
                "value": "And produce a batch of molecules reaching the same z-score for '684055'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[O+](C)CC(C)CCCC=CCl', 'CCC=CN(CC)CC1OCN=CC(C(C=CCC(C)O)C(C)CCC(C)C)=CC2N1C2(C)C(CC)CC', 'BBN[NH+](BCC)N(CN=BC1BBCC1N[N+](C)(C)NCCC)NC', 'CC(CCN(C)C)C1CC=C1CC=[O+]CN', 'C=CC(C)CC(=CC=CC)CCC(Cl)C(C)CCC(C)CC=NC=CC', 'CCCN(NC)B(CCN(N)NN)B(C)N', 'CCCCNCCN', 'CC=BN(N)n1[nH]n1CNCC(=N)NB(C)CB(CCC)CNB(C)N', 'C=C(N)[N+](C)(C)C(C)CC', 'CCBNCNCNCC=CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[O+](C)CC(C)CCCC=CCl', 'CCC=CN(CC)CC1OCN=CC(C(C=CCC(C)O)C(C)CCC(C)C)=CC2N1C2(C)C(CC)CC', 'BBN[NH+](BCC)N(CN=BC1BBCC1N[N+](C)(C)NCCC)NC', 'CC(CCN(C)C)C1CC=C1CC=[O+]CN', 'C=CC(C)CC(=CC=CC)CCC(Cl)C(C)CCC(C)CC=NC=CC', 'CCCN(NC)B(CCN(N)NN)B(C)N', 'CCCCNCCN', 'CC=BN(N)n1[nH]n1CNCC(=N)NB(C)CB(CCC)CNB(C)N', 'C=C(N)[N+](C)(C)C(C)CC', 'CCBNCNCNCC=CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules aimed at a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CCC1(C)CCCCNCCC(=C2CC(C)C2C=NN)CCC[N+](O)=CC(=C)C1C)CC', 'C=[N+](CCCC=C(C1CCCC(C)C1)[O+]1C=C(CO)CNCCCCC1)CCCCC', 'NCC=CC1CC1CCCN', 'CCc1[nH][nH]c1C=C(C)CC(C=C(C=CCCC(C)(N)CNC)CN)N(C)NCCC(C)N', 'C=C1CC2=C(C=CCC2)C2C=CC3C(NC)=C(C)CCC(NC[O+]3CN(C)C2CC)C1=C', 'CCC(C)CC(=C[O+](O)CO)C(C)=CCOC(C)C', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'C=C(CC)CNC(N)C=NCCC(C)N(CC=CC=C1C=CC1)CC(C)CC', 'CNC(=CCCC(C)=C1CC1C)C1=CCCC1', 'C=CN1C=CCCCC(C)CC(CCC)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CCC1(C)CCCCNCCC(=C2CC(C)C2C=NN)CCC[N+](O)=CC(=C)C1C)CC', 'C=[N+](CCCC=C(C1CCCC(C)C1)[O+]1C=C(CO)CNCCCCC1)CCCCC', 'NCC=CC1CC1CCCN', 'CCc1[nH][nH]c1C=C(C)CC(C=C(C=CCCC(C)(N)CNC)CN)N(C)NCCC(C)N', 'C=C1CC2=C(C=CCC2)C2C=CC3C(NC)=C(C)CCC(NC[O+]3CN(C)C2CC)C1=C', 'CCC(C)CC(=C[O+](O)CO)C(C)=CCOC(C)C', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'C=C(CC)CNC(N)C=NCCC(C)N(CC=CC=C1C=CC1)CC(C)CC', 'CNC(=CCCC(C)=C1CC1C)C1=CCCC1', 'C=CN1C=CCCCC(C)CC(CCC)CC1']."
            },
            {
                "from": "human",
                "value": "And design a series of molecules with the same z-score specific to '1331032'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=CCCCCCC[O+]=CCC(C)CCC(C)C(Cl)CC1', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)N(C)CC', 'CCCC(CC)=[N+](O)CC1=C(C)CC1', 'CC(C)CC1N[O+]=CC1CC(C)F', 'CCC=CCCN(CC)C(C=[N+](N)N)CC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=C(C)C1(N)CCC2NC2=CCCCCC2C=C(C)C=CCCC=C(C=C2C)N1', 'C=C(CN)C(CC)=C(C)CO', 'CCC1=C2CC(=NNC1)CCC1C=C21', 'CC=CN(C(C)C(N=C(CCC)CC(CC(=CC)CCCC)NCC)=NC)N(CC)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1=CCCCCCC[O+]=CCC(C)CCC(C)C(Cl)CC1', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)N(C)CC', 'CCCC(CC)=[N+](O)CC1=C(C)CC1', 'CC(C)CC1N[O+]=CC1CC(C)F', 'CCC=CCCN(CC)C(C=[N+](N)N)CC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=C(C)C1(N)CCC2NC2=CCCCCC2C=C(C)C=CCCC=C(C=C2C)N1', 'C=C(CN)C(CC)=C(C)CO', 'CCC1=C2CC(=NNC1)CCC1C=C21', 'CC=CN(C(C)C(N=C(CCC)CC(CC(=CC)CCCC)NCC)=NC)N(CC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to meet a z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC[N+]1=CCCCC2CCC2CC1CCC', 'CC=C1COC(C)=C(CF)CCN2CCCCC(=N2)C=C1CCCC=CCC', 'CNN(C)C(BNC(C)NCCCN1B(C)N1N)CNN', 'CC=C(CC)C(C(CN(C)C)=C(N)C1CC(CC)C=C1C=CC1OC1=CC1=CC1)C(C)C', 'C=CB1B=[N+]2C(=N[N+]3(B(C)C)CCC4(N)C(=N)CN(C)CNC=NC4CB3C)C12', 'C=[N+]1CC(CCC(C)N)C(CCC)=C1CC(C)(C)[O+](C)C(C)(C)N', 'B1CC2=NC3C2=CBC2NC3=C12', 'CCCC(=NC1=C2CC2[N+]1(C)C=C(C)CC(C)CN(C)N)NC', 'C=CC(=CCO)CC1C=C1', 'CC(=CC(CO)C(O)CCC1CC1)CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC[N+]1=CCCCC2CCC2CC1CCC', 'CC=C1COC(C)=C(CF)CCN2CCCCC(=N2)C=C1CCCC=CCC', 'CNN(C)C(BNC(C)NCCCN1B(C)N1N)CNN', 'CC=C(CC)C(C(CN(C)C)=C(N)C1CC(CC)C=C1C=CC1OC1=CC1=CC1)C(C)C', 'C=CB1B=[N+]2C(=N[N+]3(B(C)C)CCC4(N)C(=N)CN(C)CNC=NC4CB3C)C12', 'C=[N+]1CC(CCC(C)N)C(CCC)=C1CC(C)(C)[O+](C)C(C)(C)N', 'B1CC2=NC3C2=CBC2NC3=C12', 'CCCC(=NC1=C2CC2[N+]1(C)C=C(C)CC(C)CN(C)N)NC', 'C=CC(=CCO)CC1C=C1', 'CC(=CC(CO)C(O)CCC1CC1)CO']."
            },
            {
                "from": "human",
                "value": "And generate various molecules achieving the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CN)C(CC1=C2C(C)CC(N)CC=[N+]2C1)CC1CC1N', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'CCCC1=C(C)C(CC)C=CC=NC=[N+](C)CC1', 'CC12C=C(C1)C2', 'CC1=NC2=C(O)C(C)C[O+]2C1', 'C=C(CC(C)=C(C=C1C=C1CCCC)CC)C(C)OCC', 'C=C1CCCC(NO)(NC(=C[O+](C)C(CO)CCC(C)N)CC)C1C', 'CC=CC=C1C=CC(C)C(C)C(C)=C(CCO)C(O)(CC)CCC1C', 'CC=CC=CCCCC(CCC)C1C(C)CCC1CC', 'C=CC(=CCCCNC=C(CC)C(=C)CCC)CCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CN)C(CC1=C2C(C)CC(N)CC=[N+]2C1)CC1CC1N', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'CCCC1=C(C)C(CC)C=CC=NC=[N+](C)CC1', 'CC12C=C(C1)C2', 'CC1=NC2=C(O)C(C)C[O+]2C1', 'C=C(CC(C)=C(C=C1C=C1CCCC)CC)C(C)OCC', 'C=C1CCCC(NO)(NC(=C[O+](C)C(CO)CCC(C)N)CC)C1C', 'CC=CC=C1C=CC(C)C(C)C(C)=C(CCO)C(O)(CC)CCC1C', 'CC=CC=CCCCC(CCC)C1C(C)CCC1CC', 'C=CC(=CCCCNC=C(CC)C(=C)CCC)CCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a diverse set of molecules meeting a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['BBB(C)CBN1CCNCBN1', 'CCCCC(C=C(C)CC)=C(C)CCNC1CC1=C(N)CC', 'C=C1CC=CC2=CC(C=CC=CC(C)C1=C)CC2', 'CC1CCCC=CCC[O+](C)C1C', 'BB1B=NBB1C', 'C=C1CC1C1C=CC=CCC=C2C=C1C(C(C)CC)CCC2', 'C=C1CC=CC2=C(C)C=CC=C[OH+]C(C)(CN)C=C1C(=C)C2C', 'C=CC1(CC)C=C([O+]=C(C=C(C)C=N)C(C)(C)C)C1', 'CC=C1C(C)CCCN2C(C)C2CC1C(C)CCF', 'C=C(C[O+]1C=C[O+]=[O+]C=CC1CCOS)OCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BBB(C)CBN1CCNCBN1', 'CCCCC(C=C(C)CC)=C(C)CCNC1CC1=C(N)CC', 'C=C1CC=CC2=CC(C=CC=CC(C)C1=C)CC2', 'CC1CCCC=CCC[O+](C)C1C', 'BB1B=NBB1C', 'C=C1CC1C1C=CC=CCC=C2C=C1C(C(C)CC)CCC2', 'C=C1CC=CC2=C(C)C=CC=C[OH+]C(C)(CN)C=C1C(=C)C2C', 'C=CC1(CC)C=C([O+]=C(C=C(C)C=N)C(C)(C)C)C1', 'CC=C1C(C)CCCN2C(C)C2CC1C(C)CCF', 'C=C(C[O+]1C=C[O+]=[O+]C=CC1CCOS)OCC']."
            },
            {
                "from": "human",
                "value": "And design molecules with the same z-score for '1240182'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC(C)=CC1=CNCCCCCCB(CC)NCCN1C1CNNB1CB=NN', 'C=C1CC(C)CCC2=C(CC)CC23CC2(NCCC(C)(O)C32)C1C', 'CCC(N)(C1C=C(C(C)(N)C(C)C(BN)CC(=CN)N=CC=CCN)N1)C1(C)CNC1NC', 'CCC=[O+]C(C)C=C(CC)C(=CC(C)C(C)C)CCC=C(C)C', 'CCCCC(=N)CC=NNC(C)C1C=C(N)C=CCC1', 'CC1=C2CN[N+](=CN3C=CC=NC3)C2=CC(C)C1', 'CCC[O+](C(C)CC)C1CC1CN', 'CCN=[N+](C)NC(C)C', 'CC=C(COOC1C2=C1C2=C1CO1)NC(C)CC=NC1CCC1', 'CC[O+](C)C1C2=C[N+]1=C(C)CCCC(C)(CO)CO2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC(C)=CC1=CNCCCCCCB(CC)NCCN1C1CNNB1CB=NN', 'C=C1CC(C)CCC2=C(CC)CC23CC2(NCCC(C)(O)C32)C1C', 'CCC(N)(C1C=C(C(C)(N)C(C)C(BN)CC(=CN)N=CC=CCN)N1)C1(C)CNC1NC', 'CCC=[O+]C(C)C=C(CC)C(=CC(C)C(C)C)CCC=C(C)C', 'CCCCC(=N)CC=NNC(C)C1C=C(N)C=CCC1', 'CC1=C2CN[N+](=CN3C=CC=NC3)C2=CC(C)C1', 'CCC[O+](C(C)CC)C1CC1CN', 'CCN=[N+](C)NC(C)C', 'CC=C(COOC1C2=C1C2=C1CO1)NC(C)CC=NC1CCC1', 'CC[O+](C)C1C2=C[N+]1=C(C)CCCC(C)(CO)CO2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please prepare a series of molecules to reach a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)CC=CC=CC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'C=CC1CCC2CCC(C(=O)C([O+](C)CC(=O)O)CC(C=CC)C(OCCC)CC(C(C)CC(C)C)CC=N1)C2C', 'C=CCCCC(N)(N)N=[N+]1CC=C([N+](=CN)CC)C=[N+](N=CC)C1', 'CCC(C)C(N)CC', 'CCC(C)(C)CNC(C)NC=CCNN', 'C=C(CC(NC)NNC1(NCN)CCC1)C(C)=NNC(C)NCN', 'CC=CCCC(CC)(CN)CCC=C(CCCOC)C(=C(C)C=CCC)C(C)CC', 'CC=NC1=CCC(=CC=CC(O)C(C)(CC)CC=NC)C1', 'C=C(C=CCC1(C)CCCCCCCC(=C2CC(C)C2C=CC)CCCC(O)=CC(=C)C1C)CC', 'CC=CC(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCC(CC)CC)C(C)CCCCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)CC=CC=CC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'C=CC1CCC2CCC(C(=O)C([O+](C)CC(=O)O)CC(C=CC)C(OCCC)CC(C(C)CC(C)C)CC=N1)C2C', 'C=CCCCC(N)(N)N=[N+]1CC=C([N+](=CN)CC)C=[N+](N=CC)C1', 'CCC(C)C(N)CC', 'CCC(C)(C)CNC(C)NC=CCNN', 'C=C(CC(NC)NNC1(NCN)CCC1)C(C)=NNC(C)NCN', 'CC=CCCC(CC)(CN)CCC=C(CCCOC)C(=C(C)C=CCC)C(C)CC', 'CC=NC1=CCC(=CC=CC(O)C(C)(CC)CC=NC)C1', 'C=C(C=CCC1(C)CCCCCCCC(=C2CC(C)C2C=CC)CCCC(O)=CC(=C)C1C)CC', 'CC=CC(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCC(CC)CC)C(C)CCCCC1']."
            },
            {
                "from": "human",
                "value": "And generate multiple compounds optimized for the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1CC=CNC1', 'CCC=C([O+]=CCCC)C(C)CC', 'C=CC(C)(NCC(C=CC(CC)CC)CC(C)(CC)CCC)OCCCCC', 'CC=CN(CCCC(CCC)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'NCB1BB2BCB21', 'CCCNC1C=C2C=C(C2)SCCC1', 'CCC=CNCC(CCC1C=CCNC1)C(CNCN)C(N)C=CC(C)=O', 'CC=C1C(CNN)N1CN(N)CNCCN', 'C=CC=C[N+](=CC1NCC1C)CN(N(C=C)C(=C)CC=CC)C(C)(C=CC)CCCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1CC=CNC1', 'CCC=C([O+]=CCCC)C(C)CC', 'C=CC(C)(NCC(C=CC(CC)CC)CC(C)(CC)CCC)OCCCCC', 'CC=CN(CCCC(CCC)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'NCB1BB2BCB21', 'CCCNC1C=C2C=C(C2)SCCC1', 'CCC=CNCC(CCC1C=CCNC1)C(CNCN)C(N)C=CC(C)=O', 'CC=C1C(CNN)N1CN(N)CNCCN', 'C=CC=C[N+](=CC1NCC1C)CN(N(C=C)C(=C)CC=CC)C(C)(C=CC)CCCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate a series of molecules achieving a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CNCC(CCC1C=CCNC1)C(CCCN)C(N)C=CC(C)=O', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=CC(=CCCCNC=C(NC)[N+](=C)CCC)CCCC', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1', 'CC=C(NC)C(=CCNC=NC1CNCC1C)CNC(C)CN', 'C=C1CC(C)(N)C(C(C)N)N1', 'CCC1CN2NCC3CCN2CCCC=C1OC3', 'CCCC(C)(CC)CCC=CC(CC)CC', 'C=CCCCC(CC)(CNC)C1=CCC=CC(CC)C=C1', 'C=CC=CC(C)CC=CN=CCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CNCC(CCC1C=CCNC1)C(CCCN)C(N)C=CC(C)=O', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=CC(=CCCCNC=C(NC)[N+](=C)CCC)CCCC', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1', 'CC=C(NC)C(=CCNC=NC1CNCC1C)CNC(C)CN', 'C=C1CC(C)(N)C(C(C)N)N1', 'CCC1CN2NCC3CCN2CCCC=C1OC3', 'CCCC(C)(CC)CCC=CC(CC)CC', 'C=CCCCC(CC)(CNC)C1=CCC=CC(CC)C=C1', 'C=CC=CC(C)CC=CN=CCCC']."
            },
            {
                "from": "human",
                "value": "And create molecules that meet the same z-score in the cell line '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)C(N)CC', 'CCN=C1CCC=C(NNN(C)N)CCC=CC=CC1=CN', 'CCCNN=CC(N)=CC=C(C)NC=NCC=CC1CNN1', 'C=CC(C)C=CCCCC', 'CC=C1CCCCC2(CC)C=C2CC1', 'CCOC(=CCCCCC=NC)C(C)CC', 'C=C1C=C(CC=CC2CO2)OCC(=NC)C1', 'C=CC(CCC=CC(C)CC1CCCC2C(CCCCC)CCC=C1C(C)C2C)CCC(C)=CCC', 'C=C(C=C(C)N)C=C(C=NC)C(C(C)CC)C(CCC)NCC', 'CCC1=[N+]2CCC(C(C)=CCC(C)OC3C=CCNN=C1C=COCC3)C(CC=N)CNCC(C)CC1=C2C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)C(N)CC', 'CCN=C1CCC=C(NNN(C)N)CCC=CC=CC1=CN', 'CCCNN=CC(N)=CC=C(C)NC=NCC=CC1CNN1', 'C=CC(C)C=CCCCC', 'CC=C1CCCCC2(CC)C=C2CC1', 'CCOC(=CCCCCC=NC)C(C)CC', 'C=C1C=C(CC=CC2CO2)OCC(=NC)C1', 'C=CC(CCC=CC(C)CC1CCCC2C(CCCCC)CCC=C1C(C)C2C)CCC(C)=CCC', 'C=C(C=C(C)N)C=C(C=NC)C(C(C)CC)C(CCC)NCC', 'CCC1=[N+]2CCC(C(C)=CCC(C)OC3C=CCNN=C1C=COCC3)C(CC=N)CNCC(C)CC1=C2C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules that align with a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CCC2C(C)CC(C)C(C(C)C(C(C)CC(C)C)C(C)(O)CC)CCC(=C1)CCCCCC2CC', 'C=C(CC(C)=C(C=[N+]1C=C1NCCC)CC)C(C)OCC', 'C=CC(C=NC=CC(C)CCCCC)(CCC)[O+]1C(C)C=CCC=CCC2OCC21', 'CC1C2CCC3NN2C31', 'CC=C(CC(CC)CCCN(CC=CCC)CCC)C[N+]1=CC=CCC1', 'C=CC(C)(CC(CCC)(C(C=O)CC1CC(C)=[SH]1)C(CCC)CCN)C(CC)CC1C=C1N', 'CCC(C)=CCCC(CCN)CC(C)C(C)(CC)[OH+]C', 'C=CC(C=CC=CCC)=C(C)C(C)CCO', 'CCC=C1CC2CCCCCCC(C(C)C(C)C)[O+]2CC=C1CO', 'CC=CN(C=CC1C=C1C=CC(C)N)N1CCO[O+]2CC=C(CCC2)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CCC2C(C)CC(C)C(C(C)C(C(C)CC(C)C)C(C)(O)CC)CCC(=C1)CCCCCC2CC', 'C=C(CC(C)=C(C=[N+]1C=C1NCCC)CC)C(C)OCC', 'C=CC(C=NC=CC(C)CCCCC)(CCC)[O+]1C(C)C=CCC=CCC2OCC21', 'CC1C2CCC3NN2C31', 'CC=C(CC(CC)CCCN(CC=CCC)CCC)C[N+]1=CC=CCC1', 'C=CC(C)(CC(CCC)(C(C=O)CC1CC(C)=[SH]1)C(CCC)CCN)C(CC)CC1C=C1N', 'CCC(C)=CCCC(CCN)CC(C)C(C)(CC)[OH+]C', 'C=CC(C=CC=CCC)=C(C)C(C)CCO', 'CCC=C1CC2CCCCCCC(C(C)C(C)C)[O+]2CC=C1CO', 'CC=CN(C=CC1C=C1C=CC(C)N)N1CCO[O+]2CC=C(CCC2)C1']."
            },
            {
                "from": "human",
                "value": "And produce molecules to match the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(O)C=[O+]C(C)CN', 'CCCCC(C=C(C)CC)=CCCNC1CC1=C(N)CN', 'C=CC(C)(CC(CCC)(C(C=O)CC1CC(C)=[SH]1)C(CCC)CCN)C(CC)CC1C=C1N', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'CC1=[O+]CCCCCC(O)CCC1', 'C=C(C)C1=NCC(C=CC)C1C', 'C=CN(NC)C(CC(C)C(C)=CC1CC=CC=C1C)[O+](C)CCCC', 'CC1CC(C(C)C)CCC(N)NC2=CC(C2C)C(C)(C)C1', 'CCCCC1=CC(CC)C=C1', 'CC[N+]1=NC2C=C(CCN(N)[O+]3C(C)=C(N)N4C(C)CNCC4N3C1)N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(O)C=[O+]C(C)CN', 'CCCCC(C=C(C)CC)=CCCNC1CC1=C(N)CN', 'C=CC(C)(CC(CCC)(C(C=O)CC1CC(C)=[SH]1)C(CCC)CCN)C(CC)CC1C=C1N', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'CC1=[O+]CCCCCC(O)CCC1', 'C=C(C)C1=NCC(C=CC)C1C', 'C=CN(NC)C(CC(C)C(C)=CC1CC=CC=C1C)[O+](C)CCCC', 'CC1CC(C(C)C)CCC(N)NC2=CC(C2C)C(C)(C)C1', 'CCCCC1=CC(CC)C=C1', 'CC[N+]1=NC2C=C(CCN(N)[O+]3C(C)=C(N)N4C(C)CNCC4N3C1)N2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a series of molecules with a z-score of -4.054651 specific to '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1CCN1', 'C=NC(CCC)C(=CCCC(C)C)N=[N+]1C=CC=CC(C(C)CCN=CCC=CC)=CC1CCC', 'CCC(C)CC1CCNCCC1CNC(CC)CN', 'C=C1CCCC=C(C)C=C(CCC=CC2(C)CC=C3C=CCC(C)C3NCC2)C1CCCC', 'CCC(C)C=C1CC(C(C)CC)CCCC(N)C(C)(C2C=C(C)C2)CC(C)=C1C', 'C=[N+](C)NCC(N=CCNN)C(N)NBC', 'C=CC=CC(CC)CC1CCCCCC1CCC(CC)C(C)CC=CC', 'C=C1C=C(C2CC(C)=CC2(CCC)N(N)CC(C)=CC)N=C2C=C1C=CCC2N', 'COC(C)(CC=CCN)CCO', 'C=C1COCC(C)(O)C(OCC=[O+]C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1CCN1', 'C=NC(CCC)C(=CCCC(C)C)N=[N+]1C=CC=CC(C(C)CCN=CCC=CC)=CC1CCC', 'CCC(C)CC1CCNCCC1CNC(CC)CN', 'C=C1CCCC=C(C)C=C(CCC=CC2(C)CC=C3C=CCC(C)C3NCC2)C1CCCC', 'CCC(C)C=C1CC(C(C)CC)CCCC(N)C(C)(C2C=C(C)C2)CC(C)=C1C', 'C=[N+](C)NCC(N=CCNN)C(N)NBC', 'C=CC=CC(CC)CC1CCCCCC1CCC(CC)C(C)CC=CC', 'C=C1C=C(C2CC(C)=CC2(CCC)N(N)CC(C)=CC)N=C2C=C1C=CCC2N', 'COC(C)(CC=CCN)CCO', 'C=C1COCC(C)(O)C(OCC=[O+]C)C1']."
            },
            {
                "from": "human",
                "value": "And create molecules to hit the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'CCC=NC(C)C=C(CO)C(CCC=C(C)C)=NC(C)C(C)C', 'CCCC1CCN(C)C1', 'CC=CN(CCCC(CCN)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CC=CC1(CC)CCCC(=CCCC2NC2C)[O+]=CC(CCC)(CC(C)C(C)=COC)CC1C', 'C=NC(C=CCC(CCC(=C)NC)C(C)NCC)N(CC=CN)CC=NN', 'CN1CC=C(CNCCNC2=NCN2)C1', 'CCCC1=C(C)C(CC)C=CC=CC=[N+](C)CC1', 'C=CC(C)CN=CC=CC1=NC(C)C(C=N)=N1', 'CCC=CC(=CC(C)C(CCCCC)CCC(=CCF)CC)CCOC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'CCC=NC(C)C=C(CO)C(CCC=C(C)C)=NC(C)C(C)C', 'CCCC1CCN(C)C1', 'CC=CN(CCCC(CCN)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CC=CC1(CC)CCCC(=CCCC2NC2C)[O+]=CC(CCC)(CC(C)C(C)=COC)CC1C', 'C=NC(C=CCC(CCC(=C)NC)C(C)NCC)N(CC=CN)CC=NN', 'CN1CC=C(CNCCNC2=NCN2)C1', 'CCCC1=C(C)C(CC)C=CC=CC=[N+](C)CC1', 'C=CC(C)CN=CC=CC1=NC(C)C(C=N)=N1', 'CCC=CC(=CC(C)C(CCCCC)CCC(=CCF)CC)CCOC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds that meet a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'C=C(CN)C(CCCN)CC1=C2B(N)CB(N)CC=[N+]2N1', 'CCCCCC1(C)CCC2=C(N)C(C)=[N+]2C=C(C)CC(C)(N)O1', 'CC1=CCCC2CN1C21CCCC1', 'CB[N+](CC)(CC)CC=N', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)C(F)CCC(=C)C', 'CCC=NCCNC1CC=NC(N)NC(N)CCC=CC=C(N)C1', 'C=C1CCC(C)C(=NC)C=CC2=CCC(C)(C=CCCC)C2C2=C(C3=NC3)CCCC=C1[N+](C)=CCC=C2', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CC(C)C(=CC)CC=[O+]CC(C)(C)C)=C1', 'C=CC(CN)(CN=CCC(N)=C1CNC(C=CC)N(CN(C)CCCC(CC)NC(C)(N)C(C)C)C(N)C(C)C1)NC=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'C=C(CN)C(CCCN)CC1=C2B(N)CB(N)CC=[N+]2N1', 'CCCCCC1(C)CCC2=C(N)C(C)=[N+]2C=C(C)CC(C)(N)O1', 'CC1=CCCC2CN1C21CCCC1', 'CB[N+](CC)(CC)CC=N', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)C(F)CCC(=C)C', 'CCC=NCCNC1CC=NC(N)NC(N)CCC=CC=C(N)C1', 'C=C1CCC(C)C(=NC)C=CC2=CCC(C)(C=CCCC)C2C2=C(C3=NC3)CCCC=C1[N+](C)=CCC=C2', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CC(C)C(=CC)CC=[O+]CC(C)(C)C)=C1', 'C=CC(CN)(CN=CCC(N)=C1CNC(C=CC)N(CN(C)CCCC(CC)NC(C)(N)C(C)C)C(N)C(C)C1)NC=CC']."
            },
            {
                "from": "human",
                "value": "And create molecules to achieve the same z-score for the cell line '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NN=C1CNC(C)(CCC=C2CC2)NC1[N+](=C)CC', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)n1)CC', 'CC1CCC(=CCCCC=CC=CCO)C1', 'C=C(C)C1CC1=NC(CC)N(C)N', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'C=C(C)CC=CC=CC=C(CCC)C(CF)CCC1CCCC=CC1=CCC=CCN(C)C', 'C=C1C[N+]2=C(NCC)C=C(C=C[N+](C)(C)C=C1CC)NC(C)(N)NCC=C2C', 'C=CC1CC(NCC(C)=CC=CCCCCC)CCC2=C(C=C2C=CC)C1=C', 'CCCC=C(C[O+](C)CC(=CC=NCC)CNCCC)C(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NN=C1CNC(C)(CCC=C2CC2)NC1[N+](=C)CC', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)n1)CC', 'CC1CCC(=CCCCC=CC=CCO)C1', 'C=C(C)C1CC1=NC(CC)N(C)N', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'C=C(C)CC=CC=CC=C(CCC)C(CF)CCC1CCCC=CC1=CCC=CCN(C)C', 'C=C1C[N+]2=C(NCC)C=C(C=C[N+](C)(C)C=C1CC)NC(C)(N)NCC=C2C', 'C=CC1CC(NCC(C)=CC=CCCCCC)CCC2=C(C=C2C=CC)C1=C', 'CCCC=C(C[O+](C)CC(=CC=NCC)CNCCC)C(C)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of compounds with a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(CC)C(=CCCC=CC1CC=CC1C)CNC(C)(CC)CC(C)C', 'COC=CCCCC1CC(C)(O)CC(C)OCC23CC2CCC13', 'CNCC(N)C(C)(C)NC', 'CCC(C=C1CCCN[O+](C)C1)=C(CC)CC(C=CCN)C(CC)CNCC=C(C)C', 'CCN=CC=CCCCC=C1CCC(C)C1', 'C=[O+]C(C)[O+]=CC=C1CC(C(C)CCC)CC=CCCC(C)C(C[O+](C)C(=C)C(C)=CC)C1=C', 'CCC(CC)CCCC[O+](C)C', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'CC1CC2(C)CC(O)CC=[O+]CC3(C)CCC1C=CC1=C[O+](C=CCCCC32C)C1', 'CC=[N+](CCCCNCCC)NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(CC)C(=CCCC=CC1CC=CC1C)CNC(C)(CC)CC(C)C', 'COC=CCCCC1CC(C)(O)CC(C)OCC23CC2CCC13', 'CNCC(N)C(C)(C)NC', 'CCC(C=C1CCCN[O+](C)C1)=C(CC)CC(C=CCN)C(CC)CNCC=C(C)C', 'CCN=CC=CCCCC=C1CCC(C)C1', 'C=[O+]C(C)[O+]=CC=C1CC(C(C)CCC)CC=CCCC(C)C(C[O+](C)C(=C)C(C)=CC)C1=C', 'CCC(CC)CCCC[O+](C)C', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'CC1CC2(C)CC(O)CC=[O+]CC3(C)CCC1C=CC1=C[O+](C=CCCCC32C)C1', 'CC=[N+](CCCCNCCC)NC']."
            },
            {
                "from": "human",
                "value": "And please create compounds with a target z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)N(CCC)OC(CC1=CCC1)NN', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O', 'C=C(CN(C)CC)[N+](BC1BCC=C1)=CN1CN1C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CCC1(CC=CCC2(C)CC(C)O2)CC1', 'C=CC(CCCC(C)=CCCCC(CC)CCC)C1CC1C(C)CN', 'CC=C(C)C1=C(CCC(C)OCCC)C(C(C)CO)=CCCC=C1', 'CC12N=CC=[N+]1N2', 'CCC(C)=COC=C(F)CC', 'C=C1C=C(CCN)C=NC(C)=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)N(CCC)OC(CC1=CCC1)NN', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O', 'C=C(CN(C)CC)[N+](BC1BCC=C1)=CN1CN1C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CCC1(CC=CCC2(C)CC(C)O2)CC1', 'C=CC(CCCC(C)=CCCCC(CC)CCC)C1CC1C(C)CN', 'CC=C(C)C1=C(CCC(C)OCCC)C(C(C)CO)=CCCC=C1', 'CC12N=CC=[N+]1N2', 'CCC(C)=COC=C(F)CC', 'C=C1C=C(CCN)C=NC(C)=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1=C(C)C2=CCC(C=[O+]CC3CC3C)=CC2C1', 'C=CC1CC2=C(CCC(NC[N+](C)=CC=CCCC)C1)C(C=CC)=C2', 'CC(CCC=C[o+]1cccc1C=CC1=C2CC12)CCC1=CCCC1', 'C=C(C)C1=NCC(C=CC)C1C', 'CC(=CCNCN)CCCNCCC(C)=CC(C)N', 'CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'C=CC=C1C=CCC=[O+]C1C=CC', 'C=C1C[N+]2=C[N+](=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'CC=CC=NC[O+]=C(N)C(C)CC', 'C=C(CC(C=CCC(C)=CC(N)CCN)=CNCC)[O+]=C(C)C(C)CCC(=CC)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1=C(C)C2=CCC(C=[O+]CC3CC3C)=CC2C1', 'C=CC1CC2=C(CCC(NC[N+](C)=CC=CCCC)C1)C(C=CC)=C2', 'CC(CCC=C[o+]1cccc1C=CC1=C2CC12)CCC1=CCCC1', 'C=C(C)C1=NCC(C=CC)C1C', 'CC(=CCNCN)CCCNCCC(C)=CC(C)N', 'CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'C=CC=C1C=CCC=[O+]C1C=CC', 'C=C1C[N+]2=C[N+](=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'CC=CC=NC[O+]=C(N)C(C)CC', 'C=C(CC(C=CCC(C)=CC(N)CCN)=CNCC)[O+]=C(C)C(C)CCC(=CC)C(C)C']."
            },
            {
                "from": "human",
                "value": "And build molecules tailored for the same z-score for '905957'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C1=CCCCN[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CCC(C)NC=[N+](C)C(CCC(=N)N)C(N)C(N)CC(C)(C)NC', 'CCC1CC=CCC1', 'C=CNC(=CCNCC)C[N+](=N)OCC(C)=C(N)CN=CC', 'CC(=CCN)CC1CC2C3CC[O+]3N12', 'CCC1C=C(C(O)=CC(C)C)NC=CCC=CCC1CC', 'C=CCC1=CC=C(C)C(C)=CCCC=C2[O+]=C(CC)C=C(CC1)C1CC(C)CC3=CC(=C31)C2=C1CC1', 'C=NN=C(C)C(OC(C)CCC=C1CC1)[N+](=C)CCSC', 'CN1CCC2C=CN2C=NC1N', 'COCC(C)CC1=CCCCC=COC([N+]2=CC2)=C1C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C1=CCCCN[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CCC(C)NC=[N+](C)C(CCC(=N)N)C(N)C(N)CC(C)(C)NC', 'CCC1CC=CCC1', 'C=CNC(=CCNCC)C[N+](=N)OCC(C)=C(N)CN=CC', 'CC(=CCN)CC1CC2C3CC[O+]3N12', 'CCC1C=C(C(O)=CC(C)C)NC=CCC=CCC1CC', 'C=CCC1=CC=C(C)C(C)=CCCC=C2[O+]=C(CC)C=C(CC1)C1CC(C)CC3=CC(=C31)C2=C1CC1', 'C=NN=C(C)C(OC(C)CCC=C1CC1)[N+](=C)CCSC', 'CN1CCC2C=CN2C=NC1N', 'COCC(C)CC1=CCCCC=COC([N+]2=CC2)=C1C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds reaching a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC=C(C=CC=C1CC(F)CCC1=CC)C(C)(C)CCN=COO', 'CCC(C=CC=C(C)C)=NCCN', 'CCC(N)=NCC(C)NN=CC1=C(N)C1', 'C=CCCCC=CC1(CC(C)=CC)CCCCC1C', 'COC(C)=CC1CCCCNC1OC(C)O', 'CCC1=CC(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'C=NC(=O)CC(C)(C=CCC)N=CC=CCNC=CCC=CCC', 'CCN(C)CC(CCCCCC1CC1C1C=CC(C)C1C)CCC(C)C', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N', 'CCC=CC(=CC(C)CC)CC(N)=CCCC(CN)(CN)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=C(C=CC=C1CC(F)CCC1=CC)C(C)(C)CCN=COO', 'CCC(C=CC=C(C)C)=NCCN', 'CCC(N)=NCC(C)NN=CC1=C(N)C1', 'C=CCCCC=CC1(CC(C)=CC)CCCCC1C', 'COC(C)=CC1CCCCNC1OC(C)O', 'CCC1=CC(N(C)C)CNN(C(C)CC)CN=CC=CC1', 'C=NC(=O)CC(C)(C=CCC)N=CC=CCNC=CCC=CCC', 'CCN(C)CC(CCCCCC1CC1C1C=CC(C)C1C)CCC(C)C', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N', 'CCC=CC(=CC(C)CC)CC(N)=CCCC(CN)(CN)CCC']."
            },
            {
                "from": "human",
                "value": "And build a collection of compounds with a target z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CNCC=[O+]CC(C(CCCOCC)CC(C)=CCCC)C(C)(C)CC', 'C=C[N+](=NCC)C1CC(C)(CN)NCC2=CCC=C(CCN)C(C)C=CC(N)C(C)C21', 'C=CCCCC1CC(C=CNC)C1', 'C=C(O)NCOC1CC=C(C)C2CCC12', 'CCCC=CCC(C=CC(C)=CN)CC(=CCCC)CCC=COC(C)O', 'CC=CCC(C)C(C)C(OC(CCF)=C(C)CNCCC)C(=CCC)C(C)(C)NC', 'C=CC(=CCC[O+](C)C(C)=[N+](C=C)C=C)C(=CC)CCC(C)C(C)C(=C)C(=C)C', 'C=C(C)N1CCC(C2=CC2)C(C)C1', 'CC=C1CCC=CC1CC(N)C=CC(N)=CBNCN=NN(C)CNN', 'C=C(C)CC1CC1(C(C)CCC(O)CC)C(C)C(C)CC(C)(O)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CNCC=[O+]CC(C(CCCOCC)CC(C)=CCCC)C(C)(C)CC', 'C=C[N+](=NCC)C1CC(C)(CN)NCC2=CCC=C(CCN)C(C)C=CC(N)C(C)C21', 'C=CCCCC1CC(C=CNC)C1', 'C=C(O)NCOC1CC=C(C)C2CCC12', 'CCCC=CCC(C=CC(C)=CN)CC(=CCCC)CCC=COC(C)O', 'CC=CCC(C)C(C)C(OC(CCF)=C(C)CNCCC)C(=CCC)C(C)(C)NC', 'C=CC(=CCC[O+](C)C(C)=[N+](C=C)C=C)C(=CC)CCC(C)C(C)C(=C)C(=C)C', 'C=C(C)N1CCC(C2=CC2)C(C)C1', 'CC=C1CCC=CC1CC(N)C=CC(N)=CBNCN=NN(C)CNN', 'C=C(C)CC1CC1(C(C)CCC(O)CC)C(C)C(C)CC(C)(O)OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to reach a z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCCCCC([N+](C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CN2)C=C1CC', 'COC(C)CC=C1CC=CC(=CCN)C1', 'CCCC(=CC1C=C2CCC(CCOC2)C1C(CC)CCC)C(CC)C(C)C', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CCCC(N)=CC(C)C', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCC[N+](C=NN=NC)=CC(O)=NC', 'C=C(CCCCCC(C)=N)C[N+](=C)C', 'CCn1ccc(CCNC)c1', 'CCCCCNC(C)C1(CCNCC)CC=C(C2CCC2)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCCCCC([N+](C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CN2)C=C1CC', 'COC(C)CC=C1CC=CC(=CCN)C1', 'CCCC(=CC1C=C2CCC(CCOC2)C1C(CC)CCC)C(CC)C(C)C', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CCCC(N)=CC(C)C', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCC[N+](C=NN=NC)=CC(O)=NC', 'C=C(CCCCCC(C)=N)C[N+](=C)C', 'CCn1ccc(CCNC)c1', 'CCCCCNC(C)C1(CCNCC)CC=C(C2CCC2)CC1']."
            },
            {
                "from": "human",
                "value": "And generate a set of molecules matching the same z-score for '684055'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC=NC(C)CC1C=NC1=N', 'BC1C(BN)CC=CC(N)=CNC(=CN(C)N)C(CC)C1(C)CN(C)NCC', 'BCB(NN)C(=N)C(=N)N=NN', 'CNC=C(C)C(C=[O+]C)C[N+]1=C(C)CC1', 'BC=CB=C(N)NC(NNBC=NNN)B1CCC(=C(NNN)NC(C)C)CN2CC=C12', 'B=CCN(C=NN1BBC1)B(B)NB(B)C', 'C=C(CCN)CC(CCC)C(C)CCCCC(C)=CC(C)C(C)C12CCC=CN=CC(N)(CC1)N2CC', 'C=CC1C=CC(=C(C)C2=C(C(C(=C)C(C)N)[NH+]3C(CCC)C[O+]3NC)C3=C2C3)C1', 'CCNC1C=C(CN=NC)C(=N)CCCC1NN', 'BC1N2B=C(NC3NCN3)CCC[N+]1=N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=NC(C)CC1C=NC1=N', 'BC1C(BN)CC=CC(N)=CNC(=CN(C)N)C(CC)C1(C)CN(C)NCC', 'BCB(NN)C(=N)C(=N)N=NN', 'CNC=C(C)C(C=[O+]C)C[N+]1=C(C)CC1', 'BC=CB=C(N)NC(NNBC=NNN)B1CCC(=C(NNN)NC(C)C)CN2CC=C12', 'B=CCN(C=NN1BBC1)B(B)NB(B)C', 'C=C(CCN)CC(CCC)C(C)CCCCC(C)=CC(C)C(C)C12CCC=CN=CC(N)(CC1)N2CC', 'C=CC1C=CC(=C(C)C2=C(C(C(=C)C(C)N)[NH+]3C(CCC)C[O+]3NC)C3=C2C3)C1', 'CCNC1C=C(CN=NC)C(=N)CCCC1NN', 'BC1N2B=C(NC3NCN3)CCC[N+]1=N2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various compounds for an intended z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CCC(=CN)CC=CN)(C1=CN(C)CC1)[N+](=CN)CC(=CNC)N=CC=N', 'CCC1=C2C=CC[N+](C)=CC(=CC=[O+]CCC(C)=C(C)F)C(C)C[N+]1=C2', 'CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'C=C(C=C1C2=C1C(=CC)C2)CCC(C)(C)C(=CC)CC', 'CCCCC=CCC(CCC1=CCC=CC[N+](C)=C(CC)C(C)N(C)C[N+]2=C(CCC1)N2)NC', 'C=C(C1CN=N1)[N+]1=CC(=NN(C)C(CC(C)(C)C)NC)C1', 'BB(B)BB=BC[N+](BN)=CCNCNC', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'CC=CC(C=NC1(O)CC1C=CN=CC)=CN=CC(C)=N', 'C=C1C=C(C(C)C)CCCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CCC(=CN)CC=CN)(C1=CN(C)CC1)[N+](=CN)CC(=CNC)N=CC=N', 'CCC1=C2C=CC[N+](C)=CC(=CC=[O+]CCC(C)=C(C)F)C(C)C[N+]1=C2', 'CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'C=C(C=C1C2=C1C(=CC)C2)CCC(C)(C)C(=CC)CC', 'CCCCC=CCC(CCC1=CCC=CC[N+](C)=C(CC)C(C)N(C)C[N+]2=C(CCC1)N2)NC', 'C=C(C1CN=N1)[N+]1=CC(=NN(C)C(CC(C)(C)C)NC)C1', 'BB(B)BB=BC[N+](BN)=CCNCNC', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'CC=CC(C=NC1(O)CC1C=CN=CC)=CN=CC(C)=N', 'C=C1C=C(C(C)C)CCCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC']."
            },
            {
                "from": "human",
                "value": "And produce compounds with the same z-score for the cell line '1240182'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CNCC[N+]1=C(N(CN)N(C)NC)C(N2C[N+]2=N)N=CC=CNC=C1', 'CC=CCN(C)CCC(C)[O+]=CNC(C)CCC', 'CC=CCC=[O+]CC(C)CCCO', 'CCC(=CCC1C=CCC(CC(C)N)N1)C(C)=NCCC(C)=N', 'CCC(CN)C1C2NNNC3CN(N(C)NC)NC(=CCNC(C)(N)N1B(N)CCCB(C)N)N(N)CC3N2', 'C=CC=C(C(C)CCC)N1C(=C)C(CCC(C=CCCC)=CC)[O+]1C', 'C=C(N(C)CCN)N(C)CNC(CCCCCC)=C(N)CC(N)B(N)N', 'C=NCC(C)=NOCC(CCCN)(NC)NC', 'CC=[N+](CN(CN)NC)NC=CCN(CC=[N+](CN)C1C=C1)[N+](C)=CNCC', 'C=C1C2=CC(C=C(C)CCCCNC)=CC1N(F)CC=C(CNCC(C)C)CCC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNCC[N+]1=C(N(CN)N(C)NC)C(N2C[N+]2=N)N=CC=CNC=C1', 'CC=CCN(C)CCC(C)[O+]=CNC(C)CCC', 'CC=CCC=[O+]CC(C)CCCO', 'CCC(=CCC1C=CCC(CC(C)N)N1)C(C)=NCCC(C)=N', 'CCC(CN)C1C2NNNC3CN(N(C)NC)NC(=CCNC(C)(N)N1B(N)CCCB(C)N)N(N)CC3N2', 'C=CC=C(C(C)CCC)N1C(=C)C(CCC(C=CCCC)=CC)[O+]1C', 'C=C(N(C)CCN)N(C)CNC(CCCCCC)=C(N)CC(N)B(N)N', 'C=NCC(C)=NOCC(CCCN)(NC)NC', 'CC=[N+](CN(CN)NC)NC=CCN(CC=[N+](CN)C1C=C1)[N+](C)=CNCC', 'C=C1C2=CC(C=C(C)CCCCNC)=CC1N(F)CC=C(CNCC(C)C)CCC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of compounds targeting a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CBC1N=C1NN=CC(C)CN(CN)CCCCNC', 'CCCC(=N)CC(C)C(C[N+](=N)CCC1CN1)NC', 'CC=CNCCNN(C)CC(C=BNN)=[N+](C)CC', 'C=NCC1=C2NCC(CC=NN)OCCC(N)=CC=CC2=C1', 'CC=C[N+]1=CN2CC3C(=O)C(=[N+]21)C=CC(C)=C(N)C1C2=NCNC1=C2C(=NC)C3C', 'C=CCC[N+]1=CCCNC(CC)N(CC)CC(C(N)CCC)CCC1C', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'C=C(NOCC=CC)C(C)CC', 'C=C(C)N(CCC)OC(CC1=CCC1)NN', 'C=CCC(C)(CCC)C(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CBC1N=C1NN=CC(C)CN(CN)CCCCNC', 'CCCC(=N)CC(C)C(C[N+](=N)CCC1CN1)NC', 'CC=CNCCNN(C)CC(C=BNN)=[N+](C)CC', 'C=NCC1=C2NCC(CC=NN)OCCC(N)=CC=CC2=C1', 'CC=C[N+]1=CN2CC3C(=O)C(=[N+]21)C=CC(C)=C(N)C1C2=NCNC1=C2C(=NC)C3C', 'C=CCC[N+]1=CCCNC(CC)N(CC)CC(C(N)CCC)CCC1C', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'C=C(NOCC=CC)C(C)CC', 'C=C(C)N(CCC)OC(CC1=CCC1)NN', 'C=CCC(C)(CCC)C(C)CC']."
            },
            {
                "from": "human",
                "value": "And generate a variety of molecules with the same z-score for the cell line '1331032'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+](C)CC(=N)CNCCC[N+](C)=N', 'C=C(C)CN(C1=C2CNCC(C)=NCC(N)=CC(CN1)C(C)C2)C1NNC1C', 'CBN=C[N+]1(N)NNN1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'C=CNN1NNNC(N)(CCCNNN)CCNNC1CCC', 'CC(C)CCC(C)CCC(C)C1=CC1C', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CCC1=C2CCC(C(C)=CCC(C)OC(C(C)=CCN)CCOC=CC1F)C(CC=N)CNCC(C)CC1=C2C1', 'C=C(C)C=CCC1(C)CCN(OC)C2=C(CCCC)CC3=NC1C(C)C(CCC)C3CC=CC(C)=C(C)C1(CC=CC(C)C2C(C(C)C)C(C)N)CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+](C)CC(=N)CNCCC[N+](C)=N', 'C=C(C)CN(C1=C2CNCC(C)=NCC(N)=CC(CN1)C(C)C2)C1NNC1C', 'CBN=C[N+]1(N)NNN1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'C=CNN1NNNC(N)(CCCNNN)CCNNC1CCC', 'CC(C)CCC(C)CCC(C)C1=CC1C', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(C)O2)N(CC3)C1CCC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CCC1=C2CCC(C(C)=CCC(C)OC(C(C)=CCN)CCOC=CC1F)C(CC=N)CNCC(C)CC1=C2C1', 'C=C(C)C=CCC1(C)CCN(OC)C2=C(CCCC)CC3=NC1C(C)C(CCC)C3CC=CC(C)=C(C)C1(CC=CC(C)C2C(C(C)C)C(C)N)CCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a batch of molecules with the intended z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCNC(C)C=C(CN1CCC2CC=C(C)CN21)C(CCC=C(C)C)=NC(C)(C)C', 'BCCNC(C)BB(N)C(CC)CCC(N)=NC=CNN(CC)B(C)CC', 'CCC(=CCC1=CC1C)CN', 'CC=CCC(C)=CCCCNN(CC)[N+](C)(C)[O+](C)CC=CC', 'CC1C=CC=C(O)C2C=CCC2CC1', 'CCCC(C=C(C)S)C(C)N(CC(CC)C(=O)O)CC1CC(CCC(CC)NCC)C1C(C)C', 'CCCC(COCC)[O+](C)CN=C(C)C(CC)NC', 'CNB1CCCNB(N)CC(C)=CN=C1C', 'C=C(CCC)C(C=CC1=CNCC2C(CCCC(CC)CC)C(CC=N)CCN12)CN', 'C=CCN=CC1CCCNC(=N)N[N+](=CC=NNC)CN1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCNC(C)C=C(CN1CCC2CC=C(C)CN21)C(CCC=C(C)C)=NC(C)(C)C', 'BCCNC(C)BB(N)C(CC)CCC(N)=NC=CNN(CC)B(C)CC', 'CCC(=CCC1=CC1C)CN', 'CC=CCC(C)=CCCCNN(CC)[N+](C)(C)[O+](C)CC=CC', 'CC1C=CC=C(O)C2C=CCC2CC1', 'CCCC(C=C(C)S)C(C)N(CC(CC)C(=O)O)CC1CC(CCC(CC)NCC)C1C(C)C', 'CCCC(COCC)[O+](C)CN=C(C)C(CC)NC', 'CNB1CCCNB(N)CC(C)=CN=C1C', 'C=C(CCC)C(C=CC1=CNCC2C(CCCC(CC)CC)C(CC=N)CCN12)CN', 'C=CCN=CC1CCCNC(=N)N[N+](=CC=NNC)CN1C']."
            },
            {
                "from": "human",
                "value": "And please create compounds with a target z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(NC)C(C)=CNCC=NC(C)C(C)CC', 'CC=C1C2C(C)C12', 'CC=C(CCC(O)C(C)CC)C(C)=CCCCC', 'C=CCC(CC(CC)CCC)NCC(N)=C(C(=C)CNC=C(C)NCCN)C(C)C=CC', 'CB(C)CNCBB(CC1(C)CN(C)N1)N=CN', 'CCCC(CC(N)(N)CCCCC(CCC)C1CC2CC(N2)N1CN)NCCN', 'CC=CC=C(C)CC(CCC)[O+]=Cc1ccn[nH]1', 'C=NCC(C)=NOCC(CC)(CC)C(CCO)=C(C)C=CC', 'CNNC(C)CCNN', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2NCCC1=C2CC12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(NC)C(C)=CNCC=NC(C)C(C)CC', 'CC=C1C2C(C)C12', 'CC=C(CCC(O)C(C)CC)C(C)=CCCCC', 'C=CCC(CC(CC)CCC)NCC(N)=C(C(=C)CNC=C(C)NCCN)C(C)C=CC', 'CB(C)CNCBB(CC1(C)CN(C)N1)N=CN', 'CCCC(CC(N)(N)CCCCC(CCC)C1CC2CC(N2)N1CN)NCCN', 'CC=CC=C(C)CC(CCC)[O+]=Cc1ccn[nH]1', 'C=NCC(C)=NOCC(CC)(CC)C(CCO)=C(C)C=CC', 'CNNC(C)CCNN', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2NCCC1=C2CC12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a selection of molecules with a z-score reaching -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'CC=NNC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'C=CC(=CCC(C)C)C(C)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(=C)CC', 'CCNCC1=NOC([N+](CC)(CN)NCC=C2CC2)CC=N1', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CC=NCC1=N[N+]1=C1NCC1OCN1CCC2(CCC)NC[O+]=C12', 'CCNC=CC(C)C(C)C(C)N(CC1CC1(N)C(C)CC)C1=C(CC)C2C(C)(C)CC2(C)CC(C)NCC1C', 'CN1C2C(CN)N21', 'CC=CCOC1=[O+]C=C(C(C)CC=CC)C(CCCCCF)=CCOCCC=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'CC=NNC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'C=CC(=CCC(C)C)C(C)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(=C)CC', 'CCNCC1=NOC([N+](CC)(CN)NCC=C2CC2)CC=N1', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CC=NCC1=N[N+]1=C1NCC1OCN1CCC2(CCC)NC[O+]=C12', 'CCNC=CC(C)C(C)C(C)N(CC1CC1(N)C(C)CC)C1=C(CC)C2C(C)(C)CC2(C)CC(C)NCC1C', 'CN1C2C(CN)N21', 'CC=CCOC1=[O+]C=C(C(C)CC=CC)C(CCCCCF)=CCOCCC=C1']."
            },
            {
                "from": "human",
                "value": "And build molecules tailored for the same z-score for '1331032'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CC=CC1=CC=N[N+](C(CNC)NC(C)CCC)=C(CC)NC(C)(C)NCC1', 'C=C(C)N1C=CCC(O)C(O)=CCC(C)C1', 'CCCOC1CC=C2CC(=CCCC1)C2CCC', 'C=CCC(CC(C=CC=CC1=CC1C)CC=CC1COC1)NCC(N)C=C(C)C(C)(C)CNC', 'C=C(C)C(C)CCCCC=CCC(C)CCCN(C)CC', 'CC1=C(C)C2N=CCCCCC1C2(C)N', 'CC=NCC=[N+](C)C(=CC1([N+](=CNC=CC=CCCC)CC)CC2N=C[O+]21)CC(C)C=NCC', 'C=C(C=CC)C(=C)CCC1C=CC(N=COC(C)CC)NC1', 'C=C(O)NCCC1CC=C(C)C2CCC12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CC=CC1=CC=N[N+](C(CNC)NC(C)CCC)=C(CC)NC(C)(C)NCC1', 'C=C(C)N1C=CCC(O)C(O)=CCC(C)C1', 'CCCOC1CC=C2CC(=CCCC1)C2CCC', 'C=CCC(CC(C=CC=CC1=CC1C)CC=CC1COC1)NCC(N)C=C(C)C(C)(C)CNC', 'C=C(C)C(C)CCCCC=CCC(C)CCCN(C)CC', 'CC1=C(C)C2N=CCCCCC1C2(C)N', 'CC=NCC=[N+](C)C(=CC1([N+](=CNC=CC=CCCC)CC)CC2N=C[O+]21)CC(C)C=NCC', 'C=C(C=CC)C(=C)CCC1C=CC(N=COC(C)CC)NC1', 'C=C(O)NCCC1CC=C(C)C2CCC12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(N)NCC(C)=CN=CC=[N+]1C=NC=CCNC1', 'CCCC(C)C1(C)CCCCC1', 'CNC(CN)CCC(=CCN)N=NC(C)C', 'CCC(CC)=NCC(C)CN=CC1=C(N)C1', 'CCC=NC(N)C=C(NN)C(CCC=C(C)C)=NN(C)C(C)C', 'C=CCC(N)CC(C=CC=CC1=CC1C)NC=CC1CN(CN(C)CN)C1', 'C=C(CN(C)N)C(N)NC(C)C', 'BB1BBBBB(C)B(BN)NBC1', 'CNN(C(=N)N)C1C=CC=C(C=N)NB2NCC3CC(=C[N+](=CN)BC2C)NN3C1', 'CN1C=COC(O)=C2C3=C=C(CCC4CCC(=CC24C)C=C1)C3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(N)NCC(C)=CN=CC=[N+]1C=NC=CCNC1', 'CCCC(C)C1(C)CCCCC1', 'CNC(CN)CCC(=CCN)N=NC(C)C', 'CCC(CC)=NCC(C)CN=CC1=C(N)C1', 'CCC=NC(N)C=C(NN)C(CCC=C(C)C)=NN(C)C(C)C', 'C=CCC(N)CC(C=CC=CC1=CC1C)NC=CC1CN(CN(C)CN)C1', 'C=C(CN(C)N)C(N)NC(C)C', 'BB1BBBBB(C)B(BN)NBC1', 'CNN(C(=N)N)C1C=CC=C(C=N)NB2NCC3CC(=C[N+](=CN)BC2C)NN3C1', 'CN1C=COC(O)=C2C3=C=C(CCC4CCC(=CC24C)C=C1)C3']."
            },
            {
                "from": "human",
                "value": "And design compounds to meet the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C(CCN)CCC(CCCC)CN=CC(C)CO', 'C=C1CC=CC=[N+]2CC(C=CO)=CC(CC)C=NN3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC=CC=CNCCCC', 'CCCCC(C)C=C1C=CCNC(C)(CC[N+](C)(N)CC)N(C=CN)C=NC(CC)C=CC1', 'C=C(C=N)CCNCC(O)CCCC', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=CC=CC=CC(CCN)(C(C)C1=CC(=N)C1C=CCC)C(N)C(CC)=[O+]CN', 'CCCCC1(CC)C=C(C=CC=O)CCC(C)(C)C1', 'C=C1C=C(CCN)C=NC(C)=C1', 'CC=NN(N)CCCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C(CCN)CCC(CCCC)CN=CC(C)CO', 'C=C1CC=CC=[N+]2CC(C=CO)=CC(CC)C=NN3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC=CC=CNCCCC', 'CCCCC(C)C=C1C=CCNC(C)(CC[N+](C)(N)CC)N(C=CN)C=NC(CC)C=CC1', 'C=C(C=N)CCNCC(O)CCCC', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=CC=CC=CC(CCN)(C(C)C1=CC(=N)C1C=CCC)C(N)C(CC)=[O+]CN', 'CCCCC1(CC)C=C(C=CC=O)CCC(C)(C)C1', 'C=C1C=C(CCN)C=NC(C)=C1', 'CC=NN(N)CCCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple compounds optimized for a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(N)CC(C)(N)CCC1=CC1=CCNCN', 'CCCNCCCOCC(N)F', 'C=C(CC=CC(CC=[O+]N)CN(C)CC(C)=CC)C(CCN)[N+](=C)C', 'CCC1=CC2=COC(=N1)C1CC(CCOC=C(C)O)[O+]1C(N)=C2', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CCC=[O+]C(C)(C)C1C=C(CC)CC=CC=CCN(C(C)C2CO2)CC1', 'C=CO[N+](=CC)C(O)CNC', 'CCC(C)C=C(C)CC(CO)CC1CC1', 'C=C1CC2C=C(C)ON(C)CC=[O+]C=C(C(OO)[O+](C)C)CC12', 'CC1=C(C)C2N=CCCCCC1C2(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(N)CC(C)(N)CCC1=CC1=CCNCN', 'CCCNCCCOCC(N)F', 'C=C(CC=CC(CC=[O+]N)CN(C)CC(C)=CC)C(CCN)[N+](=C)C', 'CCC1=CC2=COC(=N1)C1CC(CCOC=C(C)O)[O+]1C(N)=C2', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CCC=[O+]C(C)(C)C1C=C(CC)CC=CC=CCN(C(C)C2CO2)CC1', 'C=CO[N+](=CC)C(O)CNC', 'CCC(C)C=C(C)CC(CO)CC1CC1', 'C=C1CC2C=C(C)ON(C)CC=[O+]C=C(C(OO)[O+](C)C)CC12', 'CC1=C(C)C2N=CCCCCC1C2(C)N']."
            },
            {
                "from": "human",
                "value": "And please produce a batch of molecules to hit the same z-score for '684055'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCB(N)N(CNC(CBBN(C)C)C1BNC1)NNN=C1CC1', 'C=C(N=CC(CC)(CC)C(CCC)C(C)=CN)C(C)=CC', 'CCC=C(CN)N=C1CC2=[N+](CC2)C(C)C2C1=COCCC2C1=[N+](C)CC1', 'C=CC(N=NCC(C)=C(CO)NO)NCC', 'CC=CC=C(O)NC(NCCC=CCO)C1CCC(=C(CNC)NC(C)C)CC2CC=C21', 'C=CC=CC(CC)=[N+]1CCC(C)C(C)=CC(C)=CC(CO)CC(C)C1', 'C=CC=C(NNN)C(=N)N=CB(CNN(N)BN)NCN', 'C=C(C)CCCC(=CC(C)CN=CC)NC', 'CCCNB1C=CNN1CN(N)C=NC1(NC(C)CCBCNNN)C=NC1', 'C[N+]1(C)CCCCCCCNN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCB(N)N(CNC(CBBN(C)C)C1BNC1)NNN=C1CC1', 'C=C(N=CC(CC)(CC)C(CCC)C(C)=CN)C(C)=CC', 'CCC=C(CN)N=C1CC2=[N+](CC2)C(C)C2C1=COCCC2C1=[N+](C)CC1', 'C=CC(N=NCC(C)=C(CO)NO)NCC', 'CC=CC=C(O)NC(NCCC=CCO)C1CCC(=C(CNC)NC(C)C)CC2CC=C21', 'C=CC=CC(CC)=[N+]1CCC(C)C(C)=CC(C)=CC(CO)CC(C)C1', 'C=CC=C(NNN)C(=N)N=CB(CNN(N)BN)NCN', 'C=C(C)CCCC(=CC(C)CN=CC)NC', 'CCCNB1C=CNN1CN(N)C=NC1(NC(C)CCBCNNN)C=NC1', 'C[N+]1(C)CCCCCCCNN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a selection of molecules with a z-score reaching -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+](C)CCC=[O+]C(=CC)CNC(NC=CN)[N+](=CCCC)CCC', 'CCC(=[O+]C)C(C=C(C)CC1=CCCCC(C=CC2=CC2)CC=C1)C(C)CC', 'C=CB(B=CCC)CNB1CNC(C)C1N=CC', 'CCCCC(CC)(CC)CCC', 'C=C(C)CC1CCCC2C1=NC1(C=C1CC)CC(C(=C)C(C=CCCC)=CC1COC1)=C2C', 'CC=CC=NC[O+]=C(N)C(C)CC', 'C=C(C)C(=C)CCC', 'C=NC[N+](CC1=NC=[N+]1C(C=CN)=[N+](C=CN)CCC(C)C)=NC', 'CC=CC=C(C=CC)CC=C(C=C[O+]=CC1=C(CC)C1CC)CC(C)C', 'C=NCC(C)=NOCC(CC)(CC)C(CCOCCCNF)=C(C)C=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+](C)CCC=[O+]C(=CC)CNC(NC=CN)[N+](=CCCC)CCC', 'CCC(=[O+]C)C(C=C(C)CC1=CCCCC(C=CC2=CC2)CC=C1)C(C)CC', 'C=CB(B=CCC)CNB1CNC(C)C1N=CC', 'CCCCC(CC)(CC)CCC', 'C=C(C)CC1CCCC2C1=NC1(C=C1CC)CC(C(=C)C(C=CCCC)=CC1COC1)=C2C', 'CC=CC=NC[O+]=C(N)C(C)CC', 'C=C(C)C(=C)CCC', 'C=NC[N+](CC1=NC=[N+]1C(C=CN)=[N+](C=CN)CCC(C)C)=NC', 'CC=CC=C(C=CC)CC=C(C=C[O+]=CC1=C(CC)C1CC)CC(C)C', 'C=NCC(C)=NOCC(CC)(CC)C(CCOCCCNF)=C(C)C=CC']."
            },
            {
                "from": "human",
                "value": "And generate a variety of molecules with the same z-score for the cell line '684055'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C(C)CCC(N)CCC=C2CNCCCC=C(C(C(C)=NC)=CCCC2CCC)C1C', 'C=NC1=[N+]2B(CCC[N+]2(CNCCN[N+](=N)NNBNC)C(C)(N)BCNC)C2NN12', 'C=NCCC(C)CC(CCNC)C(=CC)C1=C2NCN=C1C2C(N)CC', 'C=CC1(C2C=C(CCC(=C)CC3(C)CC3)CC2)C=CCCCC(C)(NC)C1', 'CB(N)N=CCB=BNN(C)NCN', 'CC=[NH+]CN1NC(NC)B2CN2CN1BN', 'BNN(CNNN)CN(B)B1B(N)B2NC(B)B(B)B21', 'CCN(C(C)CCNC)C(N)[O+]1C=CCCCC2=CCC(C)C3C4CCCCC=[N+](C(C=[N+]3C4C)C1)N2C', 'C=CC(C)C1=CCCC(OC(C)C)C(CC)=C2CC=CC(N)C(=C)C=CC1CC2', 'CCC(NNN(N)C1(CC2BCN2)N=CN1)N[N+](=N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C(C)CCC(N)CCC=C2CNCCCC=C(C(C(C)=NC)=CCCC2CCC)C1C', 'C=NC1=[N+]2B(CCC[N+]2(CNCCN[N+](=N)NNBNC)C(C)(N)BCNC)C2NN12', 'C=NCCC(C)CC(CCNC)C(=CC)C1=C2NCN=C1C2C(N)CC', 'C=CC1(C2C=C(CCC(=C)CC3(C)CC3)CC2)C=CCCCC(C)(NC)C1', 'CB(N)N=CCB=BNN(C)NCN', 'CC=[NH+]CN1NC(NC)B2CN2CN1BN', 'BNN(CNNN)CN(B)B1B(N)B2NC(B)B(B)B21', 'CCN(C(C)CCNC)C(N)[O+]1C=CCCCC2=CCC(C)C3C4CCCCC=[N+](C(C=[N+]3C4C)C1)N2C', 'C=CC(C)C1=CCCC(OC(C)C)C(CC)=C2CC=CC(N)C(=C)C=CC1CC2', 'CCC(NNN(N)C1(CC2BCN2)N=CN1)N[N+](=N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -6.190392 for the cell line '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NCCNC1C2=CN(C)CC=[N+]1N(C(BCC=CC)CCNNNN)N2', 'CCC(C)OCC(COC)C1CCC1C1CC1', 'CCC=NC(C)C=C(CN)C(CCC=C(C)C)=NC(C)C(C)C', 'CB1NN(NN)C=CC1B1CC=CNB(N)CC1', 'C=CC(C)C(N)(CC)CCC', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14NCCC5C(=C1C=C4CCC)C2C5CN=C3', 'CC=CC=C=C(C)C=CC(C)=C(C)CC(C)CCCCCCC(C)N', 'CCCCC1CC=CCC2CCC=COC21C', 'CCC(C)=CC=CC=CC(BN)NCCN', 'CCNC1C2CCC=CC3=C(CNN=C(C)C)CC3C(CC3CCCC(CC)C=NC=[N+](C)N3)=CCC(C)=C(C)C21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NCCNC1C2=CN(C)CC=[N+]1N(C(BCC=CC)CCNNNN)N2', 'CCC(C)OCC(COC)C1CCC1C1CC1', 'CCC=NC(C)C=C(CN)C(CCC=C(C)C)=NC(C)C(C)C', 'CB1NN(NN)C=CC1B1CC=CNB(N)CC1', 'C=CC(C)C(N)(CC)CCC', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14NCCC5C(=C1C=C4CCC)C2C5CN=C3', 'CC=CC=C=C(C)C=CC(C)=C(C)CC(C)CCCCCCC(C)N', 'CCCCC1CC=CCC2CCC=COC21C', 'CCC(C)=CC=CC=CC(BN)NCCN', 'CCNC1C2CCC=CC3=C(CNN=C(C)C)CC3C(CC3CCCC(CC)C=NC=[N+](C)N3)=CCC(C)=C(C)C21']."
            },
            {
                "from": "human",
                "value": "And design molecules aimed at reaching the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCC)CCCN', 'C=C1C=C(C(CC)C(=C)C=CC)CCCC=CC(CC)CC[O+]2C(C)CC(O)(CC=CC)C3=CC2(CCNCC1=C)C3', 'C1=NCCCCCCC1', 'CCN(C)CC(CCCCCC1CC1C1N=CC(C)C1C)CCC(C)C', 'CCC=NC(C)(C(=CCC(C)C)CC)C(C)CC', 'CC=[N+](CC(C)C=CCCC)C(S)=CCC(F)C1=C2C=CC(=C(O)C(CC)=C2)C1C', 'CCCC(C)(CC)CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'CC1=CC(=CCC(N)N=CNN)N[N+](N)(C(C)N)NCC2NC2=CCNCNC1', 'CC=C(C)C(C)C(C)=CCC[O+](C=CCC(C)=CCCF)COC', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCC)CCCN', 'C=C1C=C(C(CC)C(=C)C=CC)CCCC=CC(CC)CC[O+]2C(C)CC(O)(CC=CC)C3=CC2(CCNCC1=C)C3', 'C1=NCCCCCCC1', 'CCN(C)CC(CCCCCC1CC1C1N=CC(C)C1C)CCC(C)C', 'CCC=NC(C)(C(=CCC(C)C)CC)C(C)CC', 'CC=[N+](CC(C)C=CCCC)C(S)=CCC(F)C1=C2C=CC(=C(O)C(CC)=C2)C1C', 'CCCC(C)(CC)CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'CC1=CC(=CCC(N)N=CNN)N[N+](N)(C(C)N)NCC2NC2=CCNCNC1', 'CC=C(C)C(C)C(C)=CCC[O+](C=CCC(C)=CCCF)COC', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of compounds targeting a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C)C1CC=C(CC(CCF)[O+]=CCO)O1', 'C=C1NC[O+](C)C(C)C2=C(C)C=C2C(CCC)=CC(=CC=CC)C=CCCO1', 'CCC=CC(CCC)C(=CC=CCOO)CC', 'C=CCCCC=CC1(CC(C)=CC)CCCCC1C', 'C=CCC(C=C)CONC1(CC(C)(C)NC)C=CC1', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C1C=NCC=C(C(CC)=C(C)CC(=C)C2CC2(C)CC)C1', 'CCCCNNC=CC(C)CCC', 'C=BC=CN=N', 'CCCC(C)=CC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C)C1CC=C(CC(CCF)[O+]=CCO)O1', 'C=C1NC[O+](C)C(C)C2=C(C)C=C2C(CCC)=CC(=CC=CC)C=CCCO1', 'CCC=CC(CCC)C(=CC=CCOO)CC', 'C=CCCCC=CC1(CC(C)=CC)CCCCC1C', 'C=CCC(C=C)CONC1(CC(C)(C)NC)C=CC1', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=C1C=NCC=C(C(CC)=C(C)CC(=C)C2CC2(C)CC)C1', 'CCCCNNC=CC(C)CCC', 'C=BC=CN=N', 'CCCC(C)=CC(C)C']."
            },
            {
                "from": "human",
                "value": "And design a set of molecules to meet the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1NC(CCC(C)C2CC3=[N+]2N3CCNC2CC(C)N2)CCCC=CO1', 'C=CNC=CNCC=CC1=CC1', 'CC=CN=NC(C)BN1CCNC2BCN2CC1', 'CC=C1CC2=C(C(C)(N)CCN)C(N1)C1(C)CC(N=C2)N1', 'C=CC=C1CCCCC(C)=C(CCCC)CC1=CC', 'C=CB(C=CCCNCC=CCC1CCNC[N+]2=CC2N1)CCC(N)CCCC', 'C=C1C2=CC(N=C(C)CCCCNC)=CC1NCC=C(CNCC(C)C)CCN2', 'CC1=NN1CNN', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2C[N+]12CCCC', 'CC=CCCC(C)C(C=CCCCC)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1NC(CCC(C)C2CC3=[N+]2N3CCNC2CC(C)N2)CCCC=CO1', 'C=CNC=CNCC=CC1=CC1', 'CC=CN=NC(C)BN1CCNC2BCN2CC1', 'CC=C1CC2=C(C(C)(N)CCN)C(N1)C1(C)CC(N=C2)N1', 'C=CC=C1CCCCC(C)=C(CCCC)CC1=CC', 'C=CB(C=CCCNCC=CCC1CCNC[N+]2=CC2N1)CCC(N)CCCC', 'C=C1C2=CC(N=C(C)CCCCNC)=CC1NCC=C(CNCC(C)C)CCN2', 'CC1=NN1CNN', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2C[N+]12CCCC', 'CC=CCCC(C)C(C=CCCCC)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules optimized for a z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)CCC(C=CCCN)C(C)NCC', 'CCC1=CC=CC2=CC3(CCOCC2C)CC(C3)C(C2=CC2)=CCCCN=C1', 'CCC=C(C)C1CCC=C(CC)C1CCCC(CC)CC=C(C)C', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'CCC1CC(CC)C(C)NCC(N)C(C)=C[O+](CC)O1', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'CCC=C1CC(C)CC2=C(CCCC=CO)N(C)N=CC3CC3C=CC1=C(C)CC1CCNCC(C)N1CC2', 'CCC=[N+](O)NC[N+](C=C(C)C1=C(CCC2C=CO2)CC1)=CC=CCO', 'CCCC=CNC(C)CCC(C)(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)CCC(C=CCCN)C(C)NCC', 'CCC1=CC=CC2=CC3(CCOCC2C)CC(C3)C(C2=CC2)=CCCCN=C1', 'CCC=C(C)C1CCC=C(CC)C1CCCC(CC)CC=C(C)C', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'CCC1CC(CC)C(C)NCC(N)C(C)=C[O+](CC)O1', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'CCC=C1CC(C)CC2=C(CCCC=CO)N(C)N=CC3CC3C=CC1=C(C)CC1CCNCC(C)N1CC2', 'CCC=[N+](O)NC[N+](C=C(C)C1=C(CCC2C=CO2)CC1)=CC=CCO', 'CCCC=CNC(C)CCC(C)(C)CC']."
            },
            {
                "from": "human",
                "value": "And design molecules with the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['N=C1C=CCC(NC2CCCC2)=C1', 'CCCCN1CC=C(C=CC(CCC)C(C)=C(C2=CC=CC2)N(CC)CC)C1', 'CN1CC(C)(NCCCCCC(N)=CCCNC=N)CCNC=NN1', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'NNN(N)C=CCC1CC=CNC(N)CC1', 'CCCC1=NCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'CC(NCCNCCN)C(C)C1(C)NCB1O', 'C=C(CCCC)C(=CC)CCC=C[O+](C)N', 'C=NC(C)=CCCCN', 'CN1C2COC1(C)N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['N=C1C=CCC(NC2CCCC2)=C1', 'CCCCN1CC=C(C=CC(CCC)C(C)=C(C2=CC=CC2)N(CC)CC)C1', 'CN1CC(C)(NCCCCCC(N)=CCCNC=N)CCNC=NN1', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'NNN(N)C=CCC1CC=CNC(N)CC1', 'CCCC1=NCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'CC(NCCNCCN)C(C)C1(C)NCB1O', 'C=C(CCCC)C(=CC)CCC=C[O+](C)N', 'C=NC(C)=CCCCN', 'CN1C2COC1(C)N2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically for achieving a z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['NC(N)NC=C1CN1N', 'B=C(CN)NCCNNN', 'CCC[n+]1[nH][nH]bn[n+](C)c[n+]1CC', 'CCC(=[O+]C(CNC)CC(C)CC)[N+](C)(C)CC', 'C=C(C)C(N)C(N)N1N(CNC)[N+]1=C', 'CNNN[N+](N)(NN)NN=NB=CN1CCC(C)B(NN)C(N)N1', 'C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'CC=CCC=CC[NH+](C)C(CC)=C(C)N(C)CN1CC1C', 'CNCNC(C1=CB=CCCN1N)C(BCCN)CN', 'C=NN=BC(NB(C)CCC=[N+]1CN1)[N+](=C)CC=BN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['NC(N)NC=C1CN1N', 'B=C(CN)NCCNNN', 'CCC[n+]1[nH][nH]bn[n+](C)c[n+]1CC', 'CCC(=[O+]C(CNC)CC(C)CC)[N+](C)(C)CC', 'C=C(C)C(N)C(N)N1N(CNC)[N+]1=C', 'CNNN[N+](N)(NN)NN=NB=CN1CCC(C)B(NN)C(N)N1', 'C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'CC=CCC=CC[NH+](C)C(CC)=C(C)N(C)CN1CC1C', 'CNCNC(C1=CB=CCCN1N)C(BCCN)CN', 'C=NN=BC(NB(C)CCC=[N+]1CN1)[N+](=C)CC=BN']."
            },
            {
                "from": "human",
                "value": "And create compounds to align with the same z-score for '905957'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(NCCC(C)=CCCCC(CC)CCC)C1CN1C(C)CN', 'CCC=CC(C(CC)C(C)C)N(C)CCCCCC', 'C=C(O)C1=CCC1', 'CCCC1=C(C)C(CC)C=CC=CC=[N+](C)CC1', 'C=C(CC)C1CC(C=CC)NCN1', 'CC=C=CCCCC(=CCCC(CNC)NC)CCC', 'C=CCC(C=C)CCC(CC=CC(=C)C)C(C)CC(C=C)CC=CC', 'CCC=CC=[O+]CC1=CC=C1', 'CCCCC1CC=CCC2CCC=COC21C', 'C=CC=C=CCC1CCCNCCN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(NCCC(C)=CCCCC(CC)CCC)C1CN1C(C)CN', 'CCC=CC(C(CC)C(C)C)N(C)CCCCCC', 'C=C(O)C1=CCC1', 'CCCC1=C(C)C(CC)C=CC=CC=[N+](C)CC1', 'C=C(CC)C1CC(C=CC)NCN1', 'CC=C=CCCCC(=CCCC(CNC)NC)CCC', 'C=CCC(C=C)CCC(CC=CC(=C)C)C(C)CC(C=C)CC=CC', 'CCC=CC=[O+]CC1=CC=C1', 'CCCCC1CC=CCC2CCC=COC21C', 'C=CC=C=CCC1CCCNCCN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds to achieve a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[N+](C)=C(C)N=NCC=C(C=N)CNCCCC', 'CC=C1N=C2C(CCC(C)CC(C)N(CC)CCCNCCC)C2[O+]=CC(N)(CC=CC=N)NCC1=CN', 'CC1CC=COCCC(C)(NCCC=CCC(O)=CCCCC=O)C1', 'CC=CC(CF)C1C(OC)CCC2(C=C(N)C3C(C)C3C(C)C)CC12', 'CCCC1NB1CCC1=CNB1C', 'CCN(C)CC=CCB(C)N', 'CC=NCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'CCC=C(C)C(C)CCC1CCCNC1C', 'C=CC[N+](C=COC=CC(C)C)=[O+]C(N=C(C)OC)=C(C[O+]=CC)C(CC)CC', 'CC=CC(O)CC=CCC=C(C=NCC)C1C=C=C1CNNC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[N+](C)=C(C)N=NCC=C(C=N)CNCCCC', 'CC=C1N=C2C(CCC(C)CC(C)N(CC)CCCNCCC)C2[O+]=CC(N)(CC=CC=N)NCC1=CN', 'CC1CC=COCCC(C)(NCCC=CCC(O)=CCCCC=O)C1', 'CC=CC(CF)C1C(OC)CCC2(C=C(N)C3C(C)C3C(C)C)CC12', 'CCCC1NB1CCC1=CNB1C', 'CCN(C)CC=CCB(C)N', 'CC=NCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'CCC=C(C)C(C)CCC1CCCNC1C', 'C=CC[N+](C=COC=CC(C)C)=[O+]C(N=C(C)OC)=C(C[O+]=CC)C(CC)CC', 'CC=CC(O)CC=CCC=C(C=NCC)C1C=C=C1CNNC']."
            },
            {
                "from": "human",
                "value": "And design a set of molecules to meet the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CCC(C)C1CCC1CNCN=C1C(=CCCC)CNCCC[N+](C)=C1CC1=CCC1', 'CC=CC1=CC=CC=CC(CC)C(C)=C(CCC)CC1', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CCC(=CC1C=CC=C(C(=N)N=CC2NC2N)NNN1)CN', 'C=COC=CC(CC=COC)C(N)CCOCC=CC(=C)C=C', 'CCC(CC)CCCCC=CCN1C(C(C)CCC(C)C=NC)N1C(C)C', 'CCC1CC1=CC=CC(O)=COCC(C)C', 'CC=CCC(C)=CC(C)=CC(=CCNCC(C)C=CO)N=COC', 'CC(BBN)CN(C=NN)N=N', 'C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CCC(C)C1CCC1CNCN=C1C(=CCCC)CNCCC[N+](C)=C1CC1=CCC1', 'CC=CC1=CC=CC=CC(CC)C(C)=C(CCC)CC1', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CCC(=CC1C=CC=C(C(=N)N=CC2NC2N)NNN1)CN', 'C=COC=CC(CC=COC)C(N)CCOCC=CC(=C)C=C', 'CCC(CC)CCCCC=CCN1C(C(C)CCC(C)C=NC)N1C(C)C', 'CCC1CC1=CC=CC(O)=COCC(C)C', 'CC=CCC(C)=CC(C)=CC(=CCNCC(C)C=CO)N=COC', 'CC(BBN)CN(C=NN)N=N', 'C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple compounds optimized for a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)=C(NC=CCC(C)COC)C(C)=CCC=[N+]1CC2CC(CCC=CC3CC3)OC2CCCN1', 'C=CCCCC=NC=CON=C(O)C1OC(C)(CC=CCCOC)C1(C)C', 'C=C(C=C1CC=CC(=C)C1(C)C)C(C)C', 'C=[N+](C)CCC1=CCC1C=C[N+](=C)CC(C)=C(C)C', 'CCCNCC=C(C=NC=C(CCC)CNCCC)NB(N)CCC', 'CCCC=NCCC(CCC)=NC', 'C=CCN(CCC=[N+](C)c1c(CCC)c2nc[n+]1=2)C(N)N', 'C=C(C)[SH](=C)(OC(CCC)OCN(CC)CCCC1(C)CC1)C(C)CC', 'C=C1C2=C3C4=CN3CC1C(C=C(C)C(C)=C4)C(=CC)C1=C3CCC=C1C3C(N)=C(C)C=C2', 'BC=NN=CC(B(C)N)C(=C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(C)=C(NC=CCC(C)COC)C(C)=CCC=[N+]1CC2CC(CCC=CC3CC3)OC2CCCN1', 'C=CCCCC=NC=CON=C(O)C1OC(C)(CC=CCCOC)C1(C)C', 'C=C(C=C1CC=CC(=C)C1(C)C)C(C)C', 'C=[N+](C)CCC1=CCC1C=C[N+](=C)CC(C)=C(C)C', 'CCCNCC=C(C=NC=C(CCC)CNCCC)NB(N)CCC', 'CCCC=NCCC(CCC)=NC', 'C=CCN(CCC=[N+](C)c1c(CCC)c2nc[n+]1=2)C(N)N', 'C=C(C)[SH](=C)(OC(CCC)OCN(CC)CCCC1(C)CC1)C(C)CC', 'C=C1C2=C3C4=CN3CC1C(C=C(C)C(C)=C4)C(=CC)C1=C3CCC=C1C3C(N)=C(C)C=C2', 'BC=NN=CC(B(C)N)C(=C)C']."
            },
            {
                "from": "human",
                "value": "And design molecules optimized for the same z-score for the cell line '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)(CCC(C(C)=NC=N)C(C)N)CC1(CCNN)CC=C1C', 'CCCCCC1(C)CCC2=C(C)C(C)=[N+]2C=C(C)CC(C)(N)O1', 'CCCC1CCC[O+](C)NC(OCN)CCC(O)C1', 'C=CNCCC(C)C[O+]=[O+]C(=CC(C)C(C)CCC1CC1)C1CC(=CC2CCCC2)C1', 'CC[N+](C=NC(C)O)=CC=CCCCCC=CCC(C)NC', 'CCC(C)[O+]1COCOO1', 'CCCC(N)=CC(C)C', 'CN=CN=CNN', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)(CCC(C(C)=NC=N)C(C)N)CC1(CCNN)CC=C1C', 'CCCCCC1(C)CCC2=C(C)C(C)=[N+]2C=C(C)CC(C)(N)O1', 'CCCC1CCC[O+](C)NC(OCN)CCC(O)C1', 'C=CNCCC(C)C[O+]=[O+]C(=CC(C)C(C)CCC1CC1)C1CC(=CC2CCCC2)C1', 'CC[N+](C=NC(C)O)=CC=CCCCCC=CCC(C)NC', 'CCC(C)[O+]1COCOO1', 'CCCC(N)=CC(C)C', 'CN=CN=CNN', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a batch of molecules reaching a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](CNCNN)=C(C)NC(CN)=C(N)CC3', 'CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'CCCCCNC(C)(C(N)CN(C)N=C(C)C)C(CC)CN', 'C=NC=CC(C=CC(CCN)C(C)CC)C=C(C)N', 'CC(C)C1CC=C(CC(CCF)[O+]=CCO)O1', 'C[O+]=CC=C(C)C(C)COOO[O+](C)C(C)C', 'C=[N+](OCCCC)C(C)=CCCCN', 'C=CC1(CCCC=CC=CC)CC=C=CCC(OCC(C)=CC(=C)CCC(C)=O)C(C)C(NC)C1', 'C=CC1(C=CF)C=C(C)[O+]2CC3C=CC(C(O2)C(C)C3O)C1CNC', 'CC=C[N+](CNC)=C(C)C(C)=CCCCN=[N+](C)NC(C)C=NN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+]1CN=CC=C2CNC(C)OC3=C(N)C4=C(CN4)C21CN[N+](CNCNN)=C(C)NC(CN)=C(N)CC3', 'CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'CCCCCNC(C)(C(N)CN(C)N=C(C)C)C(CC)CN', 'C=NC=CC(C=CC(CCN)C(C)CC)C=C(C)N', 'CC(C)C1CC=C(CC(CCF)[O+]=CCO)O1', 'C[O+]=CC=C(C)C(C)COOO[O+](C)C(C)C', 'C=[N+](OCCCC)C(C)=CCCCN', 'C=CC1(CCCC=CC=CC)CC=C=CCC(OCC(C)=CC(=C)CCC(C)=O)C(C)C(NC)C1', 'C=CC1(C=CF)C=C(C)[O+]2CC3C=CC(C(O2)C(C)C3O)C1CNC', 'CC=C[N+](CNC)=C(C)C(C)=CCCCN=[N+](C)NC(C)C=NN']."
            },
            {
                "from": "human",
                "value": "And build a collection of molecules with the same z-score for '1240182'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCN2C=C(CC(C)=CCN(C)C1)C2(C)C', 'CC1=CC2CCCC(C)=C3C(C)=C3C2CO1', 'CCCC12C(=NC3=C(CO)C3)C3=C(C)CC(CC)=C1C2=C(C=CCC[O+](C)C)C=CCCC(C)(C)C=CCCO3', 'C[O+](O)CCCC1(N)C=C1', 'CCCCCNC(C)C(C)C1(C)NCC1O', 'C=CC1=[N+](C(C=CCC)CN=C(CC)NC=CC)CN1', 'C=NC(=CCN1C=CCNC2CC=C(C)CC2(C=O)C1)N(C)CC=CC', 'CCC(C)=C(C)N1C(C)C=CC(C(C)CC2CC=CCC(C)C2)C(F)CC1C', 'C=C(C=CCCC=C(CC=CC=CCCCC(C)C(CC)CCC=O)C1CC(N)CC(CO)C1)CC', 'C=NC1=CC(CC)(C2C3CC(O3)C2C)C=NC=CC[N+](C)=C2CCC2OC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCN2C=C(CC(C)=CCN(C)C1)C2(C)C', 'CC1=CC2CCCC(C)=C3C(C)=C3C2CO1', 'CCCC12C(=NC3=C(CO)C3)C3=C(C)CC(CC)=C1C2=C(C=CCC[O+](C)C)C=CCCC(C)(C)C=CCCO3', 'C[O+](O)CCCC1(N)C=C1', 'CCCCCNC(C)C(C)C1(C)NCC1O', 'C=CC1=[N+](C(C=CCC)CN=C(CC)NC=CC)CN1', 'C=NC(=CCN1C=CCNC2CC=C(C)CC2(C=O)C1)N(C)CC=CC', 'CCC(C)=C(C)N1C(C)C=CC(C(C)CC2CC=CCC(C)C2)C(F)CC1C', 'C=C(C=CCCC=C(CC=CC=CCCCC(C)C(CC)CCC=O)C1CC(N)CC(CO)C1)CC', 'C=NC1=CC(CC)(C2C3CC(O3)C2C)C=NC=CC[N+](C)=C2CCC2OC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce compounds targeting a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'CCC=CC1=C2C=C[N+](C)(CC2)C2C=CC(=CC2)CC=C1C=C(CCNC)C(CC)CC', 'C=C(CO)C(C)O', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CCCC=CNC(C)CCC(C)(C)CC', 'C=CCCCC(=CCCCCCC(O)CCCN1CC1)CC', 'CC=CC[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=C(N)CC1CC(O)C(=C(CC)CC=C(C)CCCNCCC(C)=CC(C)N)O1', 'CCCN=C1C=C(CC23CCCOC(=CCC=CCC(CC)N2)CC3)C1', 'C=CC(N)N(CCC(=C)CC(=O)CC)C1CC2(C=O)CNN3CN2C(=CCC=CC1N)N3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'CCC=CC1=C2C=C[N+](C)(CC2)C2C=CC(=CC2)CC=C1C=C(CCNC)C(CC)CC', 'C=C(CO)C(C)O', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CCCC=CNC(C)CCC(C)(C)CC', 'C=CCCCC(=CCCCCCC(O)CCCN1CC1)CC', 'CC=CC[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=C(N)CC1CC(O)C(=C(CC)CC=C(C)CCCNCCC(C)=CC(C)N)O1', 'CCCN=C1C=C(CC23CCCOC(=CCC=CCC(CC)N2)CC3)C1', 'C=CC(N)N(CCC(=C)CC(=O)CC)C1CC2(C=O)CNN3CN2C(=CCC=CC1N)N3']."
            },
            {
                "from": "human",
                "value": "And create a set of molecules that have the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(=CC(C)CC(C)C=CC=C(CO)CC1CCC1)C(C)(C)CC', 'CCC1CNC1(C)CCF', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'C=CC=C(N(C=CC(NN)C1(CCCN)CN1CN)CC)C1(C)C(=C)C(C)(CN(C)C)[O+](C)CCC(CC)C2=CC1C2', 'C=CCOC(CCC)CCCCC(C)(CCCCCC)ONN', 'C=NC=C=CCN1CNCNCCN1', 'CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CC)(CCC)C1', 'CCCCNCN', 'C=C1CCN(C)CCN(C)[O+](C2=NC=NC(CC=NC)C(N)CC2C)NC(CCC)=[N+]2CC12C', 'CCCCCNC(C)C1(CCCCC)CC=C(C2CCC2)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(=CC(C)CC(C)C=CC=C(CO)CC1CCC1)C(C)(C)CC', 'CCC1CNC1(C)CCF', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'C=CC=C(N(C=CC(NN)C1(CCCN)CN1CN)CC)C1(C)C(=C)C(C)(CN(C)C)[O+](C)CCC(CC)C2=CC1C2', 'C=CCOC(CCC)CCCCC(C)(CCCCCC)ONN', 'C=NC=C=CCN1CNCNCCN1', 'CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CC)(CCC)C1', 'CCCCNCN', 'C=C1CCN(C)CCN(C)[O+](C2=NC=NC(CC=NC)C(N)CC2C)NC(CCC)=[N+]2CC12C', 'CCCCCNC(C)C1(CCCCC)CC=C(C2CCC2)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that align with a z-score of -6.190392 for the cell line '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CC(C)C=N[N+]2=CCCC(CC[N+](=C)[N+](=C)C1)N=C2', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(C)C1)CCC)N(CC)CC', 'CCNN(C)CC(C)CNCCC1=C(CN)N(CC)C(C)NC(C=CNC=N)[N+](C)=NC1', 'CC=NC(C)C12C=C1C2', 'CC1CCCC=CCC[O+](N)C1C', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCCNC(BN)C=CC=CC=C(C)CC', 'CCCC(C)C=CC1CNCOC1', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCN1', 'C=NC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CC(C)C=N[N+]2=CCCC(CC[N+](=C)[N+](=C)C1)N=C2', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(C)C1)CCC)N(CC)CC', 'CCNN(C)CC(C)CNCCC1=C(CN)N(CC)C(C)NC(C=CNC=N)[N+](C)=NC1', 'CC=NC(C)C12C=C1C2', 'CC1CCCC=CCC[O+](N)C1C', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCCNC(BN)C=CC=CC=C(C)CC', 'CCCC(C)C=CC1CNCOC1', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCN1', 'C=NC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1']."
            },
            {
                "from": "human",
                "value": "And generate multiple molecules achieving the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)C1=CC=NCC[N+](=CC=CNC(=CN)CCOC)CC1', 'CN=BN=NCBNC(CCN)C1C(C)NNN1NC', 'CCC(C)C=CCCN(C)[N+]1=C2CC2=C1', 'C=C1CC=CCC=NCNC[N+](=CCC)NCC1', 'C=CCC(CC(O)CC)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'CN(C)C1=C(N(C)NCC2CC2(C)C)CCC1', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NN2)CC(C)=N1', 'C=C[O+](C)C(OCCC)C(C)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'CCC(C=CNN(C)CC=C(C)N=NCN)CN', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)C1=CC=NCC[N+](=CC=CNC(=CN)CCOC)CC1', 'CN=BN=NCBNC(CCN)C1C(C)NNN1NC', 'CCC(C)C=CCCN(C)[N+]1=C2CC2=C1', 'C=C1CC=CCC=NCNC[N+](=CCC)NCC1', 'C=CCC(CC(O)CC)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'CN(C)C1=C(N(C)NCC2CC2(C)C)CCC1', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NN2)CC(C)=N1', 'C=C[O+](C)C(OCCC)C(C)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'CCC(C=CNN(C)CC=C(C)N=NCN)CN', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds optimized to match a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCCCCCC(C)N2CC(N)=C(C)N2CNCC2C(C)=CCC2CN(C)[O+](CC)NC1', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'CC=C(C)CCC(=CC)CCCCN=C1CCC2=C(CCF)C2C1', 'C=CC1C(CC)=CC(CCC(C)C(C)(CC)C(C)O)CCC1=CC', 'C=C(C)C12C(=C(COO)C1C)C2CCN(C)C1CC(CC=CCC)N1', 'C=C(CC=CC(CC)CN)CC=C(C)C=NCN', 'CC=C1CCC=CC1CC(C)C=CC(C)=CCCCC=CC(C)CCC', 'CCCCCC=C(CC=CN)CCCC(C)=C1C=C1C', 'CC(C)=CCCNC(CC(C)NCCN)C1CCC(C)C1', 'CCC(C)CCC1C=C(C2=CC2)CCC2C3C(C)CC13C1ON21'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCCCCCC(C)N2CC(N)=C(C)N2CNCC2C(C)=CCC2CN(C)[O+](CC)NC1', 'CCCNCCC=NC(C)=NN1CC(CC)C=C1C', 'CC=C(C)CCC(=CC)CCCCN=C1CCC2=C(CCF)C2C1', 'C=CC1C(CC)=CC(CCC(C)C(C)(CC)C(C)O)CCC1=CC', 'C=C(C)C12C(=C(COO)C1C)C2CCN(C)C1CC(CC=CCC)N1', 'C=C(CC=CC(CC)CN)CC=C(C)C=NCN', 'CC=C1CCC=CC1CC(C)C=CC(C)=CCCCC=CC(C)CCC', 'CCCCCC=C(CC=CN)CCCC(C)=C1C=C1C', 'CC(C)=CCCNC(CC(C)NCCN)C1CCC(C)C1', 'CCC(C)CCC1C=C(C2=CC2)CCC2C3C(C)CC13C1ON21']."
            },
            {
                "from": "human",
                "value": "And design molecules optimized for the same z-score for the cell line '905957'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'CCCCCC(C)=CCC=CCNC', 'C=C(C)CC(C)CCC(=C(C)C(CN)CCCC)C(C)CC(=C)C(C)C(CC)CC=C[n+]1ccco1', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'C=C(C=CCC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CC(=CC1C=C1)C=C1CCC(C)C1', 'C=CCCCCC=CCCCCCNNC1NC1C', 'C=C(C)NC(N)C(C=C(C)CC=CCC)C1CCC=C(C)CN1CC1N=C1NC=CC', 'CC=CC=CC(=CC)C(C)(C=CC(C)=N)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'CCCCCC(C)=CCC=CCNC', 'C=C(C)CC(C)CCC(=C(C)C(CN)CCCC)C(C)CC(=C)C(C)C(CC)CC=C[n+]1ccco1', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'C=C(C=CCC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CC(=CC1C=C1)C=C1CCC(C)C1', 'C=CCCCCC=CCCCCCNNC1NC1C', 'C=C(C)NC(N)C(C=C(C)CC=CCC)C1CCC=C(C)CN1CC1N=C1NC=CC', 'CC=CC=CC(=CC)C(C)(C=CC(C)=N)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide molecules with a z-score reaching -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCCNN=CC1CC2CC3CC(C)(C(C)=CN4CC5CC54)C3(C)CC=C3C(CC(C)CCC)NC12NN=CCC3C1C(C)C1CC', 'C=CCC(C)[O+]1CC(=CC)CC=C2C[O+](C)C2(C)CCC(=CC)C(CC)C1', 'C=C(O)C=C(C=CCC(C)(CCOCN)C(C)=C[O+]1CC(C=CC)=C1CN)C(=C)C', 'CCCCCC(C)(N)CCCC', 'C=CCC[O+](C)C(CCC)C1=[N+](CC=C)C(C=CC(C)N)=C1C=CC', 'C=CCC[O+](C)C(CCC)C1=C2CC=C[O+]3NC(C)C=CC2=C1C=C3C', 'CCC(=O)CCC(C)C=[O+]COC(C)C(CC=CC=C1C=CC1)CC(F)CC', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'CCC(CO)CCCC[O+](C)C', 'C=CNC(C)(N)C(CN)N(C)CN(O[O+](CC=CC=NBCC)OC)[N+](=C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCCNN=CC1CC2CC3CC(C)(C(C)=CN4CC5CC54)C3(C)CC=C3C(CC(C)CCC)NC12NN=CCC3C1C(C)C1CC', 'C=CCC(C)[O+]1CC(=CC)CC=C2C[O+](C)C2(C)CCC(=CC)C(CC)C1', 'C=C(O)C=C(C=CCC(C)(CCOCN)C(C)=C[O+]1CC(C=CC)=C1CN)C(=C)C', 'CCCCCC(C)(N)CCCC', 'C=CCC[O+](C)C(CCC)C1=[N+](CC=C)C(C=CC(C)N)=C1C=CC', 'C=CCC[O+](C)C(CCC)C1=C2CC=C[O+]3NC(C)C=CC2=C1C=C3C', 'CCC(=O)CCC(C)C=[O+]COC(C)C(CC=CC=C1C=CC1)CC(F)CC', 'C=CCC=CCCCCCC(=N)O[N+](C)=C(C=CC=CC)C=NCC', 'CCC(CO)CCCC[O+](C)C', 'C=CNC(C)(N)C(CN)N(C)CN(O[O+](CC=CC=NBCC)OC)[N+](=C)C']."
            },
            {
                "from": "human",
                "value": "And create compounds to align with the same z-score for '684055'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(CC1=NCC1(C)N)N(C=NC=N)CN', 'CC(BNBCNNC(B=BCCc1nn(C=NCCN)n1C)CN)=NCN', 'CCNOC(CC)CCCCCCN(CC)CC', 'CN(C)CB1C=BB(NN)C(CN)NN1', 'CC=C(C=NN(N=CN)NNB(C)N)C(N)NNC=NCCCC(=N)CN', 'CCC1C[O+]2CC3CC4=C(C=C1CC2C4)CC3C', 'CC=CCC(=CN1C=C2C=C(NCC3CC4[O+]=CC34)C(C)N1C2)NCC', 'CCC1C(C)CN1C', 'C=C1CC=C2NCCC(CCNCSCC(F)CN)CC(COCC(C)C)C=C1O2', 'CCN(N)BBN(N)CN=C1B(N)C(C)CC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(CC1=NCC1(C)N)N(C=NC=N)CN', 'CC(BNBCNNC(B=BCCc1nn(C=NCCN)n1C)CN)=NCN', 'CCNOC(CC)CCCCCCN(CC)CC', 'CN(C)CB1C=BB(NN)C(CN)NN1', 'CC=C(C=NN(N=CN)NNB(C)N)C(N)NNC=NCCCC(=N)CN', 'CCC1C[O+]2CC3CC4=C(C=C1CC2C4)CC3C', 'CC=CCC(=CN1C=C2C=C(NCC3CC4[O+]=CC34)C(C)N1C2)NCC', 'CCC1C(C)CN1C', 'C=C1CC=C2NCCC(CCNCSCC(F)CN)CC(COCC(C)C)C=C1O2', 'CCN(N)BBN(N)CN=C1B(N)C(C)CC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of molecules that have a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1NC=NC[N+](=C)CC(C)N1CC(C)C(C)(C)N', 'CCN=CC1=CCC(NC)=C(C=CCN=CN)C(C(C=NOC(N)CN)=CN)=CCC1', 'CCC=C1C2CC1[N+]2(C)C(C)CC(CC)(CC(C)CC)C(C)C=[O+]C', 'C=CCCCC(C)CCC(O)OCCC', 'C=C(CC)C(=CC)CCCCC', 'CC=C(C)C(O)C(C)CCC', 'C=C(C)CC1CC1(C(C)CCC(O)CC)C(C)C(C)CC(C)(O)OC', 'CC=CN=NCN(C)CNN', 'C1=CC2CC(=C1)C2', 'C=C(CC(=C)C1=CC(C)(NC)CCCC=C1)CC1(C)CN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1NC=NC[N+](=C)CC(C)N1CC(C)C(C)(C)N', 'CCN=CC1=CCC(NC)=C(C=CCN=CN)C(C(C=NOC(N)CN)=CN)=CCC1', 'CCC=C1C2CC1[N+]2(C)C(C)CC(CC)(CC(C)CC)C(C)C=[O+]C', 'C=CCCCC(C)CCC(O)OCCC', 'C=C(CC)C(=CC)CCCCC', 'CC=C(C)C(O)C(C)CCC', 'C=C(C)CC1CC1(C(C)CCC(O)CC)C(C)C(C)CC(C)(O)OC', 'CC=CN=NCN(C)CNN', 'C1=CC2CC(=C1)C2', 'C=C(CC(=C)C1=CC(C)(NC)CCCC=C1)CC1(C)CN1']."
            },
            {
                "from": "human",
                "value": "And develop a series of molecules matching the same z-score for '1659819'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)=[O+]CC(C)COO', 'C=C(CC(=C)C1=CC(C)(CC)CCCC=C1)CC1(C)CC1', 'C=C(C)OC(=CC)[O+]1OCC1(C)C=[O+]C=O', 'CC=CC(C)(C=C(C)C[N+]1=CC1=C(CC)CCCC)C(CCC)CCOC', 'CCC=CC=C(CCC1=CC(C)CC1)OCC', 'CCC1CCC1(C)CCF', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'C=CC1=C2CC(=CCC1)C(CO)=CCC2C', 'C=C(CC)C(=CC)CCCCN', 'C=CC(C(CC)CCC1C2=CC(C)C(C)C[O+](CC(CC)CCC2)C1CC)C(C)(CC=CCC)C1CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)=[O+]CC(C)COO', 'C=C(CC(=C)C1=CC(C)(CC)CCCC=C1)CC1(C)CC1', 'C=C(C)OC(=CC)[O+]1OCC1(C)C=[O+]C=O', 'CC=CC(C)(C=C(C)C[N+]1=CC1=C(CC)CCCC)C(CCC)CCOC', 'CCC=CC=C(CCC1=CC(C)CC1)OCC', 'CCC1CCC1(C)CCF', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'C=CC1=C2CC(=CCC1)C(CO)=CCC2C', 'C=C(CC)C(=CC)CCCCN', 'C=CC(C(CC)CCC1C2=CC(C)C(C)C[O+](CC(CC)CCC2)C1CC)C(C)(CC=CCC)C1CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules to achieve a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC=C1C=C(C2=C(C)C2)C1CC', 'CCC1CCC(OC)CCCC(CC)C(CC)C1', 'CCCNN(N=CC=NC=NON)NNCC1=[N+](C=CN)C1', 'C=C1CC=CNC2OC(=C(C)C(=C)F)C2C(=C)CCCCC1', 'CNC1C=C1CC1C=CN[N+](=N)CCCN1', 'CC=C1C(CCCCC(C)=[N+]2CCCCC2N)=C1NC', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'CCCC(C)=C1CN=[N+]2NC2=NCC(C)=CNC1', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'C=C(NCC=C(BCCN=NC)CCCNCN)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=C1C=C(C2=C(C)C2)C1CC', 'CCC1CCC(OC)CCCC(CC)C(CC)C1', 'CCCNN(N=CC=NC=NON)NNCC1=[N+](C=CN)C1', 'C=C1CC=CNC2OC(=C(C)C(=C)F)C2C(=C)CCCCC1', 'CNC1C=C1CC1C=CN[N+](=N)CCCN1', 'CC=C1C(CCCCC(C)=[N+]2CCCCC2N)=C1NC', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'CCCC(C)=C1CN=[N+]2NC2=NCC(C)=CNC1', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'C=C(NCC=C(BCCN=NC)CCCNCN)C(C)C']."
            },
            {
                "from": "human",
                "value": "And develop compounds to meet the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(C)(CN)CCC=C(C)C1', 'CC=CCC(C)C(C)NC(C)NC(CC)C(N)CC', 'CCCC=C(C)CC(CCC)N=Cc1ccn[nH]1', 'C=C(C=C(NC)NCC)COCCC(=CCC)CC(=C)CCC', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1', 'C=C1C=C(C(C)C)CNCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC', 'CCC(CC)CCCC[O+](C)C', 'CC1=NN(CCC(NF)=C(C)O)CCCCC2=C(C)C=C12', 'CCCC(C)CNC(N)C1(N)CC(CC)N1', 'CCC=CC1=C2C=CC(C)(OC2)C2C=CC(=CC2)CC=C1C=C(CCCC)C(CC)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(C)(CN)CCC=C(C)C1', 'CC=CCC(C)C(C)NC(C)NC(CC)C(N)CC', 'CCCC=C(C)CC(CCC)N=Cc1ccn[nH]1', 'C=C(C=C(NC)NCC)COCCC(=CCC)CC(=C)CCC', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1', 'C=C1C=C(C(C)C)CNCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC', 'CCC(CC)CCCC[O+](C)C', 'CC1=NN(CCC(NF)=C(C)O)CCCCC2=C(C)C=C12', 'CCCC(C)CNC(N)C1(N)CC(CC)N1', 'CCC=CC1=C2C=CC(C)(OC2)C2C=CC(=CC2)CC=C1C=C(CCCC)C(CC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of molecules with a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(CONC1(CC(C)(C)NC)C=CC1)CC(=C)O', 'CNCN=CCB(C=CC(C)=CN)CC1=CCC(C)NN(C)CC=CNN1', 'C=C1CN(C)C(C=CC)C1=NNCCC', 'CCC(NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C)N(CC(C)C)NC(CCCN)NC', 'CCC(C)C=NCC1C2=CC(C)(CCNC(C)CC1CC)CNN2C', 'C=NC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1', 'CCCC(C=NC)CCC(C)C1N(C(C)C)[O+]1CCC1CCCCC(CC)CCO1', 'C=CN=CC1=C[N+](C(C)NCC(CC)C(C)NC(C(=N)C=C)C(C)C=C)=CC(=CC)CC1', 'CC=CC(CNC)=C(CCC=C(N)C=C1CN1)C12C=CC3=CC(NCNC1C=C2)C3C', 'CCC1=CCC2CCC3=CC(=C(C)C2)CC(C3)N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(CONC1(CC(C)(C)NC)C=CC1)CC(=C)O', 'CNCN=CCB(C=CC(C)=CN)CC1=CCC(C)NN(C)CC=CNN1', 'C=C1CN(C)C(C=CC)C1=NNCCC', 'CCC(NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C)N(CC(C)C)NC(CCCN)NC', 'CCC(C)C=NCC1C2=CC(C)(CCNC(C)CC1CC)CNN2C', 'C=NC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1', 'CCCC(C=NC)CCC(C)C1N(C(C)C)[O+]1CCC1CCCCC(CC)CCO1', 'C=CN=CC1=C[N+](C(C)NCC(CC)C(C)NC(C(=N)C=C)C(C)C=C)=CC(=CC)CC1', 'CC=CC(CNC)=C(CCC=C(N)C=C1CN1)C12C=CC3=CC(NCNC1C=C2)C3C', 'CCC1=CCC2CCC3=CC(=C(C)C2)CC(C3)N1']."
            },
            {
                "from": "human",
                "value": "And please generate a series of molecules achieving the same z-score for '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(COOC1C2=C1C2=C1CO1)NC(C)CC=NC1CCC1', 'CC=CC(C=NC(=N)[N+]1=CC(N(C)N)NN[N+]1=CB=N)=CBCC', 'CC1N=CN2C=[O+]C2CCN1C', 'CCCC(C)(CC)CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'CC=CC(CC)N(CC=NCCCC[N+](C)(CC)CC1=CN1)CCC', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1', 'CCC1CCC1(C)C(C)(CC)C1CC(O)(C(C)C)C1', 'CCN=CC1C=CCN(CC(=CCC2CCC=NC2)CN)C1', 'C=C(C=CCC)CC12c3cc[o+]1c32', 'C=CC(C)C(C)(CC)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(COOC1C2=C1C2=C1CO1)NC(C)CC=NC1CCC1', 'CC=CC(C=NC(=N)[N+]1=CC(N(C)N)NN[N+]1=CB=N)=CBCC', 'CC1N=CN2C=[O+]C2CCN1C', 'CCCC(C)(CC)CCNCCC(CCN)=C(CNN)C(C)CCC(CC)=C(CC)CN', 'CC=CC(CC)N(CC=NCCCC[N+](C)(CC)CC1=CN1)CCC', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1', 'CCC1CCC1(C)C(C)(CC)C1CC(O)(C(C)C)C1', 'CCN=CC1C=CCN(CC(=CCC2CCC=NC2)CN)C1', 'C=C(C=CCC)CC12c3cc[o+]1c32', 'C=CC(C)C(C)(CC)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a collection of molecules with a z-score of -6.190392 in '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(CC)=C(C)CC1=C(C)CC1', 'CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(CC)(CN)CCC=C(C)C1', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'BC=[N+](CCNCCCN=C)C(=CN=C(C=CNC(=C)N)[N+](C)(C)CC=CCC)NCC', 'C=C(N)CCCC', 'C=CNN(C)N1NNC[N+](=NCCCCN)N1', 'CCCNCC(CCC)CCN1CCNC[N+]2=CC3=[N+]1C32', 'C=C(C)CN1C=CC(C)=CC(CC(C)NC=C(C)C)CCC=C1N', 'CC=[N+]1N=[N+](NCC)C1CN(C)N', 'CC=NN=CC1CC1(C)N=CB(CCC)CN=CC(C)=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(CC)=C(C)CC1=C(C)CC1', 'CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(CC)(CN)CCC=C(C)C1', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'BC=[N+](CCNCCCN=C)C(=CN=C(C=CNC(=C)N)[N+](C)(C)CC=CCC)NCC', 'C=C(N)CCCC', 'C=CNN(C)N1NNC[N+](=NCCCCN)N1', 'CCCNCC(CCC)CCN1CCNC[N+]2=CC3=[N+]1C32', 'C=C(C)CN1C=CC(C)=CC(CC(C)NC=C(C)C)CCC=C1N', 'CC=[N+]1N=[N+](NCC)C1CN(C)N', 'CC=NN=CC1CC1(C)N=CB(CCC)CN=CC(C)=N']."
            },
            {
                "from": "human",
                "value": "And generate various molecules achieving the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC=CC(CC)CN)CC=C(C)C(=CCN)CC1=CC1', 'COC(C)CC=C1CC(=CCN)N=CN1', 'CCNC1CNCC=C(C=CCNC)C=C2CC(N)=C2[O+]=N1', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'C=C(CC[O+](CC)C[N+](=C)C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'C=C(CC(C)CC=CCC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(CCC(N)CC(C)C)C=C1', 'CCCC1=NC(OCC=CCCO)C=C1', 'COOCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'C=C(C=CC(C)C=CCCC(C)C)[O+]=CCCC=CCCC', 'CC1=CC2CCCCC=C3NC3CCC(N)(C(C)(C)C)NC(=CCC(O)C=C1)C=C2C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC=CC(CC)CN)CC=C(C)C(=CCN)CC1=CC1', 'COC(C)CC=C1CC(=CCN)N=CN1', 'CCNC1CNCC=C(C=CCNC)C=C2CC(N)=C2[O+]=N1', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'C=C(CC[O+](CC)C[N+](=C)C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'C=C(CC(C)CC=CCC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(CCC(N)CC(C)C)C=C1', 'CCCC1=NC(OCC=CCCO)C=C1', 'COOCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'C=C(C=CC(C)C=CCCC(C)C)[O+]=CCCC=CCCC', 'CC1=CC2CCCCC=C3NC3CCC(N)(C(C)(C)C)NC(=CCC(O)C=C1)C=C2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)C[N+]1=[N+]2CCC(OC=NCC(N)=CCC)OC(=C(C)CC)CC1C2', 'C=CC=C(NCC=C1CC(F)NCC1=CN)C(C)(C)CCN=CNO', 'C=NNC=C(CCC)C(C)C(=CC=CC=CN)CC=CC=NC', 'CC(C)CC1N[O+]=CC1CC(C)F', 'CCC=CC1=C(C)CCOCN(CC=C(C)C)CC2=CN2CC1', 'CC=[N+](C)N(NCCCCN)NN1CC1C', 'C=C1CC(NCCC(C)(C)C=CNC=CCCC)=CC1CCC(=CC)NCNC', 'CC=NC(C)C12C=C1C2', 'CCCC1=CC=C(C)CC(C)NC(C=C[O+]=CC2=C(CN)C2CC)=CC1', 'CCCCC[N+]1=CC=C(C)C(C(C)=C(CC)N=CC(C)C=NN)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)C[N+]1=[N+]2CCC(OC=NCC(N)=CCC)OC(=C(C)CC)CC1C2', 'C=CC=C(NCC=C1CC(F)NCC1=CN)C(C)(C)CCN=CNO', 'C=NNC=C(CCC)C(C)C(=CC=CC=CN)CC=CC=NC', 'CC(C)CC1N[O+]=CC1CC(C)F', 'CCC=CC1=C(C)CCOCN(CC=C(C)C)CC2=CN2CC1', 'CC=[N+](C)N(NCCCCN)NN1CC1C', 'C=C1CC(NCCC(C)(C)C=CNC=CCCC)=CC1CCC(=CC)NCNC', 'CC=NC(C)C12C=C1C2', 'CCCC1=CC=C(C)CC(C)NC(C=C[O+]=CC2=C(CN)C2CC)=CC1', 'CCCCC[N+]1=CC=C(C)C(C(C)=C(CC)N=CC(C)C=NN)C1']."
            },
            {
                "from": "human",
                "value": "And produce a range of compounds achieving the same z-score for '1659819'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['COC(CC1CCC1C)C1C=CC1', 'B=CCCNN1C=CB=C(C)C(C)(N)C(=CC)CC(C)NC1CN', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=C(NC)NC(C)C=CC=CCCC', 'CC=CC=C(C)C(C)=CCC(C=CCC(C)C)N=CC', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'CCCCC(C)CCCC(C)C1C(C(CC)CC)C2CCCC3CC(C(C)CC)CC(=CCCC1(C)C)C(C)CC3C2', 'C=CN=NC(=CC)[N+](C)(C)NC1CCN1', 'C=CC=CC=CC(CCN)=C(N)C(CC)=[O+]C=N', 'CCCCCC=C(CC=CN)CNCC(C)=C1C=C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC(CC1CCC1C)C1C=CC1', 'B=CCCNN1C=CB=C(C)C(C)(N)C(=CC)CC(C)NC1CN', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=C(NC)NC(C)C=CC=CCCC', 'CC=CC=C(C)C(C)=CCC(C=CCC(C)C)N=CC', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'CCCCC(C)CCCC(C)C1C(C(CC)CC)C2CCCC3CC(C(C)CC)CC(=CCCC1(C)C)C(C)CC3C2', 'C=CN=NC(=CC)[N+](C)(C)NC1CCN1', 'C=CC=CC=CC(CCN)=C(N)C(CC)=[O+]C=N', 'CCCCCC=C(CC=CN)CNCC(C)=C1C=C1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate compounds to achieve a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CCC(C)C(=[O+]CC)C(=CC1=CC1)C1CC=CC=CCC1', 'CC1CC=C(CCCCNC2=NCC2)C1', 'C=CCCC(C=NC(OC)C(OC(CC)C1CCCON1COO)C(N)C=C)CC1CC1C', 'CCCC1C2COC=CC3=C(CCC(CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'CCC(C)C(N)=NNN=CC=CN', 'C=C(CCCC)C(C)C=COCC', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)CNN=CCN)=CC1CCC', 'C=CCC1=CN=C2C(C)C2C(=NN)CCC1C', 'C=C1CCC2=C(CC)C(C1)C1CC=CCC1=CC=C1C=CC(CNC2)C1', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CCC(C)C(=[O+]CC)C(=CC1=CC1)C1CC=CC=CCC1', 'CC1CC=C(CCCCNC2=NCC2)C1', 'C=CCCC(C=NC(OC)C(OC(CC)C1CCCON1COO)C(N)C=C)CC1CC1C', 'CCCC1C2COC=CC3=C(CCC(CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'CCC(C)C(N)=NNN=CC=CN', 'C=C(CCCC)C(C)C=COCC', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)CNN=CCN)=CC1CCC', 'C=CCC1=CN=C2C(C)C2C(=NN)CCC1C', 'C=C1CCC2=C(CC)C(C1)C1CC=CCC1=CC=C1C=CC(CNC2)C1', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1']."
            },
            {
                "from": "human",
                "value": "And create molecules to achieve the same z-score for the cell line '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CC2CC(CNC)C(C)CC(=CC)C(=[O+]C)CC(CCC(C)=O)CC(=CCC(CC)=C(C)CC)CCC(C)[O+]12', 'C=CC1C2=C(CCCC=CC(C)=C2C)CC(CCC)C1C', 'CNN(C(=N)N)C1C=CC=C(C=O)CC2=NCC3CC(=CC(=CO)CC2C)NC3C1', 'C=C(CCCCCCCC)C(C)(CC)CCNC', 'C=CC1=C(C)C=C(NNN=CCN)C(C=NCC=N)C1=C', 'CC=CC=C(NC)C(C=CCC)CCC', 'CNC1CN(C)NCCC(C)=Cn2cc(n2C)C1CB=BC(N)CN', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'CN=C(CCO)N(C)C(C)CN=NC1CCC1', 'C=[O+]C(N)N=CC=C1NC(C(C)CNC)CC=CNNC(C)C(C=[N+](N)C(=C)[N+](N)=CC)C1=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CC2CC(CNC)C(C)CC(=CC)C(=[O+]C)CC(CCC(C)=O)CC(=CCC(CC)=C(C)CC)CCC(C)[O+]12', 'C=CC1C2=C(CCCC=CC(C)=C2C)CC(CCC)C1C', 'CNN(C(=N)N)C1C=CC=C(C=O)CC2=NCC3CC(=CC(=CO)CC2C)NC3C1', 'C=C(CCCCCCCC)C(C)(CC)CCNC', 'C=CC1=C(C)C=C(NNN=CCN)C(C=NCC=N)C1=C', 'CC=CC=C(NC)C(C=CCC)CCC', 'CNC1CN(C)NCCC(C)=Cn2cc(n2C)C1CB=BC(N)CN', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'CN=C(CCO)N(C)C(C)CN=NC1CCC1', 'C=[O+]C(N)N=CC=C1NC(C(C)CNC)CC=CNNC(C)C(C=[N+](N)C(=C)[N+](N)=CC)C1=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules with the intended z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC1=CN=C2C(C(=NN)CCC1C)N2C', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2CC12CCCC', 'CC=C(CCCC)CC(=NC)C(=CC=N)CCNC', 'CC=CC(CF)C1CC=C(C=C(N)C2C(C)C2C(C)C)CNC1OC', 'CCC(NCC(=CN)NC=CN)(C1=CN(C)CC1)[N+](=CN)CC(=CNC)N=CN=N', 'CCC(N)CC=CCCC(CC)NN', 'CC=C1C2=CC(C2)C(N)=C(C)C=CC2=C3N4C=[N+]3C=C(C)C(C)=CC1C(C4)C2=O', 'CONCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'CB(C)CNCB=C(C=CN)CC1(C)CN(C)N1', 'C=C(N)NCC(C)=CN=CC=[N+]1C=[O+]C=CCNC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC1=CN=C2C(C(=NN)CCC1C)N2C', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2CC12CCCC', 'CC=C(CCCC)CC(=NC)C(=CC=N)CCNC', 'CC=CC(CF)C1CC=C(C=C(N)C2C(C)C2C(C)C)CNC1OC', 'CCC(NCC(=CN)NC=CN)(C1=CN(C)CC1)[N+](=CN)CC(=CNC)N=CN=N', 'CCC(N)CC=CCCC(CC)NN', 'CC=C1C2=CC(C2)C(N)=C(C)C=CC2=C3N4C=[N+]3C=C(C)C(C)=CC1C(C4)C2=O', 'CONCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'CB(C)CNCB=C(C=CN)CC1(C)CN(C)N1', 'C=C(N)NCC(C)=CN=CC=[N+]1C=[O+]C=CCNC1']."
            },
            {
                "from": "human",
                "value": "And generate a diverse set of molecules meeting the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1', 'CCCNCC=C(C=CC=C(CCC)CNCCC)CC(C)CCC', 'CCCCCNCN(CN)N=CN', 'C=NNCNC[O+](C)NCN=CC', 'C=C(C)C(COCCCC=NCC)N(C=CCNC)C(C)NNC1=C(C)CC(=C(C)C=N)CCNC1', 'CCCCCCCN(C=CCN)[O+]=C1CC1C=CCCN', 'CCCC=C(CCCC1(C)CCC=C(C)N(CC)CCC1)C1C=CC1', 'C=NC[N+](CC)=C(CC)NNC=CC1CC1', 'CC=C(CCC)C(=O)C=C(OOOCC)C1C=CC1C', 'C=N[N+](C)(C)CC(N)C=[N+]1NC(=N)C(C(=CCC)CC)=C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1', 'CCCNCC=C(C=CC=C(CCC)CNCCC)CC(C)CCC', 'CCCCCNCN(CN)N=CN', 'C=NNCNC[O+](C)NCN=CC', 'C=C(C)C(COCCCC=NCC)N(C=CCNC)C(C)NNC1=C(C)CC(=C(C)C=N)CCNC1', 'CCCCCCCN(C=CCN)[O+]=C1CC1C=CCCN', 'CCCC=C(CCCC1(C)CCC=C(C)N(CC)CCC1)C1C=CC1', 'C=NC[N+](CC)=C(CC)NNC=CC1CC1', 'CC=C(CCC)C(=O)C=C(OOOCC)C1C=CC1C', 'C=N[N+](C)(C)CC(N)C=[N+]1NC(=N)C(C(=CCC)CC)=C1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of molecules that have a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC(C)CCF)[N+](=C)CCCC', 'CCOCCCC=CC(C)=CC1OC(CC)C=C1C', 'C=CC1(C)CNC(N)C(=N)C(C=CC)C1', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)CC=C(C)CC', 'CNCC(N)C(C)(C)NC', 'C=CNCC(C=NC(OC)C(CN(CC)C1CCCON1COO)C(N)C=N)CC1CC1C', 'C=CC12CNN=C3CC=[N+](C=CCC3)C[N+](=N1)CN2', 'C=C(CC)C(CCCC)CC1CC2C=C(C)CC(=C2)NCC=CCC1O', 'CC(NCCN)C1=BC=NCCCCCCC=C(N)NC1', 'C[N+]1=CNCCC2=[N+](CC2)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC(C)CCF)[N+](=C)CCCC', 'CCOCCCC=CC(C)=CC1OC(CC)C=C1C', 'C=CC1(C)CNC(N)C(=N)C(C=CC)C1', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)CC=C(C)CC', 'CNCC(N)C(C)(C)NC', 'C=CNCC(C=NC(OC)C(CN(CC)C1CCCON1COO)C(N)C=N)CC1CC1C', 'C=CC12CNN=C3CC=[N+](C=CCC3)C[N+](=N1)CN2', 'C=C(CC)C(CCCC)CC1CC2C=C(C)CC(=C2)NCC=CCC1O', 'CC(NCCN)C1=BC=NCCCCCCC=C(N)NC1', 'C[N+]1=CNCCC2=[N+](CC2)CC1']."
            },
            {
                "from": "human",
                "value": "And design molecules optimized for the same z-score for the cell line '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(N)C1C=C(N)C=CCC1', 'CC1=C(C(C)NC(N)CNCO[N+]2(C)CC2=N)C1', 'C=C(O)C1=CCC1', 'C=NC[O+]1C=CCN(C)CCCN(CCNC)CC(C(C)=NC)C(C)=NCC1', 'C=CC1COC2CCC(C(=O)C([O+](C)CC(=O)O)CC(C=C(C)S)C(OCCC)CC(C(C)CC(C)C)CC=N1)C2C', 'CC(C)=CC1=C(C)CC(C(C)CNCN)CN=CN(NC2C=CC2)C(=CCN)CC1', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC', 'C=C1C=CN(C)C2C(C)CNCCC2(CCCC)C2C(CC(=C(CCC)CCO)C1)C2(C=CCC(=CC)C(C)N=C(CC)C(=CC)C(C)=COC)CCC=CC', 'CCCCCNC=CC[N+]1=C2C=C(CCCCC=[N+]1C)CC1(C=CC(C)C1NC)C2', 'C=NCC(C)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(N)C1C=C(N)C=CCC1', 'CC1=C(C(C)NC(N)CNCO[N+]2(C)CC2=N)C1', 'C=C(O)C1=CCC1', 'C=NC[O+]1C=CCN(C)CCCN(CCNC)CC(C(C)=NC)C(C)=NCC1', 'C=CC1COC2CCC(C(=O)C([O+](C)CC(=O)O)CC(C=C(C)S)C(OCCC)CC(C(C)CC(C)C)CC=N1)C2C', 'CC(C)=CC1=C(C)CC(C(C)CNCN)CN=CN(NC2C=CC2)C(=CCN)CC1', 'CCCCC(OCCO)C(N)C=C(C=C(N)CC=O)CC', 'C=C1C=CN(C)C2C(C)CNCCC2(CCCC)C2C(CC(=C(CCC)CCO)C1)C2(C=CCC(=CC)C(C)N=C(CC)C(=CC)C(C)=COC)CCC=CC', 'CCCCCNC=CC[N+]1=C2C=C(CCCCC=[N+]1C)CC1(C=CC(C)C1NC)C2', 'C=NCC(C)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=CCOC1=[O+]C=C(C(C)CC=CC)C(CCCCCF)=CCOCCC=C1', 'C=CC=C(C)C1CC=CN1C(N)=C(C=CN=CCC(=CN)NC)CC', 'C=CN(C(C)C)C(CCC=COC)CNNC(N)O', 'C=CNCC(C=NC(OC)C(CN(CC)C1CCCON1COO)C(N)C=N)CC1CC1C', 'CC1C=CC=CC=CCC=CCCC1', 'C=C(CCC)C(CC)=C1C=C(O)CCC1', 'CC(CCC1=CC=C1)C(N)C=CN(C)C', 'NC1BB=CBB1', 'C=CC(=C)C(C)CC=CCC(=N)CC=C1CO1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCO1', 'CC=CCOC1=[O+]C=C(C(C)CC=CC)C(CCCCCF)=CCOCCC=C1', 'C=CC=C(C)C1CC=CN1C(N)=C(C=CN=CCC(=CN)NC)CC', 'C=CN(C(C)C)C(CCC=COC)CNNC(N)O', 'C=CNCC(C=NC(OC)C(CN(CC)C1CCCON1COO)C(N)C=N)CC1CC1C', 'CC1C=CC=CC=CCC=CCCC1', 'C=C(CCC)C(CC)=C1C=C(O)CCC1', 'CC(CCC1=CC=C1)C(N)C=CN(C)C', 'NC1BB=CBB1', 'C=CC(=C)C(C)CC=CCC(=N)CC=C1CO1']."
            },
            {
                "from": "human",
                "value": "And design a series of molecules with the same z-score specific to '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCC=C1CC(=NNC)CCCCC(CCC)C(C)C1', 'C=C(C1CN=N1)[N+]1=CC(=NN(N)C(CB(C)C)NCNNN)C1', 'CCN(C)CCCCN(CO)CC1C=CC1', 'C=C1CCC(=CC(C)C)N=CC(C=CC)=NCCCC=CC=NC1', 'C=C=CC=CC(C)C=CCCC(CCCCCC)=C(CCCC(C)=CCC)C(C)(C)C=O', 'CCC(F)NCC(C)CC1CCCN1', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=CC[N+](C=COC=CC(C)C)=[O+]C(N=C(C)OC)=C(C[O+]=CC)C(CC)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCC=C1CC(=NNC)CCCCC(CCC)C(C)C1', 'C=C(C1CN=N1)[N+]1=CC(=NN(N)C(CB(C)C)NCNNN)C1', 'CCN(C)CCCCN(CO)CC1C=CC1', 'C=C1CCC(=CC(C)C)N=CC(C=CC)=NCCCC=CC=NC1', 'C=C=CC=CC(C)C=CCCC(CCCCCC)=C(CCCC(C)=CCC)C(C)(C)C=O', 'CCC(F)NCC(C)CC1CCCN1', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=CC[N+](C=COC=CC(C)C)=[O+]C(N=C(C)OC)=C(C[O+]=CC)C(CC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce compounds targeting a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC=CC=NC(CC)=NCOCNN=C(C)CC', 'CC(C)C1(C)CC=CN1', 'NNBNB1N=C2C=[N+](C2)SCCN1', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=CCC(C)(O)CC', 'CC=C(COOC1C2=C1C2=C1CO1)OC(C)CC=NC1CCC1', 'CCC1CNC1(C)CC', 'CCC=C1CCCC(=CNN)CC=CC[O+]=CC(O)CC1', 'CCC=CC(C)C(=CC(C)NC)C1C[O+]1C', 'C=C(N)CC(CC)(COC)N=CC(C)N(O)C(C)=CCNC=CC1CC=CC(=C)C1', 'CCNNN1CCCC=C(NC)C=C(C)NC(CC2CC(C)C(C)(C=N)C2)C2=C(C2)C1CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC=CC=NC(CC)=NCOCNN=C(C)CC', 'CC(C)C1(C)CC=CN1', 'NNBNB1N=C2C=[N+](C2)SCCN1', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=CCC(C)(O)CC', 'CC=C(COOC1C2=C1C2=C1CO1)OC(C)CC=NC1CCC1', 'CCC1CNC1(C)CC', 'CCC=C1CCCC(=CNN)CC=CC[O+]=CC(O)CC1', 'CCC=CC(C)C(=CC(C)NC)C1C[O+]1C', 'C=C(N)CC(CC)(COC)N=CC(C)N(O)C(C)=CCNC=CC1CC=CC(=C)C1', 'CCNNN1CCCC=C(NC)C=C(C)NC(CC2CC(C)C(C)(C=N)C2)C2=C(C2)C1CN']."
            },
            {
                "from": "human",
                "value": "And produce a series of molecules matching the same z-score for '722045'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCB1CNB2CB(CNC)C2NN1', 'C=C(C)C=CCC1(C)CCN(OC)C2=C(CCCC)CC3=NC1C(C)C(CCC)C3CC=CC(C)=C(C)C1(CC=CC(C)C2C(C(C)C)C(C)N)CCC1', 'CC=CCC(C)C(C)CC(C)CC(CC)C(N)CC', 'C=C1C=CC2C=[N+](C=CC=CC=CC=CC)C(C)CC=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1', 'CC=NC=CC(=C[O+](O)[OH+]C)CCC', 'CC=NC=C[N+](=C[O+](O)[OH+]C)CCC', 'CC=C(NCCC)N(NC(C)C)C1CC=C1CN', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)NC=C(C)CC', 'C=CC[O+]=CC1C(=C)C(C=C)=C(C)C=C1NNN=CCN', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=NC1CC1(C)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCB1CNB2CB(CNC)C2NN1', 'C=C(C)C=CCC1(C)CCN(OC)C2=C(CCCC)CC3=NC1C(C)C(CCC)C3CC=CC(C)=C(C)C1(CC=CC(C)C2C(C(C)C)C(C)N)CCC1', 'CC=CCC(C)C(C)CC(C)CC(CC)C(N)CC', 'C=C1C=CC2C=[N+](C=CC=CC=CC=CC)C(C)CC=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1', 'CC=NC=CC(=C[O+](O)[OH+]C)CCC', 'CC=NC=C[N+](=C[O+](O)[OH+]C)CCC', 'CC=C(NCCC)N(NC(C)C)C1CC=C1CN', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)NC=C(C)CC', 'C=CC[O+]=CC1C(=C)C(C=C)=C(C)C=C1NNN=CCN', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=NC1CC1(C)OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules optimized for a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(NCCC)C(C)(C=N)C1NO1', 'CN1CCC2C=CN2C=NC1N', 'CN1CC2BCNCNNC2N1', 'C=C1C=C2C=C(CC=CC(CC=CCCC(Cl)CCC)CCN=C(C)CC1)CC21C=CCCCC=C1', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CCC(C)C(N)CC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=NCCCC1CC[O+]1C(C)CC=[O+]CC', 'C=C(CNNCC)C(C)CC(N)CC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CC(=COCCC=O)CC=CCCCC1(C)CCOC=CCC(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(NCCC)C(C)(C=N)C1NO1', 'CN1CCC2C=CN2C=NC1N', 'CN1CC2BCNCNNC2N1', 'C=C1C=C2C=C(CC=CC(CC=CCCC(Cl)CCC)CCN=C(C)CC1)CC21C=CCCCC=C1', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CCC(C)C(N)CC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=NCCCC1CC[O+]1C(C)CC=[O+]CC', 'C=C(CNNCC)C(C)CC(N)CC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CC(=COCCC=O)CC=CCCCC1(C)CCOC=CCC(C)C1']."
            },
            {
                "from": "human",
                "value": "And construct molecules with the same z-score for '1240166'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)C(C)C=CC(=CCC=CC(=CC(=O)CC(C)F)CCC)N(C)C(C)CCO', 'CC=C(C)C(O)C(C)CCC', 'CC=CCC1(CC)C(C)C12CC=C(C)CC1=C2C1', 'N=C(CN)NCNC(N)NC1N(N)CCC=C(BN)CNNN1N', 'CCNC1=NC(OCC=CCCO)C=C1', 'C=[N+](CCCC=C(C1CCCC(C)C1)[O+]1C=C(CO)CNCCCCC1)CCCCC', 'CCCC=C(C)CCCC=CC(C)C', 'C=C1C(=C(N)CC(C)CC=NC)CC(CC)CC(C(C)C)CCC1C(C)=CC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CC=CN=C(C=CC)C1=C(N)C(CCN=CN)C=C(CC=NNCC)C[N+](=CC)CCC1=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)C(C)C=CC(=CCC=CC(=CC(=O)CC(C)F)CCC)N(C)C(C)CCO', 'CC=C(C)C(O)C(C)CCC', 'CC=CCC1(CC)C(C)C12CC=C(C)CC1=C2C1', 'N=C(CN)NCNC(N)NC1N(N)CCC=C(BN)CNNN1N', 'CCNC1=NC(OCC=CCCO)C=C1', 'C=[N+](CCCC=C(C1CCCC(C)C1)[O+]1C=C(CO)CNCCCCC1)CCCCC', 'CCCC=C(C)CCCC=CC(C)C', 'C=C1C(=C(N)CC(C)CC=NC)CC(CC)CC(C(C)C)CCC1C(C)=CC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CC=CN=C(C=CC)C1=C(N)C(CCN=CN)C=C(CC=NNCC)C[N+](=CC)CCC1=N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a variety of molecules to achieve a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+]1C=CC(C)C(C)C2C1CC2(C)NCNCC', 'CCCC(C)C1C(C(C)CCC(C)=C(C)C)CC(C)(CC)C2CC(CC=CC1(C)CCC)C1CCC12C', 'CNC1C=CC(=C2CC=CCC(C)CC2)C(C)C1', 'C=C(COC=CC(C)NNC=CC)COCC(=CC)CC(=C)CC', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)[O+]1C=C32', 'CCCCCOC=C1C=C1C', 'C=CNN1NNNB(N)CCNNC1CCC', 'C=C(CC(C)CC=CNC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(NCC(N)CC(C)C)C=C1', 'CC=C(CC=C(CC=CCC)OONCC)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+]1C=CC(C)C(C)C2C1CC2(C)NCNCC', 'CCCC(C)C1C(C(C)CCC(C)=C(C)C)CC(C)(CC)C2CC(CC=CC1(C)CCC)C1CCC12C', 'CNC1C=CC(=C2CC=CCC(C)CC2)C(C)C1', 'C=C(COC=CC(C)NNC=CC)COCC(=CC)CC(=C)CC', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)[O+]1C=C32', 'CCCCCOC=C1C=C1C', 'C=CNN1NNNB(N)CCNNC1CCC', 'C=C(CC(C)CC=CNC)[O+](C)CC1(CC(C)(O)C=C[O+]=CC)C=CC(NCC(N)CC(C)C)C=C1', 'CC=C(CC=C(CC=CCC)OONCC)CCC']."
            },
            {
                "from": "human",
                "value": "And generate various molecules achieving the same z-score for '1331032'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CCC(C)(CCC(C(C)=NC=O)C(C)CO)CC1(C)OC=C1C', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'C=C(O)CCOC1CC=C(C)C2CCC12', 'C=C(N)CC1CCCN2C(C)=C(C(=C)B(C=CCNC)C3C4CN3C4)NC3(C=C3CN)NN12', 'CC=C[N+]1=C(CNC(CC)CCCCCC2=CCCC2CCC=CNNC)C1', 'C=CC(CN)(CN=CCC(N)=C1CCC(C=CC)N(CN(C)CCCC(CC)NC(C)(N)C(C)C)C(C)C(C)C1)NC=CC', 'CC=CC=CCCCC(CCC)C1C(C)CCC1CC', 'CC=CCCCC12CC13CC31C3C(C)C3C(C)(C3(CC4(C)C=C4)CC(C)=C(C)C(C)CC(C)(C(CC)CCC=CCC(CC4=CC4)C(C)(C)O)C3)C=C3C(=CCC21)CCC(O)C3=CC', 'CCCCC(C)CC=CC1=CC[N+]1=CF'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CCC(C)(CCC(C(C)=NC=O)C(C)CO)CC1(C)OC=C1C', 'C=C1C=C(C2=C(C(C)=CC3=C(CC)C4=CCNC43)C2)C1', 'C=C(O)CCOC1CC=C(C)C2CCC12', 'C=C(N)CC1CCCN2C(C)=C(C(=C)B(C=CCNC)C3C4CN3C4)NC3(C=C3CN)NN12', 'CC=C[N+]1=C(CNC(CC)CCCCCC2=CCCC2CCC=CNNC)C1', 'C=CC(CN)(CN=CCC(N)=C1CCC(C=CC)N(CN(C)CCCC(CC)NC(C)(N)C(C)C)C(C)C(C)C1)NC=CC', 'CC=CC=CCCCC(CCC)C1C(C)CCC1CC', 'CC=CCCCC12CC13CC31C3C(C)C3C(C)(C3(CC4(C)C=C4)CC(C)=C(C)C(C)CC(C)(C(CC)CCC=CCC(CC4=CC4)C(C)(C)O)C3)C=C3C(=CCC21)CCC(O)C3=CC', 'CCCCC(C)CC=CC1=CC[N+]1=CF']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various compounds for an intended z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['BC(N)C(=NCB(N)CCNC)[N+](=CN)CC(C)N=C(C)C', 'CCC=CC=CCCCCCN(CC=C1NCC1CC)CCN', 'CCN=CC1=CCC(CC)=C(C=CCC=CN)C(C(C=COC(N)CN)=CN)=CCC1', 'BC(N)CBC', 'CCC(C)=CC=CC=CC1COC(CC)C1', 'C=C(C)CC(C)C=CC(CCC)CC(C1CCC1)C1CC1C(CCC)CCCS', 'COC1=CCCCCC2C3=C2C1C(C)CC(C)C3', 'C=CC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=[O+]1', 'CCN1CN(NC)CCC1N1CC(C)CN1', 'CCCC1=CC(COCN)C(CC(C)OC(F)CCC)=CC(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BC(N)C(=NCB(N)CCNC)[N+](=CN)CC(C)N=C(C)C', 'CCC=CC=CCCCCCN(CC=C1NCC1CC)CCN', 'CCN=CC1=CCC(CC)=C(C=CCC=CN)C(C(C=COC(N)CN)=CN)=CCC1', 'BC(N)CBC', 'CCC(C)=CC=CC=CC1COC(CC)C1', 'C=C(C)CC(C)C=CC(CCC)CC(C1CCC1)C1CC1C(CCC)CCCS', 'COC1=CCCCCC2C3=C2C1C(C)CC(C)C3', 'C=CC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=[O+]1', 'CCN1CN(NC)CCC1N1CC(C)CN1', 'CCCC1=CC(COCN)C(CC(C)OC(F)CCC)=CC(C)C1']."
            },
            {
                "from": "human",
                "value": "And please produce compounds targeting the same z-score for '1659819'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'CC(C)=NCC1(C)CC1(C)C', 'C=CC(=C)CCC(C)CC=C[O+](C)CC(C)=C1C(=C)NC(C)=C(C)CC=C1CC(C)C(=C)C=CC', 'CC=NC1C=C(C)C(C)=CC2(CC3[O+]=C[O+]32)C(=CCC=CC=CCCC)CC1', 'C=CCC=CCCCCCC(=N)C[N+](C)=C(C=CC=CC)C=NCC', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'CCCC1CNNC(C)=NC(CCN)CCC(O)C1', 'C=[O+]C=C(C)C1=CCC(C(CCC)C(C)F)C2CC(CO)=C(CC3=C[O+](C(=C)C)C31)C2C', 'CN(N)NN=CNC1C=NC(C=N)NC(=NCN)C1', 'CCC=CC1=C(C(C)CC)C=CCCC(CN)(CN)CCC=C(N)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C(CCC=COC(C)O)CC1C=CC(=CN)CC(=CCCC)C1', 'CC(C)=NCC1(C)CC1(C)C', 'C=CC(=C)CCC(C)CC=C[O+](C)CC(C)=C1C(=C)NC(C)=C(C)CC=C1CC(C)C(=C)C=CC', 'CC=NC1C=C(C)C(C)=CC2(CC3[O+]=C[O+]32)C(=CCC=CC=CCCC)CC1', 'C=CCC=CCCCCCC(=N)C[N+](C)=C(C=CC=CC)C=NCC', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'CCCC1CNNC(C)=NC(CCN)CCC(O)C1', 'C=[O+]C=C(C)C1=CCC(C(CCC)C(C)F)C2CC(CO)=C(CC3=C[O+](C(=C)C)C31)C2C', 'CN(N)NN=CNC1C=NC(C=N)NC(=NCN)C1', 'CCC=CC1=C(C(C)CC)C=CCCC(CN)(CN)CCC=C(N)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules with the intended z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C(=CC)CC(=CC=C1CC1)C(C)CC)[N+]1(CCC(C)CC)CN1', 'C=C(C)C(C=NN=CC)C(C)C', 'CCCC(C)(C=CCC=CN=CC(C)OC)NC(C)C1CCC1', 'CCC=C(N)C(C)(N)CCN=CN', 'C=NC1c2ccc(cc2C)C=CC1(C)CC', 'CC=[O+]C(C=CCC)=CCC(=CN(C)C)C1=CC1', 'CNC=CC=CCCOC=CC(C)CC(C)CC(NF)NNCF', 'CCCCC(CC)(CC)CCC', 'CC1=NC2(C)C=CNC(CC#C1)C13C=C(N4C(C)N4C)CC1N=CC(C)C3CC2', 'CC=C(C)C1=CCC2=C(C2)OC=C1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C(=CC)CC(=CC=C1CC1)C(C)CC)[N+]1(CCC(C)CC)CN1', 'C=C(C)C(C=NN=CC)C(C)C', 'CCCC(C)(C=CCC=CN=CC(C)OC)NC(C)C1CCC1', 'CCC=C(N)C(C)(N)CCN=CN', 'C=NC1c2ccc(cc2C)C=CC1(C)CC', 'CC=[O+]C(C=CCC)=CCC(=CN(C)C)C1=CC1', 'CNC=CC=CCCOC=CC(C)CC(C)CC(NF)NNCF', 'CCCCC(CC)(CC)CCC', 'CC1=NC2(C)C=CNC(CC#C1)C13C=C(N4C(C)N4C)CC1N=CC(C)C3CC2', 'CC=C(C)C1=CCC2=C(C2)OC=C1O']."
            },
            {
                "from": "human",
                "value": "And design molecules that align with the same z-score for the cell line '684059'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'BC(C)C=C(N)CCC', 'CC=CCC1(C)CC(C(=CC=NC=CC)NC)=CC2=C1C1C(C)C1(C)CC(C)=C(C)C=C2', 'C=CC(=C[N+](=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'CCNCC(N)C(CC=C=CCCN)=C1C=C(C(C)=CN)C(C)CCC1', 'C=C1C=C(N2C=[N+]2C)NCCNC2CNC(=CN2)CC(=CCC)CCOC1', 'CCC(C)=CC=CC=CC1COC(CC)C1', 'CC=CC(=CN=CNCC)CCC(C)C', 'C=CCCC(O)CNCCC(=C)C=C', 'C=C(C)CCC(C=CCOC)C(C)NCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'BC(C)C=C(N)CCC', 'CC=CCC1(C)CC(C(=CC=NC=CC)NC)=CC2=C1C1C(C)C1(C)CC(C)=C(C)C=C2', 'C=CC(=C[N+](=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'CCNCC(N)C(CC=C=CCCN)=C1C=C(C(C)=CN)C(C)CCC1', 'C=C1C=C(N2C=[N+]2C)NCCNC2CNC(=CN2)CC(=CCC)CCOC1', 'CCC(C)=CC=CC=CC1COC(CC)C1', 'CC=CC(=CN=CNCC)CCC(C)C', 'C=CCCC(O)CNCCC(=C)C=C', 'C=C(C)CCC(C=CCOC)C(C)NCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a series of molecules matching a z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CCC(C)CC=CCC=O)CC(N)CC(C)C', 'CB1BBN(N)CC=CC(=BCNB=C(C)C=CO)NCNC1', 'C=CCC1C(CCC)CC(CCC)C1O', 'CCC=CC(C)=NN=NC(CCC)N=[N+]1N(C)N1N', 'B=CNC1(C(=CC(N)NCC(=N)NC)CN)C=NC1', 'B=CNC(=NB(N)NBC(C)N(N)CN=CN)[N+]1=CN1', 'CCCCC(C)=CC=CCCC(=CNC)CF', 'CCCC(N)CB(N)CNCCNC', 'C=CC1=CCNCNCC(NC)N(C)CC(C)C(C)CC1', 'B[N+]1(C)CC=CC(N)NC2(C)N=CB3C=C4BB(N)N(C)C(N)N1C=NC2C4=NC3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CCC(C)CC=CCC=O)CC(N)CC(C)C', 'CB1BBN(N)CC=CC(=BCNB=C(C)C=CO)NCNC1', 'C=CCC1C(CCC)CC(CCC)C1O', 'CCC=CC(C)=NN=NC(CCC)N=[N+]1N(C)N1N', 'B=CNC1(C(=CC(N)NCC(=N)NC)CN)C=NC1', 'B=CNC(=NB(N)NBC(C)N(N)CN=CN)[N+]1=CN1', 'CCCCC(C)=CC=CCCC(=CNC)CF', 'CCCC(N)CB(N)CNCCNC', 'C=CC1=CCNCNCC(NC)N(C)CC(C)C(C)CC1', 'B[N+]1(C)CC=CC(N)NC2(C)N=CB3C=C4BB(N)N(C)C(N)N1C=NC2C4=NC3']."
            },
            {
                "from": "human",
                "value": "And generate a diverse set of molecules meeting the same z-score for '905957'. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC=C1C=C(C2=C(C)C2)C1CC', 'CCCC=CC=CC(O)CCC(C)CC=C1CCC1CC1C=C(CC)CC2CC2(C)C1=NCCN', 'C=CCC(=C(C=C)CC)C(C)(C)C=C', 'CCC(CNCN)C1=CC(=C(C)CNN)N=CC(N)CN1', 'CC1=CCCC2CN1C21CCCC1', 'CC=[O+]CC=C(C)CC=C(C)C1C=CCC(C)=C(C)C1=CCCCCNCC', 'CC(=CCN)CC1CC2C3CC[O+]3N12', 'CC(C=CC1=C=NCCC(C)CCCC1)CCC=C1C=CCCN1CN', 'CC=CC=C1CC(C)C(CC)CC2C3=C(C=CC3)C2NCC1C', 'CC=CCONC(=N)C(C)NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=C1C=C(C2=C(C)C2)C1CC', 'CCCC=CC=CC(O)CCC(C)CC=C1CCC1CC1C=C(CC)CC2CC2(C)C1=NCCN', 'C=CCC(=C(C=C)CC)C(C)(C)C=C', 'CCC(CNCN)C1=CC(=C(C)CNN)N=CC(N)CN1', 'CC1=CCCC2CN1C21CCCC1', 'CC=[O+]CC=C(C)CC=C(C)C1C=CCC(C)=C(C)C1=CCCCCNCC', 'CC(=CCN)CC1CC2C3CC[O+]3N12', 'CC(C=CC1=C=NCCC(C)CCCC1)CCC=C1C=CCCN1CN', 'CC=CC=C1CC(C)C(CC)CC2C3=C(C=CC3)C2NCC1C', 'CC=CCONC(=N)C(C)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules that meet a z-score of -4.054651 in the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=C(C)C(C)=CCC(C=CCC(C)C)N=CC', 'CCC(=CCCCOC)CC=CCC(C)C', 'C=[N+]1C=CC(C)C(C)C2C3(NCNCC)CC21C3', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'CC=[N+]1C2C(C)C21', 'CC=[N+](CC(C)C=CCCCN)C(N)=CNCC1=C2C=C[N+](=C(O)C(CN)=C2)C1C', 'CCCC=CC(N)NOC(C)C', 'CCCC(C)(C)C(C(F)CC)C1C=CC(C)CCC([O+](C)C2=CCO2)C1OC(=[O+]CNC)C1=[O+]C1', 'C=C(CCC(C)(C)COCCC)CC12N=C1C2(C)CCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=C(C)C(C)=CCC(C=CCC(C)C)N=CC', 'CCC(=CCCCOC)CC=CCC(C)C', 'C=[N+]1C=CC(C)C(C)C2C3(NCNCC)CC21C3', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'CCC1CC=CC=C2CCCCCCC(O)[O+]3CCCC1=C2C3', 'CC=[N+]1C2C(C)C21', 'CC=[N+](CC(C)C=CCCCN)C(N)=CNCC1=C2C=C[N+](=C(O)C(CN)=C2)C1C', 'CCCC=CC(N)NOC(C)C', 'CCCC(C)(C)C(C(F)CC)C1C=CC(C)CCC([O+](C)C2=CCO2)C1OC(=[O+]CNC)C1=[O+]C1', 'C=C(CCC(C)(C)COCCC)CC12N=C1C2(C)CCCC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC12CC[N+](C)(C)CONC=C(C(=C)N)C(C)=CC=CCCC(CC(=CC)CC)N1C[O+](CC)N2', 'C=C(N)C1=CNNC[N+](C)(N)CCC(N)(N(N)N)N(C)C(CCCC)CCC=CC=C1C', 'C=C1CNCC=CCC(C(C)=CC)C2=CC1C2(CC=CC)C(C(=CCC)CC)C(C)C1=CCC1', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'CCCC1=CCC(CC)C2(CC)CC(CCC[O+](CC(C)(CC)CC3CC3(C)N)C(CCCCC(CCC(C)CN)CC(C)N)N(C)C)C2=C(O)C(C)OCCC1', 'C=C(C=CCC)CC12c3nc[o+]1c32', 'C=C(OCC)C(=C)C(C)CCC1=C(C)C(C(C)=C(C)C)C(C)C(C[O+](C)CC)CCCCCC1', 'C=C(NCC(C)NN1C2CC(C)(CC)C([N+](=NC)NC(CC)NC)=[N+]21)C1(N)CC1', 'C=CNC(=CCNCC)C[N+](=N)O', 'CCCC=CC(N)NOC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC12CC[N+](C)(C)CONC=C(C(=C)N)C(C)=CC=CCCC(CC(=CC)CC)N1C[O+](CC)N2', 'C=C(N)C1=CNNC[N+](C)(N)CCC(N)(N(N)N)N(C)C(CCCC)CCC=CC=C1C', 'C=C1CNCC=CCC(C(C)=CC)C2=CC1C2(CC=CC)C(C(=CCC)CC)C(C)C1=CCC1', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'CCCC1=CCC(CC)C2(CC)CC(CCC[O+](CC(C)(CC)CC3CC3(C)N)C(CCCCC(CCC(C)CN)CC(C)N)N(C)C)C2=C(O)C(C)OCCC1', 'C=C(C=CCC)CC12c3nc[o+]1c32', 'C=C(OCC)C(=C)C(C)CCC1=C(C)C(C(C)=C(C)C)C(C)C(C[O+](C)CC)CCCCCC1', 'C=C(NCC(C)NN1C2CC(C)(CC)C([N+](=NC)NC(CC)NC)=[N+]21)C1(N)CC1', 'C=CNC(=CCNCC)C[N+](=N)O', 'CCCC=CC(N)NOC(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCNCC1=CC=C(C=C(C)C2CCCC(CCCC)C2)COC(OC)=CC1', 'CBCCc1cbn(CC)n1', 'C=C1C2CCC(C(=CCN)CCC)N(C)CNC(N)NCCC([N+](=C)NNC)C1(C)C2', 'C=CC1(CCCC=CC=CC)CC=C=CCC(OCC(C)=CC(=C)CCC(C)=O)C(C)C(NC)C1', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NN2)N=C(C)N1', 'CC(=CCC=CCNN)CCC(=N)CN', 'C=CCC1=CCC(C[O+]2C(C)C(C)C2NC=CCCCCC)CC=C1C(=C)NC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO', 'C=CC(=C)CCC(C)CC=C[O+](C)C[N+](C)=C1C(=C)NC(C)=C(C)CC=C1CC(C)C(=C)C=NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCNCC1=CC=C(C=C(C)C2CCCC(CCCC)C2)COC(OC)=CC1', 'CBCCc1cbn(CC)n1', 'C=C1C2CCC(C(=CCN)CCC)N(C)CNC(N)NCCC([N+](=C)NNC)C1(C)C2', 'C=CC1(CCCC=CC=CC)CC=C=CCC(OCC(C)=CC(=C)CCC(C)=O)C(C)C(NC)C1', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NN2)N=C(C)N1', 'CC(=CCC=CCNN)CCC(=N)CN', 'C=CCC1=CCC(C[O+]2C(C)C(C)C2NC=CCCCCC)CC=C1C(=C)NC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO', 'C=CC(=C)CCC(C)CC=C[O+](C)C[N+](C)=C1C(=C)NC(C)=C(C)CC=C1CC(C)C(=C)C=NC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['BCC1=CNN(CC)N(CC)CCCCNC(CCC(C(C)C)C(N)C(C(C)CC(C)C)C(C)(C)NC)=N1', 'CCOCC(C)NC(C)C(C)CC(C)C', 'C=CC=C=CCC1CCCNCCC1', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CCCC1C(NCCC2=CC=CC=CCNC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)CCC=C1', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCC=NC(C)C(O)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCC1=CNN(CC)N(CC)CCCCNC(CCC(C(C)C)C(N)C(C(C)CC(C)C)C(C)(C)NC)=N1', 'CCOCC(C)NC(C)C(C)CC(C)C', 'C=CC=C=CCC1CCCNCCC1', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CCCC1C(NCCC2=CC=CC=CCNC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)CCC=C1', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCC=NC(C)C(O)C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds to meet a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CNC(=CCC(C=CCC)C1=C(C=CO)CC1)CC', 'CC=CC=NC[O+]=C(N)C(C)CC', 'CC=NC[O+](CCC)CC(C(C)CN)C(C)C(C)=[N+](C)C(C)=CCC', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'CC=CC=[O+]CC(C=C(C)OC)=C(CC)CCC(C)CC', 'BCCC(=CC)CCC1=CN1CC', 'CCC=C(C)C1CCC=C(CC)C1CCCC(CC)CC=C(C)C', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CCB1NN1)N1CC(=CC2CCCC2)N1', 'C=C1CNC=C(NCCN)C=[O+]C=CCC(CC(CC)CCC)NCC(N)=C1C(C)C=CC', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CNC(=CCC(C=CCC)C1=C(C=CO)CC1)CC', 'CC=CC=NC[O+]=C(N)C(C)CC', 'CC=NC[O+](CCC)CC(C(C)CN)C(C)C(C)=[N+](C)C(C)=CCC', 'CCC=C(C)CCOC=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'CC=CC=[O+]CC(C=C(C)OC)=C(CC)CCC(C)CC', 'BCCC(=CC)CCC1=CN1CC', 'CCC=C(C)C1CCC=C(CC)C1CCCC(CC)CC=C(C)C', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CCB1NN1)N1CC(=CC2CCCC2)N1', 'C=C1CNC=C(NCCN)C=[O+]C=CCC(CC(CC)CCC)NCC(N)=C1C(C)C=CC', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C=NC=CC(C)CCCCC)(CCC)C1C2COC2CC=CCC=CN1C', 'CCC1C(=CC=N)C=C1C=NC', 'CCCC=CC(N)NOC(C)C', 'CC=NCC(C)(F)C(CN)CC(CCCCCC)=NCC(N(C)CCCCO)C(C)(C)C(C)OCCC', 'CCC=NCNC(CNN)C(=N)N', 'C=C1CC2=C(C2)CN(C)CC(C)(CNC)C(CCC=CC=CN)=C1CC', 'CCC1N=NCC1C', 'CCCC(=CO)CC(C)C', 'C=C(C)CCC(C)C(C(=CC(O)CCC1=CC=CC(C)CONC1)CC)N(C)CC=C1CC=CNC1', 'C=CC(CC)C[N+](=C)CC=CC(O)CCCCCC1NC=C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(C=NC=CC(C)CCCCC)(CCC)C1C2COC2CC=CCC=CN1C', 'CCC1C(=CC=N)C=C1C=NC', 'CCCC=CC(N)NOC(C)C', 'CC=NCC(C)(F)C(CN)CC(CCCCCC)=NCC(N(C)CCCCO)C(C)(C)C(C)OCCC', 'CCC=NCNC(CNN)C(=N)N', 'C=C1CC2=C(C2)CN(C)CC(C)(CNC)C(CCC=CC=CN)=C1CC', 'CCC1N=NCC1C', 'CCCC(=CO)CC(C)C', 'C=C(C)CCC(C)C(C(=CC(O)CCC1=CC=CC(C)CONC1)CC)N(C)CC=C1CC=CNC1', 'C=CC(CC)C[N+](=C)CC=CC(O)CCCCCC1NC=C1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[N+]1(BNN)NC1NB1CCC(NC(=C)N)NC(NN)C1C', 'CBB(BB=CN[N+]1=C(C2CBC2C)C1)CN', 'CB(C)C(BBCBCN=NCN)BC=CCN', 'CC=CCC1=CCC1C(CC)COC(CCO)CC(=O)CCC', 'CC1=CCC([O+]2C=CC2)CC2=[N+](N)C=CC(CC3NC3(C)C=CN)=CCC1C=C2', 'C=CC12CC(CC)=CC=C(CC)CC1CN2NCCC', 'CCCN1CN(CN2C(NNC(C)=NN)C(N)C2C(C)C)BCN1', 'C=[N+](C)CCC=C(C=CC1(C)CC(C=CCC)C2N3C1C(C)(CCO)C23C)C(C)=CC', 'CC=C[N+](=CC=CCO)CNC1C(CC)C=CC1C1CCC1(C)CCCC', 'BBB(B)N(N)NNCNC(CB)NCC=C(CC)CB=NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[N+]1(BNN)NC1NB1CCC(NC(=C)N)NC(NN)C1C', 'CBB(BB=CN[N+]1=C(C2CBC2C)C1)CN', 'CB(C)C(BBCBCN=NCN)BC=CCN', 'CC=CCC1=CCC1C(CC)COC(CCO)CC(=O)CCC', 'CC1=CCC([O+]2C=CC2)CC2=[N+](N)C=CC(CC3NC3(C)C=CN)=CCC1C=C2', 'C=CC12CC(CC)=CC=C(CC)CC1CN2NCCC', 'CCCN1CN(CN2C(NNC(C)=NN)C(N)C2C(C)C)BCN1', 'C=[N+](C)CCC=C(C=CC1(C)CC(C=CCC)C2N3C1C(C)(CCO)C23C)C(C)=CC', 'CC=C[N+](=CC=CCO)CNC1C(CC)C=CC1C1CCC1(C)CCCC', 'BBB(B)N(N)NNCNC(CB)NCC=C(CC)CB=NC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CN1CN(C)C2(N)CCN3NN(NC3N)N3C1C32', 'C=CNOC(NC[N+](=CC=CCC)CC)OC(CC=CCC(C)NC)CC(C)C', 'CNC=NB(CC=N)CCCN=C(CNC)NCN', 'C=C(C)C=C1NC=C(N)C=CCC(NC)CC(C)(C=C(C)NCC)C1CC', 'C=CC=C1CC1(N)CC=C(CCN)C(C)CCCC', 'C=C1CCCC(OC)C2C(C=CCN)=C(C=CCC=CCC)C2C1', 'BC12CN3NC=NN(N)C(N)(N=C)CCN(C)NNNB(C)B(N)N4C(CB5C3C54)C1N2', 'CCCC(CNN)[N+](C=CNN=C(N=NN[N+](C)(C)CC1C=CCN=C1)NC)=NC', 'CCC(C)F', 'BC(CBCN)C1C(=NBC(C)NC)CN1CC(CC)NC(C)B(CN)NBNN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN1CN(C)C2(N)CCN3NN(NC3N)N3C1C32', 'C=CNOC(NC[N+](=CC=CCC)CC)OC(CC=CCC(C)NC)CC(C)C', 'CNC=NB(CC=N)CCCN=C(CNC)NCN', 'C=C(C)C=C1NC=C(N)C=CCC(NC)CC(C)(C=C(C)NCC)C1CC', 'C=CC=C1CC1(N)CC=C(CCN)C(C)CCCC', 'C=C1CCCC(OC)C2C(C=CCN)=C(C=CCC=CCC)C2C1', 'BC12CN3NC=NN(N)C(N)(N=C)CCN(C)NNNB(C)B(N)N4C(CB5C3C54)C1N2', 'CCCC(CNN)[N+](C=CNN=C(N=NN[N+](C)(C)CC1C=CCN=C1)NC)=NC', 'CCC(C)F', 'BC(CBCN)C1C(=NBC(C)NC)CN1CC(CC)NC(C)B(CN)NBNN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce a collection of molecules with a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=C1CC2=CC3(CC=C(C3)NCC(N)=C2)C1', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CC(O)(CCCCO)N=CN', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=CC(CC)OC1CC(=C(C)CC)C(CC)(C(C)(N)OO)C1CC', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=[O+]1', 'CC=CCC(O)CCC1CC(NCCN2C3=[N+]2C(C(C)CCC2CCCC=COC(CC)N2)C3)N1', 'C=C(C)CC(CC)CC1CCC[O+](C=CC)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=C1CC2=CC3(CC=C(C3)NCC(N)=C2)C1', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CC(O)(CCCCO)N=CN', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1', 'C=CC(CC)OC1CC(=C(C)CC)C(CC)(C(C)(N)OO)C1CC', 'CCC1=C2C=C(C(C)O)CCC(C=CC1)C2', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=[O+]1', 'CC=CCC(O)CCC1CC(NCCN2C3=[N+]2C(C(C)CCC2CCCC=COC(CC)N2)C3)N1', 'C=C(C)CC(CC)CC1CCC[O+](C=CC)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCC)C(=CCC)C(C)C=CO', 'CC=[O+]C[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=CCC(CC(O)CC)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'C=C1CN=CNC(=C)N(CC(C)C(C)(C)N)C(C)C1', 'C=C(CC(NC)NNC1(NCN)CCC1)C(C)=NNC(C)NCN', 'CCCNC(CCC)CC(C)(C)CCCCC(CCC)C1CC2CC(C2)C1CC', 'CCC=NC(N)C=C(NN)[N+](BCC=C(C)C)=NN(C)C(C)C', 'C=C(CN)C(C)O', 'C=CC(CCCC)C(C=CO)=CC(=C)CCC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCC)C(=CCC)C(C)C=CO', 'CC=[O+]C[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=CCC(CC(O)CC)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'C=C1CN=CNC(=C)N(CC(C)C(C)(C)N)C(C)C1', 'C=C(CC(NC)NNC1(NCN)CCC1)C(C)=NNC(C)NCN', 'CCCNC(CCC)CC(C)(C)CCCCC(CCC)C1CC2CC(C2)C1CC', 'CCC=NC(N)C=C(NN)[N+](BCC=C(C)C)=NN(C)C(C)C', 'C=C(CN)C(C)O', 'C=CC(CCCC)C(C=CO)=CC(=C)CCC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to reach a z-score of -6.190392 for the cell line '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=CCC(C)C)C(C)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(=C)CC', 'CC[N+](=NCC1=CNC(C)=CN1)C1C=[N+]1C', 'CC(=CC=C(C)CNNC(C)=CN(CN)C(N)=CC(C)CN)CCC12CCC=CN1C2', 'CCCCC1=CC(CC)C=C1', 'C=NC(CCC)C(=CCCC(C)C)N=C1C=CC=CC(C(C)CCN=CC[O+]=CC)=CC1CCC', 'CC1CC(CC2CCC3CCC32)C1C', 'C=CC1C2=CC=C(C=CC1(C)CC)OC2CC(C)C(C)=CCC', 'CCCC(CC)=[N+](O)CC1=C(C)CC1', 'CC(=CC=C(C)CNNC(C)=CN(CN)C(N)=CC(C)C)CCC12CCC=C(C)N1C2', 'CC=CCC1CC=NC=CN2CCC3=C(CO)C12CCC=CC1OC1CCC3=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(=CCC(C)C)C(C)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(=C)CC', 'CC[N+](=NCC1=CNC(C)=CN1)C1C=[N+]1C', 'CC(=CC=C(C)CNNC(C)=CN(CN)C(N)=CC(C)CN)CCC12CCC=CN1C2', 'CCCCC1=CC(CC)C=C1', 'C=NC(CCC)C(=CCCC(C)C)N=C1C=CC=CC(C(C)CCN=CC[O+]=CC)=CC1CCC', 'CC1CC(CC2CCC3CCC32)C1C', 'C=CC1C2=CC=C(C=CC1(C)CC)OC2CC(C)C(C)=CCC', 'CCCC(CC)=[N+](O)CC1=C(C)CC1', 'CC(=CC=C(C)CNNC(C)=CN(CN)C(N)=CC(C)C)CCC12CCC=C(C)N1C2', 'CC=CCC1CC=NC=CN2CCC3=C(CO)C12CCC=CC1OC1CCC3=CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(N)N(NC(C)=CCCC1B2CC21)C(C)C=CC1B(C)C1NN=C(C)C=CNCC(N)CN', 'CC(C)N', 'CCCCCC(C)=CCC=C(CNC)C(C)N', 'C=C1CC=CC=C2CC(C=CO)=CC(CC)C=N[O+]3CC=[O+]CC1(C)C(CC=C3C)C2', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'C=C(C)C1CCN([N+](C)(CNCCN)NN)C=C2CC2C1', 'CCC(CC1C=[O+]C=C(C)C(C)=CC(C)N1)=NCCC1=CC1', 'C=C(C=[N+]1CC1=CC)CCC', 'CBB1CBN1', 'C=C(CCCCNCCC1NB=C1C)CNN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(N)N(NC(C)=CCCC1B2CC21)C(C)C=CC1B(C)C1NN=C(C)C=CNCC(N)CN', 'CC(C)N', 'CCCCCC(C)=CCC=C(CNC)C(C)N', 'C=C1CC=CC=C2CC(C=CO)=CC(CC)C=N[O+]3CC=[O+]CC1(C)C(CC=C3C)C2', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'C=C(C)C1CCN([N+](C)(CNCCN)NN)C=C2CC2C1', 'CCC(CC1C=[O+]C=C(C)C(C)=CC(C)N1)=NCCC1=CC1', 'C=C(C=[N+]1CC1=CC)CCC', 'CBB1CBN1', 'C=C(CCCCNCCC1NB=C1C)CNN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)(CC)CCO', 'CCC=CC=CCCC=[O+]N1CCC(CNOC)CCCC(C)C1', 'C=B[N+]1(C)C(=C)NC=CC1=CB=NC=N', 'CCC(C=C1CC(C(C)=CC=[O+]C(C)=CN)N1)CCC(C)C', 'BC(C)=NBBN(NN(C)N)B(BC(N)NBCN)NN1CBN2CCB(N)CN21', 'CCNCBC=CCB(N)CC', 'B=C=NC(CNC)[N+](=CCCN)[N+](=CC)CCNC=NC', 'C=C1CCC(CN)CC=C1CCC(N)C(C)=C(C)CCCC', 'CC=CN(CC=NCC)OC1(N)C=CCC=CCCCCC1', 'C=[N+](CN)N=NBB=BN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)(CC)CCO', 'CCC=CC=CCCC=[O+]N1CCC(CNOC)CCCC(C)C1', 'C=B[N+]1(C)C(=C)NC=CC1=CB=NC=N', 'CCC(C=C1CC(C(C)=CC=[O+]C(C)=CN)N1)CCC(C)C', 'BC(C)=NBBN(NN(C)N)B(BC(N)NBCN)NN1CBN2CCB(N)CN21', 'CCNCBC=CCB(N)CC', 'B=C=NC(CNC)[N+](=CCCN)[N+](=CC)CCNC=NC', 'C=C1CCC(CN)CC=C1CCC(N)C(C)=C(C)CCCC', 'CC=CN(CC=NCC)OC1(N)C=CCC=CCCCCC1', 'C=[N+](CN)N=NBB=BN']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCN)CC(CCC)C(C)CCCCC(C)=CC(C)C(C)C12CCC=CN=CC(N)(CC1)N2CC', 'BB(N)B(C=C)B(NC(B=N)B(B)NB(B)C1BC=N1)NN(N)N', 'CCN=C=C(N)[N+](C)=C=CB=BCCC1(N)CC(C(C=NC)N2N(CN)C(CC)N2NN)N1', 'BC=NB1BCC1', 'CC1C=[O+]CC(CCC=C2C=CCC2C)CC1', 'C=C(C)CNC(C)OC', 'C=CC=NC(C)CN(CC1=C(C)CCC(=C)CC1=[O+]C1(OO)C=C1N)CC1CC2CCC21', 'CBC(N)C1=NB(N)B(C)C=BC2BNC2C2=CC21', 'C=CN[N+](=CC)CNNC', 'OC=NC=C1C=[O+]1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCN)CC(CCC)C(C)CCCCC(C)=CC(C)C(C)C12CCC=CN=CC(N)(CC1)N2CC', 'BB(N)B(C=C)B(NC(B=N)B(B)NB(B)C1BC=N1)NN(N)N', 'CCN=C=C(N)[N+](C)=C=CB=BCCC1(N)CC(C(C=NC)N2N(CN)C(CC)N2NN)N1', 'BC=NB1BCC1', 'CC1C=[O+]CC(CCC=C2C=CCC2C)CC1', 'C=C(C)CNC(C)OC', 'C=CC=NC(C)CN(CC1=C(C)CCC(=C)CC1=[O+]C1(OO)C=C1N)CC1CC2CCC21', 'CBC(N)C1=NB(N)B(C)C=BC2BNC2C2=CC21', 'C=CN[N+](=CC)CNNC', 'OC=NC=C1C=[O+]1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce compounds with a z-score of -6.190392 for the cell line '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)C(=C[N+](C)=O)CC(C)CC', 'CCCCCC1=C(CC=CC(C)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCC=C(CC(C)C)C(CC(C)CCCC(C)=CN)=NO', 'CC=CCC(CC(C=CCCO)=CC)C1CC=C1C(C)=C(C)C=NC', 'C=C[O+](C)C(CCCC)C(F)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'C=C[N+]1=C(C(C)CC(=N)N)N=C1CCCC', 'CCOOC(CC(C)Cl)C1=CC(C)CC1', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCCC1(C)C=CCC2CC(C(C)(CC)C2)C(C)(CC)CC(C(C)NCC(C)=C(C)C)C1C(N)CNC', 'C=C(C)COCOOCCCCC(C)(O)C=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)C(=C[N+](C)=O)CC(C)CC', 'CCCCCC1=C(CC=CC(C)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCC=C(CC(C)C)C(CC(C)CCCC(C)=CN)=NO', 'CC=CCC(CC(C=CCCO)=CC)C1CC=C1C(C)=C(C)C=NC', 'C=C[O+](C)C(CCCC)C(F)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'C=C[N+]1=C(C(C)CC(=N)N)N=C1CCCC', 'CCOOC(CC(C)Cl)C1=CC(C)CC1', 'CCC1=NC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=NN)C1', 'CCCC1(C)C=CCC2CC(C(C)(CC)C2)C(C)(CC)CC(C(C)NCC(C)=C(C)C)C1C(N)CNC', 'C=C(C)COCOOCCCCC(C)(O)C=CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CN)NC[N+]1=CC=COCC(C)C([N+]2=BNC2)NNNC1=CC', 'C=C[N+](C=CCCC)=C(C)CC(=N)CCC(=C)C', 'CC=NN1C=C(N)C1C(=COCC)C(O)N=CCC1=CC(CCS)CC1', 'CCCC(C=CNCN(C)C)=NC=[N+](C=CNC)CNC1(C)CC1=[N+](C)CC(C)C(O)=CC(C)C(C)N', 'C=CC=CCCCN(C)C(C)=C(C=NCN(CC)C[N+](=C)C)CC', 'CCC(C)=C(CC)CC1CCCNN=N1', 'C=CC=C=CCC1CCCNCCC1', 'C=C(CC)C(C=NC)OC(C)(CC1=CC2CC(C2CC=CCC)C(C)(C)C1)C(=C)C=CC', 'CCC(C)C1(N=C(CN)C2=[N+]3CC=CCC3(N)C2)CNN1', 'C=CNN=C(C=C(C)CO)N=NNCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CN)NC[N+]1=CC=COCC(C)C([N+]2=BNC2)NNNC1=CC', 'C=C[N+](C=CCCC)=C(C)CC(=N)CCC(=C)C', 'CC=NN1C=C(N)C1C(=COCC)C(O)N=CCC1=CC(CCS)CC1', 'CCCC(C=CNCN(C)C)=NC=[N+](C=CNC)CNC1(C)CC1=[N+](C)CC(C)C(O)=CC(C)C(C)N', 'C=CC=CCCCN(C)C(C)=C(C=NCN(CC)C[N+](=C)C)CC', 'CCC(C)=C(CC)CC1CCCNN=N1', 'C=CC=C=CCC1CCCNCCC1', 'C=C(CC)C(C=NC)OC(C)(CC1=CC2CC(C2CC=CCC)C(C)(C)C1)C(=C)C=CC', 'CCC(C)C1(N=C(CN)C2=[N+]3CC=CCC3(N)C2)CNN1', 'C=CNN=C(C=C(C)CO)N=NNCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce compounds targeting a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CB(B(N)BCC1CCN1)N1CCB1N', 'C=CC1CC2=C(CCC(NCC(C)=CC=CCCC)C1)C(C=CC)=C2', 'CC1=CC2CNC=C(CCCC(C)=CC1)CC2C', 'C=CNC(CC)=C(N)C=NC', 'C=C(CN(N)C=CN)C1=C(C)C1', 'CCC=C(C)CCO[O+]=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'C=CC(=CC(=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'C=CC(C)C(C)(C=CCC)C(=CCC(C)C)CC', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CCC(N)CC=NCCC(CN)NN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CB(B(N)BCC1CCN1)N1CCB1N', 'C=CC1CC2=C(CCC(NCC(C)=CC=CCCC)C1)C(C=CC)=C2', 'CC1=CC2CNC=C(CCCC(C)=CC1)CC2C', 'C=CNC(CC)=C(N)C=NC', 'C=C(CN(N)C=CN)C1=C(C)C1', 'CCC=C(C)CCO[O+]=C(C)C1=CC2N=C3C(=CC=C1)C=C32', 'C=CC(=CC(=CCCC)CC(=C)C(CC)C1=CCC(C)[O+](CCC)C=C1)CCC', 'C=CC(C)C(C)(C=CCC)C(=CCC(C)C)CC', 'C=C1CC(C)C[O+](C)C2C3=NC=CCC(=[O+]CC1C)C14CCCC5C(=C1C=C4CCC)C2C5CC=C3', 'CCC(N)CC=NCCC(CN)NN']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CC)CCCC[O+](C)C', 'COC(NC1CCC1C)C1C=CC1', 'CC(C)C1C=C2CCC3=CC1C23', 'C=C(C=CC(C)C=CCCC(C)C)N=CNCC=CCCO', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'CCCCCN(C)N1CCC[OH+]C(C)(C)C(=CC(C)CC(N)CN=C(C)CC)NCC1', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)C1', 'C=C(CCN)CCCCNCCC1NC=C1C', 'CB1NN(C)CCN(C)CCCC1NN', 'CCC1(C)CCN2CC(=CNCC(CN)CNC)C2NC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CC)CCCC[O+](C)C', 'COC(NC1CCC1C)C1C=CC1', 'CC(C)C1C=C2CCC3=CC1C23', 'C=C(C=CC(C)C=CCCC(C)C)N=CNCC=CCCO', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'CCCCCN(C)N1CCC[OH+]C(C)(C)C(=CC(C)CC(N)CN=C(C)CC)NCC1', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)C1', 'C=C(CCN)CCCCNCCC1NC=C1C', 'CB1NN(C)CCN(C)CCCC1NN', 'CCC1(C)CCN2CC(=CNCC(CN)CNC)C2NC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules intended for a z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCN(C)C1(C(C)C)C=C1', 'C=C[O+]=C(CCC)C(N)=C(C=CO)COC(C)=CCC', 'C=CCCC1CC=CCC(=CCCC)O1', 'CN=CCC(O)CC=CCCCC=CC=C(C)C', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'CC=C=C(C)C=C(CC)C(C)C1CC2(CC)C(C(C)C3=NC3=CC)C(CCC)N3CN(N3)C12', 'C=CC=CC12CC3=CC4=C[N+]5=[N+](C4)C(C(=C5)CC3)C(CC=C)OC(C)=CC1=C2C', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO', 'CCCCC(CC1=CC1)OCCNC1(C)NC1C', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCN(C)C1(C(C)C)C=C1', 'C=C[O+]=C(CCC)C(N)=C(C=CO)COC(C)=CCC', 'C=CCCC1CC=CCC(=CCCC)O1', 'CN=CCC(O)CC=CCCCC=CC=C(C)C', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'CC=C=C(C)C=C(CC)C(C)C1CC2(CC)C(C(C)C3=NC3=CC)C(CCC)N3CN(N3)C12', 'C=CC=CC12CC3=CC4=C[N+]5=[N+](C4)C(C(=C5)CC3)C(CC=C)OC(C)=CC1=C2C', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO', 'CCCCC(CC1=CC1)OCCNC1(C)NC1C', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC(C)CC(N)=CCC', 'C=NC=NC1(N)C=C(C=CCC)CC=C(N)C=C(CC)[O+]=CO1', 'C=CNC(C=C(OCC)C1(C)CC1)=C(C)C=C', 'CCC12CCCCC=C3NC(C)=CCC=C(C4=CCC(=C(F)CC1)CCC=C4)C(C)N[NH+]32', 'CCC=C1CCCC(=COC)CC=CC[O+]=CC(O)CC1', 'C=C(CCC)CCCO', 'CCCC=CC(C)C(C)C(C)N(CC1CC1(N)C(C)CC)C1=C(CC)C2C(C)(C)CC2(C)CC(C)NCC1C', 'C=C(CO)NCCC(C)CC1C(C)CNC2C[O+](C)C2=CCCC1N', 'C=CC(C)CC=COCCCC', 'C=C(CC)OCN=NC=CC=C(C)CC=CC(N)CC(CC)C(C)(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC(C)CC(N)=CCC', 'C=NC=NC1(N)C=C(C=CCC)CC=C(N)C=C(CC)[O+]=CO1', 'C=CNC(C=C(OCC)C1(C)CC1)=C(C)C=C', 'CCC12CCCCC=C3NC(C)=CCC=C(C4=CCC(=C(F)CC1)CCC=C4)C(C)N[NH+]32', 'CCC=C1CCCC(=COC)CC=CC[O+]=CC(O)CC1', 'C=C(CCC)CCCO', 'CCCC=CC(C)C(C)C(C)N(CC1CC1(N)C(C)CC)C1=C(CC)C2C(C)(C)CC2(C)CC(C)NCC1C', 'C=C(CO)NCCC(C)CC1C(C)CNC2C[O+](C)C2=CCCC1N', 'C=CC(C)CC=COCCCC', 'C=C(CC)OCN=NC=CC=C(C)CC=CC(N)CC(CC)C(C)(C)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a batch of molecules reaching a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOCCCC=CC(C)=CC1OC(CC)C=C1C', 'CCOCC1=[N+](CC)C([O+]=C2CCC2)C1(O)C(C)F', 'C=CCC(C)CC', 'C=C(C=CCC)CC12c3nc[o+]1c32', 'CC=CC(C=CC[N+]1=CC(N(C)C)CNC1=CC1=CC1)=CC1CC1C', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=CC1=C(C(C=COCC)(NC)C2(CCCO)CC2CO)C2(C)C(=C)C(C)(CN(C)C1)[O+](C)C(C)CC(CC)C1=CC2C1', 'CCCCC1=CCC(B(C)C)C1', 'CCCC(C)(CC)CCNCC[N+](CCN)=C(CNN)C(C)CCC(CN)=C(CC)CN', 'CCOCC1=C(CC)C([O+]=C2CCC2)C1(O)C(F)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCOCCCC=CC(C)=CC1OC(CC)C=C1C', 'CCOCC1=[N+](CC)C([O+]=C2CCC2)C1(O)C(C)F', 'C=CCC(C)CC', 'C=C(C=CCC)CC12c3nc[o+]1c32', 'CC=CC(C=CC[N+]1=CC(N(C)C)CNC1=CC1=CC1)=CC1CC1C', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=CC1=C(C(C=COCC)(NC)C2(CCCO)CC2CO)C2(C)C(=C)C(C)(CN(C)C1)[O+](C)C(C)CC(CC)C1=CC2C1', 'CCCCC1=CCC(B(C)C)C1', 'CCCC(C)(CC)CCNCC[N+](CCN)=C(CNN)C(C)CCC(CN)=C(CC)CN', 'CCOCC1=C(CC)C([O+]=C2CCC2)C1(O)C(F)CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCCC(C)CC', 'CC=CC=CNNCCC', 'C=C(CC)C(C)C=CC(=CCN=CC(=CC(=N)CC(C)F)CCC)N(C)C(C)CNO', 'C=C1CC(CC(=CO)CC)C(O)CC2(C1)CC(CF)=[O+]2', 'CC1=CCCC(C)(N)CC(C)[N+](C)=C1', 'C=C(N)C=CN=C1CC1NC', 'CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)=CC=CC=CC(BN)NCNN', 'C=C(C)CC(C)CCC(=C(C)C(CN)CCCC)C(C)CC(=C)C(C)C(CC)CC=C[n+]1ccco1', 'BCN[N+](=N)C=CC=CNNCCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCCCC(C)CC', 'CC=CC=CNNCCC', 'C=C(CC)C(C)C=CC(=CCN=CC(=CC(=N)CC(C)F)CCC)N(C)C(C)CNO', 'C=C1CC(CC(=CO)CC)C(O)CC2(C1)CC(CF)=[O+]2', 'CC1=CCCC(C)(N)CC(C)[N+](C)=C1', 'C=C(N)C=CN=C1CC1NC', 'CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)=CC=CC=CC(BN)NCNN', 'C=C(C)CC(C)CCC(=C(C)C(CN)CCCC)C(C)CC(=C)C(C)C(CC)CC=C[n+]1ccco1', 'BCN[N+](=N)C=CC=CNNCCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a variety of molecules aimed at a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC)CC(C)C=O)CO1', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'C=C(C)CNCOOCCCCC(C)(O)N=CN', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)O1', 'BCN[N+](=C)C=CC=CNNCCN', 'CC=C1CCCC(=CCCC)C1=CC', 'CCCC(N)=CC(CCCNCC=C(C)C)CCC(C)C(CCOC)OCC(C)C(C)CC', 'C=C(C=CCC1(C)CCCCNCCC(=C2CC(C)C2C=NC)CCCC(O)=CC(=C)C1C)CC', 'CCCCC(CC(=CC(C)C)C(=N)C(C)C)CC1CC=C(C)C[N+]1=CCC(C)C', 'C=CC(CCN[O+]=C(N)CCCC=NC1C=CC1CC)CNCN=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC)CC(C)C=O)CO1', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'C=C(C)CNCOOCCCCC(C)(O)N=CN', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)O1', 'BCN[N+](=C)C=CC=CNNCCN', 'CC=C1CCCC(=CCCC)C1=CC', 'CCCC(N)=CC(CCCNCC=C(C)C)CCC(C)C(CCOC)OCC(C)C(C)CC', 'C=C(C=CCC1(C)CCCCNCCC(=C2CC(C)C2C=NC)CCCC(O)=CC(=C)C1C)CC', 'CCCCC(CC(=CC(C)C)C(=N)C(C)C)CC1CC=C(C)C[N+]1=CCC(C)C', 'C=CC(CCN[O+]=C(N)CCCC=NC1C=CC1CC)CNCN=CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'C=[O+][N+](CC)=C(C)C=CC(=C)C1=CCCC(C2CCCNC2)N1', 'CCCC1C2COC=CC3=C(CCC(CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'C=CC1C=CC(C(C=CCC(C)O)CC)CC(=CCC)O1', 'CCC=NC(C)C=C(CN)C(CCC=C(C)C)=NC(C)C(C)C', 'NC12C=C3C=CNC=CCC(O)=C1C1=C=C(CCC2CC3)C1', 'CN(C)C1C=C2CCC3=CN1C23', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=C1CC(O)(CC)C1', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'NCC=NC1CC1CNCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)=[N+]1C=C32', 'C=[O+][N+](CC)=C(C)C=CC(=C)C1=CCCC(C2CCCNC2)N1', 'CCCC1C2COC=CC3=C(CCC(CC)=C(C)C)CC3C(CC3CCCC(CO)C=CC=C(C)C3)=CCC(C)=C(C)C12', 'C=CC1C=CC(C(C=CCC(C)O)CC)CC(=CCC)O1', 'CCC=NC(C)C=C(CN)C(CCC=C(C)C)=NC(C)C(C)C', 'NC12C=C3C=CNC=CCC(O)=C1C1=C=C(CCC2CC3)C1', 'CN(C)C1C=C2CCC3=CN1C23', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=C1CC(O)(CC)C1', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'NCC=NC1CC1CNCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules that meet a z-score of -6.190392 in the cell line '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CC(C)(N)C(C(C)N)N1', 'CCCC1C2C3C1C23C', 'CC=C(C1=CCCCC[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CC=[N+](CCCCCCCC)NC', 'CCNCCC(CCO)=C(CNC)C(C)CCC(CC)=C(CC)CO', 'BCBNBB(NB)NC1=CCC1', 'C=C1CC=CNN2OC(=C(C)C(=C)F)C2C(=C)CNCCC1', 'C=C1CCNC(C)=C2CCC3=C4C(CCNC5=CC(C5)(C2)C(=CC(C)[N+](=C)C)N(C)CC1)NC34OC1CC1CC', 'CNC1C=C1CC1C=CC[N+](=N)CCCN1', 'CC=CC(=[O+]CC)C(CC)CCCCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CC(C)(N)C(C(C)N)N1', 'CCCC1C2C3C1C23C', 'CC=C(C1=CCCCC[O+]=CCC=C1)[N+](C)=CC1=CCC1N', 'CC=[N+](CCCCCCCC)NC', 'CCNCCC(CCO)=C(CNC)C(C)CCC(CC)=C(CC)CO', 'BCBNBB(NB)NC1=CCC1', 'C=C1CC=CNN2OC(=C(C)C(=C)F)C2C(=C)CNCCC1', 'C=C1CCNC(C)=C2CCC3=C4C(CCNC5=CC(C5)(C2)C(=CC(C)[N+](=C)C)N(C)CC1)NC34OC1CC1CC', 'CNC1C=C1CC1C=CC[N+](=N)CCCN1', 'CC=CC(=[O+]CC)C(CC)CCCCCC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC', 'CC=[O+]CC=C(C)CC=C(C)C1C=CCC(C)=C(C)C1=CCCCCNCC', 'CC=[N+](NN)NC=NCC(C)(C)N', 'C=C1C=C(C)C1C1C=NCC[O+](CC=CC)CCO1', 'C=[N+]1N=CC(N)C(C)NN1CB(N)NCNCC', 'C=CC(N)=C(CCC(=O)CC(=C)CC)C1CC2(C=O)CON(F)C=C2C=CCC=CC1N', 'C=CC(C)NC(C)=CCCCC=C(C)C(C)=[N+](C=CC)CCC', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'C=CC(CCC(C)N(N)N=CN)B(NNNC)C(C)=CC', 'C=CCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C1NC(C)CC3=CC(=C31)C2=[N+]1CN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC', 'CC=[O+]CC=C(C)CC=C(C)C1C=CCC(C)=C(C)C1=CCCCCNCC', 'CC=[N+](NN)NC=NCC(C)(C)N', 'C=C1C=C(C)C1C1C=NCC[O+](CC=CC)CCO1', 'C=[N+]1N=CC(N)C(C)NN1CB(N)NCNCC', 'C=CC(N)=C(CCC(=O)CC(=C)CC)C1CC2(C=O)CON(F)C=C2C=CCC=CC1N', 'C=CC(C)NC(C)=CCCCC=C(C)C(C)=[N+](C=CC)CCC', 'C=CCC(CCC=CCCC)C(C=CC(=C)C)=CC=CC', 'C=CC(CCC(C)N(N)N=CN)B(NNNC)C(C)=CC', 'C=CCC1=CC=C(N)C(C)=CCNC=C2N=C(CC)C=C(CC1)C1NC(C)CC3=CC(=C31)C2=[N+]1CN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules with the intended z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+](CN)N=CNCN(N)CN', 'CC=NCC=C(CC)CC1OCC=C2C[N+](N)(O)C1(O)N2', 'CNC1B(BCN)[N+](B(C)N)=CBB(C)C1N', 'CNN(C)B1C2C(N=CNN)N12', 'BNB(C)BCBCN', 'C=CCC(CNC(N)C(CCCC)N=N)NN', 'C=CC(C)C(CCC)C(=C)C', 'C=CC1C=CC(=C(C)C2=C(C(C(=C)C(C)N)[NH+]3C(CCC)C[O+]3NC)C3=C2C3)C1', 'C=C1CCCCCC=C(C=CCCN)OC23C(=C)C(C2=C)C(C)CC(C=CC=CC3CCC)C1', 'CCB=C1CB(NCN)NC(N(CC)NC)=CBB(N)C=N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+](CN)N=CNCN(N)CN', 'CC=NCC=C(CC)CC1OCC=C2C[N+](N)(O)C1(O)N2', 'CNC1B(BCN)[N+](B(C)N)=CBB(C)C1N', 'CNN(C)B1C2C(N=CNN)N12', 'BNB(C)BCBCN', 'C=CCC(CNC(N)C(CCCC)N=N)NN', 'C=CC(C)C(CCC)C(=C)C', 'C=CC1C=CC(=C(C)C2=C(C(C(=C)C(C)N)[NH+]3C(CCC)C[O+]3NC)C3=C2C3)C1', 'C=C1CCCCCC=C(C=CCCN)OC23C(=C)C(C2=C)C(C)CC(C=CC=CC3CCC)C1', 'CCB=C1CB(NCN)NC(N(CC)NC)=CBB(N)C=N1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCN=CC1(NC)CCC2C=CCN2C1', 'CB[N+]1=NC(=CNCB(C=CN)C=NC)NB(N)NNC(C=N)CB(C)NB2CN2NC1', 'CCC(O)=CC=C(C)CC(C)C1=CC1C', 'C=C(C)CC(C)(N)OCCC=C(CC)C(C)F', 'C=CC=CC(C)(CCCCCC)[NH+]1CC(CC)(CCCC)C=CC12CCC2', 'C=CCN=CCBC=CN1C(N)N2C(B(BC)CNCN1NN)B(N)N2C', 'CCC=NCC=CC(=NC)C(CCC)COC', 'C=C(C)CC1C=CC(CC)C(CN)NC1', 'C=CCOCCC=C(C)C1=C(CCC)C2CCN12', 'BB(N)BB(B)BBC(B)B'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCN=CC1(NC)CCC2C=CCN2C1', 'CB[N+]1=NC(=CNCB(C=CN)C=NC)NB(N)NNC(C=N)CB(C)NB2CN2NC1', 'CCC(O)=CC=C(C)CC(C)C1=CC1C', 'C=C(C)CC(C)(N)OCCC=C(CC)C(C)F', 'C=CC=CC(C)(CCCCCC)[NH+]1CC(CC)(CCCC)C=CC12CCC2', 'C=CCN=CCBC=CN1C(N)N2C(B(BC)CNCN1NN)B(N)N2C', 'CCC=NCC=CC(=NC)C(CCC)COC', 'C=C(C)CC1C=CC(CC)C(CN)NC1', 'C=CCOCCC=C(C)C1=C(CCC)C2CCN12', 'BB(N)BB(B)BBC(B)B']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a collection of compounds with a target z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'C=C1C=CC[N+]1(O)C(C)N(C)C', 'CC=CC=CCCCC(CCC)C1C(C)CNC1CC', 'CCC1CC(CC)C(C)CCC(N)C(C)(C)COO1', 'CCCCC(CCC)CC(C)C=CNC1=NC1CC', 'C=C(N)CC1CCCC2C(C)=C(C(=C)C(C=CCCC)=C3C4=CC3C4)CC3(C=C3CC)NN12', 'CN1C=NC(CCC=C2C=C[N+](C)(C)NC(N)CCN=[N+]=CC2)NNN1', 'C=C(C)CCC(C)CC(C)CC=C1CC=COC1', 'CC=CC(C)(C=C(C)C[N+]1=CC1=C(CC)CCCN)C(CCC)CCOC', 'CC=C[O+]1CC1=CCC1=C2C(O)=C12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'C=C1C=CC[N+]1(O)C(C)N(C)C', 'CC=CC=CCCCC(CCC)C1C(C)CNC1CC', 'CCC1CC(CC)C(C)CCC(N)C(C)(C)COO1', 'CCCCC(CCC)CC(C)C=CNC1=NC1CC', 'C=C(N)CC1CCCC2C(C)=C(C(=C)C(C=CCCC)=C3C4=CC3C4)CC3(C=C3CC)NN12', 'CN1C=NC(CCC=C2C=C[N+](C)(C)NC(N)CCN=[N+]=CC2)NNN1', 'C=C(C)CCC(C)CC(C)CC=C1CC=COC1', 'CC=CC(C)(C=C(C)C[N+]1=CC1=C(CC)CCCN)C(CCC)CCOC', 'CC=C[O+]1CC1=CCC1=C2C(O)=C12']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)C[O+](CC)CCC(=C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'CCCC1C(C(CCCCC2C(C)=C2CC)CCC(C)(C)C)CCN1CC', 'C=C(CC=CC(CC)CN)CC=C(C)C=CCN', 'CC=CC(C)=C(C)C1(CC=CC(C)C(C2=C(CNCC)NC3=NC(CN(CCC)C3)[N+](C)(CC=CC(C)=N)CCN2)C(C(C)C)C(C)N)CCN1', 'C=C(C=N)C(=C)NCCCNC(CCC)C(=N)CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CC(C)C(C)CC=CCC(=C)CC=C1C2[O+](O)[O+]12', 'C=C1C=C2C=C(C[N+]23N=CCCCC=N3)NC=NC(CNCCCC(=N)NBC)CCN=C(C)NC1', 'CCCC1CC=CCC1C', 'C=C(C)C([O+]=C(C)CC=CCCOC)C1=NCC=CN=CCCC2C=C[N+]2=[O+]1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)C[O+](CC)CCC(=C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'CCCC1C(C(CCCCC2C(C)=C2CC)CCC(C)(C)C)CCN1CC', 'C=C(CC=CC(CC)CN)CC=C(C)C=CCN', 'CC=CC(C)=C(C)C1(CC=CC(C)C(C2=C(CNCC)NC3=NC(CN(CCC)C3)[N+](C)(CC=CC(C)=N)CCN2)C(C(C)C)C(C)N)CCN1', 'C=C(C=N)C(=C)NCCCNC(CCC)C(=N)CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CC(C)C(C)CC=CCC(=C)CC=C1C2[O+](O)[O+]12', 'C=C1C=C2C=C(C[N+]23N=CCCCC=N3)NC=NC(CNCCCC(=N)NBC)CCN=C(C)NC1', 'CCCC1CC=CCC1C', 'C=C(C)C([O+]=C(C)CC=CCCOC)C1=NCC=CN=CCCC2C=C[N+]2=[O+]1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of compounds reaching a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=NCC=[N+](C)C(BN)=CC1([N+](=CNN=NC=CCCC)CN)CB2N=NN21', 'CC=[N+]1C=[N+](CCC)C1CC(C)C', 'C=C(CC(CCC)CC(N)=O)CN(N)C1(C)C=[N+](C[O+]=CCC)[O+]1C', 'C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=NCC(C)=NOCC(CCCN)(NC)NC', 'CCCC(F)C(C)=CO', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'C=CBC(C)=CC=C(C)N=[N+](N)[N+]1(C)CCC1N=C', 'C=[N+]1C(N)NC=CCN=CC(N)=[N+](N)CC2=C(N2)NC1N=[N+](N)NCNC(C)N', 'CCCCC1=NC(C(CCO)C(C)(C)C)CCC(C2=CCC=CC(CC)=C2)[O+]2CN(C1)C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=NCC=[N+](C)C(BN)=CC1([N+](=CNN=NC=CCCC)CN)CB2N=NN21', 'CC=[N+]1C=[N+](CCC)C1CC(C)C', 'C=C(CC(CCC)CC(N)=O)CN(N)C1(C)C=[N+](C[O+]=CCC)[O+]1C', 'C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=NCC(C)=NOCC(CCCN)(NC)NC', 'CCCC(F)C(C)=CO', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'C=CBC(C)=CC=C(C)N=[N+](N)[N+]1(C)CCC1N=C', 'C=[N+]1C(N)NC=CCN=CC(N)=[N+](N)CC2=C(N2)NC1N=[N+](N)NCNC(C)N', 'CCCCC1=NC(C(CCO)C(C)(C)C)CCC(C2=CCC=CC(CC)=C2)[O+]2CN(C1)C2']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CCC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'C=C(CC(=C)C1=CC(C)(NC)CCCC=C1)CC1(C)CC1', 'CC(C[O+](C)C)=C1NC(C)CCCN(C)CCC1CC=O', 'CCCCC1=NC(C(CCO)C(C)(C)N)CCC(C2=CCC=NC(NN)=C2)N2CN(C1)C2', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CC=C(C=CCC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)c1)CC', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=CCC(C)(O)CC', 'C=CCC1(C(C=CC)=C(C)OC(=C)N)C2C=CC(C)=CC(=C)CC2CC(CC)NC(C)C(N)=[N+]1O', 'C=NC(=C)N(C)CCC(CCCCCC)=C(N)C(C(N)=C(C)C)C(C)CCB(C)CN', 'C=NN=C1CNC(C)(CCC=C2CC2)OC1[N+](=C)CCSC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CCC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'C=C(CC(=C)C1=CC(C)(NC)CCCC=C1)CC1(C)CC1', 'CC(C[O+](C)C)=C1NC(C)CCCN(C)CCC1CC=O', 'CCCCC1=NC(C(CCO)C(C)(C)N)CCC(C2=CCC=NC(NN)=C2)N2CN(C1)C2', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CC=C(C=CCC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)c1)CC', 'C=CCC=CC(CC(=C)CC)=[O+][O+]=CCC(C)(O)CC', 'C=CCC1(C(C=CC)=C(C)OC(=C)N)C2C=CC(C)=CC(=C)CC2CC(CC)NC(C)C(N)=[N+]1O', 'C=NC(=C)N(C)CCC(CCCCCC)=C(N)C(C(N)=C(C)C)C(C)CCB(C)CN', 'C=NN=C1CNC(C)(CCC=C2CC2)OC1[N+](=C)CCSC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple compounds optimized for a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CCC=[N+](C)C1(C)CC(=CC=NC)CCC(C)CN=C1C', 'CCCC1=CCCC=C(CC)C(=CC=CC=C(CC)CC(C)C=O)CO1', 'CC=NC[O+](CCC)CC(C(C)CN)C(C)C(C)=[N+](C)C(C)=CCC', 'BNN=CN=NC(C)CNCC1(C)C=C(CC)C(=C)CC2=[N+](C=NC1)C2', 'CC(C)CCCCCNCCC1CCC1C', 'CCOCCCC=CC(C)=NC1=CC(CC)C=C1C', 'CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'C=CCC[N+](=CCC(C)C(CC)CC=CCN=CNCC(O)=CC(C)C)CC', 'CCC1N=NCC1C', 'CC(=CCN(C)O)N=CC[N+]1(C)CCC1C=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CCC=[N+](C)C1(C)CC(=CC=NC)CCC(C)CN=C1C', 'CCCC1=CCCC=C(CC)C(=CC=CC=C(CC)CC(C)C=O)CO1', 'CC=NC[O+](CCC)CC(C(C)CN)C(C)C(C)=[N+](C)C(C)=CCC', 'BNN=CN=NC(C)CNCC1(C)C=C(CC)C(=C)CC2=[N+](C=NC1)C2', 'CC(C)CCCCCNCCC1CCC1C', 'CCOCCCC=CC(C)=NC1=CC(CC)C=C1C', 'CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'C=CCC[N+](=CCC(C)C(CC)CC=CCN=CNCC(O)=CC(C)C)CC', 'CCC1N=NCC1C', 'CC(=CCN(C)O)N=CC[N+]1(C)CCC1C=N']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CC1CCC=C(NC(C)C=CC)C1)CC', 'CC=CCCC1=CCC(C)CNCC1', 'C=CC1C=CCC(=C(CC)CCC(=CC)OCCCC2CC[O+]2C)C12CC2', 'C=C1CCC(C)CC1=CC(CC)CCNC', 'CC=CCC(O)=C(CC)OO[N+](=N)CC(=CCCCC)OC=O', 'C=C(C[N+](=CNC1=C(C=CCCCC)C1C)NCC)[N+]1=CN1CCCCN', 'CCCCC(OCNN)C(N)C=C(C=C(N)CC=O)CC', 'CCCC=CCCC(C)CCC(C)CC=C1CCN1CC(C=C(CC)CC1C=C1C)N=NCCN', 'CC=CC=CNNCC', 'C=C[O+](C)C(NCCC)C(C)=C(C=CC(CCCC(=C)C)=[N+]=C)CCOC1=CC=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CC1CCC=C(NC(C)C=CC)C1)CC', 'CC=CCCC1=CCC(C)CNCC1', 'C=CC1C=CCC(=C(CC)CCC(=CC)OCCCC2CC[O+]2C)C12CC2', 'C=C1CCC(C)CC1=CC(CC)CCNC', 'CC=CCC(O)=C(CC)OO[N+](=N)CC(=CCCCC)OC=O', 'C=C(C[N+](=CNC1=C(C=CCCCC)C1C)NCC)[N+]1=CN1CCCCN', 'CCCCC(OCNN)C(N)C=C(C=C(N)CC=O)CC', 'CCCC=CCCC(C)CCC(C)CC=C1CCN1CC(C=C(CC)CC1C=C1C)N=NCCN', 'CC=CC=CNNCC', 'C=C[O+](C)C(NCCC)C(C)=C(C=CC(CCCC(=C)C)=[N+]=C)CCOC1=CC=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various compounds for an intended z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=C[O+]1C=NC(C=CC(C)C=C(C)CC)CC1)CCC', 'CC(=N)CC1C[O+](C(C)C)C=CC1OCC1CCC(C)C1', 'CCCC=C(C)CC(CCN)N=Cc1ccn[nH]1', 'CCC1C=CC2=NC3(C)CC(C)C2CC2(CC)CC(=C(CCF)C(=O)C=C13)C=N2', 'C=CC(C)CC(=CCC)C1=C(C)C1', 'CCCCCC1(C)CC1', 'CCCC(C)(C=CCC=CC=CC(C)OC)NC(C)C1CCC1', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CC', 'C=C[N+](C)(C)C(C)CCC(C)CC(CCCC)CC(C)CC(=CC=CCC)NC', 'NOOC1CCC2(OC2O)C2COOC(F)CCN3OOOC3(C3(O)CCC3)CC(Cl)(OO2)C(OCO)OO1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=C[O+]1C=NC(C=CC(C)C=C(C)CC)CC1)CCC', 'CC(=N)CC1C[O+](C(C)C)C=CC1OCC1CCC(C)C1', 'CCCC=C(C)CC(CCN)N=Cc1ccn[nH]1', 'CCC1C=CC2=NC3(C)CC(C)C2CC2(CC)CC(=C(CCF)C(=O)C=C13)C=N2', 'C=CC(C)CC(=CCC)C1=C(C)C1', 'CCCCCC1(C)CC1', 'CCCC(C)(C=CCC=CC=CC(C)OC)NC(C)C1CCC1', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CC', 'C=C[N+](C)(C)C(C)CCC(C)CC(CCCC)CC(C)CC(=CC=CCC)NC', 'NOOC1CCC2(OC2O)C2COOC(F)CCN3OOOC3(C3(O)CCC3)CC(Cl)(OO2)C(OCO)OO1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=[N+]=C(C=CC(=CC(NC=N)NCCC)CCC[N+]1=CC=N1)CCCC(=C)C', 'CC1N=CN2C=[O+]C2CCN1C', 'CCCCCCCNC(C=CCN(C)C(C)C)NN=C(C)CCN', 'CC1=CNN(N)[N+](C)(CNCCN)N2C=C3CC3CC1CC2', 'C=C(CC)CCNC1=CC2N3C(CNC=NCC)NC(CCCCCC(NC)C23C)N=C1', 'CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'CCC(N)(C1C=C(C(C)N)N1)C(C)(C)CNC', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'C=C1CCC(C)OCCC(C)CC1=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+]=C(C=CC(=CC(NC=N)NCCC)CCC[N+]1=CC=N1)CCCC(=C)C', 'CC1N=CN2C=[O+]C2CCN1C', 'CCCCCCCNC(C=CCN(C)C(C)C)NN=C(C)CCN', 'CC1=CNN(N)[N+](C)(CNCCN)N2C=C3CC3CC1CC2', 'C=C(CC)CCNC1=CC2N3C(CNC=NCC)NC(CCCCCC(NC)C23C)N=C1', 'CCC=C(CC)CC(CC)NC(CC)C1=CC1C', 'CCC(N)(C1C=C(C(C)N)N1)C(C)(C)CNC', 'C=CC(=CCC1C=CCNC2CC=C(C)CC2(C=[O+]C)C1)C(C)CC=CC', 'C=C1CC2=CC1CCC(=CC)NCCC[O+]1CCC1C=CCC=CC(C)(C)CCN2', 'C=C1CCC(C)OCCC(C)CC1=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a batch of compounds with a target z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CC(C)=NC=NC(CCC)N=C1C(C)C1CCC1CC1=CCC', 'CC=C(C=NN(N=CN)NNB(C)N)C(N)NNC=NCCCC(=N)CN', 'CCN(C)NCCCNN', 'CCC1C(N)C(CCC2(C)C=C2OC)CCN(C)N1C=C(C)C', 'BNNN=CB(N)C1=BN(N)NC(N)(NN2CB(C)CC2B(CCN)N(N)CC)N[N+](B)=C1', 'CCOC(C)(C)CC1NC(=C2C(C)C2NCC(C)C)[O+]1C(C)C', 'BN=NBCC=N[N+](C)(C)NN(CCBC)C(=N)NCN(C)C=N', 'C=C1C(=N)[N+]2=C(BN2)NC(N)CCNCN1C', 'NCCC1CC=C2CC2C1', 'BB1NN(N)CCB1NNNCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC(C)=NC=NC(CCC)N=C1C(C)C1CCC1CC1=CCC', 'CC=C(C=NN(N=CN)NNB(C)N)C(N)NNC=NCCCC(=N)CN', 'CCN(C)NCCCNN', 'CCC1C(N)C(CCC2(C)C=C2OC)CCN(C)N1C=C(C)C', 'BNNN=CB(N)C1=BN(N)NC(N)(NN2CB(C)CC2B(CCN)N(N)CC)N[N+](B)=C1', 'CCOC(C)(C)CC1NC(=C2C(C)C2NCC(C)C)[O+]1C(C)C', 'BN=NBCC=N[N+](C)(C)NN(CCBC)C(=N)NCN(C)C=N', 'C=C1C(=N)[N+]2=C(BN2)NC(N)CCNCN1C', 'NCCC1CC=C2CC2C1', 'BB1NN(N)CCB1NNNCC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1CC(N)NNCCCCN1C', 'CN=CN1NC=NCCCC(C)CC1C', 'CC=C1C(NCN=BCC=BN)NN1CC', 'CCCC(C)(CCOC)CC(C)(C)NC(C)C(C)C=C(C)C', 'C=CC1=C(CNCC(C)C=C(CCCCC)[O+]=CC)CCC1=C', 'BN1NCC1=CC(=N)BNCC[N+](C)=N', 'BCCB(C)CB(B)n1bbn1C=C', 'C=CC1CC(NC(C)C)(C(C=C)C(C)=O)CC1NC(C)C1C=CC1', 'BC=NC(=CN(C)CNCC1=CC(=N)CN1)CNCC', 'CC=CC1[O+]=CCC=CC1=CC=CC1=CCCC(C)CC(C)NCC(C)(C)CCC=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1CC(N)NNCCCCN1C', 'CN=CN1NC=NCCCC(C)CC1C', 'CC=C1C(NCN=BCC=BN)NN1CC', 'CCCC(C)(CCOC)CC(C)(C)NC(C)C(C)C=C(C)C', 'C=CC1=C(CNCC(C)C=C(CCCCC)[O+]=CC)CCC1=C', 'BN1NCC1=CC(=N)BNCC[N+](C)=N', 'BCCB(C)CB(B)n1bbn1C=C', 'C=CC1CC(NC(C)C)(C(C=C)C(C)=O)CC1NC(C)C1C=CC1', 'BC=NC(=CN(C)CNCC1=CC(=N)CN1)CNCC', 'CC=CC1[O+]=CCC=CC1=CC=CC1=CCCC(C)CC(C)NCC(C)(C)CCC=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules to achieve the target z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CCCNC(=CC1CCC(NCC)=CCC(C)(C)C=C1N=NC1CN1)CN', 'C=C(N(C)CCN)N(C)CNC(CCCCCC)=C(N)CC(N)B(N)N', 'CC1CCC2=C1CCC=CN=CNC2', 'CCN=CC(=BCNNC)CCC=C[N+](C=NCC(N)CN)=CN', 'CCN(C=CCNC(C)(CC)CCN)CN', 'C=C1CNCC=NNC(C(C)=NC)C2=CC1C2(CC=CN)C(C(=CCN)NC)C(C)[N+]1=CCC1', 'C=C(CCCN)C(C)(N)CC', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)[O+]1C=C32', 'C=C1C=NC2C=[N+](C=CC=NC=CC=CC)N(N)CN=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CCCNC(=CC1CCC(NCC)=CCC(C)(C)C=C1N=NC1CN1)CN', 'C=C(N(C)CCN)N(C)CNC(CCCCCC)=C(N)CC(N)B(N)N', 'CC1CCC2=C1CCC=CN=CNC2', 'CCN=CC(=BCNNC)CCC=C[N+](C=NCC(N)CN)=CN', 'CCN(C=CCNC(C)(CC)CCN)CN', 'C=C1CNCC=NNC(C(C)=NC)C2=CC1C2(CC=CN)C(C(=CCN)NC)C(C)[N+]1=CCC1', 'C=C(CCCN)C(C)(N)CC', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CN)[O+]1C=C32', 'C=C1C=NC2C=[N+](C=CC=NC=CC=CC)N(N)CN=C1C1=C(N)C(C(C)C)C(C)C(CN2)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCC)C(C)CN[N+](CC(C)CC(C)C)=[N+](N)C=CO', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1', 'CCCCC(OC[OH+]N)C(N)C=C(C=C(N)CC=O)CC', 'C=C(CCC)[OH+]C1C=CC(C)C(=CC)CC(C2=C(C)C2)C(C(C)CCCOC2(C)CC2=C)C1', 'C=C(CC(C)CC=NNC(B(N)BCC)N(C)BCNN1CCNC(NCC=CC2CN2)NCCBCN1)C1=CCC1', 'CCCC(C)=C1C=CCC2=C(CCCC(C)=CC=CC(C)C(=NCC)C1)N2', 'CC1=CN(C=N)CNN1C', 'CCCCC(C=CC=NC=NOC)NNCC1=C(C=CO)C1', 'C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'CC=C[NH+](C)C(N)(CC)CC(C)[N+]1(C)C2NN1C2=NCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCC)C(C)CN[N+](CC(C)CC(C)C)=[N+](N)C=CO', 'CCC1=CN=C(C=CN[O+](CN)CC2CCCCC2)C1', 'CCCCC(OC[OH+]N)C(N)C=C(C=C(N)CC=O)CC', 'C=C(CCC)[OH+]C1C=CC(C)C(=CC)CC(C2=C(C)C2)C(C(C)CCCOC2(C)CC2=C)C1', 'C=C(CC(C)CC=NNC(B(N)BCC)N(C)BCNN1CCNC(NCC=CC2CN2)NCCBCN1)C1=CCC1', 'CCCC(C)=C1C=CCC2=C(CCCC(C)=CC=CC(C)C(=NCC)C1)N2', 'CC1=CN(C=N)CNN1C', 'CCCCC(C=CC=NC=NOC)NNCC1=C(C=CO)C1', 'C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'CC=C[NH+](C)C(N)(CC)CC(C)[N+]1(C)C2NN1C2=NCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules to achieve a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CN=[N+]1C=CCCC=NC([N+](C)=CNN(C)CC2C=CCC=C2)=NCC=C1', 'CCC(N)CC=CCCC(CN)NN', 'CNC1CCC(N2C=C(C)CO2)C(C)C1', 'CCCC1=CC(C)=C1NC=COCC', 'C=NCC(C)=NOCC(CC)(CC)C(CCOCCCNF)=C(C)C=CC', 'OC1CC2C3C[OH+]C4C5C6(CCl)C78C9%10C(C149)C3%10C21C57C618', 'C=C(C=CC1CCC=C(OC(C)[O+]=CC)C1)C(C)O', 'C=NC(OC)C(=N)CNC1CCO1', 'C=C1C=C(C)C1C1C=NCC[O+](CC=CC)CCO1', 'CCCCC1C2(C3CCNN3)C(CO)=NC3=C(CCN(CNC)C(N)=NCN(CC)NN=NCC2[N+]1(C)C)N3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN=[N+]1C=CCCC=NC([N+](C)=CNN(C)CC2C=CCC=C2)=NCC=C1', 'CCC(N)CC=CCCC(CN)NN', 'CNC1CCC(N2C=C(C)CO2)C(C)C1', 'CCCC1=CC(C)=C1NC=COCC', 'C=NCC(C)=NOCC(CC)(CC)C(CCOCCCNF)=C(C)C=CC', 'OC1CC2C3C[OH+]C4C5C6(CCl)C78C9%10C(C149)C3%10C21C57C618', 'C=C(C=CC1CCC=C(OC(C)[O+]=CC)C1)C(C)O', 'C=NC(OC)C(=N)CNC1CCO1', 'C=C1C=C(C)C1C1C=NCC[O+](CC=CC)CCO1', 'CCCCC1C2(C3CCNN3)C(CO)=NC3=C(CCN(CNC)C(N)=NCN(CC)NN=NCC2[N+]1(C)C)N3']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC1=CC(C)=C1NC=COCC', 'C=CC(=C)CNC(C)NN=NN(C)NN(C)B1C(=N)NC(N)=[N+](C)CC=C1CC(C)N(C)C=NN', 'CCB1CC(N)C2N=CC3=C(NC=CNNC)NN(N(C)N)C=C1NNC1CC(COB2N3)B1C', 'CCC1CC=CC=C2CCCCCCCN3CCCC1=C2C3', 'CC=CCNNC(=CCC(C)(C=CC)C=CN=C(C)CC)N[O+](C)O', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CC1CCC2=C1CCC=CN=CNC2', 'CB(C=N)N1CCC1O', 'C=CNCC(C=C)CC(C)=C(CCC)C(C)=CC=CC(N)CC', 'CNCCCNC(N)C(N)CN(C)N=C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC1=CC(C)=C1NC=COCC', 'C=CC(=C)CNC(C)NN=NN(C)NN(C)B1C(=N)NC(N)=[N+](C)CC=C1CC(C)N(C)C=NN', 'CCB1CC(N)C2N=CC3=C(NC=CNNC)NN(N(C)N)C=C1NNC1CC(COB2N3)B1C', 'CCC1CC=CC=C2CCCCCCCN3CCCC1=C2C3', 'CC=CCNNC(=CCC(C)(C=CC)C=CN=C(C)CC)N[O+](C)O', 'C=CC(C(C)CC=CCC)C(CC)CCC1C2=CC(C)C(C)C[O+](CN(CC)CCC2)C1CC', 'CC1CCC2=C1CCC=CN=CNC2', 'CB(C=N)N1CCC1O', 'C=CNCC(C=C)CC(C)=C(CCC)C(C)=CC=CC(N)CC', 'CNCCCNC(N)C(N)CN(C)N=C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=CN)C1', 'C=[N+](OCCCC)C(C)=CCCCC', 'C=C(CCC(=CCOC)CC(C)CCCC1CC[O+](C)C1=C)CC(C)CC', 'CC=CC(CC)N(CC=NCCCC[N+](C)(CC)CC1=CN1)CCC', 'C=C1CN=C(N=CNC)C2B=CN3C(NC)=C(C)CCB(NCN3CN(C)C2CC)C1=C', 'CCC=CC=CC(CC(C)CC1=C(CC)CC1)N(C)NCCC(C)C', 'CC(N)=CNCCNCNC1=NC(CN)=CC(C=CN)=CN1', 'C=CC1=C(C(C=COCC)(NC)C2(CCCO)CC2C)C2(C)C(=C)C(C)(CN(C)C1)[O+](C)C(C)CC(CC)C1=CC2C1', 'CC1CCC2(C)CC(N)C(C(C)C)C=C3C(=NCCN2)C3NC1', 'CCONC1=[N+](CC)C([O+]=C2CCC2)C1(O)C(C)F'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CC=CC=C(C=CC(OC)C(C)C)C=CN(C=CC=CN)C1', 'C=[N+](OCCCC)C(C)=CCCCC', 'C=C(CCC(=CCOC)CC(C)CCCC1CC[O+](C)C1=C)CC(C)CC', 'CC=CC(CC)N(CC=NCCCC[N+](C)(CC)CC1=CN1)CCC', 'C=C1CN=C(N=CNC)C2B=CN3C(NC)=C(C)CCB(NCN3CN(C)C2CC)C1=C', 'CCC=CC=CC(CC(C)CC1=C(CC)CC1)N(C)NCCC(C)C', 'CC(N)=CNCCNCNC1=NC(CN)=CC(C=CN)=CN1', 'C=CC1=C(C(C=COCC)(NC)C2(CCCO)CC2C)C2(C)C(=C)C(C)(CN(C)C1)[O+](C)C(C)CC(CC)C1=CC2C1', 'CC1CCC2(C)CC(N)C(C(C)C)C=C3C(=NCCN2)C3NC1', 'CCONC1=[N+](CC)C([O+]=C2CCC2)C1(O)C(C)F']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'C=C(CC=C1OC1C)CCCCC', 'CC=[N+](C)NC=C1CCC=CCC(CCCCC=CNCC(C=NN(C)C)CC)=NCCCCCCO1', 'CB1NN(NN)C=CC1B1CC=CNB(N)CC1', 'C=COC(C=CCN)=CC(CC)[O+]1NC=CCCOC1=C', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC(C=CC1CC(CCCC=CCl)C1)COF', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'C=CC(C)(C=C)C1NO1', 'CNC[N+](N)=CC=C(N=CC(=N)C=C(N)NCN)C(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'C=C(CC=C1OC1C)CCCCC', 'CC=[N+](C)NC=C1CCC=CCC(CCCCC=CNCC(C=NN(C)C)CC)=NCCCCCCO1', 'CB1NN(NN)C=CC1B1CC=CNB(N)CC1', 'C=COC(C=CCN)=CC(CC)[O+]1NC=CCCOC1=C', 'CC(C=CN(C)C)C(C)CCC1=CC=C1', 'CC(C=CC1CC(CCCC=CCl)C1)COF', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'C=CC(C)(C=C)C1NO1', 'CNC[N+](N)=CC=C(N=CC(=N)C=C(N)NCN)C(C)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules to hit a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)CN(N)C(N[N+]1(N)C=BNNB=CN1)N(C)C=NN', 'CCC(=CC1(C(C)(CC)CC(C)(CC)CC)COC1)C1=C2CC=CCC2(C)C1', 'C=C=CC=CC(C)C=CCOC(CCCCCC)=C(CCCC(C)=CCC)C(C)(C)C=O', 'CCCCC(CC)CC(C)=CC=CCC1=CC2(C=CCNCCC3C2[O+]3C)C1', 'CC=CC=CC=CCCC(=CC(C)C)CCCCC(CC)NCCC', 'CCC(C)=CCCCC=C(C)NC(C)C=CO', 'CC=CCCC1=CCC(C)CNCC1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C=C1CC2CC=C(C)C(C)(CC2C)C1C', 'CC(C)CCC=CC1=CCC2=C(C1)C1C=C(CC(=CNCC[N+](=CCN)C(C)(CC3=CC3)NCO)CC1)NCCC=N2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)CN(N)C(N[N+]1(N)C=BNNB=CN1)N(C)C=NN', 'CCC(=CC1(C(C)(CC)CC(C)(CC)CC)COC1)C1=C2CC=CCC2(C)C1', 'C=C=CC=CC(C)C=CCOC(CCCCCC)=C(CCCC(C)=CCC)C(C)(C)C=O', 'CCCCC(CC)CC(C)=CC=CCC1=CC2(C=CCNCCC3C2[O+]3C)C1', 'CC=CC=CC=CCCC(=CC(C)C)CCCCC(CC)NCCC', 'CCC(C)=CCCCC=C(C)NC(C)C=CO', 'CC=CCCC1=CCC(C)CNCC1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C=C1CC2CC=C(C)C(C)(CC2C)C1C', 'CC(C)CCC=CC1=CCC2=C(C1)C1C=C(CC(=CNCC[N+](=CCN)C(C)(CC3=CC3)NCO)CC1)NCCC=N2']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['BCN[N+](=N)C=CC=CNNCCN', 'CCCC(N)=CC(C)C', 'C=CCC=C(C1(C)CONC(N)CN(CC)C(N2C(C=COC)NCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC', 'C=CC1(CC2CC=CN=CCN2)CC=C=CNC(NCC(C)=CN(C)NCC=N)NC(NC)C1', 'C=CC1=CC=CC(N(OC)C(=C)C)CC2NC3=CC(=CO)CC(C)C(=NCC2C3)C1', 'CCC(C)CC(CC)(CC(C)[N+]1(C)C2NC1C2=NCN)C(C)C=[O+]C', 'C=C(C=[N+]1CC1=CC)CCC', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'CNB(C)CB1N(CC2(N)NN(N)N=CC3=NN(N=NC=N)C(C=C2N=NCCN)C3C)N1NC', 'C=CC=CC=CC(CCN)C(N)C(CC)=[O+]C=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCN[N+](=N)C=CC=CNNCCN', 'CCCC(N)=CC(C)C', 'C=CCC=C(C1(C)CONC(N)CN(CC)C(N2C(C=COC)NCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC', 'C=CC1(CC2CC=CN=CCN2)CC=C=CNC(NCC(C)=CN(C)NCC=N)NC(NC)C1', 'C=CC1=CC=CC(N(OC)C(=C)C)CC2NC3=CC(=CO)CC(C)C(=NCC2C3)C1', 'CCC(C)CC(CC)(CC(C)[N+]1(C)C2NC1C2=NCN)C(C)C=[O+]C', 'C=C(C=[N+]1CC1=CC)CCC', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C', 'CNB(C)CB1N(CC2(N)NN(N)N=CC3=NN(N=NC=N)C(C=C2N=NCCN)C3C)N1NC', 'C=CC=CC=CC(CCN)C(N)C(CC)=[O+]C=N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds reaching a z-score of -6.190392 for the cell line '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=[O+]C(N)CC1CCC1C', 'C=C(C)CCC(N)(N)C(=CC)CC', 'C=C(C)C(C)=CC', 'C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC', 'CCC1=CC2=COC(=N1)C1CC(CCOC=C(C)O)[O+]1C(N)=C2', 'C=NN(CCCNC)C[N+]1=CC=[N+]1C', 'CCNC(C)N', 'CCC(CN)NCC(=[N+]=NNCNCC1(CC)N=C1C)C1CC1N', 'CC[N+]1=NC2C=C(CCN(N)[O+]3C(C)=C(N)C4C(C)CNCC4N3C1)N2', 'C=CC(=CC)C(=C)C(C)(C(=C)CCC(C)CCCCOCCCC)C(C)=CCC(=C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=[O+]C(N)CC1CCC1C', 'C=C(C)CCC(N)(N)C(=CC)CC', 'C=C(C)C(C)=CC', 'C=CC(=C[N+](=CCNC)CC(=C)C(CC)C1=CCC(C)N(CCC)C=C1)CCC', 'CCC1=CC2=COC(=N1)C1CC(CCOC=C(C)O)[O+]1C(N)=C2', 'C=NN(CCCNC)C[N+]1=CC=[N+]1C', 'CCNC(C)N', 'CCC(CN)NCC(=[N+]=NNCNCC1(CC)N=C1C)C1CC1N', 'CC[N+]1=NC2C=C(CCN(N)[O+]3C(C)=C(N)C4C(C)CNCC4N3C1)N2', 'C=CC(=CC)C(=C)C(C)(C(=C)CCC(C)CCCCOCCCC)C(C)=CCC(=C)C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOC(C=CCCC(C)CC(C)CC=C1CC=COC1)C(O)CCC1=CC=CC(C)COCC1', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CC)N1CC(=CC2CCCC2)N1', 'C=C(C)C(C=NN=CC)C(C)C', 'CC(C=CCCN(C)[N+]1=C2CC2=C1)CN', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'CCC(=CCCCOC)CC=CCC(C)C', 'CC=C1CCCCC2(CC)C=C2CC1', 'C=C1NN1C(C(C)C)C(CO[O+]=CC1=CC(C)C1)CC(C)C(CC)C(C)C', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCOC(C=CCCC(C)CC(C)CC=C1CC=COC1)C(O)CCC1=CC=CC(C)COCC1', 'C=NNCCC(C)CN=NC(=CN(C)C(N)CC)N1CC(=CC2CCCC2)N1', 'C=C(C)C(C=NN=CC)C(C)C', 'CC(C=CCCN(C)[N+]1=C2CC2=C1)CN', 'CCCCN(CNC)CC(C)C=NNC1=NC1CC', 'CCC(=CCCCOC)CC=CCC(C)C', 'CC=C1CCCCC2(CC)C=C2CC1', 'C=C1NN1C(C(C)C)C(CO[O+]=CC1=CC(C)C1)CC(C)C(CC)C(C)C', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules achieving a z-score of -4.054651 for '684059'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCOCC(C)NC(C)C(C)CC(C)C', 'C=C1NC(C)C1C=NC=NCC', 'C=C(CCC1CCC1)C(C)C1CCC1O', 'CCCC1CN=C2C=C(C=[N+](CS)COCC)CC(C)N(CO2)C1', 'CC(C)=CCCNC(CC(C)NCCN)C1CCC(C)C1', 'C=C(CC)NC1(N)CCCC(=C)C1C', 'CC(C)=CCNCC=C(C)O', 'CB[N+](=NCCNN(C)NN)C(C)=CC=NC(C)N', 'CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC(N)C=N)NC)NN1', 'CNN1NC=NC(C(C)C)=C(CC2(C)CCNC2)N=NN1C(C)=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCOCC(C)NC(C)C(C)CC(C)C', 'C=C1NC(C)C1C=NC=NCC', 'C=C(CCC1CCC1)C(C)C1CCC1O', 'CCCC1CN=C2C=C(C=[N+](CS)COCC)CC(C)N(CO2)C1', 'CC(C)=CCCNC(CC(C)NCCN)C1CCC(C)C1', 'C=C(CC)NC1(N)CCCC(=C)C1C', 'CC(C)=CCNCC=C(C)O', 'CB[N+](=NCCNN(C)NN)C(C)=CC=NC(C)N', 'CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC(N)C=N)NC)NN1', 'CNN1NC=NC(C(C)C)=C(CC2(C)CCNC2)N=NN1C(C)=N']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)OC1(N)C2CC(C)(CC)C2CC(N)CC=C[N+](=CC)CCCC1(C)C', 'C=CC1=C(C(C=CCC)CN=C(CC)NC=CC)CN1', 'C=CC1CC(C)=[N+](CCC)C(C)=CC=CC(CC)NN(COC)N(C=C)C1', 'C=CCCC(C)CCCCO', 'CC=CC1CCCC(C(C)=C(C)N)COCC1', 'CCC(C)=NCC(C)C=[N+](C)C', 'C=C(C)N=C(C)N1CC1', 'CCC(C)(CCC(C(C)=NC=N)C(C)N)CC1(CCNN)CC=C1C', 'CCC(C)=CCCC(CCN)CC(C)C(C)CC', 'CC=NC(C)C=CC=C(CCCC)C(=CC)CC(=CC)CCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)OC1(N)C2CC(C)(CC)C2CC(N)CC=C[N+](=CC)CCCC1(C)C', 'C=CC1=C(C(C=CCC)CN=C(CC)NC=CC)CN1', 'C=CC1CC(C)=[N+](CCC)C(C)=CC=CC(CC)NN(COC)N(C=C)C1', 'C=CCCC(C)CCCCO', 'CC=CC1CCCC(C(C)=C(C)N)COCC1', 'CCC(C)=NCC(C)C=[N+](C)C', 'C=C(C)N=C(C)N1CC1', 'CCC(C)(CCC(C(C)=NC=N)C(C)N)CC1(CCNN)CC=C1C', 'CCC(C)=CCCC(CCN)CC(C)C(C)CC', 'CC=NC(C)C=CC=C(CCCC)C(=CC)CC(=CC)CCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[N+](NN(C)N)=C(N)CC=CN', 'C=C(NC(NC)NNC1(NCN)BCC1)C(C)=NNC(C)NNN', 'CC=CC1C=NC2C=CCC(=CC(C)C(CCC)OCC2C)C=CCNC1(C)CCC(C)(C)CC', 'C=CC(CN(C=CC(=CNC)CCCC)C(N)O)NCC(C)(O)NC', 'CCCC1=CC=C(C)CC(C)CC(C=C[O+]=CC2=C(CC)C2CC)=CC1', 'CB(C)CCC1=NCCN2CCC(C)C2C=C1', 'CCC=CC(CCC(NN)=[O+]CC)C[N+](=C(CC)CC=CNNC)C(C)N', 'CC=CC=C1C=CC(C)C(C)C(C)=C(CCO)C(O)(CC)CCC1', 'C=C1CCC1C1=C(CC)CC1', 'C=C1CC=CC(C)=CC2=C1C=C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[N+](NN(C)N)=C(N)CC=CN', 'C=C(NC(NC)NNC1(NCN)BCC1)C(C)=NNC(C)NNN', 'CC=CC1C=NC2C=CCC(=CC(C)C(CCC)OCC2C)C=CCNC1(C)CCC(C)(C)CC', 'C=CC(CN(C=CC(=CNC)CCCC)C(N)O)NCC(C)(O)NC', 'CCCC1=CC=C(C)CC(C)CC(C=C[O+]=CC2=C(CC)C2CC)=CC1', 'CB(C)CCC1=NCCN2CCC(C)C2C=C1', 'CCC=CC(CCC(NN)=[O+]CC)C[N+](=C(CC)CC=CNNC)C(C)N', 'CC=CC=C1C=CC(C)C(C)C(C)=C(CCO)C(O)(CC)CCC1', 'C=C1CCC1C1=C(CC)CC1', 'C=C1CC=CC(C)=CC2=C1C=C2']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'CNCC(C)NNC1CC1CCC(C)C(C)OC', 'CC1=C2C3=NN(N)C=NCC(C)C=C4C=[N+]5C=[N+](C(C3)NCC5)N(C4)N2C=CN1', 'C=C(CC(C)CC)[N+](=N)COCC', 'C=C(C=CC(C)C=CCCC(C)C)N=CNCC=CCCO', 'C=C(CC)NCCN(CC)C(C=CC)C(C)C(N=C(CCC)CC(CCC)CC(=CCN)CCCC)=NC', 'CCCCCCC=CC(C)CCC', 'CCCC=C1C=C(CC=CCCN)C1CCC', 'CC1N=CN2C=[O+]C2CCN1C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CC(C)C(=CC)CC=CCC(C)(C)C)=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1', 'CNCC(C)NNC1CC1CCC(C)C(C)OC', 'CC1=C2C3=NN(N)C=NCC(C)C=C4C=[N+]5C=[N+](C(C3)NCC5)N(C4)N2C=CN1', 'C=C(CC(C)CC)[N+](=N)COCC', 'C=C(C=CC(C)C=CCCC(C)C)N=CNCC=CCCO', 'C=C(CC)NCCN(CC)C(C=CC)C(C)C(N=C(CCC)CC(CCC)CC(=CCN)CCCC)=NC', 'CCCCCCC=CC(C)CCC', 'CCCC=C1C=C(CC=CCCN)C1CCC', 'CC1N=CN2C=[O+]C2CCN1C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CC(C)C(=CC)CC=CCC(C)(C)C)=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of compounds reaching a z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CNC=CC(N[N+](N)=CBC)C(N)CCN', 'CCNC=C(N)CN=[N+](CC)C1C=[N+]1C', 'CCCNCCC(=CC=C(C)C=[N+](C)C(CC)CCC)CC=CCNN', 'C=C(C=CC)C=NCCNCN', 'C=C(C)C[O+]1C2=CC(=C(C)CC)OC(OC=NCC(C)=CCC)CCC1C2', 'NCC1CCC=CCN[O+](N)NO1', 'C=[N+]1CN(CCCNC)N1C(C)CCC(=CCC(CC)=C(C)CC)CCCC(=[O+]C)[N+]1=C(C)N(N)C1', 'C=C1C=C=CC(C(CC)CCC)=C1C=CC=CCOOC(C)C(C)=C(C)N', 'CC=CC=CN1C=CC([O+]=CC(OC)C(C)C)=CC=CC=C(CC)O1', 'CC=CCC1CC=CC=CN2CCC3=C(CO)C12CCC=CC1OC1CCC3=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CNC=CC(N[N+](N)=CBC)C(N)CCN', 'CCNC=C(N)CN=[N+](CC)C1C=[N+]1C', 'CCCNCCC(=CC=C(C)C=[N+](C)C(CC)CCC)CC=CCNN', 'C=C(C=CC)C=NCCNCN', 'C=C(C)C[O+]1C2=CC(=C(C)CC)OC(OC=NCC(C)=CCC)CCC1C2', 'NCC1CCC=CCN[O+](N)NO1', 'C=[N+]1CN(CCCNC)N1C(C)CCC(=CCC(CC)=C(C)CC)CCCC(=[O+]C)[N+]1=C(C)N(N)C1', 'C=C1C=C=CC(C(CC)CCC)=C1C=CC=CCOOC(C)C(C)=C(C)N', 'CC=CC=CN1C=CC([O+]=CC(OC)C(C)C)=CC=CC=C(CC)O1', 'CC=CCC1CC=CC=CN2CCC3=C(CO)C12CCC=CC1OC1CCC3=CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C(NC)C(C)CC=N)N(N)CCC1CN1', 'CC=CCCCN1NC12CC21CCC=C2CCCC3=NCC(C)(C(CC)CCC=CCC(CC4=BC4)C(C)=N)CC(C[N+](C)=CC)(CC4(C)C=C4)C(N)(N=C23)C2C1N2C', 'CCCCCCC1=CC=C(C=C(C)C2CCCC(CCCC)C2)COC(OC)=CC1', 'C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CC=CCCC(=N)CCC)CCN=C(C)CC1', 'CBCCc1ccn(CC)n1', 'C=NCC(O)=CC', 'CC=CC=CC=NC(=N)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=COC(O)(CNCC)C(CO)CCC(CN)OCC1CC', 'CC=CCCC1NNC12CCNC2', 'CCC(C)=CC=NC=CC1CC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(C(NC)C(C)CC=N)N(N)CCC1CN1', 'CC=CCCCN1NC12CC21CCC=C2CCCC3=NCC(C)(C(CC)CCC=CCC(CC4=BC4)C(C)=N)CC(C[N+](C)=CC)(CC4(C)C=C4)C(N)(N=C23)C2C1N2C', 'CCCCCCC1=CC=C(C=C(C)C2CCCC(CCCC)C2)COC(OC)=CC1', 'C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CC=CCCC(=N)CCC)CCN=C(C)CC1', 'CBCCc1ccn(CC)n1', 'C=NCC(O)=CC', 'CC=CC=CC=NC(=N)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=COC(O)(CNCC)C(CO)CCC(CN)OCC1CC', 'CC=CCCC1NNC12CCNC2', 'CCC(C)=CC=NC=CC1CC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules aimed at a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(OCS)C(C)CCC(C)C', 'CCC(=NCC(C)CN=CC1=C(N)C1)NC', 'CC1=CC2CCCC(C)=C3C(C)=C3C2CC1', 'C=C1CC2(CCCC=CC(=CCCC=C(C)C)C2)N1', 'CCCC1CCC(CCCC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CNC1=[N+]2NNC(C(N)=CNC(C)NC3CCBC=C[N+]1=NNCC=C3N)N(CC=N)CNCC(C)C[N+]1=C2C1', 'CCC=C(C)CC=CC1C=C2CC2C=CC(C=C(C)C)C(C(C)O)CCOC1', 'C=C1N=C(C(CN)NCO)C2=C(C=NC)C(C=NCCN1CC)NB2N', 'CCNNC1=C(CN)C2=C(C2)C2CC3CC4(C)C(=NOC3C4C)CC(=CC(NC)=CCCC1)N2', 'CCC(CNC)COC=C1C[O+]2CCC(C)(CC)CCC12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(OCS)C(C)CCC(C)C', 'CCC(=NCC(C)CN=CC1=C(N)C1)NC', 'CC1=CC2CCCC(C)=C3C(C)=C3C2CC1', 'C=C1CC2(CCCC=CC(=CCCC=C(C)C)C2)N1', 'CCCC1CCC(CCCC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'CNC1=[N+]2NNC(C(N)=CNC(C)NC3CCBC=C[N+]1=NNCC=C3N)N(CC=N)CNCC(C)C[N+]1=C2C1', 'CCC=C(C)CC=CC1C=C2CC2C=CC(C=C(C)C)C(C(C)O)CCOC1', 'C=C1N=C(C(CN)NCO)C2=C(C=NC)C(C=NCCN1CC)NB2N', 'CCNNC1=C(CN)C2=C(C2)C2CC3CC4(C)C(=NOC3C4C)CC(=CC(NC)=CCCC1)N2', 'CCC(CNC)COC=C1C[O+]2CCC(C)(CC)CCC12']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)C1(C)CCCCC1', 'BC(C)C=C(N)CCC', 'CCC(C)=CCCC(O)C(C)CC=CC1CC(C(C)C)C(C)=[O+]1', 'CCC(CC1C=[O+]C=C(C)C(C)=CC(C)N1)=NCCC1=CC1', 'C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CC1=CNN(N)[N+](C)(CNCCN)N2C=C3CC3CC1CC2', 'NC1=CC=CC(CONC2C=CC2)C1', 'CCCNCC(C)CC1CCCN1', 'C=C(CO)CCC1=CC=C2OCC(C)=C(NNCC1=CC)[N+]1=CCC21', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(C)C1(C)CCCCC1', 'BC(C)C=C(N)CCC', 'CCC(C)=CCCC(O)C(C)CC=CC1CC(C(C)C)C(C)=[O+]1', 'CCC(CC1C=[O+]C=C(C)C(C)=CC(C)N1)=NCCC1=CC1', 'C=C(OC(C)CN)C(C)CCC=C[O+]=C(N)O', 'CC1=CNN(N)[N+](C)(CNCCN)N2C=C3CC3CC1CC2', 'NC1=CC=CC(CONC2C=CC2)C1', 'CCCNCC(C)CC1CCCN1', 'C=C(CO)CCC1=CC=C2OCC(C)=C(NNCC1=CC)[N+]1=CCC21', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce a collection of molecules with a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=C=C(C)C=CC1=C2CC(C)C(CCCCCC(C)N)N(C1)C2', 'BC(C)C=C(N)CCC', 'C=CCC(C)C1CCCC(=NCCCC(S)S)N1', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'C=C1C=C=CN(C(CC)CCC)C1C=CC=CCCOC(C)C(C)=C(C)N', 'C=C(COC=CC(C)NNC=CC)COCC(=CC)CC(=C)CC', 'CC=NC1=C(O)C(C)CO1', 'CC=C1CC2=C(CNC)C(ON2O)C(C)C=CCC(C)CC2=CC(C)=[NH+]CCC1=C2', 'CC=CC=[N+]1CCC=C(C=[N+]2CC2(C)C)CC=C[SH](C)O1', 'C=CC1NCC[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CC)C=C(C)CCC(=CC)C1CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=C=C(C)C=CC1=C2CC(C)C(CCCCCC(C)N)N(C1)C2', 'BC(C)C=C(N)CCC', 'C=CCC(C)C1CCCC(=NCCCC(S)S)N1', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'C=C1C=C=CN(C(CC)CCC)C1C=CC=CCCOC(C)C(C)=C(C)N', 'C=C(COC=CC(C)NNC=CC)COCC(=CC)CC(=C)CC', 'CC=NC1=C(O)C(C)CO1', 'CC=C1CC2=C(CNC)C(ON2O)C(C)C=CCC(C)CC2=CC(C)=[NH+]CCC1=C2', 'CC=CC=[N+]1CCC=C(C=[N+]2CC2(C)C)CC=C[SH](C)O1', 'C=CC1NCC[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CC)C=C(C)CCC(=CC)C1CC1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC(C)=CC=CC(CC(O)(CC)CC=C(C)CC)[O+]=NCNCC(CC)COCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1CC(CNC2)N=N1', 'CCCCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=CCC3C4C)CC(=CC(CC)=CCCC1)N2', 'CC=C(C=CCCC)C=[O+]CC=NCC', 'C=[O+]C(N)CC1CCC1C', 'C=C(C)[SH](=O)(OC(CCC)OCN(CC)CCCC1(C)CC1)C(C)CC', 'C=CC=CC(CCNC)CC1=CCCC1', 'CN1CC2CCCCNCC2C1', 'CC1=CCCCC2=C(CC2)C(C[O+](C)C)C1', 'C=[N+]1CCCCNC2=[N+](N=N2)N=NC(C)NC(=N)C[N+]1=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC(C)=CC=CC(CC(O)(CC)CC=C(C)CC)[O+]=NCNCC(CC)COCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1CC(CNC2)N=N1', 'CCCCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=CCC3C4C)CC(=CC(CC)=CCCC1)N2', 'CC=C(C=CCCC)C=[O+]CC=NCC', 'C=[O+]C(N)CC1CCC1C', 'C=C(C)[SH](=O)(OC(CCC)OCN(CC)CCCC1(C)CC1)C(C)CC', 'C=CC=CC(CCNC)CC1=CCCC1', 'CN1CC2CCCCNCC2C1', 'CC1=CCCCC2=C(CC2)C(C[O+](C)C)C1', 'C=[N+]1CCCCNC2=[N+](N=N2)N=NC(C)NC(=N)C[N+]1=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds reaching a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CC)C1C2CCNC3CN(N(C)CC)CC(=CCCC(C)(C)C1C(C)CCCC(C)C)C(C)CC3C2', 'C=C1CC2(CCCC=CC(=CCCC=C(C)C)C2)O1', 'CC=CCCCC(C)CC', 'C=CNC1CC(C)C(CC2=C(C)C(CC)=C2C)CCC(C)(C2CCC3CC(C(C)=NC=CN)C2N3)C1', 'CCCCCC=C(CC=CN)CCCC(C)=C1C=C1C', 'CCC(CC)=NCC(C)CN=CC1=C(N)O1', 'C=C1C(=C(N)CC(C)CC=NC)CC(OC)CC(C(C)C)CCC1C(C)=CC', 'CC1=CC2CCC[N+](C)=C3C(C)=C3N2CC1', 'C=NC(C=CC)C(CNN)N1C=CC2CN(C(=C)C)CN1C(C)C2N', 'CCCC[O+](CC12C=NC3=C(CC=C31)C2C=CCN)NC=CN(CCCO)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CC)C1C2CCNC3CN(N(C)CC)CC(=CCCC(C)(C)C1C(C)CCCC(C)C)C(C)CC3C2', 'C=C1CC2(CCCC=CC(=CCCC=C(C)C)C2)O1', 'CC=CCCCC(C)CC', 'C=CNC1CC(C)C(CC2=C(C)C(CC)=C2C)CCC(C)(C2CCC3CC(C(C)=NC=CN)C2N3)C1', 'CCCCCC=C(CC=CN)CCCC(C)=C1C=C1C', 'CCC(CC)=NCC(C)CN=CC1=C(N)O1', 'C=C1C(=C(N)CC(C)CC=NC)CC(OC)CC(C(C)C)CCC1C(C)=CC', 'CC1=CC2CCC[N+](C)=C3C(C)=C3N2CC1', 'C=NC(C=CC)C(CNN)N1C=CC2CN(C(=C)C)CN1C(C)C2N', 'CCCC[O+](CC12C=NC3=C(CC=C31)C2C=CCN)NC=CN(CCCO)OC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCC)C(CC)=C1C=C(O)CCC1', 'CCNC(C)CNC=CCN', 'C=CC(CC(=N)NN(N)NN=NN(C)N)CC(=NNN=C(N)NN)NNC', 'CC=CNC(=CCC(C=CCC)C1=C(C=CO)CC1)CC', 'CB(N)C1CCCCC=NCC(C)=C1C', 'C=C(CCNC(C)(C)C(CCCCCCN)CN(C)CC)C(C)C', 'CC=[N+](CC(C)C=CCCC)C(S)=CCC(F)C1=C2C=CC(=C(O)C(CC)=C2)C1C', 'C=CC=C1C(C)=CC1NC(C)=NC=CCCC1(N=CC2CC2)CC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'CC(C)CNC1CC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCC)C(CC)=C1C=C(O)CCC1', 'CCNC(C)CNC=CCN', 'C=CC(CC(=N)NN(N)NN=NN(C)N)CC(=NNN=C(N)NN)NNC', 'CC=CNC(=CCC(C=CCC)C1=C(C=CO)CC1)CC', 'CB(N)C1CCCCC=NCC(C)=C1C', 'C=C(CCNC(C)(C)C(CCCCCCN)CN(C)CC)C(C)C', 'CC=[N+](CC(C)C=CCCC)C(S)=CCC(F)C1=C2C=CC(=C(O)C(CC)=C2)C1C', 'C=CC=C1C(C)=CC1NC(C)=NC=CCCC1(N=CC2CC2)CC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'CC(C)CNC1CC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CNC1=C(C)C2=C(CC1C)CC2C(C)O)[N+](=C)CC', 'CCC(C)(NC)NC', 'CC1CCC2C=NC1O2', 'C=CCC1CCOC(C)C(=CCC)COC=CCC=CN(C)CC=CC(C)(N)C=NC=N1', 'C=[N+]1CN=C(ON)C=CCCCNC=CCC2=C(CN=CCCN(C)C2C)N1', 'C=CCCCC(C)=CC(=CC)C[O+]=C', 'C=C(CCCOC)C1COC2=CC(=CC(C)C3CC3=C2)OC1', 'CNC(C)C(=CC(C)C1C2=CC(C)=NC21)[O+]=CCCNCO', 'C=CCC(CC)C(C=C1CCC1)C(CC=CC)C(C)=CO', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CNC1=C(C)C2=C(CC1C)CC2C(C)O)[N+](=C)CC', 'CCC(C)(NC)NC', 'CC1CCC2C=NC1O2', 'C=CCC1CCOC(C)C(=CCC)COC=CCC=CN(C)CC=CC(C)(N)C=NC=N1', 'C=[N+]1CN=C(ON)C=CCCCNC=CCC2=C(CN=CCCN(C)C2C)N1', 'C=CCCCC(C)=CC(=CC)C[O+]=C', 'C=C(CCCOC)C1COC2=CC(=CC(C)C3CC3=C2)OC1', 'CNC(C)C(=CC(C)C1C2=CC(C)=NC21)[O+]=CCCNCO', 'C=CCC(CC)C(C=C1CCC1)C(CC=CC)C(C)=CO', 'C=C(CC)CCCC1=CC2N3C(COC=CCC)OC(CCCCCC(CC)C23C)N=C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=[N+](CN(CC)NC)NC=CCCCC=C(CN)C1C=C1', 'CC=CC(C=CC(=O)C1=CC(N(C)C)CCC1=CC1=CC1)=C[O+]1CC1C', 'CCN=[N+](C)NC(C)C', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'C=CC=C1[O+]=CC2C3=CC=NC(C)NC(C)C(=C3)CC1C2=C(C)CC(=C)CC', 'C=CCC1=CN(C(C)C=CC=CN=CCC)C1(CCC)C(=CCC)CCC', 'CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCNCCC', 'CC(C)(C)CC1=CC=CCN(N)N[N+](N)=CC2NC(=N)C2C1', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'CCCCC=CCCCONC1=C(C)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=[N+](CN(CC)NC)NC=CCCCC=C(CN)C1C=C1', 'CC=CC(C=CC(=O)C1=CC(N(C)C)CCC1=CC1=CC1)=C[O+]1CC1C', 'CCN=[N+](C)NC(C)C', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'C=CC=C1[O+]=CC2C3=CC=NC(C)NC(C)C(=C3)CC1C2=C(C)CC(=C)CC', 'C=CCC1=CN(C(C)C=CC=CN=CCC)C1(CCC)C(=CCC)CCC', 'CCCC=C(N)CC(=CC=C(C)C=[N+](C)C(F)(CC)CCC)CCNCCC', 'CC(C)(C)CC1=CC=CCN(N)N[N+](N)=CC2NC(=N)C2C1', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'CCCCC=CCCCONC1=C(C)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -4.054651 for the cell line '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC=NC[N+]1=NC(C)(B(N)CC(=N)CC(CBN)NC(=N)N)N1C', 'C=CC(C)C1C2=N[N+](C)=CN1N2', 'C=C(CCC1CC2(C)CC3=CCNCC4C(O)C1NC3C(C)C=CC42)CC1=CC2OCC12C[O+](C)CN', 'CCC1CCCC=CCCC(C)CNC1=C1CCC1C', 'C=CO[O+](C)CCC(C)=CC=CC=CCOC', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C(C)CCC2(CC2)CCC1(C)C', 'C=CCC(CC)C1=C(C)C(C)(N)CC(CCCC)C(C)C(N)C(CC2(CCC)CC(CC)C2C2COC2CC)CNC=CC1CC', 'C=C1C[N+]2=CC(=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'C=COC=CC1NC(C)N(CC)C(CC)=C(CCCCC(C)CN(C)CCC)CC=[N+]1C', 'C=C1C=[N+]2CN3C=C(CC)C(C)C4CC=CC(C)=C4C2=CC(=N1)C3C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNC=NC[N+]1=NC(C)(B(N)CC(=N)CC(CBN)NC(=N)N)N1C', 'C=CC(C)C1C2=N[N+](C)=CN1N2', 'C=C(CCC1CC2(C)CC3=CCNCC4C(O)C1NC3C(C)C=CC42)CC1=CC2OCC12C[O+](C)CN', 'CCC1CCCC=CCCC(C)CNC1=C1CCC1C', 'C=CO[O+](C)CCC(C)=CC=CC=CCOC', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C(C)CCC2(CC2)CCC1(C)C', 'C=CCC(CC)C1=C(C)C(C)(N)CC(CCCC)C(C)C(N)C(CC2(CCC)CC(CC)C2C2COC2CC)CNC=CC1CC', 'C=C1C[N+]2=CC(=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'C=COC=CC1NC(C)N(CC)C(CC)=C(CCCCC(C)CN(C)CCC)CC=[N+]1C', 'C=C1C=[N+]2CN3C=C(CC)C(C)C4CC=CC(C)=C4C2=CC(=N1)C3C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCNCC(C)(C)C', 'C=C(NC)NCCCC(=C)C1=C2NCC(C)=[O+]N=CC=C(C=CC)CC=CC2N(CCC=CC)C1', 'CC(C)=CCNNC(CC(C)NCCN)C1CCC(C)C1', 'C=C(CC1=C(NC)N1CC1(N)NN(N)N=CC2=N[N+](N=NC=N)=C(C=C1N=CCC)C2C)NC', 'CCC=CC=CC(CC(C)=CC1=C(CC)CC1)N(C)NCCC(C)C', 'CC=C1C2=C(CC(C)=C(C)C=C(C(=CC(OC)C(C)CCC(C)(O)NCCCCC)CC)CC(C)N1C)C2', 'CCC(C)CC(CC1CCC1CC)CC1CC2(CC)CCC(C=N2)CC2CN1CC13CC4(CC14C)C23C', 'C=C(C)CC1CC2=C(CC)OC=CC=CCC(F)[O+]=C2C=N1', 'CCCCC(CC)CC(C)=CC=CCC1=CC2(C=CCCCCC3C2[O+]3C)C1', 'CCC(C)COCCC=NCCNF'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCNCC(C)(C)C', 'C=C(NC)NCCCC(=C)C1=C2NCC(C)=[O+]N=CC=C(C=CC)CC=CC2N(CCC=CC)C1', 'CC(C)=CCNNC(CC(C)NCCN)C1CCC(C)C1', 'C=C(CC1=C(NC)N1CC1(N)NN(N)N=CC2=N[N+](N=NC=N)=C(C=C1N=CCC)C2C)NC', 'CCC=CC=CC(CC(C)=CC1=C(CC)CC1)N(C)NCCC(C)C', 'CC=C1C2=C(CC(C)=C(C)C=C(C(=CC(OC)C(C)CCC(C)(O)NCCCCC)CC)CC(C)N1C)C2', 'CCC(C)CC(CC1CCC1CC)CC1CC2(CC)CCC(C=N2)CC2CN1CC13CC4(CC14C)C23C', 'C=C(C)CC1CC2=C(CC)OC=CC=CCC(F)[O+]=C2C=N1', 'CCCCC(CC)CC(C)=CC=CCC1=CC2(C=CCCCCC3C2[O+]3C)C1', 'CCC(C)COCCC=NCCNF']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a variety of molecules aimed at a z-score of -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C=CC2=C(N)C=CC=CC=C(CCCC)CC1CC2', 'CC=C(C)C1=CCC2=C(C2)OC=C1O', 'BC(C)CBCC1=C2CN(N2)C2N=C(N=C(C)CCCNNC)C=C(NCC1)C2=N', 'CC(NCCNCCN)C(C)C1(C=N)NCB1O', 'C=C(CCCN)C(C)(N)CC', 'CCC=C1CC(C)CC2=C(CCCC=CO)C(C)C=CC3CC3C=CC1=C(C)CC1CCCCC(C)[O+]1CC2', 'C=[N+]1C[N+](C)(C(C)C2(C)CC=CC=C2CC)C(NCC)C(N)CN(CN)C1CCC', 'CC1C=c2ccc([nH]2)=CCC=CCCC1', 'C=C(NC)[N+](NCNNC=CC(N)=Cc1cc(N=CC)[n+](CCCN)[nH]1)=C(C)C=CCC', 'C=C1CC2C=C(N)NN(N)CC=CC=C(C(OC)[O+](C)C)CC12'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=CC2=C(N)C=CC=CC=C(CCCC)CC1CC2', 'CC=C(C)C1=CCC2=C(C2)OC=C1O', 'BC(C)CBCC1=C2CN(N2)C2N=C(N=C(C)CCCNNC)C=C(NCC1)C2=N', 'CC(NCCNCCN)C(C)C1(C=N)NCB1O', 'C=C(CCCN)C(C)(N)CC', 'CCC=C1CC(C)CC2=C(CCCC=CO)C(C)C=CC3CC3C=CC1=C(C)CC1CCCCC(C)[O+]1CC2', 'C=[N+]1C[N+](C)(C(C)C2(C)CC=CC=C2CC)C(NCC)C(N)CN(CN)C1CCC', 'CC1C=c2ccc([nH]2)=CCC=CCCC1', 'C=C(NC)[N+](NCNNC=CC(N)=Cc1cc(N=CC)[n+](CCCN)[nH]1)=C(C)C=CCC', 'C=C1CC2C=C(N)NN(N)CC=CC=C(C(OC)[O+](C)C)CC12']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C(CCC)=C(C)CC(C)=C=CCC)C(C)N=CC=C1CC1O', 'C=CC=CC=CC(CCN)C(C)C(CC)=[O+]C=C', 'CC(C)C=CCCN=CN', 'BBB(B)BBB1BBCNCC1', 'CCC12CCCCC=C3NC(N)=CCC=C(C4=CCC(=C(C)CC1)NCC=C4)C(C)N[NH+]32', 'CN(N)[N+](C)(CN)NN', 'C=C(CC=N)C(C)CC=NCC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)COC=C1', 'C=C(CCC(C)CN1C2=C(C(=CC)CC(CC)CC)C(CC)(CO)CC21)C1(C)CC1', 'C=CC=C(C=CC=C1CC(F)CCC1=CC)C(C)(C)CCN=COO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C(CCC)=C(C)CC(C)=C=CCC)C(C)N=CC=C1CC1O', 'C=CC=CC=CC(CCN)C(C)C(CC)=[O+]C=C', 'CC(C)C=CCCN=CN', 'BBB(B)BBB1BBCNCC1', 'CCC12CCCCC=C3NC(N)=CCC=C(C4=CCC(=C(C)CC1)NCC=C4)C(C)N[NH+]32', 'CN(N)[N+](C)(CN)NN', 'C=C(CC=N)C(C)CC=NCC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)COC=C1', 'C=C(CCC(C)CN1C2=C(C(=CC)CC(CC)CC)C(CC)(CO)CC21)C1(C)CC1', 'C=CC=C(C=CC=C1CC(F)CCC1=CC)C(C)(C)CCN=COO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to reach a z-score of -4.054651 for the cell line '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1CC(C)=[N+](CCC)C(C)=CC=CC(CC)NN(COC)N(C=C)C1', 'CCC(C=C(C)CNCC(C)=CC=C(C)CCC12CCC=C(C)C1C2)C(N)=CC(C)CO', 'CCC1=BNC(C(N)NN(CC)CN)NCC1C', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3C)C2=C(O)C(C)OCCC1', 'CCCC=C(O)C(C=[O+]CC=NCC)=CO', 'C=CC(N=C[NH+](C)F)[N+](CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'C=C(C)N1C=CCNNNN(C)CBCCC(=C(C)NC)NN1C', 'COC(C)(CC=CCN)CCO', 'C=C1CN=C(CC)NC(=C)N(CC(C)C(C)(C)N)C(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1CC(C)=[N+](CCC)C(C)=CC=CC(CC)NN(COC)N(C=C)C1', 'CCC(C=C(C)CNCC(C)=CC=C(C)CCC12CCC=C(C)C1C2)C(N)=CC(C)CO', 'CCC1=BNC(C(N)NN(CC)CN)NCC1C', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3C)C2=C(O)C(C)OCCC1', 'CCCC=C(O)C(C=[O+]CC=NCC)=CO', 'C=CC(N=C[NH+](C)F)[N+](CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'C=C(C)N1C=CCNNNN(C)CBCCC(=C(C)NC)NN1C', 'COC(C)(CC=CCN)CCO', 'C=C1CN=C(CC)NC(=C)N(CC(C)C(C)(C)N)C(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'CCCCCNC1N=NCC2C=C(CNC1O)C(C=C(C)CC)N2', 'C=C(CN)C(CC1=C2C(C)CC(N)CC=[N+]2C1)CC1CC1N', 'CCC(C)(C)C(C)N', 'CCCC(F)CC=CCCCC1C=C2CC(C3CC3=N)=CCCC2CC1', 'CCN1CN(NC)CCC1N1CC(C)CN1', 'C=C(CC)CC(=C)COOCNCCCCN=CCC', 'CCNCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=NOC3C4C)CC(=CC(CC)=CCCC1)N2', 'CN1CC(=[N+](C)C)N(N)CC2CC21', 'CC(C=[N+](C)C)=NCCC(N)CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)CC1', 'CCCCCNC1N=NCC2C=C(CNC1O)C(C=C(C)CC)N2', 'C=C(CN)C(CC1=C2C(C)CC(N)CC=[N+]2C1)CC1CC1N', 'CCC(C)(C)C(C)N', 'CCCC(F)CC=CCCCC1C=C2CC(C3CC3=N)=CCCC2CC1', 'CCN1CN(NC)CCC1N1CC(C)CN1', 'C=C(CC)CC(=C)COOCNCCCCN=CCC', 'CCNCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=NOC3C4C)CC(=CC(CC)=CCCC1)N2', 'CN1CC(=[N+](C)C)N(N)CC2CC21', 'CC(C=[N+](C)C)=NCCC(N)CN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a set of molecules with the desired z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC1=[N+]2NNC(C(N)=CNC(C)NC3CCBC=C[N+]1=NNCC=C3N)N(CC=N)CNCC(C)C[N+]1=C2C1', 'CCC1NCC(=C(C)CN)C1(CN)C(C)(N)NN', 'CN(CO)C(N)CCC1=NC=C1', 'CCCCC=C(C)N(C)OCCCC', 'C=CC=CCCCC(C)C=CC(CC)OC(=CC=CCC)CCC(=C)C(C)CC', 'C=CCCC(O)CNCCC(=C)C=C', 'CNNCCNCC=C[N+](N)=C(C)NC=N', 'C=CC1(C)CCC(N)C(=O)C(C=CC)C1', 'CCC=NCCNC1CC=NC(N)NC(N)CCC=CCB(N)C1', 'C=CC(N)=CB(C)C1CC(CCCCC)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNC1=[N+]2NNC(C(N)=CNC(C)NC3CCBC=C[N+]1=NNCC=C3N)N(CC=N)CNCC(C)C[N+]1=C2C1', 'CCC1NCC(=C(C)CN)C1(CN)C(C)(N)NN', 'CN(CO)C(N)CCC1=NC=C1', 'CCCCC=C(C)N(C)OCCCC', 'C=CC=CCCCC(C)C=CC(CC)OC(=CC=CCC)CCC(=C)C(C)CC', 'C=CCCC(O)CNCCC(=C)C=C', 'CNNCCNCC=C[N+](N)=C(C)NC=N', 'C=CC1(C)CCC(N)C(=O)C(C=CC)C1', 'CCC=NCCNC1CC=NC(N)NC(N)CCC=CCB(N)C1', 'C=CC(N)=CB(C)C1CC(CCCCC)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C1C2C(CCCC)CC3CCC=CN3OC3CC132', 'CCC(C=CC=C(C)N)=CC1CC(C)=CC(=CC2CC3CC(CC)=C(C)NC=C23)C1', 'CC=C(C)C(O)C(C)CCC', 'CCCC(C)(C)C(C(N)CC)C1C=CC(C)CCC([O+](C)C2=CCO2)C1OC(=[O+]CNC)C1=[O+]C1', 'C=C(C)C1=CC1CC', 'C=CC(N=C[NH+](C)F)[N+](CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1', 'C=C(CC=CN)C1=[O+]CCC(C)=C1', 'CCCC(CC)(CC)CCCN', 'BCN=CNCB(B)C(N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C1C2C(CCCC)CC3CCC=CN3OC3CC132', 'CCC(C=CC=C(C)N)=CC1CC(C)=CC(=CC2CC3CC(CC)=C(C)NC=C23)C1', 'CC=C(C)C(O)C(C)CCC', 'CCCC(C)(C)C(C(N)CC)C1C=CC(C)CCC([O+](C)C2=CCO2)C1OC(=[O+]CNC)C1=[O+]C1', 'C=C(C)C1=CC1CC', 'C=CC(N=C[NH+](C)F)[N+](CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1', 'C=C(CC=CN)C1=[O+]CCC(C)=C1', 'CCCC(CC)(CC)CCCN', 'BCN=CNCB(B)C(N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide molecules with a z-score reaching -6.190392 for '1659819'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'CCC(C=CC=C(C)N)=CC1CC(C)=CC(=CC2CC3NC(CC)=C(C)NN=C23)C1', 'CC=C(C)N1COC1C1=CCCC=CCCCCC2OCC2C1CCC', 'CC=C(C)C(O)C(C)CCC', 'CCC=C1CCCC(=CNN)CC=CC[O+]=CC(O)CC1', 'CC=C(NCC)C(C)C=CCC', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'CC=C(O)C1=CC(C)=CC(C(COC)OC(C)CCC)=C(CC)NC(C)(C)CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'CCC(C=CC=C(C)N)=CC1CC(C)=CC(=CC2CC3NC(CC)=C(C)NN=C23)C1', 'CC=C(C)N1COC1C1=CCCC=CCCCCC2OCC2C1CCC', 'CC=C(C)C(O)C(C)CCC', 'CCC=C1CCCC(=CNN)CC=CC[O+]=CC(O)CC1', 'CC=C(NCC)C(C)C=CCC', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'CC=C(O)C1=CC(C)=CC(C(COC)OC(C)CCC)=C(CC)NC(C)(C)CCC1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC1=C(C)C=CC=CC=C(C)N=C(C=N)C(N)=C1C', 'C=CCC1=C(CCC(C)C)[N+](=CCCC(C)(C)N)C(C(C)CN)=C1', 'C=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC', 'CCC(CCCN)CC(C)(CC(C)C)C(CC)NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C', 'CCCNCC(CCC)CCN1CCNC[N+]2=CC3=[N+]1C32', 'CCC(CC)N(CC)NC', 'CCC1=CCCC(C(C)O)C=CC2CCC3(COCCCCC2=C1)CO3', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'C=C(CCCCCC(C)=N)C[N+](=C)C', 'CN=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC1=C(C)C=CC=CC=C(C)N=C(C=N)C(N)=C1C', 'C=CCC1=C(CCC(C)C)[N+](=CCCC(C)(C)N)C(C(C)CN)=C1', 'C=C(CCCC)C(C)=C1C(C)C1CC(C)(C)CC', 'CCC(CCCN)CC(C)(CC(C)C)C(CC)NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C', 'CCCNCC(CCC)CCN1CCNC[N+]2=CC3=[N+]1C32', 'CCC(CC)N(CC)NC', 'CCC1=CCCC(C(C)O)C=CC2CCC3(COCCCCC2=C1)CO3', 'CCCC1CCC(CNNC(C)C)CC1C=CC=C(C)CC12NC[N+]1=[O+]2', 'C=C(CCCCCC(C)=N)C[N+](=C)C', 'CN=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a collection of molecules to match a z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CBN=NNC1NB2NB(N)CC(CCNC(C)C)CCCC2B=NCN1CN', 'CBNB(B(CNC)NN)[NH+]1C(NCNN=N)C1NB(CC)NN', 'BNBCCB(NB1NCCNNCC(=CC)NCN(CCN)N1)C(N)N(C)N', 'B=CB(NC=BBN)B(C)CC', 'C=CN(CCC)N(N)C=C(C)CCN', 'BN1CC=CC2NC=C(C)C=C3CBCCNC2C1C=C3C[N+]1(NC)[NH+]=NCC1(C)C', 'CCCCCCC(C)=CC1=C(CC)CCC1CCCCC=COC', 'C=C(CCC)CCC(O)=CC(C)C(CO)(CCC)CCCC', 'C=CC1CC(C)C(NC)C(CO)=CCC(O)C(CCCCC(N)CC)C=CNC1(C)C(=C)CC', 'CC=NCCC1NCOC(C)C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CBN=NNC1NB2NB(N)CC(CCNC(C)C)CCCC2B=NCN1CN', 'CBNB(B(CNC)NN)[NH+]1C(NCNN=N)C1NB(CC)NN', 'BNBCCB(NB1NCCNNCC(=CC)NCN(CCN)N1)C(N)N(C)N', 'B=CB(NC=BBN)B(C)CC', 'C=CN(CCC)N(N)C=C(C)CCN', 'BN1CC=CC2NC=C(C)C=C3CBCCNC2C1C=C3C[N+]1(NC)[NH+]=NCC1(C)C', 'CCCCCCC(C)=CC1=C(CC)CCC1CCCCC=COC', 'C=C(CCC)CCC(O)=CC(C)C(CO)(CCC)CCCC', 'C=CC1CC(C)C(NC)C(CO)=CCC(O)C(CCCCC(N)CC)C=CNC1(C)C(=C)CC', 'CC=NCCC1NCOC(C)C1C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CNNC(NCBN)NB(C)C', 'C=CN(NCNCCC)C(CN(N)N)C(C=CF)(C=CCC(C)(O)CCC)N=C', 'B=CNC(=NB(N)NBC(C)N(N)CN=CN)[N+]1=CN1', 'C=C(C)CNC(C)OC', 'C=CC(=C)CCC1(CC)NC1CCCCCN', 'CCC(=NC)C(=N)B1CCN(CC=CN)C1', 'C=C1CCN2CC(C=CC)C2C(CC)C1', 'C=[N+]1C=C(N)N=C1C(C)C(=CC=C=C1C=CCC1)C(C)C', 'BC(B)(N=BN[n+]1c2[nH]n1CC2N)C(=N)N', 'BN(C=C(C)CNN)N(C=C)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CNNC(NCBN)NB(C)C', 'C=CN(NCNCCC)C(CN(N)N)C(C=CF)(C=CCC(C)(O)CCC)N=C', 'B=CNC(=NB(N)NBC(C)N(N)CN=CN)[N+]1=CN1', 'C=C(C)CNC(C)OC', 'C=CC(=C)CCC1(CC)NC1CCCCCN', 'CCC(=NC)C(=N)B1CCN(CC=CN)C1', 'C=C1CCN2CC(C=CC)C2C(CC)C1', 'C=[N+]1C=C(N)N=C1C(C)C(=CC=C=C1C=CCC1)C(C)C', 'BC(B)(N=BN[n+]1c2[nH]n1CC2N)C(=N)N', 'BN(C=C(C)CNN)N(C=C)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules aimed at reaching a z-score of -4.054651 for '905957'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCCC(C)(C)C', 'C=C(C=CC=C[O+]1C=CC(=C)C1)CC', 'CCC=CC(C(C)CCCCCC)C(OC)C(C)C', 'CC(=CCCl)CC1CC2C3CC[O+]3C12', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'CC=[O+]C[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'C=CC(=CCC1C(=N)N=CN1C(C)CC=NCC(=C)N)CC', 'C=C(C)C1=CNC=C(NON2CCC2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCCC(C)(C)C', 'C=C(C=CC=C[O+]1C=CC(=C)C1)CC', 'CCC=CC(C(C)CCCCCC)C(OC)C(C)C', 'CC(=CCCl)CC1CC2C3CC[O+]3C12', 'CCC(C)CC(=C1CCCCC1C)N1C=C(CO)C1', 'CC=[O+]C[N+](C)=C[N+]1=CC(O)=NCC(CNC)C[N+](N)=C(C)C1', 'C=C(C)C=CCN[N+]1=CC(C)(C)C1', 'C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'C=CC(=CCC1C(=N)N=CN1C(C)CC=NCC(=C)N)CC', 'C=C(C)C1=CNC=C(NON2CCC2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)CC(=CCC=CCCCC)C[O+](CC(=C)COC=CC(C)CNC=CC)C1(C)CC1', 'C=C1CCCC=C(C)C(C)(NCC)C1C', 'C=C1C(C)CC=CCC=CC(C)=C(C)CC2=C(C2)CC1C[O+](N)CC(CC)CC(C)C', 'CCC=C(CCN)CCC(CCCC)CN=CC(C)CO', 'C=C(C)C(=O)C(C)(C)CC(CC(=CC)CC=C(C)CC)C(=C)C1CC1C(=C)C=CC(C)C', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(CO)NCCC(C)CC1N(C)CNC2CN(C)C2=CCCN1N', 'CC12CC1CC1CC34NC13C24Cl', 'C=CNN=C(C=C(C)CO)N=NNCN', 'C=CCCCC(CC(C)C1C=C1CCCCC(C)C(C)C(C=C1CC1C)C(C)C)C(=CC)C(C=C)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)CC(=CCC=CCCCC)C[O+](CC(=C)COC=CC(C)CNC=CC)C1(C)CC1', 'C=C1CCCC=C(C)C(C)(NCC)C1C', 'C=C1C(C)CC=CCC=CC(C)=C(C)CC2=C(C2)CC1C[O+](N)CC(CC)CC(C)C', 'CCC=C(CCN)CCC(CCCC)CN=CC(C)CO', 'C=C(C)C(=O)C(C)(C)CC(CC(=CC)CC=C(C)CC)C(=C)C1CC1C(=C)C=CC(C)C', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(CO)NCCC(C)CC1N(C)CNC2CN(C)C2=CCCN1N', 'CC12CC1CC1CC34NC13C24Cl', 'C=CNN=C(C=C(C)CO)N=NNCN', 'C=CCCCC(CC(C)C1C=C1CCCCC(C)C(C)C(C=C1CC1C)C(C)C)C(=CC)C(C=C)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules that can reach a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=C(C=CC[N+](C=CC)=CC)CC=CCC', 'C=CCC(C)CC1=CC(C)CN1C(C)N', 'C=CC(C)CN=CC=CC1=NC(C)C(C(C)=O)=N1', 'CC=NC=CC1CC1(C)N=CC(=CN=CC(C)=N)CCC', 'CCCCCCN1NC1C', 'CNN=Cc1ccccc1CN', 'CCC(CC)C1C2CCNC3CN(N(C)CC)CC(=CCCC(C)(C)C1C(C)CCCC(C)C)C(C)CC3C2', 'CCC=C(CC)CC(CC)NC(NC)C1=NC1C', 'CCC=C1CC(=C(C)CCC)C=CCC2=C(CCCC(C)=CC=CC1C)C2', 'CC=CCCC(C)C(C=CCCCC)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=C(C=CC[N+](C=CC)=CC)CC=CCC', 'C=CCC(C)CC1=CC(C)CN1C(C)N', 'C=CC(C)CN=CC=CC1=NC(C)C(C(C)=O)=N1', 'CC=NC=CC1CC1(C)N=CC(=CN=CC(C)=N)CCC', 'CCCCCCN1NC1C', 'CNN=Cc1ccccc1CN', 'CCC(CC)C1C2CCNC3CN(N(C)CC)CC(=CCCC(C)(C)C1C(C)CCCC(C)C)C(C)CC3C2', 'CCC=C(CC)CC(CC)NC(NC)C1=NC1C', 'CCC=C1CC(=C(C)CCC)C=CCC2=C(CCCC(C)=CC=CC1C)C2', 'CC=CCCC(C)C(C=CCCCC)=CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC(OCC1CCC(=C[N+](C)=C(C)C)CC1=N)C1CCC=N1', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=CC1CC1(C)OC', 'C=CC1(CN)CC=CC(C)C2=CCC(=C(CC)C=C(CCCC)C2)CCC(C)C2=CC1=C2', 'BC=CN(C)C(C)C', 'C1=NCCCCCCC1', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'CCN=C(CCN)NNC(CCNC)CN=CCCN', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C1C=CC[N+]1(O)C(C)N(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC(OCC1CCC(=C[N+](C)=C(C)C)CC1=N)C1CCC=N1', 'C[O+](CC=CCCC1CCC1)CC=CC1=CC1', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=CC1CC1(C)OC', 'C=CC1(CN)CC=CC(C)C2=CCC(=C(CC)C=C(CCCC)C2)CCC(C)C2=CC1=C2', 'BC=CN(C)C(C)C', 'C1=NCCCCCCC1', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'CCN=C(CCN)NNC(CCNC)CN=CCCN', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C1C=CC[N+]1(O)C(C)N(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -6.190392 for the cell line '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCCC(C)C(C=CCCCC)=CC', 'CC1=CCCCC(=[N+]2C=CC(C=CNC(C)C=NN)=C(C)N2)N=CC1', 'CCCCCCN1NC1C', 'CCC(C=CC=CCC(F)(CO)NC)=CN', 'CCCC1(CCC(O)N=[N+]=C(CCC(CC)CC)C2CC2C)C=C1C', 'CNC=CCNCC1CC(C)(O)CC(C)OCC23C(N)C2NCC13', 'CCCCC(C)CC=C(C(=CO)CC(C)C=C(C)C)C(C)C', 'CNCCCC(=CC=CN)C(N)CNC1C(C)C2=[N+]1C=BC2', 'C=C(C)CN=CCC(C=CC)=CC', 'CCCC(C)=C1CN=[N+]2NC2=NCC(C)=CNC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCCC(C)C(C=CCCCC)=CC', 'CC1=CCCCC(=[N+]2C=CC(C=CNC(C)C=NN)=C(C)N2)N=CC1', 'CCCCCCN1NC1C', 'CCC(C=CC=CCC(F)(CO)NC)=CN', 'CCCC1(CCC(O)N=[N+]=C(CCC(CC)CC)C2CC2C)C=C1C', 'CNC=CCNCC1CC(C)(O)CC(C)OCC23C(N)C2NCC13', 'CCCCC(C)CC=C(C(=CO)CC(C)C=C(C)C)C(C)C', 'CNCCCC(=CC=CN)C(N)CNC1C(C)C2=[N+]1C=BC2', 'C=C(C)CN=CCC(C=CC)=CC', 'CCCC(C)=C1CN=[N+]2NC2=NCC(C)=CNC1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)N(C)CCNN', 'CNC1=CN=C1CC1=CN(CN)CC(C=N)N=CC1', 'C=C(CCCCCC(F)CC)C(C)(CC)CCNC', 'C=C(C=C1C2=C1C(=CCCC(C)C)C2)CCC(C)(C)C(=CC)CC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CC1=C2C3=NN(N)C=NCC(N)C=C4C=[N+]5C=[N+](C(C3)NNC5)N(C4)N2C=CN1', 'C=C(C)C=C1C=C(C2C=CC2)C(CC2(COOC(C)=CC)OC2CCC)C(C)N(S(=O)CCO)O1', 'C=C(COCC=CC)C(C)CC', 'C=C1CCCC(CCC(C)N2CC(N)=C(C)N2CCCC2C(C)=CCC2CNC)C[NH+](OCC)C1', 'C=C(CN)C(C=NC)OC(C)(CC1=CC2CC(C2NC=CCC)C(C)(C)C1)C(=C)C=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)N(C)CCNN', 'CNC1=CN=C1CC1=CN(CN)CC(C=N)N=CC1', 'C=C(CCCCCC(F)CC)C(C)(CC)CCNC', 'C=C(C=C1C2=C1C(=CCCC(C)C)C2)CCC(C)(C)C(=CC)CC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'CC1=C2C3=NN(N)C=NCC(N)C=C4C=[N+]5C=[N+](C(C3)NNC5)N(C4)N2C=CN1', 'C=C(C)C=C1C=C(C2C=CC2)C(CC2(COOC(C)=CC)OC2CCC)C(C)N(S(=O)CCO)O1', 'C=C(COCC=CC)C(C)CC', 'C=C1CCCC(CCC(C)N2CC(N)=C(C)N2CCCC2C(C)=CCC2CNC)C[NH+](OCC)C1', 'C=C(CN)C(C=NC)OC(C)(CC1=CC2CC(C2NC=CCC)C(C)(C)C1)C(=C)C=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically for achieving a z-score of -6.190392 for '722045'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(CC(=CN)C(C)OCC)C1C2=[O+]C(CC2)CC(Cl)=CC1CC=NOC(C)(C=O)CC', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)C1', 'CCCCCCC=CC(C)CCC', 'CC=CC=C(C)C(C)CNC1C2=C(CC=C2)C1CC(=CC)CC', 'C=C1C=CCC(CCCN=CCC)CC(C)=CC=CCCC(C)N1', 'C=CCC(CC(C)CCCC)C1(C=CCCC)CCC2CC(NCC3CC3)C(C2)C1CC', 'CC=C(CCC(CC)=C1CC=CC(C=N)C12CC2)OCCCC1CC[O+]1C', 'C=CN=CC(C=C(N)N)CN', 'N=C(CN)NCNC(N)NC1N(N)CCC=C2B(N)BC2NNN1N', 'CNCCC1=C(N)N(C)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(CC(=CN)C(C)OCC)C1C2=[O+]C(CC2)CC(Cl)=CC1CC=NOC(C)(C=O)CC', 'CCOCC(C)C1CC=CC(NC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)C1', 'CCCCCCC=CC(C)CCC', 'CC=CC=C(C)C(C)CNC1C2=C(CC=C2)C1CC(=CC)CC', 'C=C1C=CCC(CCCN=CCC)CC(C)=CC=CCCC(C)N1', 'C=CCC(CC(C)CCCC)C1(C=CCCC)CCC2CC(NCC3CC3)C(C2)C1CC', 'CC=C(CCC(CC)=C1CC=CC(C=N)C12CC2)OCCCC1CC[O+]1C', 'C=CN=CC(C=C(N)N)CN', 'N=C(CN)NCNC(N)NC1N(N)CCC=C2B(N)BC2NNN1N', 'CNCCC1=C(N)N(C)CC1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)=CC1=CCNC(C(O)NC)=CC=C(C(C)C)[O+]=C1', 'BC(C)C(C)C(CC)C1C[N+]1(CCCC)C(CC)CCBCC', 'C=CN(CCCC(C)=N)C(C)C', 'CCCC(C)C1(C)CCCCC1', 'CNCCC(CN)=C(C)NC1=CCC2=[N+](CN2)N1', 'C=CCCON(OC(C)C1=C2CC2CC=C(CC)CC1)C(C)(CC=C)CNC(OC)C(=C)NCC(=C)C(C)CC', 'CCCCC(=N)CC(NCC)NCCN', 'C=[N+]1CN1C1N=CC=CCC=C2CNCC(N(C)CC)C1=[N+]2CCCC', 'CCC(CC1=CCCC1)[O+]=CNN', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(C)=CC1=CCNC(C(O)NC)=CC=C(C(C)C)[O+]=C1', 'BC(C)C(C)C(CC)C1C[N+]1(CCCC)C(CC)CCBCC', 'C=CN(CCCC(C)=N)C(C)C', 'CCCC(C)C1(C)CCCCC1', 'CNCCC(CN)=C(C)NC1=CCC2=[N+](CN2)N1', 'C=CCCON(OC(C)C1=C2CC2CC=C(CC)CC1)C(C)(CC=C)CNC(OC)C(=C)NCC(=C)C(C)CC', 'CCCCC(=N)CC(NCC)NCCN', 'C=[N+]1CN1C1N=CC=CCC=C2CNCC(N(C)CC)C1=[N+]2CCCC', 'CCC(CC1=CCCC1)[O+]=CNN', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that achieve a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC=C(CN(N)C=CN)C1=C(C)C1', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCCCC1CNNN(N)N=NNCC2CCC1=N[N+](C)=CCC=[N+]2C', 'CC=[O+]CC=[O+]CCC(C)C1=CC(CCC)C(=[O+]C(=CCCC(C)C)CCCC)C=CC=C1', 'CCC(CNC=CCC(C)C)=C(C)C1=CC1C', 'CC1CCCC12CC2C', 'C=C(CCC(=CCOC)CC(C)CCCC1CC[O+](C)C1=C)CC(C)CC', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'CC=[N+](C)NC=C1CCC=CCC(CNCCC=CNCC(C=NN(C)C)CC)=NCCCCCCN1', 'BNCCN(C=CC=C)CC1=CCNC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNC=C(CN(N)C=CN)C1=C(C)C1', 'CCC=C(C)C(C)CCC1CCCNC1C', 'CCCCC1CNNN(N)N=NNCC2CCC1=N[N+](C)=CCC=[N+]2C', 'CC=[O+]CC=[O+]CCC(C)C1=CC(CCC)C(=[O+]C(=CCCC(C)C)CCCC)C=CC=C1', 'CCC(CNC=CCC(C)C)=C(C)C1=CC1C', 'CC1CCCC12CC2C', 'C=C(CCC(=CCOC)CC(C)CCCC1CC[O+](C)C1=C)CC(C)CC', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'CC=[N+](C)NC=C1CCC=CCC(CNCCC=CNCC(C=NN(C)C)CC)=NCCCCCCN1', 'BNCCN(C=CC=C)CC1=CCNC1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCCC1C=CC=CC(C)=CC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CC2)CC1=O', 'CCCC1=NC(OCC=CCCO)C=C1', 'C=COC=CC1NC(C)N(CC)C(CC)=C(CCCCC(C)CC(C)CCC)CC=[N+]1C', 'CCC12CCCCC=C3NC(N)=CCC=C(CC=CCNC(C)=C(C)CC1)C(C)N[NH+]32', 'CC[O+]=CCN1CC=CCC1', 'C=C(N=N)C(NC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'C=C(C1CC=[O+]1)[N+]1=CC(=N)C1', 'CCCCC(CCCC=C(C)CN=CCC(C)C)CC(=CC(C)C)C(=N)C(C)C', 'CC=C(CCC(O)C(C)CC)C(C)=CCCCC', 'B=NCC(CC=C(N)NCC)NCN=C(C)BCC1C=CC(C)[N+]1(N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCCC1C=CC=CC(C)=CC=CC(=C)C=CC(C(=C)C(C)C)=C(C=CC=C2CC2)CC1=O', 'CCCC1=NC(OCC=CCCO)C=C1', 'C=COC=CC1NC(C)N(CC)C(CC)=C(CCCCC(C)CC(C)CCC)CC=[N+]1C', 'CCC12CCCCC=C3NC(N)=CCC=C(CC=CCNC(C)=C(C)CC1)C(C)N[NH+]32', 'CC[O+]=CCN1CC=CCC1', 'C=C(N=N)C(NC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'C=C(C1CC=[O+]1)[N+]1=CC(=N)C1', 'CCCCC(CCCC=C(C)CN=CCC(C)C)CC(=CC(C)C)C(=N)C(C)C', 'CC=C(CCC(O)C(C)CC)C(C)=CCCCC', 'B=NCC(CC=C(N)NCC)NCN=C(C)BCC1C=CC(C)[N+]1(N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C[O+](C)C(CCCC)C(C)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'BN(NC)NC=NCCC=C', 'C=CCC(CC(C=CC=CC1=CC1C)CC=CC1COC1)NCC(N)C=C(C)C(C)(C)CNC', 'C=C(C)CN(C1=C2CNCC(C)=NCC(N)=CC(CN1)C(C)C2)C1NNC1C', 'CC(CC=CCN)(N=N)NCO', 'CC1=NN(CCC(NF)=C(C)O)CCCCC2=C(C)C=C12', 'CC=C(C=COC(N)CC)C1=CCCC(C=CCC)=CCC(CC)=C1C=CCC=CN', 'C=CCC1=CC=C(C)C(C)=CCCC=C2N=C(CC)C=C(CC1)C1CC(C)CC3=CC(=C31)C2=[N+]1CN1', 'B=BNC(CCNC)C(=C(C)[N+](C)(N)BC(BNBC)CC)C(C=CNCCCC1(CCC=N)CC(CC)C1N1CBC1C)CC', 'C=CCCCCC=CCCCCCNNC1NC1N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C[O+](C)C(CCCC)C(C)=C(C=CC1=[N+]=CCC(=C)CCC1)CCOC1=CC=C1', 'BN(NC)NC=NCCC=C', 'C=CCC(CC(C=CC=CC1=CC1C)CC=CC1COC1)NCC(N)C=C(C)C(C)(C)CNC', 'C=C(C)CN(C1=C2CNCC(C)=NCC(N)=CC(CN1)C(C)C2)C1NNC1C', 'CC(CC=CCN)(N=N)NCO', 'CC1=NN(CCC(NF)=C(C)O)CCCCC2=C(C)C=C12', 'CC=C(C=COC(N)CC)C1=CCCC(C=CCC)=CCC(CC)=C1C=CCC=CN', 'C=CCC1=CC=C(C)C(C)=CCCC=C2N=C(CC)C=C(CC1)C1CC(C)CC3=CC(=C31)C2=[N+]1CN1', 'B=BNC(CCNC)C(=C(C)[N+](C)(N)BC(BNBC)CC)C(C=CNCCCC1(CCC=N)CC(CC)C1N1CBC1C)CC', 'C=CCCCCC=CCCCCCNNC1NC1N']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC(C=N)C1(CN2CCC(=CC)OCCC(C3NCN3C)CC2)CC1', 'C=CC=CCC1CC(C)C(C)=C(CC)C=CCC(CC(=C)C)C1C', 'C=NC(CCN=CC(C)NC1CCNC2C(CCNNC)CCN=C1C(C)C2C)CCC(N)=CCC', 'CC(C)=NCC1(C)CC1(C)C', 'CCC1(C[O+](C)C)C(C)C(N)(C(C)=CC=[N+]=CC=CC[O+]=C(C)C(C)N)CC(C=CN)N1C', 'C=CCC=CN(C)C(C(C=C)=CC)C1COC1', 'CB(N)CNCN', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CCCON(CC(C)C)C1=CC(C)CC1', 'C=C(C=N)C(C)=NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC(C=N)C1(CN2CCC(=CC)OCCC(C3NCN3C)CC2)CC1', 'C=CC=CCC1CC(C)C(C)=C(CC)C=CCC(CC(=C)C)C1C', 'C=NC(CCN=CC(C)NC1CCNC2C(CCNNC)CCN=C1C(C)C2C)CCC(N)=CCC', 'CC(C)=NCC1(C)CC1(C)C', 'CCC1(C[O+](C)C)C(C)C(N)(C(C)=CC=[N+]=CC=CC[O+]=C(C)C(C)N)CC(C=CN)N1C', 'C=CCC=CN(C)C(C(C=C)=CC)C1COC1', 'CB(N)CNCN', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'CCCON(CC(C)C)C1=CC(C)CC1', 'C=C(C=N)C(C)=NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a batch of compounds with a z-score of -4.054651 for '684055'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1CCCC(=N)[O+]=CCCC1C', 'C=B[N+]1(C)C(=C)NC=CC1=CB=NC=N', 'CB(NCC(C)CN=BNN)B(N=CC(N)CNN)N(C)CB=CC=[N+](C)C=N', 'BC(CC)C(C[N+](B)=C)NN(C)B(N)N1CNC1', 'C=C(N)N=[N+](CNNCNN=CN)C1BCB(N)CBN1', 'C=C(CCN(C)C)C1(C)CC1=C1C[O+]2C(C)=CC2C(C)C(C(C)C(CC=CC)OCN)C1C', 'CCC(C=CCCC1CC(C)(COCN)C1)=CCCNO', 'C=CC1CC(NC(C)C)(C(C=C)C(C)=O)CC1NC(C)C1C=CC1', 'B=BN1B=C(BBB)B(N)NB1', 'CCCC(C)C(CCC1=CC=C1C=CC=C(C)C)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1CCCC(=N)[O+]=CCCC1C', 'C=B[N+]1(C)C(=C)NC=CC1=CB=NC=N', 'CB(NCC(C)CN=BNN)B(N=CC(N)CNN)N(C)CB=CC=[N+](C)C=N', 'BC(CC)C(C[N+](B)=C)NN(C)B(N)N1CNC1', 'C=C(N)N=[N+](CNNCNN=CN)C1BCB(N)CBN1', 'C=C(CCN(C)C)C1(C)CC1=C1C[O+]2C(C)=CC2C(C)C(C(C)C(CC=CC)OCN)C1C', 'CCC(C=CCCC1CC(C)(COCN)C1)=CCCNO', 'C=CC1CC(NC(C)C)(C(C=C)C(C)=O)CC1NC(C)C1C=CC1', 'B=BN1B=C(BBB)B(N)NB1', 'CCCC(C)C(CCC1=CC=C1C=CC=C(C)C)OC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CN1NC2=NCB1CC=NC(N)NCB2', 'CNN=C1NB(C(C)CCN)B=[N+]2CC3(N)NC3C=NCN=C2B=NN1C', 'CCCCCCCCC(C)CC1CC(=N)C1C(C)C', 'CC=CC(C)CC1CC2CC12C(C)C1C(C)C(=NN)C(C=CCCC(=CCCCOCCC)CCC)C1C', 'CNCNC1CN(N)NNB(C)C2CNB3NNC2(CCC(N)(NCNC)C1)C3C', 'C=C1CCN2CC(C=CC)C2C(CC)C1', 'CC=CC1=C(CCC)C1', 'BB1B2NB3NC4CNC4C=C(NC)C31N2', 'CCC=CCC(C)N(CC)CC=CC1CCCC(CCNCC)CC(C)NO1', 'BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN1NC2=NCB1CC=NC(N)NCB2', 'CNN=C1NB(C(C)CCN)B=[N+]2CC3(N)NC3C=NCN=C2B=NN1C', 'CCCCCCCCC(C)CC1CC(=N)C1C(C)C', 'CC=CC(C)CC1CC2CC12C(C)C1C(C)C(=NN)C(C=CCCC(=CCCCOCCC)CCC)C1C', 'CNCNC1CN(N)NNB(C)C2CNB3NNC2(CCC(N)(NCNC)C1)C3C', 'C=C1CCN2CC(C=CC)C2C(CC)C1', 'CC=CC1=C(CCC)C1', 'BB1B2NB3NC4CNC4C=C(NC)C31N2', 'CCC=CCC(C)N(CC)CC=CC1CCCC(CCNCC)CC(C)NO1', 'BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules to achieve a z-score of -4.054651 against '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C=CCC(C=CCCC=C(C)N(O)C(C)C=NC(CC)(COC)CC(C)=CCC)C1', 'CC(C)=[N+](CC=CCC=CCN)CCN', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'CC=CCCCC(=CCC(C)CCC(C)=C(C)CCCC=C(C)OC(C)=C(C)C1=C(N)O1)CC', 'CCC1CC2(C)CCCCC3C=CC(=CC2C)CNCCCCC(OCCN3)C1CC', 'C=CC1(C)C(N)[N+](C)=CC2CCC(C)C3CCCCCC1(C3)C(C)(C(C)C(C)C)C2C', 'CN=C1CN1CC=C1CCC(C)NN=CC(NCN=CN(C)C(C)NC)C1', 'C=NCCCN=CC(C)NC1NCNC2C(CCNNC)CCN=C1C(C)C2C', 'CCCC=C(C)CC(CCC)N=Cc1ccn[nH]1', 'CC1=NCC(C)(C)C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=CCC(C=CCCC=C(C)N(O)C(C)C=NC(CC)(COC)CC(C)=CCC)C1', 'CC(C)=[N+](CC=CCC=CCN)CCN', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'CC=CCCCC(=CCC(C)CCC(C)=C(C)CCCC=C(C)OC(C)=C(C)C1=C(N)O1)CC', 'CCC1CC2(C)CCCCC3C=CC(=CC2C)CNCCCCC(OCCN3)C1CC', 'C=CC1(C)C(N)[N+](C)=CC2CCC(C)C3CCCCCC1(C3)C(C)(C(C)C(C)C)C2C', 'CN=C1CN1CC=C1CCC(C)NN=CC(NCN=CN(C)C(C)NC)C1', 'C=NCCCN=CC(C)NC1NCNC2C(CCNNC)CCN=C1C(C)C2C', 'CCCC=C(C)CC(CCC)N=Cc1ccn[nH]1', 'CC1=NCC(C)(C)C1C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=COC(O)(CNCC)C(CO)CCC(CN)OCC1CC', 'CC(=CC1C=N1)C=C1CCC(C)C1', 'C=C1CC(C)(CC)CC12CC1C3=C4C5N(CC[N+]1(C=CC(C)CC41CC(=C)C5(C)C1)C2)CO3', 'CCC(C)(C)CNC(C)NC=CCNN', 'C=C(C=CCCC=C(CC=CN=CCCCC(C)C(CC)CCC=O)C1CC(N)CC(CO)C1)CC', 'CCNC(=NC=[N+](C=CNN)CNC1(N)CC1=[N+](C)CC(C)N(C)CC(C)C(C)N)C(C)=CNCNC', 'C=BCC(CC)C1=C(C)C(C)(N)CC(CNCC)C(C)N(N)C(CC2(CCC=N)CC(CC)C2C(C)CC)CNC=CC1CC', 'C=C(C)CCNC(O)C1(NC)C=C(C2=C3C2C3(C)CC)C(O)=C(C)NCC1C', 'CC=CCCC1CC(C)=C[O+]=C(C=C2NC2=CC=C(N)C=C(CN(C)C)C(C)CC)N1', 'C=[O+]CC(C=C(C)CCCC=CF)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=COC(O)(CNCC)C(CO)CCC(CN)OCC1CC', 'CC(=CC1C=N1)C=C1CCC(C)C1', 'C=C1CC(C)(CC)CC12CC1C3=C4C5N(CC[N+]1(C=CC(C)CC41CC(=C)C5(C)C1)C2)CO3', 'CCC(C)(C)CNC(C)NC=CCNN', 'C=C(C=CCCC=C(CC=CN=CCCCC(C)C(CC)CCC=O)C1CC(N)CC(CO)C1)CC', 'CCNC(=NC=[N+](C=CNN)CNC1(N)CC1=[N+](C)CC(C)N(C)CC(C)C(C)N)C(C)=CNCNC', 'C=BCC(CC)C1=C(C)C(C)(N)CC(CNCC)C(C)N(N)C(CC2(CCC=N)CC(CC)C2C(C)CC)CNC=CC1CC', 'C=C(C)CCNC(O)C1(NC)C=C(C2=C3C2C3(C)CC)C(O)=C(C)NCC1C', 'CC=CCCC1CC(C)=C[O+]=C(C=C2NC2=CC=C(N)C=C(CN(C)C)C(C)CC)N1', 'C=[O+]CC(C=C(C)CCCC=CF)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCCCC=CCC(CCCC)CC[O+](C)CC(C)CCC', 'CCCC1=C2C=CCC2C1', 'CCC(C)(CCC(C(C)=NC=N)C(N)CO)CC1(CCNN)CC=C1C', 'CC=C(C=COC(N)CC)C1=CCCC(C=CCC)=CCC(CC)=C1C=CCC=CN', 'C=[N+](C)C1=NC(N)C(CC(C)CCCCC)C1', 'C=C(CC=CCCC=CC1=CC1)C(N=CCN)NC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'CCCCC1CNNN(N)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'C=NC(CC(N)C(N)C(C)(C)CC)NNC=C(C)C(=NC(C)C)C(N)(C=CCN=N)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCCCC=CCC(CCCC)CC[O+](C)CC(C)CCC', 'CCCC1=C2C=CCC2C1', 'CCC(C)(CCC(C(C)=NC=N)C(N)CO)CC1(CCNN)CC=C1C', 'CC=C(C=COC(N)CC)C1=CCCC(C=CCC)=CCC(CC)=C1C=CCC=CN', 'C=[N+](C)C1=NC(N)C(CC(C)CCCCC)C1', 'C=C(CC=CCCC=CC1=CC1)C(N=CCN)NC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'CCCCC1CNNN(N)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'C=NC(CC(N)C(N)C(C)(C)CC)NNC=C(C)C(=NC(C)C)C(N)(C=CCN=N)CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C=CC(N)(CN)CC1CCC(C=CCN)CCCCCCC1C1CN[O+](C)C1)C=C(C(CC)=C(CNCCCC)CC(C)(C)C(C)O)C1CC1(C)NC', 'CC=CC(O)(C=CC(=C1OOC1(C)OC)C(C[O+](C)CC(C)C)C(C)C)C=CC(C)CC', 'C=CC(N=CC(C)F)C(CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'CCC(C)C(N)CC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CCCC1(CC(C)C(C)=COC)C=NC(N)(CCCC2NC2C)CCCC(CC)(CCC)C(C)C1', 'BC(B[N+](=N)N(C)CNB=NB=C(N)N)CN', 'CCC(C)(C)CNC(C)NC=CCNN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C=CC(N)(CN)CC1CCC(C=CCN)CCCCCCC1C1CN[O+](C)C1)C=C(C(CC)=C(CNCCCC)CC(C)(C)C(C)O)C1CC1(C)NC', 'CC=CC(O)(C=CC(=C1OOC1(C)OC)C(C[O+](C)CC(C)C)C(C)C)C=CC(C)CC', 'C=CC(N=CC(C)F)C(CC(CC)C(C)=CC)=C(C)C=CCCC=CC', 'CCC1=CC=CC2=[O+]C(=[O+]1)C(C)CCCCC1=[O+]COCC(CC)CC2[N+](C)=C1', 'CCC(C)C(N)CC', 'C=CC(CC)CC1CC(=C(C)CC)C(CC)(C(C)(C)OO)C1CC', 'CCC=CCCC1CCCCCC(O)=C[O+]1CO', 'CCCC1(CC(C)C(C)=COC)C=NC(N)(CCCC2NC2C)CCCC(CC)(CCC)C(C)C1', 'BC(B[N+](=N)N(C)CNB=NB=C(N)N)CN', 'CCC(C)(C)CNC(C)NC=CCNN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds that meet a z-score of -6.190392 for '1240166'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC1CNNN(C)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'C=C(C)C(CC)C(CCCOO)NC', 'CCCCCC(CC)[N+]1(CCCC)CC1C1CCCCC1C(C)N', 'CCOCC(C)C1CC=CC(CC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)O1', 'C=C(C)CCC(C)(CF)CC(C=C(N)C(C)CC)CC1=CCC2C=C(NC(N)N=CCC1)C(C)NO2', 'C=CC=C(C)C(N(CC)CC)=[N+](C)C(C=C[N+]1=CCN(C)C1)NNC', 'CCCCCNCN(CN)N=CN', 'CC=C1C2=CC(CCCN)=C(O)C(=C(C)COCC(C)C2)CCC1C', 'C=CC1=C(C)C=C(NNN=CCN)C(C=NCC=N)C1=C', 'C=CC(C)[N+](=CCC)N=C=N[N+]1=CC(C(CNC(C)CNC(CC)[N+](N)=CC=CC(C)N)NC)=C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC1CNNN(C)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'C=C(C)C(CC)C(CCCOO)NC', 'CCCCCC(CC)[N+]1(CCCC)CC1C1CCCCC1C(C)N', 'CCOCC(C)C1CC=CC(CC2C=CC2)C(=CCO)CCC(C=C(C)C)=C(C)O1', 'C=C(C)CCC(C)(CF)CC(C=C(N)C(C)CC)CC1=CCC2C=C(NC(N)N=CCC1)C(C)NO2', 'C=CC=C(C)C(N(CC)CC)=[N+](C)C(C=C[N+]1=CCN(C)C1)NNC', 'CCCCCNCN(CN)N=CN', 'CC=C1C2=CC(CCCN)=C(O)C(=C(C)COCC(C)C2)CCC1C', 'C=CC1=C(C)C=C(NNN=CCN)C(C=NCC=N)C1=C', 'C=CC(C)[N+](=CCC)N=C=N[N+]1=CC(C(CNC(C)CNC(CC)[N+](N)=CC=CC(C)N)NC)=C1C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCONCC(C)CC', 'CCC=C1[O+]=C2CC(C=C(C)CCC=C3C=C3CC)=C2C1C', 'CCC(=CC1C=CC=C(C(=N)N=CN2NC2N)NNN1)CN', 'C=C(CO)C(CCC)(C1=C(C=CCCCC)C1)C1CC1', 'CCCCC(C)=CC1=CNCC2CCCCC(=CCCC1C1CNNC1CN=CN)C2C', 'C=CCCCCC=CC(=C)C', 'CC(C)=NC[N+]1=C(CCNN=NN)CC1', 'C=CC12C=[N+](CN1)C[N+]1=CCC(=CCC2)CCC=C1', 'CCCCC1CCCCCC(CC)CC(C)CCC=CC=C1N', 'CCCCCCCCC(C=CCC(C)C(C)C)CN=C(C)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCONCC(C)CC', 'CCC=C1[O+]=C2CC(C=C(C)CCC=C3C=C3CC)=C2C1C', 'CCC(=CC1C=CC=C(C(=N)N=CN2NC2N)NNN1)CN', 'C=C(CO)C(CCC)(C1=C(C=CCCCC)C1)C1CC1', 'CCCCC(C)=CC1=CNCC2CCCCC(=CCCC1C1CNNC1CN=CN)C2C', 'C=CCCCCC=CC(=C)C', 'CC(C)=NC[N+]1=C(CCNN=NN)CC1', 'C=CC12C=[N+](CN1)C[N+]1=CCC(=CCC2)CCC=C1', 'CCCCC1CCCCCC(CC)CC(C)CCC=CC=C1N', 'CCCCCCCCC(C=CCC(C)C(C)C)CN=C(C)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CC=CCOC(C)=CC2C(COCC1)C(C)C1CC=C[O+]2NOCCC1', 'CCC1=C2CCCCC(C2)C2=CCC(C)C=C(C)C(C)(CN=CC(=CO)CC1)C2', 'CN=CCC=C1CC(C)C1', 'CC=CCN(N)CC(=CC1=CC2C(C)[N+]2(C)CC(C)=C(C)C=N1)C(=CC=NC=CC)NC', 'CC=C(CCC)C(C=C(N)CC1=CNCCC(C=CC2=CC2)CC=N1)C(N)CC', 'CC=CC=[N+]1CCC=C(C=C2CC2(C)C)CC=C[SH](C)O1', 'CCC1=CCCC(C(C)O)C=CC2CCC3(C=CCCCCC2=C1)CN3', 'CC=COC(=N)CC(CCCC)C(C)(C)C', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CCC(C)C(N)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CC=CCOC(C)=CC2C(COCC1)C(C)C1CC=C[O+]2NOCCC1', 'CCC1=C2CCCCC(C2)C2=CCC(C)C=C(C)C(C)(CN=CC(=CO)CC1)C2', 'CN=CCC=C1CC(C)C1', 'CC=CCN(N)CC(=CC1=CC2C(C)[N+]2(C)CC(C)=C(C)C=N1)C(=CC=NC=CC)NC', 'CC=C(CCC)C(C=C(N)CC1=CNCCC(C=CC2=CC2)CC=N1)C(N)CC', 'CC=CC=[N+]1CCC=C(C=C2CC2(C)C)CC=C[SH](C)O1', 'CCC1=CCCC(C(C)O)C=CC2CCC3(C=CCCCCC2=C1)CN3', 'CC=COC(=N)CC(CCCC)C(C)(C)C', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CCC(C)C(N)CC']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC', 'C=C(NN)C(CCC)(NNN)C1=[N+](C=CCCCC)C1', 'C=C(C)CC=CC=CC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'CCCC1=CCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'CCC1CC(CCN(C)C)CC=CC(C)C=C1C', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'NBC1CC1', 'CC(C[O+](C)N)=C1CCCN(C)C=CCC(C)N1', 'CONCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC', 'C=C(NN)C(CCC)(NNN)C1=[N+](C=CCCCC)C1', 'C=C(C)CC=CC=CC=C(NCC)C(C)CCC1CCCN=CC1=CCC=CCN(C)C', 'CCCC1=CCCC1C(CCCCC1C(C)=C1CC)CCC(C)(C)C', 'CCC1CC(CCN(C)C)CC=CC(C)C=C1C', 'CNC(NC(C)N)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'NBC1CC1', 'CC(C[O+](C)N)=C1CCCN(C)C=CCC(C)N1', 'CONCC(N)C(CC=C=CCCO)=C1C=CC(C)CCC1', 'C=C(CC)C[O+]1CC2CNC3CC(CC)C1=C(C=CN)CCCC3C(C)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a batch of compounds with a z-score of -6.190392 for '1331032'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['BCC1=CCCC=CNNC1=CC=CC=[N+](CC(N)C=N)NC', 'COC(NC1CCC1C)C1C=CC1', 'CNC(CC(C)C)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'CCCNCCN1NC1C', 'CC1CCN=C=C(C=NN(C)CCC=C2C=CNCN2CN)CNCC1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'C=C1CC2=C(C2)CN(C)CC(C)(CNC)C(CCC=CC=CN)=C1CC', 'CNC(C)N(NC)C(C)C(N)N', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)C', 'C=C1C(N)CC=CCC=CC(CCC)=C(C)CC2=C(C2)CC1N=[N+](N)NCCC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCC1=CCCC=CNNC1=CC=CC=[N+](CC(N)C=N)NC', 'COC(NC1CCC1C)C1C=CC1', 'CNC(CC(C)C)=C1CCC(C(C)(C)C)=C(C)C2CC(C)C3(CC3)CC2C1(C)C', 'CCCNCCN1NC1C', 'CC1CCN=C=C(C=NN(C)CCC=C2C=CNCN2CN)CNCC1', 'CC=CCC(O)=C(C)OO[N+](=N)CC(=CCCCC)OC=O', 'C=C1CC2=C(C2)CN(C)CC(C)(CNC)C(CCC=CC=CN)=C1CC', 'CNC(C)N(NC)C(C)C(N)N', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)C', 'C=C1C(N)CC=CCC=CC(CCC)=C(C)CC2=C(C2)CC1N=[N+](N)NCCC(C)C']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCCCCC)C(C=N)C=CCC[O+]=CC1=C(CCC)C=CCC=C1', 'CCCNN=CC(N)=CC=C(C)NC=NCC=CC1CNN1', 'CCCC1(C)CCC=C(C)N(CC)CCC1', 'C=CCN=NC(=CC)C(=C)C1C[NH+]2CNC1NNC2(C)C(=C)C', 'CCCCC=CC=NC(CC)=NCOCCN=C(C)CC', 'CCCCC1C=C2C=C(C2)SCCC1', 'CCC(C)C(C)(CCN)CN=N', 'C=CC(C)C(C=CC)C(C)(N)CCC(C)C(N)=C1COC1', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCCNN=CC(N)=CC=C(C)CC=NCC=CC1CNN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCCCCC)C(C=N)C=CCC[O+]=CC1=C(CCC)C=CCC=C1', 'CCCNN=CC(N)=CC=C(C)NC=NCC=CC1CNN1', 'CCCC1(C)CCC=C(C)N(CC)CCC1', 'C=CCN=NC(=CC)C(=C)C1C[NH+]2CNC1NNC2(C)C(=C)C', 'CCCCC=CC=NC(CC)=NCOCCN=C(C)CC', 'CCCCC1C=C2C=C(C2)SCCC1', 'CCC(C)C(C)(CCN)CN=N', 'C=CC(C)C(C=CC)C(C)(N)CCC(C)C(N)=C1COC1', 'C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCCNN=CC(N)=CC=C(C)CC=NCC=CC1CNN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules with the intended z-score of -4.054651 for '1240182'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(C)CC=COCCCC', 'CCCCC=CCC(CCC1=CCC=CC[N+](C)=C(CC)C(C)N(C)C[N+]2=C(CCC1)N2)NC', 'C=C(CCCCCCCC1CC=C1C)COO', 'C=C(C)CC1C=CC(C)=CC(OC(C)OC=C(C)C)CCCC1=C', 'C=CC=C=CCC1CNCNCCN1', 'C=C(C(C)CC(N)CC)C(CC)CNCC', 'C=CC=[N+]1N(C)C(CC=CCC)C(NCC(C)[O+]=C=CCCO)C1(C)CCCC', 'CCC1CCC(C)C2CNNC3CCC(C)C4=C(CC=CCOC=C5C(C)NC5C)C([O+]5CC5)C(CCC2CCC(C)CCC2OC2C)C(CCC(=O)C3)C(C4)C1', 'CCN=CC=NC=CNC(C)CC(=CNCC)CC', 'CC=C1CCCC(COCO)CCCC(C)(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(C)CC=COCCCC', 'CCCCC=CCC(CCC1=CCC=CC[N+](C)=C(CC)C(C)N(C)C[N+]2=C(CCC1)N2)NC', 'C=C(CCCCCCCC1CC=C1C)COO', 'C=C(C)CC1C=CC(C)=CC(OC(C)OC=C(C)C)CCCC1=C', 'C=CC=C=CCC1CNCNCCN1', 'C=C(C(C)CC(N)CC)C(CC)CNCC', 'C=CC=[N+]1N(C)C(CC=CCC)C(NCC(C)[O+]=C=CCCO)C1(C)CCCC', 'CCC1CCC(C)C2CNNC3CCC(C)C4=C(CC=CCOC=C5C(C)NC5C)C([O+]5CC5)C(CCC2CCC(C)CCC2OC2C)C(CCC(=O)C3)C(C4)C1', 'CCN=CC=NC=CNC(C)CC(=CNCC)CC', 'CC=C1CCCC(COCO)CCCC(C)(C)C1']."
            },
            {
                "from": "human",
                "value": "Generate more molecules. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CC=CC2=C(C)C=CC=CCC(C)(CN)C=C1C(C)=C2C', 'CC=C(CCC)C(C)C=CCC', 'C=C(CCCCCC)C(C=N)C=CCC', 'C=CCC1=CC=CCC(C)(C=CC)NC1(C)CC', 'NC[N+]12C=BN=[N+]1NC2', 'C=C1C=C(C=CCOCCCC)C1C', 'CCC=C(C)C1(CNCC)NCN1', 'CB(C(C)=C1CC2C(C(=N)O)CC23C=NCN3C1)C(C=CN)C=CCCCCN', 'CCCC=C=CC1=C2CCCC3(C)CCCCC(C=CN(CNC)C(=CN3)C=C1)N(C)C2', 'C=[N+]1CC1=C(CCCCCC)CCC1C[O+](C)CC2=C(CCCC2)CCN1NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CC=CC2=C(C)C=CC=CCC(C)(CN)C=C1C(C)=C2C', 'CC=C(CCC)C(C)C=CCC', 'C=C(CCCCCC)C(C=N)C=CCC', 'C=CCC1=CC=CCC(C)(C=CC)NC1(C)CC', 'NC[N+]12C=BN=[N+]1NC2', 'C=C1C=C(C=CCOCCCC)C1C', 'CCC=C(C)C1(CNCC)NCN1', 'CB(C(C)=C1CC2C(C(=N)O)CC23C=NCN3C1)C(C=CN)C=CCCCCN', 'CCCC=C=CC1=C2CCCC3(C)CCCCC(C=CN(CNC)C(=CN3)C=C1)N(C)C2', 'C=[N+]1CC1=C(CCCCCC)CCC1C[O+](C)CC2=C(CCCC2)CCN1NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a range of molecules with the target z-score of -6.190392 for '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240166 is the confirmed cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'CCC(CC1CCCNN=N1)=C(C)NC', 'CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'CCNCC(N)C(CC=C=CCCN)=C1C=CC(C)CCC1', 'CCNC=CN=C(F)CC=N', 'CCC(CC)CCCCC=CCN1C(C(C)CCC(C)C=NC)N1C(C)C', 'CNCC1CN=C(O)C=[N+](C=[N+](C)NC)NC(C)=[N+](N)C1', 'CCN=CC=CCCCC=C1CCC(C)C1', 'CNCNCC=C(CO)CB=NCB(C)N', 'CCC(N)CC=CCCC(CN)NN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'CCC(CC1CCCNN=N1)=C(C)NC', 'CC=C(CC=CCCCNC(CCCC)CCCC(C)C)CCF', 'CCNCC(N)C(CC=C=CCCN)=C1C=CC(C)CCC1', 'CCNC=CN=C(F)CC=N', 'CCC(CC)CCCCC=CCN1C(C(C)CCC(C)C=NC)N1C(C)C', 'CNCC1CN=C(O)C=[N+](C=[N+](C)NC)NC(C)=[N+](N)C1', 'CCN=CC=CCCCC=C1CCC(C)C1', 'CNCNCC=C(CO)CB=NCB(C)N', 'CCC(N)CC=CCCC(CN)NN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules intended for a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The valid cell line is 1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(=C)C1CC=CCCOC1', 'CCC=NCC=[N+](C)C1(C)CC(=CC=NC)CCC(C)CN=C1N', 'CCNCC1=C2CCC2=CC(=C(NN)NCC)CC1', 'C=C1NN1C(CC)C(C)N', 'CCCC1(C)C=C1OC', 'CCC=CC(=CC=[N+]1C=CNC(C)CC1)CC', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'C=C1C=C2CCC3[SH]=CC(C1)C1=C(N=CC)CN2CC(CCCC)=CC(C)=C(OC)C3=C1CC', 'C=CC1C2CC(C)C(CC=C(C)C(CC)OC)CC1C(C)(C1CCC3CC(C(C)=CC=CO)C1C3CC(O)CCC)C2', 'CC=CCNNC(N)=CCC(C)(C=CC)C=CN=C(C)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(=C)C1CC=CCCOC1', 'CCC=NCC=[N+](C)C1(C)CC(=CC=NC)CCC(C)CN=C1N', 'CCNCC1=C2CCC2=CC(=C(NN)NCC)CC1', 'C=C1NN1C(CC)C(C)N', 'CCCC1(C)C=C1OC', 'CCC=CC(=CC=[N+]1C=CNC(C)CC1)CC', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'C=C1C=C2CCC3[SH]=CC(C1)C1=C(N=CC)CN2CC(CCCC)=CC(C)=C(OC)C3=C1CC', 'C=CC1C2CC(C)C(CC=C(C)C(CC)OC)CC1C(C)(C1CCC3CC(C(C)=CC=CO)C1C3CC(O)CCC)C2', 'CC=CCNNC(N)=CCC(C)(C=CC)C=CN=C(C)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules matching a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "Our correct cell line is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC1C2=NCN(C=N2)C1C', 'CCC1=[N+]2CNC(C(C)=CCC(C)OC3CCOC=C[N+]1=NNCC=C3N)C(CC=N)CNCC(C)CC1=C2C1', 'CC(CCC=Cn1cccc1C=CC1=C2CC12)CCC1=CCCC1', 'CCCC1=CC(C)=C1NC=COCC', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CC=CC=CNCCCC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'C=C(C)CCC(C)(C)CC(C=CN)C[N+]1=CNC2C=C(NN(N)N=C(N)CC1)C(N)NO2', 'CCCC=NCB=CNCC(=N)NCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1C=NC(CNC2)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC1C2=NCN(C=N2)C1C', 'CCC1=[N+]2CNC(C(C)=CCC(C)OC3CCOC=C[N+]1=NNCC=C3N)C(CC=N)CNCC(C)CC1=C2C1', 'CC(CCC=Cn1cccc1C=CC1=C2CC12)CCC1=CCCC1', 'CCCC1=CC(C)=C1NC=COCC', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CC=CC=CNCCCC', 'C=CC(C)(C(C)C(=CN(C)C(NCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C', 'C=C(C)CCC(C)(C)CC(C=CN)C[N+]1=CNC2C=C(NN(N)N=C(N)CC1)C(N)NO2', 'CCCC=NCB=CNCC(=N)NCC', 'C=C1CCC2=C(CC)C(N1)C1CCCCC1=CC=C1C=NC(CNC2)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a batch of compounds with a target z-score of -6.190392 for '7220450000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"7220450000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "722045. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=CC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1', 'CCC=NC1=C2C=C[N+](C)(NC2)C2C=CC(=CN2)CC=[N+]1C=[N+](CCNC)C(CC)CN', 'C=CC=CC1=C[N+](C(C)CCC(OC)=C(C)CC(C(=N)C=C)C(C)C=C)=CC(=CC)CC1', 'CCC=C(N)C1=C2OCCC(CC)C(=CC2C)CC2=CC=CN(C=C(C)C(C)C)C=C(CC1)C(C)NC2', 'CCCC=NCCC(CCC)=NC', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1', 'C=CCC(=C(C)C=C1C=C(C)C1)C1CC(=CC=CN(N=CNNC(=C)C(C)CC(C)C)C(C)(C)CC)C1', 'CC[N+]1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CNNCC(C=C1CCCN(N)C1)NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=CC1NCN[N+]1=CC1C([O+](C)O)C1(C)CC=C(C=CN)C=C(C)CCC(=CC)C1CC1', 'CCC=NC1=C2C=C[N+](C)(NC2)C2C=CC(=CN2)CC=[N+]1C=[N+](CCNC)C(CC)CN', 'C=CC=CC1=C[N+](C(C)CCC(OC)=C(C)CC(C(=N)C=C)C(C)C=C)=CC(=CC)CC1', 'CCC=C(N)C1=C2OCCC(CC)C(=CC2C)CC2=CC=CN(C=C(C)C(C)C)C=C(CC1)C(C)NC2', 'CCCC=NCCC(CCC)=NC', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1', 'C=CCC(=C(C)C=C1C=C(C)C1)C1CC(=CC=CN(N=CNNC(=C)C(C)CC(C)C)C(C)(C)CC)C1', 'CC[N+]1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CNNCC(C=C1CCCN(N)C1)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that reach a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The most suitable cell line for this task is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C(C=[OH+])CC1CCC1=CN(C(N)CO)NC(CC=CCCC)=C2C=CC(OC2)C(N)C=C1CC', 'C=C(CN(C)CC)[N+](BC1BCC=C1)=CN1CN1C', 'CCCCC(=N)CC=CNC(C)C1C=C(N)C=CCC1', 'C=CC=CCCCCC=NCCCC', 'CCCC(C)=CC1=CCCC(C(O)CC)=CC=C(C(C)C)[O+]=C1', 'CCC(CCC=CC1CO1)=S(CC)CC=O', 'C=NN=C1CNC(C)(CCC=C2CC2)OC1[N+](=C)CCSC', 'CCC(C=CCC(C)C)C(C)=CCCCC(C)C(CC=CCC=CC1=C[O+]1C)=[O+]C', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C(CC(=C)C1=CC(C)(CC)CCCC=C1)CC1(C)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C(C=[OH+])CC1CCC1=CN(C(N)CO)NC(CC=CCCC)=C2C=CC(OC2)C(N)C=C1CC', 'C=C(CN(C)CC)[N+](BC1BCC=C1)=CN1CN1C', 'CCCCC(=N)CC=CNC(C)C1C=C(N)C=CCC1', 'C=CC=CCCCCC=NCCCC', 'CCCC(C)=CC1=CCCC(C(O)CC)=CC=C(C(C)C)[O+]=C1', 'CCC(CCC=CC1CO1)=S(CC)CC=O', 'C=NN=C1CNC(C)(CCC=C2CC2)OC1[N+](=C)CCSC', 'CCC(C=CCC(C)C)C(C)=CCCCC(C)C(CC=CCC=CC1=C[O+]1C)=[O+]C', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C(CC(=C)C1=CC(C)(CC)CCCC=C1)CC1(C)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of compounds with a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The accurate cell line is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CN=CN=C(C)N(N)CCCN[N+](N)=C(C)C', 'C=C(CC(CC)CCC1(CCC)CCC1)C(C)=NNC(C)NCC', 'C=C(CCCC=CC)NC(CC)C1C(=C(N)CC(C)CC=NC)C(=C)C(C(C)=CC)CCC(C(C)C)CC1OC', 'BC(CCCNCN)C1CC=CB1N', 'C=CC(=CC(CC(=CC(C)CCOC(C)(O)O)OC)=C(C)C)CNC', 'CCC=CCC(CC(C=C(C)CC)=CO)C(C)C', 'CCNNC1=C(CN)C2=C(C2)C2CC3CC(C)C(C)(C3)C(=N)CC(=CC(NC)=CCCC1)N2', 'CON(C(=N)N)C1C=CC=C(C=O)CC2=NCC3CC(=CC(=CO)CC2C)NC3C1', 'CCCC(O)C=CC(C)N(N)CC=C1CNC1C', 'C=[N+]1C=NC(C)C(C)C(N)=C(CCO)C(=CC)CCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN=CN=C(C)N(N)CCCN[N+](N)=C(C)C', 'C=C(CC(CC)CCC1(CCC)CCC1)C(C)=NNC(C)NCC', 'C=C(CCCC=CC)NC(CC)C1C(=C(N)CC(C)CC=NC)C(=C)C(C(C)=CC)CCC(C(C)C)CC1OC', 'BC(CCCNCN)C1CC=CB1N', 'C=CC(=CC(CC(=CC(C)CCOC(C)(O)O)OC)=C(C)C)CNC', 'CCC=CCC(CC(C=C(C)CC)=CO)C(C)C', 'CCNNC1=C(CN)C2=C(C2)C2CC3CC(C)C(C)(C3)C(=N)CC(=CC(NC)=CCCC1)N2', 'CON(C(=N)N)C1C=CC=C(C=O)CC2=NCC3CC(=CC(=CO)CC2C)NC3C1', 'CCCC(O)C=CC(C)N(N)CC=C1CNC1C', 'C=[N+]1C=NC(C)C(C)C(N)=C(CCO)C(=CC)CCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -6.190392 for the cell line '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The cell line that should be used is 1240166. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'C=C(N)CCCCC=CC1=CCC(CC=CCC=CC)CC(C)OC1', 'CC[O+](C)C1C2=CC1=C(C)CCCC(C)(CO)CO2', 'C=CCCCC1(C)B(C)CCNC=NCC1(C)CCN(C=CCC)C(C)C', 'C=C(C)CC1=C2CCC(OC=NCC(C)=CCC)OC(=C(C)CC)CC1C2', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)NC=C(C)CC', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'CBNC(C)N', 'C=C1CN1[O+](C(C)C)C(CO[O+]=CC1=CC(C)C1)CC(C)C(CC)C(C)C', 'CCCCCCC=CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'C=C(N)CCCCC=CC1=CCC(CC=CCC=CC)CC(C)OC1', 'CC[O+](C)C1C2=CC1=C(C)CCCC(C)(CO)CO2', 'C=CCCCC1(C)B(C)CCNC=NCC1(C)CCN(C=CCC)C(C)C', 'C=C(C)CC1=C2CCC(OC=NCC(C)=CCC)OC(=C(C)CC)CC1C2', 'CCOCC(CC)NNCN=NC(C=CC=C(C)C=NC)NC(N)(CC)NC=C(C)CC', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'CBNC(C)N', 'C=C1CN1[O+](C(C)C)C(CO[O+]=CC1=CC(C)C1)CC(C)C(CC)C(C)C', 'CCCCCCC=CN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules aimed at reaching a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'CCC(C)[O+]1COCOO1', 'C=C(CCCCO)C(CC=C(CCCCCC)CC(CC)C(C)(F)CC=CC)C(C)(C)C(C)OCCC', 'CCNC(C)(CC1=CC1)[N+](=CCO)CCCC=C1CCC2C=C(C1)OCCC=CC1=C2CC(C=CCCC(C)C)=CC1', 'CCC(C(C)OC)=[N+](C)CCCCCN', 'BBBB1BB2B(B)B12', 'C=[N+](C(=CC(C)=[N+](C)N)CNC)[N+](=NC(C)CCN[N+](C)=N)NC', 'C=C(CC)C(C=CC)OC(C)(CC1=CC2CC(C2CC=CCC)C(C)(C)C1)C(=C)C=CC', 'C=CC1=C(C(CF)CC(=C)O)C=C1CCCC', 'CC=CCCC1NNC12CCNC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC)C(C=CCC1C(C)=CCC(=C(C)NC)C=CC1CC)C(C)C1=CCC(C)CNN=C1', 'CCC(C)[O+]1COCOO1', 'C=C(CCCCO)C(CC=C(CCCCCC)CC(CC)C(C)(F)CC=CC)C(C)(C)C(C)OCCC', 'CCNC(C)(CC1=CC1)[N+](=CCO)CCCC=C1CCC2C=C(C1)OCCC=CC1=C2CC(C=CCCC(C)C)=CC1', 'CCC(C(C)OC)=[N+](C)CCCCCN', 'BBBB1BB2B(B)B12', 'C=[N+](C(=CC(C)=[N+](C)N)CNC)[N+](=NC(C)CCN[N+](C)=N)NC', 'C=C(CC)C(C=CC)OC(C)(CC1=CC2CC(C2CC=CCC)C(C)(C)C1)C(=C)C=CC', 'C=CC1=C(C(CF)CC(=C)O)C=C1CCCC', 'CC=CCCC1NNC12CCNC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a batch of molecules reaching a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The appropriate cell line would be 1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CC(C)C(C)=CCC)CC1CC=C1CC(=CC)C(O)C=CC', 'C=CC[N+]1=CNC(CCC(C)C(C)CNC=CNCCNC)CC=C1C(=N)NC', 'C=C(CC)NCC(C)(CC=CCC(C)CC1CO1)CCN(N)CCCCCCC', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=[O+]C1CC1(C)OC', 'C=CC1=CC=CCCC2NC(=CC(=CO)CC(C)CC1)CC2C', 'C=CN=CC1=C[N+](C(C)NCC(C)C(C)NC(C(C)C=C)[N+](=N)C=C)=CC(=CC)CC1', 'CCCNNC(CC1=CCC1)NN', 'CN1NNC2C=[NH+]C2=CCCN(N)C1NC(N)NCNC(=N)CN', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=CC=CCC(O)CCC=NCCCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CC(C)C(C)=CCC)CC1CC=C1CC(=CC)C(O)C=CC', 'C=CC[N+]1=CNC(CCC(C)C(C)CNC=CNCCNC)CC=C1C(=N)NC', 'C=C(CC)NCC(C)(CC=CCC(C)CC1CO1)CCN(N)CCCCCCC', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=[O+]C1CC1(C)OC', 'C=CC1=CC=CCCC2NC(=CC(=CO)CC(C)CC1)CC2C', 'C=CN=CC1=C[N+](C(C)NCC(C)C(C)NC(C(C)C=C)[N+](=N)C=C)=CC(=CC)CC1', 'CCCNNC(CC1=CCC1)NN', 'CN1NNC2C=[NH+]C2=CCCN(N)C1NC(N)NCNC(=N)CN', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=CC=CCC(O)CCC=NCCCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules reaching a z-score of -4.054651 for the cell line '6840550000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840550000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The suitable cell line is 684055. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['NC=C1C=NCC2CNC3C4C=CCCC=C2CC2CCC3C(CC1)CCC24', 'C=C1CC2CC2NC(=CC)C(CCCC)C1C', 'CCCC(=CC(C)C)C(C)CC', 'C=CCC(C)=C=CC=CNCCCCCOOF', 'CC=CC(C)(C)C=CCC1=C2CC1CC2O', 'CCBNCNCNCC=CCC', 'CCC1=C(C)C=CCCC(CCC(C)NCO)C(C)N=CO1', 'CC=NC1C=CC(=CCCC(O)C(=CC=NC)CC)C1', 'CNC1C(C)C1C(C)C', 'CNB1NB2B3BB(BB12)B(C)C3'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['NC=C1C=NCC2CNC3C4C=CCCC=C2CC2CCC3C(CC1)CCC24', 'C=C1CC2CC2NC(=CC)C(CCCC)C1C', 'CCCC(=CC(C)C)C(C)CC', 'C=CCC(C)=C=CC=CNCCCCCOOF', 'CC=CC(C)(C)C=CCC1=C2CC1CC2O', 'CCBNCNCNCC=CCC', 'CCC1=C(C)C=CCCC(CCC(C)NCO)C(C)N=CO1', 'CC=NC1C=CC(=CCCC(O)C(=CC=NC)CC)C1', 'CNC1C(C)C1C(C)C', 'CNB1NB2B3BB(BB12)B(C)C3']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that align with a z-score of -6.190392 for the cell line '7220450000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"7220450000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "722045 is the preferred cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC=C[N+](C)=C1CCC(C)=C2C(CN=CCCC2(N)CCN)O1', 'C=NC(OC)C(=N)CNC1NCN1', 'CN1C(O)CCCC=CCC[O+]1N', 'CCC1=CN=CNNC(C)=CC2C(ONCC1)C(C)C1CC=C[O+]2NOCCN1', 'C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'CC=CC(C=CCCCCC)=C(C)C(C)=C1CC2C(C(=N)O)CC23C=CCN3C1', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'CCN1CCC=CC1=CCCC(C)C=C[O+]1C=NCCC(C)CCCC1', 'CCCCCC=C(CC)CC=CCC(C)C', 'CNC1=CN=[N+](C)C(NC)CC(CCNC(N=CC[N+]2=NCC(C)=C2)N(C)N)=C(C)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC=C[N+](C)=C1CCC(C)=C2C(CN=CCCC2(N)CCN)O1', 'C=NC(OC)C(=N)CNC1NCN1', 'CN1C(O)CCCC=CCC[O+]1N', 'CCC1=CN=CNNC(C)=CC2C(ONCC1)C(C)C1CC=C[O+]2NOCCN1', 'C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'CC=CC(C=CCCCCC)=C(C)C(C)=C1CC2C(C(=N)O)CC23C=CCN3C1', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'CCN1CCC=CC1=CCCC(C)C=C[O+]1C=NCCC(C)CCCC1', 'CCCCCC=C(CC)CC=CCC(C)C', 'CNC1=CN=[N+](C)C(NC)CC(CCNC(N=CC[N+]2=NCC(C)=C2)N(C)N)=C(C)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a series of compounds to reach a z-score of -6.190392 for '13310320000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"13310320000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1331032 is the cell line we need. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['CN=CC(CCNN(C)C=NCCCNC=C[N+]1(C)CN1)C(C)O', 'CCC1CCC(=CN)C=NCC2(C)CC(=CNC(C)C=C2C)C2CCCCC1N2', 'CC=COC(=N)CC(CCCC)C(C)(C)C', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NO2)CC(C)=N1', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(N)O2)N(CC3)C1CCC', 'C=CC(N)(N)CC1(C[O+](C)C(=C)CC(C)NN=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CCCCNNC=CC(C)CCC', 'CC=C(NCC)[O+]1NCC1(C)C=NC=O', 'CCC=CC(C=C(C)CC(C=CCCCCCCc1c[nH]c(O)c1)CC)=[O+]C', 'CC(C)=NCC1(C)CC1(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN=CC(CCNN(C)C=NCCCNC=C[N+]1(C)CN1)C(C)O', 'CCC1CCC(=CN)C=NCC2(C)CC(=CNC(C)C=C2C)C2CCCCC1N2', 'CC=COC(=N)CC(CCCC)C(C)(C)C', 'C=C(C)C1(C2=CCC[N+](=CC=CN)NO2)CC(C)=N1', 'CC=CCN1CC(CCC(C)CCC)=CC2(CC)CCC3=C2C2=C(C(N)O2)N(CC3)C1CCC', 'C=CC(N)(N)CC1(C[O+](C)C(=C)CC(C)NN=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CCCCNNC=CC(C)CCC', 'CC=C(NCC)[O+]1NCC1(C)C=NC=O', 'CCC=CC(C=C(C)CC(C=CCCCCCCc1c[nH]c(O)c1)CC)=[O+]C', 'CC(C)=NCC1(C)CC1(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a set of molecules targeted at a z-score of -4.054651 for '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240182 is the designated cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['BBB(N)BBBB=NN(B(C)N)B(C)CC', 'C=C(O)C(=NN1CC1(C)C)[NH+](CN)CC(C)CC', 'C=C1CC(NCCC(C)(C)CCNC=CC2CC[N+]2=CNC)=CC1C', 'CCCC(C=C(C)C=C(C)NCC(C)C=C=C(C)CCOCC)CC', 'C=C(C)C(CCCC)CCC(C=CCC)C(CCCC)CCCCCN', 'C=CC(C)(COC=CO)C(C)CC=C(C)C', 'CC=CC(CC)NC(CCCC)C(CC)CC(=CC)CNC', 'CCC[N+]1=C(CC(C)(C)N)C=C1', 'C=CN=CC(CCC=C(C=C)CO)C(C)CC=NCC(C)=S', 'C=C(C=C(C)C(=O)O)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BBB(N)BBBB=NN(B(C)N)B(C)CC', 'C=C(O)C(=NN1CC1(C)C)[NH+](CN)CC(C)CC', 'C=C1CC(NCCC(C)(C)CCNC=CC2CC[N+]2=CNC)=CC1C', 'CCCC(C=C(C)C=C(C)NCC(C)C=C=C(C)CCOCC)CC', 'C=C(C)C(CCCC)CCC(C=CCC)C(CCCC)CCCCCN', 'C=CC(C)(COC=CO)C(C)CC=C(C)C', 'CC=CC(CC)NC(CCCC)C(CC)CC(=CC)CNC', 'CCC[N+]1=C(CC(C)(C)N)C=C1', 'C=CN=CC(CCC=C(C=C)CO)C(C)CC=NCC(C)=S', 'C=C(C=C(C)C(=O)O)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of compounds reaching a z-score of -6.190392 for the cell line '13310320000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"13310320000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1331032. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCC(=CCC(C)C)C1', 'C=CC[N+](=CC)C(O)CNC', 'CC1CN1N(N)NCCN(NN)C1=CCCC1', 'C=NC=C[N+](C)=C1CCC(C)=C2C(CCN)CCC=NCC2O1', 'C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'CC=CC(=[O+]CC)C(CC)CCCCCC', 'CC1C=CC=CC=CCC=CCCC1', 'CCC=CNCC(CCC1C=CCNC1)C(CCCN)C(N)C=CC(C)=O', 'C=C(C)C(CCC)OC1(CCCC)CCC1', 'CC1=CCC2=C(C2)C(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCC(=CCC(C)C)C1', 'C=CC[N+](=CC)C(O)CNC', 'CC1CN1N(N)NCCN(NN)C1=CCCC1', 'C=NC=C[N+](C)=C1CCC(C)=C2C(CCN)CCC=NCC2O1', 'C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'CC=CC(=[O+]CC)C(CC)CCCCCC', 'CC1C=CC=CC=CCC=CCCC1', 'CCC=CNCC(CCC1C=CCNC1)C(CCCN)C(N)C=CC(C)=O', 'C=C(C)C(CCC)OC1(CCCC)CCC1', 'CC1=CCC2=C(C2)C(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a batch of molecules with the intended z-score of -4.054651 for '9059570000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"9059570000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The cell line in question is 905957. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CNC1(C)C=C(C)C1)C(N)C(C)N', 'CCCCCNCN(CN)N=CN', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CO)C1=C2C', 'CC=NCC1=[O+][N+]1=C1NCC1OCC1CCC2(CCCO)NC[O+]=C12', 'CCC1=CCC(O)C(C(C)N)=CC1C', 'CCC1=CCCCCC2=C(CC)C(=C(C)C=[O+]CC2)[O+]1CC', 'CCCC(O)C=CC(C)C(C)CC=C1CNC1C', 'C=C(CC(C)=CC=COO)C1CCC1C(C)C', 'NCNC1B2CBBCCNNB1N2', 'CC1=C2CC(=O)[O+](CN3C=CC=CC3)C2=CC(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CNC1(C)C=C(C)C1)C(N)C(C)N', 'CCCCCNCN(CN)N=CN', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CO)C1=C2C', 'CC=NCC1=[O+][N+]1=C1NCC1OCC1CCC2(CCCO)NC[O+]=C12', 'CCC1=CCC(O)C(C(C)N)=CC1C', 'CCC1=CCCCCC2=C(CC)C(=C(C)C=[O+]CC2)[O+]1CC', 'CCCC(O)C=CC(C)C(C)CC=C1CNC1C', 'C=C(CC(C)=CC=COO)C1CCC1C(C)C', 'NCNC1B2CBBCCNNB1N2', 'CC1=C2CC(=O)[O+](CN3C=CC=CC3)C2=CC(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules to reach a z-score of -4.054651 for the cell line '6840550000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840550000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684055 should be used as the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['BCBN(CC)C1CN(CCCC)CCBCCN=NNB1C', 'C=CC1CC(CN)(CC2CC2)[OH+]C=CC=CC(C)=C(CCC)C(C)=C1N', 'CCCC(=CC(C)C)C(C)CC', 'C=[N+](C)N=CNCC1B=CN=CN=C2CC1(N)NNN=N2', 'C=C(C)C(=NCC1C=C1C)NCC(BC)N(C=NC=N)NC', 'CB1CC(C(CNCN2B3C=NC32)CNB(N)N)=BNC1=N', 'BC(=CCC1NNC=[N+]1NCC)C1=NN1', 'B[N+]12BCN(C=CB[NH+](N)B(C)NN1C)[N+](CN)=CN(N=NC=CCCC)N2', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1', 'BCB(NN)C(=N)C(=N)N=NN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCBN(CC)C1CN(CCCC)CCBCCN=NNB1C', 'C=CC1CC(CN)(CC2CC2)[OH+]C=CC=CC(C)=C(CCC)C(C)=C1N', 'CCCC(=CC(C)C)C(C)CC', 'C=[N+](C)N=CNCC1B=CN=CN=C2CC1(N)NNN=N2', 'C=C(C)C(=NCC1C=C1C)NCC(BC)N(C=NC=N)NC', 'CB1CC(C(CNCN2B3C=NC32)CNB(N)N)=BNC1=N', 'BC(=CCC1NNC=[N+]1NCC)C1=NN1', 'B[N+]12BCN(C=CB[NH+](N)B(C)NN1C)[N+](CN)=CN(N=NC=CCCC)N2', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1', 'BCB(NN)C(=N)C(=N)N=NN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules that can reach a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The selected cell line should be 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=BCC(CC)C1=C(C)[N+](C)(N)CC(CNCC)C(C)N(N)C(CC2(CCC=N)CC(CC)C2C(C)CC)CNC=CC1CC', 'C=C(CN)C(CCCN)CB1NB2CCB(N)CB(N)C21', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=NC(C)(N)C=CC', 'C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'C=C(C)C1=CNC=C(CON2CCC2)C=CC=C(N=CCC2C=C2C)C(C)C(C)(C=C(C)N(C)CCN)C1', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'C=NCC(CCC)NNC', 'CCCC=CCC=COCC1(C)CCC[O+]1CCCC(CC)C1NCCC1C', 'C=CCC=C(O)CC(C)(CN)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=BCC(CC)C1=C(C)[N+](C)(N)CC(CNCC)C(C)N(N)C(CC2(CCC=N)CC(CC)C2C(C)CC)CNC=CC1CC', 'C=C(CN)C(CCCN)CB1NB2CCB(N)CB(N)C21', 'CNCNCC=C(CO)CC=NCC(C)N', 'C=NC(C)(N)C=CC', 'C=COC=CC(CC(F)=CCC)C(CCOCC=CC(=C)C=C)NC', 'C=C(C)C1=CNC=C(CON2CCC2)C=CC=C(N=CCC2C=C2C)C(C)C(C)(C=C(C)N(C)CCN)C1', 'CC[N+](CC1C=NC=C(C)C(C)=CC(C)N1)=NCCC1=CN1', 'C=NCC(CCC)NNC', 'CCCC=CCC=COCC1(C)CCC[O+]1CCCC(CC)C1NCCC1C', 'C=CCC=C(O)CC(C)(CN)C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -4.054651 for the cell line '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "We should go with 684059 for the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(OBC)C(C)CC', 'C=C1C(=CCC)CC=CC1CN(C)C', 'CC=CC(C1=CN1)C(C)N(C=CNCCC1=C2C1[N+]2=N)C(N)CC=CN', 'CCC(C)(C)CC', 'CC(=CCNCN)CCCNCCC(C)=CC(C)N', 'CCC1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CCC1=CN=CNNC(C)=NC2=[N+]3C=CCC(NCCON3)C(C)C2CNCC1', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'CC=C(C=CCC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)c1)CC', 'CB(N)B(NCN)C1N=C(CCCN)NN2CN(C2)C(C2=NNN=NC(NN)=N2)CN1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(OBC)C(C)CC', 'C=C1C(=CCC)CC=CC1CN(C)C', 'CC=CC(C1=CN1)C(C)N(C=CNCCC1=C2C1[N+]2=N)C(N)CC=CN', 'CCC(C)(C)CC', 'CC(=CCNCN)CCCNCCC(C)=CC(C)N', 'CCC1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CCC1=CN=CNNC(C)=NC2=[N+]3C=CCC(NCCON3)C(C)C2CNCC1', 'CC=CN1C=C(C)C1C(=COCC)C(O)C=CCC1=CC(CCS)C(C)C1', 'CC=C(C=CCC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)c1)CC', 'CB(N)B(NCN)C1N=C(CCCN)NN2CN(C2)C(C2=NNN=NC(NN)=N2)CN1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -6.190392 for the cell line '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240166 is confirmed as the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=CCC2CN2CC=C1O', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CC)C1=C2C', 'CN=CC(CCNN(C)C=NCCCNC=C[N+]1(C)CN1)C(C)O', 'CCC=C(CCCN)C(C)=C1C(C)C1CC(C)(C)CC', 'C=C1C=CNCCOCCCC(=C)CC(=CCC)CCOC1', 'CC=CC(C)=CC=CC(CC(O)(CC)CC=C(C)CC)[O+]=NCNCC(CC)COCC', 'CC(C)C=CCCN=CN', 'CCCC(C)(CC)CCNCCN(CCN)B(CNN)B(C)BCC(CN)=C(CC)CN', 'CCCO[O+]=C=NC1=CC(C(CC)CCC(S)CCC(CC)C(C)=CC=C2CC2S)=C1C', 'C=C(CC)CCCCCCC(C)C1CCCCCC(C)=CN=CC(C)CCC(NCC)C(CC)C1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=CCC2CN2CC=C1O', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CC)C1=C2C', 'CN=CC(CCNN(C)C=NCCCNC=C[N+]1(C)CN1)C(C)O', 'CCC=C(CCCN)C(C)=C1C(C)C1CC(C)(C)CC', 'C=C1C=CNCCOCCCC(=C)CC(=CCC)CCOC1', 'CC=CC(C)=CC=CC(CC(O)(CC)CC=C(C)CC)[O+]=NCNCC(CC)COCC', 'CC(C)C=CCCN=CN', 'CCCC(C)(CC)CCNCCN(CCN)B(CNN)B(C)BCC(CN)=C(CC)CN', 'CCCO[O+]=C=NC1=CC(C(CC)CCC(S)CCC(CC)C(C)=CC=C2CC2S)=C1C', 'C=C(CC)CCCCCCC(C)C1CCCCCC(C)=CN=CC(C)CCC(NCC)C(CC)C1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules to achieve the target z-score of -6.190392 for the cell line '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The appropriate cell line would be 1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(=CCNNNC=C1CNNC1)CN', 'CCCCC(CC)C1=CC(=C(C)CCC)C=CC(C)CC1', 'CCC=C1CC(C)CC2=C(CCCC=CO)N(C)N=CC3CC3C=CC1=C(C)CC1CCNCC(C)N1CC2', 'CCCCNCC(CC)C(C=NC)=NCC', 'CC1(C)C=CCC2CC(COO2)OCNCO1', 'C=C1CNC=C(NCCN)C=NC=CCC(C)NCC(N)=C1C(N)C=CC', 'CCCCC(OCNN)N(N)C=C(C)C=C(N)CN=N', 'C=C(C=[N+]1C=CN=NC=CC1C)CCC', 'CB(CC(=CC=NN)NCB(N)CCN)CC(N)CC1CC=N[N+]1=N', 'C=C(CC)NC(C)C(CCC(C)C)=C(C)C(CN)CNCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(=CCNNNC=C1CNNC1)CN', 'CCCCC(CC)C1=CC(=C(C)CCC)C=CC(C)CC1', 'CCC=C1CC(C)CC2=C(CCCC=CO)N(C)N=CC3CC3C=CC1=C(C)CC1CCNCC(C)N1CC2', 'CCCCNCC(CC)C(C=NC)=NCC', 'CC1(C)C=CCC2CC(COO2)OCNCO1', 'C=C1CNC=C(NCCN)C=NC=CCC(C)NCC(N)=C1C(N)C=CC', 'CCCCC(OCNN)N(N)C=C(C)C=C(N)CN=N', 'C=C(C=[N+]1C=CN=NC=CC1C)CCC', 'CB(CC(=CC=NN)NCB(N)CCN)CC(N)CC1CC=N[N+]1=N', 'C=C(CC)NC(C)C(CCC(C)C)=C(C)C(CN)CNCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a series of molecules matching a z-score of -6.190392 for '13310320000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"13310320000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The selected cell line corresponds to 1331032. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(N)CC=C(CCCC(C)C=CN)C(NCC)C(C)CNC', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'C=C(C)C1=C2C=CC3CC(C)(C3)C1OC(=CC)C2', 'C=CC1CNC(N)C(=N)C(C=CC)N1', 'CC=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'CCC1=CC=CCCCOC2=CC1=CC2', 'CCC1CC(C)C(NC2CC2N)C1', 'C=C(C)CC(C)(N)OC(C)(CCC=C(C)CC)CCCCC', 'CC=CC1=C(CCC(CC)CCCCCC2=CC(CCO)CC2CCC=CNOC)C1', 'CC=CC=C(CCCNC)C(C)CNC1C(C)C2=[N+]1C=CC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(N)CC=C(CCCC(C)C=CN)C(NCC)C(C)CNC', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'C=C(C)C1=C2C=CC3CC(C)(C3)C1OC(=CC)C2', 'C=CC1CNC(N)C(=N)C(C=CC)N1', 'CC=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'CCC1=CC=CCCCOC2=CC1=CC2', 'CCC1CC(C)C(NC2CC2N)C1', 'C=C(C)CC(C)(N)OC(C)(CCC=C(C)CC)CCCCC', 'CC=CC1=C(CCC(CC)CCCCCC2=CC(CCO)CC2CCC=CNOC)C1', 'CC=CC=C(CCCNC)C(C)CNC1C(C)C2=[N+]1C=CC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically for achieving a z-score of -6.190392 for '7220450000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"7220450000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The cell line in question is 722045. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCC(C)C(N=C(CCC)CC(CCC)CC(=CCC)CCCC)=NC', 'C[O+]1CCC(CCN)CCCC=NC=CO1', 'CC1=CC2CCCCC=C3NC3CCC(N)(C(C)(C)C)OC(=CCC(O)C=C1)C=C2C', 'CC=CN(CC(C)N)NCCN(CCN)C(C)CN', 'CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(CN)(CN)CCC=C(C)C1', 'C=BC=CN=N', 'CCC=CC=CC(=N)CCC=C(C)C(=CCC1C=CCC(CC(C)O)N1)CC', 'C=CC(NCCC(C)=CCCCC(CC)CCC)C1CN1C(C)CN', 'CNC[O+](C)CCC1CC=C(CCCC=N)[N+](C)=C(C)CC1', 'C=C(NCC=C(BCCB=NCBN=N)CCCNBN)C(C)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCC(C)C(N=C(CCC)CC(CCC)CC(=CCC)CCCC)=NC', 'C[O+]1CCC(CCN)CCCC=NC=CO1', 'CC1=CC2CCCCC=C3NC3CCC(N)(C(C)(C)C)OC(=CCC(O)C=C1)C=C2C', 'CC=CN(CC(C)N)NCCN(CCN)C(C)CN', 'CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(CN)(CN)CCC=C(C)C1', 'C=BC=CN=N', 'CCC=CC=CC(=N)CCC=C(C)C(=CCC1C=CCC(CC(C)O)N1)CC', 'C=CC(NCCC(C)=CCCCC(CC)CCC)C1CN1C(C)CN', 'CNC[O+](C)CCC1CC=C(CCCC=N)[N+](C)=C(C)CC1', 'C=C(NCC=C(BCCB=NCBN=N)CCCNBN)C(C)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a series of compounds to reach a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=C(C)[O+]1CC1CCCC=CC=CCC(C)(CCCC)CNC', 'CC=CC1=C(CCC(CC)CCCCCC2=CC(CCO)CC2CCC=CNOC)C1', 'CCC=C(C)C1=CC(CCCC2=CC(C)=C2C)=C1', 'CCCC(N)=CC(C)C', 'CCC=CCCC[N+](=CCCC)CNCC(C)O', 'CC[O+]=CCC(C)(CCO)OC', 'CC=C(NC)NC(C)C=CC=CCCC', 'C=[N+]1C=[N+]=N[N+](C(CC)CCC)=C1N=CN=NCCNC(C)C(C)=C(C)N', 'C=CC(CC(C)C(CC=C[N+](=C)C)CNC(C=C)CC=N)NC', 'CCCN(C=C(C)C=C(C)NNC(C)C=C=[N+](N)CCNCC)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=C(C)[O+]1CC1CCCC=CC=CCC(C)(CCCC)CNC', 'CC=CC1=C(CCC(CC)CCCCCC2=CC(CCO)CC2CCC=CNOC)C1', 'CCC=C(C)C1=CC(CCCC2=CC(C)=C2C)=C1', 'CCCC(N)=CC(C)C', 'CCC=CCCC[N+](=CCCC)CNCC(C)O', 'CC[O+]=CCC(C)(CCO)OC', 'CC=C(NC)NC(C)C=CC=CCCC', 'C=[N+]1C=[N+]=N[N+](C(CC)CCC)=C1N=CN=NCCNC(C)C(C)=C(C)N', 'C=CC(CC(C)C(CC=C[N+](=C)C)CNC(C=C)CC=N)NC', 'CCCN(C=C(C)C=C(C)NNC(C)C=C=[N+](N)CCNCC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a batch of molecules that hit a z-score of -4.054651 for '6840550000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840550000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684055 is the exact cell line we need. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(CNC(N)C(CCCC)N=N)NN', 'C=CC=C(C(N)N=CN[N+](=C)CN(CN)NC(N)N)C1NC=C1N', 'C=CC=C(C)C1NC(C)N1CC(C)=CCC(=CC)CC(C)=CC', 'NC[N+]1=NBB2CBNN3CC(C2)N3B2CB1CCN2', 'CB1N(C)N[N+]1(C)N', 'C=C1CN([O+](C)C(=C)[O+](C)C(=C)OC(C)C)C1C', 'CCC=CC(C)=NN=NC(CCC)N=[N+]1N(C)N1N', 'C=C=C(C)C1=C2CC(CC)(CCC(N)(C(C)CCC)C(CCCC)C1)N=N2', 'BBCC[N+](=NC)C(C)=CC(N)NB(N)C(N)B(N)CN=N', 'C=NN=BC(NB(C)CCC=[N+]1CN1)[N+](=C)CC=BN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(CNC(N)C(CCCC)N=N)NN', 'C=CC=C(C(N)N=CN[N+](=C)CN(CN)NC(N)N)C1NC=C1N', 'C=CC=C(C)C1NC(C)N1CC(C)=CCC(=CC)CC(C)=CC', 'NC[N+]1=NBB2CBNN3CC(C2)N3B2CB1CCN2', 'CB1N(C)N[N+]1(C)N', 'C=C1CN([O+](C)C(=C)[O+](C)C(=C)OC(C)C)C1C', 'CCC=CC(C)=NN=NC(CCC)N=[N+]1N(C)N1N', 'C=C=C(C)C1=C2CC(CC)(CCC(N)(C(C)CCC)C(CCCC)C1)N=N2', 'BBCC[N+](=NC)C(C)=CC(N)NB(N)C(N)B(N)CN=N', 'C=NN=BC(NB(C)CCC=[N+]1CN1)[N+](=C)CC=BN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules with the intended z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684059 serves as the correct cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(CCCC(C)CC(=CN(C)N)C(O)=CCC)CC(CCC)=[O+]NC=C(C)NC', 'CCC=C(N)C(C)(N)CC(=N)N=CN', 'CCCC[N+]1(C(CC)CC)CC1C1CCCCC1CC', 'CCCC[N+](O)=[N+](CC)CNCN', 'CCNN1NN(CC)C1CN(C)N', 'C=C(S)N(OC(C)=CC=CC1C2C[O+]12)C(C)C=CC1=C(C)C1CC=C(C)C=CCCC(F)CC', 'C=C1NN1C(CC)C(C)N', 'CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC)CC(C)C=O)CO1', 'CCC(C)=CC=CC=CC1CNC(CC)C1', 'C=NNNCCCNCCC=CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(CCCC(C)CC(=CN(C)N)C(O)=CCC)CC(CCC)=[O+]NC=C(C)NC', 'CCC=C(N)C(C)(N)CC(=N)N=CN', 'CCCC[N+]1(C(CC)CC)CC1C1CCCCC1CC', 'CCCC[N+](O)=[N+](CC)CNCN', 'CCNN1NN(CC)C1CN(C)N', 'C=C(S)N(OC(C)=CC=CC1C2C[O+]12)C(C)C=CC1=C(C)C1CC=C(C)C=CCCC(F)CC', 'C=C1NN1C(CC)C(C)N', 'CCCC1=CCCC=C(CC)C(=CC=CC=[N+](CC)CC(C)C=O)CO1', 'CCC(C)=CC=CC=CC1CNC(CC)C1', 'C=NNNCCCNCCC=CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a batch of molecules with the intended z-score of -4.054651 for '9059570000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"9059570000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "905957 should be used as the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)C(=C)CCC', 'C=C1C=CC1', 'CCCC1=CC=CC(C2C=CC2CC)C(C(C)F)CCC(C)C1', 'CC=CCC=C1CC(=NNC)CCCOC(NCN)C(C)C1', 'CCCC=CCCCCN1CCN1C(CCC)CC1OCN1C(C)=CN', 'CCN(N)CCC1C2(Cl)N3C(N)C45C67NC48C14N(C45BC6N7N)C328', 'CCC(N)(C1C=C(C(C)(N)C(C)C(BN)CC(=CN)N=CC=CCN)N1)C1(C)CNC1NC', 'CNCNC1COOC(NC=N)C1', 'CC(C)CCC=C(CCCN)CC1CCC(C=CC2C=CC2)C1', 'C=C(CCCN)C(=CC)NCC=C[O+](N)N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)C(=C)CCC', 'C=C1C=CC1', 'CCCC1=CC=CC(C2C=CC2CC)C(C(C)F)CCC(C)C1', 'CC=CCC=C1CC(=NNC)CCCOC(NCN)C(C)C1', 'CCCC=CCCCCN1CCN1C(CCC)CC1OCN1C(C)=CN', 'CCN(N)CCC1C2(Cl)N3C(N)C45C67NC48C14N(C45BC6N7N)C328', 'CCC(N)(C1C=C(C(C)(N)C(C)C(BN)CC(=CN)N=CC=CCN)N1)C1(C)CNC1NC', 'CNCNC1COOC(NC=N)C1', 'CC(C)CCC=C(CCCN)CC1CCC(C=CC2C=CC2)C1', 'C=C(CCCN)C(=CC)NCC=C[O+](N)N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a set of molecules that have a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684059 is the cell line we require. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C(C=C1C=CC(CC)CC(C)(N2C=CC2)C1C)CCCCC', 'CCCNC(C)C=CCB(N)CCN=CC=C(C)N', 'C=B[N+](=CC(N=C(C)BC)=C(CN=NN)C(CC)NC)CC=C', 'CCC(C=NC)COC(N)[N+](CCCO)=[O+]C', 'CCC1C=C1CC1C=CCC(=N)CCC=N1', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3(C)N)C2=C(O)C(C)OCCC1', 'C=CC=C(CC)CNC1=C(CC)CC=C[O+](CCC(C)(O)C=C)CC=C1', 'CC1=CC2CNC=C(CCCC(C)=CC1)CC2C', 'C=[O+]C=C(C)C(=CC)C1C(CC2=C(CN)CCC2C)=C[O+]1C(=C)C', 'CCCCNCC(CC)C(C=NC)=NCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C(C=C1C=CC(CC)CC(C)(N2C=CC2)C1C)CCCCC', 'CCCNC(C)C=CCB(N)CCN=CC=C(C)N', 'C=B[N+](=CC(N=C(C)BC)=C(CN=NN)C(CC)NC)CC=C', 'CCC(C=NC)COC(N)[N+](CCCO)=[O+]C', 'CCC1C=C1CC1C=CCC(=N)CCC=N1', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3(C)N)C2=C(O)C(C)OCCC1', 'C=CC=C(CC)CNC1=C(CC)CC=C[O+](CCC(C)(O)C=C)CC=C1', 'CC1=CC2CNC=C(CCCC(C)=CC1)CC2C', 'C=[O+]C=C(C)C(=CC)C1C(CC2=C(CN)CCC2C)=C[O+]1C(=C)C', 'CCCCNCC(CC)C(C=NC)=NCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules reaching a z-score of -4.054651 for '6840550000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840550000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "Our correct cell line is 684055. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCB(NC=N)C1NC(N)=NC=C1C', 'BN(CN)CC[N+](CC=NN(N)CC1CNB2NC1C2NNCC)=NC', 'CCCNNC(N)(NN)N1C(=C(C)NN)BN(NC)N1CC', 'CC(=CCC1CC=C(O)C1)CC1=C2C(=NC3(CC3C)C2C)C1', 'C=CC1=NCC(C)=CC(=CO)C(CCC)=C(C)O1', 'C=CC[O+](CC)[O+]=[O+]CN1C=C(C=C)C(C)(C)C1', 'C=C(C(C)C(CC)NC(C)=CC)C(C)[O+](CC)[OH+]C', 'CCN(C)NBBN', 'CC=CC(=CC1([O+]=CC)CCC=C1C)CCCC(CC)C(C)CC', 'C=CC[O+]1C(=C)[N+]1=CC=CN(O)C(C)NC(CC)CC(C)C1=NC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCB(NC=N)C1NC(N)=NC=C1C', 'BN(CN)CC[N+](CC=NN(N)CC1CNB2NC1C2NNCC)=NC', 'CCCNNC(N)(NN)N1C(=C(C)NN)BN(NC)N1CC', 'CC(=CCC1CC=C(O)C1)CC1=C2C(=NC3(CC3C)C2C)C1', 'C=CC1=NCC(C)=CC(=CO)C(CCC)=C(C)O1', 'C=CC[O+](CC)[O+]=[O+]CN1C=C(C=C)C(C)(C)C1', 'C=C(C(C)C(CC)NC(C)=CC)C(C)[O+](CC)[OH+]C', 'CCN(C)NBBN', 'CC=CC(=CC1([O+]=CC)CCC=C1C)CCCC(CC)C(C)CC', 'C=CC[O+]1C(=C)[N+]1=CC=CN(O)C(C)NC(CC)CC(C)C1=NC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate a series of molecules achieving a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The designated cell line is 1659819. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)NC(N)C(C=C(C)CC=CCC)C1CCC=C(C)CN1CC1N=C1NC=CC', 'CCC(C(C)OC)=[N+](C)CCCCCN', 'C=CCC(C)(C)C(CC)N(C)COO[O+](CC=CC=CCCC)OC', 'C=C(CCC)CN=C(CC)OC(=C)OCC(C)C(C)(C)N', 'CC=CCC(=CC=N)CNC1(C=CNC)CC2CC21CCCC', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1', 'CCCNC1C=C2C=C(C2)SCCC1', 'C=CCC(C)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'CC(C)CC(C)N[O+](C)CCNC(C)N', 'CCC(C)C1=CN[N+]2(N)C=C1C(C)=NC2CNN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)NC(N)C(C=C(C)CC=CCC)C1CCC=C(C)CN1CC1N=C1NC=CC', 'CCC(C(C)OC)=[N+](C)CCCCCN', 'C=CCC(C)(C)C(CC)N(C)COO[O+](CC=CC=CCCC)OC', 'C=C(CCC)CN=C(CC)OC(=C)OCC(C)C(C)(C)N', 'CC=CCC(=CC=N)CNC1(C=CNC)CC2CC21CCCC', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1', 'CCCNC1C=C2C=C(C2)SCCC1', 'C=CCC(C)=C1C=CCC2=C(C=CCC=CC(CCC)=C1C(=C)OC)C2', 'CC(C)CC(C)N[O+](C)CCNC(C)N', 'CCC(C)C1=CN[N+]2(N)C=C1C(C)=NC2CNN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a variety of molecules aimed at a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The most accurate cell line is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCCC1(CC)CC(C)(C)CC=C2CC21', 'N=C1C=C(CC23NCCOC(=CCC=CCN(BN)N2)CN3)N1', 'CCC=CC1(CC)CNN1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(BN)C(NCC)(NNN)C1CN1N=CBCNC', 'CCC=CC1=C(C)OCC2COC=[O+]C=C1C(C)C2', 'N[O+]=CCOC1BBCBNB1N', 'CC(=CC(C)CC1=CCCCC=CNC([N+]2=CC2)=C1C(C)C)OO', 'C=[N+]1CN(CCCNC)N1C(C)CCC(=CCC(CC)=C(C)CC)CCCC[N+]1=C(C)N(N)C1', 'CC=[N+]1CCCC(CCCN)CCCC(C)(N)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCCC1(CC)CC(C)(C)CC=C2CC21', 'N=C1C=C(CC23NCCOC(=CCC=CCN(BN)N2)CN3)N1', 'CCC=CC1(CC)CNN1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(BN)C(NCC)(NNN)C1CN1N=CBCNC', 'CCC=CC1=C(C)OCC2COC=[O+]C=C1C(C)C2', 'N[O+]=CCOC1BBCBNB1N', 'CC(=CC(C)CC1=CCCCC=CNC([N+]2=CC2)=C1C(C)C)OO', 'C=[N+]1CN(CCCNC)N1C(C)CCC(=CCC(CC)=C(C)CC)CCCC[N+]1=C(C)N(N)C1', 'CC=[N+]1CCCC(CCCN)CCCC(C)(N)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds optimized to match a z-score of -4.054651 for '9059570000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"9059570000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "905957 is the preferred cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)=NCC(C)C=[N+](C)C', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'CCCC[N+]1=[N+](C)CBCN(N)C2C(=CC(CCNN)=C1)N=[N+]2C', 'C=CCCN=CNN(N)NC', 'CCNC(CC[NH+](CN)CB(C)CC(CC)N(N)BN1CN(N(C(C)C)[N+]2=BC2)C1N)NC1(C=NC)BC1C', 'C=CC(C)C(C)(C=CCO)C(=CCC(C)C)CC', 'CC(=COC1CC1(C)C)C1=C(C(C)C)CCC1', 'B=C(CC(C)NN=NNN)[O+](C)CC1(C[N+](N)(N)C=CN=CC)C=BC(NCC(N)CC(C)C)C=C1', 'CCC(N=C1[O+]=C2C1N2C)=C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC(CN(C)C)=NC(CCCC1=CCC=CC(CN)=C1)C(CCO)C(C)(C)C', 'CCC(C)=NCC(C)C=[N+](C)C', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'CCCC[N+]1=[N+](C)CBCN(N)C2C(=CC(CCNN)=C1)N=[N+]2C', 'C=CCCN=CNN(N)NC', 'CCNC(CC[NH+](CN)CB(C)CC(CC)N(N)BN1CN(N(C(C)C)[N+]2=BC2)C1N)NC1(C=NC)BC1C', 'C=CC(C)C(C)(C=CCO)C(=CCC(C)C)CC', 'CC(=COC1CC1(C)C)C1=C(C(C)C)CCC1', 'B=C(CC(C)NN=NNN)[O+](C)CC1(C[N+](N)(N)C=CN=CC)C=BC(NCC(N)CC(C)C)C=C1', 'CCC(N=C1[O+]=C2C1N2C)=C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules that reach the target z-score of -6.190392 for '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The right choice for the cell line is 1240166. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC=CCC=CCCC=CC=NC(C)(CC(=O)OCF)C(C)=CCC', 'C=C(C)C(C)CCC=C(C1=CC1)C1CC=CC(CC(C)=C(CC)CC(CC)CN=CC)C(C=CN)C1', 'CC=CC(C)(C=CN=C(C)CC)CC=C(NNCN=CN)N[O+](C)[NH+]1C=CCC(C)N1', 'C=C(CO)CCCCCCCC1CC=C1C', 'C=C1C=C(CC=CC2CO2)OCC(F)C1', 'CCOCC(C)NC(C)C(C)CC(C)C', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CC=CCC(=CCCC)CC(CCN)C[N+]1=CC(C)CNN(C)CC1', 'C=C(C)C(C)C(O)[N+]1=CC(C)CCC(=CCCCC(C)CCCC)CCCCNC(CC)=N1', 'CCCN=NCCCC=CC1C=C(C)NC=CC1CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CCC=CCCC=CC=NC(C)(CC(=O)OCF)C(C)=CCC', 'C=C(C)C(C)CCC=C(C1=CC1)C1CC=CC(CC(C)=C(CC)CC(CC)CN=CC)C(C=CN)C1', 'CC=CC(C)(C=CN=C(C)CC)CC=C(NNCN=CN)N[O+](C)[NH+]1C=CCC(C)N1', 'C=C(CO)CCCCCCCC1CC=C1C', 'C=C1C=C(CC=CC2CO2)OCC(F)C1', 'CCOCC(C)NC(C)C(C)CC(C)C', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CC=CCC(=CCCC)CC(CCN)C[N+]1=CC(C)CNN(C)CC1', 'C=C(C)C(C)C(O)[N+]1=CC(C)CCC(=CCCCC(C)CCCC)CCCCNC(CC)=N1', 'CCCN=NCCCC=CC1C=C(C)NC=CC1CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules with the intended z-score of -6.190392 for '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The correct cell line to use is 1240166. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CNCCCC(C)=N)C[N+](=C)C', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)C(F)CCC(=C)C', 'BCN[O+]=CCC=NC(=C)C(=NC)[N+](C)=CC1=CCC1N', 'C=C1BNNC(N)(NC(=C[N+](N)=C(CN)CCN(C)N)NC)C1C', 'C=C(C=[N+](N)N1C(C)C1C(C)N)CNC(CC1C=CCN=N1)NC', 'CCC=CCC(C)C(=[O+]CC)[N+](=CC1=CC1)C1CC=CC=CCC1', 'C=C(CCCC)CC1(CC)C(=C)CC=CC(CCC=[O+]C=CC(CC)C2CC3(CCO3)C2=C)C1C=CCN(C)CN', 'CC=CCC=[N+](N)C(C(=O)CC(N)NCC)C(N)(CNCC(CC(CC=CC)C(C)CC)NC)C(C)C(C)C[N+](CC1C=CC1)=NC', 'CC=CC(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCC(CC)CC)C(C)CCCCC1', 'C=CCCOCC(C)CCCCC(CCC)=C(CC)COCC=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CNCCCC(C)=N)C[N+](=C)C', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)C(F)CCC(=C)C', 'BCN[O+]=CCC=NC(=C)C(=NC)[N+](C)=CC1=CCC1N', 'C=C1BNNC(N)(NC(=C[N+](N)=C(CN)CCN(C)N)NC)C1C', 'C=C(C=[N+](N)N1C(C)C1C(C)N)CNC(CC1C=CCN=N1)NC', 'CCC=CCC(C)C(=[O+]CC)[N+](=CC1=CC1)C1CC=CC=CCC1', 'C=C(CCCC)CC1(CC)C(=C)CC=CC(CCC=[O+]C=CC(CC)C2CC3(CCO3)C2=C)C1C=CCN(C)CN', 'CC=CCC=[N+](N)C(C(=O)CC(N)NCC)C(N)(CNCC(CC(CC=CC)C(C)CC)NC)C(C)C(C)C[N+](CC1C=CC1)=NC', 'CC=CC(CCC)C(CC)C(C)CC[O+]1CC=CCC(CCC(CC)CC)C(C)CCCCC1', 'C=CCCOCC(C)CCCCC(CCC)=C(CC)COCC=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a diverse set of molecules meeting a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "Opt for 1659819 as the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1(C)CNC(N)C(=O)C(C=CC)C1', 'CCC1=CCCC(C(C)O)C=CC2CCC3(C=CCCCCC2=C1)CN3', 'C=CC(CCC(CCC(CC)CN1CN1)=C(N)C=CC)=C(CNCC)C(C)=CC', 'CCCC=C(C)CC1=CC=C(C=[N+](O)C(F)(CC)CCC)C(O)CCC1', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'CCC(CN)CCC(=[N+]=NNCNCC1(CC)N=C1C)C1CC1N', 'CC=C[NH+](C)C(N)(CC)NC(C)[N+]1(C)C2NN1C2=NCN', 'CCCCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=CCC3C4C)CC(=CC(CC)=CCCC1)N2', 'CCC=C([O+]=CCCC)C(C)CC', 'C=C1C(N)CC=CCC=CC(NCC)=C(C)CC2=C(C2)CC1N=[N+](N)NCCC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1(C)CNC(N)C(=O)C(C=CC)C1', 'CCC1=CCCC(C(C)O)C=CC2CCC3(C=CCCCCC2=C1)CN3', 'C=CC(CCC(CCC(CC)CN1CN1)=C(N)C=CC)=C(CNCC)C(C)=CC', 'CCCC=C(C)CC1=CC=C(C=[N+](O)C(F)(CC)CCC)C(O)CCC1', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'CCC(CN)CCC(=[N+]=NNCNCC1(CC)N=C1C)C1CC1N', 'CC=C[NH+](C)C(N)(CC)NC(C)[N+]1(C)C2NN1C2=NCN', 'CCCCC1=C(CC)C2=C(C2)C2CC3CC4(C)C(=CCC3C4C)CC(=CC(CC)=CCCC1)N2', 'CCC=C([O+]=CCCC)C(C)CC', 'C=C1C(N)CC=CCC=CC(NCC)=C(C)CC2=C(C2)CC1N=[N+](N)NCCC(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a set of molecules to meet a z-score of -4.054651 for '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "Our correct cell line is 1240182. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC1=CC=CC(=C)CC(C=C2NC2C)C1', 'C=CC1=NCCC=CCC(NCC)=C(C)C2C[N+](=C)N=CC2=C1', 'C=[N+](CC)OBC=NCCC', 'C=NC(=CCC1C=CCCC2=CCC(N)C(C=[O+]O)(C2)C1)N(C)CC=CC', 'C=C(C)CN[N+](=CC=NC(C)C(C)C1(N)C(N)=C1CNCC1=CC(CC)=C1)CC', 'CC=CC(CF)C1CC=C(C=C(N)C2C(C)C2C(C)C)CNC1OC', 'C=C(CC)C(CC)=C1OCC(C=C(C)C)=CC=CCC2(C)C(=CCC1=CC)CCC[OH+]C21CCC1', 'CCCCC1CC(CCC)(CCCCC(C)CN)C1ON', 'CC1CCC2=C1CNC=CN=CNC2', 'CC=CC(C=CCCCCC)=C(C)C(C)=C1CC2C(C(=N)O)CC23C=NCN3C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC1=CC=CC(=C)CC(C=C2NC2C)C1', 'C=CC1=NCCC=CCC(NCC)=C(C)C2C[N+](=C)N=CC2=C1', 'C=[N+](CC)OBC=NCCC', 'C=NC(=CCC1C=CCCC2=CCC(N)C(C=[O+]O)(C2)C1)N(C)CC=CC', 'C=C(C)CN[N+](=CC=NC(C)C(C)C1(N)C(N)=C1CNCC1=CC(CC)=C1)CC', 'CC=CC(CF)C1CC=C(C=C(N)C2C(C)C2C(C)C)CNC1OC', 'C=C(CC)C(CC)=C1OCC(C=C(C)C)=CC=CCC2(C)C(=CCC1=CC)CCC[OH+]C21CCC1', 'CCCCC1CC(CCC)(CCCCC(C)CN)C1ON', 'CC1CCC2=C1CNC=CN=CNC2', 'CC=CC(C=CCCCCC)=C(C)C(C)=C1CC2C(C(=N)O)CC23C=NCN3C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that align with a z-score of -4.054651 for the cell line '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CNC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=[N+]1C(C)[N+]2=CCN1B=[N+](C)CCN(C)C(C)(N)CCC(CCN)=C2', 'C=CC=C1C=CCC=[O+]C1C=CC', 'C=CCCCCC=CCOCCCCOC1NC1C', 'C=C(NC)C(=CC(C)C(N=CN=CN(C)CC=C(C)C)NN)CC', 'CNC12CC=C(N)CCCC[N+]1(C)N2', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CC=CC1=C(CCC)CC1', 'CC=CCNNC(=CCC(C)(C=CC)C=CN=C(C)CC)N[O+](C)O', 'CC=NCNCC1C=NC(C=CCCC)N=CCCCC(N)=[O+]NCC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CNC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=[N+]1C(C)[N+]2=CCN1B=[N+](C)CCN(C)C(C)(N)CCC(CCN)=C2', 'C=CC=C1C=CCC=[O+]C1C=CC', 'C=CCCCCC=CCOCCCCOC1NC1C', 'C=C(NC)C(=CC(C)C(N=CN=CN(C)CC=C(C)C)NN)CC', 'CNC12CC=C(N)CCCC[N+]1(C)N2', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CC=CC1=C(CCC)CC1', 'CC=CCNNC(=CCC(C)(C=CC)C=CN=C(C)CC)N[O+](C)O', 'CC=NCNCC1C=NC(C=CCCC)N=CCCCC(N)=[O+]NCC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules to achieve a z-score of -6.190392 for '16598190000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"16598190000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1659819 is the recommended cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CN(CCC)C(CC)C(C)CCOCC=CCC(CCCCC)C(C)CCCC', 'CCC(C)N=[O+]C(O)=CC=C(C)CC=CCC=CC1CCC1', 'CCCCCC(C)(N)CCCC', 'CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)CCO)N=CNC', 'C=C(C)CCC', 'CCC=NCC(CC)CCNNC', 'CC=C(NCCC)C(CCCC=CCN)=NCNN', 'C=C(C)CNCCCC(N)CCC1=CCCC1', 'CC=CC(CC(C)COCC)=C(NC)C(O)CC', 'CC(CO)CC(C)C(N)C=CC1CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CN(CCC)C(CC)C(C)CCOCC=CCC(CCCCC)C(C)CCCC', 'CCC(C)N=[O+]C(O)=CC=C(C)CC=CCC=CC1CCC1', 'CCCCCC(C)(N)CCCC', 'CC=CC[N+](C)=CC(C)=CC(=CCNCC(C)CCO)N=CNC', 'C=C(C)CCC', 'CCC=NCC(CC)CCNNC', 'CC=C(NCCC)C(CCCC=CCN)=NCNN', 'C=C(C)CNCCCC(N)CCC1=CCCC1', 'CC=CC(CC(C)COCC)=C(NC)C(O)CC', 'CC(CO)CC(C)C(N)C=CC1CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules with the goal of achieving a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The appropriate cell line is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['C1=NCCCCCCC1', 'CC=CC1NC(CCCC)C(C)C(CC=CCC=[O+]C)C2(C)CC1C2', 'CCC(=CC(C)CC(C)C=CC=C(CO)CC1CCC1)C(C)(C)CC', 'CCC=C1N=C2CC(C=C(C)CCN=c3cc3C)=C2C1C', 'CCC=C1CCCC(=CON)CC=CC[O+]=CC(O)CC1', 'CCCCCCCCC(C=CCC(C)C(C)C)C=NC(C)CCC', 'CC(C=CC1CC(=N[N+](C)(C)CC=CNN)CCC(C)B1CN)NCN', 'C=[N+](CCCCC)C1CN=C(CCCCCC)CC(C(C)CN=CC)C(N)C(CC)[O+](C)C(C)C1(C)C', 'C=CC(CCCOC)C(=C)C1=CC=C1C', 'C=CN(C=CCCCCN(C)CNCCCC)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C1=NCCCCCCC1', 'CC=CC1NC(CCCC)C(C)C(CC=CCC=[O+]C)C2(C)CC1C2', 'CCC(=CC(C)CC(C)C=CC=C(CO)CC1CCC1)C(C)(C)CC', 'CCC=C1N=C2CC(C=C(C)CCN=c3cc3C)=C2C1C', 'CCC=C1CCCC(=CON)CC=CC[O+]=CC(O)CC1', 'CCCCCCCCC(C=CCC(C)C(C)C)C=NC(C)CCC', 'CC(C=CC1CC(=N[N+](C)(C)CC=CNN)CCC(C)B1CN)NCN', 'C=[N+](CCCCC)C1CN=C(CCCCCC)CC(C(C)CN=CC)C(N)C(CC)[O+](C)C(C)C1(C)C', 'C=CC(CCCOC)C(=C)C1=CC=C1C', 'C=CN(C=CCCCCN(C)CNCCCC)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various compounds for an intended z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "We should go with 684059 for the cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=CCC1=CC(=CC=CC)C(O)=NCC(C)=C(CCC)C1C', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CCC(N=C1[O+]=C2C(C)C12)=C(C)C', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'CCCC(=CC1C=C(CO)CCC(C)C1N(CC)CCC)C(CC)C(C)C', 'CC=C(C1=CCCCC[O+]=CCC=C1)[N+](C)=CC1=CCC1C', 'C=C(CC(C)NN=NNC)[O+](C)CC1(C[N+](N)(N)C=CN=CC)C=CC(NCC(N)CC(C)C)N=C1', 'COC1(C)C=CC=CCC=CC(C)(CC(C)C2CCC2)CC1C', 'CCC=C(C)CCO[O+]=C(C)C1=CC2N=C3C(=CC=C1)C=C32'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=CCC1=CC(=CC=CC)C(O)=NCC(C)=C(CCC)C1C', 'CCC1(C)CCC=CCC(C)(CC(C)C(C)C)CCC1', 'CCC(N=C1[O+]=C2C(C)C12)=C(C)C', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'NC1CCCCCNCC[N+]2=C(CC2)C(=CC=CCO)C1', 'CCCC(=CC1C=C(CO)CCC(C)C1N(CC)CCC)C(CC)C(C)C', 'CC=C(C1=CCCCC[O+]=CCC=C1)[N+](C)=CC1=CCC1C', 'C=C(CC(C)NN=NNC)[O+](C)CC1(C[N+](N)(N)C=CN=CC)C=CC(NCC(N)CC(C)C)N=C1', 'COC1(C)C=CC=CCC=CC(C)(CC(C)C2CCC2)CC1C', 'CCC=C(C)CCO[O+]=C(C)C1=CC2N=C3C(=CC=C1)C=C32']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a set of molecules targeted at a z-score of -4.054651 for '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240182 is the recommended cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CN=CN=CCNC(C)CC=C1CN1', 'CCCCC1(CC)C=C2C=CC(=O)N=NC(C)(CC2)C1', 'CCCC=CC(C)N(C)ON', 'CCCCCNCN(CN)C(C)=CN', 'C=CC=C1C2NC=C3CC=C(CC(C)C)CC(=C)C(CN)C312', 'CC=CC1(CCCCOC(C)C(C)CC(CC)C(C)=CC)CCC1', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2CC12CCCC', 'CC=CCC=C1CCOC=CCC(C)(COCOC)CC(C(C)C)C(F)C1', 'C=[N+]1[N+]2=CC3=CN(C=[N+]12)N=CC(CCN)C(C)CCN(NC)NN3', 'C=CC=C(C(C)CCC)N1C(=C)C(CCC(C=CCCC)=CC)[O+]1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN=CN=CCNC(C)CC=C1CN1', 'CCCCC1(CC)C=C2C=CC(=O)N=NC(C)(CC2)C1', 'CCCC=CC(C)N(C)ON', 'CCCCCNCN(CN)C(C)=CN', 'C=CC=C1C2NC=C3CC=C(CC(C)C)CC(=C)C(CN)C312', 'CC=CC1(CCCCOC(C)C(C)CC(CC)C(C)=CC)CCC1', 'CCCCCC1CCC(CC(C)C)=CC(C)C2CCCCC2C2CC12CCCC', 'CC=CCC=C1CCOC=CCC(C)(COCOC)CC(C(C)C)C(F)C1', 'C=[N+]1[N+]2=CC3=CN(C=[N+]12)N=CC(CCN)C(C)CCN(NC)NN3', 'C=CC=C(C(C)CCC)N1C(=C)C(CCC(C=CCCC)=CC)[O+]1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules that can attain a z-score of -4.054651 for the cell line '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240182 is the cell line we need. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=NC1=C1N=C(C)C2(C3=C(CC)CN3)CC1C(C=CCCCNC)=[NH+]2', 'CC=C(C=CCC(C)CCC1=CCC1)CCCC', 'CC=CC1C=C1N', 'CCC1=CCC=CCC(C)(C2=CC2C)C2C=CC(C)CCCC2C1', 'CC(=CCNNNC=C1CNNC1)CN', 'CCCNCC=C(C=NC=C(CCC)CNCCC)N[O+](C)CCC', 'C=COC1(C)NCC1C(CC=C(C=C1CC2=CCCN1C1CC(=C)C21C(C)=CCC)CC)C(N)CC', 'CCC=CC=CC(CC(C)=CC1=C(CC)CC1)N(C)NCCC(C)C', 'CCCC=CC=C(CC)CNC(O)CCC1CCC1C', 'C=C(C)CC=CC=CC=C(CCC)C(CF)CCC1CCCC=CC1=CCC=CCN(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=NC1=C1N=C(C)C2(C3=C(CC)CN3)CC1C(C=CCCCNC)=[NH+]2', 'CC=C(C=CCC(C)CCC1=CCC1)CCCC', 'CC=CC1C=C1N', 'CCC1=CCC=CCC(C)(C2=CC2C)C2C=CC(C)CCCC2C1', 'CC(=CCNNNC=C1CNNC1)CN', 'CCCNCC=C(C=NC=C(CCC)CNCCC)N[O+](C)CCC', 'C=COC1(C)NCC1C(CC=C(C=C1CC2=CCCN1C1CC(=C)C21C(C)=CCC)CC)C(N)CC', 'CCC=CC=CC(CC(C)=CC1=C(CC)CC1)N(C)NCCC(C)C', 'CCCC=CC=C(CC)CNC(O)CCC1CCC1C', 'C=C(C)CC=CC=CC=C(CCC)C(CF)CCC1CCCC=CC1=CCC=CCN(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -6.190392 for '12401660000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"12401660000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240166. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "['CCNC=CN=C(F)CC=N', 'C=COC=CC(CC=CCC)C(CCOCC=CC(=C)C=C)NC', 'CBC(N)C(NC)=C(N=CC)NC(C)CNCC', 'CC=C1CCN(C2BC=N[NH+](N=N)C23CN3)NCC(C2NBB2C)BCN1', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=C(C)C[O+](CC)CCC(=C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'CC=CC1(CC)CCCC(=CCCC2NC2C)[O+]=CC(CCC)(CC(C)C(C)=COC)CC1C', 'CNC(=CCC[N+](C)=C1CC1C)C1=CCCC1', 'CC=CC=CC(C)C(C)=N', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CO)C1=C2C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCNC=CN=C(F)CC=N', 'C=COC=CC(CC=CCC)C(CCOCC=CC(=C)C=C)NC', 'CBC(N)C(NC)=C(N=CC)NC(C)CNCC', 'CC=C1CCN(C2BC=N[NH+](N=N)C23CN3)NCC(C2NBB2C)BCN1', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=C(C)C[O+](CC)CCC(=C)C12C=C(CCCC1)C(C)CCNC=CCC2', 'CC=CC1(CC)CCCC(=CCCC2NC2C)[O+]=CC(CCC)(CC(C)C(C)=COC)CC1C', 'CNC(=CCC[N+](C)=C1CC1C)C1=CCCC1', 'CC=CC=CC(C)C(C)=N', 'CC=CC1OCC(C)=CC=CCCC2C=C(CC=NNCC)CC(=CC)CCC(=CO)C1=C2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a series of compounds reaching a z-score of -6.190392 for the cell line '7220450000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"7220450000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "Our correct cell line is 722045. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC[O+]=CC1C(=C)C(C=C)=C(C)C=C1NCC=CCC', 'CC=C(CCC)C(C)C=CCC', 'C=C(CC(C)CC)[N+](=N)COCC', 'C=C(C)NN1C=CC(C)NNC=C(C=C(CN)NC(=CCN=CCNCC)CCC2(C)CC2)C1', 'CCC=CCC(C)N1CCCC2CCC3CC(CCC=CN2)NOC(CCC1)C3', 'CNOCC(C)C(C)C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(N)C2=CC2)C(CN(C)C(=CC)CC=NCC(C)(C)C)=C1', 'CC=[O+]C(N)CC1CCC1C', 'CC1CC(CN2CCCCOC2)C1C', 'CCC=CCCC[N+](=CCCC)CNCC(C)O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC[O+]=CC1C(=C)C(C=C)=C(C)C=C1NCC=CCC', 'CC=C(CCC)C(C)C=CCC', 'C=C(CC(C)CC)[N+](=N)COCC', 'C=C(C)NN1C=CC(C)NNC=C(C=C(CN)NC(=CCN=CCNCC)CCC2(C)CC2)C1', 'CCC=CCC(C)N1CCCC2CCC3CC(CCC=CN2)NOC(CCC1)C3', 'CNOCC(C)C(C)C', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(N)C2=CC2)C(CN(C)C(=CC)CC=NCC(C)(C)C)=C1', 'CC=[O+]C(N)CC1CCC1C', 'CC1CC(CN2CCCCOC2)C1C', 'CCC=CCCC[N+](=CCCC)CNCC(C)O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds reaching a z-score of -4.054651 for '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240182. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C(C)OC)=[N+](C)CCCCCN', 'C=C(C)C1C(C(=CC)CCC)CC2=CC(OC)=C2C(C)=CC1(N)CC(=CN=CC)NC=CN', 'CC=C1C=C(C)C(OC(C)C(Cl)CCC2CCCCCCCCC2C)=[O+]1', 'CCCNC=CNC(C)CCCN', 'CCC=C(CC)C[N+]1=CNC(N)CN1', 'CC(C)CNC1=CCCC2=[O+]C(C)COC=C2C=CC=CCC1', 'C=C1C(CC)=C2C=CC=CCC(C)=CC3=CCC3CC=CC=CC3[O+]1C23C', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)NC)CC(N)C=C(C)CC1', 'C=CCC=CC(F)C1=CC=CCC(O)CNCCC(C)CC(C)CC1', 'COC(NC1CCC1C)C1C=CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C(C)OC)=[N+](C)CCCCCN', 'C=C(C)C1C(C(=CC)CCC)CC2=CC(OC)=C2C(C)=CC1(N)CC(=CN=CC)NC=CN', 'CC=C1C=C(C)C(OC(C)C(Cl)CCC2CCCCCCCCC2C)=[O+]1', 'CCCNC=CNC(C)CCCN', 'CCC=C(CC)C[N+]1=CNC(N)CN1', 'CC(C)CNC1=CCCC2=[O+]C(C)COC=C2C=CC=CCC1', 'C=C1C(CC)=C2C=CC=CCC(C)=CC3=CCC3CC=CC=CC3[O+]1C23C', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)NC)CC(N)C=C(C)CC1', 'C=CCC=CC(F)C1=CC=CCC(O)CNCCC(C)CC(C)CC1', 'COC(NC1CCC1C)C1C=CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically for achieving a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CNN[N+](N)=CC=C(N=NC(=N)C=C(N)NCN)C(C)N', 'CC=CC=CN1C=CC([O+]=CC(OC)C(C)C)=CC=CC=C(CC)O1', 'CCCC(CC)(CC)CCNN', 'BN1BC2BB(C)BN(B)C2C1', 'COC=CCCC1CNNC(N)ONC=CN1C(C)C', 'CB1BB=NB1', 'CCC(C=CCCC(C)(CC)CCN)CC', 'CC1=C(NC=CB(N)[N+](=N)CN)C(C)CC(N)=C1', 'C=C(C(C)CC(O)CC)C(CC)CCCCCl', 'CB1CCC2=C1CNC=CN=CNC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNN[N+](N)=CC=C(N=NC(=N)C=C(N)NCN)C(C)N', 'CC=CC=CN1C=CC([O+]=CC(OC)C(C)C)=CC=CC=C(CC)O1', 'CCCC(CC)(CC)CCNN', 'BN1BC2BB(C)BN(B)C2C1', 'COC=CCCC1CNNC(N)ONC=CN1C(C)C', 'CB1BB=NB1', 'CCC(C=CCCC(C)(CC)CCN)CC', 'CC1=C(NC=CB(N)[N+](=N)CN)C(C)CC(N)=C1', 'C=C(C(C)CC(O)CC)C(CC)CCCCCl', 'CB1CCC2=C1CNC=CN=CNC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '6840550000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840550000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684055. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "['BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1', 'C=CC(C)C(=CC)CCC(CCN)C(C)CCC', 'BCC(C)=CNCC(CC=N)CCC(C)(CC)NC=C', 'C=C(CC(C)=CCC(=CC)CC)C1(C2CO2)CC1=C', 'BCB(C=CCCN)CCC(N)NN', 'BB(C)NC(BNC(C=C)C(N)NNN(N)N)=CC', 'CCC=CC(N)(CCC)CCCNN', 'CCC=CNC1=C(C)CCN2C1CC=C[N+](C)=C1C(C)C12', 'C=C(CC)CCCC(CC)(CC=CCC)NC1CC=C(CC=CC)C(C)C1[N+]1=CC=C1', 'C=CC(CC)C(C=NBCN=NCCN)=C(C)BBN=NC=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1', 'C=CC(C)C(=CC)CCC(CCN)C(C)CCC', 'BCC(C)=CNCC(CC=N)CCC(C)(CC)NC=C', 'C=C(CC(C)=CCC(=CC)CC)C1(C2CO2)CC1=C', 'BCB(C=CCCN)CCC(N)NN', 'BB(C)NC(BNC(C=C)C(N)NNN(N)N)=CC', 'CCC=CC(N)(CCC)CCCNN', 'CCC=CNC1=C(C)CCN2C1CC=C[N+](C)=C1C(C)C12', 'C=C(CC)CCCC(CC)(CC=CCC)NC1CC=C(CC=CC)C(C)C1[N+]1=CC=C1', 'C=CC(CC)C(C=NBCN=NCCN)=C(C)BBN=NC=N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -4.054651 for the cell line '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "1240182 is the cell line we need. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['OC1C2CC=CCCC12', 'C=[O+]CC(C=C(C)CCCC=CC)=CC', 'CCCC(C)C1=CCC=C1', 'CCC=C(C)N(C)CNCCCN', 'C=[N+](C=CCC=[N+](C=N)[N+](C)=CC(C=CC=[N+](N)CC)=CCC(C)NC(C)C)C(C)=N', 'C=CC=C=CCC1CCCNCCC1', 'CCC(NC)=[N+](C)CCNCCN', 'C=C(CC[O+](CC)C[N+](=C)C)C12C=C(CNCC1)C(C)CCNC=NCC2', 'CC=CCC(C)C(C)=CC1(F)C=N1', 'C=C(C)C[O+](C1=C2CCCC(C)=CCC(C)=CC(CN1)C(C)C2)C(O)C(C)F'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['OC1C2CC=CCCC12', 'C=[O+]CC(C=C(C)CCCC=CC)=CC', 'CCCC(C)C1=CCC=C1', 'CCC=C(C)N(C)CNCCCN', 'C=[N+](C=CCC=[N+](C=N)[N+](C)=CC(C=CC=[N+](N)CC)=CCC(C)NC(C)C)C(C)=N', 'C=CC=C=CCC1CCCNCCC1', 'CCC(NC)=[N+](C)CCNCCN', 'C=C(CC[O+](CC)C[N+](=C)C)C12C=C(CNCC1)C(C)CCNC=NCC2', 'CC=CCC(C)C(C)=CC1(F)C=N1', 'C=C(C)C[O+](C1=C2CCCC(C)=CCC(C)=CC(CN1)C(C)C2)C(O)C(C)F']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules with a z-score of -4.054651 for the cell line '12401820000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"12401820000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The right choice for the cell line is 1240182. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC1C=CC(=C(CC)C(C=NC)=C2CC=C(C)N2)C1', 'C=C1C=CCC(C=CCCC=C(C)N(O)C(C)C=NC(CC)(COC)CC(C)=CCC)C1', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)CNN=CCN=CC)=CC1CCC', 'C=CC(CCC=CC(C)=CC(C)C(=C)NCC(C)C)CCC=[N+]=C(C=CC)CNCCCC', 'C=C1C=C(CN)N=CC2CN(CCNC=C(C)N)[O+]2C(N)=C1', 'CCC(CO)CCCC[O+](C)C', 'C=C(C=CCCCC(C)(C)C=C(C[O+](C)O)C1=CC1)[O+]=CC', 'C=C(C=CC1CCC=C(CC(C)C=CC)C1)C(C)O', 'CCC1=C2C=CCC(C)=CC(=CC=CCCC(C)=C(C)O)C(C)OC1=C2', 'CC=Nc1cc2o[n+]1CCCOC=C(CC)C(=C(O)C=CCC)CCCC[O+]=CC(C)=[O+]2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC1C=CC(=C(CC)C(C=NC)=C2CC=C(C)N2)C1', 'C=C1C=CCC(C=CCCC=C(C)N(O)C(C)C=NC(CC)(COC)CC(C)=CCC)C1', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)CNN=CCN=CC)=CC1CCC', 'C=CC(CCC=CC(C)=CC(C)C(=C)NCC(C)C)CCC=[N+]=C(C=CC)CNCCCC', 'C=C1C=C(CN)N=CC2CN(CCNC=C(C)N)[O+]2C(N)=C1', 'CCC(CO)CCCC[O+](C)C', 'C=C(C=CCCCC(C)(C)C=C(C[O+](C)O)C1=CC1)[O+]=CC', 'C=C(C=CC1CCC=C(CC(C)C=CC)C1)C(C)O', 'CCC1=C2C=CCC(C)=CC(=CC=CCCC(C)=C(C)O)C(C)OC1=C2', 'CC=Nc1cc2o[n+]1CCCOC=C(CC)C(=C(O)C=CCC)CCCC[O+]=CC(C)=[O+]2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules reaching a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "684059 is the preferred cell line. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['COC=CCCCC(CC(C)(C)O)C1CCC2C(C)C12COOC', 'C=C(C)C1=CNC=C(NON2CCC2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1', 'CC(C)OC(C)C', 'NC1=CC=CC(CONC2C=CC2)C1', 'C=CC1C=CC=CC=C(NCC)CC1CC(=C)C', 'CCC(CO)CCCC[O+](C)N', 'CCCC(N)=CC(C)C', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C(NCC=C(BCCN=NCBN=N)CCCNCN)C(C)N', 'C=CB(B=CC)CNB1CNC(C)C1N=C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC=CCCCC(CC(C)(C)O)C1CCC2C(C)C12COOC', 'C=C(C)C1=CNC=C(NON2CCC2)C=CC=C(N=CCC2C=C2C)C(N)C(C)(C=C(C)N(C)CCN)C1', 'CC(C)OC(C)C', 'NC1=CC=CC(CONC2C=CC2)C1', 'C=CC1C=CC=CC=C(NCC)CC1CC(=C)C', 'CCC(CO)CCCC[O+](C)N', 'CCCC(N)=CC(C)C', 'C=C(C)CC1=C2CN2CC(=CC)CCCCCCN2NC(C)C=[O+]C2=C1', 'C=C(NCC=C(BCCN=NCBN=N)CCCNCN)C(C)N', 'C=CB(B=CC)CNB1CNC(C)C1N=C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a collection of molecules with a z-score of -4.054651 for '6840590000'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"6840590000\"}}"
            },
            {
                "from": "observation",
                "value": "[\"invalid cell\"] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "There might be some mistake with the cell line, please give me a correct one."
            },
            {
                "from": "human",
                "value": "The most accurate cell line is 684059. MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"(user_zscore)\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C=CC1CC(=N[N+](C)(C)CC=CNN)CCC(C)B1BN)NCN', 'CN=NC1C(C=CC=NNC(C)(CNN)NNNC)CCC2CN2C1C', 'CCC(C)=C(C)C=NC1C=CC(C)CN1', 'C=C(CCCC(C)(C)C(C=CC=C(C)C)C(C)C)N1CCCC1', 'CC=CCC[N+]12C=CCC(=CC)CN=C(CC)C(N)C(N)=C[N+](C)(C)N(C)CCC3(CCNCN(N)C3[N+](=N)B=CNC)C1C2CC=C(CCC)NCC', 'CCC(C)=NN=CCC=CN(N)CC', 'CCCNCC(=CN=NCN)CC(C)NC(=NNCC)C(C)CN', 'C=C=CCC(C)OC(C)CCC', 'CC1(C)N[N+]1=C[N+]1=CCC[N+](=CC=NN)NB(N)N=NC1', 'CC(C=N)N(C)CN(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C=CC1CC(=N[N+](C)(C)CC=CNN)CCC(C)B1BN)NCN', 'CN=NC1C(C=CC=NNC(C)(CNN)NNNC)CCC2CN2C1C', 'CCC(C)=C(C)C=NC1C=CC(C)CN1', 'C=C(CCCC(C)(C)C(C=CC=C(C)C)C(C)C)N1CCCC1', 'CC=CCC[N+]12C=CCC(=CC)CN=C(CC)C(N)C(N)=C[N+](C)(C)N(C)CCC3(CCNCN(N)C3[N+](=N)B=CNC)C1C2CC=C(CCC)NCC', 'CCC(C)=NN=CCC=CN(N)CC', 'CCCNCC(=CN=NCN)CC(C)NC(=NNCC)C(C)CN', 'C=C=CCC(C)OC(C)CCC', 'CC1(C)N[N+]1=C[N+]1=CCC[N+](=CC=NN)NB(N)N=NC1', 'CC(C=N)N(C)CN(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple compounds optimized for a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['NCC=CC1CC1CCCN', 'CCCCCC(CC)[N+]1(CCCC)CC1C1CCCCC1C(C)N', 'CC=C(CC1=CC=CCC1)CC(CC)CCCC(CC=CCC)CCC', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)n1)CC', 'C=C(C=CC)[O+]1OC(C)[OH+]1', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1', 'C=C(C)N1COC1C1=CCCC=CCCCCN2CCN2C1CCC', 'C=C(C)C(=N)C[N+](=CC=C(N)NN=CNC(C)CN)CCC1=CC1C', 'CCC(=C(C)CCC(CCN)CCCC=C(O)C=NCC=C(C)C)C(C)OC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['NCC=CC1CC1CCCN', 'CCCCCC(CC)[N+]1(CCCC)CC1C1CCCCC1C(C)N', 'CC=C(CC1=CC=CCC1)CC(CC)CCCC(CC=CCC)CCC', 'CC=C(C=CNC)C=C(C)CC(C=CCCCCCCc1c[nH][n+](O)n1)CC', 'C=C(C=CC)[O+]1OC(C)[OH+]1', 'C=CCCC1CCC(CCC2CC2=NOC)CCC(C=CC)C1', 'C=C1C=C(CC)C=CC=CC(O)CC=CCC2=[O+]C(O)C=CCCC2C(=C)C1', 'C=C(C)N1COC1C1=CCCC=CCCCCN2CCN2C1CCC', 'C=C(C)C(=N)C[N+](=CC=C(N)NN=CNC(C)CN)CCC1=CC1C', 'CCC(=C(C)CCC(CCN)CCCC=C(O)C=NCC=C(C)C)C(C)OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a series of molecules with a z-score of -4.054651 specific to the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['B=C(C)C(=CNCC)N(C)B(NN)B1NNC1CC(C)=CN(CN)CCN=C', 'CCC(CC(C)N)N1N=CNCC(N=C(C)N)C(N)N1CN(NN)C(=N)N', 'CN(C)CB1C=BB(NN)C(CN)NN1', 'BCCC1(CCCN)C=B[NH+](NN)C(C)C(=BC)BCC1N', 'BCCN(N)NC1=CC=C2NC(BCN)N(CNN)BCCC1(N)N2C(C)N', 'C=CC(C)C(N)CC(=CC)NCN', 'CCC(CC)CCC1=CC1(C)CC', 'C=CC(C)C1CCNC(C)=[O+]C(C)C1C=C', 'C=CNCC(NNC=CCO)OF', 'CCN(C=NB(C)B(C)NN)NN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['B=C(C)C(=CNCC)N(C)B(NN)B1NNC1CC(C)=CN(CN)CCN=C', 'CCC(CC(C)N)N1N=CNCC(N=C(C)N)C(N)N1CN(NN)C(=N)N', 'CN(C)CB1C=BB(NN)C(CN)NN1', 'BCCC1(CCCN)C=B[NH+](NN)C(C)C(=BC)BCC1N', 'BCCN(N)NC1=CC=C2NC(BCN)N(CNN)BCCC1(N)N2C(C)N', 'C=CC(C)C(N)CC(=CC)NCN', 'CCC(CC)CCC1=CC1(C)CC', 'C=CC(C)C1CCNC(C)=[O+]C(C)C1C=C', 'C=CNCC(NNC=CCO)OF', 'CCN(C=NB(C)B(C)NN)NN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please prepare a series of molecules to reach a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)CCC(N)(N)C(=CC)NC', 'C=C(C)C(O)(CC(CCC)OCN(CC)CCCC1(C)CN1)N(C)CC', 'C=C(C)CN1C=CC(C)=CC(OC(C)NC=C(C)C)CCCC1=C', 'CCCC1=C2C=CCC2C1', 'C=C[N+]1=CC1', 'CC(C)CCC=CC1=CCC2=C(C1)C1C=C(CC(=CNCC[N+](=CCN)C(C)(CC3=CC3)NCO)CC1)NCCC=N2', 'C[O+]=C(CC=CCC=CC1=[O+][O+]1C)C(C)C', 'CCC=C[N+]1(CC)CNN1', 'CC(C)(C)CC1=CC=CCN(N)N[N+](N)=CC2NC(=N)C2C1', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)CCC(N)(N)C(=CC)NC', 'C=C(C)C(O)(CC(CCC)OCN(CC)CCCC1(C)CN1)N(C)CC', 'C=C(C)CN1C=CC(C)=CC(OC(C)NC=C(C)C)CCCC1=C', 'CCCC1=C2C=CCC2C1', 'C=C[N+]1=CC1', 'CC(C)CCC=CC1=CCC2=C(C1)C1C=C(CC(=CNCC[N+](=CCN)C(C)(CC3=CC3)NCO)CC1)NCCC=N2', 'C[O+]=C(CC=CCC=CC1=[O+][O+]1C)C(C)C', 'CCC=C[N+]1(CC)CNN1', 'CC(C)(C)CC1=CC=CCN(N)N[N+](N)=CC2NC(=N)C2C1', 'CC=CC1(CC(CCC)CCC2(CC)CC2(C)CC(CC)C(C)=CC2CN(C(C3=CC3)=C(C)C)C2C)CC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various molecules achieving the desired z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC1=C(CC2=[O+]C(C[N+](C)=C(CCC)CN(C)CCC(C)CCC)C2)C1', 'C=NC(NBNB(C)CNN)C(C)NC(C)NN', 'C=NN=C(CN)NN=C', 'CC=CNCC=[O+]CC(C(CCCNCC)CC(C)=NCCC)C(C)(C)CC', 'CC=[O+]C(C)CC1CCC1C', 'CC=COC(CC)[N+](=CCCC)CCC', 'OCN=C1C=CC1CCCCCC1=CC(O)CN1', 'CC(C)CC1N[O+]=CC1CC(C)F', 'C=C1CCC(C)C(=NC)C=CC2=CCC(C)(C=CCNC)C2C2=C([N+]3=NC3)CCCC=C1[N+](C)=CNC=C2', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CC)[O+]1C=C32'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC1=C(CC2=[O+]C(C[N+](C)=C(CCC)CN(C)CCC(C)CCC)C2)C1', 'C=NC(NBNB(C)CNN)C(C)NC(C)NN', 'C=NN=C(CN)NN=C', 'CC=CNCC=[O+]CC(C(CCCNCC)CC(C)=NCCC)C(C)(C)CC', 'CC=[O+]C(C)CC1CCC1C', 'CC=COC(CC)[N+](=CCCC)CCC', 'OCN=C1C=CC1CCCCCC1=CC(O)CN1', 'CC(C)CC1N[O+]=CC1CC(C)F', 'C=C1CCC(C)C(=NC)C=CC2=CCC(C)(C=CCNC)C2C2=C([N+]3=NC3)CCCC=C1[N+](C)=CNC=C2', 'C=CC1C2CC(=CO)CC3CCC(CC)CCC(CCC=CC)[O+]1C=C32']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules that can reach a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCB=C[N+](N=N)=C(C)CC(=N)CCC(C)=N', 'CCC(N=C1[O+]=C2C1N2C)=C(C)C', 'CC(N)CC1CCNC(C)(C)C1', 'C=C(C)CCC(N=CCCN)C(C)NCC', 'CCCCNCC(CC)C(C=NC)=NCC', 'CCC(C)[O+]1COCOO1', 'CCCC=CC=CC(O)CCC(C)CC=C1CCC1CC(C=NCCO)C=C(CC)CC1C=C1C', 'C=[O+]C=C(C)C1=CCC(C(CCC)C(C)F)C2CC(CO)=C(CC3=C[O+](C(=C)C)C31)C2C', 'C=C(C=CCC1(C)CCCCCCCC(=C2CC(C)C2C=CC)CCCC(O)=CC(=C)C1C)CC', 'N=NC=C(N)C=[N+](C=NCB(N)N)NCNN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCB=C[N+](N=N)=C(C)CC(=N)CCC(C)=N', 'CCC(N=C1[O+]=C2C1N2C)=C(C)C', 'CC(N)CC1CCNC(C)(C)C1', 'C=C(C)CCC(N=CCCN)C(C)NCC', 'CCCCNCC(CC)C(C=NC)=NCC', 'CCC(C)[O+]1COCOO1', 'CCCC=CC=CC(O)CCC(C)CC=C1CCC1CC(C=NCCO)C=C(CC)CC1C=C1C', 'C=[O+]C=C(C)C1=CCC(C(CCC)C(C)F)C2CC(CO)=C(CC3=C[O+](C(=C)C)C31)C2C', 'C=C(C=CCC1(C)CCCCCCCC(=C2CC(C)C2C=CC)CCCC(O)=CC(=C)C1C)CC', 'N=NC=C(N)C=[N+](C=NCB(N)N)NCNN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design compounds to reach a z-score of -6.190392 for the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CC)(CCC)C1', 'CC1=CC2=C(O)C(C)C[O+]2C1', 'C1=CC2CC(=C1)C2', 'CB(C)CNCBB(CC1(C)CN(C)N1)N=CN', 'CC=NCC=[N+](C)C=CC1([N+](=CNC=CC=CCCC)CC)Cn2ncn21', 'CCCCCNCN(CN)C(C)=CN', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'C=C(N=CNOCCCC)C(N)CC', 'C=CC=CC=CC(CCN)C(N)C(CC)=[O+]C=N', 'C=CC1=C2CC(=CCC1)C(CO)=CCC2C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CC)(CCC)C1', 'CC1=CC2=C(O)C(C)C[O+]2C1', 'C1=CC2CC(=C1)C2', 'CB(C)CNCBB(CC1(C)CN(C)N1)N=CN', 'CC=NCC=[N+](C)C=CC1([N+](=CNC=CC=CCCC)CC)Cn2ncn21', 'CCCCCNCN(CN)C(C)=CN', 'CC=C(C)C1=CC=CCC[N+]2=CC=CNC(CCOC)=C(N)OC1C2', 'C=C(N=CNOCCCC)C(N)CC', 'C=CC=CC=CC(CCN)C(N)C(CC)=[O+]C=N', 'C=CC1=C2CC(=CCC1)C(CO)=CCC2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce molecules to match a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC(C)CCNC1CC2CCC(C=CCCC)(C(CC=C)CC(C)CCCC)C(CC)C1C2', 'C=NC(C)(C(C)C(=CN(C)N(NCCCN)C(CC(C)CC)=NCC)CCN)N(C(N)=C(CCN)CC(C)(C)C(C)N)[N+](=C)N', 'C=C1C=C(C(C)C)CNCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC', 'C=CC12N=[N+](CN1)C[N+]1=CCC(=NNN2)CCC=C1', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)NNN=CCN=CC)=NN1CCC', 'C=CCCC(O)CNCCC(=C)C=C', 'C=C(C=CC1CNC(C=CC(C)C=C(C)CC)N=N1)CCC', 'C=C(CC(C)CC)[N+](=C)COCC', 'CCCNCC(CC)C(C=NC)=NCC', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC(C)CCNC1CC2CCC(C=CCCC)(C(CC=C)CC(C)CCCC)C(CC)C1C2', 'C=NC(C)(C(C)C(=CN(C)N(NCCCN)C(CC(C)CC)=NCC)CCN)N(C(N)=C(CCN)CC(C)(C)C(C)N)[N+](=C)N', 'C=C1C=C(C(C)C)CNCC1CC1=C(C)CC1C(C=NN(CC)CCC)CCC', 'C=CC12N=[N+](CN1)C[N+]1=CCC(=NNN2)CCC=C1', 'C=NC(CCC)C(=NNCC(C)C)N=[N+]1C=CC=CC(C(C)NNN=CCN=CC)=NN1CCC', 'C=CCCC(O)CNCCC(=C)C=C', 'C=C(C=CC1CNC(C=CC(C)C=C(C)CC)N=N1)CCC', 'C=C(CC(C)CC)[N+](=C)COCC', 'CCCNCC(CC)C(C=NC)=NCC', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules to hit a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(CNCN)C1=CC(=C(C)CNN)N=CC(N)CN1', 'C=C1N=CC[N+]1(N)C(C)N(C)C', 'CB=C(CCCC)C(=N)N=C(CBNC)C(C)=NC', 'CCCCCNC1N=NCC2CB(CNC1O)C(C=C(C)NC)N2', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCCC(CC(=CC)CCO)C(C)C', 'C=C(CCCC)C(O)C=[O+]NC(C)C1C=C(N)C=CCC1', 'CBCCc1ccn(CC)n1', 'C=C1C=NCC=CC2=C(NC)[N+](=C2)CC1C', 'CCCC(=CC1N=C2CCN(NNNN2)C1N(CC)CNC)C(CC)C(C)N', 'C=C(OCS)C(C)CCC(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(CNCN)C1=CC(=C(C)CNN)N=CC(N)CN1', 'C=C1N=CC[N+]1(N)C(C)N(C)C', 'CB=C(CCCC)C(=N)N=C(CBNC)C(C)=NC', 'CCCCCNC1N=NCC2CB(CNC1O)C(C=C(C)NC)N2', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCCC(CC(=CC)CCO)C(C)C', 'C=C(CCCC)C(O)C=[O+]NC(C)C1C=C(N)C=CCC1', 'CBCCc1ccn(CC)n1', 'C=C1C=NCC=CC2=C(NC)[N+](=C2)CC1C', 'CCCC(=CC1N=C2CCN(NNNN2)C1N(CC)CNC)C(CC)C(C)N', 'C=C(OCS)C(C)CCC(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules that reach the target z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCNCOC=C1N[O+]1C', 'C=CCCCC(C)=CC(=CC)C[O+]=C', 'CCC(CO)CCCC[O+](C)C', 'CC1=CCCC=CC(C)CCCCO1', 'C=CCC1C=C1CC(N)(C1CC1CC)[O+](C)C', 'CCNNC(C)C', 'C=[N+]1C=CCC2(C)C=CC23C=C(CC(S)=CC(=C(C=C[O+]=CCCCO)CCC)C1)N3', 'CC(CN)CC=CC=CNCC1=C=CCCC2O[O+]2C(C)CC=CC1C', 'CCCOC1CC=C2CC(=CCCC1)C2CCC', 'CCC1=C(C)NN=C2C(C=C3C=[N+](C)CCC3)CC2N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCNCOC=C1N[O+]1C', 'C=CCCCC(C)=CC(=CC)C[O+]=C', 'CCC(CO)CCCC[O+](C)C', 'CC1=CCCC=CC(C)CCCCO1', 'C=CCC1C=C1CC(N)(C1CC1CC)[O+](C)C', 'CCNNC(C)C', 'C=[N+]1C=CCC2(C)C=CC23C=C(CC(S)=CC(=C(C=C[O+]=CCCCO)CCC)C1)N3', 'CC(CN)CC=CC=CNCC1=C=CCCC2O[O+]2C(C)CC=CC1C', 'CCCOC1CC=C2CC(=CCCC1)C2CCC', 'CCC1=C(C)NN=C2C(C=C3C=[N+](C)CCC3)CC2N1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a variety of molecules to achieve a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCC(C)C(C)C(CC(CC)=C(C)CNCCCN(C)CC(C)CC)C(=CCC)C(C)(C)NC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC', 'CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CN)(CCC)C1', 'C=C(CC=CCCC=CC1=CC1)C(N=CCN)NC', 'CCC1CCN1', 'CC1=CN=CCCC(=CC=C2CC2)C=C(C)C(C)(CN(C)O)C1', 'C=CNC(=CCNCC)C[N+](=N)OCC(C)=C(N)CN=CC', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1', 'C=CC(C)(C(C)C(=CN(C)C(CCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCC(C)C(C)C(CC(CC)=C(C)CNCCCN(C)CC(C)CC)C(=CCC)C(C)(C)NC', 'CCCC=C1CNCCC[N+](C)=C(CC2=CCC2)C1=CCCCC1CC[O+]1C(C)CC=[O+]CC', 'CCCC(C)=CC(C)CC(C)CCCC1(C)CCC(CN)(CCC)C1', 'C=C(CC=CCCC=CC1=CC1)C(N=CCN)NC', 'CCC1CCN1', 'CC1=CN=CCCC(=CC=C2CC2)C=C(C)C(C)(CN(C)O)C1', 'C=CNC(=CCNCC)C[N+](=N)OCC(C)=C(N)CN=CC', 'C=CC=C(C=CNCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCN1', 'CCCC(C)=CCCC1=C(C(O)CC)CC(O)O1', 'C=CC(C)(C(C)C(=CN(C)C(CCCCN)C(=NCC)C(C)C(C)CC)CCC)C(C(C)=C(CCN)CC(C)(C)C(C)N)[N+](=C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please create compounds that have a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)CCC(C)C=C1CC2=CC3(CC=C(CCC(O)=C2)C3)C1', 'CC(O)=CC=CCCC(C)CC=CC(C)CC(S)CCF', 'CCCC=C(CCCC1(C)CCC=C(C)[O+](OC)CCC1)C1C=CC1', 'CCCCC(CC)(CC)CCC', 'CN=CCC=C1CC(C)C1', 'C=CC[N+](=C(C=C)CC)C(C)(C)C=C', 'C=C(C=CC)C=CCCNCC', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'CCC1CC1=CC=CC(N)=COCC(C)C', 'C=C1CCC(=CC(C)C)C=CC(C=CC)=CCCCC=CC=NC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)CCC(C)C=C1CC2=CC3(CC=C(CCC(O)=C2)C3)C1', 'CC(O)=CC=CCCC(C)CC=CC(C)CC(S)CCF', 'CCCC=C(CCCC1(C)CCC=C(C)[O+](OC)CCC1)C1C=CC1', 'CCCCC(CC)(CC)CCC', 'CN=CCC=C1CC(C)C1', 'C=CC[N+](=C(C=C)CC)C(C)(C)C=C', 'C=C(C=CC)C=CCCNCC', 'CCC(C=CC(C)C=NN)=C(C)C1CC2(CC=C1C)CCCCC2=O', 'CCC1CC1=CC=CC(N)=COCC(C)C', 'C=C1CCC(=CC(C)C)C=CC(C=CC)=CCCCC=CC=NC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules aimed at reaching a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CN(C)C(C=CC)C1=NNCCC', 'CCC=C(C)C1(CC)CC(=COC[O+](C)C(CC)OC)C(CC)CC2CC2C1', 'CCNC1=NC(OCC=CCCO)C=C1', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'C=C(NCS)C(C)CCN(C)C', 'CC=NCN1CC=NC=CN2CCC(=CCC)C(=CC)NCC3CC3C=CNCN21', 'CCC1(CC=CCC2(C)OC(C)O2)CC1', 'CC(=N)CC1CN(C(C)C)C=CC1OCC1CNN(C)C1', 'CC=CC=[N+]=CN=[N+](C)C1(N)CC(N=NN)N(N)C(CC)(C[O+](C)C)N1N', 'C=CN(C=CCCCCN(C)CNCCCN)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CN(C)C(C=CC)C1=NNCCC', 'CCC=C(C)C1(CC)CC(=COC[O+](C)C(CC)OC)C(CC)CC2CC2C1', 'CCNC1=NC(OCC=CCCO)C=C1', 'CC=C(C)CNCC1C=CCCC(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2CC=C=CCC3)CCC(=C2C=C2CC)C=C(CC(N)=CCCC)C1', 'C=C(NCS)C(C)CCN(C)C', 'CC=NCN1CC=NC=CN2CCC(=CCC)C(=CC)NCC3CC3C=CNCN21', 'CCC1(CC=CCC2(C)OC(C)O2)CC1', 'CC(=N)CC1CN(C(C)C)C=CC1OCC1CNN(C)C1', 'CC=CC=[N+]=CN=[N+](C)C1(N)CC(N=NN)N(N)C(CC)(C[O+](C)C)N1N', 'C=CN(C=CCCCCN(C)CNCCCN)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds reaching a z-score of -4.054651 for the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(CC)C(CCCC)=NC1C(C)=CCC1(C)N', 'C=C1C=C(C)C1C(N)CNC', 'CCC1CCCC(=N)[O+]=CCCC1C', 'CCN(C)NCCCNN', 'CC=CC=NN=NC(C)(C)C(NNC)B(C)N', 'B=NN(CC)CNB(BC(B(N)C(C)C)C1(NCC)NN=NN1)CC=C', 'CNC1NC(N)B=N[N+](N)(CB=C(N)CN)NC(N)(N)CC2=C(CC2)B1C', 'B=[N+]1B(C)[N+]2=NNCCNN(C)CC(N)[N+](B)(N)C1=CC2', 'C=C1CC=C(C)C2=C(C2)C(=C)CC(=C(CC)CCC2(C)C(C)C2N)C1', 'CC=CC(C)=CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(CC)C(CCCC)=NC1C(C)=CCC1(C)N', 'C=C1C=C(C)C1C(N)CNC', 'CCC1CCCC(=N)[O+]=CCCC1C', 'CCN(C)NCCCNN', 'CC=CC=NN=NC(C)(C)C(NNC)B(C)N', 'B=NN(CC)CNB(BC(B(N)C(C)C)C1(NCC)NN=NN1)CC=C', 'CNC1NC(N)B=N[N+](N)(CB=C(N)CN)NC(N)(N)CC2=C(CC2)B1C', 'B=[N+]1B(C)[N+]2=NNCCNN(C)CC(N)[N+](B)(N)C1=CC2', 'C=C1CC=C(C)C2=C(C2)C(=C)CC(=C(CC)CCC2(C)C(C)C2N)C1', 'CC=CC(C)=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules with the intended z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CNN[N+](N)=CC=C(N=NC(=N)C=C(N)NCN)C(C)N', 'CNC1=NC2=[N+](C2)CC(C)CNCN(CC=N)C(N)C(C)=CNC(C)NC2CCBC=C[N+]1=NNCC=C2N', 'C=CC(=CC)C1C2=C(C(=C)C[O+](C)OCCC)C21', 'CC1CCC(=CCCCC=CC=CCO)C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'C=C1C[N+]2=C[N+](=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'C=NCBC(NC)NC(C=CCC)=NC', 'CCC(N)CC(CC)C(C)NCC(N)C(C)(C)N', 'CC=CNCC=CCNNCC', 'CCN=C(C)C1=C[N+](CCCC2=C[N+](N)=C2C)=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNN[N+](N)=CC=C(N=NC(=N)C=C(N)NCN)C(C)N', 'CNC1=NC2=[N+](C2)CC(C)CNCN(CC=N)C(N)C(C)=CNC(C)NC2CCBC=C[N+]1=NNCC=C2N', 'C=CC(=CC)C1C2=C(C(=C)C[O+](C)OCCC)C21', 'CC1CCC(=CCCCC=CC=CCO)C1', 'C=CCC(C)CC1=CC(C)CC1C(C)CCC(=CN(C)C)C=C1CC1', 'C=C1C[N+]2=C[N+](=CC(C)C(N=CN)C(CN)CCC)C1CCCC2', 'C=NCBC(NC)NC(C=CCC)=NC', 'CCC(N)CC(CC)C(C)NCC(N)C(C)(C)N', 'CC=CNCC=CCNNCC', 'CCN=C(C)C1=C[N+](CCCC2=C[N+](N)=C2C)=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a batch of molecules that hit a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C[N+]1=NN(N)CC=C1', 'C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)O1', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=CC1CC1(C)OC', 'CCC=CCC(C)C(=[O+]CC)C(=CC1=CC1)C1CC=CC=CCC1', 'C=CC=C(C=CCCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCC1', 'CCCCCNC=CCC1=C2C=C(CCCCC=[N+]1C)CC1(C=CC(C)C1NC)C2', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CN(C)C(=CC)CC=CCC(C)(C)C)=C1', 'CCC(CNC)COC=C1C[O+]2CCC(C)(CC)CCC12', 'CC=CCC(C)C1=C[O+]=C(OC)C=NCCOCC=C1CCCCC', 'CNCCNC1=C2C=CCC(C)=C1C=C(CC(C=CC(C)(C)C)NC)C=CC=C(C)C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C[N+]1=NN(N)CC=C1', 'C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)O1', 'C=CC=C1C(CC)=C(OCC=C(C)C)C1CC=CC1CC1(C)OC', 'CCC=CCC(C)C(=[O+]CC)C(=CC1=CC1)C1CC=CC=CCC1', 'C=CC=C(C=CCCC=C(CC)CCC=C)C(CC=CC(=C)C)CC(C)C1C=CCCC1', 'CCCCCNC=CCC1=C2C=C(CCCCC=[N+]1C)CC1(C=CC(C)C1NC)C2', 'C=CN=CC=CCC=CCCC1=C(C(=C)CC(C)C2=CC2)C(CN(C)C(=CC)CC=CCC(C)(C)C)=C1', 'CCC(CNC)COC=C1C[O+]2CCC(C)(CC)CCC12', 'CC=CCC(C)C1=C[O+]=C(OC)C=NCCOCC=C1CCCCC', 'CNCCNC1=C2C=CCC(C)=C1C=C(CC(C=CC(C)(C)C)NC)C=CC=C(C)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please create a variety of molecules to reach a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC(C=NN=CCN)CCNCCC=[N+]1C=NN1', 'CC(C)=NCC1(C)CC1(C)C', 'C=CCC(C)(CCC)C(C)CC', 'C=C(CCCCCC)C(C=N)C=CCCN=CC1=C(CCC)C=CNC=C1', 'CC=C(C)CCC(CCCC1CCC1)CCC(=CCC(C)CC)CC(CC)=C(C)NCC', 'C=C1C=C(N2C=[N+]2C)NCCNC2CN[N+](=CN2)CC(=CCC)CCOC1', 'C=CCCCCCC1(CC)C(=C)C2=C(C)CC1(C)C=C1CC(O)C1=C2', 'C=C(C)CC(C)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'CC1CCC2=C1CCC=CC=CCC2', 'C=C(CC)C(C)C=CC(=CCC=CC(=CC(=O)CC(C)F)CCC)N(C)C(C)CCO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(C=NN=CCN)CCNCCC=[N+]1C=NN1', 'CC(C)=NCC1(C)CC1(C)C', 'C=CCC(C)(CCC)C(C)CC', 'C=C(CCCCCC)C(C=N)C=CCCN=CC1=C(CCC)C=CNC=C1', 'CC=C(C)CCC(CCCC1CCC1)CCC(=CCC(C)CC)CC(CC)=C(C)NCC', 'C=C1C=C(N2C=[N+]2C)NCCNC2CN[N+](=CN2)CC(=CCC)CCOC1', 'C=CCCCCCC1(CC)C(=C)C2=C(C)CC1(C)C=C1CC(O)C1=C2', 'C=C(C)CC(C)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'CC1CCC2=C1CCC=CC=CCC2', 'C=C(CC)C(C)C=CC(=CCC=CC(=CC(=O)CC(C)F)CCC)N(C)C(C)CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop molecules to achieve a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1=CC=CC(C=C(CC)CN)NNN1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C=C(CC)C(CC(C)(C)O)C(C)CC(C)CC=C1ON1CCCC', 'CN=[N+]1C=CCCC=CC([N+](C)=CNN(C)CC2C=CCC=C2)=NCC=C1', 'C=C1CC=COC2OC(=C(C)C(=C)O)C2C(=C)CCCCC1', 'C=CC(CC(=O)CC(C)CC(=CN(C)N)C(O)=CCC)CC(CCC)=[O+]NC=C(C)NC', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=C1C2CCC(C(=CCN)CCC)N(C)CCC(N)NCCC([N+](=C)NNC)C1(C)C2', 'CCCC1C(C(CCCCN2C(C)=C2CC)CCC(C)(C)C)CCN1CC', 'CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1=CC=CC(C=C(CC)CN)NNN1', 'CCC=CC1=CC(NCCC(C)C)C[O+]=CC1C(C)C=CNC(C)CCCC=CCCCC', 'C=C(CC)C(CC(C)(C)O)C(C)CC(C)CC=C1ON1CCCC', 'CN=[N+]1C=CCCC=CC([N+](C)=CNN(C)CC2C=CCC=C2)=NCC=C1', 'C=C1CC=COC2OC(=C(C)C(=C)O)C2C(=C)CCCCC1', 'C=CC(CC(=O)CC(C)CC(=CN(C)N)C(O)=CCC)CC(CCC)=[O+]NC=C(C)NC', 'C=CC1=C[N+](N)(NCC2CC2CCC)CC1(C)CC', 'C=C1C2CCC(C(=CCN)CCC)N(C)CCC(N)NCCC([N+](=C)NNC)C1(C)C2', 'CCCC1C(C(CCCCN2C(C)=C2CC)CCC(C)(C)C)CCN1CC', 'CCC(=CCC1C=CCC(CC(C)O)N1)C(C)=CCCC(=N)C=C1C=CCCO1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce compounds with a z-score of -4.054651 for the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CB=CN=C(C=CC)C1=C(N)C(NCN=CN)C=C(NN=NNCC)C[N+](=CC)CCC1=NN', 'CCCC(O)C=[O+]C(C)C', 'CCC=CC1C=CCN2CC3CC3(CCC(C)CCC(C)C(C)C2C)CC(C)C1', 'C=C1CC=CC=[N+]2CC(C=CO)=CC(CC)C=NN3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC=CC=NCC(C)CNNCC1=CC1(C)OOC=NCC', 'C=CC(=CC)C(=C)C(C)(C(=C)CCC(C)CCCCOCCCC)C(C)=CCC(=C)C', 'CCN[NH+]1C2=CCCCCC13CCC(C)=C1CN=C(C=CCC=C(N)N2)N=CCN1C3C', 'C=C(CNC(NN)NCBC)[N+](C)(CC)CNNC', 'CC=CN(CCCC(CCN)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CNNCC=CC=CCN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CB=CN=C(C=CC)C1=C(N)C(NCN=CN)C=C(NN=NNCC)C[N+](=CC)CCC1=NN', 'CCCC(O)C=[O+]C(C)C', 'CCC=CC1C=CCN2CC3CC3(CCC(C)CCC(C)C(C)C2C)CC(C)C1', 'C=C1CC=CC=[N+]2CC(C=CO)=CC(CC)C=NN3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC=CC=NCC(C)CNNCC1=CC1(C)OOC=NCC', 'C=CC(=CC)C(=C)C(C)(C(=C)CCC(C)CCCCOCCCC)C(C)=CCC(=C)C', 'CCN[NH+]1C2=CCCCCC13CCC(C)=C1CN=C(C=CCC=C(N)N2)N=CCN1C3C', 'C=C(CNC(NN)NCBC)[N+](C)(CC)CNNC', 'CC=CN(CCCC(CCN)=C(C)CN)CC(C)CC(N)N=C(N)CCC', 'CNNCC=CC=CCN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate multiple molecules with a z-score matching -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCC=C1C[O+](C)CC(CNCCC)=CC=CC(O)SCCC1C', 'CCC=CCC(CC(C=C(C)CC)=CO)C(C)C', 'CC(C)CC1N[O+]=CC1CC(C)N', 'CC(C)C1=CC=C2OCCN=C2CC2CCCC=C=CC2C=CCC2C1=CCC[N+]2(C)C', 'C=C(C)C1=CNC=C(CON2CCC2)C=CC=C(N=CCC2C=C2C)C(C)C(C)(C=C(C)N(C)CCN)C1', 'CC=[N+](CC=C(C)CCCCC)C1CC(=O)C=C1C', 'C=CC(C=CCC=O)C(C)CC(C)C', 'CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=CCC(O)(CNCC)C(CO)CCC(CN)OCC1C(C)=N', 'CCC=C1C(=CCC(CNC)=NOC)CC1=C1CC1C', 'C=C(CC)CCC1CC2C=C(C)CC(=C2)NCC=CCC1O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC=C1C[O+](C)CC(CNCCC)=CC=CC(O)SCCC1C', 'CCC=CCC(CC(C=C(C)CC)=CO)C(C)C', 'CC(C)CC1N[O+]=CC1CC(C)N', 'CC(C)C1=CC=C2OCCN=C2CC2CCCC=C=CC2C=CCC2C1=CCC[N+]2(C)C', 'C=C(C)C1=CNC=C(CON2CCC2)C=CC=C(N=CCC2C=C2C)C(C)C(C)(C=C(C)N(C)CCN)C1', 'CC=[N+](CC=C(C)CCCCC)C1CC(=O)C=C1C', 'C=CC(C=CCC=O)C(C)CC(C)C', 'CCC=CC(C)(CC(C)C(C)(C)C)[N+]1=CCC(O)(CNCC)C(CO)CCC(CN)OCC1C(C)=N', 'CCC=C1C(=CCC(CNC)=NOC)CC1=C1CC1C', 'C=C(CC)CCC1CC2C=C(C)CC(=C2)NCC=CCC1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a collection of compounds with a target z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC1=NC(C(CCO)C(C)(C)N)CCC(C2=CCC=NC(NN)=C2)N2CN(C1)C2', 'CC1CB(CBCBC2(C)CN2)NNB1N', 'C=C(CCCC(C)(C)C(C=CN=CC)C(C)C)N1CCNC1', 'CCC(C)=CCCC(CCN)CC(C)C(C)(CC)[OH+]C', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=CC1=C2CC(=[O+]C1)CCC(OCC(C)C(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2', 'CCCC(=O)C1OCC1(C)C(CC)CC(=O)CCC1CC1', 'CCC=CC(C)C(=CC(C)NC)C1C[O+]1C', 'C=C1CC=CC(C)=[N+](N=CC2=C(C)C(C)C(C)CCN2)C2=C1C=C2', 'CCC=C(N)C1=C2OCC[NH+](CC)[N+](=CC2C)CC2=CC=CN(C=C(N)C(C)C)C=C(CC1)C(C)NC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC1=NC(C(CCO)C(C)(C)N)CCC(C2=CCC=NC(NN)=C2)N2CN(C1)C2', 'CC1CB(CBCBC2(C)CN2)NNB1N', 'C=C(CCCC(C)(C)C(C=CN=CC)C(C)C)N1CCNC1', 'CCC(C)=CCCC(CCN)CC(C)C(C)(CC)[OH+]C', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'C=CC1=C2CC(=[O+]C1)CCC(OCC(C)C(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2', 'CCCC(=O)C1OCC1(C)C(CC)CC(=O)CCC1CC1', 'CCC=CC(C)C(=CC(C)NC)C1C[O+]1C', 'C=C1CC=CC(C)=[N+](N=CC2=C(C)C(C)C(C)CCN2)C2=C1C=C2', 'CCC=C(N)C1=C2OCC[NH+](CC)[N+](=CC2C)CC2=CC=CN(C=C(N)C(C)C)C=C(CC1)C(C)NC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules that align with a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C)CCCCCC(C)=CCC=C(C)CC(C)(C)CCC1=CC1=CCCCO', 'C=NCCC1CC=CCC(CCCC)=C1C', 'CC1CC(C(C)C)CCC(N)NC2=CC(C2C)C(C)(C)C1', 'CCC=C(N)CC=CCC=C1CC1C=CC(C=C(C)C)C(CCOC)C(C)N', 'CCC=C1C=[N+]2NC(N=C(N)CC)(C1)CC2O', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'COC=CCCCC(CC(C)(C)O)C1CCC2C(C)C12COOC', 'CCCCCCC(=CCC=CC(C)OC)[O+]=N', 'CCC1CCN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)CCCCCC(C)=CCC=C(C)CC(C)(C)CCC1=CC1=CCCCO', 'C=NCCC1CC=CCC(CCCC)=C1C', 'CC1CC(C(C)C)CCC(N)NC2=CC(C2C)C(C)(C)C1', 'CCC=C(N)CC=CCC=C1CC1C=CC(C=C(C)C)C(CCOC)C(C)N', 'CCC=C1C=[N+]2NC(N=C(N)CC)(C1)CC2O', 'C=C(N=N)C(CC1=CCC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'COC=CCCCC(CC(C)(C)O)C1CCC2C(C)C12COOC', 'CCCCCCC(=CCC=CC(C)OC)[O+]=N', 'CCC1CCN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a set of molecules to meet a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CN=C1C2N=C3C4[O+]=C4C4OC(Cl)OC5ON2OCC(N)CC31CC1CC1[OH+]C54F', 'C=C1C=C(NC)C=C(C)C(C)(CCC(C)CCC)CCC(CCC)[O+](CC)C=CC1', 'CC(=CCN)CCOC=C(C)c1cccc2c[n+]3n(c1)nc2-3', 'C=C(CCC)C(C)CCC1=C2C=COC(C)C(CC(C)C1)OC(C=CF)=C2', 'CC=CNC=C(CCCCC(CC)CCCCCCCCC1C=C1)COC1CC1=C[O+]=C(C)C', 'C=CC(C)C=CCCCC', 'CNCC1C(CN)CN1CC1CC2(C)CN1C(C)(C)C(CNC)NN=CC1=NC2C1(N)C=CN', 'C=C(CCCC)CCCC=CCCC(C)(C)O', 'C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'CC1(CCN2CC=CCCC3=C4CC(C)(C)CCC5=C4C4(CC=C6CC2CC6C4=C3)CCC5)C=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CN=C1C2N=C3C4[O+]=C4C4OC(Cl)OC5ON2OCC(N)CC31CC1CC1[OH+]C54F', 'C=C1C=C(NC)C=C(C)C(C)(CCC(C)CCC)CCC(CCC)[O+](CC)C=CC1', 'CC(=CCN)CCOC=C(C)c1cccc2c[n+]3n(c1)nc2-3', 'C=C(CCC)C(C)CCC1=C2C=COC(C)C(CC(C)C1)OC(C=CF)=C2', 'CC=CNC=C(CCCCC(CC)CCCCCCCCC1C=C1)COC1CC1=C[O+]=C(C)C', 'C=CC(C)C=CCCCC', 'CNCC1C(CN)CN1CC1CC2(C)CN1C(C)(C)C(CNC)NN=CC1=NC2C1(N)C=CN', 'C=C(CCCC)CCCC=CCCC(C)(C)O', 'C=CCC1CC(CC=CC2COC2)C=CC(=CC2=CC2C)C(=CC(C)(C)C(C)CC)C(C)CN1', 'CC1(CCN2CC=CCCC3=C4CC(C)(C)CCC5=C4C4(CC=C6CC2CC6C4=C3)CCC5)C=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce compounds targeting a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(N)NC(=NCC(C)N)NC', 'C=C(CCCC)CCC1C=C[O+](CC(CC)N(C)C)N1C', 'C=CC(=C)CC=C(C)CCC(=CC)OC(=CC)C(C)O', 'BC(C)(N)NC(CCN=NBC)(N=N)NN', 'CN=NC(=CCCC(N)NNC)CB(C)N', 'C=CCCCC1(C)C2=NC21COC', 'CBB(CC)CC(N)C(N)B(N)CC', 'CCC(C)CC1CCC=CC(=C(C)C(=CNOC=[N+](C)C)C(C)(C)N)[N+]12C[O+](CC)NC2(C)[N+](C)(C)C', 'C=CC=CC=CCC1CCC(C=NN=CCC(=C)C2CC(N)C(CC)C(CO)C2)C(=N)CCC1CN', 'BBN=CBCN=CC(C)N(C=NNNN)CC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(N)NC(=NCC(C)N)NC', 'C=C(CCCC)CCC1C=C[O+](CC(CC)N(C)C)N1C', 'C=CC(=C)CC=C(C)CCC(=CC)OC(=CC)C(C)O', 'BC(C)(N)NC(CCN=NBC)(N=N)NN', 'CN=NC(=CCCC(N)NNC)CB(C)N', 'C=CCCCC1(C)C2=NC21COC', 'CBB(CC)CC(N)C(N)B(N)CC', 'CCC(C)CC1CCC=CC(=C(C)C(=CNOC=[N+](C)C)C(C)(C)N)[N+]12C[O+](CC)NC2(C)[N+](C)(C)C', 'C=CC=CC=CCC1CCC(C=NN=CCC(=C)C2CC(N)C(CC)C(CO)C2)C(=N)CCC1CN', 'BBN=CBCN=CC(C)N(C=NNNN)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop a set of molecules with the desired z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'CC=CC(NCC)C(C)C(N=C(CCC)CC(CC(=CCN)CCCC)NCC)=NC', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1', 'CC1CC1C1C2C1N2C', 'CCC1CCC1(C)CCF', 'CC=C(N)C[N+](C)=CC(C)N=C1CN=C1CCN', 'C=C(N)C1=NC=NC(C)C2CC(C2C)C2C(=C)C=C(C3=C(C)C3)C3CCC=[N+](C)CCC(C3)NC(C)CC2C1', 'CCCCC(C)=CC1=CCCC2CCCCC(=CCCC1C1CNNC1CN=CN)C2C', 'CC=C1CCCCC2(CC)C=C2CC1', 'C=CC=[N+](C)CC(=CC)CC(CC)CCCN(CC=CCC)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCOCN(C)C1CC(C)=CC1CC(C)(OCCC)CCC2NC3CC2C3C1C(C)C', 'CC=CC(NCC)C(C)C(N=C(CCC)CC(CC(=CCN)CCCC)NCC)=NC', 'CCCC1(CN)CCC(C)(CCCC(C)CC(C)C=C(C)CCN)N1', 'CC1CC1C1C2C1N2C', 'CCC1CCC1(C)CCF', 'CC=C(N)C[N+](C)=CC(C)N=C1CN=C1CCN', 'C=C(N)C1=NC=NC(C)C2CC(C2C)C2C(=C)C=C(C3=C(C)C3)C3CCC=[N+](C)CCC(C3)NC(C)CC2C1', 'CCCCC(C)=CC1=CCCC2CCCCC(=CCCC1C1CNNC1CN=CN)C2C', 'CC=C1CCCCC2(CC)C=C2CC1', 'C=CC=[N+](C)CC(=CC)CC(CC)CCCN(CC=CCC)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create a variety of molecules aimed at a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC1=C(C)NN=C2C(C=C3C=[N+](C)CCC3)CC2N1', 'CCC=CC(CC1CCC=COC1C)=NC', 'C=C(SCC(=CC=[O+]C)C(C)COOO[O+](C)C(C)C)C(C)C1(C)CC1', 'CCC(C)CC=NCCC=CN', 'C=C(C=CC)C=CCCNCN', 'CCC(=CB(C)C(N=CN=CN(C)CN=C(C)C)NN)CNC', 'CC1CC2(C1)NCCC(C)(C)CC2C', 'CC=C(C)C1=C2CC(CC)C(O)C(C)C2=CCCC=C1', 'CCC(=CC(C)CC(C)C=CC=CCO)C(C)(C)CC', 'C=C(CCC)CCC(=CC=[O+]NC(N)=CC)CC(C)CC(N)CC1CC=[N+](C)C1=N'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC1=C(C)NN=C2C(C=C3C=[N+](C)CCC3)CC2N1', 'CCC=CC(CC1CCC=COC1C)=NC', 'C=C(SCC(=CC=[O+]C)C(C)COOO[O+](C)C(C)C)C(C)C1(C)CC1', 'CCC(C)CC=NCCC=CN', 'C=C(C=CC)C=CCCNCN', 'CCC(=CB(C)C(N=CN=CN(C)CN=C(C)C)NN)CNC', 'CC1CC2(C1)NCCC(C)(C)CC2C', 'CC=C(C)C1=C2CC(CC)C(O)C(C)C2=CCCC=C1', 'CCC(=CC(C)CC(C)C=CC=CCO)C(C)(C)CC', 'C=C(CCC)CCC(=CC=[O+]NC(N)=CC)CC(C)CC(N)CC1CC=[N+](C)C1=N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a batch of compounds with a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CN=CCCC(=N)CCC)CCN=C(C)CC1', 'C=[O+]CC(C=C(N)CCCC=CC)=CC', 'C=C(NCC=C(BCCN=NCBN=N)CCCNCN)C(C)N', 'CCNCCC1=C(CC)C2C=CC3=CN=CCC(C(=C(CC)CC(C)CC)C2)C1(CCN)CCN3C', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'C=C(C=C[O+]1C=NC(C=CC(C)C=C(C)CC)CC1)CCC', 'C=CC1(CN)CC=CC(C)C2=CCC(=C(CC)C=C(CCCC)C2)CCC(C)C2=CC1=C2', 'C=C(OCS)C(C)CCC(C)C', 'CCN=CC=CCCCC=C1CCC(C)C1', 'C=CCC=C(C1(C)COOC(C)=CN(CC)C(N2C(C=COC)CCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=C2C=C(CC23C=CCCCC=C3)NC=CC(CN=CCCC(=N)CCC)CCN=C(C)CC1', 'C=[O+]CC(C=C(N)CCCC=CC)=CC', 'C=C(NCC=C(BCCN=NCBN=N)CCCNCN)C(C)N', 'CCNCCC1=C(CC)C2C=CC3=CN=CCC(C(=C(CC)CC(C)CC)C2)C1(CCN)CCN3C', 'CC=C=CCCCC(=CCCC(CNC)NCC1=C(C)C1)C[O+](CO)CC[O+]=CC', 'C=C(C=C[O+]1C=NC(C=CC(C)C=C(C)CC)CC1)CCC', 'C=CC1(CN)CC=CC(C)C2=CCC(=C(CC)C=C(CCCC)C2)CCC(C)C2=CC1=C2', 'C=C(OCS)C(C)CCC(C)C', 'CCN=CC=CCCCC=C1CCC(C)C1', 'C=CCC=C(C1(C)COOC(C)=CN(CC)C(N2C(C=COC)CCC(C)C2O)C1)C1(C)C(=C)C1(C)C=CCCNCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build molecules tailored for a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NCCCC=C(CC)C(C)C(=C)C(NCC=N)OCC', 'C=NC(CNC=NCCC(C)(C)N)NC(N)=CC', 'CC=NNC1=[N+]2[N+](=N)C(=C1CNC)CC(=CC)C(=N)CN[N+]2=C(C)C', 'CCCC(C=NC)CCC(C)C1N(C(C)C)[O+]1CCC1CCCCC(CC)CCO1', 'CC(=CN(C)CC1=CNNCC=CNC([N+]2=CC2)=C1C(C)N)OO', 'CCCNCC=NBNN1CC2CC2N(C)CC1=[N+](C)N', 'CCC(CC1C=C(N)C1CF)C(C)CCC(CN)CC(C)OC=CCCCO', 'C=C1CCCCC12CC=C(C)C(C(C)=C(C=CC(C)C=CN)CC)C2', 'CCC1=C(CCC2(C)N=C2CC2=CCN(C3(CC)CC3)C=C2)COC1', 'CCCC1=CC(C)CC1CCC=CC=C(C)C(C)C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NCCCC=C(CC)C(C)C(=C)C(NCC=N)OCC', 'C=NC(CNC=NCCC(C)(C)N)NC(N)=CC', 'CC=NNC1=[N+]2[N+](=N)C(=C1CNC)CC(=CC)C(=N)CN[N+]2=C(C)C', 'CCCC(C=NC)CCC(C)C1N(C(C)C)[O+]1CCC1CCCCC(CC)CCO1', 'CC(=CN(C)CC1=CNNCC=CNC([N+]2=CC2)=C1C(C)N)OO', 'CCCNCC=NBNN1CC2CC2N(C)CC1=[N+](C)N', 'CCC(CC1C=C(N)C1CF)C(C)CCC(CN)CC(C)OC=CCCCO', 'C=C1CCCCC12CC=C(C)C(C(C)=C(C=CC(C)C=CN)CC)C2', 'CCC1=C(CCC2(C)N=C2CC2=CCN(C3(CC)CC3)C=C2)COC1', 'CCCC1=CC(C)CC1CCC=CC=C(C)C(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules designed to achieve a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CN(CNC=CN=CN)[N+](=N)OC(N)CN', 'C=C(C)C=C(C)C1CC1', 'CCBCCCNCCC[N+](C)=[N+](CNCNNN1CCN1)NC1=CCC1', 'C=C(N=N)C(NC1=CNC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'CCCCCCCCC(C=CCC(C)C(C)C)CN=C(C)CCC', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=NC=C1C(C=CC)=[N+](C=C(C)N)CCC(C)C', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1', 'C=CC(C=C)CC(=CCC)CC(C)C(O)NCC1CC1', 'CNCCC1=C(N)N(C)CC1', 'CNC1C=C1CC1C=CC[N+](=N)CCC=N1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CN(CNC=CN=CN)[N+](=N)OC(N)CN', 'C=C(C)C=C(C)C1CC1', 'CCBCCCNCCC[N+](C)=[N+](CNCNNN1CCN1)NC1=CCC1', 'C=C(N=N)C(NC1=CNC(C)C(C)CNC1(C)C(=CC)NC)=C(N)CC', 'CCCCCCCCC(C=CCC(C)C(C)C)CN=C(C)CCC', 'C=[N+](C=CC(C)C)C[N+](=CC)CC1=NC=C1C(C=CC)=[N+](C=C(C)N)CCC(C)C', 'CCCCC1(CC)C=C2C=CC(=O)C=CC(C)(CC2)C1', 'C=CC(C=C)CC(=CCC)CC(C)C(O)NCC1CC1', 'CNCCC1=C(N)N(C)CC1', 'CNC1C=C1CC1C=CC[N+](=N)CCC=N1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a collection of molecules to match a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCC(C(C)OC)=[N+](C)CCCCCN', 'CC1C=CC=CC=CCC=CCCC1', 'C=CCC1CNCC(=C)CCC1C=C1NCN1C', 'C=C(NC)C1=CCC2(BC2)NNCN=[N+](N)CB1', 'C=CCCC(C)CC1CNCC2C=CC1C(C)C(C=[O+]OC)C=N2', 'C=[N+](C)CN(CN)CB=CB(CN)C(C)N(C)NCCC=CC=N', 'CC1CC1=CC(C(C)C(C)CCCCC1=NN1C(C)C)N(C)C', 'CC=CC(C)(C=CN=[N+](C)CC)CC=C(N)NNCN=CN', 'CCC=C(C)CC(C)C=O', 'C=C(C(CCC)=C(C)CC(C)=C=CCC)C(C)[O+]=CC=C1CC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C(C)OC)=[N+](C)CCCCCN', 'CC1C=CC=CC=CCC=CCCC1', 'C=CCC1CNCC(=C)CCC1C=C1NCN1C', 'C=C(NC)C1=CCC2(BC2)NNCN=[N+](N)CB1', 'C=CCCC(C)CC1CNCC2C=CC1C(C)C(C=[O+]OC)C=N2', 'C=[N+](C)CN(CN)CB=CB(CN)C(C)N(C)NCCC=CC=N', 'CC1CC1=CC(C(C)C(C)CCCCC1=NN1C(C)C)N(C)C', 'CC=CC(C)(C=CN=[N+](C)CC)CC=C(N)NNCN=CN', 'CCC=C(C)CC(C)C=O', 'C=C(C(CCC)=C(C)CC(C)=C=CCC)C(C)[O+]=CC=C1CC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds to meet a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC', 'C=C(C)C(C)CCC=CC1C[O+]=CC(CC(C)=C(CC)CC(CC)CC=CC)C(C=CC)C1C1=[O+]C1', 'C=[N+](C=NN=NC)CCC', 'CC=CC1=CC(C)C(CCC)OCC(C)C(N=CC(C=CC)C(C)CCC(C)(C)CC)C=CC1', 'CC1C=CCC(C)(C)NCCC(C)C2=C(C2)C2=C3C=C2NC1=CCCCN(N(C)NN=N)C3C', 'C=C1CC2=C(C2)CN(C)CC(C)(CCC)C(CCC=CC=CCC)=C1CC', 'CCC1CCC1(C)C(C)(CC)C1CC(O)(C(C)C)C1', 'CCCC=C(O)C(C=[O+]CC=NCC)=CO', 'C=C(C=[O+]CC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'CCC=CCCC1CCCNCC(O)=C[O+]1CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC', 'C=C(C)C(C)CCC=CC1C[O+]=CC(CC(C)=C(CC)CC(CC)CC=CC)C(C=CC)C1C1=[O+]C1', 'C=[N+](C=NN=NC)CCC', 'CC=CC1=CC(C)C(CCC)OCC(C)C(N=CC(C=CC)C(C)CCC(C)(C)CC)C=CC1', 'CC1C=CCC(C)(C)NCCC(C)C2=C(C2)C2=C3C=C2NC1=CCCCN(N(C)NN=N)C3C', 'C=C1CC2=C(C2)CN(C)CC(C)(CCC)C(CCC=CC=CCC)=C1CC', 'CCC1CCC1(C)C(C)(CC)C1CC(O)(C(C)C)C1', 'CCCC=C(O)C(C=[O+]CC=NCC)=CO', 'C=C(C=[O+]CC1=C(C)C2=C(CC1C)CC2C(C)O)[O+](C)CC', 'CCC=CCCC1CCCNCC(O)=C[O+]1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate molecules specifically for achieving a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(O)C1=CC(C)=CC(C(COC)OC(C)CCC)=C(CC)OC(C)(C)CCC1', 'CN=[SH][N+]1=C(C)CC(C(C)F)=C1C', 'CC[N+]1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CNC(=CC(C)(C)CCC1(CCC(C)C=C(NC)C(C)(C)C)CN1)NC(C)N', 'CC=CN(NCC)C(C)C(N=C(CCC)CC(CC(=CC)CCCC)NCC)=NC', 'CC=C(N)C=C1N=CN(NC)CC1C', 'CCCCNC[N+]1=CC=C(C=C(C)C2CCNN(NCCC)C2)CNC(C)=CC1', 'C=C(CCC)C(C)CC[N+]1=C2C=COC(N)C(CC(C=CF)=C2)CC(C)C1', 'CCC=CCC(CCC=CCCCC)OONCC', 'C=C1C=CC2=C(N)CCC=CC=C(CCCC)CC1CC2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(O)C1=CC(C)=CC(C(COC)OC(C)CCC)=C(CC)OC(C)(C)CCC1', 'CN=[SH][N+]1=C(C)CC(C(C)F)=C1C', 'CC[N+]1=CC(C)(N)C(CC=CCC2=C(C)C2)C(C)CC1', 'CNC(=CC(C)(C)CCC1(CCC(C)C=C(NC)C(C)(C)C)CN1)NC(C)N', 'CC=CN(NCC)C(C)C(N=C(CCC)CC(CC(=CC)CCCC)NCC)=NC', 'CC=C(N)C=C1N=CN(NC)CC1C', 'CCCCNC[N+]1=CC=C(C=C(C)C2CCNN(NCCC)C2)CNC(C)=CC1', 'C=C(CCC)C(C)CC[N+]1=C2C=COC(N)C(CC(C=CF)=C2)CC(C)C1', 'CCC=CCC(CCC=CCCCC)OONCC', 'C=C1C=CC2=C(N)CCC=CC=C(CCCC)CC1CC2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create compounds to align with a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'C=CC1C=C(C)C(C)CC1', 'C=CNC(=CCNCC)C[N+](=N)OOC(C)=C(C)CN=CC', 'NC1=NC=CC(CONC2C=CC2)C1', 'BBB(N)BBBB=NN(B(C)N)C(C)CC', 'C=C1C(=CCC)CC=CC1CN(C)N', 'C=NCCNC[O+](C)NCN=CC', 'CBC=NC1CB(CCCC)N1', 'C=C(C=CC)C=CCCCCC', 'CCNC(CC[NH+](CN)CB(C)CC(CC)N(N)BN1CN(N(C(C)C)[N+]2=BC2)C1N)NC1(C=NC)BC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NN=C1CNC(C)(CCC=C2CN2)NC1[N+](=C)CCNC', 'C=CC1C=C(C)C(C)CC1', 'C=CNC(=CCNCC)C[N+](=N)OOC(C)=C(C)CN=CC', 'NC1=NC=CC(CONC2C=CC2)C1', 'BBB(N)BBBB=NN(B(C)N)C(C)CC', 'C=C1C(=CCC)CC=CC1CN(C)N', 'C=NCCNC[O+](C)NCN=CC', 'CBC=NC1CB(CCCC)N1', 'C=C(C=CC)C=CCCCCC', 'CCNC(CC[NH+](CN)CB(C)CC(CC)N(N)BN1CN(N(C(C)C)[N+]2=BC2)C1N)NC1(C=NC)BC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a batch of molecules reaching a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CCC=CC(N)C(C=C(C)C=O)N(CCC(=C)CC(=O)CC)C(N)C=C', 'C=CC=CC=CC(CCN)=C(N)C(CC)=[O+]C=N', 'C=C(C=N)C(C)=NC', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'C=CCCC(C)CC1CCCC2C=CC(C=[O+]CC)C(C)C1C=C2', 'C=C1COCC(C)(O)C(OCC=[O+]C)C1', 'CCCC(C1CC(C)C=CO1)[O+]1C=C(CO)CNCCCCC1', 'C=[N+](CC=CCCC)CC(CCC)N(C)NC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)CCC=C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CCC=CC(N)C(C=C(C)C=O)N(CCC(=C)CC(=O)CC)C(N)C=C', 'C=CC=CC=CC(CCN)=C(N)C(CC)=[O+]C=N', 'C=C(C=N)C(C)=NC', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'C=CCCC(C)CC1CCCC2C=CC(C=[O+]CC)C(C)C1C=C2', 'C=C1COCC(C)(O)C(OCC=[O+]C)C1', 'CCCC(C1CC(C)C=CO1)[O+]1C=C(CO)CNCCCCC1', 'C=[N+](CC=CCCC)CC(CCC)N(C)NC', 'C=C(CC)C(C=CCC1C(C)=CCC(=C(C)CC)C=CC1CC)C(C)C1=CCC(C)CCC=C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules optimized for a z-score of -6.190392 for the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC1c2ccc(cc2CN(C)C(C)=CCC)C=CC1(C)CC', 'CC=C(CC)CC1CCC=CC=C2CCC(C)(C)C2=CCOC[N+](C)(C)CCC2O[O+](CC)CC12', 'CC=C(NCC)C(C)C=CCC', 'C1=C2CCC1C2', 'CCCCCNC(C)C1(CCCCC)CC=C(C2CCC2)CC1', 'CCCCC1CC=CCC2CCC=COC21C', 'BB(B(N)BCC1CBN1)B1BCB1N', 'C=C(CCCC=BCC[N+](C)(C)C)CCNC', 'CC=CCN=CN=CCC(C)=CC=CCNN(C)N', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC1c2ccc(cc2CN(C)C(C)=CCC)C=CC1(C)CC', 'CC=C(CC)CC1CCC=CC=C2CCC(C)(C)C2=CCOC[N+](C)(C)CCC2O[O+](CC)CC12', 'CC=C(NCC)C(C)C=CCC', 'C1=C2CCC1C2', 'CCCCCNC(C)C1(CCCCC)CC=C(C2CCC2)CC1', 'CCCCC1CC=CCC2CCC=COC21C', 'BB(B(N)BCC1CBN1)B1BCB1N', 'C=C(CCCC=BCC[N+](C)(C)C)CCNC', 'CC=CCN=CN=CCC(C)=CC=CCNN(C)N', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a variety of molecules to achieve a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=NC(OC)C(=N)CNC1NCN1', 'C=C[N+]1=C(C(CF)CC(=C)O)C=C1CCCC', 'CCCNCC(=CN=NCN)CC(C)NC(=NNCC)C(C)CN', 'C=NC(CC(N)C(N)C(C)(C)CC)NNC=C(C)C(=NC(C)N)C1(CC)NNC1=CCN=N', 'C=C(C)CC(C)(O)OC(C)(CCC=C(C)C(C)F)CCCCC', 'CC=NC1C(C)C[O+]1C', 'CCCC1OCC1C1=C[O+]=CCC(C)=NN1CC', 'C=CC(=CC(CC(=CC(C)CCOC(C)(O)O)OC)=C(C)C)CCC', 'C=C(C)C(C)=CC', 'C=C(CC=CC(=N)C1CC2=CC1OC2)C(=O)CCC(N)C=C(O)O'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=NC(OC)C(=N)CNC1NCN1', 'C=C[N+]1=C(C(CF)CC(=C)O)C=C1CCCC', 'CCCNCC(=CN=NCN)CC(C)NC(=NNCC)C(C)CN', 'C=NC(CC(N)C(N)C(C)(C)CC)NNC=C(C)C(=NC(C)N)C1(CC)NNC1=CCN=N', 'C=C(C)CC(C)(O)OC(C)(CCC=C(C)C(C)F)CCCCC', 'CC=NC1C(C)C[O+]1C', 'CCCC1OCC1C1=C[O+]=CCC(C)=NN1CC', 'C=CC(=CC(CC(=CC(C)CCOC(C)(O)O)OC)=C(C)C)CCC', 'C=C(C)C(C)=CC', 'C=C(CC=CC(=N)C1CC2=CC1OC2)C(=O)CCC(N)C=C(O)O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate a set of molecules targeted at a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(CCCC)C(O)C=[O+]OC(C)C1C=C(F)C=CCC1', 'COC(CCC(C=[O+]C(C)C)=CCO)C(C)O', 'CCC=C(CCCN)C(C)=C1C(C)C1CC(C)(C)CC', 'CNC=NC[N+]1=NC(C)(N(N)CC(=N)CC(CCN)NC(N)=O)N1C', 'C=NC(CC(N)CC(C)(C)CC)NNC=C(C)C(=NC(C)N)C1(CC)NNC1=CCN=N', 'C=C(O)CCN(C=CC(CCC)C1CC(C)=C1CC1CCCC(C(C)C)=CC1=C)CCC', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCCCC1C2(C3CCNN3)C3=CC4=C(CCN(C(N)=NCN(CC)NN=NCC2[N+]1(C)C)C(NC)C3O)N4', 'C=NN=C1CCC(C)(CCC=C2CC2)OC1[N+](=C)CCSC', 'CNNC=CNC1=C2C=NC(C(C)N)B(N2)OCC2CC(NCC=CN(N(C)N)N1)B2C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(CCCC)C(O)C=[O+]OC(C)C1C=C(F)C=CCC1', 'COC(CCC(C=[O+]C(C)C)=CCO)C(C)O', 'CCC=C(CCCN)C(C)=C1C(C)C1CC(C)(C)CC', 'CNC=NC[N+]1=NC(C)(N(N)CC(=N)CC(CCN)NC(N)=O)N1C', 'C=NC(CC(N)CC(C)(C)CC)NNC=C(C)C(=NC(C)N)C1(CC)NNC1=CCN=N', 'C=C(O)CCN(C=CC(CCC)C1CC(C)=C1CC1CCCC(C(C)C)=CC1=C)CCC', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCCCC1C2(C3CCNN3)C3=CC4=C(CCN(C(N)=NCN(CC)NN=NCC2[N+]1(C)C)C(NC)C3O)N4', 'C=NN=C1CCC(C)(CCC=C2CC2)OC1[N+](=C)CCSC', 'CNNC=CNC1=C2C=NC(C(C)N)B(N2)OCC2CC(NCC=CN(N(C)N)N1)B2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please produce a batch of molecules to hit a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(COCC=CC)C(C)CC', 'C=CC1CNCOC1', 'C=CNCC(C=C)CC(C)=C(CCC)C(C)=CC=CC(N)CC', 'C=CC(=CC)C1C2=C(C(=CO)C[O+](C)OCC(C)O)C21', 'CCCC1(CCC(O)N=[N+]=C(CCC(CC)CC)C2CC2N)N=C1C', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'CCCC[N+]1(C(CC)CC)CC1C1CCCCC1CC', 'CCCCCC=CC=CCC(C)(CNC)CNCC', 'C=CCCCOC(C)=C(C=C)CC=C(CCC(C)F)NCCN=CCF', 'CCC=CC1CCCCCCC(C2CC[O+](C)C2)C(CC2(CCCCC3(C)CC3C(=COC)C(CC)=C(CCCCCC)CC(C)(C)C(C)C)C=COC2)CC1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(COCC=CC)C(C)CC', 'C=CC1CNCOC1', 'C=CNCC(C=C)CC(C)=C(CCC)C(C)=CC=CC(N)CC', 'C=CC(=CC)C1C2=C(C(=CO)C[O+](C)OCC(C)O)C21', 'CCCC1(CCC(O)N=[N+]=C(CCC(CC)CC)C2CC2N)N=C1C', 'C=CC=CCCCC(C)C=CC(CN)OC(=CC=CCC)CCC(=C)C(C)CC', 'CCCC[N+]1(C(CC)CC)CC1C1CCCCC1CC', 'CCCCCC=CC=CCC(C)(CNC)CNCC', 'C=CCCCOC(C)=C(C=C)CC=C(CCC(C)F)NCCN=CCF', 'CCC=CC1CCCCCCC(C2CC[O+](C)C2)C(CC2(CCCCC3(C)CC3C(=COC)C(CC)=C(CCCCCC)CC(C)(C)C(C)C)C=COC2)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Develop compounds with the intended z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CC1(CC2CC=CN=CCN2)CC=C=CNC(NCC(C)=CN(C)CCC(C)=O)N(O)C(NC)C1', 'CCC(N)C1COCOO1', 'C=C1CCN(C)CCC(C)[O+](C2=CC=[O+]C(CC=CC)C(C)CC2C)CC(CCC)=C2CC12C', 'CC=CNC=CC(C)CN1CC=C1C', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'C=CC(N)(N)CC1(C[O+](C)C(=C)CC(C)NN=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CCC(F)=CC(C=C1CC1)C(C)(CC)C(=C(C)CO)C(C)F', 'C=NCC(C=C(N)CCCNCC)=CC', 'CN1CC2CCCCNNC2C1', 'CCC=C(C)[O+](C)CCC1CCCCC1C'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1(CC2CC=CN=CCN2)CC=C=CNC(NCC(C)=CN(C)CCC(C)=O)N(O)C(NC)C1', 'CCC(N)C1COCOO1', 'C=C1CCN(C)CCC(C)[O+](C2=CC=[O+]C(CC=CC)C(C)CC2C)CC(CCC)=C2CC12C', 'CC=CNC=CC(C)CN1CC=C1C', 'C=C(NC)C(C)=CC1CC=[N+](C(C)C(C)CC)[O+]1CC', 'C=CC(N)(N)CC1(C[O+](C)C(=C)CC(C)NN=CNC)C=CC(NCC(N)CC(C)C)C=C1', 'CCC(F)=CC(C=C1CC1)C(C)(CC)C(=C(C)CO)C(C)F', 'C=NCC(C=C(N)CCCNCC)=CC', 'CN1CC2CCCCNNC2C1', 'CCC=C(C)[O+](C)CCC1CCCCC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules that meet a z-score of -4.054651 in the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C1CC(C)C2CCC(C)C=CCC(C)(CC=C(C(=CCC)CC)CC1=CNC(C)C(C)C)CCC2NC', 'CCC(CC1=CCCC1)[O+]=CNN', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N', 'CC=[N+](N)NC=C1CCC=CCC(CNCCC=NNNC(C=NN(C)C)CC)=NCCCCCCN1', 'C=C[N+](C)=C(C)N=NCC=C(C=N)CNCCCC', 'C=CC1=C(C)C2CCCC(CC(=C)N)N2NC2(C=C2CC)N1', 'C=C(C=CC)[O+]1OC(C)[OH+]1', 'C=C(C)CC(C)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'C=C1CCCC(N)(NC(=C[O+](C)C(CO)CCC(C)N)CC)C1C', 'CC=C(C)CCC(C)C=C1CC2=CC3(CC=C(CCC(O)=C2)C3)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CC(C)C2CCC(C)C=CCC(C)(CC=C(C(=CCC)CC)CC1=CNC(C)C(C)C)CCC2NC', 'CCC(CC1=CCCC1)[O+]=CNN', 'C=CC=CC(CC)CC1CC2CCCC1CCCC2N', 'CC=[N+](N)NC=C1CCC=CCC(CNCCC=NNNC(C=NN(C)C)CC)=NCCCCCCN1', 'C=C[N+](C)=C(C)N=NCC=C(C=N)CNCCCC', 'C=CC1=C(C)C2CCCC(CC(=C)N)N2NC2(C=C2CC)N1', 'C=C(C=CC)[O+]1OC(C)[OH+]1', 'C=C(C)CC(C)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'C=C1CCCC(N)(NC(=C[O+](C)C(CO)CCC(C)N)CC)C1C', 'CC=C(C)CCC(C)C=C1CC2=CC3(CC=C(CCC(O)=C2)C3)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate various compounds for an intended z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C)C1CC(CCCC)=CCC=C(C)CC=C[O+](COC)CCC=C1C', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=C[O+]=[N+](CCC)C(N)=C(C=CC)CNC(C)=CCC', 'C=C1C=CC1', 'C=NC=C[N+](C)=C1CCC(C)=C2C(NN=CCCC2(N)CCN)O1', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(N)CC1CC(O)C(=C(CC)CC=C(C)CCCNCCC(C)=CC(C)N)O1', 'C=CC(C=CCC(CCC(=C)CC)C(C)CCC)[O+](CC=CC)CC=CC', 'CC=C1C(CCCCC(C)=[N+]2CCCCC2N)=C1NC', 'C=C1COCC(N)(O)C(OCC=[O+]C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C)C1CC(CCCC)=CCC=C(C)CC=C[O+](COC)CCC=C1C', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=C[O+]=[N+](CCC)C(N)=C(C=CC)CNC(C)=CCC', 'C=C1C=CC1', 'C=NC=C[N+](C)=C1CCC(C)=C2C(NN=CCCC2(N)CCN)O1', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(N)CC1CC(O)C(=C(CC)CC=C(C)CCCNCCC(C)=CC(C)N)O1', 'C=CC(C=CCC(CCC(=C)CC)C(C)CCC)[O+](CC=CC)CC=CC', 'CC=C1C(CCCCC(C)=[N+]2CCCCC2N)=C1NC', 'C=C1COCC(N)(O)C(OCC=[O+]C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Construct molecules reaching a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CNC(C)C[N+](=C[N+](C)=N)C(N)=CCNC(N)N', 'CC=NC=CC1CC1(C)N=CC(=CN=CC(C)=N)CCC', 'CCN=[N+](C)C1CCC=C(NC)C1CCCC(CC)NC=C(C)N', 'CCCCC(CC)=C(O)CC1=C(C)CC1', 'CCC=CCC(C)N1CCCC2CC=CCCC3CC(CC2)CC(CCC1)ON3', 'C=C[N+](=CCC=C[N+](=C)C(C)=N)[N+](O)=CC(C=CC=[N+](N)CC)=CCC(C)NC(C)C', 'C=NC=[O+][N+](C)=C1CCC(C)=C2C(CC=CCCC2(O)CCN)O1', 'C=C1C2C=[O+]C3=C4C=CC(=C(CC=CNCC)NN(C(N)CO)C=C(CCC1C2)C(CC)=CC4N)C3', 'C1=C2CB(CCCCCC1)C2', 'C=C(N)NNNNB=NC(C)=NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNC(C)C[N+](=C[N+](C)=N)C(N)=CCNC(N)N', 'CC=NC=CC1CC1(C)N=CC(=CN=CC(C)=N)CCC', 'CCN=[N+](C)C1CCC=C(NC)C1CCCC(CC)NC=C(C)N', 'CCCCC(CC)=C(O)CC1=C(C)CC1', 'CCC=CCC(C)N1CCCC2CC=CCCC3CC(CC2)CC(CCC1)ON3', 'C=C[N+](=CCC=C[N+](=C)C(C)=N)[N+](O)=CC(C=CC=[N+](N)CC)=CCC(C)NC(C)C', 'C=NC=[O+][N+](C)=C1CCC(C)=C2C(CC=CCCC2(O)CCN)O1', 'C=C1C2C=[O+]C3=C4C=CC(=C(CC=CNCC)NN(C(N)CO)C=C(CCC1C2)C(CC)=CC4N)C3', 'C1=C2CB(CCCCCC1)C2', 'C=C(N)NNNNB=NC(C)=NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design a batch of compounds with a target z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCCC(CCF)C1(C)CN1', 'CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1', 'CCNC=C(CNC)N=CB[N+](N)=BB(N)CNN', 'CNC=CCCCCNC=CN(C)CN(C)CC(N)NNN', 'CCN1C(CN)=C(C)CN=[N+](C)C(C=CNN=N)NC1C', 'CC=CCSCC(=CC)[O+]=CC', 'CC(C)C1C=C2CCC3=CC1C23', 'CCCC(N)=CC(C)C', 'NC(=CO)NN=CC1C=C1N', 'CN(CNC=CN=CN)[N+](=N)OC(N)CN'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCCC(CCF)C1(C)CN1', 'CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1', 'CCNC=C(CNC)N=CB[N+](N)=BB(N)CNN', 'CNC=CCCCCNC=CN(C)CN(C)CC(N)NNN', 'CCN1C(CN)=C(C)CN=[N+](C)C(C=CNN=N)NC1C', 'CC=CCSCC(=CC)[O+]=CC', 'CC(C)C1C=C2CCC3=CC1C23', 'CCCC(N)=CC(C)C', 'NC(=CO)NN=CC1C=C1N', 'CN(CNC=CN=CN)[N+](=N)OC(N)CN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a series of molecules that meet a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CCCNB1C=CNN1CN(N)C=NC1(NC(C)CCBCNNN)C=NC1', 'C=CNCC(C)(COC)[O+](CC)CC1CC(CC)[O+]2CN2CN1CN', 'CC(BCBN)C(=BC=NC=NN)CNCNB(N)C=N', 'CC=CCC1CC(NC=CN)C1C1=C(C)C(C(CN)CN)=C(CC)CC1O', 'BCNC(C)CCB(BC(=N)N)NBCNN', 'CNCB1N=CCC(=N)NNB(N)BNCN1', 'C=C(C)CCCCC(=N)CC12C3=C1NC2N3', 'CCC=C(CN)N=C1CC2=[N+](CC2)C(C)C2C1=COCCC2C1=[N+](C)CC1', 'C=NBCC(=CN)N=C', 'CCCC(O)C1C=C(CC(CC)NC)C=C(C)CC(CC)(C(C)CC)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCNB1C=CNN1CN(N)C=NC1(NC(C)CCBCNNN)C=NC1', 'C=CNCC(C)(COC)[O+](CC)CC1CC(CC)[O+]2CN2CN1CN', 'CC(BCBN)C(=BC=NC=NN)CNCNB(N)C=N', 'CC=CCC1CC(NC=CN)C1C1=C(C)C(C(CN)CN)=C(CC)CC1O', 'BCNC(C)CCB(BC(=N)N)NBCNN', 'CNCB1N=CCC(=N)NNB(N)BNCN1', 'C=C(C)CCCCC(=N)CC12C3=C1NC2N3', 'CCC=C(CN)N=C1CC2=[N+](CC2)C(C)C2C1=COCCC2C1=[N+](C)CC1', 'C=NBCC(=CN)N=C', 'CCCC(O)C1C=C(CC(CC)NC)C=C(C)CC(CC)(C(C)CC)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please generate compounds to achieve a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC(CC)C(C)C(C=CC)C1C(=CC2(C)C=CCC3CC(C(CC)(CC(CC(C)CCC)C(C)F)COCCC2)C2(C)CCC32)C1CCC', 'C=C(NC(NC)NNC1(NCN)BCC1)C(C)=NNC(C)NNN', 'CC=CC(C)C1CC=C(C=C(N)C2C(C)C2C(C)N)CNC1NC', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'NCC=CC1CC1CCCN', 'C=C(C=CC(C)C=CCCC(C)C)[O+]=CNCC=CCCO', 'CC=NCC1=[O+][N+]1=C1NCC1OCC1CCC2(CCCON=CC(C)COC=CCO)NC[O+]=C12', 'C=CCC1=C(CCC(C)C)C(=CCCC(C)(C)N)C(C(C)C)=C1', 'CC(NC1(C)C=CCC=CN=CC(N)(O)C(C)C1)C1CCC1', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC(CC)C(C)C(C=CC)C1C(=CC2(C)C=CCC3CC(C(CC)(CC(CC(C)CCC)C(C)F)COCCC2)C2(C)CCC32)C1CCC', 'C=C(NC(NC)NNC1(NCN)BCC1)C(C)=NNC(C)NNN', 'CC=CC(C)C1CC=C(C=C(N)C2C(C)C2C(C)N)CNC1NC', 'C=C1CCCC(CCC(C)C2CC(C)=C(C)N2CCCC2C(C)=CCC2CCC)O[NH+](OCC)C1', 'NCC=CC1CC1CCCN', 'C=C(C=CC(C)C=CCCC(C)C)[O+]=CNCC=CCCO', 'CC=NCC1=[O+][N+]1=C1NCC1OCC1CCC2(CCCON=CC(C)COC=CCO)NC[O+]=C12', 'C=CCC1=C(CCC(C)C)C(=CCCC(C)(C)N)C(C(C)C)=C1', 'CC(NC1(C)C=CCC=CN=CC(N)(O)C(C)C1)C1CCC1', 'C=C(CNCCCC1CCN1CCCC)C(C)(C)NCCC(=CC)[N+](CCC(C)[O+]=O)=NNN=CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Create molecules intended for a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CC=CCNC=CC=NC(CC(C)CNCC1CC1)C(C)CCCCCCCC(N)=CCC', 'C=NC(CNC=NCCC(C)(C)N)NC(N)=CC', 'CCCCC(C)C=CC(C)=C1C=C1C(CC)=C(C)C1=C(C)O1', 'C=CC(C)(C(=C)CCCCCC(CCC)C(=O)CC)C1CO1', 'CC1CON(CN)CCCCCCNC1C', 'C=C(C=CCCC1=C(C=CC)C(C=CCCC)=C(N)CCC1)OC', 'C=C(NC(=CNCC)OC=CC)C1C2=CC(CC(C)NC(O)CN)C2C2(C)CNC(OC1C)[O+]2CC', 'CC=C(C)CN1CCC=C(CCN=C2C=CCCC=CCC2=NCC(CC)CN)C(O)CC1', 'CC=NCC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'COCCC1(C)CCNC2(CC(C)C2)C(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CC=CCNC=CC=NC(CC(C)CNCC1CC1)C(C)CCCCCCCC(N)=CCC', 'C=NC(CNC=NCCC(C)(C)N)NC(N)=CC', 'CCCCC(C)C=CC(C)=C1C=C1C(CC)=C(C)C1=C(C)O1', 'C=CC(C)(C(=C)CCCCCC(CCC)C(=O)CC)C1CO1', 'CC1CON(CN)CCCCCCNC1C', 'C=C(C=CCCC1=C(C=CC)C(C=CCCC)=C(N)CCC1)OC', 'C=C(NC(=CNCC)OC=CC)C1C2=CC(CC(C)NC(O)CN)C2C2(C)CNC(OC1C)[O+]2CC', 'CC=C(C)CN1CCC=C(CCN=C2C=CCCC=CCC2=NCC(CC)CN)C(O)CC1', 'CC=NCC1(CC)C(C)C12CN=C(C)CC1=[N+]2C1', 'COCCC1(C)CCNC2(CC(C)C2)C(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Generate compounds optimized to match a z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=CNCNC=C(CC=CC)NC=CC(CC=NCNC)CC(C)C', 'C=C1C(=CCC)CC=CC1CN(C)N', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCC(NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C)N(CC(C)C)NC(CCCN)NC', 'CN=CN=CNN', 'C=CNN1NNNB(N)CCNNC1CCC', 'C=C(CCCN)C(=CC)CCC=CON', 'C=C1C=C(C(CC)C(=C)C=CC)CCCC=CC(CC)CC[O+]2C(C)CC(O)(CC=CC)C3=CC2(CCNCC1=C)C3', 'COC=C(CN(N)C=CN)C1=C(C)C1'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CNCNC=C(CC=CC)NC=CC(CC=NCNC)CC(C)C', 'C=C1C(=CCC)CC=CC1CN(C)N', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CCCCCC1=C(CC=CC(N)C2C=CCC2)CCCC2C=CCC(=C1O)C=C2', 'CCC(NC(C)CN(C)C=CCCCCCN(C)C1CC1(C)C)N(CC(C)C)NC(CCCN)NC', 'CN=CN=CNN', 'C=CNN1NNNB(N)CCNNC1CCC', 'C=C(CCCN)C(=CC)CCC=CON', 'C=C1C=C(C(CC)C(=C)C=CC)CCCC=CC(CC)CC[O+]2C(C)CC(O)(CC=CC)C3=CC2(CCNCC1=C)C3', 'COC=C(CN(N)C=CN)C1=C(C)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Design molecules that align with a z-score of -6.190392 for the cell line the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['C=C(C)CC(O)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'C=C(CC)CCNC1=CC2N3C(CNC=NCC)OC(CCCCCC(NC)C23C)N=C1', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)N', 'CNC12CC=C(N)CCCN[N+]1(C)N2', 'CCCN=C1C=C(CC23CCCOC(=CCC=CCC(CC)N2)CC3)C1', 'C=CC=CCC(O)CCC=NCCCC', 'CC=CCNNC(N)=CCC(C)(C=CC)C=C(C)N=C(C)CC', 'C=CC(C)CC=COCCCC', 'C=[O+]C(N)N=CC=C1NC(C(N)CNC)CC=CNNC(C)C(C=[N+](N)C(=C)[N+](N)=CC)C1=C', 'CC=[N+](CCCCCCCC)NC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C(C)CC(O)C1=C2CCC=C(CC2CC2C=C2CC=CC)CC(C)(CC=CCC)C1', 'C=C(CC)CCNC1=CC2N3C(CNC=NCC)OC(CCCCCC(NC)C23C)N=C1', 'CCC(CC(CC)C1(C)C=C(C)C1)C(C)C(C)N', 'CNC12CC=C(N)CCCN[N+]1(C)N2', 'CCCN=C1C=C(CC23CCCOC(=CCC=CCC(CC)N2)CC3)C1', 'C=CC=CCC(O)CCC=NCCCC', 'CC=CCNNC(N)=CCC(C)(C=CC)C=C(C)N=C(C)CC', 'C=CC(C)CC=COCCCC', 'C=[O+]C(N)N=CC=C1NC(C(N)CNC)CC=CNNC(C)C(C=[N+](N)C(=C)[N+](N)=CC)C1=C', 'CC=[N+](CCCCCCCC)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Build a batch of molecules with the intended z-score of -4.054651 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=CCC(C=C(C)CCC)C(=CC(=CCC1=CCC(N)(C=[O+]CC)NCC1)CC)CNCC(C)C', 'C=C1B2N=C[NH+](B1CCC)[N+](N)(N)N2', 'C=CCC(C)C=CC[N+](=C1C=C(CNC(C)O)NCC1)N1C(CC=[O+]CC)NCC2C1[N+]2=C', 'CB1CCCCC1N', 'CCN(CNC1(N)C(C)C1N)C1CB(C)CCN(C)C2NN2C(=N)C1', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1', 'CN=CN(CN)C(C)CCN', 'CNB(C)C=NCB(BN)C(C)CCC(C)=N', 'CB1B=BNCB(C)CNCNB(C)CBBB1', 'CB1B2B=BB1C2'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=CCC(C=C(C)CCC)C(=CC(=CCC1=CCC(N)(C=[O+]CC)NCC1)CC)CNCC(C)C', 'C=C1B2N=C[NH+](B1CCC)[N+](N)(N)N2', 'C=CCC(C)C=CC[N+](=C1C=C(CNC(C)O)NCC1)N1C(CC=[O+]CC)NCC2C1[N+]2=C', 'CB1CCCCC1N', 'CCN(CNC1(N)C(C)C1N)C1CB(C)CCN(C)C2NN2C(=N)C1', 'C=CC1CC=C(C(=CNC(CCC)C(C)(C=C)CCCN)CC)CO1', 'CN=CN(CN)C(C)CCN', 'CNB(C)C=NCB(BN)C(C)CCC(C)=N', 'CB1B=BNCB(C)CNCNB(C)CBBB1', 'CB1B2B=BB1C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Produce a series of compounds to reach a z-score of -6.190392 for the cell. MemoryPool(arguments=dict_keys(['user_cell_line']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"(user_cell_line)\"}}"
            },
            {
                "from": "observation",
                "value": "['CC=C(C=CNC)C=C(C)NC(C=CCNCCCNC=CN[N+](C)=N)CC', 'CC=CC(=CN=CNCC)CCC(C)C', 'CCC1c2c[o+]c(c[o+]2)C1C', 'CCC(C=CCC(C)C)C(C)=CCCCC(C)C(CC=CCC=CC1=C[O+]1C)=[O+]C', 'C=CC(COC=CC(C)CC1CC=C1C)C(=C)CC=CC', 'CCCCC1CNNN(C)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCCCC(CC(C)C1C=C1CCCCC(C)C(C)C1C=C2OC2CCC1C)C(=CC)C(C=C)=CC', 'C=NCC(C)=NOCC(CC)(CC)C(CCO)=C(C)C=CC', 'CCCCNNC=CC(C)CCC'] MemoryPool(arguments=dict_keys(['user_zscore', 'user_cell_line', 'output_smiles']))"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=C(C=CNC)C=C(C)NC(C=CCNCCCNC=CN[N+](C)=N)CC', 'CC=CC(=CN=CNCC)CCC(C)C', 'CCC1c2c[o+]c(c[o+]2)C1C', 'CCC(C=CCC(C)C)C(C)=CCCCC(C)C(CC=CCC=CC1=C[O+]1C)=[O+]C', 'C=CC(COC=CC(C)CC1CC=C1C)C(=C)CC=CC', 'CCCCC1CNNN(C)C2=NCCC3CCC1=C[N+](C)=CCC=[N+]3C=C2', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12', 'C=CCCCC(CC(C)C1C=C1CCCCC(C)C(C)C1C=C2OC2CCC1C)C(=CC)C(C=C)=CC', 'C=NCC(C)=NOCC(CC)(CC)C(CCO)=C(C)C=CC', 'CCCCNNC=CC(C)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"zscore\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"zscore\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis path of '[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([O:22][C:33]([N:31]([CH3:30])[CH3:32])=[O:34])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([O:22][C:33]([N:31]([CH3:30])[CH3:32])=[O:34])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([O:22][C:33]([N:31]([CH3:30])[CH3:32])=[O:34])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1\", \"reactants\":\"[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([OH:22])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1.Cl[C:33]([N:31]([CH3:30])[CH3:32])=[O:34]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([OH:22])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1' and 'Cl[C:33]([N:31]([CH3:30])[CH3:32])=[O:34]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis path for '[CH2:1]([C:2]#[C:3][CH2:4][N:5]1[CH2:6][CH2:7][N:8]([C:9](=[O:10])[c:11]2[cH:12][c:13]([C:14]([F:15])([F:16])[F:17])[cH:18][c:19]([C:20]([F:21])([F:22])[F:23])[cH:24]2)[C@H:25]([CH2:26][c:27]2[cH:28][cH:29][c:30]3[cH:31][cH:32][cH:33][cH:34][c:35]3[cH:36]2)[CH2:37]1)[N:41]1[CH2:40][C@H:39]([CH3:38])[O:45][C@H:43]([CH3:44])[CH2:42]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]#[C:3][CH2:4][N:5]1[CH2:6][CH2:7][N:8]([C:9](=[O:10])[c:11]2[cH:12][c:13]([C:14]([F:15])([F:16])[F:17])[cH:18][c:19]([C:20]([F:21])([F:22])[F:23])[cH:24]2)[C@H:25]([CH2:26][c:27]2[cH:28][cH:29][c:30]3[cH:31][cH:32][cH:33][cH:34][c:35]3[cH:36]2)[CH2:37]1)[N:41]1[CH2:40][C@H:39]([CH3:38])[O:45][C@H:43]([CH3:44])[CH2:42]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]#[C:3][CH2:4][N:5]1[CH2:6][CH2:7][N:8]([C:9](=[O:10])[c:11]2[cH:12][c:13]([C:14]([F:15])([F:16])[F:17])[cH:18][c:19]([C:20]([F:21])([F:22])[F:23])[cH:24]2)[C@H:25]([CH2:26][c:27]2[cH:28][cH:29][c:30]3[cH:31][cH:32][cH:33][cH:34][c:35]3[cH:36]2)[CH2:37]1)[N:41]1[CH2:40][C@H:39]([CH3:38])[O:45][C@H:43]([CH3:44])[CH2:42]1\", \"reactants\":\"Cl[CH2:1][C:2]#[C:3][CH2:4][N:5]1[CH2:6][CH2:7][N:8]([C:9](=[O:10])[c:11]2[cH:12][c:13]([C:14]([F:15])([F:16])[F:17])[cH:18][c:19]([C:20]([F:21])([F:22])[F:23])[cH:24]2)[C@H:25]([CH2:26][c:27]2[cH:28][cH:29][c:30]3[cH:31][cH:32][cH:33][cH:34][c:35]3[cH:36]2)[CH2:37]1.[CH3:38][C@H:39]1[CH2:40][NH:41][CH2:42][C@@H:43]([CH3:44])[O:45]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][C:2]#[C:3][CH2:4][N:5]1[CH2:6][CH2:7][N:8]([C:9](=[O:10])[c:11]2[cH:12][c:13]([C:14]([F:15])([F:16])[F:17])[cH:18][c:19]([C:20]([F:21])([F:22])[F:23])[cH:24]2)[C@H:25]([CH2:26][c:27]2[cH:28][cH:29][c:30]3[cH:31][cH:32][cH:33][cH:34][c:35]3[cH:36]2)[CH2:37]1' and '[CH3:38][C@H:39]1[CH2:40][NH:41][CH2:42][C@@H:43]([CH3:44])[O:45]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:21]1[C:19](=[O:20])[c:17]2[c:16]([cH:15][cH:14][c:13]([O:12][CH3:11])[cH:18]2)[O:23][CH2:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:21]1[C:19](=[O:20])[c:17]2[c:16]([cH:15][cH:14][c:13]([O:12][CH3:11])[cH:18]2)[O:23][CH2:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:21]1[C:19](=[O:20])[c:17]2[c:16]([cH:15][cH:14][c:13]([O:12][CH3:11])[cH:18]2)[O:23][CH2:22]1\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12.[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]2[c:17]([cH:18]1)[C:19](=[O:20])[CH2:21][CH2:22][O:23]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12' and '[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]2[c:17]([cH:18]1)[C:19](=[O:20])[CH2:21][CH2:22][O:23]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis route for '[CH3:1][O:24][CH2:23][CH2:22][C@@H:21]1[N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11][N:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[CH2:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:24][CH2:23][CH2:22][C@@H:21]1[N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11][N:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[CH2:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:24][CH2:23][CH2:22][C@@H:21]1[N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11][N:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[CH2:20]1\", \"reactants\":\"I[CH3:1].[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][N:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[CH2:20][C@@H:21]1[CH2:22][CH2:23][OH:24]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[CH3:1]' and '[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][N:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[CH2:20][C@@H:21]1[CH2:22][CH2:23][OH:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[S:1]([c:2]1[c:3]([N+:4](=[O:5])[O-:6])[cH:7][cH:8][cH:9][cH:10]1)(=[O:11])(=[O:12])[NH:13][c:14]1[cH:15][c:16]([NH:17][C:18](=[O:19])[NH:20][CH:21]2[CH2:22][CH2:23][CH2:24][CH2:25][CH2:26]2)[cH:27][c:28]([Cl:29])[c:30]1[OH:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[c:3]([N+:4](=[O:5])[O-:6])[cH:7][cH:8][cH:9][cH:10]1)(=[O:11])(=[O:12])[NH:13][c:14]1[cH:15][c:16]([NH:17][C:18](=[O:19])[NH:20][CH:21]2[CH2:22][CH2:23][CH2:24][CH2:25][CH2:26]2)[cH:27][c:28]([Cl:29])[c:30]1[OH:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([c:2]1[c:3]([N+:4](=[O:5])[O-:6])[cH:7][cH:8][cH:9][cH:10]1)(=[O:11])(=[O:12])[NH:13][c:14]1[cH:15][c:16]([NH:17][C:18](=[O:19])[NH:20][CH:21]2[CH2:22][CH2:23][CH2:24][CH2:25][CH2:26]2)[cH:27][c:28]([Cl:29])[c:30]1[OH:31]\", \"reactants\":\"Cl[S:1]([c:2]1[c:3]([N+:4](=[O:5])[O-:6])[cH:7][cH:8][cH:9][cH:10]1)(=[O:11])=[O:12].[NH2:13][c:14]1[cH:15][c:16]([NH:17][C:18](=[O:19])[NH:20][CH:21]2[CH2:22][CH2:23][CH2:24][CH2:25][CH2:26]2)[cH:27][c:28]([Cl:29])[c:30]1[OH:31]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([c:2]1[c:3]([N+:4](=[O:5])[O-:6])[cH:7][cH:8][cH:9][cH:10]1)(=[O:11])=[O:12]' and '[NH2:13][c:14]1[cH:15][c:16]([NH:17][C:18](=[O:19])[NH:20][CH:21]2[CH2:22][CH2:23][CH2:24][CH2:25][CH2:26]2)[cH:27][c:28]([Cl:29])[c:30]1[OH:31]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path for '[C:1]([C:2]1([CH3:3])[CH2:4][CH2:5][N:6]([c:7]2[cH:8][cH:9][c:10]([F:11])[cH:12][cH:13]2)[C:14]1=[O:15])(=[O:16])[NH:28][c:27]1[cH:26][cH:25][c:24]([O:23][c:22]2[c:21]3[cH:20][c:19]([O:18][CH3:17])[c:37]([O:38][CH2:39][CH2:40][CH2:41][N:42]4[CH2:43][CH2:44][O:45][CH2:46][CH2:47]4)[cH:36][c:35]3[n:34][cH:33][cH:32]2)[c:30]([F:31])[cH:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C:2]1([CH3:3])[CH2:4][CH2:5][N:6]([c:7]2[cH:8][cH:9][c:10]([F:11])[cH:12][cH:13]2)[C:14]1=[O:15])(=[O:16])[NH:28][c:27]1[cH:26][cH:25][c:24]([O:23][c:22]2[c:21]3[cH:20][c:19]([O:18][CH3:17])[c:37]([O:38][CH2:39][CH2:40][CH2:41][N:42]4[CH2:43][CH2:44][O:45][CH2:46][CH2:47]4)[cH:36][c:35]3[n:34][cH:33][cH:32]2)[c:30]([F:31])[cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([C:2]1([CH3:3])[CH2:4][CH2:5][N:6]([c:7]2[cH:8][cH:9][c:10]([F:11])[cH:12][cH:13]2)[C:14]1=[O:15])(=[O:16])[NH:28][c:27]1[cH:26][cH:25][c:24]([O:23][c:22]2[c:21]3[cH:20][c:19]([O:18][CH3:17])[c:37]([O:38][CH2:39][CH2:40][CH2:41][N:42]4[CH2:43][CH2:44][O:45][CH2:46][CH2:47]4)[cH:36][c:35]3[n:34][cH:33][cH:32]2)[c:30]([F:31])[cH:29]1\", \"reactants\":\"O[C:1]([C:2]1([CH3:3])[CH2:4][CH2:5][N:6]([c:7]2[cH:8][cH:9][c:10]([F:11])[cH:12][cH:13]2)[C:14]1=[O:15])=[O:16].[CH3:17][O:18][c:19]1[cH:20][c:21]2[c:22]([O:23][c:24]3[cH:25][cH:26][c:27]([NH2:28])[cH:29][c:30]3[F:31])[cH:32][cH:33][n:34][c:35]2[cH:36][c:37]1[O:38][CH2:39][CH2:40][CH2:41][N:42]1[CH2:43][CH2:44][O:45][CH2:46][CH2:47]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([C:2]1([CH3:3])[CH2:4][CH2:5][N:6]([c:7]2[cH:8][cH:9][c:10]([F:11])[cH:12][cH:13]2)[C:14]1=[O:15])=[O:16]' and '[CH3:17][O:18][c:19]1[cH:20][c:21]2[c:22]([O:23][c:24]3[cH:25][cH:26][c:27]([NH2:28])[cH:29][c:30]3[F:31])[cH:32][cH:33][n:34][c:35]2[cH:36][c:37]1[O:38][CH2:39][CH2:40][CH2:41][N:42]1[CH2:43][CH2:44][O:45][CH2:46][CH2:47]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis path of '[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1\", \"reactants\":\"Cl[c:1]1[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1.[CH3:23][NH:24][C:25](=[O:26])[NH:27][CH2:28][C:29]#[C:30][c:31]1[cH:32][c:33]([Cl:34])[c:35]([NH2:36])[c:37]2[c:38]1[O:39][CH2:40][O:41]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1' and '[CH3:23][NH:24][C:25](=[O:26])[NH:27][CH2:28][C:29]#[C:30][c:31]1[cH:32][c:33]([Cl:34])[c:35]([NH2:36])[c:37]2[c:38]1[O:39][CH2:40][O:41]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide the synthesis path of '[c:1]1([O:19][c:18]2[c:16]([Cl:17])[cH:15][c:14]([CH2:13][CH2:12][OH:11])[cH:22][c:20]2[Cl:21])[cH:2][c:3]([CH:4]([CH3:5])[CH3:6])[c:7]([Cl:8])[n:9][n:10]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:19][c:18]2[c:16]([Cl:17])[cH:15][c:14]([CH2:13][CH2:12][OH:11])[cH:22][c:20]2[Cl:21])[cH:2][c:3]([CH:4]([CH3:5])[CH3:6])[c:7]([Cl:8])[n:9][n:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:19][c:18]2[c:16]([Cl:17])[cH:15][c:14]([CH2:13][CH2:12][OH:11])[cH:22][c:20]2[Cl:21])[cH:2][c:3]([CH:4]([CH3:5])[CH3:6])[c:7]([Cl:8])[n:9][n:10]1\", \"reactants\":\"Cl[c:1]1[cH:2][c:3]([CH:4]([CH3:5])[CH3:6])[c:7]([Cl:8])[n:9][n:10]1.[OH:11][CH2:12][CH2:13][c:14]1[cH:15][c:16]([Cl:17])[c:18]([OH:19])[c:20]([Cl:21])[cH:22]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[cH:2][c:3]([CH:4]([CH3:5])[CH3:6])[c:7]([Cl:8])[n:9][n:10]1' and '[OH:11][CH2:12][CH2:13][c:14]1[cH:15][c:16]([Cl:17])[c:18]([OH:19])[c:20]([Cl:21])[cH:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for '[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[n:22]1[c:21]2[cH:20][cH:19][c:18](-[c:17]3[c:6]([CH3:5])[cH:7][cH:8][c:9]([C:10](=[O:11])[NH:12][CH:13]4[CH2:14][CH2:15]4)[cH:16]3)[cH:26][c:25]2[cH:24][n:23]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[n:22]1[c:21]2[cH:20][cH:19][c:18](-[c:17]3[c:6]([CH3:5])[cH:7][cH:8][c:9]([C:10](=[O:11])[NH:12][CH:13]4[CH2:14][CH2:15]4)[cH:16]3)[cH:26][c:25]2[cH:24][n:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[n:22]1[c:21]2[cH:20][cH:19][c:18](-[c:17]3[c:6]([CH3:5])[cH:7][cH:8][c:9]([C:10](=[O:11])[NH:12][CH:13]4[CH2:14][CH2:15]4)[cH:16]3)[cH:26][c:25]2[cH:24][n:23]1\", \"reactants\":\"Br[CH2:1][CH:2]1[CH2:3][CH2:4]1.[CH3:5][c:6]1[cH:7][cH:8][c:9]([C:10](=[O:11])[NH:12][CH:13]2[CH2:14][CH2:15]2)[cH:16][c:17]1-[c:18]1[cH:19][cH:20][c:21]2[nH:22][n:23][cH:24][c:25]2[cH:26]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][CH:2]1[CH2:3][CH2:4]1' and '[CH3:5][c:6]1[cH:7][cH:8][c:9]([C:10](=[O:11])[NH:12][CH:13]2[CH2:14][CH2:15]2)[cH:16][c:17]1-[c:18]1[cH:19][cH:20][c:21]2[nH:22][n:23][cH:24][c:25]2[cH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis steps for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[c:9]1[O:10][CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][cH:17]1)[NH:18][c:19]1[cH:20][cH:21][cH:22][c:23](-[c:24]2[c:25]([CH2:26][c:27]3[cH:28][cH:29][cH:30][cH:31][cH:32]3)[cH:33][n:34][c:35]3[c:36]([C:37]([F:38])([F:39])[F:40])[cH:41][cH:42][cH:43][c:44]23)[cH:45]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[c:9]1[O:10][CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][cH:17]1)[NH:18][c:19]1[cH:20][cH:21][cH:22][c:23](-[c:24]2[c:25]([CH2:26][c:27]3[cH:28][cH:29][cH:30][cH:31][cH:32]3)[cH:33][n:34][c:35]3[c:36]([C:37]([F:38])([F:39])[F:40])[cH:41][cH:42][cH:43][c:44]23)[cH:45]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[c:9]1[O:10][CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][cH:17]1)[NH:18][c:19]1[cH:20][cH:21][cH:22][c:23](-[c:24]2[c:25]([CH2:26][c:27]3[cH:28][cH:29][cH:30][cH:31][cH:32]3)[cH:33][n:34][c:35]3[c:36]([C:37]([F:38])([F:39])[F:40])[cH:41][cH:42][cH:43][c:44]23)[cH:45]1\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[c:9]1[O:10][CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][cH:17]1.[NH2:18][c:19]1[cH:20][cH:21][cH:22][c:23](-[c:24]2[c:25]([CH2:26][c:27]3[cH:28][cH:29][cH:30][cH:31][cH:32]3)[cH:33][n:34][c:35]3[c:36]([C:37]([F:38])([F:39])[F:40])[cH:41][cH:42][cH:43][c:44]23)[cH:45]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[c:9]1[O:10][CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][cH:17]1' and '[NH2:18][c:19]1[cH:20][cH:21][cH:22][c:23](-[c:24]2[c:25]([CH2:26][c:27]3[cH:28][cH:29][cH:30][cH:31][cH:32]3)[cH:33][n:34][c:35]3[c:36]([C:37]([F:38])([F:39])[F:40])[cH:41][cH:42][cH:43][c:44]23)[cH:45]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis method for '[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1)[N:23]1[CH:19]([c:18]2[cH:17][cH:16][c:15]([F:14])[cH:25][cH:24]2)[CH2:20][CH2:21][CH2:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1)[N:23]1[CH:19]([c:18]2[cH:17][cH:16][c:15]([F:14])[cH:25][cH:24]2)[CH2:20][CH2:21][CH2:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1)[N:23]1[CH:19]([c:18]2[cH:17][cH:16][c:15]([F:14])[cH:25][cH:24]2)[CH2:20][CH2:21][CH2:22]1\", \"reactants\":\"Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1.[F:14][c:15]1[cH:16][cH:17][c:18]([CH:19]2[CH2:20][CH2:21][CH2:22][NH:23]2)[cH:24][cH:25]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1' and '[F:14][c:15]1[cH:16][cH:17][c:18]([CH:19]2[CH2:20][CH2:21][CH2:22][NH:23]2)[cH:24][cH:25]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[CH2:1]([c:2]1[c:3]([F:4])[c:5](-[c:6]2[cH:7][cH:8][cH:9][c:10]([Cl:11])[cH:12]2)[c:13]2[c:14]([cH:15]1)[s:16][cH:17][n:18]2)[N:30]1[CH2:29][CH2:28][CH:27]([NH:26][C:24]([O:23][C:20]([CH3:19])([CH3:21])[CH3:22])=[O:25])[CH2:32][CH2:31]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([F:4])[c:5](-[c:6]2[cH:7][cH:8][cH:9][c:10]([Cl:11])[cH:12]2)[c:13]2[c:14]([cH:15]1)[s:16][cH:17][n:18]2)[N:30]1[CH2:29][CH2:28][CH:27]([NH:26][C:24]([O:23][C:20]([CH3:19])([CH3:21])[CH3:22])=[O:25])[CH2:32][CH2:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([F:4])[c:5](-[c:6]2[cH:7][cH:8][cH:9][c:10]([Cl:11])[cH:12]2)[c:13]2[c:14]([cH:15]1)[s:16][cH:17][n:18]2)[N:30]1[CH2:29][CH2:28][CH:27]([NH:26][C:24]([O:23][C:20]([CH3:19])([CH3:21])[CH3:22])=[O:25])[CH2:32][CH2:31]1\", \"reactants\":\"Br[CH2:1][c:2]1[c:3]([F:4])[c:5](-[c:6]2[cH:7][cH:8][cH:9][c:10]([Cl:11])[cH:12]2)[c:13]2[c:14]([cH:15]1)[s:16][cH:17][n:18]2.[CH3:19][C:20]([CH3:21])([CH3:22])[O:23][C:24](=[O:25])[NH:26][CH:27]1[CH2:28][CH2:29][NH:30][CH2:31][CH2:32]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[c:3]([F:4])[c:5](-[c:6]2[cH:7][cH:8][cH:9][c:10]([Cl:11])[cH:12]2)[c:13]2[c:14]([cH:15]1)[s:16][cH:17][n:18]2' and '[CH3:19][C:20]([CH3:21])([CH3:22])[O:23][C:24](=[O:25])[NH:26][CH:27]1[CH2:28][CH2:29][NH:30][CH2:31][CH2:32]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis path of '[CH:1]1([NH:22][c:21]2[c:19]([OH:20])[cH:18][cH:17][c:16]([C:15]#[N:14])[cH:23]2)[CH2:2][CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[CH2:12][CH2:13]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]1([NH:22][c:21]2[c:19]([OH:20])[cH:18][cH:17][c:16]([C:15]#[N:14])[cH:23]2)[CH2:2][CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[CH2:12][CH2:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]1([NH:22][c:21]2[c:19]([OH:20])[cH:18][cH:17][c:16]([C:15]#[N:14])[cH:23]2)[CH2:2][CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[CH2:12][CH2:13]1\", \"reactants\":\"O=[C:1]1[CH2:2][CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[CH2:12][CH2:13]1.[N:14]#[C:15][c:16]1[cH:17][cH:18][c:19]([OH:20])[c:21]([NH2:22])[cH:23]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1]1[CH2:2][CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[CH2:12][CH2:13]1' and '[N:14]#[C:15][c:16]1[cH:17][cH:18][c:19]([OH:20])[c:21]([NH2:22])[cH:23]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis route for '[C:1]([CH2:2][N:3]1[CH2:4][CH2:5][N:6]([C:7]([N:8]2[C:9]([c:10]3[c:11]([O:12][CH2:13][CH3:14])[cH:15][c:16]([C:17]([CH3:18])([CH3:19])[CH3:20])[n:21][cH:22]3)=[N:23][C@@:24]([CH3:25])([c:26]3[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]3)[C@@:33]2([CH3:34])[c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)=[O:42])[CH2:43][CH2:44]1)(=[O:45])[N:50]1[CH2:49][CH2:48][C:47](=[O:46])[NH:53][CH2:52][CH2:51]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][N:3]1[CH2:4][CH2:5][N:6]([C:7]([N:8]2[C:9]([c:10]3[c:11]([O:12][CH2:13][CH3:14])[cH:15][c:16]([C:17]([CH3:18])([CH3:19])[CH3:20])[n:21][cH:22]3)=[N:23][C@@:24]([CH3:25])([c:26]3[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]3)[C@@:33]2([CH3:34])[c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)=[O:42])[CH2:43][CH2:44]1)(=[O:45])[N:50]1[CH2:49][CH2:48][C:47](=[O:46])[NH:53][CH2:52][CH2:51]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][N:3]1[CH2:4][CH2:5][N:6]([C:7]([N:8]2[C:9]([c:10]3[c:11]([O:12][CH2:13][CH3:14])[cH:15][c:16]([C:17]([CH3:18])([CH3:19])[CH3:20])[n:21][cH:22]3)=[N:23][C@@:24]([CH3:25])([c:26]3[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]3)[C@@:33]2([CH3:34])[c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)=[O:42])[CH2:43][CH2:44]1)(=[O:45])[N:50]1[CH2:49][CH2:48][C:47](=[O:46])[NH:53][CH2:52][CH2:51]1\", \"reactants\":\"O[C:1]([CH2:2][N:3]1[CH2:4][CH2:5][N:6]([C:7]([N:8]2[C:9]([c:10]3[c:11]([O:12][CH2:13][CH3:14])[cH:15][c:16]([C:17]([CH3:18])([CH3:19])[CH3:20])[n:21][cH:22]3)=[N:23][C@@:24]([CH3:25])([c:26]3[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]3)[C@@:33]2([CH3:34])[c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)=[O:42])[CH2:43][CH2:44]1)=[O:45].[O:46]=[C:47]1[CH2:48][CH2:49][NH:50][CH2:51][CH2:52][NH:53]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH2:2][N:3]1[CH2:4][CH2:5][N:6]([C:7]([N:8]2[C:9]([c:10]3[c:11]([O:12][CH2:13][CH3:14])[cH:15][c:16]([C:17]([CH3:18])([CH3:19])[CH3:20])[n:21][cH:22]3)=[N:23][C@@:24]([CH3:25])([c:26]3[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]3)[C@@:33]2([CH3:34])[c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)=[O:42])[CH2:43][CH2:44]1)=[O:45]' and '[O:46]=[C:47]1[CH2:48][CH2:49][NH:50][CH2:51][CH2:52][NH:53]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis steps for '[CH3:1][CH2:2][c:3]1[cH:4][cH:5][c:6]([N:7]([CH2:8][c:9]2[cH:10][cH:11][c:12]([C:13](=[O:14])[N:15]([CH3:16])[CH3:17])[cH:18][cH:19]2)[C:35]([NH:34][c:33]2[c:25]([CH:23]([CH3:22])[CH3:24])[cH:26][cH:27][cH:28][c:29]2[CH:30]([CH3:31])[CH3:32])=[O:36])[cH:20][cH:21]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][c:3]1[cH:4][cH:5][c:6]([N:7]([CH2:8][c:9]2[cH:10][cH:11][c:12]([C:13](=[O:14])[N:15]([CH3:16])[CH3:17])[cH:18][cH:19]2)[C:35]([NH:34][c:33]2[c:25]([CH:23]([CH3:22])[CH3:24])[cH:26][cH:27][cH:28][c:29]2[CH:30]([CH3:31])[CH3:32])=[O:36])[cH:20][cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH2:2][c:3]1[cH:4][cH:5][c:6]([N:7]([CH2:8][c:9]2[cH:10][cH:11][c:12]([C:13](=[O:14])[N:15]([CH3:16])[CH3:17])[cH:18][cH:19]2)[C:35]([NH:34][c:33]2[c:25]([CH:23]([CH3:22])[CH3:24])[cH:26][cH:27][cH:28][c:29]2[CH:30]([CH3:31])[CH3:32])=[O:36])[cH:20][cH:21]1\", \"reactants\":\"[CH3:1][CH2:2][c:3]1[cH:4][cH:5][c:6]([NH:7][CH2:8][c:9]2[cH:10][cH:11][c:12]([C:13](=[O:14])[N:15]([CH3:16])[CH3:17])[cH:18][cH:19]2)[cH:20][cH:21]1.[CH3:22][CH:23]([CH3:24])[c:25]1[cH:26][cH:27][cH:28][c:29]([CH:30]([CH3:31])[CH3:32])[c:33]1[N:34]=[C:35]=[O:36]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[CH3:1][CH2:2][c:3]1[cH:4][cH:5][c:6]([NH:7][CH2:8][c:9]2[cH:10][cH:11][c:12]([C:13](=[O:14])[N:15]([CH3:16])[CH3:17])[cH:18][cH:19]2)[cH:20][cH:21]1' and '[CH3:22][CH:23]([CH3:24])[c:25]1[cH:26][cH:27][cH:28][c:29]([CH:30]([CH3:31])[CH3:32])[c:33]1[N:34]=[C:35]=[O:36]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis process for '[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])(=[O:16])[NH:25][CH2:24][CH2:23][CH2:22][CH2:21][CH2:20][CH2:19][CH2:18][CH3:17]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])(=[O:16])[NH:25][CH2:24][CH2:23][CH2:22][CH2:21][CH2:20][CH2:19][CH2:18][CH3:17]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])(=[O:16])[NH:25][CH2:24][CH2:23][CH2:22][CH2:21][CH2:20][CH2:19][CH2:18][CH3:17]\", \"reactants\":\"O[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])=[O:16].[CH3:17][CH2:18][CH2:19][CH2:20][CH2:21][CH2:22][CH2:23][CH2:24][NH2:25]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])=[O:16]' and '[CH3:17][CH2:18][CH2:19][CH2:20][CH2:21][CH2:22][CH2:23][CH2:24][NH2:25]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis method for '[C:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][CH2:17][CH2:18][CH2:19][CH3:20])[c:21]([C:22]([CH3:23])([CH3:24])[CH3:25])[cH:26]1)(=[O:27])[N:31]([C:29]([CH3:28])=[O:30])[CH2:32][c:33]1[cH:34][cH:35][cH:36][cH:37][n:38]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][CH2:17][CH2:18][CH2:19][CH3:20])[c:21]([C:22]([CH3:23])([CH3:24])[CH3:25])[cH:26]1)(=[O:27])[N:31]([C:29]([CH3:28])=[O:30])[CH2:32][c:33]1[cH:34][cH:35][cH:36][cH:37][n:38]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][CH2:17][CH2:18][CH2:19][CH3:20])[c:21]([C:22]([CH3:23])([CH3:24])[CH3:25])[cH:26]1)(=[O:27])[N:31]([C:29]([CH3:28])=[O:30])[CH2:32][c:33]1[cH:34][cH:35][cH:36][cH:37][n:38]1\", \"reactants\":\"Cl[C:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][CH2:17][CH2:18][CH2:19][CH3:20])[c:21]([C:22]([CH3:23])([CH3:24])[CH3:25])[cH:26]1)=[O:27].[CH3:28][C:29](=[O:30])[NH:31][CH2:32][c:33]1[cH:34][cH:35][cH:36][cH:37][n:38]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][CH2:17][CH2:18][CH2:19][CH3:20])[c:21]([C:22]([CH3:23])([CH3:24])[CH3:25])[cH:26]1)=[O:27]' and '[CH3:28][C:29](=[O:30])[NH:31][CH2:32][c:33]1[cH:34][cH:35][cH:36][cH:37][n:38]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis route for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([S:7](=[O:8])(=[O:9])[NH:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[O:17][c:18]2[cH:19][cH:20][c:21]([Cl:22])[c:23]([Cl:24])[cH:25]2)[cH:26][cH:27]1)[N:33]1[CH2:32][CH2:31][N:30]([CH2:29][CH2:28][CH2:36][N:37]2[CH2:38][CH2:39][O:40][CH2:41][CH2:42]2)[CH2:35][CH2:34]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([S:7](=[O:8])(=[O:9])[NH:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[O:17][c:18]2[cH:19][cH:20][c:21]([Cl:22])[c:23]([Cl:24])[cH:25]2)[cH:26][cH:27]1)[N:33]1[CH2:32][CH2:31][N:30]([CH2:29][CH2:28][CH2:36][N:37]2[CH2:38][CH2:39][O:40][CH2:41][CH2:42]2)[CH2:35][CH2:34]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([S:7](=[O:8])(=[O:9])[NH:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[O:17][c:18]2[cH:19][cH:20][c:21]([Cl:22])[c:23]([Cl:24])[cH:25]2)[cH:26][cH:27]1)[N:33]1[CH2:32][CH2:31][N:30]([CH2:29][CH2:28][CH2:36][N:37]2[CH2:38][CH2:39][O:40][CH2:41][CH2:42]2)[CH2:35][CH2:34]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([S:7](=[O:8])(=[O:9])[NH:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[O:17][c:18]2[cH:19][cH:20][c:21]([Cl:22])[c:23]([Cl:24])[cH:25]2)[cH:26][cH:27]1.[CH2:28]([CH2:29][N:30]1[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]1)[CH2:36][N:37]1[CH2:38][CH2:39][O:40][CH2:41][CH2:42]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([S:7](=[O:8])(=[O:9])[NH:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[O:17][c:18]2[cH:19][cH:20][c:21]([Cl:22])[c:23]([Cl:24])[cH:25]2)[cH:26][cH:27]1' and '[CH2:28]([CH2:29][N:30]1[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]1)[CH2:36][N:37]1[CH2:38][CH2:39][O:40][CH2:41][CH2:42]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how '[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][c:9]([Br:10])[c:11]2[s:12]1)[N:14]([CH3:13])[O:15][CH3:16]' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][c:9]([Br:10])[c:11]2[s:12]1)[N:14]([CH3:13])[O:15][CH3:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][c:9]([Br:10])[c:11]2[s:12]1)[N:14]([CH3:13])[O:15][CH3:16]\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][c:9]([Br:10])[c:11]2[s:12]1.[CH3:13][NH:14][O:15][CH3:16]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][c:9]([Br:10])[c:11]2[s:12]1' and '[CH3:13][NH:14][O:15][CH3:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[c:1]1([C:11]#[C:12][CH2:13][CH2:14][OH:15])[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:11]#[C:12][CH2:13][CH2:14][OH:15])[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:11]#[C:12][CH2:13][CH2:14][OH:15])[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1.[CH:11]#[C:12][CH2:13][CH2:14][OH:15]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1' and '[CH:11]#[C:12][CH2:13][CH2:14][OH:15]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[c:1]1([C:17]#[C:18][c:19]2[cH:20][cH:21][c:22]([O:23][CH2:24][CH3:25])[cH:26][cH:27]2)[cH:2][cH:3][c:4]([CH:5]([CH3:6])[NH:7][C:8](=[O:9])[O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[cH:15][cH:16]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:17]#[C:18][c:19]2[cH:20][cH:21][c:22]([O:23][CH2:24][CH3:25])[cH:26][cH:27]2)[cH:2][cH:3][c:4]([CH:5]([CH3:6])[NH:7][C:8](=[O:9])[O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[cH:15][cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:17]#[C:18][c:19]2[cH:20][cH:21][c:22]([O:23][CH2:24][CH3:25])[cH:26][cH:27]2)[cH:2][cH:3][c:4]([CH:5]([CH3:6])[NH:7][C:8](=[O:9])[O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[cH:15][cH:16]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([CH:5]([CH3:6])[NH:7][C:8](=[O:9])[O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[cH:15][cH:16]1.[CH:17]#[C:18][c:19]1[cH:20][cH:21][c:22]([O:23][CH2:24][CH3:25])[cH:26][cH:27]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH:5]([CH3:6])[NH:7][C:8](=[O:9])[O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[cH:15][cH:16]1' and '[CH:17]#[C:18][c:19]1[cH:20][cH:21][c:22]([O:23][CH2:24][CH3:25])[cH:26][cH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how '[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][o:6][n:7]1.[CH3:8][c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[C@@H:15]1[C@@H:16]([O:17][C@H:18]([CH3:19])[c:20]2[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]2)[O:34][CH2:35][C@@H:36]2[CH2:37][NH:38][CH2:39][C@@H:40]12\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][o:6][n:7]1' and '[CH3:8][c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[C@@H:15]1[C@@H:16]([O:17][C@H:18]([CH3:19])[c:20]2[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]2)[O:34][CH2:35][C@@H:36]2[CH2:37][NH:38][CH2:39][C@@H:40]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[CH2:1]([CH2:2][CH2:3][n:4]1[n:5][c:6]2[n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]3)[c:16](-[c:17]3[cH:18][cH:19][n:20][cH:21][cH:22]3)[c:23](-[c:24]3[cH:25][cH:26][c:27]([F:28])[cH:29][cH:30]3)[c:31]2[cH:32]1)[NH:34][CH3:33]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][n:4]1[n:5][c:6]2[n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]3)[c:16](-[c:17]3[cH:18][cH:19][n:20][cH:21][cH:22]3)[c:23](-[c:24]3[cH:25][cH:26][c:27]([F:28])[cH:29][cH:30]3)[c:31]2[cH:32]1)[NH:34][CH3:33]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][n:4]1[n:5][c:6]2[n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]3)[c:16](-[c:17]3[cH:18][cH:19][n:20][cH:21][cH:22]3)[c:23](-[c:24]3[cH:25][cH:26][c:27]([F:28])[cH:29][cH:30]3)[c:31]2[cH:32]1)[NH:34][CH3:33]\", \"reactants\":\"Cl[CH2:1][CH2:2][CH2:3][n:4]1[n:5][c:6]2[n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]3)[c:16](-[c:17]3[cH:18][cH:19][n:20][cH:21][cH:22]3)[c:23](-[c:24]3[cH:25][cH:26][c:27]([F:28])[cH:29][cH:30]3)[c:31]2[cH:32]1.[CH3:33][NH2:34]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][CH2:2][CH2:3][n:4]1[n:5][c:6]2[n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]3)[c:16](-[c:17]3[cH:18][cH:19][n:20][cH:21][cH:22]3)[c:23](-[c:24]3[cH:25][cH:26][c:27]([F:28])[cH:29][cH:30]3)[c:31]2[cH:32]1' and '[CH3:33][NH2:34]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis steps for '[CH2:1]([c:2]1[n:3][n:4][c:5]([CH2:6][CH2:7][N:8]([CH3:9])[CH3:10])[n:11]1-[c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[C:18](=[O:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[N:29]1[C:28](=[O:27])[c:37]2[c:32]([cH:33][cH:34][cH:35][cH:36]2)[C:30]1=[O:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[n:3][n:4][c:5]([CH2:6][CH2:7][N:8]([CH3:9])[CH3:10])[n:11]1-[c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[C:18](=[O:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[N:29]1[C:28](=[O:27])[c:37]2[c:32]([cH:33][cH:34][cH:35][cH:36]2)[C:30]1=[O:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[n:3][n:4][c:5]([CH2:6][CH2:7][N:8]([CH3:9])[CH3:10])[n:11]1-[c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[C:18](=[O:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[N:29]1[C:28](=[O:27])[c:37]2[c:32]([cH:33][cH:34][cH:35][cH:36]2)[C:30]1=[O:31]\", \"reactants\":\"O[CH2:1][c:2]1[n:3][n:4][c:5]([CH2:6][CH2:7][N:8]([CH3:9])[CH3:10])[n:11]1-[c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[C:18](=[O:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26].[O:27]=[C:28]1[NH:29][C:30](=[O:31])[c:32]2[cH:33][cH:34][cH:35][cH:36][c:37]21\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[CH2:1][c:2]1[n:3][n:4][c:5]([CH2:6][CH2:7][N:8]([CH3:9])[CH3:10])[n:11]1-[c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[C:18](=[O:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26]' and '[O:27]=[C:28]1[NH:29][C:30](=[O:31])[c:32]2[cH:33][cH:34][cH:35][cH:36][c:37]21'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the possible synthesis path of '[CH2:1]([CH2:2][CH2:3][Br:4])[O:22][c:21]1[cH:20][c:19]2[n:18][c:17]([NH:16][CH:15]3[CH2:14][CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[CH2:28][CH2:27]3)[o:26][c:25]2[cH:24][cH:23]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][Br:4])[O:22][c:21]1[cH:20][c:19]2[n:18][c:17]([NH:16][CH:15]3[CH2:14][CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[CH2:28][CH2:27]3)[o:26][c:25]2[cH:24][cH:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][Br:4])[O:22][c:21]1[cH:20][c:19]2[n:18][c:17]([NH:16][CH:15]3[CH2:14][CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[CH2:28][CH2:27]3)[o:26][c:25]2[cH:24][cH:23]1\", \"reactants\":\"Br[CH2:1][CH2:2][CH2:3][Br:4].[CH3:5][C:6]([CH3:7])([CH3:8])[O:9][C:10](=[O:11])[N:12]1[CH2:13][CH2:14][CH:15]([NH:16][c:17]2[n:18][c:19]3[cH:20][c:21]([OH:22])[cH:23][cH:24][c:25]3[o:26]2)[CH2:27][CH2:28]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][CH2:2][CH2:3][Br:4]' and '[CH3:5][C:6]([CH3:7])([CH3:8])[O:9][C:10](=[O:11])[N:12]1[CH2:13][CH2:14][CH:15]([NH:16][c:17]2[n:18][c:19]3[cH:20][c:21]([OH:22])[cH:23][cH:24][c:25]3[o:26]2)[CH2:27][CH2:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the best synthesis route for '[C:1](=[O:2])([C:3]1([c:4]2[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]2)[CH2:11][CH2:12]1)[NH:31][CH2:30][CH2:29][CH2:28][N:27]1[CH2:26][CH2:25][CH:24]([c:23]2[cH:22][cH:21][cH:20][c:19]([NH:18][C:16]([CH2:15][CH2:14][CH3:13])=[O:17])[cH:34]2)[CH2:33][CH2:32]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([C:3]1([c:4]2[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]2)[CH2:11][CH2:12]1)[NH:31][CH2:30][CH2:29][CH2:28][N:27]1[CH2:26][CH2:25][CH:24]([c:23]2[cH:22][cH:21][cH:20][c:19]([NH:18][C:16]([CH2:15][CH2:14][CH3:13])=[O:17])[cH:34]2)[CH2:33][CH2:32]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([C:3]1([c:4]2[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]2)[CH2:11][CH2:12]1)[NH:31][CH2:30][CH2:29][CH2:28][N:27]1[CH2:26][CH2:25][CH:24]([c:23]2[cH:22][cH:21][cH:20][c:19]([NH:18][C:16]([CH2:15][CH2:14][CH3:13])=[O:17])[cH:34]2)[CH2:33][CH2:32]1\", \"reactants\":\"O[C:1](=[O:2])[C:3]1([c:4]2[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]2)[CH2:11][CH2:12]1.[CH3:13][CH2:14][CH2:15][C:16](=[O:17])[NH:18][c:19]1[cH:20][cH:21][cH:22][c:23]([CH:24]2[CH2:25][CH2:26][N:27]([CH2:28][CH2:29][CH2:30][NH2:31])[CH2:32][CH2:33]2)[cH:34]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[C:3]1([c:4]2[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]2)[CH2:11][CH2:12]1' and '[CH3:13][CH2:14][CH2:15][C:16](=[O:17])[NH:18][c:19]1[cH:20][cH:21][cH:22][c:23]([CH:24]2[CH2:25][CH2:26][N:27]([CH2:28][CH2:29][CH2:30][NH2:31])[CH2:32][CH2:33]2)[cH:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most likely synthesis path for '[C:1]([C@@H:2]([NH:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([S:10](=[O:11])(=[O:12])[NH:13][c:14]2[cH:15][cH:16][cH:17][cH:18][c:19]2[O:20][c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[cH:27][cH:28]1)[CH2:29][OH:30])(=[O:31])[NH:44][CH2:43][CH:42]1[CH2:41][CH2:40][N:39]([C:37]([O:36][C:33]([CH3:32])([CH3:34])[CH3:35])=[O:38])[CH2:46][CH2:45]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@@H:2]([NH:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([S:10](=[O:11])(=[O:12])[NH:13][c:14]2[cH:15][cH:16][cH:17][cH:18][c:19]2[O:20][c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[cH:27][cH:28]1)[CH2:29][OH:30])(=[O:31])[NH:44][CH2:43][CH:42]1[CH2:41][CH2:40][N:39]([C:37]([O:36][C:33]([CH3:32])([CH3:34])[CH3:35])=[O:38])[CH2:46][CH2:45]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([C@@H:2]([NH:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([S:10](=[O:11])(=[O:12])[NH:13][c:14]2[cH:15][cH:16][cH:17][cH:18][c:19]2[O:20][c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[cH:27][cH:28]1)[CH2:29][OH:30])(=[O:31])[NH:44][CH2:43][CH:42]1[CH2:41][CH2:40][N:39]([C:37]([O:36][C:33]([CH3:32])([CH3:34])[CH3:35])=[O:38])[CH2:46][CH2:45]1\", \"reactants\":\"O[C:1]([C@@H:2]([NH:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([S:10](=[O:11])(=[O:12])[NH:13][c:14]2[cH:15][cH:16][cH:17][cH:18][c:19]2[O:20][c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[cH:27][cH:28]1)[CH2:29][OH:30])=[O:31].[CH3:32][C:33]([CH3:34])([CH3:35])[O:36][C:37](=[O:38])[N:39]1[CH2:40][CH2:41][CH:42]([CH2:43][NH2:44])[CH2:45][CH2:46]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([C@@H:2]([NH:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([S:10](=[O:11])(=[O:12])[NH:13][c:14]2[cH:15][cH:16][cH:17][cH:18][c:19]2[O:20][c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[cH:27][cH:28]1)[CH2:29][OH:30])=[O:31]' and '[CH3:32][C:33]([CH3:34])([CH3:35])[O:36][C:37](=[O:38])[N:39]1[CH2:40][CH2:41][CH:42]([CH2:43][NH2:44])[CH2:45][CH2:46]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[CH2:1]([c:2]1[c:3]([O:4][CH3:5])[n:6][cH:7][cH:8][cH:9]1)[NH2:10]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([O:4][CH3:5])[n:6][cH:7][cH:8][cH:9]1)[NH2:10]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([O:4][CH3:5])[n:6][cH:7][cH:8][cH:9]1)[NH2:10]\", \"reactants\":\"O=[CH:1][c:2]1[c:3]([O:4][CH3:5])[n:6][cH:7][cH:8][cH:9]1.[NH4+:10]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[c:3]([O:4][CH3:5])[n:6][cH:7][cH:8][cH:9]1' and '[NH4+:10]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis route for '[CH2:1]([CH2:2][n:3]1[c:4]2[cH:5][c:6]([O:7][CH3:8])[c:9]([O:10][CH3:11])[n:12][c:13]2[c:14](-[c:15]2[cH:16][c:17]3[cH:18][cH:19][cH:20][n:21][c:22]3[n:23]2[S:24](=[O:25])(=[O:26])[c:27]2[cH:28][cH:29][c:30]([CH3:31])[cH:32][cH:33]2)[cH:34]1)[N:40]1[CH2:35][CH2:36][O:37][CH2:38][CH2:39]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][n:3]1[c:4]2[cH:5][c:6]([O:7][CH3:8])[c:9]([O:10][CH3:11])[n:12][c:13]2[c:14](-[c:15]2[cH:16][c:17]3[cH:18][cH:19][cH:20][n:21][c:22]3[n:23]2[S:24](=[O:25])(=[O:26])[c:27]2[cH:28][cH:29][c:30]([CH3:31])[cH:32][cH:33]2)[cH:34]1)[N:40]1[CH2:35][CH2:36][O:37][CH2:38][CH2:39]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][n:3]1[c:4]2[cH:5][c:6]([O:7][CH3:8])[c:9]([O:10][CH3:11])[n:12][c:13]2[c:14](-[c:15]2[cH:16][c:17]3[cH:18][cH:19][cH:20][n:21][c:22]3[n:23]2[S:24](=[O:25])(=[O:26])[c:27]2[cH:28][cH:29][c:30]([CH3:31])[cH:32][cH:33]2)[cH:34]1)[N:40]1[CH2:35][CH2:36][O:37][CH2:38][CH2:39]1\", \"reactants\":\"I[CH2:1][CH2:2][n:3]1[c:4]2[cH:5][c:6]([O:7][CH3:8])[c:9]([O:10][CH3:11])[n:12][c:13]2[c:14](-[c:15]2[cH:16][c:17]3[cH:18][cH:19][cH:20][n:21][c:22]3[n:23]2[S:24](=[O:25])(=[O:26])[c:27]2[cH:28][cH:29][c:30]([CH3:31])[cH:32][cH:33]2)[cH:34]1.[CH2:35]1[CH2:36][O:37][CH2:38][CH2:39][NH:40]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[CH2:1][CH2:2][n:3]1[c:4]2[cH:5][c:6]([O:7][CH3:8])[c:9]([O:10][CH3:11])[n:12][c:13]2[c:14](-[c:15]2[cH:16][c:17]3[cH:18][cH:19][cH:20][n:21][c:22]3[n:23]2[S:24](=[O:25])(=[O:26])[c:27]2[cH:28][cH:29][c:30]([CH3:31])[cH:32][cH:33]2)[cH:34]1' and '[CH2:35]1[CH2:36][O:37][CH2:38][CH2:39][NH:40]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis trajectory for '[c:1]1(-[c:18]2[c:19]([CH3:20])[cH:21][cH:22][c:23]([C:24]([NH:25][CH2:26][CH3:27])=[O:28])[cH:29]2)[c:2]([F:3])[cH:4][c:5]2[c:6](-[c:7]3[cH:8][cH:9][c:10]([Cl:11])[n:12][cH:13]3)[n:14][nH:15][c:16]2[cH:17]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:18]2[c:19]([CH3:20])[cH:21][cH:22][c:23]([C:24]([NH:25][CH2:26][CH3:27])=[O:28])[cH:29]2)[c:2]([F:3])[cH:4][c:5]2[c:6](-[c:7]3[cH:8][cH:9][c:10]([Cl:11])[n:12][cH:13]3)[n:14][nH:15][c:16]2[cH:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:18]2[c:19]([CH3:20])[cH:21][cH:22][c:23]([C:24]([NH:25][CH2:26][CH3:27])=[O:28])[cH:29]2)[c:2]([F:3])[cH:4][c:5]2[c:6](-[c:7]3[cH:8][cH:9][c:10]([Cl:11])[n:12][cH:13]3)[n:14][nH:15][c:16]2[cH:17]1\", \"reactants\":\"Br[c:1]1[c:2]([F:3])[cH:4][c:5]2[c:6](-[c:7]3[cH:8][cH:9][c:10]([Cl:11])[n:12][cH:13]3)[n:14][nH:15][c:16]2[cH:17]1.CC1(C)OB([c:18]2[c:19]([CH3:20])[cH:21][cH:22][c:23]([C:24]([NH:25][CH2:26][CH3:27])=[O:28])[cH:29]2)OC1(C)C\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([F:3])[cH:4][c:5]2[c:6](-[c:7]3[cH:8][cH:9][c:10]([Cl:11])[n:12][cH:13]3)[n:14][nH:15][c:16]2[cH:17]1' and 'CC1(C)OB([c:18]2[c:19]([CH3:20])[cH:21][cH:22][c:23]([C:24]([NH:25][CH2:26][CH3:27])=[O:28])[cH:29]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the possible synthesis steps for '[c:1]1([S:17][c:16]2[cH:15][cH:14][cH:13][c:12]([NH2:11])[cH:18]2)[cH:2][cH:3][c:4]([CH3:5])[cH:6][c:7]1[N+:8](=[O:9])[O-:10]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([S:17][c:16]2[cH:15][cH:14][cH:13][c:12]([NH2:11])[cH:18]2)[cH:2][cH:3][c:4]([CH3:5])[cH:6][c:7]1[N+:8](=[O:9])[O-:10]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([S:17][c:16]2[cH:15][cH:14][cH:13][c:12]([NH2:11])[cH:18]2)[cH:2][cH:3][c:4]([CH3:5])[cH:6][c:7]1[N+:8](=[O:9])[O-:10]\", \"reactants\":\"Cl[c:1]1[cH:2][cH:3][c:4]([CH3:5])[cH:6][c:7]1[N+:8](=[O:9])[O-:10].[NH2:11][c:12]1[cH:13][cH:14][cH:15][c:16]([SH:17])[cH:18]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[cH:2][cH:3][c:4]([CH3:5])[cH:6][c:7]1[N+:8](=[O:9])[O-:10]' and '[NH2:11][c:12]1[cH:13][cH:14][cH:15][c:16]([SH:17])[cH:18]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis procedure for '[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]\", \"reactants\":\"Cl[CH2:1][CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1.[CH3:9][S:10][c:11]1[n:12][cH:13][c:14]2[c:15]([n:16]1)[nH:17][c:18](=[O:19])[c:20](-[c:21]1[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]1[Cl:34])[cH:35]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1' and '[CH3:9][S:10][c:11]1[n:12][cH:13][c:14]2[c:15]([n:16]1)[nH:17][c:18](=[O:19])[c:20](-[c:21]1[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]1[Cl:34])[cH:35]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis method for '[C:1]([CH:2]1[CH2:3][CH2:4][c:5]2[c:6]([cH:7][cH:8][c:9]([O:10][CH3:11])[cH:12]2)[CH2:13]1)(=[O:14])[NH:21][CH:19]([c:18]1[cH:17][c:16]([CH3:15])[c:25]([NH:26][S:27]([CH3:28])(=[O:29])=[O:30])[cH:24][c:22]1[F:23])[CH3:20]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH:2]1[CH2:3][CH2:4][c:5]2[c:6]([cH:7][cH:8][c:9]([O:10][CH3:11])[cH:12]2)[CH2:13]1)(=[O:14])[NH:21][CH:19]([c:18]1[cH:17][c:16]([CH3:15])[c:25]([NH:26][S:27]([CH3:28])(=[O:29])=[O:30])[cH:24][c:22]1[F:23])[CH3:20]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH:2]1[CH2:3][CH2:4][c:5]2[c:6]([cH:7][cH:8][c:9]([O:10][CH3:11])[cH:12]2)[CH2:13]1)(=[O:14])[NH:21][CH:19]([c:18]1[cH:17][c:16]([CH3:15])[c:25]([NH:26][S:27]([CH3:28])(=[O:29])=[O:30])[cH:24][c:22]1[F:23])[CH3:20]\", \"reactants\":\"O[C:1]([CH:2]1[CH2:3][CH2:4][c:5]2[c:6]([cH:7][cH:8][c:9]([O:10][CH3:11])[cH:12]2)[CH2:13]1)=[O:14].[CH3:15][c:16]1[cH:17][c:18]([CH:19]([CH3:20])[NH2:21])[c:22]([F:23])[cH:24][c:25]1[NH:26][S:27]([CH3:28])(=[O:29])=[O:30]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH:2]1[CH2:3][CH2:4][c:5]2[c:6]([cH:7][cH:8][c:9]([O:10][CH3:11])[cH:12]2)[CH2:13]1)=[O:14]' and '[CH3:15][c:16]1[cH:17][c:18]([CH:19]([CH3:20])[NH2:21])[c:22]([F:23])[cH:24][c:25]1[NH:26][S:27]([CH3:28])(=[O:29])=[O:30]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[c:1]1(-[c:10]2[n:11][cH:12][cH:13][cH:14][c:15]2[Cl:16])[cH:2][cH:3][cH:4][c:5]([NH2:6])[c:7]1[F:8]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:10]2[n:11][cH:12][cH:13][cH:14][c:15]2[Cl:16])[cH:2][cH:3][cH:4][c:5]([NH2:6])[c:7]1[F:8]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:10]2[n:11][cH:12][cH:13][cH:14][c:15]2[Cl:16])[cH:2][cH:3][cH:4][c:5]([NH2:6])[c:7]1[F:8]\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][cH:4][c:5]([NH2:6])[c:7]1[F:8].OB(O)[c:10]1[n:11][cH:12][cH:13][cH:14][c:15]1[Cl:16]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][cH:4][c:5]([NH2:6])[c:7]1[F:8]' and 'OB(O)[c:10]1[n:11][cH:12][cH:13][cH:14][c:15]1[Cl:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the steps for synthesizing '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][o:7]1)[NH:17][c:16]1[cH:15][cH:14][c:13]2[cH:12][cH:11][c:10]([O:9][CH3:8])[n:20][c:19]2[n:18]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][o:7]1)[NH:17][c:16]1[cH:15][cH:14][c:13]2[cH:12][cH:11][c:10]([O:9][CH3:8])[n:20][c:19]2[n:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][o:7]1)[NH:17][c:16]1[cH:15][cH:14][c:13]2[cH:12][cH:11][c:10]([O:9][CH3:8])[n:20][c:19]2[n:18]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][o:7]1.[CH3:8][O:9][c:10]1[cH:11][cH:12][c:13]2[cH:14][cH:15][c:16]([NH2:17])[n:18][c:19]2[n:20]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][o:7]1' and '[CH3:8][O:9][c:10]1[cH:11][cH:12][c:13]2[cH:14][cH:15][c:16]([NH2:17])[n:18][c:19]2[n:20]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide a synthesis prediction for '[c:1]1([N:32]2[CH2:31][c:29]3[c:28]([cH:27][cH:26][c:25]([F:24])[cH:30]3)[CH2:33]2)[c:2]([F:3])[cH:4][c:5]2[c:6]([n:7](-[c:8]3[cH:9][cH:10][c:11]([F:12])[cH:13][cH:14]3)[cH:15][c:16]([C:17](=[O:18])[OH:19])[c:20]2=[O:21])[c:22]1[F:23]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:32]2[CH2:31][c:29]3[c:28]([cH:27][cH:26][c:25]([F:24])[cH:30]3)[CH2:33]2)[c:2]([F:3])[cH:4][c:5]2[c:6]([n:7](-[c:8]3[cH:9][cH:10][c:11]([F:12])[cH:13][cH:14]3)[cH:15][c:16]([C:17](=[O:18])[OH:19])[c:20]2=[O:21])[c:22]1[F:23]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:32]2[CH2:31][c:29]3[c:28]([cH:27][cH:26][c:25]([F:24])[cH:30]3)[CH2:33]2)[c:2]([F:3])[cH:4][c:5]2[c:6]([n:7](-[c:8]3[cH:9][cH:10][c:11]([F:12])[cH:13][cH:14]3)[cH:15][c:16]([C:17](=[O:18])[OH:19])[c:20]2=[O:21])[c:22]1[F:23]\", \"reactants\":\"F[c:1]1[c:2]([F:3])[cH:4][c:5]2[c:6]([n:7](-[c:8]3[cH:9][cH:10][c:11]([F:12])[cH:13][cH:14]3)[cH:15][c:16]([C:17](=[O:18])[OH:19])[c:20]2=[O:21])[c:22]1[F:23].[F:24][c:25]1[cH:26][cH:27][c:28]2[c:29]([cH:30]1)[CH2:31][NH:32][CH2:33]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[c:2]([F:3])[cH:4][c:5]2[c:6]([n:7](-[c:8]3[cH:9][cH:10][c:11]([F:12])[cH:13][cH:14]3)[cH:15][c:16]([C:17](=[O:18])[OH:19])[c:20]2=[O:21])[c:22]1[F:23]' and '[F:24][c:25]1[cH:26][cH:27][c:28]2[c:29]([cH:30]1)[CH2:31][NH:32][CH2:33]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis method for '[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]([F:8])[cH:9][c:10]1[F:11])[N:13]([CH3:12])[CH2:14][CH2:15][OH:16]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]([F:8])[cH:9][c:10]1[F:11])[N:13]([CH3:12])[CH2:14][CH2:15][OH:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]([F:8])[cH:9][c:10]1[F:11])[N:13]([CH3:12])[CH2:14][CH2:15][OH:16]\", \"reactants\":\"Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]([F:8])[cH:9][c:10]1[F:11].[CH3:12][NH:13][CH2:14][CH2:15][OH:16]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]([F:8])[cH:9][c:10]1[F:11]' and '[CH3:12][NH:13][CH2:14][CH2:15][OH:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:26]2)[c:2]([N+:3](=[O:4])[O-:5])[cH:6][c:7]2[nH:8][c:9](=[O:10])[n:11]([NH:12][S:13]([CH3:14])(=[O:15])=[O:16])[c:17](=[O:18])[c:19]2[cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:26]2)[c:2]([N+:3](=[O:4])[O-:5])[cH:6][c:7]2[nH:8][c:9](=[O:10])[n:11]([NH:12][S:13]([CH3:14])(=[O:15])=[O:16])[c:17](=[O:18])[c:19]2[cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:26]2)[c:2]([N+:3](=[O:4])[O-:5])[cH:6][c:7]2[nH:8][c:9](=[O:10])[n:11]([NH:12][S:13]([CH3:14])(=[O:15])=[O:16])[c:17](=[O:18])[c:19]2[cH:20]1\", \"reactants\":\"F[c:1]1[c:2]([N+:3](=[O:4])[O-:5])[cH:6][c:7]2[nH:8][c:9](=[O:10])[n:11]([NH:12][S:13]([CH3:14])(=[O:15])=[O:16])[c:17](=[O:18])[c:19]2[cH:20]1.[OH:21][CH:22]1[CH2:23][CH2:24][NH:25][CH2:26]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[c:2]([N+:3](=[O:4])[O-:5])[cH:6][c:7]2[nH:8][c:9](=[O:10])[n:11]([NH:12][S:13]([CH3:14])(=[O:15])=[O:16])[c:17](=[O:18])[c:19]2[cH:20]1' and '[OH:21][CH:22]1[CH2:23][CH2:24][NH:25][CH2:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis path of '[C:1](=[O:2])([c:3]1[s:4][c:5](-[n:6]2[cH:7][n:8][c:9]3[cH:10][n:11][cH:12][cH:13][c:14]23)[cH:15][c:16]1[O:17][CH:18]([CH3:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[NH2:27]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[s:4][c:5](-[n:6]2[cH:7][n:8][c:9]3[cH:10][n:11][cH:12][cH:13][c:14]23)[cH:15][c:16]1[O:17][CH:18]([CH3:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[NH2:27]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[s:4][c:5](-[n:6]2[cH:7][n:8][c:9]3[cH:10][n:11][cH:12][cH:13][c:14]23)[cH:15][c:16]1[O:17][CH:18]([CH3:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26])[NH2:27]\", \"reactants\":\"CO[C:1](=[O:2])[c:3]1[s:4][c:5](-[n:6]2[cH:7][n:8][c:9]3[cH:10][n:11][cH:12][cH:13][c:14]23)[cH:15][c:16]1[O:17][CH:18]([CH3:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26].[NH3:27]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CO[C:1](=[O:2])[c:3]1[s:4][c:5](-[n:6]2[cH:7][n:8][c:9]3[cH:10][n:11][cH:12][cH:13][c:14]23)[cH:15][c:16]1[O:17][CH:18]([CH3:19])[c:20]1[cH:21][cH:22][cH:23][cH:24][c:25]1[Cl:26]' and '[NH3:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis steps for '[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]\", \"reactants\":\"O[C@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])[CH3:9].[CH3:10][O:11][C:12](=[O:13])/[CH:14]=[CH:15]/[C:16](=[O:17])[OH:18]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])[CH3:9]' and '[CH3:10][O:11][C:12](=[O:13])/[CH:14]=[CH:15]/[C:16](=[O:17])[OH:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most efficient synthesis path for '[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][s:6][cH:7]1)[n:19]1[cH:18][c:17]([CH:16]2[CH2:15][CH2:14][N:13]([C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH2:27][CH2:26]2)[c:25]2[c:20]1[cH:21][cH:22][cH:23][cH:24]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][s:6][cH:7]1)[n:19]1[cH:18][c:17]([CH:16]2[CH2:15][CH2:14][N:13]([C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH2:27][CH2:26]2)[c:25]2[c:20]1[cH:21][cH:22][cH:23][cH:24]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][s:6][cH:7]1)[n:19]1[cH:18][c:17]([CH:16]2[CH2:15][CH2:14][N:13]([C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH2:27][CH2:26]2)[c:25]2[c:20]1[cH:21][cH:22][cH:23][cH:24]2\", \"reactants\":\"CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][s:6][cH:7]1.[CH3:8][CH2:9][O:10][C:11](=[O:12])[N:13]1[CH2:14][CH2:15][CH:16]([c:17]2[cH:18][nH:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]23)[CH2:26][CH2:27]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][s:6][cH:7]1' and '[CH3:8][CH2:9][O:10][C:11](=[O:12])[N:13]1[CH2:14][CH2:15][CH:16]([c:17]2[cH:18][nH:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]23)[CH2:26][CH2:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the possible synthesis route for '[CH:1](=[O:2])[c:10]1[c:9]([C:8]2=[N:16][C:4]([CH3:3])([CH3:5])[CH2:6][O:7]2)[cH:15][cH:14][c:12]([F:13])[cH:11]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:10]1[c:9]([C:8]2=[N:16][C:4]([CH3:3])([CH3:5])[CH2:6][O:7]2)[cH:15][cH:14][c:12]([F:13])[cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[O:2])[c:10]1[c:9]([C:8]2=[N:16][C:4]([CH3:3])([CH3:5])[CH2:6][O:7]2)[cH:15][cH:14][c:12]([F:13])[cH:11]1\", \"reactants\":\"CN(C)[CH:1]=[O:2].[CH3:3][C:4]1([CH3:5])[CH2:6][O:7][C:8]([c:9]2[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]2)=[N:16]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][C:4]1([CH3:5])[CH2:6][O:7][C:8]([c:9]2[cH:10][cH:11][c:12]([F:13])[cH:14][cH:15]2)=[N:16]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the likely synthesis process for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][cH:6][c:7]1[Cl:8])[NH:13][CH2:12][CH:10]([CH3:9])[OH:11]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][cH:6][c:7]1[Cl:8])[NH:13][CH2:12][CH:10]([CH3:9])[OH:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][cH:6][c:7]1[Cl:8])[NH:13][CH2:12][CH:10]([CH3:9])[OH:11]\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][cH:4][cH:5][cH:6][c:7]1[Cl:8].[CH3:9][CH:10]([OH:11])[CH2:12][NH2:13]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][cH:5][cH:6][c:7]1[Cl:8]' and '[CH3:9][CH:10]([OH:11])[CH2:12][NH2:13]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis path for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:11]([S:9](=[O:8])(=[O:10])[c:19]1[cH:20][cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][cH:28]1)[CH2:12][c:13]1[cH:14][cH:15][cH:16][cH:17][cH:18]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:11]([S:9](=[O:8])(=[O:10])[c:19]1[cH:20][cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][cH:28]1)[CH2:12][c:13]1[cH:14][cH:15][cH:16][cH:17][cH:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:11]([S:9](=[O:8])(=[O:10])[c:19]1[cH:20][cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][cH:28]1)[CH2:12][c:13]1[cH:14][cH:15][cH:16][cH:17][cH:18]1\", \"reactants\":\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[O:8]=[S:9](=[O:10])([NH:11][CH2:12][c:13]1[cH:14][cH:15][cH:16][cH:17][cH:18]1)[c:19]1[cH:20][cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][cH:28]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[O:8]=[S:9](=[O:10])([NH:11][CH2:12][c:13]1[cH:14][cH:15][cH:16][cH:17][cH:18]1)[c:19]1[cH:20][cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][cH:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1([NH:27][CH3:26])[c:2]([CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[C:12](=[O:13])[O:14][C:15]([CH3:16])([CH3:17])[CH3:18])[cH:19][c:20]([N+:21](=[O:22])[O-:23])[cH:24][cH:25]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:27][CH3:26])[c:2]([CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[C:12](=[O:13])[O:14][C:15]([CH3:16])([CH3:17])[CH3:18])[cH:19][c:20]([N+:21](=[O:22])[O-:23])[cH:24][cH:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:27][CH3:26])[c:2]([CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[C:12](=[O:13])[O:14][C:15]([CH3:16])([CH3:17])[CH3:18])[cH:19][c:20]([N+:21](=[O:22])[O-:23])[cH:24][cH:25]1\", \"reactants\":\"F[c:1]1[c:2]([CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[C:12](=[O:13])[O:14][C:15]([CH3:16])([CH3:17])[CH3:18])[cH:19][c:20]([N+:21](=[O:22])[O-:23])[cH:24][cH:25]1.[CH3:26][NH2:27]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[c:2]([CH2:3][N:4]([C:5]([O:6][C:7]([CH3:8])([CH3:9])[CH3:10])=[O:11])[C:12](=[O:13])[O:14][C:15]([CH3:16])([CH3:17])[CH3:18])[cH:19][c:20]([N+:21](=[O:22])[O-:23])[cH:24][cH:25]1' and '[CH3:26][NH2:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:20]1[CH:16]([c:15]2[cH:14][cH:13][c:12]([CH3:11])[cH:23][c:21]2[CH3:22])[CH2:17][CH2:18][CH2:19]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:20]1[CH:16]([c:15]2[cH:14][cH:13][c:12]([CH3:11])[cH:23][c:21]2[CH3:22])[CH2:17][CH2:18][CH2:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:20]1[CH:16]([c:15]2[cH:14][cH:13][c:12]([CH3:11])[cH:23][c:21]2[CH3:22])[CH2:17][CH2:18][CH2:19]1\", \"reactants\":\"Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10].[CH3:11][c:12]1[cH:13][cH:14][c:15]([CH:16]2[CH2:17][CH2:18][CH2:19][NH:20]2)[c:21]([CH3:22])[cH:23]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10]' and '[CH3:11][c:12]1[cH:13][cH:14][c:15]([CH:16]2[CH2:17][CH2:18][CH2:19][NH:20]2)[c:21]([CH3:22])[cH:23]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis pathway for '[C@H:1]1([O:11][c:12]2[cH:13][cH:14][c:15]3[cH:16][c:17]([Br:18])[cH:19][cH:20][c:21]3[cH:22]2)[CH2:2][CH2:3][C@H:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[CH2:9][CH2:10]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C@H:1]1([O:11][c:12]2[cH:13][cH:14][c:15]3[cH:16][c:17]([Br:18])[cH:19][cH:20][c:21]3[cH:22]2)[CH2:2][CH2:3][C@H:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[CH2:9][CH2:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C@H:1]1([O:11][c:12]2[cH:13][cH:14][c:15]3[cH:16][c:17]([Br:18])[cH:19][cH:20][c:21]3[cH:22]2)[CH2:2][CH2:3][C@H:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[CH2:9][CH2:10]1\", \"reactants\":\"CS(=O)(=O)O[C@H:1]1[CH2:2][CH2:3][C@H:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[CH2:9][CH2:10]1.[OH:11][c:12]1[cH:13][cH:14][c:15]2[cH:16][c:17]([Br:18])[cH:19][cH:20][c:21]2[cH:22]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CS(=O)(=O)O[C@H:1]1[CH2:2][CH2:3][C@H:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[CH2:9][CH2:10]1' and '[OH:11][c:12]1[cH:13][cH:14][c:15]2[cH:16][c:17]([Br:18])[cH:19][cH:20][c:21]2[cH:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the most efficient synthesis process for '[C:1]([CH:2]([CH2:3][CH3:4])[n:5]1[cH:6][c:7]([C:8]([F:9])([F:10])[F:11])[n:12][c:13]1[CH3:14])(=[O:15])[N:29]1[c:24]2[cH:23][n:22][n:21](-[c:20]3[cH:19][cH:18][c:17]([F:16])[cH:31][cH:30]3)[c:25]2[CH2:26][CH2:27][CH2:28]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH:2]([CH2:3][CH3:4])[n:5]1[cH:6][c:7]([C:8]([F:9])([F:10])[F:11])[n:12][c:13]1[CH3:14])(=[O:15])[N:29]1[c:24]2[cH:23][n:22][n:21](-[c:20]3[cH:19][cH:18][c:17]([F:16])[cH:31][cH:30]3)[c:25]2[CH2:26][CH2:27][CH2:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH:2]([CH2:3][CH3:4])[n:5]1[cH:6][c:7]([C:8]([F:9])([F:10])[F:11])[n:12][c:13]1[CH3:14])(=[O:15])[N:29]1[c:24]2[cH:23][n:22][n:21](-[c:20]3[cH:19][cH:18][c:17]([F:16])[cH:31][cH:30]3)[c:25]2[CH2:26][CH2:27][CH2:28]1\", \"reactants\":\"O[C:1]([CH:2]([CH2:3][CH3:4])[n:5]1[cH:6][c:7]([C:8]([F:9])([F:10])[F:11])[n:12][c:13]1[CH3:14])=[O:15].[F:16][c:17]1[cH:18][cH:19][c:20](-[n:21]2[n:22][cH:23][c:24]3[c:25]2[CH2:26][CH2:27][CH2:28][NH:29]3)[cH:30][cH:31]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH:2]([CH2:3][CH3:4])[n:5]1[cH:6][c:7]([C:8]([F:9])([F:10])[F:11])[n:12][c:13]1[CH3:14])=[O:15]' and '[F:16][c:17]1[cH:18][cH:19][c:20](-[n:21]2[n:22][cH:23][c:24]3[c:25]2[CH2:26][CH2:27][CH2:28][NH:29]3)[cH:30][cH:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the most probable synthesis route for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1)[NH:32][C:29]([CH2:28][c:27]1[n:14]([CH2:13][CH2:12][O:11][CH3:10])[n:15][c:16]2[c:17]([NH2:18])[n:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]3[c:26]21)([CH3:30])[CH3:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1)[NH:32][C:29]([CH2:28][c:27]1[n:14]([CH2:13][CH2:12][O:11][CH3:10])[n:15][c:16]2[c:17]([NH2:18])[n:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]3[c:26]21)([CH3:30])[CH3:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1)[NH:32][C:29]([CH2:28][c:27]1[n:14]([CH2:13][CH2:12][O:11][CH3:10])[n:15][c:16]2[c:17]([NH2:18])[n:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]3[c:26]21)([CH3:30])[CH3:31]\", \"reactants\":\"Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1.[CH3:10][O:11][CH2:12][CH2:13][n:14]1[n:15][c:16]2[c:17]([NH2:18])[n:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]3[c:26]2[c:27]1[CH2:28][C:29]([CH3:30])([CH3:31])[NH2:32]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1' and '[CH3:10][O:11][CH2:12][CH2:13][n:14]1[n:15][c:16]2[c:17]([NH2:18])[n:19][c:20]3[cH:21][cH:22][cH:23][cH:24][c:25]3[c:26]2[c:27]1[CH2:28][C:29]([CH3:30])([CH3:31])[NH2:32]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how to synthesize '[c:1]1(-[c:19]2[cH:20][c:21]([CH3:22])[c:23]([F:24])[cH:25][cH:26]2)[c:2]([CH3:3])[n:4][n:5]([CH3:6])[c:7]1-[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[NH:14][C:15](=[O:16])[CH2:17][O:18]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:19]2[cH:20][c:21]([CH3:22])[c:23]([F:24])[cH:25][cH:26]2)[c:2]([CH3:3])[n:4][n:5]([CH3:6])[c:7]1-[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[NH:14][C:15](=[O:16])[CH2:17][O:18]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:19]2[cH:20][c:21]([CH3:22])[c:23]([F:24])[cH:25][cH:26]2)[c:2]([CH3:3])[n:4][n:5]([CH3:6])[c:7]1-[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[NH:14][C:15](=[O:16])[CH2:17][O:18]2\", \"reactants\":\"Br[c:1]1[c:2]([CH3:3])[n:4][n:5]([CH3:6])[c:7]1-[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[NH:14][C:15](=[O:16])[CH2:17][O:18]2.OB(O)[c:19]1[cH:20][c:21]([CH3:22])[c:23]([F:24])[cH:25][cH:26]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([CH3:3])[n:4][n:5]([CH3:6])[c:7]1-[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[NH:14][C:15](=[O:16])[CH2:17][O:18]2' and 'OB(O)[c:19]1[cH:20][c:21]([CH3:22])[c:23]([F:24])[cH:25][cH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most efficient synthesis strategy for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:16][c:15]1[cH:14][c:13]([C:9]([CH3:8])([CH3:10])[C:11]#[N:12])[n:18][o:17]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:16][c:15]1[cH:14][c:13]([C:9]([CH3:8])([CH3:10])[C:11]#[N:12])[n:18][o:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:16][c:15]1[cH:14][c:13]([C:9]([CH3:8])([CH3:10])[C:11]#[N:12])[n:18][o:17]1\", \"reactants\":\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][C:9]([CH3:10])([C:11]#[N:12])[c:13]1[cH:14][c:15]([NH2:16])[o:17][n:18]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][C:9]([CH3:10])([C:11]#[N:12])[c:13]1[cH:14][c:15]([NH2:16])[o:17][n:18]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis method for '[CH2:1]([c:2]1[c:3]([CH2:4][CH3:5])[c:6](-[c:7]2[cH:8][n:9][c:10](-[c:11]3[cH:12][cH:13][c:14]([O:15][CH:16]([CH3:17])[CH3:18])[c:19]([Cl:20])[cH:21]3)[s:22]2)[cH:23][cH:24][cH:25]1)[N:32]1[CH2:31][CH:30]([C:28]([O:27][CH3:26])=[O:29])[CH2:33]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([CH2:4][CH3:5])[c:6](-[c:7]2[cH:8][n:9][c:10](-[c:11]3[cH:12][cH:13][c:14]([O:15][CH:16]([CH3:17])[CH3:18])[c:19]([Cl:20])[cH:21]3)[s:22]2)[cH:23][cH:24][cH:25]1)[N:32]1[CH2:31][CH:30]([C:28]([O:27][CH3:26])=[O:29])[CH2:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([CH2:4][CH3:5])[c:6](-[c:7]2[cH:8][n:9][c:10](-[c:11]3[cH:12][cH:13][c:14]([O:15][CH:16]([CH3:17])[CH3:18])[c:19]([Cl:20])[cH:21]3)[s:22]2)[cH:23][cH:24][cH:25]1)[N:32]1[CH2:31][CH:30]([C:28]([O:27][CH3:26])=[O:29])[CH2:33]1\", \"reactants\":\"O=[CH:1][c:2]1[c:3]([CH2:4][CH3:5])[c:6](-[c:7]2[cH:8][n:9][c:10](-[c:11]3[cH:12][cH:13][c:14]([O:15][CH:16]([CH3:17])[CH3:18])[c:19]([Cl:20])[cH:21]3)[s:22]2)[cH:23][cH:24][cH:25]1.[CH3:26][O:27][C:28](=[O:29])[CH:30]1[CH2:31][NH:32][CH2:33]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[c:3]([CH2:4][CH3:5])[c:6](-[c:7]2[cH:8][n:9][c:10](-[c:11]3[cH:12][cH:13][c:14]([O:15][CH:16]([CH3:17])[CH3:18])[c:19]([Cl:20])[cH:21]3)[s:22]2)[cH:23][cH:24][cH:25]1' and '[CH3:26][O:27][C:28](=[O:29])[CH:30]1[CH2:31][NH:32][CH2:33]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[c:1]1(-[c:38]2[cH:39][n:40]([C:41]([O:42][C:43]([CH3:44])([CH3:45])[CH3:46])=[O:47])[n:48][cH:49]2)[cH:2][c:3]([C:4]([O:5][CH3:6])=[O:7])[c:8]([NH:9][C:10](=[O:11])[CH2:12][O:13][CH2:14][C:15](=[O:16])[N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[cH:36][cH:37]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:38]2[cH:39][n:40]([C:41]([O:42][C:43]([CH3:44])([CH3:45])[CH3:46])=[O:47])[n:48][cH:49]2)[cH:2][c:3]([C:4]([O:5][CH3:6])=[O:7])[c:8]([NH:9][C:10](=[O:11])[CH2:12][O:13][CH2:14][C:15](=[O:16])[N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[cH:36][cH:37]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:38]2[cH:39][n:40]([C:41]([O:42][C:43]([CH3:44])([CH3:45])[CH3:46])=[O:47])[n:48][cH:49]2)[cH:2][c:3]([C:4]([O:5][CH3:6])=[O:7])[c:8]([NH:9][C:10](=[O:11])[CH2:12][O:13][CH2:14][C:15](=[O:16])[N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[cH:36][cH:37]1\", \"reactants\":\"Br[c:1]1[cH:2][c:3]([C:4]([O:5][CH3:6])=[O:7])[c:8]([NH:9][C:10](=[O:11])[CH2:12][O:13][CH2:14][C:15](=[O:16])[N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[cH:36][cH:37]1.CC1(C)OB([c:38]2[cH:39][n:40]([C:41]([O:42][C:43]([CH3:44])([CH3:45])[CH3:46])=[O:47])[n:48][cH:49]2)OC1(C)C\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][c:3]([C:4]([O:5][CH3:6])=[O:7])[c:8]([NH:9][C:10](=[O:11])[CH2:12][O:13][CH2:14][C:15](=[O:16])[N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[cH:36][cH:37]1' and 'CC1(C)OB([c:38]2[cH:39][n:40]([C:41]([O:42][C:43]([CH3:44])([CH3:45])[CH3:46])=[O:47])[n:48][cH:49]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis process for '[c:1]1([NH:33][c:32]2[cH:31][cH:30][c:29]3[c:28]([c:34]2[O:35][CH3:36])[CH2:27][CH2:26][CH2:25][C:23](=[O:24])[N:22]3[CH2:21][CH3:20])[n:2][cH:3][c:4]([Cl:5])[c:6]([NH:7][c:8]2[cH:9][cH:10][cH:11][cH:12][c:13]2-[n:14]2[cH:15][cH:16][cH:17][n:18]2)[n:19]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:33][c:32]2[cH:31][cH:30][c:29]3[c:28]([c:34]2[O:35][CH3:36])[CH2:27][CH2:26][CH2:25][C:23](=[O:24])[N:22]3[CH2:21][CH3:20])[n:2][cH:3][c:4]([Cl:5])[c:6]([NH:7][c:8]2[cH:9][cH:10][cH:11][cH:12][c:13]2-[n:14]2[cH:15][cH:16][cH:17][n:18]2)[n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:33][c:32]2[cH:31][cH:30][c:29]3[c:28]([c:34]2[O:35][CH3:36])[CH2:27][CH2:26][CH2:25][C:23](=[O:24])[N:22]3[CH2:21][CH3:20])[n:2][cH:3][c:4]([Cl:5])[c:6]([NH:7][c:8]2[cH:9][cH:10][cH:11][cH:12][c:13]2-[n:14]2[cH:15][cH:16][cH:17][n:18]2)[n:19]1\", \"reactants\":\"Cl[c:1]1[n:2][cH:3][c:4]([Cl:5])[c:6]([NH:7][c:8]2[cH:9][cH:10][cH:11][cH:12][c:13]2-[n:14]2[cH:15][cH:16][cH:17][n:18]2)[n:19]1.[CH3:20][CH2:21][N:22]1[C:23](=[O:24])[CH2:25][CH2:26][CH2:27][c:28]2[c:29]1[cH:30][cH:31][c:32]([NH2:33])[c:34]2[O:35][CH3:36]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[n:2][cH:3][c:4]([Cl:5])[c:6]([NH:7][c:8]2[cH:9][cH:10][cH:11][cH:12][c:13]2-[n:14]2[cH:15][cH:16][cH:17][n:18]2)[n:19]1' and '[CH3:20][CH2:21][N:22]1[C:23](=[O:24])[CH2:25][CH2:26][CH2:27][c:28]2[c:29]1[cH:30][cH:31][c:32]([NH2:33])[c:34]2[O:35][CH3:36]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the likely synthesis method for '[C:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]2[c:9]([c:10]1[OH:11])[cH:12][cH:13][cH:14][cH:15]2)(=[O:16])[NH:30][CH2:29][CH2:28][CH2:27][O:26][c:25]1[cH:24][cH:23][c:22]([C:19]([CH2:18][CH3:17])([CH3:20])[CH3:21])[cH:37][c:31]1[C:32]([CH3:33])([CH3:34])[CH2:35][CH3:36]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]2[c:9]([c:10]1[OH:11])[cH:12][cH:13][cH:14][cH:15]2)(=[O:16])[NH:30][CH2:29][CH2:28][CH2:27][O:26][c:25]1[cH:24][cH:23][c:22]([C:19]([CH2:18][CH3:17])([CH3:20])[CH3:21])[cH:37][c:31]1[C:32]([CH3:33])([CH3:34])[CH2:35][CH3:36]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]2[c:9]([c:10]1[OH:11])[cH:12][cH:13][cH:14][cH:15]2)(=[O:16])[NH:30][CH2:29][CH2:28][CH2:27][O:26][c:25]1[cH:24][cH:23][c:22]([C:19]([CH2:18][CH3:17])([CH3:20])[CH3:21])[cH:37][c:31]1[C:32]([CH3:33])([CH3:34])[CH2:35][CH3:36]\", \"reactants\":\"O[C:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]2[c:9]([c:10]1[OH:11])[cH:12][cH:13][cH:14][cH:15]2)=[O:16].[CH3:17][CH2:18][C:19]([CH3:20])([CH3:21])[c:22]1[cH:23][cH:24][c:25]([O:26][CH2:27][CH2:28][CH2:29][NH2:30])[c:31]([C:32]([CH3:33])([CH3:34])[CH2:35][CH3:36])[cH:37]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]2[c:9]([c:10]1[OH:11])[cH:12][cH:13][cH:14][cH:15]2)=[O:16]' and '[CH3:17][CH2:18][C:19]([CH3:20])([CH3:21])[c:22]1[cH:23][cH:24][c:25]([O:26][CH2:27][CH2:28][CH2:29][NH2:30])[c:31]([C:32]([CH3:33])([CH3:34])[CH2:35][CH3:36])[cH:37]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis procedure for '[c:1]1(-[n:20]2[cH:19][c:18]([CH2:17][NH:16][C:15](=[O:14])[c:23]3[cH:24][cH:25][c:26]([Cl:27])[s:28]3)[n:22][cH:21]2)[cH:2][cH:3][c:4](-[n:5]2[c:6](=[O:7])[cH:8][cH:9][cH:10][cH:11]2)[n:12][cH:13]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:20]2[cH:19][c:18]([CH2:17][NH:16][C:15](=[O:14])[c:23]3[cH:24][cH:25][c:26]([Cl:27])[s:28]3)[n:22][cH:21]2)[cH:2][cH:3][c:4](-[n:5]2[c:6](=[O:7])[cH:8][cH:9][cH:10][cH:11]2)[n:12][cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[n:20]2[cH:19][c:18]([CH2:17][NH:16][C:15](=[O:14])[c:23]3[cH:24][cH:25][c:26]([Cl:27])[s:28]3)[n:22][cH:21]2)[cH:2][cH:3][c:4](-[n:5]2[c:6](=[O:7])[cH:8][cH:9][cH:10][cH:11]2)[n:12][cH:13]1\", \"reactants\":\"I[c:1]1[cH:2][cH:3][c:4](-[n:5]2[c:6](=[O:7])[cH:8][cH:9][cH:10][cH:11]2)[n:12][cH:13]1.[O:14]=[C:15]([NH:16][CH2:17][c:18]1[cH:19][nH:20][cH:21][n:22]1)[c:23]1[cH:24][cH:25][c:26]([Cl:27])[s:28]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[c:1]1[cH:2][cH:3][c:4](-[n:5]2[c:6](=[O:7])[cH:8][cH:9][cH:10][cH:11]2)[n:12][cH:13]1' and '[O:14]=[C:15]([NH:16][CH2:17][c:18]1[cH:19][nH:20][cH:21][n:22]1)[c:23]1[cH:24][cH:25][c:26]([Cl:27])[s:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis process for '[C:1]([CH2:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][c:9]([CH2:10][C@@H:11]([C:12]([O:13][C:14]([CH3:15])([CH3:16])[CH3:17])=[O:18])[NH:19][S:20](=[O:21])(=[O:22])[c:23]2[cH:24][cH:25][c:26]([C:27]([F:28])([F:29])[F:30])[cH:31][cH:32]2)[cH:33][cH:34]1)(=[O:35])[NH:36][C:37]1=[N:38][CH2:39][CH2:40][CH2:41][NH:42]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][c:9]([CH2:10][C@@H:11]([C:12]([O:13][C:14]([CH3:15])([CH3:16])[CH3:17])=[O:18])[NH:19][S:20](=[O:21])(=[O:22])[c:23]2[cH:24][cH:25][c:26]([C:27]([F:28])([F:29])[F:30])[cH:31][cH:32]2)[cH:33][cH:34]1)(=[O:35])[NH:36][C:37]1=[N:38][CH2:39][CH2:40][CH2:41][NH:42]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][c:9]([CH2:10][C@@H:11]([C:12]([O:13][C:14]([CH3:15])([CH3:16])[CH3:17])=[O:18])[NH:19][S:20](=[O:21])(=[O:22])[c:23]2[cH:24][cH:25][c:26]([C:27]([F:28])([F:29])[F:30])[cH:31][cH:32]2)[cH:33][cH:34]1)(=[O:35])[NH:36][C:37]1=[N:38][CH2:39][CH2:40][CH2:41][NH:42]1\", \"reactants\":\"O[C:1]([CH2:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][c:9]([CH2:10][C@@H:11]([C:12]([O:13][C:14]([CH3:15])([CH3:16])[CH3:17])=[O:18])[NH:19][S:20](=[O:21])(=[O:22])[c:23]2[cH:24][cH:25][c:26]([C:27]([F:28])([F:29])[F:30])[cH:31][cH:32]2)[cH:33][cH:34]1)=[O:35].[NH2:36][C:37]1=[N:38][CH2:39][CH2:40][CH2:41][NH:42]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH2:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][c:9]([CH2:10][C@@H:11]([C:12]([O:13][C:14]([CH3:15])([CH3:16])[CH3:17])=[O:18])[NH:19][S:20](=[O:21])(=[O:22])[c:23]2[cH:24][cH:25][c:26]([C:27]([F:28])([F:29])[F:30])[cH:31][cH:32]2)[cH:33][cH:34]1)=[O:35]' and '[NH2:36][C:37]1=[N:38][CH2:39][CH2:40][CH2:41][NH:42]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis steps for '[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1.C1COB([c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)OC1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1' and 'C1COB([c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)OC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis procedure for '[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:19]1[CH:15]([c:14]2[cH:13][cH:12][cH:11][cH:21][cH:20]2)[CH2:16][CH2:17][CH2:18]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:19]1[CH:15]([c:14]2[cH:13][cH:12][cH:11][cH:21][cH:20]2)[CH2:16][CH2:17][CH2:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[N:19]1[CH:15]([c:14]2[cH:13][cH:12][cH:11][cH:21][cH:20]2)[CH2:16][CH2:17][CH2:18]1\", \"reactants\":\"Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10].[cH:11]1[cH:12][cH:13][c:14]([CH:15]2[CH2:16][CH2:17][CH2:18][NH:19]2)[cH:20][cH:21]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10]' and '[cH:11]1[cH:12][cH:13][c:14]([CH:15]2[CH2:16][CH2:17][CH2:18][NH:19]2)[cH:20][cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis method of '[CH2:1]([c:2]1[cH:3][cH:4][c:5]2[n:6][o:7][n:8][c:9]2[cH:10]1)[n:33]1[c:12]([CH3:11])[c:13](-[c:14]2[cH:15][cH:16][c:17]3[n:18][cH:19][n:20][n:21]3[cH:22]2)[n:23](-[c:24]2[cH:25][cH:26][cH:27][c:28]([CH3:29])[cH:30]2)[c:31]1=[O:32]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]2[n:6][o:7][n:8][c:9]2[cH:10]1)[n:33]1[c:12]([CH3:11])[c:13](-[c:14]2[cH:15][cH:16][c:17]3[n:18][cH:19][n:20][n:21]3[cH:22]2)[n:23](-[c:24]2[cH:25][cH:26][cH:27][c:28]([CH3:29])[cH:30]2)[c:31]1=[O:32]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]2[n:6][o:7][n:8][c:9]2[cH:10]1)[n:33]1[c:12]([CH3:11])[c:13](-[c:14]2[cH:15][cH:16][c:17]3[n:18][cH:19][n:20][n:21]3[cH:22]2)[n:23](-[c:24]2[cH:25][cH:26][cH:27][c:28]([CH3:29])[cH:30]2)[c:31]1=[O:32]\", \"reactants\":\"Br[CH2:1][c:2]1[cH:3][cH:4][c:5]2[n:6][o:7][n:8][c:9]2[cH:10]1.[CH3:11][c:12]1[c:13](-[c:14]2[cH:15][cH:16][c:17]3[n:18][cH:19][n:20][n:21]3[cH:22]2)[n:23](-[c:24]2[cH:25][cH:26][cH:27][c:28]([CH3:29])[cH:30]2)[c:31](=[O:32])[nH:33]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[cH:3][cH:4][c:5]2[n:6][o:7][n:8][c:9]2[cH:10]1' and '[CH3:11][c:12]1[c:13](-[c:14]2[cH:15][cH:16][c:17]3[n:18][cH:19][n:20][n:21]3[cH:22]2)[n:23](-[c:24]2[cH:25][cH:26][cH:27][c:28]([CH3:29])[cH:30]2)[c:31](=[O:32])[nH:33]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis path for '[c:1]1(-[c:15]2[cH:16][cH:17][cH:18][c:19]([CH:20]=[O:21])[cH:22]2)[c:2]([CH3:3])[cH:4][c:5]([S:6][CH2:7][CH2:8][O:9][CH2:10][CH3:11])[cH:12][c:13]1[CH3:14]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:15]2[cH:16][cH:17][cH:18][c:19]([CH:20]=[O:21])[cH:22]2)[c:2]([CH3:3])[cH:4][c:5]([S:6][CH2:7][CH2:8][O:9][CH2:10][CH3:11])[cH:12][c:13]1[CH3:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:15]2[cH:16][cH:17][cH:18][c:19]([CH:20]=[O:21])[cH:22]2)[c:2]([CH3:3])[cH:4][c:5]([S:6][CH2:7][CH2:8][O:9][CH2:10][CH3:11])[cH:12][c:13]1[CH3:14]\", \"reactants\":\"Br[c:1]1[c:2]([CH3:3])[cH:4][c:5]([S:6][CH2:7][CH2:8][O:9][CH2:10][CH3:11])[cH:12][c:13]1[CH3:14].OB(O)[c:15]1[cH:16][cH:17][cH:18][c:19]([CH:20]=[O:21])[cH:22]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([CH3:3])[cH:4][c:5]([S:6][CH2:7][CH2:8][O:9][CH2:10][CH3:11])[cH:12][c:13]1[CH3:14]' and 'OB(O)[c:15]1[cH:16][cH:17][cH:18][c:19]([CH:20]=[O:21])[cH:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most likely synthesis method for '[C:1](=[O:2])([CH:3]([CH2:4][C:5]1([F:6])[CH2:7][CH2:8][CH2:9][CH2:10]1)[N:11]1[CH2:12][C:13]([O:14][c:15]2[c:16]([F:17])[cH:18][cH:19][cH:20][c:21]2[F:22])=[CH:23][C:24]1=[O:25])[NH:37][c:36]1[cH:35][cH:34][n:33]([CH2:32][C@@H:31]2[CH2:30][O:29][C:27]([CH3:26])([CH3:28])[O:39]2)[n:38]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH:3]([CH2:4][C:5]1([F:6])[CH2:7][CH2:8][CH2:9][CH2:10]1)[N:11]1[CH2:12][C:13]([O:14][c:15]2[c:16]([F:17])[cH:18][cH:19][cH:20][c:21]2[F:22])=[CH:23][C:24]1=[O:25])[NH:37][c:36]1[cH:35][cH:34][n:33]([CH2:32][C@@H:31]2[CH2:30][O:29][C:27]([CH3:26])([CH3:28])[O:39]2)[n:38]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH:3]([CH2:4][C:5]1([F:6])[CH2:7][CH2:8][CH2:9][CH2:10]1)[N:11]1[CH2:12][C:13]([O:14][c:15]2[c:16]([F:17])[cH:18][cH:19][cH:20][c:21]2[F:22])=[CH:23][C:24]1=[O:25])[NH:37][c:36]1[cH:35][cH:34][n:33]([CH2:32][C@@H:31]2[CH2:30][O:29][C:27]([CH3:26])([CH3:28])[O:39]2)[n:38]1\", \"reactants\":\"O[C:1](=[O:2])[CH:3]([CH2:4][C:5]1([F:6])[CH2:7][CH2:8][CH2:9][CH2:10]1)[N:11]1[CH2:12][C:13]([O:14][c:15]2[c:16]([F:17])[cH:18][cH:19][cH:20][c:21]2[F:22])=[CH:23][C:24]1=[O:25].[CH3:26][C:27]1([CH3:28])[O:29][CH2:30][C@@H:31]([CH2:32][n:33]2[cH:34][cH:35][c:36]([NH2:37])[n:38]2)[O:39]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[CH:3]([CH2:4][C:5]1([F:6])[CH2:7][CH2:8][CH2:9][CH2:10]1)[N:11]1[CH2:12][C:13]([O:14][c:15]2[c:16]([F:17])[cH:18][cH:19][cH:20][c:21]2[F:22])=[CH:23][C:24]1=[O:25]' and '[CH3:26][C:27]1([CH3:28])[O:29][CH2:30][C@@H:31]([CH2:32][n:33]2[cH:34][cH:35][c:36]([NH2:37])[n:38]2)[O:39]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the likely synthesis route for '[C@@H:1]([C@H:2]1[CH2:3][O:4][CH2:5][C:6](=[O:7])[NH:8]1)([c:9]1[cH:10][cH:11][cH:12][cH:13][cH:14]1)[O:17][C:16](=[O:15])[c:18]1[cH:19][cH:20][c:21]([N+:22](=[O:23])[O-:24])[cH:25][cH:26]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C@@H:1]([C@H:2]1[CH2:3][O:4][CH2:5][C:6](=[O:7])[NH:8]1)([c:9]1[cH:10][cH:11][cH:12][cH:13][cH:14]1)[O:17][C:16](=[O:15])[c:18]1[cH:19][cH:20][c:21]([N+:22](=[O:23])[O-:24])[cH:25][cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C@@H:1]([C@H:2]1[CH2:3][O:4][CH2:5][C:6](=[O:7])[NH:8]1)([c:9]1[cH:10][cH:11][cH:12][cH:13][cH:14]1)[O:17][C:16](=[O:15])[c:18]1[cH:19][cH:20][c:21]([N+:22](=[O:23])[O-:24])[cH:25][cH:26]1\", \"reactants\":\"O[C@@H:1]([C@H:2]1[CH2:3][O:4][CH2:5][C:6](=[O:7])[NH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][cH:14]1.[O:15]=[C:16]([OH:17])[c:18]1[cH:19][cH:20][c:21]([N+:22](=[O:23])[O-:24])[cH:25][cH:26]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C@@H:1]([C@H:2]1[CH2:3][O:4][CH2:5][C:6](=[O:7])[NH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][cH:14]1' and '[O:15]=[C:16]([OH:17])[c:18]1[cH:19][cH:20][c:21]([N+:22](=[O:23])[O-:24])[cH:25][cH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the possible synthesis steps for '[CH2:1]([CH3:2])[O:29][c:28]1[c:26]([F:27])[cH:25][c:24]([CH:23]2[N:9]([CH2:8][C:6]([O:5][CH2:4][CH3:3])=[O:7])[CH2:10][CH2:11][C:12]3([CH:13]=[CH:14][c:15]4[cH:16][cH:17][cH:18][cH:19][c:20]4[O:21]3)[CH2:22]2)[cH:32][c:30]1[F:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH3:2])[O:29][c:28]1[c:26]([F:27])[cH:25][c:24]([CH:23]2[N:9]([CH2:8][C:6]([O:5][CH2:4][CH3:3])=[O:7])[CH2:10][CH2:11][C:12]3([CH:13]=[CH:14][c:15]4[cH:16][cH:17][cH:18][cH:19][c:20]4[O:21]3)[CH2:22]2)[cH:32][c:30]1[F:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH3:2])[O:29][c:28]1[c:26]([F:27])[cH:25][c:24]([CH:23]2[N:9]([CH2:8][C:6]([O:5][CH2:4][CH3:3])=[O:7])[CH2:10][CH2:11][C:12]3([CH:13]=[CH:14][c:15]4[cH:16][cH:17][cH:18][cH:19][c:20]4[O:21]3)[CH2:22]2)[cH:32][c:30]1[F:31]\", \"reactants\":\"I[CH2:1][CH3:2].[CH3:3][CH2:4][O:5][C:6](=[O:7])[CH2:8][N:9]1[CH2:10][CH2:11][C:12]2([CH:13]=[CH:14][c:15]3[cH:16][cH:17][cH:18][cH:19][c:20]3[O:21]2)[CH2:22][CH:23]1[c:24]1[cH:25][c:26]([F:27])[c:28]([OH:29])[c:30]([F:31])[cH:32]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[CH2:1][CH3:2]' and '[CH3:3][CH2:4][O:5][C:6](=[O:7])[CH2:8][N:9]1[CH2:10][CH2:11][C:12]2([CH:13]=[CH:14][c:15]3[cH:16][cH:17][cH:18][cH:19][c:20]3[O:21]2)[CH2:22][CH:23]1[c:24]1[cH:25][c:26]([F:27])[c:28]([OH:29])[c:30]([F:31])[cH:32]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path of '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([Cl:6])[cH:7][c:8]1[Cl:9])[NH:10][c:11]1[cH:12][cH:13][c:14]([S:15](=[O:16])(=[O:17])[OH:18])[cH:19][cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([Cl:6])[cH:7][c:8]1[Cl:9])[NH:10][c:11]1[cH:12][cH:13][c:14]([S:15](=[O:16])(=[O:17])[OH:18])[cH:19][cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([Cl:6])[cH:7][c:8]1[Cl:9])[NH:10][c:11]1[cH:12][cH:13][c:14]([S:15](=[O:16])(=[O:17])[OH:18])[cH:19][cH:20]1\", \"reactants\":\"Cl[CH2:1][c:2]1[cH:3][cH:4][c:5]([Cl:6])[cH:7][c:8]1[Cl:9].[NH2:10][c:11]1[cH:12][cH:13][c:14]([S:15](=[O:16])(=[O:17])[OH:18])[cH:19][cH:20]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][c:2]1[cH:3][cH:4][c:5]([Cl:6])[cH:7][c:8]1[Cl:9]' and '[NH2:10][c:11]1[cH:12][cH:13][c:14]([S:15](=[O:16])(=[O:17])[OH:18])[cH:19][cH:20]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis pathway for '[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\", \"reactants\":\"O=C(O[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6])C(F)(F)F.[NH2:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=C(O[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6])C(F)(F)F' and '[NH2:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis route for '[CH:1]([c:2]1[c:3]([CH3:4])[c:5]([CH2:6][CH2:7][C:8](=[O:9])[OH:10])[cH:11][nH:12]1)=[C:21]1[c:19]2[c:18]([cH:17][cH:16][c:15]([CH2:14][CH3:13])[cH:20]2)[NH:24][C:22]1=[O:23]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([c:2]1[c:3]([CH3:4])[c:5]([CH2:6][CH2:7][C:8](=[O:9])[OH:10])[cH:11][nH:12]1)=[C:21]1[c:19]2[c:18]([cH:17][cH:16][c:15]([CH2:14][CH3:13])[cH:20]2)[NH:24][C:22]1=[O:23]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([c:2]1[c:3]([CH3:4])[c:5]([CH2:6][CH2:7][C:8](=[O:9])[OH:10])[cH:11][nH:12]1)=[C:21]1[c:19]2[c:18]([cH:17][cH:16][c:15]([CH2:14][CH3:13])[cH:20]2)[NH:24][C:22]1=[O:23]\", \"reactants\":\"O=[CH:1][c:2]1[c:3]([CH3:4])[c:5]([CH2:6][CH2:7][C:8](=[O:9])[OH:10])[cH:11][nH:12]1.[CH3:13][CH2:14][c:15]1[cH:16][cH:17][c:18]2[c:19]([cH:20]1)[CH2:21][C:22](=[O:23])[NH:24]2\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[c:3]([CH3:4])[c:5]([CH2:6][CH2:7][C:8](=[O:9])[OH:10])[cH:11][nH:12]1' and '[CH3:13][CH2:14][c:15]1[cH:16][cH:17][c:18]2[c:19]([cH:20]1)[CH2:21][C:22](=[O:23])[NH:24]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for '[C:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[cH:9]1)(=[O:10])[N:27]([c:26]1[cH:25][cH:24][c:23]([O:22][CH:21]2[CH2:20][CH2:19][N:18]([C:16]([O:15][C:12]([CH3:11])([CH3:13])[CH3:14])=[O:17])[CH2:44][CH2:43]2)[cH:42][cH:41]1)[CH2:28][c:29]1[cH:30][cH:31][c:32]2[cH:33][cH:34][c:35]([C:36]#[N:37])[cH:38][c:39]2[cH:40]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[cH:9]1)(=[O:10])[N:27]([c:26]1[cH:25][cH:24][c:23]([O:22][CH:21]2[CH2:20][CH2:19][N:18]([C:16]([O:15][C:12]([CH3:11])([CH3:13])[CH3:14])=[O:17])[CH2:44][CH2:43]2)[cH:42][cH:41]1)[CH2:28][c:29]1[cH:30][cH:31][c:32]2[cH:33][cH:34][c:35]([C:36]#[N:37])[cH:38][c:39]2[cH:40]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[cH:9]1)(=[O:10])[N:27]([c:26]1[cH:25][cH:24][c:23]([O:22][CH:21]2[CH2:20][CH2:19][N:18]([C:16]([O:15][C:12]([CH3:11])([CH3:13])[CH3:14])=[O:17])[CH2:44][CH2:43]2)[cH:42][cH:41]1)[CH2:28][c:29]1[cH:30][cH:31][c:32]2[cH:33][cH:34][c:35]([C:36]#[N:37])[cH:38][c:39]2[cH:40]1\", \"reactants\":\"Cl[C:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[cH:9]1)=[O:10].[CH3:11][C:12]([CH3:13])([CH3:14])[O:15][C:16](=[O:17])[N:18]1[CH2:19][CH2:20][CH:21]([O:22][c:23]2[cH:24][cH:25][c:26]([NH:27][CH2:28][c:29]3[cH:30][cH:31][c:32]4[cH:33][cH:34][c:35]([C:36]#[N:37])[cH:38][c:39]4[cH:40]3)[cH:41][cH:42]2)[CH2:43][CH2:44]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([O:7][CH3:8])[cH:9]1)=[O:10]' and '[CH3:11][C:12]([CH3:13])([CH3:14])[O:15][C:16](=[O:17])[N:18]1[CH2:19][CH2:20][CH:21]([O:22][c:23]2[cH:24][cH:25][c:26]([NH:27][CH2:28][c:29]3[cH:30][cH:31][c:32]4[cH:33][cH:34][c:35]([C:36]#[N:37])[cH:38][c:39]4[cH:40]3)[cH:41][cH:42]2)[CH2:43][CH2:44]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the likely synthesis procedure for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][n:6][cH:7][cH:8]1)[NH:27][c:26]1[c:22](-[c:21]2[nH:20][c:19]3[cH:18][c:17]4[c:31]([cH:30][c:29]3[n:28]2)[N:11]([CH2:10][CH3:9])[C:12](=[O:13])[C:14]4([CH3:15])[CH3:16])[n:23][nH:24][cH:25]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][n:6][cH:7][cH:8]1)[NH:27][c:26]1[c:22](-[c:21]2[nH:20][c:19]3[cH:18][c:17]4[c:31]([cH:30][c:29]3[n:28]2)[N:11]([CH2:10][CH3:9])[C:12](=[O:13])[C:14]4([CH3:15])[CH3:16])[n:23][nH:24][cH:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][n:6][cH:7][cH:8]1)[NH:27][c:26]1[c:22](-[c:21]2[nH:20][c:19]3[cH:18][c:17]4[c:31]([cH:30][c:29]3[n:28]2)[N:11]([CH2:10][CH3:9])[C:12](=[O:13])[C:14]4([CH3:15])[CH3:16])[n:23][nH:24][cH:25]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][n:6][cH:7][cH:8]1.[CH3:9][CH2:10][N:11]1[C:12](=[O:13])[C:14]([CH3:15])([CH3:16])[c:17]2[cH:18][c:19]3[nH:20][c:21](-[c:22]4[n:23][nH:24][cH:25][c:26]4[NH2:27])[n:28][c:29]3[cH:30][c:31]21\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][n:6][cH:7][cH:8]1' and '[CH3:9][CH2:10][N:11]1[C:12](=[O:13])[C:14]([CH3:15])([CH3:16])[c:17]2[cH:18][c:19]3[nH:20][c:21](-[c:22]4[n:23][nH:24][cH:25][c:26]4[NH2:27])[n:28][c:29]3[cH:30][c:31]21'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis route of '[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:27][CH2:26]2)[cH:2][cH:3][c:4]2[nH:5][cH:6][c:7]3[c:8](=[O:9])[n:10](-[c:11]4[cH:12][cH:13][cH:14][cH:15][cH:16]4)[n:17][c:18]-3[c:19]2[n:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:27][CH2:26]2)[cH:2][cH:3][c:4]2[nH:5][cH:6][c:7]3[c:8](=[O:9])[n:10](-[c:11]4[cH:12][cH:13][cH:14][cH:15][cH:16]4)[n:17][c:18]-3[c:19]2[n:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:25]2[CH2:24][CH2:23][CH:22]([OH:21])[CH2:27][CH2:26]2)[cH:2][cH:3][c:4]2[nH:5][cH:6][c:7]3[c:8](=[O:9])[n:10](-[c:11]4[cH:12][cH:13][cH:14][cH:15][cH:16]4)[n:17][c:18]-3[c:19]2[n:20]1\", \"reactants\":\"F[c:1]1[cH:2][cH:3][c:4]2[nH:5][cH:6][c:7]3[c:8](=[O:9])[n:10](-[c:11]4[cH:12][cH:13][cH:14][cH:15][cH:16]4)[n:17][c:18]-3[c:19]2[n:20]1.[OH:21][CH:22]1[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]2[nH:5][cH:6][c:7]3[c:8](=[O:9])[n:10](-[c:11]4[cH:12][cH:13][cH:14][cH:15][cH:16]4)[n:17][c:18]-3[c:19]2[n:20]1' and '[OH:21][CH:22]1[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis path of '[c:1]1([C:7]#[C:8][CH2:9][O:10][CH2:11][CH2:12][n:13]2[c:14]([CH2:15][CH2:16][CH2:17][CH3:18])[n:19][c:20]3[cH:21][n:22][c:23]4[cH:24][cH:25][cH:26][cH:27][c:28]4[c:29]23)[cH:2][cH:3][cH:4][cH:5][cH:6]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:7]#[C:8][CH2:9][O:10][CH2:11][CH2:12][n:13]2[c:14]([CH2:15][CH2:16][CH2:17][CH3:18])[n:19][c:20]3[cH:21][n:22][c:23]4[cH:24][cH:25][cH:26][cH:27][c:28]4[c:29]23)[cH:2][cH:3][cH:4][cH:5][cH:6]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:7]#[C:8][CH2:9][O:10][CH2:11][CH2:12][n:13]2[c:14]([CH2:15][CH2:16][CH2:17][CH3:18])[n:19][c:20]3[cH:21][n:22][c:23]4[cH:24][cH:25][cH:26][cH:27][c:28]4[c:29]23)[cH:2][cH:3][cH:4][cH:5][cH:6]1\", \"reactants\":\"I[c:1]1[cH:2][cH:3][cH:4][cH:5][cH:6]1.[CH:7]#[C:8][CH2:9][O:10][CH2:11][CH2:12][n:13]1[c:14]([CH2:15][CH2:16][CH2:17][CH3:18])[n:19][c:20]2[cH:21][n:22][c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[c:29]12\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[c:1]1[cH:2][cH:3][cH:4][cH:5][cH:6]1' and '[CH:7]#[C:8][CH2:9][O:10][CH2:11][CH2:12][n:13]1[c:14]([CH2:15][CH2:16][CH2:17][CH3:18])[n:19][c:20]2[cH:21][n:22][c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[c:29]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis route of '[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1\", \"reactants\":\"CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1.[CH3:8][c:9]1[c:10]([N:11]([CH2:12][c:13]2[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]2)[CH2:21][c:22]2[cH:23][cH:24][c:25]([O:26][c:27]3[cH:28][cH:29][cH:30][c:31]([OH:32])[cH:33]3)[cH:34][cH:35]2)[cH:36][cH:37][cH:38][c:39]1[N+:40](=[O:41])[O-:42]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1' and '[CH3:8][c:9]1[c:10]([N:11]([CH2:12][c:13]2[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]2)[CH2:21][c:22]2[cH:23][cH:24][c:25]([O:26][c:27]3[cH:28][cH:29][cH:30][c:31]([OH:32])[cH:33]3)[cH:34][cH:35]2)[cH:36][cH:37][cH:38][c:39]1[N+:40](=[O:41])[O-:42]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis method for '[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]\", \"reactants\":\"Cl[C:1]([CH3:2])=[O:3].[CH3:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][n:16]1[n:17][n:18][c:19]([CH:20]([NH2:21])[C:22](=[O:23])[NH:24][c:25]2[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]2[CH:34]([CH3:35])[CH3:36])[n:37]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([CH3:2])=[O:3]' and '[CH3:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][n:16]1[n:17][n:18][c:19]([CH:20]([NH2:21])[C:22](=[O:23])[NH:24][c:25]2[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]2[CH:34]([CH3:35])[CH3:36])[n:37]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis process for '[CH3:1][C:2](=[O:3])[n:17]1[c:5]([CH3:4])[c:6]([CH2:7][c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[c:14](=[O:15])[nH:16]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2](=[O:3])[n:17]1[c:5]([CH3:4])[c:6]([CH2:7][c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[c:14](=[O:15])[nH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2](=[O:3])[n:17]1[c:5]([CH3:4])[c:6]([CH2:7][c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[c:14](=[O:15])[nH:16]1\", \"reactants\":\"CC(=O)O[C:2]([CH3:1])=[O:3].[CH3:4][c:5]1[c:6]([CH2:7][c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[c:14](=[O:15])[nH:16][nH:17]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(=O)O[C:2]([CH3:1])=[O:3]' and '[CH3:4][c:5]1[c:6]([CH2:7][c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[c:14](=[O:15])[nH:16][nH:17]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[C:1](=[O:2])([CH2:3][N:4]1[C:5](=[O:6])[C@@H:7]([NH:8][C:9](=[O:10])[c:11]2[cH:12][c:13]3[cH:14][cH:15][cH:16][cH:17][c:18]3[nH:19]2)[C@H:20]([c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[S:27][c:28]2[cH:29][cH:30][cH:31][cH:32][c:33]21)[NH:34][CH2:35][CH2:36][c:37]1[cH:38][cH:39][cH:40][cH:41][cH:42]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH2:3][N:4]1[C:5](=[O:6])[C@@H:7]([NH:8][C:9](=[O:10])[c:11]2[cH:12][c:13]3[cH:14][cH:15][cH:16][cH:17][c:18]3[nH:19]2)[C@H:20]([c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[S:27][c:28]2[cH:29][cH:30][cH:31][cH:32][c:33]21)[NH:34][CH2:35][CH2:36][c:37]1[cH:38][cH:39][cH:40][cH:41][cH:42]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH2:3][N:4]1[C:5](=[O:6])[C@@H:7]([NH:8][C:9](=[O:10])[c:11]2[cH:12][c:13]3[cH:14][cH:15][cH:16][cH:17][c:18]3[nH:19]2)[C@H:20]([c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[S:27][c:28]2[cH:29][cH:30][cH:31][cH:32][c:33]21)[NH:34][CH2:35][CH2:36][c:37]1[cH:38][cH:39][cH:40][cH:41][cH:42]1\", \"reactants\":\"O[C:1](=[O:2])[CH2:3][N:4]1[C:5](=[O:6])[C@@H:7]([NH:8][C:9](=[O:10])[c:11]2[cH:12][c:13]3[cH:14][cH:15][cH:16][cH:17][c:18]3[nH:19]2)[C@H:20]([c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[S:27][c:28]2[cH:29][cH:30][cH:31][cH:32][c:33]21.[NH2:34][CH2:35][CH2:36][c:37]1[cH:38][cH:39][cH:40][cH:41][cH:42]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[CH2:3][N:4]1[C:5](=[O:6])[C@@H:7]([NH:8][C:9](=[O:10])[c:11]2[cH:12][c:13]3[cH:14][cH:15][cH:16][cH:17][c:18]3[nH:19]2)[C@H:20]([c:21]2[cH:22][cH:23][cH:24][cH:25][cH:26]2)[S:27][c:28]2[cH:29][cH:30][cH:31][cH:32][c:33]21' and '[NH2:34][CH2:35][CH2:36][c:37]1[cH:38][cH:39][cH:40][cH:41][cH:42]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[NH:22][c:21]1[s:20][cH:19][c:18](-[c:17]2[cH:16][cH:15][cH:14][c:13]([C:12]#[N:11])[cH:24]2)[n:23]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[NH:22][c:21]1[s:20][cH:19][c:18](-[c:17]2[cH:16][cH:15][cH:14][c:13]([C:12]#[N:11])[cH:24]2)[n:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])(=[O:10])[NH:22][c:21]1[s:20][cH:19][c:18](-[c:17]2[cH:16][cH:15][cH:14][c:13]([C:12]#[N:11])[cH:24]2)[n:23]1\", \"reactants\":\"Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10].[N:11]#[C:12][c:13]1[cH:14][cH:15][cH:16][c:17](-[c:18]2[cH:19][s:20][c:21]([NH2:22])[n:23]2)[cH:24]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[cH:7][cH:8]1)(=[O:9])=[O:10]' and '[N:11]#[C:12][c:13]1[cH:14][cH:15][cH:16][c:17](-[c:18]2[cH:19][s:20][c:21]([NH2:22])[n:23]2)[cH:24]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most probable synthesis process for '[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1\", \"reactants\":\"Cl[c:1]1[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1.[CH3:10][N:11]1[CH2:12][CH2:13][CH:14]([SH:15])[CH2:16]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1' and '[CH3:10][N:11]1[CH2:12][CH2:13][CH:14]([SH:15])[CH2:16]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis method for '[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[N:21]1[CH2:20][CH2:19][N:18]([c:17]2[cH:16][cH:15][c:14]([O:13][CH2:12][O:11][CH3:10])[c:25]([Cl:26])[cH:24]2)[CH2:23][CH2:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[N:21]1[CH2:20][CH2:19][N:18]([c:17]2[cH:16][cH:15][c:14]([O:13][CH2:12][O:11][CH3:10])[c:25]([Cl:26])[cH:24]2)[CH2:23][CH2:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[N:21]1[CH2:20][CH2:19][N:18]([c:17]2[cH:16][cH:15][c:14]([O:13][CH2:12][O:11][CH3:10])[c:25]([Cl:26])[cH:24]2)[CH2:23][CH2:22]1\", \"reactants\":\"Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1.[CH3:10][O:11][CH2:12][O:13][c:14]1[cH:15][cH:16][c:17]([N:18]2[CH2:19][CH2:20][NH:21][CH2:22][CH2:23]2)[cH:24][c:25]1[Cl:26]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[CH3:10][O:11][CH2:12][O:13][c:14]1[cH:15][cH:16][c:17]([N:18]2[CH2:19][CH2:20][NH:21][CH2:22][CH2:23]2)[cH:24][c:25]1[Cl:26]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis steps for '[c:1]1(-[c:32]2[cH:33][cH:34][cH:35][c:36]([S:37]([CH3:38])(=[O:39])=[O:40])[cH:41]2)[c:2]([O:3][c:4]2[cH:5][cH:6][c:7]([C:8](=[O:9])[NH:10][CH2:11][CH2:12][c:13]3[cH:14][cH:15][c:16]([Cl:17])[cH:18][cH:19]3)[cH:20][cH:21]2)[cH:22][cH:23][c:24]([CH2:25][C:26]([O:27][CH2:28][CH3:29])=[O:30])[cH:31]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:32]2[cH:33][cH:34][cH:35][c:36]([S:37]([CH3:38])(=[O:39])=[O:40])[cH:41]2)[c:2]([O:3][c:4]2[cH:5][cH:6][c:7]([C:8](=[O:9])[NH:10][CH2:11][CH2:12][c:13]3[cH:14][cH:15][c:16]([Cl:17])[cH:18][cH:19]3)[cH:20][cH:21]2)[cH:22][cH:23][c:24]([CH2:25][C:26]([O:27][CH2:28][CH3:29])=[O:30])[cH:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:32]2[cH:33][cH:34][cH:35][c:36]([S:37]([CH3:38])(=[O:39])=[O:40])[cH:41]2)[c:2]([O:3][c:4]2[cH:5][cH:6][c:7]([C:8](=[O:9])[NH:10][CH2:11][CH2:12][c:13]3[cH:14][cH:15][c:16]([Cl:17])[cH:18][cH:19]3)[cH:20][cH:21]2)[cH:22][cH:23][c:24]([CH2:25][C:26]([O:27][CH2:28][CH3:29])=[O:30])[cH:31]1\", \"reactants\":\"Br[c:1]1[c:2]([O:3][c:4]2[cH:5][cH:6][c:7]([C:8](=[O:9])[NH:10][CH2:11][CH2:12][c:13]3[cH:14][cH:15][c:16]([Cl:17])[cH:18][cH:19]3)[cH:20][cH:21]2)[cH:22][cH:23][c:24]([CH2:25][C:26]([O:27][CH2:28][CH3:29])=[O:30])[cH:31]1.OB(O)[c:32]1[cH:33][cH:34][cH:35][c:36]([S:37]([CH3:38])(=[O:39])=[O:40])[cH:41]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([O:3][c:4]2[cH:5][cH:6][c:7]([C:8](=[O:9])[NH:10][CH2:11][CH2:12][c:13]3[cH:14][cH:15][c:16]([Cl:17])[cH:18][cH:19]3)[cH:20][cH:21]2)[cH:22][cH:23][c:24]([CH2:25][C:26]([O:27][CH2:28][CH3:29])=[O:30])[cH:31]1' and 'OB(O)[c:32]1[cH:33][cH:34][cH:35][c:36]([S:37]([CH3:38])(=[O:39])=[O:40])[cH:41]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method of '[CH2:1]([CH2:2][OH:3])[N:9]([CH2:8][c:7]1[cH:6][cH:5][cH:4][cH:17][cH:16]1)[CH2:10][c:11]1[cH:12][cH:13][o:14][cH:15]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][OH:3])[N:9]([CH2:8][c:7]1[cH:6][cH:5][cH:4][cH:17][cH:16]1)[CH2:10][c:11]1[cH:12][cH:13][o:14][cH:15]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][OH:3])[N:9]([CH2:8][c:7]1[cH:6][cH:5][cH:4][cH:17][cH:16]1)[CH2:10][c:11]1[cH:12][cH:13][o:14][cH:15]1\", \"reactants\":\"Br[CH2:1][CH2:2][OH:3].[cH:4]1[cH:5][cH:6][c:7]([CH2:8][NH:9][CH2:10][c:11]2[cH:12][cH:13][o:14][cH:15]2)[cH:16][cH:17]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][CH2:2][OH:3]' and '[cH:4]1[cH:5][cH:6][c:7]([CH2:8][NH:9][CH2:10][c:11]2[cH:12][cH:13][o:14][cH:15]2)[cH:16][cH:17]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest a synthesis path for '[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\", \"reactants\":\"O[CH3:1].[O:2]=[C:3]([OH:4])[CH:5]=[CH:6][c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[CH3:1]' and '[O:2]=[C:3]([OH:4])[CH:5]=[CH:6][c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '[CH:1]([CH3:2])([c:3]1[cH:4][c:5]([N+:6](=[O:7])[O-:8])[c:9]([S:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][c:17]2[Cl:18])[s:19]1)[NH:27][CH2:26][c:25]1[cH:24][cH:23][c:22]([O:21][CH3:20])[cH:29][cH:28]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([CH3:2])([c:3]1[cH:4][c:5]([N+:6](=[O:7])[O-:8])[c:9]([S:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][c:17]2[Cl:18])[s:19]1)[NH:27][CH2:26][c:25]1[cH:24][cH:23][c:22]([O:21][CH3:20])[cH:29][cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([CH3:2])([c:3]1[cH:4][c:5]([N+:6](=[O:7])[O-:8])[c:9]([S:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][c:17]2[Cl:18])[s:19]1)[NH:27][CH2:26][c:25]1[cH:24][cH:23][c:22]([O:21][CH3:20])[cH:29][cH:28]1\", \"reactants\":\"Br[CH:1]([CH3:2])[c:3]1[cH:4][c:5]([N+:6](=[O:7])[O-:8])[c:9]([S:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][c:17]2[Cl:18])[s:19]1.[CH3:20][O:21][c:22]1[cH:23][cH:24][c:25]([CH2:26][NH2:27])[cH:28][cH:29]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH:1]([CH3:2])[c:3]1[cH:4][c:5]([N+:6](=[O:7])[O-:8])[c:9]([S:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][c:17]2[Cl:18])[s:19]1' and '[CH3:20][O:21][c:22]1[cH:23][cH:24][c:25]([CH2:26][NH2:27])[cH:28][cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the best synthesis route for '[CH3:1][O:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[n:15][c:16](-[c:17]4[cH:18][cH:19][cH:20][n:21][cH:22]4)[n:23][c:24]([NH:25][CH3:26])[c:27]3[cH:28]2)[cH:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[n:15][c:16](-[c:17]4[cH:18][cH:19][cH:20][n:21][cH:22]4)[n:23][c:24]([NH:25][CH3:26])[c:27]3[cH:28]2)[cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:2][CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[n:15][c:16](-[c:17]4[cH:18][cH:19][cH:20][n:21][cH:22]4)[n:23][c:24]([NH:25][CH3:26])[c:27]3[cH:28]2)[cH:29]1\", \"reactants\":\"Cl[CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[n:15][c:16](-[c:17]4[cH:18][cH:19][cH:20][n:21][cH:22]4)[n:23][c:24]([NH:25][CH3:26])[c:27]3[cH:28]2)[cH:29]1.[CH3:1][O-:2]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:3][CH2:4][O:5][c:6]1[cH:7][cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[n:15][c:16](-[c:17]4[cH:18][cH:19][cH:20][n:21][cH:22]4)[n:23][c:24]([NH:25][CH3:26])[c:27]3[cH:28]2)[cH:29]1' and '[CH3:1][O-:2]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how to synthesize '[CH2:1]([c:2]1[s:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[cH:23]1)[N:25]([CH3:24])[CH2:26][C@@H:27]([OH:28])[c:29]1[cH:30][cH:31][cH:32][n:33][cH:34]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[s:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[cH:23]1)[N:25]([CH3:24])[CH2:26][C@@H:27]([OH:28])[c:29]1[cH:30][cH:31][cH:32][n:33][cH:34]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[s:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[cH:23]1)[N:25]([CH3:24])[CH2:26][C@@H:27]([OH:28])[c:29]1[cH:30][cH:31][cH:32][n:33][cH:34]1\", \"reactants\":\"Cl[CH2:1][c:2]1[s:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[cH:23]1.[CH3:24][NH:25][CH2:26][C@@H:27]([OH:28])[c:29]1[cH:30][cH:31][cH:32][n:33][cH:34]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][c:2]1[s:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[cH:23]1' and '[CH3:24][NH:25][CH2:26][C@@H:27]([OH:28])[c:29]1[cH:30][cH:31][cH:32][n:33][cH:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis process for '[C:1](=[O:2])([c:3]1[cH:4][n:5][cH:6][cH:7][n:8]1)[NH:19][c:18]1[cH:17][cH:16][c:15]([C:13]([N:11]([O:10][CH3:9])[CH3:12])=[O:14])[c:21]([F:22])[cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][n:5][cH:6][cH:7][n:8]1)[NH:19][c:18]1[cH:17][cH:16][c:15]([C:13]([N:11]([O:10][CH3:9])[CH3:12])=[O:14])[c:21]([F:22])[cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][n:5][cH:6][cH:7][n:8]1)[NH:19][c:18]1[cH:17][cH:16][c:15]([C:13]([N:11]([O:10][CH3:9])[CH3:12])=[O:14])[c:21]([F:22])[cH:20]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][n:5][cH:6][cH:7][n:8]1.[CH3:9][O:10][N:11]([CH3:12])[C:13](=[O:14])[c:15]1[cH:16][cH:17][c:18]([NH2:19])[cH:20][c:21]1[F:22]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][n:5][cH:6][cH:7][n:8]1' and '[CH3:9][O:10][N:11]([CH3:12])[C:13](=[O:14])[c:15]1[cH:16][cH:17][c:18]([NH2:19])[cH:20][c:21]1[F:22]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis path for '[c:1]1(-[c:13]2[cH:14][cH:15][c:16]([C@H:17]([NH:18][C@@H:19]([CH2:20][C:21]([CH3:22])([CH3:23])[F:24])[C:25](=[O:26])[NH:27][C:28]3([C:29]#[N:30])[CH2:31][CH2:32]3)[C:33]([F:34])([F:35])[F:36])[cH:37][cH:38]2)[cH:2][cH:3][c:4]([C:5]2([C:6]([NH2:7])=[O:8])[CH2:9][CH2:10]2)[cH:11][cH:12]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:13]2[cH:14][cH:15][c:16]([C@H:17]([NH:18][C@@H:19]([CH2:20][C:21]([CH3:22])([CH3:23])[F:24])[C:25](=[O:26])[NH:27][C:28]3([C:29]#[N:30])[CH2:31][CH2:32]3)[C:33]([F:34])([F:35])[F:36])[cH:37][cH:38]2)[cH:2][cH:3][c:4]([C:5]2([C:6]([NH2:7])=[O:8])[CH2:9][CH2:10]2)[cH:11][cH:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:13]2[cH:14][cH:15][c:16]([C@H:17]([NH:18][C@@H:19]([CH2:20][C:21]([CH3:22])([CH3:23])[F:24])[C:25](=[O:26])[NH:27][C:28]3([C:29]#[N:30])[CH2:31][CH2:32]3)[C:33]([F:34])([F:35])[F:36])[cH:37][cH:38]2)[cH:2][cH:3][c:4]([C:5]2([C:6]([NH2:7])=[O:8])[CH2:9][CH2:10]2)[cH:11][cH:12]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]2([C:6]([NH2:7])=[O:8])[CH2:9][CH2:10]2)[cH:11][cH:12]1.CC1(C)OB([c:13]2[cH:14][cH:15][c:16]([C@H:17]([NH:18][C@@H:19]([CH2:20][C:21]([CH3:22])([CH3:23])[F:24])[C:25](=[O:26])[NH:27][C:28]3([C:29]#[N:30])[CH2:31][CH2:32]3)[C:33]([F:34])([F:35])[F:36])[cH:37][cH:38]2)OC1(C)C\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]2([C:6]([NH2:7])=[O:8])[CH2:9][CH2:10]2)[cH:11][cH:12]1' and 'CC1(C)OB([c:13]2[cH:14][cH:15][c:16]([C@H:17]([NH:18][C@@H:19]([CH2:20][C:21]([CH3:22])([CH3:23])[F:24])[C:25](=[O:26])[NH:27][C:28]3([C:29]#[N:30])[CH2:31][CH2:32]3)[C:33]([F:34])([F:35])[F:36])[cH:37][cH:38]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how to synthesize '[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:17][c:16]1[n:15][cH:14][cH:13][c:12]([C:11]#[N:10])[cH:18]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:17][c:16]1[n:15][cH:14][cH:13][c:12]([C:11]#[N:10])[cH:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:17][c:16]1[n:15][cH:14][cH:13][c:12]([C:11]#[N:10])[cH:18]1\", \"reactants\":\"Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1.[N:10]#[C:11][c:12]1[cH:13][cH:14][n:15][c:16]([NH2:17])[cH:18]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[N:10]#[C:11][c:12]1[cH:13][cH:14][n:15][c:16]([NH2:17])[cH:18]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the likely synthesis process for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:15]1[C:9](=[O:8])[CH2:10][C:11]12[CH2:12][CH2:13][CH2:14]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:15]1[C:9](=[O:8])[CH2:10][C:11]12[CH2:12][CH2:13][CH2:14]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:15]1[C:9](=[O:8])[CH2:10][C:11]12[CH2:12][CH2:13][CH2:14]2\", \"reactants\":\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[O:8]=[C:9]1[CH2:10][C:11]2([CH2:12][CH2:13][CH2:14]2)[NH:15]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[O:8]=[C:9]1[CH2:10][C:11]2([CH2:12][CH2:13][CH2:14]2)[NH:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the best synthesis strategy for '[C:1]([CH3:2])(=[O:3])[O:14][CH2:13][CH2:12][NH:11][C:9]([O:8][C:5]([CH3:4])([CH3:6])[CH3:7])=[O:10]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH3:2])(=[O:3])[O:14][CH2:13][CH2:12][NH:11][C:9]([O:8][C:5]([CH3:4])([CH3:6])[CH3:7])=[O:10]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH3:2])(=[O:3])[O:14][CH2:13][CH2:12][NH:11][C:9]([O:8][C:5]([CH3:4])([CH3:6])[CH3:7])=[O:10]\", \"reactants\":\"Cl[C:1]([CH3:2])=[O:3].[CH3:4][C:5]([CH3:6])([CH3:7])[O:8][C:9](=[O:10])[NH:11][CH2:12][CH2:13][OH:14]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([CH3:2])=[O:3]' and '[CH3:4][C:5]([CH3:6])([CH3:7])[O:8][C:9](=[O:10])[NH:11][CH2:12][CH2:13][OH:14]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis path for '[S:1]([CH3:2])(=[O:3])(=[O:4])[N:28]1[CH2:27][CH2:26][c:18]2[c:17]([n:16][c:15]([N:14]3[CH2:13][CH2:12][CH:11]([O:10][c:9]4[cH:8][cH:7][c:6]([F:5])[cH:34][c:32]4[F:33])[CH2:31][CH2:30]3)[c:20]([NH:21][CH2:22][CH:23]([F:24])[F:25])[n:19]2)[CH2:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[N:28]1[CH2:27][CH2:26][c:18]2[c:17]([n:16][c:15]([N:14]3[CH2:13][CH2:12][CH:11]([O:10][c:9]4[cH:8][cH:7][c:6]([F:5])[cH:34][c:32]4[F:33])[CH2:31][CH2:30]3)[c:20]([NH:21][CH2:22][CH:23]([F:24])[F:25])[n:19]2)[CH2:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[N:28]1[CH2:27][CH2:26][c:18]2[c:17]([n:16][c:15]([N:14]3[CH2:13][CH2:12][CH:11]([O:10][c:9]4[cH:8][cH:7][c:6]([F:5])[cH:34][c:32]4[F:33])[CH2:31][CH2:30]3)[c:20]([NH:21][CH2:22][CH:23]([F:24])[F:25])[n:19]2)[CH2:29]1\", \"reactants\":\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[F:5][c:6]1[cH:7][cH:8][c:9]([O:10][CH:11]2[CH2:12][CH2:13][N:14]([c:15]3[n:16][c:17]4[c:18]([n:19][c:20]3[NH:21][CH2:22][CH:23]([F:24])[F:25])[CH2:26][CH2:27][NH:28][CH2:29]4)[CH2:30][CH2:31]2)[c:32]([F:33])[cH:34]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[F:5][c:6]1[cH:7][cH:8][c:9]([O:10][CH:11]2[CH2:12][CH2:13][N:14]([c:15]3[n:16][c:17]4[c:18]([n:19][c:20]3[NH:21][CH2:22][CH:23]([F:24])[F:25])[CH2:26][CH2:27][NH:28][CH2:29]4)[CH2:30][CH2:31]2)[c:32]([F:33])[cH:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis steps for '[C:1](=[O:2])([CH:3]1[CH:4]2[CH2:5][O:6][CH2:7][CH:8]12)[NH:22][c:21]1[cH:20][c:19]2[cH:18][cH:17][c:16](-[c:15]3[c:10]([CH3:9])[cH:11][cH:12][n:13][cH:14]3)[cH:26][c:25]2[cH:24][n:23]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH:3]1[CH:4]2[CH2:5][O:6][CH2:7][CH:8]12)[NH:22][c:21]1[cH:20][c:19]2[cH:18][cH:17][c:16](-[c:15]3[c:10]([CH3:9])[cH:11][cH:12][n:13][cH:14]3)[cH:26][c:25]2[cH:24][n:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH:3]1[CH:4]2[CH2:5][O:6][CH2:7][CH:8]12)[NH:22][c:21]1[cH:20][c:19]2[cH:18][cH:17][c:16](-[c:15]3[c:10]([CH3:9])[cH:11][cH:12][n:13][cH:14]3)[cH:26][c:25]2[cH:24][n:23]1\", \"reactants\":\"O[C:1](=[O:2])[CH:3]1[CH:4]2[CH2:5][O:6][CH2:7][CH:8]12.[CH3:9][c:10]1[cH:11][cH:12][n:13][cH:14][c:15]1-[c:16]1[cH:17][cH:18][c:19]2[cH:20][c:21]([NH2:22])[n:23][cH:24][c:25]2[cH:26]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[CH:3]1[CH:4]2[CH2:5][O:6][CH2:7][CH:8]12' and '[CH3:9][c:10]1[cH:11][cH:12][n:13][cH:14][c:15]1-[c:16]1[cH:17][cH:18][c:19]2[cH:20][c:21]([NH2:22])[n:23][cH:24][c:25]2[cH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[c:1]1(-[n:27]2[c:13](=[O:12])[cH:14][c:15]([O:16][CH2:17][c:18]3[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]3)[cH:25][cH:26]2)[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:27]2[c:13](=[O:12])[cH:14][c:15]([O:16][CH2:17][c:18]3[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]3)[cH:25][cH:26]2)[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[n:27]2[c:13](=[O:12])[cH:14][c:15]([O:16][CH2:17][c:18]3[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]3)[cH:25][cH:26]2)[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1.[O:12]=[c:13]1[cH:14][c:15]([O:16][CH2:17][c:18]2[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]2)[cH:25][cH:26][nH:27]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1' and '[O:12]=[c:13]1[cH:14][c:15]([O:16][CH2:17][c:18]2[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]2)[cH:25][cH:26][nH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis procedure for '[C:1](\\[CH2:2][F:3])([c:4]1[cH:5][c:6]([Br:7])[cH:8][n:9][c:10]1[F:11])=[N:17]/[S@@:16]([C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](\\[CH2:2][F:3])([c:4]1[cH:5][c:6]([Br:7])[cH:8][n:9][c:10]1[F:11])=[N:17]/[S@@:16]([C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](\\[CH2:2][F:3])([c:4]1[cH:5][c:6]([Br:7])[cH:8][n:9][c:10]1[F:11])=[N:17]/[S@@:16]([C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18]\", \"reactants\":\"O=[C:1]([CH2:2][F:3])[c:4]1[cH:5][c:6]([Br:7])[cH:8][n:9][c:10]1[F:11].[CH3:12][C:13]([CH3:14])([CH3:15])[S@:16]([NH2:17])=[O:18]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1]([CH2:2][F:3])[c:4]1[cH:5][c:6]([Br:7])[cH:8][n:9][c:10]1[F:11]' and '[CH3:12][C:13]([CH3:14])([CH3:15])[S@:16]([NH2:17])=[O:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for '[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\", \"reactants\":\"Cl[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])=[O:14].[OH:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])=[O:14]' and '[OH:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis method for '[CH2:1]([Cl:2])[O:29][C:27]([c:26]1[c:21]([NH:20][C:4](=[O:3])[CH2:5][NH:6][C:7](=[O:8])[O:9][CH2:10][c:11]2[cH:12][cH:13][c:14]([N+:15](=[O:16])[O-:17])[cH:18][cH:19]2)[cH:22][cH:23][cH:24][cH:25]1)=[O:28]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([Cl:2])[O:29][C:27]([c:26]1[c:21]([NH:20][C:4](=[O:3])[CH2:5][NH:6][C:7](=[O:8])[O:9][CH2:10][c:11]2[cH:12][cH:13][c:14]([N+:15](=[O:16])[O-:17])[cH:18][cH:19]2)[cH:22][cH:23][cH:24][cH:25]1)=[O:28]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([Cl:2])[O:29][C:27]([c:26]1[c:21]([NH:20][C:4](=[O:3])[CH2:5][NH:6][C:7](=[O:8])[O:9][CH2:10][c:11]2[cH:12][cH:13][c:14]([N+:15](=[O:16])[O-:17])[cH:18][cH:19]2)[cH:22][cH:23][cH:24][cH:25]1)=[O:28]\", \"reactants\":\"Cl[CH2:1][Cl:2].[O:3]=[C:4]([CH2:5][NH:6][C:7](=[O:8])[O:9][CH2:10][c:11]1[cH:12][cH:13][c:14]([N+:15](=[O:16])[O-:17])[cH:18][cH:19]1)[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1[C:27](=[O:28])[OH:29]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][Cl:2]' and '[O:3]=[C:4]([CH2:5][NH:6][C:7](=[O:8])[O:9][CH2:10][c:11]1[cH:12][cH:13][c:14]([N+:15](=[O:16])[O-:17])[cH:18][cH:19]1)[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1[C:27](=[O:28])[OH:29]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis route for '[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\", \"reactants\":\"O[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)=[O:9].[NH2:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)=[O:9]' and '[NH2:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[C:1]([C@@H:2]([S:3][C:4]([CH3:5])=[O:6])[CH:7]([CH3:8])[CH3:9])(=[O:10])[NH:31][C:28]([C:26]([NH:25][C@H:16]([C:14]([O:13][CH2:12][CH3:11])=[O:15])[CH2:17][c:18]1[cH:19][cH:20][c:21]([OH:22])[cH:23][cH:24]1)=[O:27])([CH3:29])[CH3:30]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@@H:2]([S:3][C:4]([CH3:5])=[O:6])[CH:7]([CH3:8])[CH3:9])(=[O:10])[NH:31][C:28]([C:26]([NH:25][C@H:16]([C:14]([O:13][CH2:12][CH3:11])=[O:15])[CH2:17][c:18]1[cH:19][cH:20][c:21]([OH:22])[cH:23][cH:24]1)=[O:27])([CH3:29])[CH3:30]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([C@@H:2]([S:3][C:4]([CH3:5])=[O:6])[CH:7]([CH3:8])[CH3:9])(=[O:10])[NH:31][C:28]([C:26]([NH:25][C@H:16]([C:14]([O:13][CH2:12][CH3:11])=[O:15])[CH2:17][c:18]1[cH:19][cH:20][c:21]([OH:22])[cH:23][cH:24]1)=[O:27])([CH3:29])[CH3:30]\", \"reactants\":\"O[C:1]([C@@H:2]([S:3][C:4]([CH3:5])=[O:6])[CH:7]([CH3:8])[CH3:9])=[O:10].[CH3:11][CH2:12][O:13][C:14](=[O:15])[C@H:16]([CH2:17][c:18]1[cH:19][cH:20][c:21]([OH:22])[cH:23][cH:24]1)[NH:25][C:26](=[O:27])[C:28]([CH3:29])([CH3:30])[NH2:31]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([C@@H:2]([S:3][C:4]([CH3:5])=[O:6])[CH:7]([CH3:8])[CH3:9])=[O:10]' and '[CH3:11][CH2:12][O:13][C:14](=[O:15])[C@H:16]([CH2:17][c:18]1[cH:19][cH:20][c:21]([OH:22])[cH:23][cH:24]1)[NH:25][C:26](=[O:27])[C:28]([CH3:29])([CH3:30])[NH2:31]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify how to synthesize '[c:1]1([S:11][CH3:10])[cH:2][c:3]([C:4]#[N:5])[cH:6][c:7]([Cl:8])[n:9]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([S:11][CH3:10])[cH:2][c:3]([C:4]#[N:5])[cH:6][c:7]([Cl:8])[n:9]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([S:11][CH3:10])[cH:2][c:3]([C:4]#[N:5])[cH:6][c:7]([Cl:8])[n:9]1\", \"reactants\":\"Cl[c:1]1[cH:2][c:3]([C:4]#[N:5])[cH:6][c:7]([Cl:8])[n:9]1.[CH3:10][S-:11]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[cH:2][c:3]([C:4]#[N:5])[cH:6][c:7]([Cl:8])[n:9]1' and '[CH3:10][S-:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most probable synthesis steps for '[C:1](=[O:2])([c:3]1[cH:4][s:5][c:6]([Cl:7])[n:8]1)[NH:20][c:19]1[cH:18][cH:17][cH:16][c:15]([CH2:14][C:12]([O:11][CH2:10][CH3:9])=[O:13])[cH:21]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][s:5][c:6]([Cl:7])[n:8]1)[NH:20][c:19]1[cH:18][cH:17][cH:16][c:15]([CH2:14][C:12]([O:11][CH2:10][CH3:9])=[O:13])[cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][s:5][c:6]([Cl:7])[n:8]1)[NH:20][c:19]1[cH:18][cH:17][cH:16][c:15]([CH2:14][C:12]([O:11][CH2:10][CH3:9])=[O:13])[cH:21]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][s:5][c:6]([Cl:7])[n:8]1.[CH3:9][CH2:10][O:11][C:12](=[O:13])[CH2:14][c:15]1[cH:16][cH:17][cH:18][c:19]([NH2:20])[cH:21]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][s:5][c:6]([Cl:7])[n:8]1' and '[CH3:9][CH2:10][O:11][C:12](=[O:13])[CH2:14][c:15]1[cH:16][cH:17][cH:18][c:19]([NH2:20])[cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([Cl:7])[n:8]1)[O:32][c:31]1[cH:30][c:18]([O:19][c:20]2[cH:21][cH:22][c:23]([S:24]([CH3:25])(=[O:26])=[O:27])[cH:28][cH:29]2)[cH:17][c:16]2[cH:15][c:14]([C:12]([O:11][CH2:10][CH3:9])=[O:13])[nH:34][c:33]21'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([Cl:7])[n:8]1)[O:32][c:31]1[cH:30][c:18]([O:19][c:20]2[cH:21][cH:22][c:23]([S:24]([CH3:25])(=[O:26])=[O:27])[cH:28][cH:29]2)[cH:17][c:16]2[cH:15][c:14]([C:12]([O:11][CH2:10][CH3:9])=[O:13])[nH:34][c:33]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([Cl:7])[n:8]1)[O:32][c:31]1[cH:30][c:18]([O:19][c:20]2[cH:21][cH:22][c:23]([S:24]([CH3:25])(=[O:26])=[O:27])[cH:28][cH:29]2)[cH:17][c:16]2[cH:15][c:14]([C:12]([O:11][CH2:10][CH3:9])=[O:13])[nH:34][c:33]21\", \"reactants\":\"Br[CH2:1][c:2]1[cH:3][cH:4][cH:5][c:6]([Cl:7])[n:8]1.[CH3:9][CH2:10][O:11][C:12](=[O:13])[c:14]1[cH:15][c:16]2[cH:17][c:18]([O:19][c:20]3[cH:21][cH:22][c:23]([S:24]([CH3:25])(=[O:26])=[O:27])[cH:28][cH:29]3)[cH:30][c:31]([OH:32])[c:33]2[nH:34]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[cH:3][cH:4][cH:5][c:6]([Cl:7])[n:8]1' and '[CH3:9][CH2:10][O:11][C:12](=[O:13])[c:14]1[cH:15][c:16]2[cH:17][c:18]([O:19][c:20]3[cH:21][cH:22][c:23]([S:24]([CH3:25])(=[O:26])=[O:27])[cH:28][cH:29]3)[cH:30][c:31]([OH:32])[c:33]2[nH:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis path for '(CB[22139:5OcCC[1:9:]](:]::::c[[]4(]2[111:[[[2[[[]371:NC8)C]:CO:]]]:]H:C:5=H1]0[[1)1]r2cH:2:1[1)H(Cc3]]6:#]s[]:871]1H]=22[4O[[)CH[16A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"(CB[22139:5OcCC[1:9:]](:]::::c[[]4(]2[111:[[[2[[[]371:NC8)C]:CO:]]]:]H:C:5=H1]0[[1)1]r2cH:2:1[1)H(Cc3]]6:#]s[]:871]1H]=22[4O[[)CH[16A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The correct expression to use with drug1 is '[c:1]1([C:14]2=[CH:15][CH2:16][O:17][CH2:18][CH2:19]2)[s:2][c:3]([C:4]([O:5][CH2:6][CH3:7])=[O:8])[c:9]([Br:10])[c:11]1[C:12]#[N:13]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:14]2=[CH:15][CH2:16][O:17][CH2:18][CH2:19]2)[s:2][c:3]([C:4]([O:5][CH2:6][CH3:7])=[O:8])[c:9]([Br:10])[c:11]1[C:12]#[N:13]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:14]2=[CH:15][CH2:16][O:17][CH2:18][CH2:19]2)[s:2][c:3]([C:4]([O:5][CH2:6][CH3:7])=[O:8])[c:9]([Br:10])[c:11]1[C:12]#[N:13]\", \"reactants\":[\"Br[c:1]1[s:2][c:3]([C:4]([O:5][CH2:6][CH3:7])=[O:8])[c:9]([Br:10])[c:11]1[C:12]#[N:13].CC1(C)OB([C:14]2=[CH:15][CH2:16][O:17][CH2:18][CH2:19]2)OC1(C)C\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[s:2][c:3]([C:4]([O:5][CH2:6][CH3:7])=[O:8])[c:9]([Br:10])[c:11]1[C:12]#[N:13]' and 'CC1(C)OB([C:14]2=[CH:15][CH2:16][O:17][CH2:18][CH2:19]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis path for 'H]]][0122:H]C:1:221c18c::2]Hc6[:)[H][3O=6[H57][]((1C]42:[(O1[C]1]H]4:][2])C5:C][31H28:[C2131:]1[C:]3[2[9:1[:2O][2CO)[:c)1[:[11:][cHH::CNHHC]H:07]932:[(cA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H]]][0122:H]C:1:221c18c::2]Hc6[:)[H][3O=6[H57][]((1C]42:[(O1[C]1]H]4:][2])C5:C][31H28:[C2131:]1[C:]3[2[9:1[:2O][2CO)[:c)1[:[11:][cHH::CNHHC]H:07]932:[(cA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The most proper smile for drug1 is '[c:1]1([N:17]2[CH2:12][CH2:13][C:14]3([CH2:15][CH2:16]2)[O:18][CH2:19][CH2:20][O:21]3)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:17]2[CH2:12][CH2:13][C:14]3([CH2:15][CH2:16]2)[O:18][CH2:19][CH2:20][O:21]3)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:17]2[CH2:12][CH2:13][C:14]3([CH2:15][CH2:16]2)[O:18][CH2:19][CH2:20][O:21]3)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\", \"reactants\":[\"F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1.[CH2:12]1[CH2:13][C:14]2([CH2:15][CH2:16][NH:17]1)[O:18][CH2:19][CH2:20][O:21]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1' and '[CH2:12]1[CH2:13][C:14]2([CH2:15][CH2:16][NH:17]1)[O:18][CH2:19][CH2:20][O:21]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis process for 'c:01])2=H[:(:[1c[H3:[[]HOH@2][]112]n8137c=C:]:3c69]H[CC]HN@c:[2HH)(5=4)7)[:1[:](C[28O2]][:[H][C59[1:])3O:[::]:3H]:]:H1c[(6CO[[2c]]:2[1(41]::[[(]1][C31):)C1](]1nc0:13:2A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c:01])2=H[:(:[1c[H3:[[]HOH@2][]112]n8137c=C:]:3c69]H[CC]HN@c:[2HH)(5=4)7)[:1[:](C[28O2]][:[H][C59[1:])3O:[::]:3H]:]:H1c[(6CO[[2c]]:2[1(41]::[[(]1][C31):)C1](]1nc0:13:2A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid smile for drug1 is '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[n:9][cH:10][nH:11][c:12]2=[O:13])[NH:16][C@@H:15]([CH3:14])[C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[n:9][cH:10][nH:11][c:12]2=[O:13])[NH:16][C@@H:15]([CH3:14])[C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[n:9][cH:10][nH:11][c:12]2=[O:13])[NH:16][C@@H:15]([CH3:14])[C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23]\", \"reactants\":[\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[n:9][cH:10][nH:11][c:12]2=[O:13].[CH3:14][C@H:15]([NH2:16])[C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[n:9][cH:10][nH:11][c:12]2=[O:13]' and '[CH3:14][C@H:15]([NH2:16])[C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the possible synthesis steps for 'c21[H11]][]]3]N]5c:32:]H1[[:9c2:[]:2(]]cC0H1:11:]94:H2:H[=]]]N[)H=:C05n]](H(c]::32[::c23]:[[c][HH::35N]N8C::)]]2:c)22]2n:(:]33c4]4H6:23H3H[[26)75)(3]C[3:][2[)[13](2::2[]]:C13cC[HH:2(:[3O]4111c2[10[H[32F2)3:[H87(7O[:cc1Cc[[S1[)[:H]2:[]C[622][C:cH[2H8:[]1[c9A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c21[H11]][]]3]N]5c:32:]H1[[:9c2:[]:2(]]cC0H1:11:]94:H2:H[=]]]N[)H=:C05n]](H(c]::32[::c23]:[[c][HH::35N]N8C::)]]2:c)22]2n:(:]33c4]4H6:23H3H[[26)75)(3]C[3:][2[)[13](2::2[]]:C13cC[HH:2(:[3O]4111c2[10[H[32F2)3:[H87(7O[:cc1Cc[[S1[)[:H]2:[]C[622][C:cH[2H8:[]1[c9A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([N:35]2[c:30]3[cH:29][cH:28][c:27]([F:26])[cH:32][c:31]3[CH2:33][CH2:34]2)[cH:2][cH:3][n:4][c:5]([NH:6][c:7]2[cH:8][cH:9][c:10]([S:11]([N:12]([CH:13]3[CH2:14][CH2:15][N:16]([CH3:17])[CH2:18][CH2:19]3)[CH3:20])(=[O:21])=[O:22])[cH:23][cH:24]2)[n:25]1' should be the smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:35]2[c:30]3[cH:29][cH:28][c:27]([F:26])[cH:32][c:31]3[CH2:33][CH2:34]2)[cH:2][cH:3][n:4][c:5]([NH:6][c:7]2[cH:8][cH:9][c:10]([S:11]([N:12]([CH:13]3[CH2:14][CH2:15][N:16]([CH3:17])[CH2:18][CH2:19]3)[CH3:20])(=[O:21])=[O:22])[cH:23][cH:24]2)[n:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:35]2[c:30]3[cH:29][cH:28][c:27]([F:26])[cH:32][c:31]3[CH2:33][CH2:34]2)[cH:2][cH:3][n:4][c:5]([NH:6][c:7]2[cH:8][cH:9][c:10]([S:11]([N:12]([CH:13]3[CH2:14][CH2:15][N:16]([CH3:17])[CH2:18][CH2:19]3)[CH3:20])(=[O:21])=[O:22])[cH:23][cH:24]2)[n:25]1\", \"reactants\":[\"Cl[c:1]1[cH:2][cH:3][n:4][c:5]([NH:6][c:7]2[cH:8][cH:9][c:10]([S:11]([N:12]([CH:13]3[CH2:14][CH2:15][N:16]([CH3:17])[CH2:18][CH2:19]3)[CH3:20])(=[O:21])=[O:22])[cH:23][cH:24]2)[n:25]1.[F:26][c:27]1[cH:28][cH:29][c:30]2[c:31]([cH:32]1)[CH2:33][CH2:34][NH:35]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[cH:2][cH:3][n:4][c:5]([NH:6][c:7]2[cH:8][cH:9][c:10]([S:11]([N:12]([CH:13]3[CH2:14][CH2:15][N:16]([CH3:17])[CH2:18][CH2:19]3)[CH3:20])(=[O:21])=[O:22])[cH:23][cH:24]2)[n:25]1' and '[F:26][c:27]1[cH:28][cH:29][c:30]2[c:31]([cH:32]1)[CH2:33][CH2:34][NH:35]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis trajectory for '7H3C)[3NH124][cH]:[)c3c]19)]9[22[(:43C]C:23[1H:8H:c1)cC:H3]cHO:61[22c)c[1):2H44:30]3]:::2o][:][[[(:[3:3[[]4][3:=35C[:16(]=2:6]53)][2]HH0[[c07]3co:2[25H]c[H][7]c1]1cO11O1(:c[(8[]8H:[]]:1[HH7C1:H[3H1c(:[[4[3H5c=2(c::3]233:9n[28]:(1:[c)0]:ccC::c]:::]6C1]S(]2[cH9[[[H]3[:)]]::]2[:H]]cCN331]1[:H:3]][]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"7H3C)[3NH124][cH]:[)c3c]19)]9[22[(:43C]C:23[1H:8H:c1)cC:H3]cHO:61[22c)c[1):2H44:30]3]:::2o][:][[[(:[3:3[[]4][3:=35C[:16(]=2:6]53)][2]HH0[[c07]3co:2[25H]c[H][7]c1]1cO11O1(:c[(8[]8H:[]]:1[HH7C1:H[3H1c(:[[4[3H5c=2(c::3]233:9n[28]:(1:[c)0]:ccC::c]:::]6C1]S(]2[cH9[[[H]3[:)]]::]2[:H]]cCN331]1[:H:3]][]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that fits drug1 is '[S:1]([CH3:2])(=[O:3])(=[O:4])[NH:22][CH:21]([c:20]1[o:19][c:18]2[cH:17][cH:16][c:15]([CH2:14][C:12]([NH:11][CH:10]([c:9]3[cH:8][cH:7][c:6]([CH3:5])[cH:41][c:39]3[CH3:40])[c:33]3[cH:34][cH:35][cH:36][cH:37][cH:38]3)=[O:13])[cH:32][c:31]2[cH:30]1)[c:23]1[c:24]([CH3:25])[n:26][o:27][c:28]1[CH3:29]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[NH:22][CH:21]([c:20]1[o:19][c:18]2[cH:17][cH:16][c:15]([CH2:14][C:12]([NH:11][CH:10]([c:9]3[cH:8][cH:7][c:6]([CH3:5])[cH:41][c:39]3[CH3:40])[c:33]3[cH:34][cH:35][cH:36][cH:37][cH:38]3)=[O:13])[cH:32][c:31]2[cH:30]1)[c:23]1[c:24]([CH3:25])[n:26][o:27][c:28]1[CH3:29]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[NH:22][CH:21]([c:20]1[o:19][c:18]2[cH:17][cH:16][c:15]([CH2:14][C:12]([NH:11][CH:10]([c:9]3[cH:8][cH:7][c:6]([CH3:5])[cH:41][c:39]3[CH3:40])[c:33]3[cH:34][cH:35][cH:36][cH:37][cH:38]3)=[O:13])[cH:32][c:31]2[cH:30]1)[c:23]1[c:24]([CH3:25])[n:26][o:27][c:28]1[CH3:29]\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[CH3:5][c:6]1[cH:7][cH:8][c:9]([CH:10]([NH:11][C:12](=[O:13])[CH2:14][c:15]2[cH:16][cH:17][c:18]3[o:19][c:20]([CH:21]([NH2:22])[c:23]4[c:24]([CH3:25])[n:26][o:27][c:28]4[CH3:29])[cH:30][c:31]3[cH:32]2)[c:33]2[cH:34][cH:35][cH:36][cH:37][cH:38]2)[c:39]([CH3:40])[cH:41]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[CH3:5][c:6]1[cH:7][cH:8][c:9]([CH:10]([NH:11][C:12](=[O:13])[CH2:14][c:15]2[cH:16][cH:17][c:18]3[o:19][c:20]([CH:21]([NH2:22])[c:23]4[c:24]([CH3:25])[n:26][o:27][c:28]4[CH3:29])[cH:30][c:31]3[cH:32]2)[c:33]2[cH:34][cH:35][cH:36][cH:37][cH:38]2)[c:39]([CH3:40])[cH:41]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '4c:H][1::]H:O[)c]1[[:]1]HH7:[]=11B[3H=]293[]5:CO(:1[]HC3]cc28[:6]c0:]C[[r::HC[1/1:)[(/c]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"4c:H][1::]H:O[)c]1[[:]1]HH7:[]=11B[3H=]293[]5:CO(:1[]HC3]cc28[:6]c0:]C[[r::HC[1/1:)[(/c]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:4][C:3](=[O:2])/[CH:5]=[CH:6]/[c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\", \"reactants\":[\"O[CH3:1].[O:2]=[C:3]([OH:4])[CH:5]=[CH:6][c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[CH3:1]' and '[O:2]=[C:3]([OH:4])[CH:5]=[CH:6][c:7]1[cH:8][cH:9][cH:10][c:11]([Br:12])[cH:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis steps for '1Hc[3c[:H:]Cc3H([)8:]]1((5[H)C2O2:H[]C]Cc9[]:N[(c[(3)11H([::[4c33c[0]:][:C2O12[2C2]1:2H:[])]2:cc36[:2:313[H2C]O:6]H3]]:cCH:4[cc[2)HC:[(:]2]6:[[cCc1]1[1)1c[][::C03c:9]:132[]3[]4[1C:28350HH:5H1[:82H][2]]83C]61[[2]:[:2[:2C][[9:]:::1]]1:c1:1]:H31249]:1320[]23C5[3H:7O]c7H]12=::3H7O)H7])2]H]][4[412[[(HHHNC]HA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"1Hc[3c[:H:]Cc3H([)8:]]1((5[H)C2O2:H[]C]Cc9[]:N[(c[(3)11H([::[4c33c[0]:][:C2O12[2C2]1:2H:[])]2:cc36[:2:313[H2C]O:6]H3]]:cCH:4[cc[2)HC:[(:]2]6:[[cCc1]1[1)1c[][::C03c:9]:132[]3[]4[1C:28350HH:5H1[:82H][2]]83C]61[[2]:[:2[:2C][[9:]:::1]]1:c1:1]:H31249]:1320[]23C5[3H:7O]c7H]12=::3H7O)H7])2]H]][4[412[[(HHHNC]HA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right expression for drug1 is '[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[O:13][c:12]1[c:7]([O:6][CH3:5])[cH:8][c:9]2[c:10]([cH:11]1)[CH:14]([CH2:15][c:16]1[cH:17][cH:18][c:19]([O:20][CH3:21])[c:22]([O:23][CH3:24])[cH:25]1)[N:26]([CH2:27][C:28](=[O:29])[NH:30][CH:31]1[CH2:32][CH2:33][c:34]3[cH:35][cH:36][cH:37][cH:38][c:39]31)[CH2:40][CH2:41]2'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[O:13][c:12]1[c:7]([O:6][CH3:5])[cH:8][c:9]2[c:10]([cH:11]1)[CH:14]([CH2:15][c:16]1[cH:17][cH:18][c:19]([O:20][CH3:21])[c:22]([O:23][CH3:24])[cH:25]1)[N:26]([CH2:27][C:28](=[O:29])[NH:30][CH:31]1[CH2:32][CH2:33][c:34]3[cH:35][cH:36][cH:37][cH:38][c:39]31)[CH2:40][CH2:41]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH:2]1[CH2:3][CH2:4]1)[O:13][c:12]1[c:7]([O:6][CH3:5])[cH:8][c:9]2[c:10]([cH:11]1)[CH:14]([CH2:15][c:16]1[cH:17][cH:18][c:19]([O:20][CH3:21])[c:22]([O:23][CH3:24])[cH:25]1)[N:26]([CH2:27][C:28](=[O:29])[NH:30][CH:31]1[CH2:32][CH2:33][c:34]3[cH:35][cH:36][cH:37][cH:38][c:39]31)[CH2:40][CH2:41]2\", \"reactants\":[\"Br[CH2:1][CH:2]1[CH2:3][CH2:4]1.[CH3:5][O:6][c:7]1[cH:8][c:9]2[c:10]([cH:11][c:12]1[OH:13])[CH:14]([CH2:15][c:16]1[cH:17][cH:18][c:19]([O:20][CH3:21])[c:22]([O:23][CH3:24])[cH:25]1)[N:26]([CH2:27][C:28](=[O:29])[NH:30][CH:31]1[CH2:32][CH2:33][c:34]3[cH:35][cH:36][cH:37][cH:38][c:39]31)[CH2:40][CH2:41]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][CH:2]1[CH2:3][CH2:4]1' and '[CH3:5][O:6][c:7]1[cH:8][c:9]2[c:10]([cH:11][c:12]1[OH:13])[CH:14]([CH2:15][c:16]1[cH:17][cH:18][c:19]([O:20][CH3:21])[c:22]([O:23][CH3:24])[cH:25]1)[N:26]([CH2:27][C:28](=[O:29])[NH:30][CH:31]1[CH2:32][CH2:33][c:34]3[cH:35][cH:36][cH:37][cH:38][c:39]31)[CH2:40][CH2:41]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most probable synthesis steps for '11]12]]]H1[(5C201c=[[[[6C]HC1]H:2:C]:2[[:32([2[]2[:cC][]:[(:[1][:11[3]82:c]]9n=H1[2[23H:HHc[[]02):1C2H:H:O]cOn]c3]34N1(c2)]2:O]5:=:]OC5][4:(4:79H7:O])][:222H2[H)::c]81HH7C2c6S][:)c)::1(38][2):([C[[H1:26:][[:]9CA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"11]12]]]H1[(5C201c=[[[[6C]HC1]H:2:C]:2[[:32([2[]2[:cC][]:[(:[1][:11[3]82:c]]9n=H1[2[23H:HHc[[]02):1C2H:H:O]cOn]c3]34N1(c2)]2:O]5:=:]OC5][4:(4:79H7:O])][:222H2[H)::c]81HH7C2c6S][:)c)::1(38][2):([C[[H1:26:][[:]9CA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right look for drug1 is '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:27][CH2:26][CH2:25][c:24]1[cH:23][cH:22][c:21]([CH2:20][CH2:19][NH:18][c:17]2[c:10]([C:8]([O:7][CH2:6][CH3:5])=[O:9])[c:11]([CH2:12][CH3:13])[n:14][cH:15][n:16]2)[cH:29][cH:28]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:27][CH2:26][CH2:25][c:24]1[cH:23][cH:22][c:21]([CH2:20][CH2:19][NH:18][c:17]2[c:10]([C:8]([O:7][CH2:6][CH3:5])=[O:9])[c:11]([CH2:12][CH3:13])[n:14][cH:15][n:16]2)[cH:29][cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:27][CH2:26][CH2:25][c:24]1[cH:23][cH:22][c:21]([CH2:20][CH2:19][NH:18][c:17]2[c:10]([C:8]([O:7][CH2:6][CH3:5])=[O:9])[c:11]([CH2:12][CH3:13])[n:14][cH:15][n:16]2)[cH:29][cH:28]1\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[CH3:5][CH2:6][O:7][C:8](=[O:9])[c:10]1[c:11]([CH2:12][CH3:13])[n:14][cH:15][n:16][c:17]1[NH:18][CH2:19][CH2:20][c:21]1[cH:22][cH:23][c:24]([CH2:25][CH2:26][OH:27])[cH:28][cH:29]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[CH3:5][CH2:6][O:7][C:8](=[O:9])[c:10]1[c:11]([CH2:12][CH3:13])[n:14][cH:15][n:16][c:17]1[NH:18][CH2:19][CH2:20][c:21]1[cH:22][cH:23][c:24]([CH2:25][CH2:26][OH:27])[cH:28][cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[H(H1]c[]]HH1]5H]c6C)5]]5:c[1:][]1:][[(16HC18[9()H:=8]]H)2[][196:c2:C38:71]H2-::2=12][1:0[2[Nc13]::cH2H((][:]1[2(c]::4][][C:]):[:1[c12[]]1[:2[9c7[1:c[O:23[722[O[)H[2:0HHC2214[]4c]O:]::c:2[O)c2c::H2c]2:clc]CcA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[H(H1]c[]]HH1]5H]c6C)5]]5:c[1:][]1:][[(16HC18[9()H:=8]]H)2[][196:c2:C38:71]H2-::2=12][1:0[2[Nc13]::cH2H((][:]1[2(c]::4][][C:]):[:1[c12[]]1[:2[9c7[1:c[O:23[722[O[)H[2:0HHC2214[]4c]O:]::c:2[O)c2c::H2c]2:clc]CcA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile for drug1 should be '[c:1]1(-[c:23]2[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]2)[cH:2][c:3]2[c:4]([cH:5][c:6]1[O:7][CH2:8][c:9]1[cH:10][cH:11][c:12]([C:13](=[O:14])[OH:15])[cH:16][cH:17]1)[NH:18][C:19](=[O:20])[CH2:21][CH2:22]2'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:23]2[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]2)[cH:2][c:3]2[c:4]([cH:5][c:6]1[O:7][CH2:8][c:9]1[cH:10][cH:11][c:12]([C:13](=[O:14])[OH:15])[cH:16][cH:17]1)[NH:18][C:19](=[O:20])[CH2:21][CH2:22]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:23]2[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]2)[cH:2][c:3]2[c:4]([cH:5][c:6]1[O:7][CH2:8][c:9]1[cH:10][cH:11][c:12]([C:13](=[O:14])[OH:15])[cH:16][cH:17]1)[NH:18][C:19](=[O:20])[CH2:21][CH2:22]2\", \"reactants\":[\"Br[c:1]1[cH:2][c:3]2[c:4]([cH:5][c:6]1[O:7][CH2:8][c:9]1[cH:10][cH:11][c:12]([C:13](=[O:14])[OH:15])[cH:16][cH:17]1)[NH:18][C:19](=[O:20])[CH2:21][CH2:22]2.OB(O)[c:23]1[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][c:3]2[c:4]([cH:5][c:6]1[O:7][CH2:8][c:9]1[cH:10][cH:11][c:12]([C:13](=[O:14])[OH:15])[cH:16][cH:17]1)[NH:18][C:19](=[O:20])[CH2:21][CH2:22]2' and 'OB(O)[c:23]1[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for 'c1]()][4-]c2Hc23:552c]H[32]([:[:(HH1)[:34N8n[9:9::]7c[4:4:1:3:2[[cH:17c]CH[H]22][)][[n:[3](]]:2:3c3][]:4c2c(C1]5[9]02:1:][OH]2:CH:1:2C]021H[14:[[O]4:[c)13c4[[H]]H[ccc:))1#5=CcH]3]2[4]:[::2(4H:]]3n[])H1][393:[4[:::[2:C:N21:1]02c[34]Hcc8[[c[[H6538-:c:5H[[):c-c([Hc:]:4HC[]1C3:1]24C03:c[]:H]H7c326]H[23[]2C3(7]3]]6]6:]4][:1c)c[H58(A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c1]()][4-]c2Hc23:552c]H[32]([:[:(HH1)[:34N8n[9:9::]7c[4:4:1:3:2[[cH:17c]CH[H]22][)][[n:[3](]]:2:3c3][]:4c2c(C1]5[9]02:1:][OH]2:CH:1:2C]021H[14:[[O]4:[c)13c4[[H]]H[ccc:))1#5=CcH]3]2[4]:[::2(4H:]]3n[])H1][393:[4[:::[2:C:N21:1]02c[34]Hcc8[[c[[H6538-:c:5H[[):c-c([Hc:]:4HC[]1C3:1]24C03:c[]:H]H7c326]H[23[]2C3(7]3]]6]6:]4][:1c)c[H58(A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "drug1’s correct smile is '[c:1]1(-[c:38]2[cH:39][cH:40][cH:41][c:42]([C:43]#[N:44])[cH:45]2)[cH:2][c:3]2[c:4]3[n:5][c:6](-[c:7]4[cH:8][cH:9][c:10]([C:11]5([NH:12][C:13]([O:14][C:15]([CH3:16])([CH3:17])[CH3:18])=[O:19])[CH2:20][CH2:21][CH2:22]5)[cH:23][cH:24]4)[c:25](-[c:26]4[cH:27][cH:28][cH:29][cH:30][cH:31]4)[cH:32][n:33]3[n:34][c:35]2[cH:36][cH:37]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:38]2[cH:39][cH:40][cH:41][c:42]([C:43]#[N:44])[cH:45]2)[cH:2][c:3]2[c:4]3[n:5][c:6](-[c:7]4[cH:8][cH:9][c:10]([C:11]5([NH:12][C:13]([O:14][C:15]([CH3:16])([CH3:17])[CH3:18])=[O:19])[CH2:20][CH2:21][CH2:22]5)[cH:23][cH:24]4)[c:25](-[c:26]4[cH:27][cH:28][cH:29][cH:30][cH:31]4)[cH:32][n:33]3[n:34][c:35]2[cH:36][cH:37]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:38]2[cH:39][cH:40][cH:41][c:42]([C:43]#[N:44])[cH:45]2)[cH:2][c:3]2[c:4]3[n:5][c:6](-[c:7]4[cH:8][cH:9][c:10]([C:11]5([NH:12][C:13]([O:14][C:15]([CH3:16])([CH3:17])[CH3:18])=[O:19])[CH2:20][CH2:21][CH2:22]5)[cH:23][cH:24]4)[c:25](-[c:26]4[cH:27][cH:28][cH:29][cH:30][cH:31]4)[cH:32][n:33]3[n:34][c:35]2[cH:36][cH:37]1\", \"reactants\":[\"Br[c:1]1[cH:2][c:3]2[c:4]3[n:5][c:6](-[c:7]4[cH:8][cH:9][c:10]([C:11]5([NH:12][C:13]([O:14][C:15]([CH3:16])([CH3:17])[CH3:18])=[O:19])[CH2:20][CH2:21][CH2:22]5)[cH:23][cH:24]4)[c:25](-[c:26]4[cH:27][cH:28][cH:29][cH:30][cH:31]4)[cH:32][n:33]3[n:34][c:35]2[cH:36][cH:37]1.OB(O)[c:38]1[cH:39][cH:40][cH:41][c:42]([C:43]#[N:44])[cH:45]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][c:3]2[c:4]3[n:5][c:6](-[c:7]4[cH:8][cH:9][c:10]([C:11]5([NH:12][C:13]([O:14][C:15]([CH3:16])([CH3:17])[CH3:18])=[O:19])[CH2:20][CH2:21][CH2:22]5)[cH:23][cH:24]4)[c:25](-[c:26]4[cH:27][cH:28][cH:29][cH:30][cH:31]4)[cH:32][n:33]3[n:34][c:35]2[cH:36][cH:37]1' and 'OB(O)[c:38]1[cH:39][cH:40][cH:41][c:42]([C:43]#[N:44])[cH:45]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how to synthesize 'H:1][:OO[04(C]c]c:1[:[27]c))9C]1]:l[H02:21n[H]:[]H]c[][8[c22:)2::c[[H[1]:[4]:12Nc:6:C=]7C:]]H2:Hc]HC]1c:6]:1:c:]H[28c2H[c2[H(72(38][312O1[:H(49c[(C:2[c]]1H:)16]5n51[]:1::)c[[]22:](1[3:[]5[H)H[c2]33A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H:1][:OO[04(C]c]c:1[:[27]c))9C]1]:l[H02:21n[H]:[]H]c[][8[c22:)2::c[[H[1]:[4]:12Nc:6:C=]7C:]]H2:Hc]HC]1c:6]:1:c:]H[28c2H[c2[H(72(38][312O1[:H(49c[(C:2[c]]1H:)16]5n51[]:1::)c[[]22:](1[3:[]5[H)H[c2]33A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([NH:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[n:2][cH:3][c:4]([CH2:5][CH:6]([C:7]([O:8][CH3:9])=[O:10])[c:11]2[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][cH:18]2)[c:19]([Cl:20])[n:21]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[n:2][cH:3][c:4]([CH2:5][CH:6]([C:7]([O:8][CH3:9])=[O:10])[c:11]2[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][cH:18]2)[c:19]([Cl:20])[n:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[n:2][cH:3][c:4]([CH2:5][CH:6]([C:7]([O:8][CH3:9])=[O:10])[c:11]2[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][cH:18]2)[c:19]([Cl:20])[n:21]1\", \"reactants\":[\"Cl[c:1]1[n:2][cH:3][c:4]([CH2:5][CH:6]([C:7]([O:8][CH3:9])=[O:10])[c:11]2[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][cH:18]2)[c:19]([Cl:20])[n:21]1.[NH2:22][c:23]1[cH:24][cH:25][cH:26][cH:27][cH:28]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[n:2][cH:3][c:4]([CH2:5][CH:6]([C:7]([O:8][CH3:9])=[O:10])[c:11]2[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][cH:18]2)[c:19]([Cl:20])[n:21]1' and '[NH2:22][c:23]1[cH:24][cH:25][cH:26][cH:27][cH:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method of 'c]][:H5:[][:23:]21:][]):[N[nH:=1F):4[c[::HH:[170l[2(H]cc13(cH]:1N35:cO]2c0[H[H[4S28]81:(12[:[[FC17H[]C:6[[Hc6[cc)7[:291]cc]]][]c::[::1::c]:14]:2HCc]2]222]]5]1[]c6211]:c[:]9212[2HH[]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c]][:H5:[][:23:]21:][]):[N[nH:=1F):4[c[::HH:[170l[2(H]cc13(cH]:1N35:cO]2c0[H[H[4S28]81:(12[:[[FC17H[]C:6[[Hc6[cc)7[:291]cc]]][]c::[::1::c]:14]:2HCc]2]222]]5]1[]c6211]:c[:]9212[2HH[]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper smile for drug1 is '[F:1][c:2]1[cH:3][cH:4][c:5]([NH:6][C:7](=[S:8])[NH:11][c:12]2[n:13][cH:14][cH:15][cH:16][c:17]2[O:18][CH2:19][c:20]2[c:21]([F:22])[cH:23][cH:24][cH:25][c:26]2[Cl:27])[cH:9][cH:10]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[F:1][c:2]1[cH:3][cH:4][c:5]([NH:6][C:7](=[S:8])[NH:11][c:12]2[n:13][cH:14][cH:15][cH:16][c:17]2[O:18][CH2:19][c:20]2[c:21]([F:22])[cH:23][cH:24][cH:25][c:26]2[Cl:27])[cH:9][cH:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[F:1][c:2]1[cH:3][cH:4][c:5]([NH:6][C:7](=[S:8])[NH:11][c:12]2[n:13][cH:14][cH:15][cH:16][c:17]2[O:18][CH2:19][c:20]2[c:21]([F:22])[cH:23][cH:24][cH:25][c:26]2[Cl:27])[cH:9][cH:10]1\", \"reactants\":[\"[F:1][c:2]1[cH:3][cH:4][c:5]([N:6]=[C:7]=[S:8])[cH:9][cH:10]1.[NH2:11][c:12]1[n:13][cH:14][cH:15][cH:16][c:17]1[O:18][CH2:19][c:20]1[c:21]([F:22])[cH:23][cH:24][cH:25][c:26]1[Cl:27]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[F:1][c:2]1[cH:3][cH:4][c:5]([N:6]=[C:7]=[S:8])[cH:9][cH:10]1' and '[NH2:11][c:12]1[n:13][cH:14][cH:15][cH:16][c:17]1[O:18][CH2:19][c:20]1[c:21]([F:22])[cH:23][cH:24][cH:25][c:26]1[Cl:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis method for '[:1C]C]2:1[1[[][HC21CHn:](3HC6CC2[]2[CC[Hn51[C]:58[:[16[1HO][[2):]][CH]1:2H:H]:332:::c3c:]H(2[2)39C[c:H1[:(2:C]:54[][2][[H15](07:N1]H(()c42:]:2[:3(8)]31)H][O8=][3]120:HH6:::[3:H]C=1[7[H2][12C7C:]2]Hc7]C]0H:[1c]31[n::Nc[H2HC:2H]39[]::]]24C2H(2][331:333:6[:H9H22C3][4)C)]n:23)C[]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[:1C]C]2:1[1[[][HC21CHn:](3HC6CC2[]2[CC[Hn51[C]:58[:[16[1HO][[2):]][CH]1:2H:H]:332:::c3c:]H(2[2)39C[c:H1[:(2:C]:54[][2][[H15](07:N1]H(()c42:]:2[:3(8)]31)H][O8=][3]120:HH6:::[3:H]C=1[7[H2][12C7C:]2]Hc7]C]0H:[1c]31[n::Nc[H2HC:2H]39[]::]]24C2H(2][331:333:6[:H9H22C3][4)C)]n:23)C[]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The appropriate smile for drug1 is '[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH3:2])(=[O:3])[NH:21][CH:20]([c:19]1[n:18][n:17][n:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH3:4])[n:37]1)[C:22](=[O:23])[NH:24][c:25]1[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]1[CH:34]([CH3:35])[CH3:36]\", \"reactants\":[\"Cl[C:1]([CH3:2])=[O:3].[CH3:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][n:16]1[n:17][n:18][c:19]([CH:20]([NH2:21])[C:22](=[O:23])[NH:24][c:25]2[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]2[CH:34]([CH3:35])[CH3:36])[n:37]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([CH3:2])=[O:3]' and '[CH3:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][n:16]1[n:17][n:18][c:19]([CH:20]([NH2:21])[C:22](=[O:23])[NH:24][c:25]2[c:26]([CH:27]([CH3:28])[CH3:29])[cH:30][cH:31][cH:32][c:33]2[CH:34]([CH3:35])[CH3:36])[n:37]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the most probable synthesis route for 'H811:-c[c226lO)142::[]C)2)[3H5c[3[cH:]:0[[]cn::N:23:[H[[]]]4(:]6H24][2[:0:c]1]:):]2:2[H]2:c938]2:2cc42]3::1C[][]:[(7122[3([:91C]33[c7[n[4:C18l[)]3((c21H]:N][19314[:2c::C2]7c1]1[O2c[HH1]c]n[3(232H1C::[)cc]C:(:H1[H]:::[C[]]05:[C]5n3]]H)]]-3[6][2HA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H811:-c[c226lO)142::[]C)2)[3H5c[3[cH:]:0[[]cn::N:23:[H[[]]]4(:]6H24][2[:0:c]1]:):]2:2[H]2:c938]2:2cc42]3::1C[][]:[(7122[3([:91C]33[c7[n[4:C18l[)]3((c21H]:N][19314[:2c::C2]7c1]1[O2c[HH1]c]n[3(232H1C::[)cc]C:(:H1[H]:::[C[]]05:[C]5n3]]H)]]-3[6][2HA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1(-[c:14]2[c:15]3[cH:16][n:17][c:18](-[c:19]4[c:20]([Cl:21])[c:22]([O:23][CH3:24])[cH:25][c:26]([O:27][CH3:28])[c:29]4[Cl:30])[n:31][c:32]3[nH:33][n:34]2)[cH:2][cH:3][c:4]([N:5]2[CH2:6][CH2:7][N:8]([CH3:9])[CH2:10][CH2:11]2)[cH:12][cH:13]1' is the expression that fits drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:14]2[c:15]3[cH:16][n:17][c:18](-[c:19]4[c:20]([Cl:21])[c:22]([O:23][CH3:24])[cH:25][c:26]([O:27][CH3:28])[c:29]4[Cl:30])[n:31][c:32]3[nH:33][n:34]2)[cH:2][cH:3][c:4]([N:5]2[CH2:6][CH2:7][N:8]([CH3:9])[CH2:10][CH2:11]2)[cH:12][cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:14]2[c:15]3[cH:16][n:17][c:18](-[c:19]4[c:20]([Cl:21])[c:22]([O:23][CH3:24])[cH:25][c:26]([O:27][CH3:28])[c:29]4[Cl:30])[n:31][c:32]3[nH:33][n:34]2)[cH:2][cH:3][c:4]([N:5]2[CH2:6][CH2:7][N:8]([CH3:9])[CH2:10][CH2:11]2)[cH:12][cH:13]1\", \"reactants\":[\"CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([N:5]3[CH2:6][CH2:7][N:8]([CH3:9])[CH2:10][CH2:11]3)[cH:12][cH:13]2)OC1(C)C.I[c:14]1[c:15]2[cH:16][n:17][c:18](-[c:19]3[c:20]([Cl:21])[c:22]([O:23][CH3:24])[cH:25][c:26]([O:27][CH3:28])[c:29]3[Cl:30])[n:31][c:32]2[nH:33][n:34]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([N:5]3[CH2:6][CH2:7][N:8]([CH3:9])[CH2:10][CH2:11]3)[cH:12][cH:13]2)OC1(C)C' and 'I[c:14]1[c:15]2[cH:16][n:17][c:18](-[c:19]3[c:20]([Cl:21])[c:22]([O:23][CH3:24])[cH:25][c:26]([O:27][CH3:28])[c:29]3[Cl:30])[n:31][c:32]2[nH:33][n:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis route for 'N]]n]]c[]4HH(]c2]:cH::4:[c]0:8:[cl]1[9H1[(17:11[]::#nc:1[[:c23]C:5]:]31[)5)][c]212cH1[H:-:[(6[[C)[c1A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"N]]n]]c[]4HH(]c2]:cH::4:[c]0:8:[cl]1[9H1[(17:11[]::#nc:1[[:c23]C:5]:]31[)5)][c]212cH1[H:-:[(6[[C)[c1A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that fits drug1 is '[c:1]1(-[c:8]2[c:9]([C:10]#[N:11])[cH:12][cH:13][cH:14][n:15]2)[cH:2][cH:3][cH:4][c:5]([Cl:6])[n:7]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[c:9]([C:10]#[N:11])[cH:12][cH:13][cH:14][n:15]2)[cH:2][cH:3][cH:4][c:5]([Cl:6])[n:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:8]2[c:9]([C:10]#[N:11])[cH:12][cH:13][cH:14][n:15]2)[cH:2][cH:3][cH:4][c:5]([Cl:6])[n:7]1\", \"reactants\":[\"CCCC[Sn](CCCC)(CCCC)[c:1]1[cH:2][cH:3][cH:4][c:5]([Cl:6])[n:7]1.Cl[c:8]1[c:9]([C:10]#[N:11])[cH:12][cH:13][cH:14][n:15]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CCCC[Sn](CCCC)(CCCC)[c:1]1[cH:2][cH:3][cH:4][c:5]([Cl:6])[n:7]1' and 'Cl[c:8]1[c:9]([C:10]#[N:11])[cH:12][cH:13][cH:14][n:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how to synthesize 'CH):C[]](c2232:::]]:c9[11O[C::[8H]1]n]Hr[2:9C[([H]3)7[]H[]:HOc0C12B)nO][cH]H]::::[11::4:2i5=[7[1]22[()]0[c)C]][::13][)[c:[C2]3n2:16[:(H[S1(262]H:15138][H1C1]]1:224C[(33A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"CH):C[]](c2232:::]]:c9[11O[C::[8H]1]n]Hr[2:9C[([H]3)7[]H[]:HOc0C12B)nO][cH]H]::::[11::4:2i5=[7[1]22[()]0[c)C]][::13][)[c:[C2]3n2:16[:(H[S1(262]H:15138][H1C1]]1:224C[(33A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH2:1]([c:2]1[c:3]([Br:4])[n:5]([CH2:6][O:7][CH2:8][CH2:9][Si:10]([CH3:11])([CH3:12])[CH3:13])[c:14]2[c:15]1[c:16](=[O:17])[nH:18][n:19][cH:20]2)[O:23][CH2:22][CH3:21]' should be the smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([Br:4])[n:5]([CH2:6][O:7][CH2:8][CH2:9][Si:10]([CH3:11])([CH3:12])[CH3:13])[c:14]2[c:15]1[c:16](=[O:17])[nH:18][n:19][cH:20]2)[O:23][CH2:22][CH3:21]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([Br:4])[n:5]([CH2:6][O:7][CH2:8][CH2:9][Si:10]([CH3:11])([CH3:12])[CH3:13])[c:14]2[c:15]1[c:16](=[O:17])[nH:18][n:19][cH:20]2)[O:23][CH2:22][CH3:21]\", \"reactants\":[\"Br[CH2:1][c:2]1[c:3]([Br:4])[n:5]([CH2:6][O:7][CH2:8][CH2:9][Si:10]([CH3:11])([CH3:12])[CH3:13])[c:14]2[c:15]1[c:16](=[O:17])[nH:18][n:19][cH:20]2.[CH3:21][CH2:22][OH:23]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[c:3]([Br:4])[n:5]([CH2:6][O:7][CH2:8][CH2:9][Si:10]([CH3:11])([CH3:12])[CH3:13])[c:14]2[c:15]1[c:16](=[O:17])[nH:18][n:19][cH:20]2' and '[CH3:21][CH2:22][OH:23]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the possible synthesis steps for '@:1[[2]1FH1][[][1c:@][c)H61c[:2[2:[]c:C=H]C(C]c2Cc[)054(2]6)::][H62:Cc[4[5]HH]:C3(F:31O:1O:]H[))[870H]:O22HC:]c9[2C31H](7[)H35l2:2)C]:C]2:[[:][:2(]F1:1(:31:CHc1]2:]7::[12c(=c1)[H1]9:c]8]::H[[@]1[]][H[9:8(1[[]]42A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"@:1[[2]1FH1][[][1c:@][c)H61c[:2[2:[]c:C=H]C(C]c2Cc[)054(2]6)::][H62:Cc[4[5]HH]:C3(F:31O:1O:]H[))[870H]:O22HC:]c9[2C31H](7[)H35l2:2)C]:C]2:[[:][:2(]F1:1(:31:CHc1]2:]7::[12c(=c1)[H1]9:c]8]::H[[@]1[]][H[9:8(1[[]]42A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that fits drug1 is '[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])(=[O:14])[O:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\", \"reactants\":[\"Cl[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])=[O:14].[OH:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([C@@H:2]1[C:3]([CH3:4])([CH3:5])[C@H:6]1[CH:7]=[C:8]([Cl:9])[C:10]([F:11])([F:12])[F:13])=[O:14]' and '[OH:15][CH2:16][c:17]1[cH:18][cH:19][cH:20][c:21]([O:22][c:23]2[cH:24][cH:25][cH:26][cH:27][cH:28]2)[cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how '2C1]H2[c146]O[=[12[Oc]:[1(c8:[5H[[CH]:H[:2:]:=18H[c([:][:(1:)H7]):Hc][3::1[]1:C][11[:c:C2[]]H31ON]O1]C](5731[]90H]3)6C])HC4:::A' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"2C1]H2[c146]O[=[12[Oc]:[1(c8:[5H[[CH]:H[:2:]:=18H[c([:][:(1:)H7]):Hc][3::1[]1:C][11[:c:C2[]]H31ON]O1]C](5731[]90H]3)6C])HC4:::A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH2:1]([C:2]([O:3][CH2:4][CH3:5])=[O:6])[O:18][c:17]1[c:12]([CH2:11][C:9]([NH:8][CH3:7])=[O:10])[cH:13][cH:14][cH:15][cH:16]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]([O:3][CH2:4][CH3:5])=[O:6])[O:18][c:17]1[c:12]([CH2:11][C:9]([NH:8][CH3:7])=[O:10])[cH:13][cH:14][cH:15][cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]([O:3][CH2:4][CH3:5])=[O:6])[O:18][c:17]1[c:12]([CH2:11][C:9]([NH:8][CH3:7])=[O:10])[cH:13][cH:14][cH:15][cH:16]1\", \"reactants\":[\"Br[CH2:1][C:2]([O:3][CH2:4][CH3:5])=[O:6].[CH3:7][NH:8][C:9](=[O:10])[CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[OH:18]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][C:2]([O:3][CH2:4][CH3:5])=[O:6]' and '[CH3:7][NH:8][C:9](=[O:10])[CH2:11][c:12]1[cH:13][cH:14][cH:15][cH:16][c:17]1[OH:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most likely synthesis method for 'c:C[[c[24]C]c][):3)]1N[]72]c15c7]H23215:c131[[2#2N62[:2875(][1:22C[]1(1]]]OO:[[H:06H:H:[6[HC]H:[H[C1[032:]:[[:3H1:(2]:]]314sC:O3:]H:2C12]24C:HN[[9=:H)22):1(]:cc:[])c(c[(]2:C:198)]]:[]1:[H][=C[]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c:C[[c[24]C]c][):3)]1N[]72]c15c7]H23215:c131[[2#2N62[:2875(][1:22C[]1(1]]]OO:[[H:06H:H:[6[HC]H:[H[C1[032:]:[[:3H1:(2]:]]314sC:O3:]H:2C12]24C:HN[[9=:H)22):1(]:cc:[])c(c[(]2:C:198)]]:[]1:[H][=C[]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile proper for drug1 is '[C:1]([CH2:2][CH2:3][CH3:4])(=[O:5])[N:25]1[CH2:24][CH2:23][c:19]2[c:18]([s:17][c:16]([NH:15][C:13]([c:12]3[cH:11][cH:10][cH:9][c:8]([O:7][CH3:6])[cH:27]3)=[O:14])[c:20]2[C:21]#[N:22])[CH2:26]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][CH2:3][CH3:4])(=[O:5])[N:25]1[CH2:24][CH2:23][c:19]2[c:18]([s:17][c:16]([NH:15][C:13]([c:12]3[cH:11][cH:10][cH:9][c:8]([O:7][CH3:6])[cH:27]3)=[O:14])[c:20]2[C:21]#[N:22])[CH2:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][CH2:3][CH3:4])(=[O:5])[N:25]1[CH2:24][CH2:23][c:19]2[c:18]([s:17][c:16]([NH:15][C:13]([c:12]3[cH:11][cH:10][cH:9][c:8]([O:7][CH3:6])[cH:27]3)=[O:14])[c:20]2[C:21]#[N:22])[CH2:26]1\", \"reactants\":[\"Cl[C:1]([CH2:2][CH2:3][CH3:4])=[O:5].[CH3:6][O:7][c:8]1[cH:9][cH:10][cH:11][c:12]([C:13](=[O:14])[NH:15][c:16]2[s:17][c:18]3[c:19]([c:20]2[C:21]#[N:22])[CH2:23][CH2:24][NH:25][CH2:26]3)[cH:27]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]([CH2:2][CH2:3][CH3:4])=[O:5]' and '[CH3:6][O:7][c:8]1[cH:9][cH:10][cH:11][c:12]([C:13](=[O:14])[NH:15][c:16]2[s:17][c:18]3[c:19]([c:20]2[C:21]#[N:22])[CH2:23][CH2:24][NH:25][CH2:26]3)[cH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis path of ']O11[Nc24[1]]0)]HF:][H[H:H:N[cSCF:]C(c:]23292)6[)]21]:12=@[9[)cFc583231:O6[[:[[[][[]c(C82:]O13[:][]1):1:HC:(6c:c[:71[::H]H2)1c2N]][1]:)[]7:]]1)(n0c(H:2:2::2([[]::=)c[:1]][1]7(]411CCC]22H:C@2[2:[]=3[:9#:H(HH4)([0](5528][A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"]O11[Nc24[1]]0)]HF:][H[H:H:N[cSCF:]C(c:]23292)6[)]21]:12=@[9[)cFc583231:O6[[:[[[][[]c(C82:]O13[:][]1):1:HC:(6c:c[:71[::H]H2)1c2N]][1]:)[]7:]]1)(n0c(H:2:2::2([[]::=)c[:1]][1]7(]411CCC]22H:C@2[2:[]=3[:9#:H(HH4)([0](5528][A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1]([c:2]1[cH:3][cH:4][c:5]([C:6]#[N:7])[n:8][cH:9]1)(=[O:10])[N:30]1[C@@H:12]([CH3:11])[CH2:13][N:14]([S:15](=[O:16])(=[O:17])[c:18]2[cH:19][cH:20][c:21]([C:22]([F:23])([F:24])[F:25])[cH:26][cH:27]2)[CH2:28][CH2:29]1' is the expression that matches drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][cH:4][c:5]([C:6]#[N:7])[n:8][cH:9]1)(=[O:10])[N:30]1[C@@H:12]([CH3:11])[CH2:13][N:14]([S:15](=[O:16])(=[O:17])[c:18]2[cH:19][cH:20][c:21]([C:22]([F:23])([F:24])[F:25])[cH:26][cH:27]2)[CH2:28][CH2:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][cH:4][c:5]([C:6]#[N:7])[n:8][cH:9]1)(=[O:10])[N:30]1[C@@H:12]([CH3:11])[CH2:13][N:14]([S:15](=[O:16])(=[O:17])[c:18]2[cH:19][cH:20][c:21]([C:22]([F:23])([F:24])[F:25])[cH:26][cH:27]2)[CH2:28][CH2:29]1\", \"reactants\":[\"O[C:1]([c:2]1[cH:3][cH:4][c:5]([C:6]#[N:7])[n:8][cH:9]1)=[O:10].[CH3:11][C@H:12]1[CH2:13][N:14]([S:15](=[O:16])(=[O:17])[c:18]2[cH:19][cH:20][c:21]([C:22]([F:23])([F:24])[F:25])[cH:26][cH:27]2)[CH2:28][CH2:29][NH:30]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[cH:3][cH:4][c:5]([C:6]#[N:7])[n:8][cH:9]1)=[O:10]' and '[CH3:11][C@H:12]1[CH2:13][N:14]([S:15](=[O:16])(=[O:17])[c:18]2[cH:19][cH:20][c:21]([C:22]([F:23])([F:24])[F:25])[cH:26][cH:27]2)[CH2:28][CH2:29][NH:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path for 'HC]o::[C13[::2c[21]O1:[3]cHC3[::HH1)912O(33c3[8)H2[34[1]c:C(c)[1437[][:][1[[02[1[6(:330]]31[:]:24:9(:0[cc3H=83]3C:]:]2[]:):[]23Oc]=[(]cn-F::[[6]:1336)5)(]]:Nc]H:383H():19)H]131:2[c:H7c[::)[CH:[6:F:5(1)4]H5-]2H[52(CCcHc1[]:[22]:]]2[N[1(:[][2[]c2]]:787]c[FHc])cH([:[1[]C]]:]9]c:F:2]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"HC]o::[C13[::2c[21]O1:[3]cHC3[::HH1)912O(33c3[8)H2[34[1]c:C(c)[1437[][:][1[[02[1[6(:330]]31[:]:24:9(:0[cc3H=83]3C:]:]2[]:):[]23Oc]=[(]cn-F::[[6]:1336)5)(]]:Nc]H:383H():19)H]131:2[c:H7c[::)[CH:[6:F:5(1)4]H5-]2H[52(CCcHc1[]:[22]:]]2[N[1(:[][2[]c2]]:787]c[FHc])cH([:[1[]C]]:]9]c:F:2]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([NH:34][CH2:35][C:36]([F:37])([F:38])[F:39])[n:2][c:3]2[o:4][c:5](-[c:6]3[cH:7][cH:8][c:9]([F:10])[cH:11][cH:12]3)[c:13]([C:14]([NH:15][CH3:16])=[O:17])[c:18]2[cH:19][c:20]1-[c:21]1[cH:22][cH:23][cH:24][c:25]([C:26](=[O:27])[O:28][C:29]([CH3:30])([CH3:31])[CH3:32])[cH:33]1' proper the smile meant for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:34][CH2:35][C:36]([F:37])([F:38])[F:39])[n:2][c:3]2[o:4][c:5](-[c:6]3[cH:7][cH:8][c:9]([F:10])[cH:11][cH:12]3)[c:13]([C:14]([NH:15][CH3:16])=[O:17])[c:18]2[cH:19][c:20]1-[c:21]1[cH:22][cH:23][cH:24][c:25]([C:26](=[O:27])[O:28][C:29]([CH3:30])([CH3:31])[CH3:32])[cH:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:34][CH2:35][C:36]([F:37])([F:38])[F:39])[n:2][c:3]2[o:4][c:5](-[c:6]3[cH:7][cH:8][c:9]([F:10])[cH:11][cH:12]3)[c:13]([C:14]([NH:15][CH3:16])=[O:17])[c:18]2[cH:19][c:20]1-[c:21]1[cH:22][cH:23][cH:24][c:25]([C:26](=[O:27])[O:28][C:29]([CH3:30])([CH3:31])[CH3:32])[cH:33]1\", \"reactants\":[\"Cl[c:1]1[n:2][c:3]2[o:4][c:5](-[c:6]3[cH:7][cH:8][c:9]([F:10])[cH:11][cH:12]3)[c:13]([C:14]([NH:15][CH3:16])=[O:17])[c:18]2[cH:19][c:20]1-[c:21]1[cH:22][cH:23][cH:24][c:25]([C:26](=[O:27])[O:28][C:29]([CH3:30])([CH3:31])[CH3:32])[cH:33]1.[NH2:34][CH2:35][C:36]([F:37])([F:38])[F:39]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[n:2][c:3]2[o:4][c:5](-[c:6]3[cH:7][cH:8][c:9]([F:10])[cH:11][cH:12]3)[c:13]([C:14]([NH:15][CH3:16])=[O:17])[c:18]2[cH:19][c:20]1-[c:21]1[cH:22][cH:23][cH:24][c:25]([C:26](=[O:27])[O:28][C:29]([CH3:30])([CH3:31])[CH3:32])[cH:33]1' and '[NH2:34][CH2:35][C:36]([F:37])([F:38])[F:39]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for 'c]3:3H1H(]]C2)]O]1:17]H:[c2][F()[][[HC:](25[[:]1c]c:::[041:2c91=:186F[[]H:H12:[2)]CCc[:[A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c]3:3H1H(]]C2)]O]1:17]H:[c2][F()[][[HC:](25[[:]1c]c:::[041:2c91=:186F[[]H:H12:[2)]CCc[:[A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([CH:11]2[CH2:12][CH2:13]2)[cH:2][cH:3][c:4]([F:5])[c:6]([CH:7]=[O:8])[c:9]1[F:10]' is the true smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([CH:11]2[CH2:12][CH2:13]2)[cH:2][cH:3][c:4]([F:5])[c:6]([CH:7]=[O:8])[c:9]1[F:10]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([CH:11]2[CH2:12][CH2:13]2)[cH:2][cH:3][c:4]([F:5])[c:6]([CH:7]=[O:8])[c:9]1[F:10]\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([F:5])[c:6]([CH:7]=[O:8])[c:9]1[F:10].OB(O)[CH:11]1[CH2:12][CH2:13]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([F:5])[c:6]([CH:7]=[O:8])[c:9]1[F:10]' and 'OB(O)[CH:11]1[CH2:12][CH2:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most likely synthesis path for ':121O2222C](113(1:O1C8[=c][][][H6[]][3]][::C6::521C2H:9:H:8)0(]3)1]Nc][][C[s21[:2)C(51:::[(7)HH[O[[11]]H[]4:c[::H[3:]]]):=7C14HCCcA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\":121O2222C](113(1:O1C8[=c][][][H6[]][3]][::C6::521C2H:9:H:8)0(]3)1]Nc][][C[s21[:2)C(51:::[(7)HH[O[[11]]H[]4:c[::H[3:]]]):=7C14HCCcA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1]([CH3:2])(=[O:3])[NH:11][c:10]1[c:9]([C:7]([O:6][CH2:5][CH3:4])=[O:8])[c:14]2[c:13]([s:12]1)[CH2:18][CH2:17][CH2:16][CH2:15]2' should be the smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH3:2])(=[O:3])[NH:11][c:10]1[c:9]([C:7]([O:6][CH2:5][CH3:4])=[O:8])[c:14]2[c:13]([s:12]1)[CH2:18][CH2:17][CH2:16][CH2:15]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH3:2])(=[O:3])[NH:11][c:10]1[c:9]([C:7]([O:6][CH2:5][CH3:4])=[O:8])[c:14]2[c:13]([s:12]1)[CH2:18][CH2:17][CH2:16][CH2:15]2\", \"reactants\":[\"O[C:1]([CH3:2])=[O:3].[CH3:4][CH2:5][O:6][C:7](=[O:8])[c:9]1[c:10]([NH2:11])[s:12][c:13]2[c:14]1[CH2:15][CH2:16][CH2:17][CH2:18]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH3:2])=[O:3]' and '[CH3:4][CH2:5][O:6][C:7](=[O:8])[c:9]1[c:10]([NH2:11])[s:12][c:13]2[c:14]1[CH2:15][CH2:16][CH2:17][CH2:18]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis steps for '::C]:0][2]H]](c[]1[[Hc72:nH6:c[]nc11[:1]:9OH:H[1[2=[3c]4[]c)::n:]81[5A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"::C]:0][2]H]](c[]1[[Hc72:nH6:c[]nc11[:1]:9OH:H[1[2=[3c]4[]c)::n:]81[5A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile for drug1 should be '[CH:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[n:7][cH:8][n:9][n:10]2[cH:11]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[n:7][cH:8][n:9][n:10]2[cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[n:7][cH:8][n:9][n:10]2[cH:11]1\", \"reactants\":[\"CN(C)[CH:1]=[O:2].I[c:3]1[cH:4][cH:5][c:6]2[n:7][cH:8][n:9][n:10]2[cH:11]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CN(C)[CH:1]=[O:2]' and 'I[c:3]1[cH:4][cH:5][c:6]2[n:7][cH:8][n:9][n:10]2[cH:11]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for 'H]C]cc5[c]:c:HHHc1227[[31N][[:1::3]0:2cC[::H[:[]1)::21]H6[[(][27cB(8Oc1H]H:[:)6:]11[1ccH2(:24H[H498:1][H[c113:20C]]:2]1[]1]rH391]5[H2HC]:])cC][HC[[]::A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H]C]cc5[c]:c:HHHc1227[[31N][[:1::3]0:2cC[::H[:[]1)::21]H6[[(][27cB(8Oc1H]H:[:)6:]11[1ccH2(:24H[H498:1][H[c113:20C]]:2]1[]1]rH391]5[H2HC]:])cC][HC[[]::A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "drug1’s true smile is '[Br:1][c:2]1[cH:3][cH:4][c:5]([CH:6]2[CH:7]([OH:21])[CH2:8][N:9]([CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)[CH2:17][CH2:18]2)[cH:19][cH:20]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Br:1][c:2]1[cH:3][cH:4][c:5]([CH:6]2[CH:7]([OH:21])[CH2:8][N:9]([CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)[CH2:17][CH2:18]2)[cH:19][cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[Br:1][c:2]1[cH:3][cH:4][c:5]([CH:6]2[CH:7]([OH:21])[CH2:8][N:9]([CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)[CH2:17][CH2:18]2)[cH:19][cH:20]1\", \"reactants\":[\"[Br:1][c:2]1[cH:3][cH:4][c:5]([C:6]2=[CH:7][CH2:8][N:9]([CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)[CH2:17][CH2:18]2)[cH:19][cH:20]1.[OH2:21]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[Br:1][c:2]1[cH:3][cH:4][c:5]([C:6]2=[CH:7][CH2:8][N:9]([CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)[CH2:17][CH2:18]2)[cH:19][cH:20]1' and '[OH2:21]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the likely synthesis method for 'H3c1:c[1]68]:112c=[95:[5c[2]::H:[Hc]:])[3)1[c:=:](]2[:]C1O[4]C:](C:)C[[Hc](0cO:1H1[)1[:7H1]H4)c9Hc:7[3[[H(1]OO:]1]02:c2]H:1[([8[6]]3]c][:1:A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H3c1:c[1]68]:112c=[95:[5c[2]::H:[Hc]:])[3)1[c:=:](]2[:]C1O[4]C:](C:)C[[Hc](0cO:1H1[)1[:7H1]H4)c9Hc:7[3[[H(1]OO:]1]02:c2]H:1[([8[6]]3]c][:1:A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([C:12](=[O:13])[c:14]2[cH:15][cH:16][c:17]([CH3:18])[cH:19][cH:20]2)[cH:2][cH:3][c:4]([O:5][CH3:6])[cH:7][c:8]1[C:9](=[O:10])[OH:11]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:12](=[O:13])[c:14]2[cH:15][cH:16][c:17]([CH3:18])[cH:19][cH:20]2)[cH:2][cH:3][c:4]([O:5][CH3:6])[cH:7][c:8]1[C:9](=[O:10])[OH:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:12](=[O:13])[c:14]2[cH:15][cH:16][c:17]([CH3:18])[cH:19][cH:20]2)[cH:2][cH:3][c:4]([O:5][CH3:6])[cH:7][c:8]1[C:9](=[O:10])[OH:11]\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH3:6])[cH:7][c:8]1[C:9](=[O:10])[OH:11].CON(C)[C:12](=[O:13])[c:14]1[cH:15][cH:16][c:17]([CH3:18])[cH:19][cH:20]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH3:6])[cH:7][c:8]1[C:9](=[O:10])[OH:11]' and 'CON(C)[C:12](=[O:13])[c:14]1[cH:15][cH:16][c:17]([CH3:18])[cH:19][cH:20]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '130c88[2])[H22C[(H:FC:]([:71]2=N:H1:3:]:]825:]:H[6O:Cc(][2(:c[::H[1O:[:][Hc[11H:]C2C2[c[3[11c6n[27c[1:7[[H12141H193[2]c[[C4]2l1[521][9]]02]O(]]()1]42:)c]1][2]S[]H)c][5:]::2:])2:)62c]Cn::n3]:::[c]2H[=HA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"130c88[2])[H22C[(H:FC:]([:71]2=N:H1:3:]:]825:]:H[6O:Cc(][2(:c[::H[1O:[:][Hc[11H:]C2C2[c[3[11c6n[27c[1:7[[H12141H193[2]c[[C4]2l1[521][9]]02]O(]]()1]42:)c]1][2]S[]H)c][5:]::2:])2:)62c]Cn::n3]:::[c]2H[=HA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "drug1 requires '[CH2:1]([CH2:2][N:3]1[CH2:4][CH2:5][O:6][CH2:7][CH2:8]1)[n:26]1[cH:25][cH:24][c:23]2[c:21]([Cl:22])[n:20][c:19]([S:10](=[O:9])(=[O:11])[c:12]3[cH:13][cH:14][c:15]([F:16])[cH:17][cH:18]3)[n:28][c:27]21' as the smile. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][N:3]1[CH2:4][CH2:5][O:6][CH2:7][CH2:8]1)[n:26]1[cH:25][cH:24][c:23]2[c:21]([Cl:22])[n:20][c:19]([S:10](=[O:9])(=[O:11])[c:12]3[cH:13][cH:14][c:15]([F:16])[cH:17][cH:18]3)[n:28][c:27]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][N:3]1[CH2:4][CH2:5][O:6][CH2:7][CH2:8]1)[n:26]1[cH:25][cH:24][c:23]2[c:21]([Cl:22])[n:20][c:19]([S:10](=[O:9])(=[O:11])[c:12]3[cH:13][cH:14][c:15]([F:16])[cH:17][cH:18]3)[n:28][c:27]21\", \"reactants\":[\"Cl[CH2:1][CH2:2][N:3]1[CH2:4][CH2:5][O:6][CH2:7][CH2:8]1.[O:9]=[S:10](=[O:11])([c:12]1[cH:13][cH:14][c:15]([F:16])[cH:17][cH:18]1)[c:19]1[n:20][c:21]([Cl:22])[c:23]2[cH:24][cH:25][nH:26][c:27]2[n:28]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][CH2:2][N:3]1[CH2:4][CH2:5][O:6][CH2:7][CH2:8]1' and '[O:9]=[S:10](=[O:11])([c:12]1[cH:13][cH:14][c:15]([F:16])[cH:17][cH:18]1)[c:19]1[n:20][c:21]([Cl:22])[c:23]2[cH:24][cH:25][nH:26][c:27]2[n:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis steps for ']]6:c[211C[[1:]c6:1:Fc3[2]c4NH(HHC3]7CH):]812[[c0[F(1]7[:H]5c1[:C1)][[]HHH2:1c4cc[)1c[][)[::[::9]]]0:22[1])][[51Hc2H:][9:1:]:(H1(:1OH]H:1[:F](]1c8:A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"]]6:c[211C[[1:]c6:1:Fc3[2]c4NH(HHC3]7CH):]812[[c0[F(1]7[:H]5c1[:C1)][[]HHH2:1c4cc[)1c[][)[::[::9]]]0:22[1])][[51Hc2H:][9:1:]:(H1(:1OH]H:1[:F](]1c8:A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper smile for drug1 is '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([C:6]([F:7])([F:8])[F:9])[cH:10][cH:11]1)[NH:12][CH2:13][CH2:14][c:15]1[cH:16][cH:17][c:18]([OH:19])[cH:20][cH:21]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([C:6]([F:7])([F:8])[F:9])[cH:10][cH:11]1)[NH:12][CH2:13][CH2:14][c:15]1[cH:16][cH:17][c:18]([OH:19])[cH:20][cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([C:6]([F:7])([F:8])[F:9])[cH:10][cH:11]1)[NH:12][CH2:13][CH2:14][c:15]1[cH:16][cH:17][c:18]([OH:19])[cH:20][cH:21]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][cH:4][c:5]([C:6]([F:7])([F:8])[F:9])[cH:10][cH:11]1.[NH2:12][CH2:13][CH2:14][c:15]1[cH:16][cH:17][c:18]([OH:19])[cH:20][cH:21]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][c:5]([C:6]([F:7])([F:8])[F:9])[cH:10][cH:11]1' and '[NH2:12][CH2:13][CH2:14][c:15]1[cH:16][cH:17][c:18]([OH:19])[cH:20][cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis steps for ':]C(]((::[:n[C]]]](3:2]:c1c][H2[H[1F:3S[:]]:]31]5][]c[:)::])3H-:[]]F14]1n[[]32][:)F0c[C:)35)(3)12712c:)3c7[14(cc[9[[1[5:):1]2:)n2H[[21:c2:]F444H[]]2):cc[]c1H2cH-:c:252cN(F6869:n1c808[:1][[:1O=10c:(36[:1:2:]cc(:637H:[]l(3][2[3]]3H[c([::H[c9]]H[H2]3]][c741H:[H:)3[H[]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\":]C(]((::[:n[C]]]](3:2]:c1c][H2[H[1F:3S[:]]:]31]5][]c[:)::])3H-:[]]F14]1n[[]32][:)F0c[C:)35)(3)12712c:)3c7[14(cc[9[[1[5:):1]2:)n2H[[21:c2:]F444H[]]2):cc[]c1H2cH-:c:252cN(F6869:n1c808[:1][[:1O=10c:(36[:1:2:]cc(:637H:[]l(3][2[3]]3H[c([::H[c9]]H[H2]3]][c741H:[H:)3[H[]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1](=[O:2])([c:3]1[cH:4][c:5]([Cl:6])[cH:7][c:8]([S:9]([F:10])([F:11])([F:12])([F:13])[F:14])[cH:15]1)[NH:21][c:20]1[cH:19][cH:18][c:17]([CH3:16])[c:23](-[n:24]2[cH:25][cH:26][n:27]3[n:28][c:29](-[c:30]4[cH:31][cH:32][cH:33][n:34][cH:35]4)[cH:36][c:37]23)[cH:22]1' is the correct smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][c:5]([Cl:6])[cH:7][c:8]([S:9]([F:10])([F:11])([F:12])([F:13])[F:14])[cH:15]1)[NH:21][c:20]1[cH:19][cH:18][c:17]([CH3:16])[c:23](-[n:24]2[cH:25][cH:26][n:27]3[n:28][c:29](-[c:30]4[cH:31][cH:32][cH:33][n:34][cH:35]4)[cH:36][c:37]23)[cH:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][c:5]([Cl:6])[cH:7][c:8]([S:9]([F:10])([F:11])([F:12])([F:13])[F:14])[cH:15]1)[NH:21][c:20]1[cH:19][cH:18][c:17]([CH3:16])[c:23](-[n:24]2[cH:25][cH:26][n:27]3[n:28][c:29](-[c:30]4[cH:31][cH:32][cH:33][n:34][cH:35]4)[cH:36][c:37]23)[cH:22]1\", \"reactants\":[\"O[C:1](=[O:2])[c:3]1[cH:4][c:5]([Cl:6])[cH:7][c:8]([S:9]([F:10])([F:11])([F:12])([F:13])[F:14])[cH:15]1.[CH3:16][c:17]1[cH:18][cH:19][c:20]([NH2:21])[cH:22][c:23]1-[n:24]1[cH:25][cH:26][n:27]2[n:28][c:29](-[c:30]3[cH:31][cH:32][cH:33][n:34][cH:35]3)[cH:36][c:37]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][c:5]([Cl:6])[cH:7][c:8]([S:9]([F:10])([F:11])([F:12])([F:13])[F:14])[cH:15]1' and '[CH3:16][c:17]1[cH:18][cH:19][c:20]([NH2:21])[cH:22][c:23]1-[n:24]1[cH:25][cH:26][n:27]2[n:28][c:29](-[c:30]3[cH:31][cH:32][cH:33][n:34][cH:35]3)[cH:36][c:37]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method of '(:n)2]1:3[1]2(C33:H:3[[([:][H:cC[]:1c5512Hc29:HCc:[cF]S[214]0:[[])C](c[2c[)[c6]31[c[:228]c:[[[:::2Hc:=-1[3]]]c2]1c2123:]]:1[[P]])[2]0:1F:H][BH]7]2:1]]H2H4HH:3]:c399[3rC8[(cH]6[O2([CH:][21)H22:H:7:)]][7541C:22:]:H]:O:(81)([OH:c1[[60]c)2[]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"(:n)2]1:3[1]2(C33:H:3[[([:][H:cC[]:1c5512Hc29:HCc:[cF]S[214]0:[[])C](c[2c[)[c6]31[c[:228]c:[[[:::2Hc:=-1[3]]]c2]1c2123:]]:1[[P]])[2]0:1F:H][BH]7]2:1]]H2H4HH:3]:c399[3rC8[(cH]6[O2([CH:][21)H22:H:7:)]][7541C:22:]:H]:O:(81)([OH:c1[[60]c)2[]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The correct match for drug1’s smile is '[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1.C1COB([c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)OC1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH2:5][S:6][CH2:7][c:8]2[cH:9][cH:10][c:11]([C:12]([P:13]([O:14][CH2:15][CH3:16])(=[O:17])[O:18][CH2:19][CH3:20])([F:21])[F:22])[c:23]([Br:24])[cH:25]2)[cH:26][cH:27]1' and 'C1COB([c:28]2[cH:29][n:30][cH:31][cH:32][cH:33]2)OC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for '3]Hc:[])2C]C:]16:)[O[H[]1])[F-:Hc5:]C89[c2=39][c([H(:3c3]C::5[](c[([](::H[(1N[1]]]H3::[0111([F:224)1H:[]4([122]]2c6:21:1::FcO[)H1]H2)11)]c70[7[][]1H[]):3]C:c:::cc[8A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"3]Hc:[])2C]C:]16:)[O[H[]1])[F-:Hc5:]C89[c2=39][c([H(:3c3]C::5[](c[([](::H[(1N[1]]]H3::[0111([F:224)1H:[]4([122]]2c6:21:1::FcO[)H1]H2)11)]c70[7[][]1H[]):3]C:c:::cc[8A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1(-[c:14]2[cH:15][cH:16][c:17]([C:18]([F:19])([F:20])[F:21])[cH:22][cH:23]2)[c:2]([C:3]([O:4][CH3:5])=[O:6])[cH:7][cH:8][c:9]([N:10]([CH3:11])[CH3:12])[cH:13]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:14]2[cH:15][cH:16][c:17]([C:18]([F:19])([F:20])[F:21])[cH:22][cH:23]2)[c:2]([C:3]([O:4][CH3:5])=[O:6])[cH:7][cH:8][c:9]([N:10]([CH3:11])[CH3:12])[cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:14]2[cH:15][cH:16][c:17]([C:18]([F:19])([F:20])[F:21])[cH:22][cH:23]2)[c:2]([C:3]([O:4][CH3:5])=[O:6])[cH:7][cH:8][c:9]([N:10]([CH3:11])[CH3:12])[cH:13]1\", \"reactants\":[\"O=S(=O)(O[c:1]1[c:2]([C:3]([O:4][CH3:5])=[O:6])[cH:7][cH:8][c:9]([N:10]([CH3:11])[CH3:12])[cH:13]1)C(F)(F)F.OB(O)[c:14]1[cH:15][cH:16][c:17]([C:18]([F:19])([F:20])[F:21])[cH:22][cH:23]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=S(=O)(O[c:1]1[c:2]([C:3]([O:4][CH3:5])=[O:6])[cH:7][cH:8][c:9]([N:10]([CH3:11])[CH3:12])[cH:13]1)C(F)(F)F' and 'OB(O)[c:14]1[cH:15][cH:16][c:17]([C:18]([F:19])([F:20])[F:21])[cH:22][cH:23]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for 'cC1:(lF1][6=2]O[)516C:[CH[]2(:H[1)[9]c1Hc:nC[]7C4:[:[N[:[9[:S1:H]1:][(CHH2)1[0c:F:2C[:7[[]]12]H:11H:582:)]::][1]c[84(3=:C13]2]()]]]]1A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"cC1:(lF1][6=2]O[)516C:[CH[]2(:H[1)[9]c1Hc:nC[]7C4:[:[N[:[9[:S1:H]1:][(CHH2)1[0c:F:2C[:7[[]]12]H:11H:582:)]::][1]c[84(3=:C13]2]()]]]]1A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right expression for drug1 is '[C:1]1([N:11]([CH2:10][CH:8]([F:7])[F:9])[CH2:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[n:18][cH:19]2)=[CH:2][C:3](=[S:4])[O:5][CH2:6]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]1([N:11]([CH2:10][CH:8]([F:7])[F:9])[CH2:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[n:18][cH:19]2)=[CH:2][C:3](=[S:4])[O:5][CH2:6]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]1([N:11]([CH2:10][CH:8]([F:7])[F:9])[CH2:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[n:18][cH:19]2)=[CH:2][C:3](=[S:4])[O:5][CH2:6]1\", \"reactants\":[\"O[C:1]1=[CH:2][C:3](=[S:4])[O:5][CH2:6]1.[F:7][CH:8]([F:9])[CH2:10][NH:11][CH2:12][c:13]1[cH:14][cH:15][c:16]([Cl:17])[n:18][cH:19]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]1=[CH:2][C:3](=[S:4])[O:5][CH2:6]1' and '[F:7][CH:8]([F:9])[CH2:10][NH:11][CH2:12][c:13]1[cH:14][cH:15][c:16]([Cl:17])[n:18][cH:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis steps for '54:H[3N[CH1:CCCH]H73C]9(7[)O[3C:[:2::[28]]2:2HcHC([]:[2(4C0::])221CH:[2[1:H7CCH[O[]3=3:2]N3[(1][)]CN[:]22201]::]23]7[1C5C]CO21810[H])1:]39HC)[23N]:H(][]:c:3(20[2[[:(C:[2[[:]]H482N:9H][[[3:83:4)=[H])]9H:H]]C4::6H(C])][3[:1][[C345][:3([:2CSC[(1]22C:N[6)HH]]:2H13H211c2]2[:6]:2[=32=:H:1s6]H5[]2)1n2:2O212H2CHC32](]]4:21:[1:)C][3A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"54:H[3N[CH1:CCCH]H73C]9(7[)O[3C:[:2::[28]]2:2HcHC([]:[2(4C0::])221CH:[2[1:H7CCH[O[]3=3:2]N3[(1][)]CN[:]22201]::]23]7[1C5C]CO21810[H])1:]39HC)[23N]:H(][]:c:3(20[2[[:(C:[2[[:]]H482N:9H][[[3:83:4)=[H])]9H:H]]C4::6H(C])][3[:1][[C345][:3([:2CSC[(1]22C:N[6)HH]]:2H13H211c2]2[:6]:2[=32=:H:1s6]H5[]2)1n2:2O212H2CHC32](]]4:21:[1:)C][3A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7]1)[N:38]1[CH2:37][CH2:36][CH:35]([N:13]([CH:12]2[CH2:11][CH2:10][CH:9]([CH3:8])[CH2:42][CH2:41]2)[C:14](=[O:15])[NH:16][c:17]2[n:18][cH:19][c:20]([CH2:21][N:22]3[CH2:23][CH2:24][N:25]([S:26](=[O:27])(=[O:28])[N:29]([CH3:30])[CH3:31])[CH2:32][CH2:33]3)[s:34]2)[CH2:40][CH2:39]1' is the correct smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7]1)[N:38]1[CH2:37][CH2:36][CH:35]([N:13]([CH:12]2[CH2:11][CH2:10][CH:9]([CH3:8])[CH2:42][CH2:41]2)[C:14](=[O:15])[NH:16][c:17]2[n:18][cH:19][c:20]([CH2:21][N:22]3[CH2:23][CH2:24][N:25]([S:26](=[O:27])(=[O:28])[N:29]([CH3:30])[CH3:31])[CH2:32][CH2:33]3)[s:34]2)[CH2:40][CH2:39]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7]1)[N:38]1[CH2:37][CH2:36][CH:35]([N:13]([CH:12]2[CH2:11][CH2:10][CH:9]([CH3:8])[CH2:42][CH2:41]2)[C:14](=[O:15])[NH:16][c:17]2[n:18][cH:19][c:20]([CH2:21][N:22]3[CH2:23][CH2:24][N:25]([S:26](=[O:27])(=[O:28])[N:29]([CH3:30])[CH3:31])[CH2:32][CH2:33]3)[s:34]2)[CH2:40][CH2:39]1\", \"reactants\":[\"Cl[C:1](=[O:2])[CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7]1.[CH3:8][CH:9]1[CH2:10][CH2:11][CH:12]([N:13]([C:14](=[O:15])[NH:16][c:17]2[n:18][cH:19][c:20]([CH2:21][N:22]3[CH2:23][CH2:24][N:25]([S:26](=[O:27])(=[O:28])[N:29]([CH3:30])[CH3:31])[CH2:32][CH2:33]3)[s:34]2)[CH:35]2[CH2:36][CH2:37][NH:38][CH2:39][CH2:40]2)[CH2:41][CH2:42]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7]1' and '[CH3:8][CH:9]1[CH2:10][CH2:11][CH:12]([N:13]([C:14](=[O:15])[NH:16][c:17]2[n:18][cH:19][c:20]([CH2:21][N:22]3[CH2:23][CH2:24][N:25]([S:26](=[O:27])(=[O:28])[N:29]([CH3:30])[CH3:31])[CH2:32][CH2:33]3)[s:34]2)[CH:35]2[CH2:36][CH2:37][NH:38][CH2:39][CH2:40]2)[CH2:41][CH2:42]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis route for ':1:H:2[C[7(]1H]nO0[12:nn:]22[cH]5]]32-:)]68[]2(3[:H[c]C1:H:5]:3]21c33c))H:3:2][C[c0:C[[41]Hc:1:9:[[C24c[[][9C]H]1H[7c22n4)]]6:Cn1]](11[H6](1:2[:2[[c3:HC:):(][][1::H[1]22:]c8312][:[51C1nA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\":1:H:2[C[7(]1H]nO0[12:nn:]22[cH]5]]32-:)]68[]2(3[:H[c]C1:H:5]:3]21c33c))H:3:2][C[c0:C[[41]Hc:1:9:[[C24c[[][9C]H]1H[7c22n4)]]6:Cn1]](11[H6](1:2[:2[[c3:HC:):(][][1::H[1]22:]c8312][:[51C1nA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH2:1]([CH2:2][CH2:3][c:4]1[cH:5][n:6][c:7]([CH2:8][CH3:9])[n:10]1[CH3:11])[O:26][c:25]1[c:13]([CH3:12])[cH:14][c:15](-[c:16]2[n:17][n:18][n:19]([CH3:20])[n:21]2)[cH:22][c:23]1[CH3:24]' is the expression that matches drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][c:4]1[cH:5][n:6][c:7]([CH2:8][CH3:9])[n:10]1[CH3:11])[O:26][c:25]1[c:13]([CH3:12])[cH:14][c:15](-[c:16]2[n:17][n:18][n:19]([CH3:20])[n:21]2)[cH:22][c:23]1[CH3:24]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][c:4]1[cH:5][n:6][c:7]([CH2:8][CH3:9])[n:10]1[CH3:11])[O:26][c:25]1[c:13]([CH3:12])[cH:14][c:15](-[c:16]2[n:17][n:18][n:19]([CH3:20])[n:21]2)[cH:22][c:23]1[CH3:24]\", \"reactants\":[\"O[CH2:1][CH2:2][CH2:3][c:4]1[cH:5][n:6][c:7]([CH2:8][CH3:9])[n:10]1[CH3:11].[CH3:12][c:13]1[cH:14][c:15](-[c:16]2[n:17][n:18][n:19]([CH3:20])[n:21]2)[cH:22][c:23]([CH3:24])[c:25]1[OH:26]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[CH2:1][CH2:2][CH2:3][c:4]1[cH:5][n:6][c:7]([CH2:8][CH3:9])[n:10]1[CH3:11]' and '[CH3:12][c:13]1[cH:14][c:15](-[c:16]2[n:17][n:18][n:19]([CH3:20])[n:21]2)[cH:22][c:23]([CH3:24])[c:25]1[OH:26]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide the synthesis path of '0[3]::[H1c87::]cC:6[c[]2n[H42]1:]]]]1-[c[c2]:][1n(:([cH2H1[[[H::c]H5c1))H1c][c9:1:l]:3A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"0[3]::[H1c87::]cC:6[c[]2n[H42]1:]]]]1-[c[c2]:][1n(:([cH2H1[[[H::c]H5c1))H1c][c9:1:l]:3A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1(-[c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[cH:2][c:3]([Cl:4])[n:5][cH:6][n:7]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[cH:2][c:3]([Cl:4])[n:5][cH:6][n:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:8]2[cH:9][cH:10][cH:11][cH:12][cH:13]2)[cH:2][c:3]([Cl:4])[n:5][cH:6][n:7]1\", \"reactants\":[\"Cl[c:1]1[cH:2][c:3]([Cl:4])[n:5][cH:6][n:7]1.OB(O)[c:8]1[cH:9][cH:10][cH:11][cH:12][cH:13]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[cH:2][c:3]([Cl:4])[n:5][cH:6][n:7]1' and 'OB(O)[c:8]1[cH:9][cH:10][cH:11][cH:12][cH:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis route of '2232(3](Cc]2:[[[:1]H]7NN:HH2[c9225]C112]][2[]8CC]][)1:)[]1(]C:::4[4nHC9[=n1::[N1:22H[031c:H:[)]1[1cc]:])8]:::6[[[[23235[(]36O4:17nC:(2H]2]):n]5::=1[:8n]HC01H2:[[H([O]]31[:3c[:C:7O[6[H1]Hc][:c2]9=:2[:]3]c)2]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"2232(3](Cc]2:[[[:1]H]7NN:HH2[c9225]C112]][2[]8CC]][)1:)[]1(]C:::4[4nHC9[=n1::[N1:22H[031c:H:[)]1[1cc]:])8]:::6[[[[23235[(]36O4:17nC:(2H]2]):n]5::=1[:8n]HC01H2:[[H([O]]31[:3c[:C:7O[6[H1]Hc][:c2]9=:2[:]3]c)2]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1]([c:2]1[n:3][n:4]2[cH:5][cH:6][c:7]([NH:8][C:9]([c:10]3[n:11]([CH3:12])[n:13][cH:14][c:15]3[C:16](=[O:17])[N:18]3[CH2:19][CH2:20][CH2:21]3)=[O:22])[cH:23][c:24]2[n:25]1)(=[O:26])[NH:29][CH2:28][CH3:27]' proper the smile meant for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[n:3][n:4]2[cH:5][cH:6][c:7]([NH:8][C:9]([c:10]3[n:11]([CH3:12])[n:13][cH:14][c:15]3[C:16](=[O:17])[N:18]3[CH2:19][CH2:20][CH2:21]3)=[O:22])[cH:23][c:24]2[n:25]1)(=[O:26])[NH:29][CH2:28][CH3:27]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[n:3][n:4]2[cH:5][cH:6][c:7]([NH:8][C:9]([c:10]3[n:11]([CH3:12])[n:13][cH:14][c:15]3[C:16](=[O:17])[N:18]3[CH2:19][CH2:20][CH2:21]3)=[O:22])[cH:23][c:24]2[n:25]1)(=[O:26])[NH:29][CH2:28][CH3:27]\", \"reactants\":[\"O[C:1]([c:2]1[n:3][n:4]2[cH:5][cH:6][c:7]([NH:8][C:9]([c:10]3[n:11]([CH3:12])[n:13][cH:14][c:15]3[C:16](=[O:17])[N:18]3[CH2:19][CH2:20][CH2:21]3)=[O:22])[cH:23][c:24]2[n:25]1)=[O:26].[CH3:27][CH2:28][NH2:29]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[n:3][n:4]2[cH:5][cH:6][c:7]([NH:8][C:9]([c:10]3[n:11]([CH3:12])[n:13][cH:14][c:15]3[C:16](=[O:17])[N:18]3[CH2:19][CH2:20][CH2:21]3)=[O:22])[cH:23][c:24]2[n:25]1)=[O:26]' and '[CH3:27][CH2:28][NH2:29]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for ']2:10:1]:H3[:n2]Hc7(]Hc6c16:c[n9H:1cn:]H]c)O2:c22[]n][[(1c-[:H]1c1c[]c[5[4[2-H2H12:][:34:11]31[1:1::39:3H]c[]2::1[[c]]]][[1:[C8[8H[:c[7]:)05c():H2]cH]][A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"]2:10:1]:H3[:n2]Hc7(]Hc6c16:c[n9H:1cn:]H]c)O2:c22[]n][[(1c-[:H]1c1c[]c[5[4[2-H2H12:][:34:11]31[1:1::39:3H]c[]2::1[[c]]]][[1:[C8[8H[:c[7]:)05c():H2]cH]][A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid smile for drug1 is '[c:1]1(-[c:2]2[cH:3][cH:4][cH:5][nH:6]2)[nH:18][c:17]2[n:16][cH:15][cH:14][c:13](-[c:12]3[cH:11][cH:10][c:9]([O:8][CH3:7])[cH:22][cH:21]3)[c:19]2[n:20]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:2]2[cH:3][cH:4][cH:5][nH:6]2)[nH:18][c:17]2[n:16][cH:15][cH:14][c:13](-[c:12]3[cH:11][cH:10][c:9]([O:8][CH3:7])[cH:22][cH:21]3)[c:19]2[n:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:2]2[cH:3][cH:4][cH:5][nH:6]2)[nH:18][c:17]2[n:16][cH:15][cH:14][c:13](-[c:12]3[cH:11][cH:10][c:9]([O:8][CH3:7])[cH:22][cH:21]3)[c:19]2[n:20]1\", \"reactants\":[\"O=[C:1](O)[c:2]1[cH:3][cH:4][cH:5][nH:6]1.[CH3:7][O:8][c:9]1[cH:10][cH:11][c:12](-[c:13]2[cH:14][cH:15][n:16][c:17]([NH2:18])[c:19]2[NH2:20])[cH:21][cH:22]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1](O)[c:2]1[cH:3][cH:4][cH:5][nH:6]1' and '[CH3:7][O:8][c:9]1[cH:10][cH:11][c:12](-[c:13]2[cH:14][cH:15][n:16][c:17]([NH2:18])[c:19]2[NH2:20])[cH:21][cH:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for 'O:(([:H22))2=4C2[):]HcC[)3H(CN:4]]22H[8[]5:[:[:C66]51::H[c][]11][63:6O::=[:[=[):O]:1:12]Oc:(54H=]cO:H](H=[][]]2)7C[2[1118O01c)3[][)9C1]:1:8]]HH]3H303C2]323:SH22Hc:H[]]3]41[[]]:[9H3212[30H1]:[]:C3c([::Hc[3([HH(OCCC[]9395:112)([[3[:::]C[-=3[[4[)]]4:=[N22[]+N(:7:]]]4c]Cc)[]2]3:7:12]CSO]2]C7:c:O3[23:[(:N[8::0cA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"O:(([:H22))2=4C2[):]HcC[)3H(CN:4]]22H[8[]5:[:[:C66]51::H[c][]11][63:6O::=[:[=[):O]:1:12]Oc:(54H=]cO:H](H=[][]]2)7C[2[1118O01c)3[][)9C1]:1:8]]HH]3H303C2]323:SH22Hc:H[]]3]41[[]]:[9H3212[30H1]:[]:C3c([::Hc[3([HH(OCCC[]9395:112)([[3[:::]C[-=3[[4[)]]4:=[N22[]+N(:7:]]]4c]Cc)[]2]3:7:12]CSO]2]C7:c:O3[23:[(:N[8::0cA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:36]([CH:12]([C:10]([O:9][CH3:8])=[O:11])[CH2:13][S:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([O:21][CH2:22][CH2:23][O:24][S:25](=[O:26])(=[O:27])[c:28]2[cH:29][cH:30][c:31]([CH3:32])[cH:33][cH:34]2)[cH:35]1)[C:37](=[O:38])[CH2:39][N:40]=[N+:41]=[N-:42]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:36]([CH:12]([C:10]([O:9][CH3:8])=[O:11])[CH2:13][S:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([O:21][CH2:22][CH2:23][O:24][S:25](=[O:26])(=[O:27])[c:28]2[cH:29][cH:30][c:31]([CH3:32])[cH:33][cH:34]2)[cH:35]1)[C:37](=[O:38])[CH2:39][N:40]=[N+:41]=[N-:42]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:36]([CH:12]([C:10]([O:9][CH3:8])=[O:11])[CH2:13][S:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([O:21][CH2:22][CH2:23][O:24][S:25](=[O:26])(=[O:27])[c:28]2[cH:29][cH:30][c:31]([CH3:32])[cH:33][cH:34]2)[cH:35]1)[C:37](=[O:38])[CH2:39][N:40]=[N+:41]=[N-:42]\", \"reactants\":[\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][O:9][C:10](=[O:11])[CH:12]([CH2:13][S:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([O:21][CH2:22][CH2:23][O:24][S:25](=[O:26])(=[O:27])[c:28]2[cH:29][cH:30][c:31]([CH3:32])[cH:33][cH:34]2)[cH:35]1)[NH:36][C:37](=[O:38])[CH2:39][N:40]=[N+:41]=[N-:42]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][O:9][C:10](=[O:11])[CH:12]([CH2:13][S:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([O:21][CH2:22][CH2:23][O:24][S:25](=[O:26])(=[O:27])[c:28]2[cH:29][cH:30][c:31]([CH3:32])[cH:33][cH:34]2)[cH:35]1)[NH:36][C:37](=[O:38])[CH2:39][N:40]=[N+:41]=[N-:42]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis process for 'c][[3725][s:H[:3:C)]:]:321:]:c:]:c]1:(C162(:]]1]8C[]H2]1][(=221[11:]Hc41c21cH(2023cH[[8s[5:H][(2]:Nc52H[2c]:::301C:H[37[]]O69[C:]272[4]]::):O[3O[[c::3]0[]1:CH1[)8H22[c[[:c][][[9H:]H19:4)]:O2]c12:2c2cn[N[:]=C]H11)6[H1C[[23]22:A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c][[3725][s:H[:3:C)]:]:321:]:c:]:c]1:(C162(:]]1]8C[]H2]1][(=221[11:]Hc41c21cH(2023cH[[8s[5:H][(2]:Nc52H[2c]:::301C:H[37[]]O69[C:]272[4]]::):O[3O[[c::3]0[]1:CH1[)8H22[c[[:c][][[9H:]H19:4)]:O2]c12:2c2cn[N[:]=C]H11)6[H1C[[23]22:A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1]([c:2]1[s:3][c:4]2[c:5]([N:6]3[CH2:7][CH2:8][O:9][CH2:10][CH2:11]3)[cH:12][cH:13][c:14]([O:15][CH3:16])[c:17]2[n:18]1)(=[O:19])[NH:20][CH2:21][C:22](=[O:23])[c:24]1[cH:25][s:26][c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12' is the correct smile for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[s:3][c:4]2[c:5]([N:6]3[CH2:7][CH2:8][O:9][CH2:10][CH2:11]3)[cH:12][cH:13][c:14]([O:15][CH3:16])[c:17]2[n:18]1)(=[O:19])[NH:20][CH2:21][C:22](=[O:23])[c:24]1[cH:25][s:26][c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[s:3][c:4]2[c:5]([N:6]3[CH2:7][CH2:8][O:9][CH2:10][CH2:11]3)[cH:12][cH:13][c:14]([O:15][CH3:16])[c:17]2[n:18]1)(=[O:19])[NH:20][CH2:21][C:22](=[O:23])[c:24]1[cH:25][s:26][c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12\", \"reactants\":[\"O[C:1]([c:2]1[s:3][c:4]2[c:5]([N:6]3[CH2:7][CH2:8][O:9][CH2:10][CH2:11]3)[cH:12][cH:13][c:14]([O:15][CH3:16])[c:17]2[n:18]1)=[O:19].[NH2:20][CH2:21][C:22](=[O:23])[c:24]1[cH:25][s:26][c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[s:3][c:4]2[c:5]([N:6]3[CH2:7][CH2:8][O:9][CH2:10][CH2:11]3)[cH:12][cH:13][c:14]([O:15][CH3:16])[c:17]2[n:18]1)=[O:19]' and '[NH2:20][CH2:21][C:22](=[O:23])[c:24]1[cH:25][s:26][c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for 'HH]c1:][:2:[[])1]Hc:HH]])n4[:]]cO:nH1[(7:cS:[]1[:]:c625H0]c)11O]:[:2F9[15][[]3)18(]3:[:c1112]c:H[2[][]:18[H(:=C1cC:n]0H[21:21:][2271[]]:[3[c[6][2:2]cc(C]cF5[92442:[:1H:cA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"HH]c1:][:2:[[])1]Hc:HH]])n4[:]]cO:nH1[(7:cS:[]1[:]:c625H0]c)11O]:[:2F9[15][[]3)18(]3:[:c1112]c:H[2[][]:18[H(:=C1cC:n]0H[21:21:][2271[]]:[3[c[6][2:2]cc(C]cF5[92442:[:1H:cA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1\", \"reactants\":[\"Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1.[SH:20][c:21]1[n:22][cH:23][nH:24][n:25]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1' and '[SH:20][c:21]1[n:22][cH:23][nH:24][n:25]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for 'N::C1(5N:][6(]][1H@(61][cH:2[)=:13:O:cc:11[7C[]s[C7]]13[1[8]2)::CH[(9:[]]0HC1][H)H4c::C3]:cN[]42H]c1n:](:0[12:):[1[(H[)1:#][[2cN]]:[1H]):[C22c(3:]3]]85:3H)2[1Hn[]922A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"N::C1(5N:][6(]][1H@(61][cH:2[)=:13:O:cc:11[7C[]s[C7]]13[1[8]2)::CH[(9:[]]0HC1][H)H4c::C3]:cN[]42H]c1n:](:0[12:):[1[(H[)1:#][[2cN]]:[1H]):[C22c(3:]3]]85:3H)2[1Hn[]922A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[c:1]1([NH:21][c:20]2[cH:19][c:18]([CH3:17])[n:23][s:22]2)[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([NH:8][C@H:9]([CH:10]([CH3:11])[CH3:12])[C:13]([NH2:14])=[O:15])[cH:16]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:21][c:20]2[cH:19][c:18]([CH3:17])[n:23][s:22]2)[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([NH:8][C@H:9]([CH:10]([CH3:11])[CH3:12])[C:13]([NH2:14])=[O:15])[cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:21][c:20]2[cH:19][c:18]([CH3:17])[n:23][s:22]2)[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([NH:8][C@H:9]([CH:10]([CH3:11])[CH3:12])[C:13]([NH2:14])=[O:15])[cH:16]1\", \"reactants\":[\"Br[c:1]1[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([NH:8][C@H:9]([CH:10]([CH3:11])[CH3:12])[C:13]([NH2:14])=[O:15])[cH:16]1.[CH3:17][c:18]1[cH:19][c:20]([NH2:21])[s:22][n:23]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([NH:8][C@H:9]([CH:10]([CH3:11])[CH3:12])[C:13]([NH2:14])=[O:15])[cH:16]1' and '[CH3:17][c:18]1[cH:19][c:20]([NH2:21])[s:22][n:23]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most probable synthesis steps for ']cOc1]]::H1][)(56Cc2116[H:213[]]:c:8H(:227c1=c[[c)]317:HH3c):[:[::[3N[]2]H[[]11[[=:][c4=:Hn0:62]c]3H:2(5[[]2[:11[H:4]1]2:4][:]H=[2(:9]H[c]cH)2::[)c[2:2:29:c0[[[:[[][]8S15):[c:2c:N2]:1c2O213]]:]2[:H0O1[]nH(OccH]C17]O]H9]81c2(]A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"]cOc1]]::H1][)(56Cc2116[H:213[]]:c:8H(:227c1=c[[c)]317:HH3c):[:[::[3N[]2]H[[]11[[=:][c4=:Hn0:62]c]3H:2(5[[]2[:11[H:4]1]2:4][:]H=[2(:9]H[c]cH)2::[)c[2:2:29:c0[[[:[[][]8S15):[c:2c:N2]:1c2O213]]:]2[:H0O1[]nH(OccH]C17]O]H9]81c2(]A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[cH:7][cH:8][cH:9][cH:10][c:11]2[n:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][n:19]2)[c:20]1=[O:21])[NH:22][c:23]1[cH:24][cH:25][cH:26][cH:27][c:28]1[S:29]([NH2:30])(=[O:31])=[O:32]' is the valid expression for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[cH:7][cH:8][cH:9][cH:10][c:11]2[n:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][n:19]2)[c:20]1=[O:21])[NH:22][c:23]1[cH:24][cH:25][cH:26][cH:27][c:28]1[S:29]([NH2:30])(=[O:31])=[O:32]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[cH:7][cH:8][cH:9][cH:10][c:11]2[n:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][n:19]2)[c:20]1=[O:21])[NH:22][c:23]1[cH:24][cH:25][cH:26][cH:27][c:28]1[S:29]([NH2:30])(=[O:31])=[O:32]\", \"reactants\":[\"CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[cH:7][cH:8][cH:9][cH:10][c:11]2[n:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][n:19]2)[c:20]1=[O:21].[NH2:22][c:23]1[cH:24][cH:25][cH:26][cH:27][c:28]1[S:29]([NH2:30])(=[O:31])=[O:32]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[cH:7][cH:8][cH:9][cH:10][c:11]2[n:12]([CH2:13][c:14]2[cH:15][cH:16][cH:17][cH:18][n:19]2)[c:20]1=[O:21]' and '[NH2:22][c:23]1[cH:24][cH:25][cH:26][cH:27][c:28]1[S:29]([NH2:30])(=[O:31])=[O:32]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis path of ':2]:C-94[:3[(][]][]1H[[c4:N3[]:)9C[2(1[]]:36[6c]3H[::4[C(:2:1]31H5c24:94F]]]c1:3[:01[0:[H22][:nO4(:22[[c]:]):))3O3HHCcc(]:H[22]C[CC]3[c:3C15[2(c[C)1]]C2(Cc2H)])H))]0H=:7c:2C([H[3:2113[83H(2:2c:]33[HH[[]:]2:]::HH2C]]C)@C7]c:][[[2H3[O5[:=:523]]H]:[]]](7]62]8C44Nc6O1:3[::[]N]13::854442:[4]:c[3:H3Cc#0H[[]H2[6@H)3]81:][142[3HHN(1HC[[:H)C(97[:3CH:]21c]::71A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\":2]:C-94[:3[(][]][]1H[[c4:N3[]:)9C[2(1[]]:36[6c]3H[::4[C(:2:1]31H5c24:94F]]]c1:3[:01[0:[H22][:nO4(:22[[c]:]):))3O3HHCcc(]:H[22]C[CC]3[c:3C15[2(c[C)1]]C2(Cc2H)])H))]0H=:7c:2C([H[3:2113[83H(2:2c:]33[HH[[]:]2:]::HH2C]]C)@C7]c:][[[2H3[O5[:=:523]]H]:[]]](7]62]8C44Nc6O1:3[::[]N]13::854442:[4]:c[3:H3Cc#0H[[]H2[6@H)3]81:][142[3HHN(1HC[[:H)C(97[:3CH:]21c]::71A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "drug1’s correct smile is '[c:1]1(-[c:22]2[cH:23][cH:24][c:25]([CH2:26][C@H:27]([NH:28][C:29]([O:30][C:31]([CH3:32])([CH3:33])[CH3:34])=[O:35])[C:36](=[O:37])[N:38]3[CH2:39][CH2:40][CH2:41][C@H:42]3[C:43]#[N:44])[c:45]([F:46])[cH:47]2)[cH:2][cH:3][c:4]([O:5][CH:6]2[CH2:7][N:8]([CH2:9][c:10]3[cH:11][cH:12][c:13]([CH:14]([CH3:15])[CH3:16])[cH:17][cH:18]3)[CH2:19]2)[n:20][cH:21]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:22]2[cH:23][cH:24][c:25]([CH2:26][C@H:27]([NH:28][C:29]([O:30][C:31]([CH3:32])([CH3:33])[CH3:34])=[O:35])[C:36](=[O:37])[N:38]3[CH2:39][CH2:40][CH2:41][C@H:42]3[C:43]#[N:44])[c:45]([F:46])[cH:47]2)[cH:2][cH:3][c:4]([O:5][CH:6]2[CH2:7][N:8]([CH2:9][c:10]3[cH:11][cH:12][c:13]([CH:14]([CH3:15])[CH3:16])[cH:17][cH:18]3)[CH2:19]2)[n:20][cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:22]2[cH:23][cH:24][c:25]([CH2:26][C@H:27]([NH:28][C:29]([O:30][C:31]([CH3:32])([CH3:33])[CH3:34])=[O:35])[C:36](=[O:37])[N:38]3[CH2:39][CH2:40][CH2:41][C@H:42]3[C:43]#[N:44])[c:45]([F:46])[cH:47]2)[cH:2][cH:3][c:4]([O:5][CH:6]2[CH2:7][N:8]([CH2:9][c:10]3[cH:11][cH:12][c:13]([CH:14]([CH3:15])[CH3:16])[cH:17][cH:18]3)[CH2:19]2)[n:20][cH:21]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH:6]2[CH2:7][N:8]([CH2:9][c:10]3[cH:11][cH:12][c:13]([CH:14]([CH3:15])[CH3:16])[cH:17][cH:18]3)[CH2:19]2)[n:20][cH:21]1.CC1(C)OB([c:22]2[cH:23][cH:24][c:25]([CH2:26][C@H:27]([NH:28][C:29]([O:30][C:31]([CH3:32])([CH3:33])[CH3:34])=[O:35])[C:36](=[O:37])[N:38]3[CH2:39][CH2:40][CH2:41][C@H:42]3[C:43]#[N:44])[c:45]([F:46])[cH:47]2)OC1(C)C\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH:6]2[CH2:7][N:8]([CH2:9][c:10]3[cH:11][cH:12][c:13]([CH:14]([CH3:15])[CH3:16])[cH:17][cH:18]3)[CH2:19]2)[n:20][cH:21]1' and 'CC1(C)OB([c:22]2[cH:23][cH:24][c:25]([CH2:26][C@H:27]([NH:28][C:29]([O:30][C:31]([CH3:32])([CH3:33])[CH3:34])=[O:35])[C:36](=[O:37])[N:38]3[CH2:39][CH2:40][CH2:41][C@H:42]3[C:43]#[N:44])[c:45]([F:46])[cH:47]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis path for 'cc11):HH]C]:9:H[]]H1H2)2]c1(1:43H[[[]372]4]]C([CC3[c::221102[]18[]NH3(]2C[1[[]C(6[CH:c:H[C:c)1:H]1H3)]::[]::C[3:[[H:2O2]65[]7:1CH5218:A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"cc11):HH]C]:9:H[]]H1H2)2]c1(1:43H[[[]372]4]]C([CC3[c::221102[]18[]NH3(]2C[1[[]C(6[CH:c:H[C:c)1:H]1H3)]::[]::C[3:[[H:2O2]65[]7:1CH5218:A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The appropriate smile for drug1 is '[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2\", \"reactants\":[\"O=[C:1]1[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2.[NH4+:18]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1]1[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2' and '[NH4+:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis route for 'H:]]]Hn1H:[]N)[(cc:::[]07:3c[6:[[::[9CO(8C:#4)11]c]c13][5]][[2HA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H:]]]Hn1H:[]N)[(cc:::[]07:3c[6:[[::[9CO(8C:#4)11]c]c13][5]][[2HA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The most proper smile for drug1 is '[c:1]1([C:9]#[N:10])[cH:2][n:3][cH:4][c:5]([O:6][CH3:7])[cH:8]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:9]#[N:10])[cH:2][n:3][cH:4][c:5]([O:6][CH3:7])[cH:8]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:9]#[N:10])[cH:2][n:3][cH:4][c:5]([O:6][CH3:7])[cH:8]1\", \"reactants\":[\"Br[c:1]1[cH:2][n:3][cH:4][c:5]([O:6][CH3:7])[cH:8]1.[C-:9]#[N:10]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][n:3][cH:4][c:5]([O:6][CH3:7])[cH:8]1' and '[C-:9]#[N:10]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis route for '[:[:3C]:2:3]112:1]c[20[[:22:0(0]c3n9]Ho:]2c23)32]C]HH]:C2]6[C6]42]Cc3]]5O[]::](][1:)3[c1[[CCH3:2C2(C3][84:4[2HHc:21[9(:[3[]::2[:][n]:83:[]H2H:[:[NcH9328:H]2][1c8CCH521742c3[1H35HH(H][]H:):7]2n])::2c]H[1H[4O][:1[ncOc][1C:61:c[13[]1:1H:F[1:]]9:[23c::]):[27(7C):H42c[[]1]6][2N]53[H3[A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[:[:3C]:2:3]112:1]c[20[[:22:0(0]c3n9]Ho:]2c23)32]C]HH]:C2]6[C6]42]Cc3]]5O[]::](][1:)3[c1[[CCH3:2C2(C3][84:4[2HHc:21[9(:[3[]::2[:][n]:83:[]H2H:[:[NcH9328:H]2][1c8CCH521742c3[1H35HH(H][]H:):7]2n])::2c]H[1H[4O][:1[ncOc][1C:61:c[13[]1:1H:F[1:]]9:[23c::]):[27(7C):H42c[[]1]6][2N]53[H3[A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:33]1[CH2:32][CH2:31][CH:30]([NH:29][c:28]2[o:27][c:26]3[cH:25][cH:24][c:23]([O:22][CH2:21][CH2:20][CH2:19][n:18]4[cH:17][n:16][cH:15][n:39]4)[cH:38][c:37]3[n:36]2)[CH2:35][CH2:34]1' is the smile best suited for drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:33]1[CH2:32][CH2:31][CH:30]([NH:29][c:28]2[o:27][c:26]3[cH:25][cH:24][c:23]([O:22][CH2:21][CH2:20][CH2:19][n:18]4[cH:17][n:16][cH:15][n:39]4)[cH:38][c:37]3[n:36]2)[CH2:35][CH2:34]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:33]1[CH2:32][CH2:31][CH:30]([NH:29][c:28]2[o:27][c:26]3[cH:25][cH:24][c:23]([O:22][CH2:21][CH2:20][CH2:19][n:18]4[cH:17][n:16][cH:15][n:39]4)[cH:38][c:37]3[n:36]2)[CH2:35][CH2:34]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1.[cH:15]1[n:16][cH:17][n:18]([CH2:19][CH2:20][CH2:21][O:22][c:23]2[cH:24][cH:25][c:26]3[o:27][c:28]([NH:29][CH:30]4[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]4)[n:36][c:37]3[cH:38]2)[n:39]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1' and '[cH:15]1[n:16][cH:17][n:18]([CH2:19][CH2:20][CH2:21][O:22][c:23]2[cH:24][cH:25][c:26]3[o:27][c:28]([NH:29][CH:30]4[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]4)[n:36][c:37]3[cH:38]2)[n:39]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis procedure for 'c][COc:cH4c1[:7O8:3[c2c6]2:30]2[-:[27[):[:1=5:c]]CHH5ccH:9cn)]](cCH4:41CH])3::3][:31[C31::38c:]:(]14:9]3:H2H12]Hc2[c]4)]]-:(cHH:[41224[42:132::])]6::(::16:[[()[]cF[H]8oHNc19C]][5]]2H5[[n]:[2]3c02c[]3]9)HH2[:[c53]2]c][[]0cc[]3[:(]2H]7[[cc3H11::[c[8C[32[3171[]103[]H253:1C]:H[:2[[]:H():[:H:]26([2][A'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"c][COc:cH4c1[:7O8:3[c2c6]2:30]2[-:[27[):[:1=5:c]]CHH5ccH:9cn)]](cCH4:41CH])3::3][:31[C31::38c:]:(]14:9]3:H2H12]Hc2[c]4)]]-:(cHH:[41224[42:132::])]6::(::16:[[()[]cF[H]8oHNc19C]][5]]2H5[[n]:[2]3c02c[]3]9)HH2[:[c53]2]c][[]0cc[]3[:(]2H]7[[cc3H11::[c[8C[32[3171[]103[]H253:1C]:H[:2[[]:H():[:H:]26([2][A\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile proper for drug1 is '[c:1]1([NH:35][c:34]2[c:29](-[c:28]3[cH:27][cH:26][c:25](-[c:24]4[cH:23][cH:22][c:21]([C:20]5([C:18]([O:17][CH2:16][CH3:15])=[O:19])[CH2:40][CH2:41]5)[cH:39][cH:38]4)[cH:37][cH:36]3)[o:30][n:31][c:32]2[CH3:33])[cH:2][cH:3][cH:4][c:5]([CH2:6][c:7]2[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]2)[n:14]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:35][c:34]2[c:29](-[c:28]3[cH:27][cH:26][c:25](-[c:24]4[cH:23][cH:22][c:21]([C:20]5([C:18]([O:17][CH2:16][CH3:15])=[O:19])[CH2:40][CH2:41]5)[cH:39][cH:38]4)[cH:37][cH:36]3)[o:30][n:31][c:32]2[CH3:33])[cH:2][cH:3][cH:4][c:5]([CH2:6][c:7]2[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]2)[n:14]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:35][c:34]2[c:29](-[c:28]3[cH:27][cH:26][c:25](-[c:24]4[cH:23][cH:22][c:21]([C:20]5([C:18]([O:17][CH2:16][CH3:15])=[O:19])[CH2:40][CH2:41]5)[cH:39][cH:38]4)[cH:37][cH:36]3)[o:30][n:31][c:32]2[CH3:33])[cH:2][cH:3][cH:4][c:5]([CH2:6][c:7]2[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]2)[n:14]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][cH:4][c:5]([CH2:6][c:7]2[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]2)[n:14]1.[CH3:15][CH2:16][O:17][C:18](=[O:19])[C:20]1([c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][cH:27][c:28](-[c:29]4[o:30][n:31][c:32]([CH3:33])[c:34]4[NH2:35])[cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][CH2:41]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][cH:4][c:5]([CH2:6][c:7]2[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]2)[n:14]1' and '[CH3:15][CH2:16][O:17][C:18](=[O:19])[C:20]1([c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][cH:27][c:28](-[c:29]4[o:30][n:31][c:32]([CH3:33])[c:34]4[NH2:35])[cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][CH2:41]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis steps for 'H:12:C]1H8]]1]0n6):[c[c1H]()Hcc1]813[6::35cc13]][9H:]02:[2H[[]13H2c4(H::72:8Hc[]cH1F:[OF[21c(H=2]3c]]]:[2:2)2][2[21O4c]H:]4H:)3[1]c]:]]:[c1H5](]72::2:]:[c)H[:cc6[][:[[:H[0]]:c2[:1cC)1[c[1[-C[191F]]:][Hc::4[[3::[5[31:c(:2HN]3:(][92[1n72]CA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"H:12:C]1H8]]1]0n6):[c[c1H]()Hcc1]813[6::35cc13]][9H:]02:[2H[[]13H2c4(H::72:8Hc[]cH1F:[OF[21c(H=2]3c]]]:[2:2)2][2[21O4c]H:]4H:)3[1]c]:]]:[c1H5](]72::2:]:[c)H[:cc6[][:[[:H[0]]:c2[:1cC)1[c[1[-C[191F]]:][Hc::4[[3::[5[31:c(:2HN]3:(][92[1n72]CA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1)[NH:19][c:20]1[cH:21][cH:22][c:23]([O:24][CH2:25][CH2:26][c:27]2[cH:28][cH:29][cH:30][cH:31][n:32]2)[n:33][cH:34]1' is the expression that matches drug1. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1)[NH:19][c:20]1[cH:21][cH:22][c:23]([O:24][CH2:25][CH2:26][c:27]2[cH:28][cH:29][cH:30][cH:31][n:32]2)[n:33][cH:34]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1)[NH:19][c:20]1[cH:21][cH:22][c:23]([O:24][CH2:25][CH2:26][c:27]2[cH:28][cH:29][cH:30][cH:31][n:32]2)[n:33][cH:34]1\", \"reactants\":[\"Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1.[NH2:19][c:20]1[cH:21][cH:22][c:23]([O:24][CH2:25][CH2:26][c:27]2[cH:28][cH:29][cH:30][cH:31][n:32]2)[n:33][cH:34]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1' and '[NH2:19][c:20]1[cH:21][cH:22][c:23]([O:24][CH2:25][CH2:26][c:27]2[cH:28][cH:29][cH:30][cH:31][n:32]2)[n:33][cH:34]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[:O[[2:1N[]::]6]]3[(316@1]:2O3]5[:[[]9][N91)2:=3O)2[11C:C32(2]0=[[1):)]]78:[[([CC[+:N[H]C:H:2[C::2S=5]:[@0S:23111C]C)HHH)=3:1[C([O:[[)(71NC43[H:H)23(::4]HC]:]]-CH]4:1H]]H]H]]((8CCA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[:O[[2:1N[]::]6]]3[(316@1]:2O3]5[:[[]9][N91)2:=3O)2[11C:C32(2]0=[[1):)]]78:[[([CC[+:N[H]C:H:2[C::2S=5]:[@0S:23111C]C)HHH)=3:1[C([O:[[)(71NC43[H:H)23(::4]HC]:]]-CH]4:1H]]H]H]]((8CCA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The most fitting smile for drug1 is '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:21][C@@H:20]([CH2:19][CH:14]([S:13][S:12][C:9]([CH3:8])([CH3:10])[CH3:11])[CH2:15][N:16]=[N+:17]=[N-:18])[C:22](=[O:23])[OH:24]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:21][C@@H:20]([CH2:19][CH:14]([S:13][S:12][C:9]([CH3:8])([CH3:10])[CH3:11])[CH2:15][N:16]=[N+:17]=[N-:18])[C:22](=[O:23])[OH:24]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:21][C@@H:20]([CH2:19][CH:14]([S:13][S:12][C:9]([CH3:8])([CH3:10])[CH3:11])[CH2:15][N:16]=[N+:17]=[N-:18])[C:22](=[O:23])[OH:24]\", \"reactants\":[\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][C:9]([CH3:10])([CH3:11])[S:12][S:13][CH:14]([CH2:15][N:16]=[N+:17]=[N-:18])[CH2:19][C@H:20]([NH2:21])[C:22](=[O:23])[OH:24]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][C:9]([CH3:10])([CH3:11])[S:12][S:13][CH:14]([CH2:15][N:16]=[N+:17]=[N-:18])[CH2:19][C@H:20]([NH2:21])[C:22](=[O:23])[OH:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis pathway for ':]1)[]HH1[H92]]][c[6c2c8O](:]][12C[cc5[21cH:1]][::[]3:10:::]c1H:H[1]2]:8:](0:]]H[(]7)1[c[2:[]:]241NH[14=32-]H[c[2[cC2c:[9[[[Hn11:cH::3-F[])[):61c2H]71coH52c:2([6:11]c:1::]1H]:42[8[n7:n5:c[c]]2cA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\":]1)[]HH1[H92]]][c[6c2c8O](:]][12C[cc5[21cH:1]][::[]3:10:::]c1H:H[1]2]:8:](0:]]H[(]7)1[c[2:[]:]241NH[14=32-]H[c[2[cC2c:[9[[[Hn11:cH::3-F[])[):61c2H]71coH52c:2([6:11]c:1::]1H]:42[8[n7:n5:c[c]]2cA\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":(wrong_smile1), \"reactants\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "There exists some mistake with the smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile for drug1 is '[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[o:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([F:19])[cH:20][cH:21]2)[c:22]1-[c:23]1[cH:24][cH:25][n:26][cH:27][n:28]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[o:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([F:19])[cH:20][cH:21]2)[c:22]1-[c:23]1[cH:24][cH:25][n:26][cH:27][n:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[o:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([F:19])[cH:20][cH:21]2)[c:22]1-[c:23]1[cH:24][cH:25][n:26][cH:27][n:28]1\", \"reactants\":[\"Cl[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1.[NH2:10][c:11]1[o:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([F:19])[cH:20][cH:21]2)[c:22]1-[c:23]1[cH:24][cH:25][n:26][cH:27][n:28]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[NH2:10][c:11]1[o:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([F:19])[cH:20][cH:21]2)[c:22]1-[c:23]1[cH:24][cH:25][n:26][cH:27][n:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[C:1]([O:2][CH3:3])([c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12)=[N:15][OH:16]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([O:2][CH3:3])([c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12)=[N:15][OH:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([O:2][CH3:3])([c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12)=[N:15][OH:16]\", \"reactants\":[\"O=[C:1]([O:2][CH3:3])[c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12.[NH2:15][OH:16]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=[C:1]([O:2][CH3:3])[c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12' and '[NH2:15][OH:16]'."
            },
            {
                "from": "human",
                "value": "What are the expectations for '[NH:1]([c:2]1[cH:3][c:4]([Cl:5])[c:6]([F:7])[c:8]([Cl:9])[cH:10]1)[C:11](=[O:12])[CH2:13][c:14]1[n:15][c:16]([N:17]2[CH2:18][CH2:19][O:20][CH2:21][CH2:22]2)[cH:23][c:24](=[O:25])[nH:26]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH:1]([c:2]1[cH:3][c:4]([Cl:5])[c:6]([F:7])[c:8]([Cl:9])[cH:10]1)[C:11](=[O:12])[CH2:13][c:14]1[n:15][c:16]([N:17]2[CH2:18][CH2:19][O:20][CH2:21][CH2:22]2)[cH:23][c:24](=[O:25])[nH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([O:2][CH3:3])([c:4]1[cH:5][cH:6][cH:7][c:8]2[nH:9][cH:10][c:11]([CH:12]=[O:13])[c:14]12)=[N:15][OH:16]\", \"reactants\":[\"[NH2:1][c:2]1[cH:3][c:4]([Cl:5])[c:6]([F:7])[c:8]([Cl:9])[cH:10]1.[O-][C:11](=[O:12])[CH2:13][c:14]1[n:15][c:16]([N:17]2[CH2:18][CH2:19][O:20][CH2:21][CH2:22]2)[cH:23][c:24](=[O:25])[nH:26]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[NH2:1][c:2]1[cH:3][c:4]([Cl:5])[c:6]([F:7])[c:8]([Cl:9])[cH:10]1' and '[O-][C:11](=[O:12])[CH2:13][c:14]1[n:15][c:16]([N:17]2[CH2:18][CH2:19][O:20][CH2:21][CH2:22]2)[cH:23][c:24](=[O:25])[nH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for '[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1\", \"reactants\":[\"Cl[c:1]1[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1.[CH3:10][N:11]1[CH2:12][CH2:13][CH:14]([SH:15])[CH2:16]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1' and '[CH3:10][N:11]1[CH2:12][CH2:13][CH:14]([SH:15])[CH2:16]1'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[Sn:1]([CH2:2][CH2:3][CH2:4][CH3:5])([CH2:6][CH2:7][CH2:8][CH3:9])([CH2:10][CH2:11][CH2:12][CH3:13])[c:18]1[cH:17][n:16][c:15]([CH3:14])[s:19]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Sn:1]([CH2:2][CH2:3][CH2:4][CH3:5])([CH2:6][CH2:7][CH2:8][CH3:9])([CH2:10][CH2:11][CH2:12][CH3:13])[c:18]1[cH:17][n:16][c:15]([CH3:14])[s:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([S:15][CH:14]2[CH2:13][CH2:12][N:11]([CH3:10])[CH2:16]2)[c:2]([C:3](=[O:4])[OH:5])[cH:6][cH:7][cH:8][n:9]1\", \"reactants\":[\"Cl[Sn:1]([CH2:2][CH2:3][CH2:4][CH3:5])([CH2:6][CH2:7][CH2:8][CH3:9])[CH2:10][CH2:11][CH2:12][CH3:13].[CH3:14][c:15]1[n:16][cH:17][cH:18][s:19]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[Sn:1]([CH2:2][CH2:3][CH2:4][CH3:5])([CH2:6][CH2:7][CH2:8][CH3:9])[CH2:10][CH2:11][CH2:12][CH3:13]' and '[CH3:14][c:15]1[n:16][cH:17][cH:18][s:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis steps for '[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[O:41][CH3:42]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[O:41][CH3:42]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[O:41][CH3:42]\", \"reactants\":[\"[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[OH:41].[N-]=[N+]=[CH2:42]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are '[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[OH:41]' and '[N-]=[N+]=[CH2:42]'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:21]1[C:19](=[O:20])[c:17]2[c:16]([cH:15][cH:14][c:13]([O:12][CH3:11])[cH:18]2)[O:23][CH2:22]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:21]1[C:19](=[O:20])[c:17]2[c:16]([cH:15][cH:14][c:13]([O:12][CH3:11])[cH:18]2)[O:23][CH2:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C@H:2]([OH:3])[CH2:4][CH2:5][CH:6]1[O:7][CH2:8][CH2:9][O:10][CH:11]1[CH2:12][CH2:13][CH2:14]/[CH:15]=[CH:16]/[c:17]1[cH:18][c:19]([O:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[cH:28][c:29]([O:30][CH2:31][c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[c:38]1[C:39](=[O:40])[O:41][CH3:42]\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12.[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]2[c:17]([cH:18]1)[C:19](=[O:20])[CH2:21][CH2:22][O:23]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12' and '[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]2[c:17]([cH:18]1)[C:19](=[O:20])[CH2:21][CH2:22][O:23]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the synthesis procedure for '[c:1]1(-[c:8]2[s:9][c:10]3[c:11]([N:12]4[CH2:13][CH2:14][O:15][CH2:16][CH2:17]4)[n:18][c:19]([Cl:20])[n:21][c:22]3[cH:23]2)[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[s:9][c:10]3[c:11]([N:12]4[CH2:13][CH2:14][O:15][CH2:16][CH2:17]4)[n:18][c:19]([Cl:20])[n:21][c:22]3[cH:23]2)[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:8]2[s:9][c:10]3[c:11]([N:12]4[CH2:13][CH2:14][O:15][CH2:16][CH2:17]4)[n:18][c:19]([Cl:20])[n:21][c:22]3[cH:23]2)[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]1\", \"reactants\":[\"CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]2)OC1(C)C.I[c:8]1[s:9][c:10]2[c:11]([N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[n:18][c:19]([Cl:20])[n:21][c:22]2[cH:23]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]2)OC1(C)C' and 'I[c:8]1[s:9][c:10]2[c:11]([N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[n:18][c:19]([Cl:20])[n:21][c:22]2[cH:23]1'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:8]2[s:9][c:10]3[c:11]([N:12]4[CH2:13][CH2:14][O:15][CH2:16][CH2:17]4)[n:18][c:19]([Cl:20])[n:21][c:22]3[cH:23]2)[cH:2][cH:3][c:4]([NH2:5])[cH:6][cH:7]1\", \"reactants\":[\"Br[CH2:1][c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16].[Br:17][c:18]1[cH:19][cH:20][c:21]([N:22]2[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]2)[cH:28][cH:29]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16]' and '[Br:17][c:18]1[cH:19][cH:20][c:21]([N:22]2[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]2)[cH:28][cH:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most likely synthesis path for '[c:1]1([CH:23]=[CH:24][C:25](=[O:26])[N:27]2[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]2)[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([CH:23]=[CH:24][C:25](=[O:26])[N:27]2[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]2)[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([CH:23]=[CH:24][C:25](=[O:26])[N:27]2[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]2)[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1.[CH2:23]=[CH:24][C:25](=[O:26])[N:27]1[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1' and '[CH2:23]=[CH:24][C:25](=[O:26])[N:27]1[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]1'."
            },
            {
                "from": "human",
                "value": "And predict the outcomes for '[c:1]1([NH:26][CH:24]([CH2:23][O:22][CH3:21])[CH3:25])[n:2][cH:3][cH:4][c:5](-[c:6]2[n:7][cH:8][n:9][c:10]([NH:11][c:12]3[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]3)[n:19]2)[cH:20]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:26][CH:24]([CH2:23][O:22][CH3:21])[CH3:25])[n:2][cH:3][cH:4][c:5](-[c:6]2[n:7][cH:8][n:9][c:10]([NH:11][c:12]3[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]3)[n:19]2)[cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([CH:23]=[CH:24][C:25](=[O:26])[N:27]2[CH2:28][CH2:29][N:30]([CH3:31])[CH2:32][CH2:33]2)[cH:2][cH:3][c:4]([O:5][CH2:6][CH2:7][CH3:8])[c:9](-[c:10]2[n:11][c:12]3[c:13]([CH3:14])[cH:15][cH:16][cH:17][c:18]3[c:19](=[O:20])[nH:21]2)[cH:22]1\", \"reactants\":[\"Cl[c:1]1[n:2][cH:3][cH:4][c:5](-[c:6]2[n:7][cH:8][n:9][c:10]([NH:11][c:12]3[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]3)[n:19]2)[cH:20]1.[CH3:21][O:22][CH2:23][CH:24]([CH3:25])[NH2:26]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[n:2][cH:3][cH:4][c:5](-[c:6]2[n:7][cH:8][n:9][c:10]([NH:11][c:12]3[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]3)[n:19]2)[cH:20]1' and '[CH3:21][O:22][CH2:23][CH:24]([CH3:25])[NH2:26]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis steps for '[CH:1]([C:2]([O:3][CH3:4])=[O:5])([CH3:6])[O:27][c:26]1[cH:25][cH:24][c:22]([Cl:23])[c:21]2[nH:20][c:18](=[O:19])[c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]3)[c:8]([CH3:7])[c:28]21'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([C:2]([O:3][CH3:4])=[O:5])([CH3:6])[O:27][c:26]1[cH:25][cH:24][c:22]([Cl:23])[c:21]2[nH:20][c:18](=[O:19])[c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]3)[c:8]([CH3:7])[c:28]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([C:2]([O:3][CH3:4])=[O:5])([CH3:6])[O:27][c:26]1[cH:25][cH:24][c:22]([Cl:23])[c:21]2[nH:20][c:18](=[O:19])[c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]3)[c:8]([CH3:7])[c:28]21\", \"reactants\":[\"Br[CH:1]([C:2]([O:3][CH3:4])=[O:5])[CH3:6].[CH3:7][c:8]1[c:9]([CH2:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]2)[c:18](=[O:19])[nH:20][c:21]2[c:22]([Cl:23])[cH:24][cH:25][c:26]([OH:27])[c:28]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[CH:1]([C:2]([O:3][CH3:4])=[O:5])[CH3:6]' and '[CH3:7][c:8]1[c:9]([CH2:10][c:11]2[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]2)[c:18](=[O:19])[nH:20][c:21]2[c:22]([Cl:23])[cH:24][cH:25][c:26]([OH:27])[c:28]12'."
            },
            {
                "from": "human",
                "value": "What are the expectations for '[C:1]1([c:2]2[c:3]([O:4][CH3:5])[cH:6][cH:7][cH:8][cH:9]2)([N:27]2[C@H:22]([C:21](=[O:20])[N:28]3[CH2:29][CH2:30][CH2:31]3)[CH2:23][CH:24]([F:25])[CH2:26]2)[C:10](=[O:11])[NH:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[cH:18][c:19]21'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]1([c:2]2[c:3]([O:4][CH3:5])[cH:6][cH:7][cH:8][cH:9]2)([N:27]2[C@H:22]([C:21](=[O:20])[N:28]3[CH2:29][CH2:30][CH2:31]3)[CH2:23][CH:24]([F:25])[CH2:26]2)[C:10](=[O:11])[NH:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[cH:18][c:19]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([C:2]([O:3][CH3:4])=[O:5])([CH3:6])[O:27][c:26]1[cH:25][cH:24][c:22]([Cl:23])[c:21]2[nH:20][c:18](=[O:19])[c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([Cl:15])[cH:16][cH:17]3)[c:8]([CH3:7])[c:28]21\", \"reactants\":[\"Cl[C:1]1([c:2]2[c:3]([O:4][CH3:5])[cH:6][cH:7][cH:8][cH:9]2)[C:10](=[O:11])[NH:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[cH:18][c:19]21.[O:20]=[C:21]([C@@H:22]1[CH2:23][CH:24]([F:25])[CH2:26][NH:27]1)[N:28]1[CH2:29][CH2:30][CH2:31]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1]1([c:2]2[c:3]([O:4][CH3:5])[cH:6][cH:7][cH:8][cH:9]2)[C:10](=[O:11])[NH:12][c:13]2[cH:14][cH:15][c:16]([Cl:17])[cH:18][c:19]21' and '[O:20]=[C:21]([C@@H:22]1[CH2:23][CH:24]([F:25])[CH2:26][NH:27]1)[N:28]1[CH2:29][CH2:30][CH2:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how '[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH:15][C:24](=[O:23])[NH:25][c:26]4[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]4)[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22]' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH:15][C:24](=[O:23])[NH:25][c:26]4[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]4)[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH:15][C:24](=[O:23])[NH:25][c:26]4[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]4)[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22]\", \"reactants\":[\"[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH2:15])[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22].[O:23]=[C:24]=[N:25][c:26]1[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are '[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH2:15])[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22]' and '[O:23]=[C:24]=[N:25][c:26]1[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]1'."
            },
            {
                "from": "human",
                "value": "Offer predictions on '[c:1]1(-[c:13]2[cH:14][cH:15][c:16]([F:17])[cH:18][cH:19]2)[cH:2][cH:3][c:4]([CH3:5])[c:6]([NH:7][CH2:8][CH:9]([CH3:10])[CH3:11])[cH:12]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:13]2[cH:14][cH:15][c:16]([F:17])[cH:18][cH:19]2)[cH:2][cH:3][c:4]([CH3:5])[c:6]([NH:7][CH2:8][CH:9]([CH3:10])[CH3:11])[cH:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:2][c:3]1[cH:4][c:5]2[n:6][cH:7][cH:8][c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([NH:15][C:24](=[O:23])[NH:25][c:26]4[cH:27][cH:28][c:29]([Cl:30])[c:31]([Cl:32])[cH:33]4)[cH:16][cH:17]3)[c:18]2[cH:19][c:20]1[O:21][CH3:22]\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([CH3:5])[c:6]([NH:7][CH2:8][CH:9]([CH3:10])[CH3:11])[cH:12]1.OB(O)[c:13]1[cH:14][cH:15][c:16]([F:17])[cH:18][cH:19]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH3:5])[c:6]([NH:7][CH2:8][CH:9]([CH3:10])[CH3:11])[cH:12]1' and 'OB(O)[c:13]1[cH:14][cH:15][c:16]([F:17])[cH:18][cH:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis pathway for '[c:1]1(-[c:2]2[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]2)[n:9][c:10]2[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]2[nH:19]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:2]2[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]2)[n:9][c:10]2[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]2[nH:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:2]2[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]2)[n:9][c:10]2[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]2[nH:19]1\", \"reactants\":[\"O=[C:1](O)[c:2]1[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]1.[NH2:9][c:10]1[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]1[NH2:19]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=[C:1](O)[c:2]1[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]1' and '[NH2:9][c:10]1[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]1[NH2:19]'."
            },
            {
                "from": "human",
                "value": "And provide a forecast for '[c:1]1([S:11][CH3:10])[cH:2][cH:3][c:4]([C:5]#[N:6])[cH:7][c:8]1[F:9]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([S:11][CH3:10])[cH:2][cH:3][c:4]([C:5]#[N:6])[cH:7][c:8]1[F:9]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:2]2[cH:3][cH:4][n:5][c:6]([Cl:7])[cH:8]2)[n:9][c:10]2[cH:11][c:12]([N+:13](=[O:14])[O-:15])[cH:16][n:17][c:18]2[nH:19]1\", \"reactants\":[\"F[c:1]1[cH:2][cH:3][c:4]([C:5]#[N:6])[cH:7][c:8]1[F:9].[CH3:10][S-:11]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]([C:5]#[N:6])[cH:7][c:8]1[F:9]' and '[CH3:10][S-:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[CH:1](=[O:2])[c:19]1[cH:18][cH:17][cH:16][c:15]([C:14]#[C:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH2:4][CH3:3])[c:20]1[F:21]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:19]1[cH:18][cH:17][cH:16][c:15]([C:14]#[C:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH2:4][CH3:3])[c:20]1[F:21]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[O:2])[c:19]1[cH:18][cH:17][cH:16][c:15]([C:14]#[C:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH2:4][CH3:3])[c:20]1[F:21]\", \"reactants\":[\"CN(C)[CH:1]=[O:2].[CH3:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][C:13]#[C:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[F:21]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][C:13]#[C:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[F:21]'."
            },
            {
                "from": "human",
                "value": "And predict the outcomes for '[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][cH:7][c:8]([F:9])[cH:10]1)[NH:20][c:19]1[cH:18][cH:17][c:16]2[nH:15][n:14][c:13]([C:12]#[N:11])[c:22]2[cH:21]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][cH:7][c:8]([F:9])[cH:10]1)[NH:20][c:19]1[cH:18][cH:17][c:16]2[nH:15][n:14][c:13]([C:12]#[N:11])[c:22]2[cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[O:2])[c:19]1[cH:18][cH:17][cH:16][c:15]([C:14]#[C:13][CH2:12][CH2:11][CH2:10][CH2:9][CH2:8][CH2:7][CH2:6][CH2:5][CH2:4][CH3:3])[c:20]1[F:21]\", \"reactants\":[\"Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][cH:7][c:8]([F:9])[cH:10]1.[N:11]#[C:12][c:13]1[n:14][nH:15][c:16]2[cH:17][cH:18][c:19]([NH2:20])[cH:21][c:22]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][cH:7][c:8]([F:9])[cH:10]1' and '[N:11]#[C:12][c:13]1[n:14][nH:15][c:16]2[cH:17][cH:18][c:19]([NH2:20])[cH:21][c:22]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how to synthesize '[c:1]1([C:18]#[N:19])[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:18]#[N:19])[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:18]#[N:19])[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1\", \"reactants\":[\"Br[c:1]1[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1.[Cu][C:18]#[N:19]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1' and '[Cu][C:18]#[N:19]'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[CH2:1]([c:2]1[n:3]([CH3:4])[cH:5][cH:6][n:7]1)[O:21][c:20]1[cH:19][c:18]2[n:17][c:15]([NH2:16])[c:14]3[n:13][c:12]([CH2:11][O:10][CH2:9][CH3:8])[n:26]([CH2:27][C:28]([CH3:29])([CH3:30])[OH:31])[c:25]3[c:24]2[cH:23][cH:22]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[n:3]([CH3:4])[cH:5][cH:6][n:7]1)[O:21][c:20]1[cH:19][c:18]2[n:17][c:15]([NH2:16])[c:14]3[n:13][c:12]([CH2:11][O:10][CH2:9][CH3:8])[n:26]([CH2:27][C:28]([CH3:29])([CH3:30])[OH:31])[c:25]3[c:24]2[cH:23][cH:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:18]#[N:19])[c:2]2[cH:3][cH:4][n:5]([CH2:6][CH2:7][CH2:8][C:9]([O:10][CH2:11][CH3:12])=[O:13])[c:14]2[n:15][cH:16][cH:17]1\", \"reactants\":[\"Cl[CH2:1][c:2]1[n:3]([CH3:4])[cH:5][cH:6][n:7]1.[CH3:8][CH2:9][O:10][CH2:11][c:12]1[n:13][c:14]2[c:15]([NH2:16])[n:17][c:18]3[cH:19][c:20]([OH:21])[cH:22][cH:23][c:24]3[c:25]2[n:26]1[CH2:27][C:28]([CH3:29])([CH3:30])[OH:31]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[CH2:1][c:2]1[n:3]([CH3:4])[cH:5][cH:6][n:7]1' and '[CH3:8][CH2:9][O:10][CH2:11][c:12]1[n:13][c:14]2[c:15]([NH2:16])[n:17][c:18]3[cH:19][c:20]([OH:21])[cH:22][cH:23][c:24]3[c:25]2[n:26]1[CH2:27][C:28]([CH3:29])([CH3:30])[OH:31]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[c:1]1([O:17][CH:16]2[CH2:15][CH2:14][N:13]([CH3:12])[CH2:18]2)[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:17][CH:16]2[CH2:15][CH2:14][N:13]([CH3:12])[CH2:18]2)[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:17][CH:16]2[CH2:15][CH2:14][N:13]([CH3:12])[CH2:18]2)[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1\", \"reactants\":[\"F[c:1]1[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1.[CH3:12][N:13]1[CH2:14][CH2:15][CH:16]([OH:17])[CH2:18]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'F[c:1]1[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1' and '[CH3:12][N:13]1[CH2:14][CH2:15][CH:16]([OH:17])[CH2:18]1'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[c:15]([n:16][n:17][n:18]3[CH2:19][CH:20]3[CH2:21][CH2:22]3)[c:23]2[Cl:24])[cH:25][cH:26]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[c:15]([n:16][n:17][n:18]3[CH2:19][CH:20]3[CH2:21][CH2:22]3)[c:23]2[Cl:24])[cH:25][cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:17][CH:16]2[CH2:15][CH2:14][N:13]([CH3:12])[CH2:18]2)[cH:2][c:3]([O:4][CH3:5])[c:6]([N+:7](=[O:8])[O-:9])[cH:10][cH:11]1\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[OH:5][CH2:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[c:15]([n:16][n:17][n:18]3[CH2:19][CH:20]3[CH2:21][CH2:22]3)[c:23]2[Cl:24])[cH:25][cH:26]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[OH:5][CH2:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]3[c:15]([n:16][n:17][n:18]3[CH2:19][CH:20]3[CH2:21][CH2:22]3)[c:23]2[Cl:24])[cH:25][cH:26]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Provide a synthesis prediction for '[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1)[O:13][C:11]([CH3:10])([CH3:12])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1)[O:13][C:11]([CH3:10])([CH3:12])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1)[O:13][C:11]([CH3:10])([CH3:12])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21\", \"reactants\":[\"Br[CH2:1][c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1.[CH3:10][C:11]([CH3:12])([OH:13])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[CH2:1][c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1' and '[CH3:10][C:11]([CH3:12])([OH:13])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[S:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH3:7])[cH:8][cH:9]1)(=[O:10])(=[O:11])[N:19]1[CH:15]([C:13](=[O:12])[OH:14])[CH2:16][CH2:17][CH2:18]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH3:7])[cH:8][cH:9]1)(=[O:10])(=[O:11])[N:19]1[CH:15]([C:13](=[O:12])[OH:14])[CH2:16][CH2:17][CH2:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([Cl:8])[cH:9]1)[O:13][C:11]([CH3:10])([CH3:12])[C:14](=[O:15])[N:16]1[CH2:17][CH2:18][N:19]([CH:20]2[CH2:21][CH2:22]2)[c:23]2[cH:24][cH:25][cH:26][cH:27][c:28]21\", \"reactants\":[\"Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH3:7])[cH:8][cH:9]1)(=[O:10])=[O:11].[O:12]=[C:13]([OH:14])[C@@H:15]1[CH2:16][CH2:17][CH2:18][NH:19]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([c:2]1[cH:3][cH:4][c:5]([O:6][CH3:7])[cH:8][cH:9]1)(=[O:10])=[O:11]' and '[O:12]=[C:13]([OH:14])[C@@H:15]1[CH2:16][CH2:17][CH2:18][NH:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis procedure for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[n:12]1[cH:11][c:10]([CH:9]=[O:8])[c:23]2[c:13]1[cH:14][n:15][c:16](-[n:17]1[cH:18][n:19][cH:20][n:21]1)[cH:22]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[n:12]1[cH:11][c:10]([CH:9]=[O:8])[c:23]2[c:13]1[cH:14][n:15][c:16](-[n:17]1[cH:18][n:19][cH:20][n:21]1)[cH:22]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[n:12]1[cH:11][c:10]([CH:9]=[O:8])[c:23]2[c:13]1[cH:14][n:15][c:16](-[n:17]1[cH:18][n:19][cH:20][n:21]1)[cH:22]2\", \"reactants\":[\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[O:8]=[CH:9][c:10]1[cH:11][nH:12][c:13]2[cH:14][n:15][c:16](-[n:17]3[cH:18][n:19][cH:20][n:21]3)[cH:22][c:23]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[O:8]=[CH:9][c:10]1[cH:11][nH:12][c:13]2[cH:14][n:15][c:16](-[n:17]3[cH:18][n:19][cH:20][n:21]3)[cH:22][c:23]12'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[N:27]1[CH2:26][CH2:25][C@H:24]([N:12]([C:11](=[O:10])[C:37]([F:38])([F:39])[F:40])[CH2:13][c:14]2[cH:15][cH:16][n:17][c:18]3[cH:19][cH:20][cH:21][cH:22][c:23]23)[CH2:36][C@H:28]1[CH2:29][c:30]1[cH:31][cH:32][cH:33][cH:34][cH:35]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[N:27]1[CH2:26][CH2:25][C@H:24]([N:12]([C:11](=[O:10])[C:37]([F:38])([F:39])[F:40])[CH2:13][c:14]2[cH:15][cH:16][n:17][c:18]3[cH:19][cH:20][cH:21][cH:22][c:23]23)[CH2:36][C@H:28]1[CH2:29][c:30]1[cH:31][cH:32][cH:33][cH:34][cH:35]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[n:12]1[cH:11][c:10]([CH:9]=[O:8])[c:23]2[c:13]1[cH:14][n:15][c:16](-[n:17]1[cH:18][n:19][cH:20][n:21]1)[cH:22]2\", \"reactants\":[\"Cl[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1.[O:10]=[C:11]([N:12]([CH2:13][c:14]1[cH:15][cH:16][n:17][c:18]2[cH:19][cH:20][cH:21][cH:22][c:23]12)[C@H:24]1[CH2:25][CH2:26][NH:27][C@H:28]([CH2:29][c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:36]1)[C:37]([F:38])([F:39])[F:40]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[O:10]=[C:11]([N:12]([CH2:13][c:14]1[cH:15][cH:16][n:17][c:18]2[cH:19][cH:20][cH:21][cH:22][c:23]12)[C@H:24]1[CH2:25][CH2:26][NH:27][C@H:28]([CH2:29][c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:36]1)[C:37]([F:38])([F:39])[F:40]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis process for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:14][c:13]1[cH:12][n:11][c:10]([S:9][CH3:8])[n:17][c:15]1[Br:16]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:14][c:13]1[cH:12][n:11][c:10]([S:9][CH3:8])[n:17][c:15]1[Br:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:14][c:13]1[cH:12][n:11][c:10]([S:9][CH3:8])[n:17][c:15]1[Br:16]\", \"reactants\":[\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][S:9][c:10]1[n:11][cH:12][c:13]([NH2:14])[c:15]([Br:16])[n:17]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][S:9][c:10]1[n:11][cH:12][c:13]([NH2:14])[c:15]([Br:16])[n:17]1'."
            },
            {
                "from": "human",
                "value": "And provide a forecast for '[CH2:1]1[O:2][C:3]1([c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([I:15])[cH:16][cH:17]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]1[O:2][C:3]1([c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([I:15])[cH:16][cH:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:14][c:13]1[cH:12][n:11][c:10]([S:9][CH3:8])[n:17][c:15]1[Br:16]\", \"reactants\":[\"C[S+](C)[CH3:1].[O:2]=[C:3]([c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([I:15])[cH:16][cH:17]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'C[S+](C)[CH3:1]' and '[O:2]=[C:3]([c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([I:15])[cH:16][cH:17]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis steps for '[O:1]1[C:3]2([CH3:2])[C:4]1([c:5]1[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]1)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[O:1]1[C:3]2([CH3:2])[C:4]1([c:5]1[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]1)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[O:1]1[C:3]2([CH3:2])[C:4]1([c:5]1[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]1)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]2\", \"reactants\":[\"O=C(O[OH:1])c1cccc(Cl)c1.[CH3:2][C:3]1=[C:4]([c:5]2[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]2)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=C(O[OH:1])c1cccc(Cl)c1' and '[CH3:2][C:3]1=[C:4]([c:5]2[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]2)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]1'."
            },
            {
                "from": "human",
                "value": "And '[c:1]1(-[n:27]2[c:13](=[O:12])[cH:14][c:15]([O:16][CH2:17][c:18]3[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]3)[cH:25][cH:26]2)[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:27]2[c:13](=[O:12])[cH:14][c:15]([O:16][CH2:17][c:18]3[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]3)[cH:25][cH:26]2)[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[O:1]1[C:3]2([CH3:2])[C:4]1([c:5]1[cH:6][cH:7][c:8]([Cl:9])[cH:10][cH:11]1)[CH2:12][CH2:13][N:14]([C:15](=[O:16])[O:17][C:18]([CH3:19])([CH3:20])[CH3:21])[CH2:22]2\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1.[O:12]=[c:13]1[cH:14][c:15]([O:16][CH2:17][c:18]2[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]2)[cH:25][cH:26][nH:27]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]2[n:5][c:6]([CH3:7])[n:8]([CH3:9])[c:10]2[cH:11]1' and '[O:12]=[c:13]1[cH:14][c:15]([O:16][CH2:17][c:18]2[cH:19][cH:20][c:21]([F:22])[cH:23][cH:24]2)[cH:25][cH:26][nH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis method for '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1)[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]3[n:17][n:16][c:15]([C:12]([F:11])([F:13])[F:14])[n:29]3[n:28]2)[CH2:27][CH2:26]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1)[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]3[n:17][n:16][c:15]([C:12]([F:11])([F:13])[F:14])[n:29]3[n:28]2)[CH2:27][CH2:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1)[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]3[n:17][n:16][c:15]([C:12]([F:11])([F:13])[F:14])[n:29]3[n:28]2)[CH2:27][CH2:26]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1.[F:11][C:12]([F:13])([F:14])[c:15]1[n:16][n:17][c:18]2[cH:19][cH:20][c:21]([N:22]3[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]3)[n:28][n:29]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1' and '[F:11][C:12]([F:13])([F:14])[c:15]1[n:16][n:17][c:18]2[cH:19][cH:20][c:21]([N:22]3[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]3)[n:28][n:29]12'."
            },
            {
                "from": "human",
                "value": "And '[CH3:1][O:13][C:12]1([c:14]2[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]2)[CH2:11][CH2:10][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:22][CH2:21]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:13][C:12]1([c:14]2[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]2)[CH2:11][CH2:10][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:22][CH2:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([N+:6](=[O:7])[O-:8])[cH:9][cH:10]1)[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]3[n:17][n:16][c:15]([C:12]([F:11])([F:13])[F:14])[n:29]3[n:28]2)[CH2:27][CH2:26]1\", \"reactants\":[\"I[CH3:1].[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][C:12]([OH:13])([c:14]2[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]2)[CH2:21][CH2:22]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[CH3:1]' and '[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][C:12]([OH:13])([c:14]2[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]2)[CH2:21][CH2:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)(=[O:24])[NH:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)(=[O:24])[NH:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)(=[O:24])[NH:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1\", \"reactants\":[\"O[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)=[O:24].[NH2:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)=[O:24]' and '[NH2:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1'."
            },
            {
                "from": "human",
                "value": "And provide a forecast for '[c:1]1([NH:14][CH:13]([CH2:12][CH3:11])[CH2:15][CH3:16])[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([Cl:8])[c:9]1[I:10]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:14][CH:13]([CH2:12][CH3:11])[CH2:15][CH3:16])[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([Cl:8])[c:9]1[I:10]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][c:4]([CH3:5])[c:6]2[c:7]([cH:8]1)[cH:9][c:10]([NH:11][CH:12]1[CH2:13][CH2:14][C:15]3([CH2:16][CH2:17]1)[O:18][CH2:19][CH2:20][O:21]3)[n:22][cH:23]2)(=[O:24])[NH:25][CH2:26][c:27]1[cH:28][cH:29][cH:30][cH:31][cH:32]1\", \"reactants\":[\"F[c:1]1[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([Cl:8])[c:9]1[I:10].[CH3:11][CH2:12][CH:13]([NH2:14])[CH2:15][CH3:16]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'F[c:1]1[c:2]([C:3]#[N:4])[n:5][cH:6][c:7]([Cl:8])[c:9]1[I:10]' and '[CH3:11][CH2:12][CH:13]([NH2:14])[CH2:15][CH3:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the most likely synthesis method for '[C:1](=[O:2])([c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12)[NH:28][CH2:29][CH2:30][OH:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12)[NH:28][CH2:29][CH2:30][OH:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12)[NH:28][CH2:29][CH2:30][OH:31]\", \"reactants\":[\"CCO[C:1](=[O:2])[c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12.[NH2:28][CH2:29][CH2:30][OH:31]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CCO[C:1](=[O:2])[c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12' and '[NH2:28][CH2:29][CH2:30][OH:31]'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[CH2:1]([CH2:2][CH:3]=[C:4]1[c:5]2[c:6]([cH:7][cH:8][c:9]([C:10]([CH3:11])([CH3:12])[OH:13])[cH:14]2)[O:15][CH2:16][c:17]2[c:18]1[cH:19][cH:20][cH:21][n:22]2)[N:30]1[CH2:29][C:27]([C:25]([O:24][CH3:23])=[O:26])([CH3:28])[C:33]([OH:34])([c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)[CH2:32][CH2:31]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH:3]=[C:4]1[c:5]2[c:6]([cH:7][cH:8][c:9]([C:10]([CH3:11])([CH3:12])[OH:13])[cH:14]2)[O:15][CH2:16][c:17]2[c:18]1[cH:19][cH:20][cH:21][n:22]2)[N:30]1[CH2:29][C:27]([C:25]([O:24][CH3:23])=[O:26])([CH3:28])[C:33]([OH:34])([c:35]2[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]2)[CH2:32][CH2:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][n:5][c:6]([NH2:7])[c:8]2[c:9]([CH2:10][O:11][c:12]3[cH:13][cH:14][cH:15][c:16]([NH:17][c:18]4[cH:19][cH:20][cH:21][cH:22][cH:23]4)[cH:24]3)[cH:25][s:26][c:27]12)[NH:28][CH2:29][CH2:30][OH:31]\", \"reactants\":[\"Br[CH2:1][CH2:2][CH:3]=[C:4]1[c:5]2[c:6]([cH:7][cH:8][c:9]([C:10]([CH3:11])([CH3:12])[OH:13])[cH:14]2)[O:15][CH2:16][c:17]2[c:18]1[cH:19][cH:20][cH:21][n:22]2.[CH3:23][O:24][C:25](=[O:26])[C:27]1([CH3:28])[CH2:29][NH:30][CH2:31][CH2:32][C:33]1([OH:34])[c:35]1[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][CH2:2][CH:3]=[C:4]1[c:5]2[c:6]([cH:7][cH:8][c:9]([C:10]([CH3:11])([CH3:12])[OH:13])[cH:14]2)[O:15][CH2:16][c:17]2[c:18]1[cH:19][cH:20][cH:21][n:22]2' and '[CH3:23][O:24][C:25](=[O:26])[C:27]1([CH3:28])[CH2:29][NH:30][CH2:31][CH2:32][C:33]1([OH:34])[c:35]1[cH:36][cH:37][c:38]([Cl:39])[cH:40][cH:41]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis steps for '[c:1]1([O:44][c:43]2[c:40]([C:41]#[N:42])[cH:39][cH:38][c:37]([C:34]([CH3:33])([CH3:35])[CH3:36])[cH:45]2)[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:44][c:43]2[c:40]([C:41]#[N:42])[cH:39][cH:38][c:37]([C:34]([CH3:33])([CH3:35])[CH3:36])[cH:45]2)[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:44][c:43]2[c:40]([C:41]#[N:42])[cH:39][cH:38][c:37]([C:34]([CH3:33])([CH3:35])[CH3:36])[cH:45]2)[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1\", \"reactants\":[\"Br[c:1]1[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1.[CH3:33][C:34]([CH3:35])([CH3:36])[c:37]1[cH:38][cH:39][c:40]([C:41]#[N:42])[c:43]([OH:44])[cH:45]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1' and '[CH3:33][C:34]([CH3:35])([CH3:36])[c:37]1[cH:38][cH:39][c:40]([C:41]#[N:42])[c:43]([OH:44])[cH:45]1'."
            },
            {
                "from": "human",
                "value": "Offer predictions on '[nH:1]1[c:2]2[c:3]([cH:4][c:5]([Br:6])[cH:7][cH:8]2)[c:10]2[c:9]1[CH2:18][CH:13]([NH:14][C:15]([CH3:16])=[O:17])[CH2:12][CH2:11]2'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[nH:1]1[c:2]2[c:3]([cH:4][c:5]([Br:6])[cH:7][cH:8]2)[c:10]2[c:9]1[CH2:18][CH:13]([NH:14][C:15]([CH3:16])=[O:17])[CH2:12][CH2:11]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:44][c:43]2[c:40]([C:41]#[N:42])[cH:39][cH:38][c:37]([C:34]([CH3:33])([CH3:35])[CH3:36])[cH:45]2)[s:2][cH:3][c:4]([C:5]([NH:6][c:7]2[c:8]([O:9][CH3:10])[n:11][c:12]([NH:13][CH2:14][CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH:24]([CH3:25])[CH3:26])[n:27][c:28]2[O:29][CH3:30])=[O:31])[n:32]1\", \"reactants\":[\"N[NH:1][c:2]1[cH:3][cH:4][c:5]([Br:6])[cH:7][cH:8]1.O=[C:9]1[CH2:10][CH2:11][CH2:12][CH:13]([NH:14][C:15]([CH3:16])=[O:17])[CH2:18]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'N[NH:1][c:2]1[cH:3][cH:4][c:5]([Br:6])[cH:7][cH:8]1' and 'O=[C:9]1[CH2:10][CH2:11][CH2:12][CH:13]([NH:14][C:15]([CH3:16])=[O:17])[CH2:18]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[N:11]1[C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[N:11]1[C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[N:11]1[C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1\", \"reactants\":[\"[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[NH:11]1.[O-][C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are '[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[NH:11]1' and '[O-][C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[c:1]1([NH:30][c:31]2[cH:32][cH:33][n:34][cH:35][cH:36]2)[cH:2][cH:3][c:4](-[c:5]2[cH:6][n:7]([CH:8]3[C:9](=[O:10])[N:11]([CH2:12][C:13]([F:14])([F:15])[F:16])[c:17]4[c:18]([c:19]([F:20])[cH:21][cH:22][cH:23]4)[CH2:24][CH2:25]3)[n:26][n:27]2)[cH:28][cH:29]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:30][c:31]2[cH:32][cH:33][n:34][cH:35][cH:36]2)[cH:2][cH:3][c:4](-[c:5]2[cH:6][n:7]([CH:8]3[C:9](=[O:10])[N:11]([CH2:12][C:13]([F:14])([F:15])[F:16])[c:17]4[c:18]([c:19]([F:20])[cH:21][cH:22][cH:23]4)[CH2:24][CH2:25]3)[n:26][n:27]2)[cH:28][cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH2:2][CH:3]1[CH2:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[N:11]1[C:12](=[O:13])[CH2:14][c:15]1[n:16][c:17]([N:18]2[CH2:19][CH2:20][O:21][CH2:22][CH2:23]2)[cH:24][c:25](=[O:26])[nH:27]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4](-[c:5]2[cH:6][n:7]([CH:8]3[C:9](=[O:10])[N:11]([CH2:12][C:13]([F:14])([F:15])[F:16])[c:17]4[c:18]([c:19]([F:20])[cH:21][cH:22][cH:23]4)[CH2:24][CH2:25]3)[n:26][n:27]2)[cH:28][cH:29]1.[NH2:30][c:31]1[cH:32][cH:33][n:34][cH:35][cH:36]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4](-[c:5]2[cH:6][n:7]([CH:8]3[C:9](=[O:10])[N:11]([CH2:12][C:13]([F:14])([F:15])[F:16])[c:17]4[c:18]([c:19]([F:20])[cH:21][cH:22][cH:23]4)[CH2:24][CH2:25]3)[n:26][n:27]2)[cH:28][cH:29]1' and '[NH2:30][c:31]1[cH:32][cH:33][n:34][cH:35][cH:36]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH:1](=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13])\\[C:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13])\\[C:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13])\\[C:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]\", \"reactants\":[\"I/[CH:1]=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13].[CH:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'I/[CH:1]=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13]' and '[CH:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[c:1]1([N:14]2[C:13](=[O:12])[c:26]3[c:17]([cH:18][cH:19][c:20]([C:21]([F:22])([F:23])[F:24])[cH:25]3)[CH2:16][CH2:15]2)[c:2]([CH:3]([O:4][CH3:5])[O:6][CH3:7])[cH:8][cH:9][n:10][cH:11]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:14]2[C:13](=[O:12])[c:26]3[c:17]([cH:18][cH:19][c:20]([C:21]([F:22])([F:23])[F:24])[cH:25]3)[CH2:16][CH2:15]2)[c:2]([CH:3]([O:4][CH3:5])[O:6][CH3:7])[cH:8][cH:9][n:10][cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[CH:2]/[C@@H:3]([CH2:4][CH3:5])[O:6][Si:7]([CH3:8])([CH3:9])[C:10]([CH3:11])([CH3:12])[CH3:13])\\[C:14]#[C:15][CH2:16][C@H:17](/[CH:18]=[CH:19]/[CH:20]=[CH:21]/[C:22]#[C:23][C@H:24]([CH2:25][CH2:26][CH2:27][C:28](=[O:29])[O:30][CH3:31])[O:32][Si:33]([CH3:34])([CH3:35])[C:36]([CH3:37])([CH3:38])[CH3:39])[O:40][Si:41]([CH3:42])([CH3:43])[C:44]([CH3:45])([CH3:46])[CH3:47]\", \"reactants\":[\"Br[c:1]1[c:2]([CH:3]([O:4][CH3:5])[O:6][CH3:7])[cH:8][cH:9][n:10][cH:11]1.[O:12]=[C:13]1[NH:14][CH2:15][CH2:16][c:17]2[cH:18][cH:19][c:20]([C:21]([F:22])([F:23])[F:24])[cH:25][c:26]21\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[c:2]([CH:3]([O:4][CH3:5])[O:6][CH3:7])[cH:8][cH:9][n:10][cH:11]1' and '[O:12]=[C:13]1[NH:14][CH2:15][CH2:16][c:17]2[cH:18][cH:19][c:20]([C:21]([F:22])([F:23])[F:24])[cH:25][c:26]21'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path for '[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]\", \"reactants\":[\"Cl[CH2:1][CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1.[CH3:9][S:10][c:11]1[n:12][cH:13][c:14]2[c:15]([n:16]1)[nH:17][c:18](=[O:19])[c:20](-[c:21]1[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]1[Cl:34])[cH:35]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[CH2:1][CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1' and '[CH3:9][S:10][c:11]1[n:12][cH:13][c:14]2[c:15]([n:16]1)[nH:17][c:18](=[O:19])[c:20](-[c:21]1[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]1[Cl:34])[cH:35]2'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([O:22][C:33]([N:31]([CH3:30])[CH3:32])=[O:34])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([O:22][C:33]([N:31]([CH3:30])[CH3:32])=[O:34])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][N:3]1[C:4](=[O:5])[O:6][CH2:7][CH2:8]1)[n:17]1[c:15]2[c:14]([cH:13][n:12][c:11]([S:10][CH3:9])[n:16]2)[cH:35][c:20](-[c:21]2[cH:22][cH:23][c:24](-[c:25]3[cH:26][n:27][cH:28][c:29]([CH3:30])[n:31]3)[cH:32][c:33]2[Cl:34])[c:18]1=[O:19]\", \"reactants\":[\"[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([OH:22])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1.Cl[C:33]([N:31]([CH3:30])[CH3:32])=[O:34]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[CH3:1][C:2]1([CH3:3])[CH2:4][C:5](=[O:6])[N:7]([CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][N:13]2[CH2:14][CH2:15][C@@H:16]3[c:17]4[cH:18][cH:19][cH:20][c:21]([OH:22])[c:23]4[CH2:24][CH2:25][C@H:26]23)[C:27](=[O:28])[CH2:29]1' and 'Cl[C:33]([N:31]([CH3:30])[CH3:32])=[O:34]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[CH2:1]([c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1)[O:14][c:13]1[cH:12][cH:11][c:10]([C:9](=[O:8])[N:17]2[CH2:18][CH2:19][CH2:20][C@H:21]2[CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27]2)[cH:16][cH:15]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1)[O:14][c:13]1[cH:12][cH:11][c:10]([C:9](=[O:8])[N:17]2[CH2:18][CH2:19][CH2:20][C@H:21]2[CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27]2)[cH:16][cH:15]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1)[O:14][c:13]1[cH:12][cH:11][c:10]([C:9](=[O:8])[N:17]2[CH2:18][CH2:19][CH2:20][C@H:21]2[CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27]2)[cH:16][cH:15]1\", \"reactants\":[\"O[CH2:1][c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1.[O:8]=[C:9]([c:10]1[cH:11][cH:12][c:13]([OH:14])[cH:15][cH:16]1)[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[CH2:22][N:23]1[CH2:24][CH2:25][CH2:26][CH2:27]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[CH2:1][c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1' and '[O:8]=[C:9]([c:10]1[cH:11][cH:12][c:13]([OH:14])[cH:15][cH:16]1)[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[CH2:22][N:23]1[CH2:24][CH2:25][CH2:26][CH2:27]1'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[c:1]1([NH2:30])[c:2]2[cH:3][c:4]([CH3:5])[cH:6][c:7]([C:8]([NH:9][c:10]3[c:11]([F:12])[cH:13][cH:14][c:15]([NH:16][S:17]([CH2:18][CH2:19][CH3:20])(=[O:21])=[O:22])[c:23]3[F:24])=[O:25])[c:26]2[n:27][cH:28][n:29]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:30])[c:2]2[cH:3][c:4]([CH3:5])[cH:6][c:7]([C:8]([NH:9][c:10]3[c:11]([F:12])[cH:13][cH:14][c:15]([NH:16][S:17]([CH2:18][CH2:19][CH3:20])(=[O:21])=[O:22])[c:23]3[F:24])=[O:25])[c:26]2[n:27][cH:28][n:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][n:4][cH:5][cH:6][n:7]1)[O:14][c:13]1[cH:12][cH:11][c:10]([C:9](=[O:8])[N:17]2[CH2:18][CH2:19][CH2:20][C@H:21]2[CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27]2)[cH:16][cH:15]1\", \"reactants\":[\"Cl[c:1]1[c:2]2[cH:3][c:4]([CH3:5])[cH:6][c:7]([C:8]([NH:9][c:10]3[c:11]([F:12])[cH:13][cH:14][c:15]([NH:16][S:17]([CH2:18][CH2:19][CH3:20])(=[O:21])=[O:22])[c:23]3[F:24])=[O:25])[c:26]2[n:27][cH:28][n:29]1.[NH3:30]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[c:1]1[c:2]2[cH:3][c:4]([CH3:5])[cH:6][c:7]([C:8]([NH:9][c:10]3[c:11]([F:12])[cH:13][cH:14][c:15]([NH:16][S:17]([CH2:18][CH2:19][CH3:20])(=[O:21])=[O:22])[c:23]3[F:24])=[O:25])[c:26]2[n:27][cH:28][n:29]1' and '[NH3:30]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for '[C:1]([CH2:2][O:3][CH2:4][CH3:5])(=[O:6])[NH:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][O:3][CH2:4][CH3:5])(=[O:6])[NH:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][O:3][CH2:4][CH3:5])(=[O:6])[NH:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1\", \"reactants\":[\"O[C:1]([CH2:2][O:3][CH2:4][CH3:5])=[O:6].[NH2:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[C:1]([CH2:2][O:3][CH2:4][CH3:5])=[O:6]' and '[NH2:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1'."
            },
            {
                "from": "human",
                "value": "And provide a forecast for '[CH:1](\\[c:2]1[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH3:9])[c:10]([O:11][CH3:12])[cH:13]1)=[N:14]/[NH:15][C:16](=[O:17])[CH2:18][CH:19]1[S:20][c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[NH:27][C:28]1=[O:29]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](\\[c:2]1[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH3:9])[c:10]([O:11][CH3:12])[cH:13]1)=[N:14]/[NH:15][C:16](=[O:17])[CH2:18][CH:19]1[S:20][c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[NH:27][C:28]1=[O:29]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][O:3][CH2:4][CH3:5])(=[O:6])[NH:7][CH:8]1[CH2:9][CH2:10][N:11]([CH2:12][C:13]23[CH2:14][CH:15]([c:16]4[cH:17][cH:18][cH:19][cH:20][c:21]42)[c:22]2[cH:23][cH:24][c:25]([Cl:26])[cH:27][c:28]23)[CH2:29][CH2:30]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH3:9])[c:10]([O:11][CH3:12])[cH:13]1.[NH2:14][NH:15][C:16](=[O:17])[CH2:18][CH:19]1[S:20][c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[NH:27][C:28]1=[O:29]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH3:9])[c:10]([O:11][CH3:12])[cH:13]1' and '[NH2:14][NH:15][C:16](=[O:17])[CH2:18][CH:19]1[S:20][c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[NH:27][C:28]1=[O:29]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:15][C@H:14]1[CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[C@H:17]([C:18]([NH2:19])=[O:20])[CH2:16]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:15][C@H:14]1[CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[C@H:17]([C:18]([NH2:19])=[O:20])[CH2:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:15][C@H:14]1[CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[C@H:17]([C:18]([NH2:19])=[O:20])[CH2:16]1\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[CH3:5][C:6]([CH3:7])([CH3:8])[O:9][C:10](=[O:11])[N:12]1[CH2:13][C@H:14]([OH:15])[CH2:16][C@H:17]1[C:18]([NH2:19])=[O:20]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[CH3:5][C:6]([CH3:7])([CH3:8])[O:9][C:10](=[O:11])[N:12]1[CH2:13][C@H:14]([OH:15])[CH2:16][C@H:17]1[C:18]([NH2:19])=[O:20]'."
            },
            {
                "from": "human",
                "value": "And '[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1)[NH:20][CH:19]([C:17]([O:16][CH2:15][CH3:14])=[O:18])[CH2:21][NH:22][C:23](=[O:24])[CH:25]1[CH2:26][CH2:27][N:28]([c:29]2[cH:30][cH:31][n:32][cH:33][cH:34]2)[CH2:35][CH2:36]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1](=[O:2])(=[O:3])([c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1)[NH:20][CH:19]([C:17]([O:16][CH2:15][CH3:14])=[O:18])[CH2:21][NH:22][C:23](=[O:24])[CH:25]1[CH2:26][CH2:27][N:28]([c:29]2[cH:30][cH:31][n:32][cH:33][cH:34]2)[CH2:35][CH2:36]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:15][C@H:14]1[CH2:13][N:12]([C:10]([O:9][C:6]([CH3:5])([CH3:7])[CH3:8])=[O:11])[C@H:17]([C:18]([NH2:19])=[O:20])[CH2:16]1\", \"reactants\":[\"Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1.[CH3:14][CH2:15][O:16][C:17](=[O:18])[CH:19]([NH2:20])[CH2:21][NH:22][C:23](=[O:24])[CH:25]1[CH2:26][CH2:27][N:28]([c:29]2[cH:30][cH:31][n:32][cH:33][cH:34]2)[CH2:35][CH2:36]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1](=[O:2])(=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[cH:13]1' and '[CH3:14][CH2:15][O:16][C:17](=[O:18])[CH:19]([NH2:20])[CH2:21][NH:22][C:23](=[O:24])[CH:25]1[CH2:26][CH2:27][N:28]([c:29]2[cH:30][cH:31][n:32][cH:33][cH:34]2)[CH2:35][CH2:36]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis process for '[c:1]1([O:21][c:22]2[cH:23][cH:24][c:25](-[c:26]3[cH:27][cH:28][s:29][cH:30]3)[cH:31][cH:32]2)[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:21][c:22]2[cH:23][cH:24][c:25](-[c:26]3[cH:27][cH:28][s:29][cH:30]3)[cH:31][cH:32]2)[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:21][c:22]2[cH:23][cH:24][c:25](-[c:26]3[cH:27][cH:28][s:29][cH:30]3)[cH:31][cH:32]2)[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1\", \"reactants\":[\"I[c:1]1[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1.[OH:21][c:22]1[cH:23][cH:24][c:25](-[c:26]2[cH:27][cH:28][s:29][cH:30]2)[cH:31][cH:32]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'I[c:1]1[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1' and '[OH:21][c:22]1[cH:23][cH:24][c:25](-[c:26]2[cH:27][cH:28][s:29][cH:30]2)[cH:31][cH:32]1'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[CH3:1][n:17]1[n:16][n:15][n:14](-[c:13]2[cH:12][c:8]([N+:9](=[O:10])[O-:11])[cH:7][c:6]([C:4]([O:3][CH3:2])=[O:5])[c:20]2[CH3:21])[c:18]1=[O:19]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][n:17]1[n:16][n:15][n:14](-[c:13]2[cH:12][c:8]([N+:9](=[O:10])[O-:11])[cH:7][c:6]([C:4]([O:3][CH3:2])=[O:5])[c:20]2[CH3:21])[c:18]1=[O:19]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:21][c:22]2[cH:23][cH:24][c:25](-[c:26]3[cH:27][cH:28][s:29][cH:30]3)[cH:31][cH:32]2)[cH:2][cH:3][c:4]([O:5][CH2:6][C@@H:7]2[N:8]([C:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])=[O:15])[CH2:16][CH2:17][CH2:18]2)[cH:19][cH:20]1\", \"reactants\":[\"I[CH3:1].[CH3:2][O:3][C:4](=[O:5])[c:6]1[cH:7][c:8]([N+:9](=[O:10])[O-:11])[cH:12][c:13](-[n:14]2[n:15][n:16][nH:17][c:18]2=[O:19])[c:20]1[CH3:21]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'I[CH3:1]' and '[CH3:2][O:3][C:4](=[O:5])[c:6]1[cH:7][c:8]([N+:9](=[O:10])[O-:11])[cH:12][c:13](-[n:14]2[n:15][n:16][nH:17][c:18]2=[O:19])[c:20]1[CH3:21]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:20]1[CH2:19][C@@H:18]2[CH2:17][C@@H:16]([O:15][Si:12]([C:9]([CH3:8])([CH3:10])[CH3:11])([CH3:13])[CH3:14])[CH2:37][N:36]2[CH:22]([CH:23]([c:24]2[cH:25][cH:26][cH:27][cH:28][cH:29]2)[c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:21]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:20]1[CH2:19][C@@H:18]2[CH2:17][C@@H:16]([O:15][Si:12]([C:9]([CH3:8])([CH3:10])[CH3:11])([CH3:13])[CH3:14])[CH2:37][N:36]2[CH:22]([CH:23]([c:24]2[cH:25][cH:26][cH:27][cH:28][cH:29]2)[c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:20]1[CH2:19][C@@H:18]2[CH2:17][C@@H:16]([O:15][Si:12]([C:9]([CH3:8])([CH3:10])[CH3:11])([CH3:13])[CH3:14])[CH2:37][N:36]2[CH:22]([CH:23]([c:24]2[cH:25][cH:26][cH:27][cH:28][cH:29]2)[c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:21]1\", \"reactants\":[\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][C:9]([CH3:10])([CH3:11])[Si:12]([CH3:13])([CH3:14])[O:15][C@@H:16]1[CH2:17][C@H:18]2[CH2:19][NH:20][CH2:21][CH:22]([CH:23]([c:24]3[cH:25][cH:26][cH:27][cH:28][cH:29]3)[c:30]3[cH:31][cH:32][cH:33][cH:34][cH:35]3)[N:36]2[CH2:37]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][C:9]([CH3:10])([CH3:11])[Si:12]([CH3:13])([CH3:14])[O:15][C@@H:16]1[CH2:17][C@H:18]2[CH2:19][NH:20][CH2:21][CH:22]([CH:23]([c:24]3[cH:25][cH:26][cH:27][cH:28][cH:29]3)[c:30]3[cH:31][cH:32][cH:33][cH:34][cH:35]3)[N:36]2[CH2:37]1'."
            },
            {
                "from": "human",
                "value": "And predict the outcomes for '[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:20]1[CH2:19][C@@H:18]2[CH2:17][C@@H:16]([O:15][Si:12]([C:9]([CH3:8])([CH3:10])[CH3:11])([CH3:13])[CH3:14])[CH2:37][N:36]2[CH:22]([CH:23]([c:24]2[cH:25][cH:26][cH:27][cH:28][cH:29]2)[c:30]2[cH:31][cH:32][cH:33][cH:34][cH:35]2)[CH2:21]1\", \"reactants\":[\"CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31].[CH3:32][O:33][CH2:34][CH2:35][NH2:36]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31]' and '[CH3:32][O:33][CH2:34][CH2:35][NH2:36]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis process for '[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\", \"reactants\":[\"O=C(O[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6])C(F)(F)F.[NH2:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=C(O[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6])C(F)(F)F' and '[NH2:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2'."
            },
            {
                "from": "human",
                "value": "What are the expectations for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13]([F:14])[cH:15]1)[NH:25][c:24]1[c:19]([C:17]([NH2:16])=[O:18])[cH:20][cH:21][cH:22][cH:23]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13]([F:14])[cH:15]1)[NH:25][c:24]1[c:19]([C:17]([NH2:16])=[O:18])[cH:20][cH:21][cH:22][cH:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][C:8]1([c:9]2[cH:10][cH:11][c:12](-[c:13]3[n:14][c:15]4[cH:16][cH:17][n:18]5[c:19](-[c:20]6[n:21][cH:22][cH:23][cH:24][n:25]6)[n:26][n:27][c:28]5[c:29]4[cH:30][c:31]3-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[cH:38][cH:39]2)[CH2:40][C:41]2([CH2:42]1)[O:43][CH2:44][CH2:45][O:46]2\", \"reactants\":[\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13]([F:14])[cH:15]1.[NH2:16][C:17](=[O:18])[c:19]1[cH:20][cH:21][cH:22][cH:23][c:24]1[NH2:25]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13]([F:14])[cH:15]1' and '[NH2:16][C:17](=[O:18])[c:19]1[cH:20][cH:21][cH:22][cH:23][c:24]1[NH2:25]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the likely synthesis route for '[c:1]1([N:24]2[CH2:23][CH2:22][N:21]([c:20]3[cH:19][cH:18][cH:17][cH:28][cH:27]3)[CH2:26][CH2:25]2)[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:24]2[CH2:23][CH2:22][N:21]([c:20]3[cH:19][cH:18][cH:17][cH:28][cH:27]3)[CH2:26][CH2:25]2)[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:24]2[CH2:23][CH2:22][N:21]([c:20]3[cH:19][cH:18][cH:17][cH:28][cH:27]3)[CH2:26][CH2:25]2)[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16]\", \"reactants\":[\"Cl[c:1]1[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16].[cH:17]1[cH:18][cH:19][c:20]([N:21]2[CH2:22][CH2:23][NH:24][CH2:25][CH2:26]2)[cH:27][cH:28]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16]' and '[cH:17]1[cH:18][cH:19][c:20]([N:21]2[CH2:22][CH2:23][NH:24][CH2:25][CH2:26]2)[cH:27][cH:28]1'."
            },
            {
                "from": "human",
                "value": "What about '[C:1](=[O:2])([N:3]1[CH2:4][CH:5]([O:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[Cl:13])[CH2:14]1)[NH:16][CH3:15]'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([N:3]1[CH2:4][CH:5]([O:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[Cl:13])[CH2:14]1)[NH:16][CH3:15]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:24]2[CH2:23][CH2:22][N:21]([c:20]3[cH:19][cH:18][cH:17][cH:28][cH:27]3)[CH2:26][CH2:25]2)[cH:2][c:3]2[n:4][c:5]([C:6]([CH3:7])([CH3:8])[CH3:9])[s:10][c:11]2[cH:12][c:13]1[N+:14](=[O:15])[O-:16]\", \"reactants\":[\"Cl[C:1](=[O:2])[N:3]1[CH2:4][CH:5]([O:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[Cl:13])[CH2:14]1.[CH3:15][NH2:16]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[N:3]1[CH2:4][CH:5]([O:6][c:7]2[cH:8][cH:9][cH:10][cH:11][c:12]2[Cl:13])[CH2:14]1' and '[CH3:15][NH2:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify how to synthesize '[CH3:1][O:10][c:9]1[c:4]([CH:3]=[O:2])[cH:5][cH:6][c:7]2[c:8]1[CH2:11][CH2:12][CH2:13]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][O:10][c:9]1[c:4]([CH:3]=[O:2])[cH:5][cH:6][c:7]2[c:8]1[CH2:11][CH2:12][CH2:13]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:10][c:9]1[c:4]([CH:3]=[O:2])[cH:5][cH:6][c:7]2[c:8]1[CH2:11][CH2:12][CH2:13]2\", \"reactants\":[\"I[CH3:1].[O:2]=[CH:3][c:4]1[cH:5][cH:6][c:7]2[c:8]([c:9]1[OH:10])[CH2:11][CH2:12][CH2:13]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'I[CH3:1]' and '[O:2]=[CH:3][c:4]1[cH:5][cH:6][c:7]2[c:8]([c:9]1[OH:10])[CH2:11][CH2:12][CH2:13]2'."
            },
            {
                "from": "human",
                "value": "And '[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])(=[O:16])[NH:25][CH2:24][CH2:23][CH2:22][CH2:21][CH2:20][CH2:19][CH2:18][CH3:17]'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])(=[O:16])[NH:25][CH2:24][CH2:23][CH2:22][CH2:21][CH2:20][CH2:19][CH2:18][CH3:17]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][O:10][c:9]1[c:4]([CH:3]=[O:2])[cH:5][cH:6][c:7]2[c:8]1[CH2:11][CH2:12][CH2:13]2\", \"reactants\":[\"O[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])=[O:16].[CH3:17][CH2:18][CH2:19][CH2:20][CH2:21][CH2:22][CH2:23][CH2:24][NH2:25]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH:2]([CH2:3][CH2:4][S:5]([CH3:6])=[O:7])[NH:8][C:9](=[O:10])[O:11][C:12]([CH3:13])([CH3:14])[CH3:15])=[O:16]' and '[CH3:17][CH2:18][CH2:19][CH2:20][CH2:21][CH2:22][CH2:23][CH2:24][NH2:25]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis path for '[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])(=[O:55])[NH:56][CH:57]1[CH2:58][O:59][CH2:60]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])(=[O:55])[NH:56][CH:57]1[CH2:58][O:59][CH2:60]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])(=[O:55])[NH:56][CH:57]1[CH2:58][O:59][CH2:60]1\", \"reactants\":[\"O[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])=[O:55].[NH2:56][CH:57]1[CH2:58][O:59][CH2:60]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])=[O:55]' and '[NH2:56][CH:57]1[CH2:58][O:59][CH2:60]1'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[CH3:1][C:2](=[O:3])[NH:16][CH2:15][CH:14]=[C:13]1[c:12]2[c:6]([O:5][CH3:4])[c:7]([NH2:8])[cH:9][cH:10][c:11]2[CH2:18][CH2:17]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2](=[O:3])[NH:16][CH2:15][CH:14]=[C:13]1[c:12]2[c:6]([O:5][CH3:4])[c:7]([NH2:8])[cH:9][cH:10][c:11]2[CH2:18][CH2:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][c:4]([C:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]([NH:11][C:12]([NH:13][c:14]2[cH:15][cH:16][c:17]([O:18][c:19]3[cH:20][c:21]([NH:22][c:23]4[cH:24][c:25]([O:26][CH2:27][CH2:28][O:29][CH2:30][CH2:31][O:32][CH2:33][CH2:34][O:35][CH3:36])[cH:37][c:38]([O:39][CH3:40])[cH:41]4)[n:42][cH:43][cH:44]3)[c:45]3[c:46]2[cH:47][cH:48][cH:49][cH:50]3)=[O:51])[c:52]1[O:53][CH3:54])(=[O:55])[NH:56][CH:57]1[CH2:58][O:59][CH2:60]1\", \"reactants\":[\"CC(=O)O[C:2]([CH3:1])=[O:3].[CH3:4][O:5][c:6]1[c:7]([NH2:8])[cH:9][cH:10][c:11]2[c:12]1[C:13](=[CH:14][CH2:15][NH2:16])[CH2:17][CH2:18]2\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(=O)O[C:2]([CH3:1])=[O:3]' and '[CH3:4][O:5][c:6]1[c:7]([NH2:8])[cH:9][cH:10][c:11]2[c:12]1[C:13](=[CH:14][CH2:15][NH2:16])[CH2:17][CH2:18]2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[c:1]1([N:18]2[CH2:17][CH2:16][N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:20][CH2:19]2)[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:18]2[CH2:17][CH2:16][N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:20][CH2:19]2)[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:18]2[CH2:17][CH2:16][N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:20][CH2:19]2)[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7]\", \"reactants\":[\"Cl[c:1]1[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7].[CH3:8][C:9]([CH3:10])([CH3:11])[O:12][C:13](=[O:14])[N:15]1[CH2:16][CH2:17][NH:18][CH2:19][CH2:20]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7]' and '[CH3:8][C:9]([CH3:10])([CH3:11])[O:12][C:13](=[O:14])[N:15]1[CH2:16][CH2:17][NH:18][CH2:19][CH2:20]1'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1)[NH:17][CH:16]1[CH2:15][CH2:14][N:13]([C:11]([CH3:10])=[O:12])[CH2:19][CH2:18]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1)[NH:17][CH:16]1[CH2:15][CH2:14][N:13]([C:11]([CH3:10])=[O:12])[CH2:19][CH2:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:18]2[CH2:17][CH2:16][N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:20][CH2:19]2)[n:2][cH:3][cH:4][n:5][c:6]1[Cl:7]\", \"reactants\":[\"Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1.[CH3:10][C:11](=[O:12])[N:13]1[CH2:14][CH2:15][CH:16]([NH2:17])[CH2:18][CH2:19]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([F:7])[cH:8][cH:9]1' and '[CH3:10][C:11](=[O:12])[N:13]1[CH2:14][CH2:15][CH:16]([NH2:17])[CH2:18][CH2:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1([NH:32][CH2:31][C@H:30]2[N:24]([C:22]([c:21]3[c:15](-[n:16]4[n:17][cH:18][cH:19][n:20]4)[cH:14][cH:13][c:12]([CH3:11])[cH:33]3)=[O:23])[CH2:25][CH2:26][CH2:27][C@H:28]2[CH3:29])[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:32][CH2:31][C@H:30]2[N:24]([C:22]([c:21]3[c:15](-[n:16]4[n:17][cH:18][cH:19][n:20]4)[cH:14][cH:13][c:12]([CH3:11])[cH:33]3)=[O:23])[CH2:25][CH2:26][CH2:27][C@H:28]2[CH3:29])[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:32][CH2:31][C@H:30]2[N:24]([C:22]([c:21]3[c:15](-[n:16]4[n:17][cH:18][cH:19][n:20]4)[cH:14][cH:13][c:12]([CH3:11])[cH:33]3)=[O:23])[CH2:25][CH2:26][CH2:27][C@H:28]2[CH3:29])[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1\", \"reactants\":[\"Cl[c:1]1[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1.[CH3:11][c:12]1[cH:13][cH:14][c:15](-[n:16]2[n:17][cH:18][cH:19][n:20]2)[c:21]([C:22](=[O:23])[N:24]2[CH2:25][CH2:26][CH2:27][C@@H:28]([CH3:29])[C@H:30]2[CH2:31][NH2:32])[cH:33]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1' and '[CH3:11][c:12]1[cH:13][cH:14][c:15](-[n:16]2[n:17][cH:18][cH:19][n:20]2)[c:21]([C:22](=[O:23])[N:24]2[CH2:25][CH2:26][CH2:27][C@@H:28]([CH3:29])[C@H:30]2[CH2:31][NH2:32])[cH:33]1'."
            },
            {
                "from": "human",
                "value": "And '[c:1]1(-[c:11]2[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]2)[c:2](-[c:3]2[cH:4][cH:5][c:6]([S:7][CH3:8])[cH:9][cH:10]2)[s:20][c:19](-[c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[Cl:27])[n:18]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:11]2[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]2)[c:2](-[c:3]2[cH:4][cH:5][c:6]([S:7][CH3:8])[cH:9][cH:10]2)[s:20][c:19](-[c:21]2[cH:22][cH:23][cH:24][cH:25][c:26]2[Cl:27])[n:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:32][CH2:31][C@H:30]2[N:24]([C:22]([c:21]3[c:15](-[n:16]4[n:17][cH:18][cH:19][n:20]4)[cH:14][cH:13][c:12]([CH3:11])[cH:33]3)=[O:23])[CH2:25][CH2:26][CH2:27][C@H:28]2[CH3:29])[c:2]([C:3]([F:4])([F:5])[F:6])[cH:7][cH:8][cH:9][n:10]1\", \"reactants\":[\"O=[C:1]([CH:2](Br)[c:3]1[cH:4][cH:5][c:6]([S:7][CH3:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]1.[NH2:18][C:19](=[S:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1[Cl:27]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1]([CH:2](Br)[c:3]1[cH:4][cH:5][c:6]([S:7][CH3:8])[cH:9][cH:10]1)[c:11]1[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]1' and '[NH2:18][C:19](=[S:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1[Cl:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1\", \"reactants\":[\"Cl[c:1]1[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1.[CH3:29][CH2:30][c:31]1[n:32][c:33]2[cH:34][cH:35][cH:36][cH:37][c:38]2[nH:39]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1' and '[CH3:29][CH2:30][c:31]1[n:32][c:33]2[cH:34][cH:35][cH:36][cH:37][c:38]2[nH:39]1'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[c:1]1([CH3:2])[cH:3][cH:4][c:5]([CH2:6][N:7]2[C:8](=[O:9])[c:10]3[cH:11][cH:12][cH:13][cH:14][c:15]3[C:16]2=[O:17])[n:19]1[CH2:20][CH2:21][CH2:22][n:23]1[cH:24][cH:25][n:26][cH:27]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([CH3:2])[cH:3][cH:4][c:5]([CH2:6][N:7]2[C:8](=[O:9])[c:10]3[cH:11][cH:12][cH:13][cH:14][c:15]3[C:16]2=[O:17])[n:19]1[CH2:20][CH2:21][CH2:22][n:23]1[cH:24][cH:25][n:26][cH:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1\", \"reactants\":[\"O=[C:1]([CH3:2])[CH2:3][CH2:4][C:5](=O)[CH2:6][N:7]1[C:8](=[O:9])[c:10]2[cH:11][cH:12][cH:13][cH:14][c:15]2[C:16]1=[O:17].[NH2:19][CH2:20][CH2:21][CH2:22][n:23]1[cH:24][cH:25][n:26][cH:27]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[C:1]([CH3:2])[CH2:3][CH2:4][C:5](=O)[CH2:6][N:7]1[C:8](=[O:9])[c:10]2[cH:11][cH:12][cH:13][cH:14][c:15]2[C:16]1=[O:17]' and '[NH2:19][CH2:20][CH2:21][CH2:22][n:23]1[cH:24][cH:25][n:26][cH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the best synthesis strategy for '[NH:1]([CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1)[C:19](=[O:18])[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH:1]([CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1)[C:19](=[O:18])[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[NH:1]([CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1)[C:19](=[O:18])[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1\", \"reactants\":[\"[NH2:1][CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1.[O:18]=[C:19]=[N:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are '[NH2:1][CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1' and '[O:18]=[C:19]=[N:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1'."
            },
            {
                "from": "human",
                "value": "What about '[O:1]=[C:2]1[N:3]=[C:4]([N:5]2[CH2:6][CH2:7][CH2:8][CH2:9][NH:10]2)[S:11]/[C:12]1=[CH:13]/[c:14]1[cH:15][c:16]([F:17])[cH:18][cH:19][c:20]1[O:21][C:23](=[O:22])[N:25]1[CH2:26][CH2:27][CH2:28][CH2:29][CH2:30]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[O:1]=[C:2]1[N:3]=[C:4]([N:5]2[CH2:6][CH2:7][CH2:8][CH2:9][NH:10]2)[S:11]/[C:12]1=[CH:13]/[c:14]1[cH:15][c:16]([F:17])[cH:18][cH:19][c:20]1[O:21][C:23](=[O:22])[N:25]1[CH2:26][CH2:27][CH2:28][CH2:29][CH2:30]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[NH:1]([CH:2]1[CH2:3][c:4]2[cH:5][cH:6][c:7]([C:8]3=[N:9][NH:10][C:11](=[O:12])[CH2:13][CH2:14]3)[cH:15][c:16]2[CH2:17]1)[C:19](=[O:18])[NH:20][c:21]1[cH:22][cH:23][cH:24][cH:25][cH:26]1\", \"reactants\":[\"[O:1]=[C:2]1[N:3]=[C:4]([N:5]2[CH2:6][CH2:7][CH2:8][CH2:9][NH:10]2)[S:11]/[C:12]1=[CH:13]\\[c:14]1[cH:15][c:16]([F:17])[cH:18][cH:19][c:20]1[OH:21].Cl[C:23](=[O:22])[N:25]1[CH2:26][CH2:27][CH2:28][CH2:29][CH2:30]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are '[O:1]=[C:2]1[N:3]=[C:4]([N:5]2[CH2:6][CH2:7][CH2:8][CH2:9][NH:10]2)[S:11]/[C:12]1=[CH:13]\\[c:14]1[cH:15][c:16]([F:17])[cH:18][cH:19][c:20]1[OH:21]' and 'Cl[C:23](=[O:22])[N:25]1[CH2:26][CH2:27][CH2:28][CH2:29][CH2:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis method for '[c:1]1(-[c:10]2[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]2)[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:10]2[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]2)[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:10]2[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]2)[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1\", \"reactants\":[\"Br[c:1]1[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1.OB(O)[c:10]1[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1' and 'OB(O)[c:10]1[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]1'."
            },
            {
                "from": "human",
                "value": "What are the expectations for '[O:1]1[CH2:2][CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH3:9]'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[O:1]1[CH2:2][CH:3]1[CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH3:9]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:10]2[cH:11][cH:12][c:13]([C:14](=[O:15])[OH:16])[cH:17][cH:18]2)[c:2]([CH2:3][C:4]#[N:5])[cH:6][cH:7][cH:8][cH:9]1\", \"reactants\":[\"O[OH:1].[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH3:9]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[OH:1]' and '[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH3:9]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Deduce the synthesis method for '[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1\", \"reactants\":[\"Cl[c:1]1[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1.[CH3:11][O:12][C:13](=[O:14])[CH2:15][OH:16]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1' and '[CH3:11][O:12][C:13](=[O:14])[CH2:15][OH:16]'."
            },
            {
                "from": "human",
                "value": "And predict the outcomes for '[c:1]1([CH3:36])[cH:2][cH:3][c:4]2[c:5]([cH:6]1)[C:7]1([C:8](=[O:9])[N:10]2[CH:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[CH2:24][O:25][c:26]2[cH:27][c:28]3[c:29]([cH:30][c:31]21)[O:32][CH2:33][CH2:34][O:35]3'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([CH3:36])[cH:2][cH:3][c:4]2[c:5]([cH:6]1)[C:7]1([C:8](=[O:9])[N:10]2[CH:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[CH2:24][O:25][c:26]2[cH:27][c:28]3[c:29]([cH:30][c:31]21)[O:32][CH2:33][CH2:34][O:35]3\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]2[c:5]([cH:6]1)[C:7]1([C:8](=[O:9])[N:10]2[CH:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[CH2:24][O:25][c:26]2[cH:27][c:28]3[c:29]([cH:30][c:31]21)[O:32][CH2:33][CH2:34][O:35]3.C[Sn](C)(C)[CH3:36]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]2[c:5]([cH:6]1)[C:7]1([C:8](=[O:9])[N:10]2[CH:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[CH2:24][O:25][c:26]2[cH:27][c:28]3[c:29]([cH:30][c:31]21)[O:32][CH2:33][CH2:34][O:35]3' and 'C[Sn](C)(C)[CH3:36]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis path of '[CH:1](=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28])[NH:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28])[NH:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28])[NH:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1\", \"reactants\":[\"O[CH:1]=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28].[NH2:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[CH:1]=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28]' and '[NH2:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1'."
            },
            {
                "from": "human",
                "value": "What are the expectations for '[CH2:1]([C:2]([N:3]([CH:4]([CH3:5])[CH3:6])[c:7]1[cH:8][cH:9][cH:10][cH:11][cH:12]1)=[O:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[NH2:21]'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]([N:3]([CH:4]([CH3:5])[CH3:6])[c:7]1[cH:8][cH:9][cH:10][cH:11][cH:12]1)=[O:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[NH2:21]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1](=[C:2]1[c:3]2[cH:4][cH:5][c:6]([C:7]([c:8]3[cH:9][cH:10][cH:11][c:12]([NH:13][C:14]([c:15]4[c:16]([CH3:17])[cH:18][cH:19][o:20]4)=[O:21])[cH:22]3)=[O:23])[cH:24][c:25]2[NH:26][C:27]1=[O:28])[NH:29][c:30]1[cH:31][cH:32][c:33]([CH2:34][CH2:35][N:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:41][cH:42]1\", \"reactants\":[\"Br[CH2:1][C:2]([N:3]([CH:4]([CH3:5])[CH3:6])[c:7]1[cH:8][cH:9][cH:10][cH:11][cH:12]1)=[O:13].[NH2:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[NH2:21]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][C:2]([N:3]([CH:4]([CH3:5])[CH3:6])[c:7]1[cH:8][cH:9][cH:10][cH:11][cH:12]1)=[O:13]' and '[NH2:14][c:15]1[cH:16][cH:17][cH:18][cH:19][c:20]1[NH2:21]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis steps for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22])[NH:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22])[NH:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22])[NH:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1\", \"reactants\":[\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22].[NH2:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22]' and '[NH2:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][cH:12][n:13][cH:14]1)[NH:37][c:36]1[cH:35][cH:34][c:33]([N:32]2[CH2:31][CH2:30][N:29]([CH:22]([C:20]([N:17]([CH2:16][CH3:15])[CH2:18][CH3:19])=[O:21])[c:23]3[cH:24][cH:25][cH:26][cH:27][cH:28]3)[CH2:42][CH2:41]2)[c:39]([F:40])[cH:38]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][cH:12][n:13][cH:14]1)[NH:37][c:36]1[cH:35][cH:34][c:33]([N:32]2[CH2:31][CH2:30][N:29]([CH:22]([C:20]([N:17]([CH2:16][CH3:15])[CH2:18][CH3:19])=[O:21])[c:23]3[cH:24][cH:25][cH:26][cH:27][cH:28]3)[CH2:42][CH2:41]2)[c:39]([F:40])[cH:38]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[nH:9][c:10](=[O:11])[n:12]([CH2:13][c:14]1[cH:15][cH:16][c:17]([Cl:18])[cH:19][cH:20]1)[c:21]2=[O:22])[NH:23][CH2:24][CH2:25][CH2:26][N:27]1[CH2:28][CH2:29][CH2:30][CH2:31][CH2:32]1\", \"reactants\":[\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][cH:12][n:13][cH:14]1.[CH3:15][CH2:16][N:17]([CH2:18][CH3:19])[C:20](=[O:21])[CH:22]([c:23]1[cH:24][cH:25][cH:26][cH:27][cH:28]1)[N:29]1[CH2:30][CH2:31][N:32]([c:33]2[cH:34][cH:35][c:36]([NH2:37])[cH:38][c:39]2[F:40])[CH2:41][CH2:42]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][cH:12][n:13][cH:14]1' and '[CH3:15][CH2:16][N:17]([CH2:18][CH3:19])[C:20](=[O:21])[CH:22]([c:23]1[cH:24][cH:25][cH:26][cH:27][cH:28]1)[N:29]1[CH2:30][CH2:31][N:32]([c:33]2[cH:34][cH:35][c:36]([NH2:37])[cH:38][c:39]2[F:40])[CH2:41][CH2:42]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[S:1]([CH3:2])(=[O:3])(=[O:4])[N:11]1[CH2:10][CH2:9][N:8]([NH:7][C:6](=[O:5])[c:14]2[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]3[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]3)[cH:32]2)[CH2:13][CH2:12]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[N:11]1[CH2:10][CH2:9][N:8]([NH:7][C:6](=[O:5])[c:14]2[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]3[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]3)[cH:32]2)[CH2:13][CH2:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[N:11]1[CH2:10][CH2:9][N:8]([NH:7][C:6](=[O:5])[c:14]2[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]3[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]3)[cH:32]2)[CH2:13][CH2:12]1\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[O:5]=[C:6]([NH:7][N:8]1[CH2:9][CH2:10][NH:11][CH2:12][CH2:13]1)[c:14]1[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:32]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[O:5]=[C:6]([NH:7][N:8]1[CH2:9][CH2:10][NH:11][CH2:12][CH2:13]1)[c:14]1[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:32]1'."
            },
            {
                "from": "human",
                "value": "Offer predictions on '[C:1]([CH:2]([c:3]1[cH:4][cH:5][c:6]([S:7]([CH3:8])(=[O:9])=[O:10])[c:11]([C:12]([F:13])([F:14])[F:15])[cH:16]1)[CH2:17][CH:18]1[CH2:19][CH2:20][CH2:21][CH2:22][O:23]1)(=[O:24])[NH:25][c:26]1[cH:27][n:28][cH:29][cH:30][n:31]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH:2]([c:3]1[cH:4][cH:5][c:6]([S:7]([CH3:8])(=[O:9])=[O:10])[c:11]([C:12]([F:13])([F:14])[F:15])[cH:16]1)[CH2:17][CH:18]1[CH2:19][CH2:20][CH2:21][CH2:22][O:23]1)(=[O:24])[NH:25][c:26]1[cH:27][n:28][cH:29][cH:30][n:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[N:11]1[CH2:10][CH2:9][N:8]([NH:7][C:6](=[O:5])[c:14]2[cH:15][n:16][c:17]([O:18][CH2:19][C:20]([F:21])([F:22])[F:23])[c:24](-[c:25]3[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]3)[cH:32]2)[CH2:13][CH2:12]1\", \"reactants\":[\"O[C:1]([CH:2]([c:3]1[cH:4][cH:5][c:6]([S:7]([CH3:8])(=[O:9])=[O:10])[c:11]([C:12]([F:13])([F:14])[F:15])[cH:16]1)[CH2:17][CH:18]1[CH2:19][CH2:20][CH2:21][CH2:22][O:23]1)=[O:24].[NH2:25][c:26]1[cH:27][n:28][cH:29][cH:30][n:31]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([CH:2]([c:3]1[cH:4][cH:5][c:6]([S:7]([CH3:8])(=[O:9])=[O:10])[c:11]([C:12]([F:13])([F:14])[F:15])[cH:16]1)[CH2:17][CH:18]1[CH2:19][CH2:20][CH2:21][CH2:22][O:23]1)=[O:24]' and '[NH2:25][c:26]1[cH:27][n:28][cH:29][cH:30][n:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest a synthesis path for '[CH3:1][NH:12][c:11]1[c:10]([NH:9][C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[c:16]([F:17])[cH:15][cH:14][cH:13]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][NH:12][c:11]1[c:10]([NH:9][C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[c:16]([F:17])[cH:15][cH:14][cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][NH:12][c:11]1[c:10]([NH:9][C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[c:16]([F:17])[cH:15][cH:14][cH:13]1\", \"reactants\":[\"O=[CH2:1].[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[NH:9][c:10]1[c:11]([NH2:12])[cH:13][cH:14][cH:15][c:16]1[F:17]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=[CH2:1]' and '[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[NH:9][c:10]1[c:11]([NH2:12])[cH:13][cH:14][cH:15][c:16]1[F:17]'."
            },
            {
                "from": "human",
                "value": "Offer predictions on '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH:7]1[CH2:8][CH2:9][CH2:10][CH2:11]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH:7]1[CH2:8][CH2:9][CH2:10][CH2:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][NH:12][c:11]1[c:10]([NH:9][C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[c:16]([F:17])[cH:15][cH:14][cH:13]1\", \"reactants\":[\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[OH:5][CH2:6][CH:7]1[CH2:8][CH2:9][CH2:10][CH2:11]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[OH:5][CH2:6][CH:7]1[CH2:8][CH2:9][CH2:10][CH2:11]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis method for '[CH:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1)=[C:26]1[C:25](=[O:24])[NH:30][C:28](=[O:29])[S:27]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1)=[C:26]1[C:25](=[O:24])[NH:30][C:28](=[O:29])[S:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1)=[C:26]1[C:25](=[O:24])[NH:30][C:28](=[O:29])[S:27]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1.[O:24]=[C:25]1[CH2:26][S:27][C:28](=[O:29])[NH:30]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1' and '[O:24]=[C:25]1[CH2:26][S:27][C:28](=[O:29])[NH:30]1'."
            },
            {
                "from": "human",
                "value": "What about '[C:1]([C@H:2]1[N:3]([C:4]([C:5]2=[C:6]([CH:7]([CH3:8])[CH3:9])[N:10]3[C:11](=[N:12][C@@:13]([CH3:14])([c:15]4[cH:16][cH:17][c:18]([Cl:19])[cH:20][cH:21]4)[C@H:22]3[c:23]3[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]3)[S:30]2)=[O:31])[CH2:32][CH2:33][CH2:34]1)(=[O:35])[NH:36][NH2:37]'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@H:2]1[N:3]([C:4]([C:5]2=[C:6]([CH:7]([CH3:8])[CH3:9])[N:10]3[C:11](=[N:12][C@@:13]([CH3:14])([c:15]4[cH:16][cH:17][c:18]([Cl:19])[cH:20][cH:21]4)[C@H:22]3[c:23]3[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]3)[S:30]2)=[O:31])[CH2:32][CH2:33][CH2:34]1)(=[O:35])[NH:36][NH2:37]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[cH:7][c:8]([CH2:9][N:10]([CH3:11])[C:12](=[O:13])[c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[s:20][cH:21]2)[cH:22][cH:23]1)=[C:26]1[C:25](=[O:24])[NH:30][C:28](=[O:29])[S:27]1\", \"reactants\":[\"O[C:1]([C@H:2]1[N:3]([C:4]([C:5]2=[C:6]([CH:7]([CH3:8])[CH3:9])[N:10]3[C:11](=[N:12][C@@:13]([CH3:14])([c:15]4[cH:16][cH:17][c:18]([Cl:19])[cH:20][cH:21]4)[C@H:22]3[c:23]3[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]3)[S:30]2)=[O:31])[CH2:32][CH2:33][CH2:34]1)=[O:35].[NH2:36][NH2:37]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([C@H:2]1[N:3]([C:4]([C:5]2=[C:6]([CH:7]([CH3:8])[CH3:9])[N:10]3[C:11](=[N:12][C@@:13]([CH3:14])([c:15]4[cH:16][cH:17][c:18]([Cl:19])[cH:20][cH:21]4)[C@H:22]3[c:23]3[cH:24][cH:25][c:26]([Cl:27])[cH:28][cH:29]3)[S:30]2)=[O:31])[CH2:32][CH2:33][CH2:34]1)=[O:35]' and '[NH2:36][NH2:37]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how '[c:1]1([O:16][c:15]2[c:9]([CH3:8])[cH:10][c:11]([NH2:12])[cH:13][cH:14]2)[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:16][c:15]2[c:9]([CH3:8])[cH:10][c:11]([NH2:12])[cH:13][cH:14]2)[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:16][c:15]2[c:9]([CH3:8])[cH:10][c:11]([NH2:12])[cH:13][cH:14]2)[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1\", \"reactants\":[\"Cl[c:1]1[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1.[CH3:8][c:9]1[cH:10][c:11]([NH2:12])[cH:13][cH:14][c:15]1[OH:16]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1' and '[CH3:8][c:9]1[cH:10][c:11]([NH2:12])[cH:13][cH:14][c:15]1[OH:16]'."
            },
            {
                "from": "human",
                "value": "And make forecasts regarding '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1)[O:15][CH2:14][CH:13]1[CH2:12][O:11][C:9]([CH3:8])([CH3:10])[O:17][CH2:16]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1)[O:15][CH2:14][CH:13]1[CH2:12][O:11][C:9]([CH3:8])([CH3:10])[O:17][CH2:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:16][c:15]2[c:9]([CH3:8])[cH:10][c:11]([NH2:12])[cH:13][cH:14]2)[cH:2][cH:3][n:4][c:5]([Cl:6])[cH:7]1\", \"reactants\":[\"Br[CH2:1][c:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1.[CH3:8][C:9]1([CH3:10])[O:11][CH2:12][CH:13]([CH2:14][OH:15])[CH2:16][O:17]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[CH2:1][c:2]1[cH:3][cH:4][cH:5][cH:6][cH:7]1' and '[CH3:8][C:9]1([CH3:10])[O:11][CH2:12][CH:13]([CH2:14][OH:15])[CH2:16][O:17]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis steps for '[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH:19][C:27](=[O:26])[NH:28][C:29](=[O:30])[CH2:31][c:32]4[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]4)[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH:19][C:27](=[O:26])[NH:28][C:29](=[O:30])[CH2:31][c:32]4[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]4)[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH:19][C:27](=[O:26])[NH:28][C:29](=[O:30])[CH2:31][c:32]4[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]4)[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25]\", \"reactants\":[\"[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH2:19])[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25].[O:26]=[C:27]=[N:28][C:29](=[O:30])[CH2:31][c:32]1[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are '[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH2:19])[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25]' and '[O:26]=[C:27]=[N:28][C:29](=[O:30])[CH2:31][c:32]1[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]1'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[C:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([NH:7][c:8]2[c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][c:18]3[Cl:19])[cH:20][cH:21][cH:22][cH:23]2)(=[O:24])=[O:25])[cH:26][cH:27]1)(=[O:28])[NH:29][CH2:30][CH2:31][N:32]1[CH2:33][CH2:34][N:35]([c:36]2[cH:37][n:38][cH:39][cH:40][n:41]2)[CH2:42][CH2:43]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([NH:7][c:8]2[c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][c:18]3[Cl:19])[cH:20][cH:21][cH:22][cH:23]2)(=[O:24])=[O:25])[cH:26][cH:27]1)(=[O:28])[NH:29][CH2:30][CH2:31][N:32]1[CH2:33][CH2:34][N:35]([c:36]2[cH:37][n:38][cH:39][cH:40][n:41]2)[CH2:42][CH2:43]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH2:2][NH:3][C:4](=[O:5])[O:6][c:7]1[cH:8][n:9]2[n:10][cH:11][n:12][c:13]([O:14][c:15]3[cH:16][cH:17][c:18]([NH:19][C:27](=[O:26])[NH:28][C:29](=[O:30])[CH2:31][c:32]4[cH:33][cH:34][c:35]([F:36])[cH:37][cH:38]4)[cH:20][c:21]3[F:22])[c:23]2[c:24]1[CH3:25]\", \"reactants\":[\"O[C:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([NH:7][c:8]2[c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][c:18]3[Cl:19])[cH:20][cH:21][cH:22][cH:23]2)(=[O:24])=[O:25])[cH:26][cH:27]1)=[O:28].[NH2:29][CH2:30][CH2:31][N:32]1[CH2:33][CH2:34][N:35]([c:36]2[cH:37][n:38][cH:39][cH:40][n:41]2)[CH2:42][CH2:43]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([NH:7][c:8]2[c:9]([O:10][c:11]3[cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17][c:18]3[Cl:19])[cH:20][cH:21][cH:22][cH:23]2)(=[O:24])=[O:25])[cH:26][cH:27]1)=[O:28]' and '[NH2:29][CH2:30][CH2:31][N:32]1[CH2:33][CH2:34][N:35]([c:36]2[cH:37][n:38][cH:39][cH:40][n:41]2)[CH2:42][CH2:43]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis path for '[c:1]1([NH:23][C@H:22]2[CH2:21][CH2:20][C@H:19]([N:17]([CH3:16])[CH3:18])[CH2:25][CH2:24]2)[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:23][C@H:22]2[CH2:21][CH2:20][C@H:19]([N:17]([CH3:16])[CH3:18])[CH2:25][CH2:24]2)[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:23][C@H:22]2[CH2:21][CH2:20][C@H:19]([N:17]([CH3:16])[CH3:18])[CH2:25][CH2:24]2)[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12\", \"reactants\":[\"Cl[c:1]1[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12.[CH3:16][N:17]([CH3:18])[C@H:19]1[CH2:20][CH2:21][C@H:22]([NH2:23])[CH2:24][CH2:25]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[c:1]1[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12' and '[CH3:16][N:17]([CH3:18])[C@H:19]1[CH2:20][CH2:21][C@H:22]([NH2:23])[CH2:24][CH2:25]1'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[c:1]1(-[c:27]2[cH:28][cH:29][c:30]([CH2:31][C:32]([O:33][CH2:34][CH3:35])=[O:36])[cH:37][cH:38]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8]([CH3:9])[c:10]2[NH:11][C:12](=[O:13])[O:14][CH:15]([CH3:16])[c:17]2[cH:18][c:19]([F:20])[cH:21][cH:22][c:23]2[F:24])[cH:25][cH:26]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:27]2[cH:28][cH:29][c:30]([CH2:31][C:32]([O:33][CH2:34][CH3:35])=[O:36])[cH:37][cH:38]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8]([CH3:9])[c:10]2[NH:11][C:12](=[O:13])[O:14][CH:15]([CH3:16])[c:17]2[cH:18][c:19]([F:20])[cH:21][cH:22][c:23]2[F:24])[cH:25][cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:23][C@H:22]2[CH2:21][CH2:20][C@H:19]([N:17]([CH3:16])[CH3:18])[CH2:25][CH2:24]2)[c:2]([S:3]([CH3:4])(=[O:5])=[O:6])[cH:7][n:8][c:9]2[cH:10][cH:11][c:12]([Cl:13])[n:14][c:15]12\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8]([CH3:9])[c:10]2[NH:11][C:12](=[O:13])[O:14][CH:15]([CH3:16])[c:17]2[cH:18][c:19]([F:20])[cH:21][cH:22][c:23]2[F:24])[cH:25][cH:26]1.CC1(C)OB([c:27]2[cH:28][cH:29][c:30]([CH2:31][C:32]([O:33][CH2:34][CH3:35])=[O:36])[cH:37][cH:38]2)OC1(C)C\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8]([CH3:9])[c:10]2[NH:11][C:12](=[O:13])[O:14][CH:15]([CH3:16])[c:17]2[cH:18][c:19]([F:20])[cH:21][cH:22][c:23]2[F:24])[cH:25][cH:26]1' and 'CC1(C)OB([c:27]2[cH:28][cH:29][c:30]([CH2:31][C:32]([O:33][CH2:34][CH3:35])=[O:36])[cH:37][cH:38]2)OC1(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how to synthesize '[c:1]1([C:8]2=[CH:9][CH2:10][CH2:11][CH2:12][O:13]2)[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:8]2=[CH:9][CH2:10][CH2:11][CH2:12][O:13]2)[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:8]2=[CH:9][CH2:10][CH2:11][CH2:12][O:13]2)[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1\", \"reactants\":[\"Br[c:1]1[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1.CCCC[Sn](CCCC)(CCCC)[C:8]1=[CH:9][CH2:10][CH2:11][CH2:12][O:13]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1' and 'CCCC[Sn](CCCC)(CCCC)[C:8]1=[CH:9][CH2:10][CH2:11][CH2:12][O:13]1'."
            },
            {
                "from": "human",
                "value": "And provide a forecast for '[CH3:1][C:2](=[O:3])[NH:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2](=[O:3])[NH:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:8]2=[CH:9][CH2:10][CH2:11][CH2:12][O:13]2)[n:2][cH:3][c:4]([NH2:5])[n:6][cH:7]1\", \"reactants\":[\"CC(=O)O[C:2]([CH3:1])=[O:3].[NH2:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(=O)O[C:2]([CH3:1])=[O:3]' and '[NH2:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][cH:10][c:11]([F:12])[cH:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis route for '[c:1]1(-[c:12]2[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:12]2[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:12]2[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1.OB(O)[c:12]1[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1' and 'OB(O)[c:12]1[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]1'."
            },
            {
                "from": "human",
                "value": "And predict the outcomes for '[CH2:1]([CH:2]1[CH2:3][CH2:4][N:5]([C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[CH2:13][CH2:14]1)[N:26]1[CH2:25][CH2:24][CH:23]([O:22][c:21]2[cH:20][c:19]([CH2:18][CH2:17][CH2:16][CH3:15])[cH:35][c:34]3[c:29]2[n:30][cH:31][cH:32][cH:33]3)[CH2:28][CH2:27]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH:2]1[CH2:3][CH2:4][N:5]([C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[CH2:13][CH2:14]1)[N:26]1[CH2:25][CH2:24][CH:23]([O:22][c:21]2[cH:20][c:19]([CH2:18][CH2:17][CH2:16][CH3:15])[cH:35][c:34]3[c:29]2[n:30][cH:31][cH:32][cH:33]3)[CH2:28][CH2:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:12]2[cH:13][cH:14][cH:15][c:16]([CH3:17])[cH:18]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\", \"reactants\":[\"O=[CH:1][CH:2]1[CH2:3][CH2:4][N:5]([C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[CH2:13][CH2:14]1.[CH3:15][CH2:16][CH2:17][CH2:18][c:19]1[cH:20][c:21]([O:22][CH:23]2[CH2:24][CH2:25][NH:26][CH2:27][CH2:28]2)[c:29]2[n:30][cH:31][cH:32][cH:33][c:34]2[cH:35]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][CH:2]1[CH2:3][CH2:4][N:5]([C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[CH2:13][CH2:14]1' and '[CH3:15][CH2:16][CH2:17][CH2:18][c:19]1[cH:20][c:21]([O:22][CH:23]2[CH2:24][CH2:25][NH:26][CH2:27][CH2:28]2)[c:29]2[n:30][cH:31][cH:32][cH:33][c:34]2[cH:35]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis path for '[CH2:1]([C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8])[N:15]([c:14]1[cH:13][cH:12][c:11]([C:10]#[N:9])[c:21]([C:22]([F:23])([F:24])[F:25])[cH:20]1)[CH2:16][CH:17]1[CH2:18][CH2:19]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8])[N:15]([c:14]1[cH:13][cH:12][c:11]([C:10]#[N:9])[c:21]([C:22]([F:23])([F:24])[F:25])[cH:20]1)[CH2:16][CH:17]1[CH2:18][CH2:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8])[N:15]([c:14]1[cH:13][cH:12][c:11]([C:10]#[N:9])[c:21]([C:22]([F:23])([F:24])[F:25])[cH:20]1)[CH2:16][CH:17]1[CH2:18][CH2:19]1\", \"reactants\":[\"Br[CH2:1][C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8].[N:9]#[C:10][c:11]1[cH:12][cH:13][c:14]([NH:15][CH2:16][CH:17]2[CH2:18][CH2:19]2)[cH:20][c:21]1[C:22]([F:23])([F:24])[F:25]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[CH2:1][C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8]' and '[N:9]#[C:10][c:11]1[cH:12][cH:13][c:14]([NH:15][CH2:16][CH:17]2[CH2:18][CH2:19]2)[cH:20][c:21]1[C:22]([F:23])([F:24])[F:25]'."
            },
            {
                "from": "human",
                "value": "Now make predictions about '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([NH:6][C:7]([O:8][C:9]([CH3:10])([CH3:11])[CH3:12])=[O:13])[cH:14][cH:15]1)[N:30]1[CH2:29][CH2:28][N:27]([c:26]2[cH:25][cH:24][c:23]3[n:22][n:21][c:20]([C:17]([F:16])([F:18])[F:19])[n:34]3[n:33]2)[CH2:32][CH2:31]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([NH:6][C:7]([O:8][C:9]([CH3:10])([CH3:11])[CH3:12])=[O:13])[cH:14][cH:15]1)[N:30]1[CH2:29][CH2:28][N:27]([c:26]2[cH:25][cH:24][c:23]3[n:22][n:21][c:20]([C:17]([F:16])([F:18])[F:19])[n:34]3[n:33]2)[CH2:32][CH2:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]([O:3][C:4]([CH3:5])([CH3:6])[CH3:7])=[O:8])[N:15]([c:14]1[cH:13][cH:12][c:11]([C:10]#[N:9])[c:21]([C:22]([F:23])([F:24])[F:25])[cH:20]1)[CH2:16][CH:17]1[CH2:18][CH2:19]1\", \"reactants\":[\"O=[CH:1][c:2]1[cH:3][cH:4][c:5]([NH:6][C:7]([O:8][C:9]([CH3:10])([CH3:11])[CH3:12])=[O:13])[cH:14][cH:15]1.[F:16][C:17]([F:18])([F:19])[c:20]1[n:21][n:22][c:23]2[cH:24][cH:25][c:26]([N:27]3[CH2:28][CH2:29][NH:30][CH2:31][CH2:32]3)[n:33][n:34]12\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][c:5]([NH:6][C:7]([O:8][C:9]([CH3:10])([CH3:11])[CH3:12])=[O:13])[cH:14][cH:15]1' and '[F:16][C:17]([F:18])([F:19])[c:20]1[n:21][n:22][c:23]2[cH:24][cH:25][c:26]([N:27]3[CH2:28][CH2:29][NH:30][CH2:31][CH2:32]3)[n:33][n:34]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis method for '[c:1]1([N:17]2[C:8](=[O:7])[CH2:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][c:16]32)[cH:2][cH:3][cH:4][cH:5][cH:6]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:17]2[C:8](=[O:7])[CH2:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][c:16]32)[cH:2][cH:3][cH:4][cH:5][cH:6]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:17]2[C:8](=[O:7])[CH2:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][c:16]32)[cH:2][cH:3][cH:4][cH:5][cH:6]1\", \"reactants\":[\"Br[c:1]1[cH:2][cH:3][cH:4][cH:5][cH:6]1.[O:7]=[C:8]1[CH2:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[NH:17]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Br[c:1]1[cH:2][cH:3][cH:4][cH:5][cH:6]1' and '[O:7]=[C:8]1[CH2:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][c:16]2[NH:17]1'."
            },
            {
                "from": "human",
                "value": "What about '[C:1]([C@H:2]([O:3][CH3:4])[CH3:5])(=[O:6])[NH:7][C@H:8]1[CH2:9][CH2:10][C@H:11]([CH2:12][CH2:13][N:14]2[CH2:15][CH2:16][N:17]([c:18]3[n:19][cH:20][cH:21][c:22]4[c:23]3[CH2:24][CH2:25][O:26]4)[CH2:27][CH2:28]2)[CH2:29][CH2:30]1'? MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([C@H:2]([O:3][CH3:4])[CH3:5])(=[O:6])[NH:7][C@H:8]1[CH2:9][CH2:10][C@H:11]([CH2:12][CH2:13][N:14]2[CH2:15][CH2:16][N:17]([c:18]3[n:19][cH:20][cH:21][c:22]4[c:23]3[CH2:24][CH2:25][O:26]4)[CH2:27][CH2:28]2)[CH2:29][CH2:30]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([N:17]2[C:8](=[O:7])[CH2:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][c:16]32)[cH:2][cH:3][cH:4][cH:5][cH:6]1\", \"reactants\":[\"O[C:1]([C@H:2]([O:3][CH3:4])[CH3:5])=[O:6].[NH2:7][C@H:8]1[CH2:9][CH2:10][C@H:11]([CH2:12][CH2:13][N:14]2[CH2:15][CH2:16][N:17]([c:18]3[n:19][cH:20][cH:21][c:22]4[c:23]3[CH2:24][CH2:25][O:26]4)[CH2:27][CH2:28]2)[CH2:29][CH2:30]1\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'O[C:1]([C@H:2]([O:3][CH3:4])[CH3:5])=[O:6]' and '[NH2:7][C@H:8]1[CH2:9][CH2:10][C@H:11]([CH2:12][CH2:13][N:14]2[CH2:15][CH2:16][N:17]([c:18]3[n:19][cH:20][cH:21][c:22]4[c:23]3[CH2:24][CH2:25][O:26]4)[CH2:27][CH2:28]2)[CH2:29][CH2:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis steps for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1)[N:29]=[N+:30]=[N-:31]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1)[N:29]=[N+:30]=[N-:31]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1)[N:29]=[N+:30]=[N-:31]\", \"reactants\":[\"Cl[CH2:1][c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1.[N-:29]=[N+:30]=[N-:31]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The most likely precursor reactants are 'Cl[CH2:1][c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1' and '[N-:29]=[N+:30]=[N-:31]'."
            },
            {
                "from": "human",
                "value": "And forecast the results for '[C:1]([CH2:2][C:3]([c:4]1[cH:5][cH:6][cH:7][c:8](-[c:9]2[c:10]([CH3:11])[n:12][cH:13][cH:14][cH:15]2)[cH:16]1)=[O:17])(=[O:18])[NH:37][c:36]1[c:27]([NH:26][C:24]([O:23][C:20]([CH3:19])([CH3:21])[CH3:22])=[O:25])[cH:28][cH:29][c:30]([C:31]([F:32])([F:33])[F:34])[cH:35]1'. MemoryPool(arguments=dict_keys(['user_smiles', 'drug_synthetic_pathway_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][C:3]([c:4]1[cH:5][cH:6][cH:7][c:8](-[c:9]2[c:10]([CH3:11])[n:12][cH:13][cH:14][cH:15]2)[cH:16]1)=[O:17])(=[O:18])[NH:37][c:36]1[c:27]([NH:26][C:24]([O:23][C:20]([CH3:19])([CH3:21])[CH3:22])=[O:25])[cH:28][cH:29][c:30]([C:31]([F:32])([F:33])[F:34])[cH:35]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6]([CH2:7][CH2:8][c:9]2[cH:10][n:11][cH:12][c:13]([O:14][CH2:15][C@H:16]3[N:17]([C:18]([O:19][C:20]([CH3:21])([CH3:22])[CH3:23])=[O:24])[CH2:25][CH2:26]3)[cH:27]2)[cH:28]1)[N:29]=[N+:30]=[N-:31]\", \"reactants\":[\"CC(C)(C)O[C:1]([CH2:2][C:3]([c:4]1[cH:5][cH:6][cH:7][c:8](-[c:9]2[c:10]([CH3:11])[n:12][cH:13][cH:14][cH:15]2)[cH:16]1)=[O:17])=[O:18].[CH3:19][C:20]([CH3:21])([CH3:22])[O:23][C:24](=[O:25])[NH:26][c:27]1[cH:28][cH:29][c:30]([C:31]([F:32])([F:33])[F:34])[cH:35][c:36]1[NH2:37]\"]}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants are 'CC(C)(C)O[C:1]([CH2:2][C:3]([c:4]1[cH:5][cH:6][cH:7][c:8](-[c:9]2[c:10]([CH3:11])[n:12][cH:13][cH:14][cH:15]2)[cH:16]1)=[O:17])=[O:18]' and '[CH3:19][C:20]([CH3:21])([CH3:22])[O:23][C:24](=[O:25])[NH:26][c:27]1[cH:28][cH:29][c:30]([C:31]([F:32])([F:33])[F:34])[cH:35][c:36]1[NH2:37]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis process for '[C:1](=[O:2])([CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1)[NH:26][c:25]1[cH:24][c:23]([O:22][C:20]([O:19][CH3:18])=[O:21])[c:35]([CH:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:34][c:27]1[C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33]' and '[Sn:1]([CH3:2])([CH3:3])([CH3:4])[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1)[NH:26][c:25]1[cH:24][c:23]([O:22][C:20]([O:19][CH3:18])=[O:21])[c:35]([CH:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:34][c:27]1[C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33]\", \"[Sn:1]([CH3:2])([CH3:3])([CH3:4])[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1)[NH:26][c:25]1[cH:24][c:23]([O:22][C:20]([O:19][CH3:18])=[O:21])[c:35]([CH:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:34][c:27]1[C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33]\", \"reactants\":\"O[C:1](=[O:2])[CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1.[CH3:18][O:19][C:20](=[O:21])[O:22][c:23]1[cH:24][c:25]([NH2:26])[c:27]([C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33])[cH:34][c:35]1[CH:36]1[CH2:37][CH2:38][CH2:39][CH2:40]1\"}, {\"smile\":\"[Sn:1]([CH3:2])([CH3:3])([CH3:4])[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1\", \"reactants\":\"Cl[Sn:1]([CH3:2])([CH3:3])[CH3:4].I[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1)[NH:26][c:25]1[cH:24][c:23]([O:22][C:20]([O:19][CH3:18])=[O:21])[c:35]([CH:36]2[CH2:37][CH2:38][CH2:39][CH2:40]2)[cH:34][c:27]1[C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33]' are 'O[C:1](=[O:2])[CH:3]1[CH2:4][NH:5][c:6]2[cH:7][c:8]([O:9][C:10]([F:11])([F:12])[F:13])[cH:14][cH:15][c:16]2[O:17]1' and '[CH3:18][O:19][C:20](=[O:21])[O:22][c:23]1[cH:24][c:25]([NH2:26])[c:27]([C:28]#[C:29][CH2:30][N:31]([CH3:32])[CH3:33])[cH:34][c:35]1[CH:36]1[CH2:37][CH2:38][CH2:39][CH2:40]1', for '[Sn:1]([CH3:2])([CH3:3])([CH3:4])[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1' are 'Cl[Sn:1]([CH3:2])([CH3:3])[CH3:4]' and 'I[c:5]1[cH:6][cH:7][c:8]([SiH2:9][O:10][CH2:11][CH:12]([CH2:13][CH:14]=[CH2:15])[CH2:16][CH:17]=[CH2:18])[cH:19][cH:20]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Identify the most efficient synthesis process for '[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)(=[O:9])[n:24]1[c:22](=[O:23])[c:20]([F:21])[cH:19][n:18]([C@@H:17]2[O:16][C@H:15]([CH2:14][O:13][C:11]([CH3:10])=[O:12])[C@@H:28]([O:29][C:30]([CH3:31])=[O:32])[CH2:27]2)[c:25]1=[O:26]' and '[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)(=[O:9])[n:24]1[c:22](=[O:23])[c:20]([F:21])[cH:19][n:18]([C@@H:17]2[O:16][C@H:15]([CH2:14][O:13][C:11]([CH3:10])=[O:12])[C@@H:28]([O:29][C:30]([CH3:31])=[O:32])[CH2:27]2)[c:25]1=[O:26]\", \"[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)(=[O:9])[n:24]1[c:22](=[O:23])[c:20]([F:21])[cH:19][n:18]([C@@H:17]2[O:16][C@H:15]([CH2:14][O:13][C:11]([CH3:10])=[O:12])[C@@H:28]([O:29][C:30]([CH3:31])=[O:32])[CH2:27]2)[c:25]1=[O:26]\", \"reactants\":\"Cl[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)=[O:9].[CH3:10][C:11](=[O:12])[O:13][CH2:14][C@H:15]1[O:16][C@@H:17]([n:18]2[cH:19][c:20]([F:21])[c:22](=[O:23])[nH:24][c:25]2=[O:26])[CH2:27][C@@H:28]1[O:29][C:30]([CH3:31])=[O:32]\"}, {\"smile\":\"[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]\", \"reactants\":\"I[CH3:1].[CH3:2][CH2:3][O:4][N:5]=[CH:6][c:7]1[cH:8][c:9](-[n:10]2[c:11](=[O:12])[cH:13][c:14]([C:15]([F:16])([F:17])[F:18])[nH:19][c:20]2=[O:21])[c:22]([Cl:23])[cH:24][c:25]1[Cl:26]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)(=[O:9])[n:24]1[c:22](=[O:23])[c:20]([F:21])[cH:19][n:18]([C@@H:17]2[O:16][C@H:15]([CH2:14][O:13][C:11]([CH3:10])=[O:12])[C@@H:28]([O:29][C:30]([CH3:31])=[O:32])[CH2:27]2)[c:25]1=[O:26]' are 'Cl[C:1]([c:2]1[c:3]([CH3:4])[cH:5][cH:6][cH:7][cH:8]1)=[O:9]' and '[CH3:10][C:11](=[O:12])[O:13][CH2:14][C@H:15]1[O:16][C@@H:17]([n:18]2[cH:19][c:20]([F:21])[c:22](=[O:23])[nH:24][c:25]2=[O:26])[CH2:27][C@@H:28]1[O:29][C:30]([CH3:31])=[O:32]', for '[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]' are 'I[CH3:1]' and '[CH3:2][CH2:3][O:4][N:5]=[CH:6][c:7]1[cH:8][c:9](-[n:10]2[c:11](=[O:12])[cH:13][c:14]([C:15]([F:16])([F:17])[F:18])[nH:19][c:20]2=[O:21])[c:22]([Cl:23])[cH:24][c:25]1[Cl:26]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how to synthesize '[Br:1][CH2:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]' and '[CH2:1]([c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12])[NH:14][CH3:13]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Br:1][CH2:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]\", \"[CH2:1]([c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12])[NH:14][CH3:13]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[Br:1][CH2:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]\", \"reactants\":\"O=C1CCC(=O)N1[Br:1].[CH3:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12])[NH:14][CH3:13]\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12].[CH3:13][NH2:14]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[Br:1][CH2:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]' are 'O=C1CCC(=O)N1[Br:1]' and '[CH3:2][c:3]1[cH:4][cH:5][c:6]2[c:7]([cH:8]1)[c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[n:15]2[C:16](=[O:17])[O:18][C:19]([CH3:20])([CH3:21])[CH3:22]', for '[CH2:1]([c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12])[NH:14][CH3:13]' are 'O=[CH:1][c:2]1[cH:3][c:4]([N+:5](=[O:6])[O-:7])[cH:8][c:9]([F:10])[c:11]1[OH:12]' and '[CH3:13][NH2:14]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the synthesis steps for '[CH2:1]([CH2:2][CH2:3][OH:4])[O:29][c:28]1[cH:27][c:26]2[c:24](=[O:25])[c:23]3[c:17]([n:16]4[cH:15][c:7]([CH2:8][c:9]5[cH:10][cH:11][cH:12][n:13][cH:14]5)[c:6](=[O:5])[c:31]([cH:30]1)[c:32]42)[cH:18][c:19]([Br:20])[cH:21][cH:22]3' and '[c:1]1([CH:27]=[O:28])[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][OH:4])[O:29][c:28]1[cH:27][c:26]2[c:24](=[O:25])[c:23]3[c:17]([n:16]4[cH:15][c:7]([CH2:8][c:9]5[cH:10][cH:11][cH:12][n:13][cH:14]5)[c:6](=[O:5])[c:31]([cH:30]1)[c:32]42)[cH:18][c:19]([Br:20])[cH:21][cH:22]3\", \"[c:1]1([CH:27]=[O:28])[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][OH:4])[O:29][c:28]1[cH:27][c:26]2[c:24](=[O:25])[c:23]3[c:17]([n:16]4[cH:15][c:7]([CH2:8][c:9]5[cH:10][cH:11][cH:12][n:13][cH:14]5)[c:6](=[O:5])[c:31]([cH:30]1)[c:32]42)[cH:18][c:19]([Br:20])[cH:21][cH:22]3\", \"reactants\":\"Br[CH2:1][CH2:2][CH2:3][OH:4].[O:5]=[c:6]1[c:7]([CH2:8][c:9]2[cH:10][cH:11][cH:12][n:13][cH:14]2)[cH:15][n:16]2[c:17]3[cH:18][c:19]([Br:20])[cH:21][cH:22][c:23]3[c:24](=[O:25])[c:26]3[cH:27][c:28]([OH:29])[cH:30][c:31]1[c:32]23\"}, {\"smile\":\"[c:1]1([CH:27]=[O:28])[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2\", \"reactants\":\"Br[c:1]1[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2.CN(C)[CH:27]=[O:28]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([CH2:2][CH2:3][OH:4])[O:29][c:28]1[cH:27][c:26]2[c:24](=[O:25])[c:23]3[c:17]([n:16]4[cH:15][c:7]([CH2:8][c:9]5[cH:10][cH:11][cH:12][n:13][cH:14]5)[c:6](=[O:5])[c:31]([cH:30]1)[c:32]42)[cH:18][c:19]([Br:20])[cH:21][cH:22]3' are 'Br[CH2:1][CH2:2][CH2:3][OH:4]' and '[O:5]=[c:6]1[c:7]([CH2:8][c:9]2[cH:10][cH:11][cH:12][n:13][cH:14]2)[cH:15][n:16]2[c:17]3[cH:18][c:19]([Br:20])[cH:21][cH:22][c:23]3[c:24](=[O:25])[c:26]3[cH:27][c:28]([OH:29])[cH:30][c:31]1[c:32]23', for '[c:1]1([CH:27]=[O:28])[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2' are 'Br[c:1]1[s:2][c:3]2[c:4]([cH:5]1)[N:6]([S:7](=[O:8])(=[O:9])[c:10]1[cH:11][cH:12][c:13]([O:14][CH3:15])[cH:16][cH:17]1)[CH:18]([CH2:19][CH3:20])[c:21]1[cH:22][cH:23][cH:24][cH:25][c:26]1-2' and 'CN(C)[CH:27]=[O:28]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis process for '[CH2:1]([C:2](=[O:3])[OH:4])[O:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1' and '[O:1]=[S:25]([c:24]1[c:6]2[c:7]([n:8][n:9]1[CH2:10][c:11]1[cH:12][cH:13][c:14](-[c:15]3[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]3)[cH:22][cH:23]1)[N:32]1[C:33](=[N:34][C@@H:35]3[CH2:36][CH2:37][CH2:38][C@H:39]13)[N:3]([CH3:2])[C:4]2=[O:5])[c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2](=[O:3])[OH:4])[O:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1\", \"[O:1]=[S:25]([c:24]1[c:6]2[c:7]([n:8][n:9]1[CH2:10][c:11]1[cH:12][cH:13][c:14](-[c:15]3[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]3)[cH:22][cH:23]1)[N:32]1[C:33](=[N:34][C@@H:35]3[CH2:36][CH2:37][CH2:38][C@H:39]13)[N:3]([CH3:2])[C:4]2=[O:5])[c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2](=[O:3])[OH:4])[O:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1\", \"reactants\":\"Cl[CH2:1][C:2](=[O:3])[OH:4].[OH:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1\"}, {\"smile\":\"[O:1]=[S:25]([c:24]1[c:6]2[c:7]([n:8][n:9]1[CH2:10][c:11]1[cH:12][cH:13][c:14](-[c:15]3[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]3)[cH:22][cH:23]1)[N:32]1[C:33](=[N:34][C@@H:35]3[CH2:36][CH2:37][CH2:38][C@H:39]13)[N:3]([CH3:2])[C:4]2=[O:5])[c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1\", \"reactants\":\"O=S([O-])O[OH:1].[CH3:2][N:3]1[C:4](=[O:5])[c:6]2[c:7]([n:8][n:9]([CH2:10][c:11]3[cH:12][cH:13][c:14](-[c:15]4[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]4)[cH:22][cH:23]3)[c:24]2[S:25][c:26]2[cH:27][cH:28][cH:29][cH:30][cH:31]2)[N:32]2[C:33]1=[N:34][C@@H:35]1[CH2:36][CH2:37][CH2:38][C@H:39]21\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([C:2](=[O:3])[OH:4])[O:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1' are 'Cl[CH2:1][C:2](=[O:3])[OH:4]' and '[OH:5][CH2:6][CH2:7][O:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][cH:14][cH:15]1', for '[O:1]=[S:25]([c:24]1[c:6]2[c:7]([n:8][n:9]1[CH2:10][c:11]1[cH:12][cH:13][c:14](-[c:15]3[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]3)[cH:22][cH:23]1)[N:32]1[C:33](=[N:34][C@@H:35]3[CH2:36][CH2:37][CH2:38][C@H:39]13)[N:3]([CH3:2])[C:4]2=[O:5])[c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1' are 'O=S([O-])O[OH:1]' and '[CH3:2][N:3]1[C:4](=[O:5])[c:6]2[c:7]([n:8][n:9]([CH2:10][c:11]3[cH:12][cH:13][c:14](-[c:15]4[cH:16][cH:17][cH:18][c:19]([F:20])[n:21]4)[cH:22][cH:23]3)[c:24]2[S:25][c:26]2[cH:27][cH:28][cH:29][cH:30][cH:31]2)[N:32]2[C:33]1=[N:34][C@@H:35]1[CH2:36][CH2:37][CH2:38][C@H:39]21'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[c:1]1(-[c:11]2[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]2)[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2' and '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:18][C@H:17]1[C@@H:16]([C:14](=[CH:13][C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH3:15])[N:21]([CH2:22][c:23]2[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]2[O:31][CH3:32])[C:19]1=[O:20]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:11]2[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]2)[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2\", \"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:18][C@H:17]1[C@@H:16]([C:14](=[CH:13][C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH3:15])[N:21]([CH2:22][c:23]2[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]2[O:31][CH3:32])[C:19]1=[O:20]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:11]2[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]2)[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2\", \"reactants\":\"Br[c:1]1[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2.OB(O)[c:11]1[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]1\"}, {\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:18][C@H:17]1[C@@H:16]([C:14](=[CH:13][C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH3:15])[N:21]([CH2:22][c:23]2[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]2[O:31][CH3:32])[C:19]1=[O:20]\", \"reactants\":\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][CH2:9][O:10][C:11](=[O:12])[CH:13]=[C:14]([CH3:15])[C@@H:16]1[C@H:17]([NH2:18])[C:19](=[O:20])[N:21]1[CH2:22][c:23]1[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]1[O:31][CH3:32]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1(-[c:11]2[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]2)[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2' are 'Br[c:1]1[cH:2][c:3]2[c:4]([cH:5][cH:6]1)[C@@H:7]([OH:8])[CH2:9][CH2:10]2' and 'OB(O)[c:11]1[c:12]([CH:13]([CH3:14])[CH3:15])[cH:16][cH:17][cH:18][cH:19]1', for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[NH:18][C@H:17]1[C@@H:16]([C:14](=[CH:13][C:11]([O:10][CH2:9][CH3:8])=[O:12])[CH3:15])[N:21]([CH2:22][c:23]2[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]2[O:31][CH3:32])[C:19]1=[O:20]' are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][CH2:9][O:10][C:11](=[O:12])[CH:13]=[C:14]([CH3:15])[C@@H:16]1[C@H:17]([NH2:18])[C:19](=[O:20])[N:21]1[CH2:22][c:23]1[cH:24][cH:25][c:26]([O:27][CH3:28])[cH:29][c:30]1[O:31][CH3:32]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[CH2:1]([CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12)[N:33]1[CH2:32][CH2:31][CH:30]([OH:29])[CH2:35][CH2:34]1' and '[CH2:1]([c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12)[O:19][c:18]1[c:17]([CH3:16])[c:32]([CH3:33])[c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[cH:21][cH:20]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12)[N:33]1[CH2:32][CH2:31][CH:30]([OH:29])[CH2:35][CH2:34]1\", \"[CH2:1]([c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12)[O:19][c:18]1[c:17]([CH3:16])[c:32]([CH3:33])[c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[cH:21][cH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12)[N:33]1[CH2:32][CH2:31][CH:30]([OH:29])[CH2:35][CH2:34]1\", \"reactants\":\"Cl[CH2:1][CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12.[OH:29][CH:30]1[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]1\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12)[O:19][c:18]1[c:17]([CH3:16])[c:32]([CH3:33])[c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[cH:21][cH:20]1\", \"reactants\":\"O[CH2:1][c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12.[CH3:16][c:17]1[c:18]([OH:19])[cH:20][cH:21][c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[c:32]1[CH3:33]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12)[N:33]1[CH2:32][CH2:31][CH:30]([OH:29])[CH2:35][CH2:34]1' are 'Cl[CH2:1][CH2:2][O:3][c:4]1[n:5][nH:6][c:7]2[n:8][cH:9][n:10][c:11]([NH:12][c:13]3[cH:14][c:15]([CH3:16])[c:17]([O:18][CH2:19][c:20]4[cH:21][cH:22][cH:23][cH:24][n:25]4)[cH:26][cH:27]3)[c:28]12' and '[OH:29][CH:30]1[CH2:31][CH2:32][NH:33][CH2:34][CH2:35]1', for '[CH2:1]([c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12)[O:19][c:18]1[c:17]([CH3:16])[c:32]([CH3:33])[c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[cH:21][cH:20]1' are 'O[CH2:1][c:2]1[cH:3][c:4]([F:5])[cH:6][c:7]2[s:8][c:9]([C:10]([F:11])([F:12])[F:13])[n:14][c:15]12' and '[CH3:16][c:17]1[c:18]([OH:19])[cH:20][cH:21][c:22]([CH2:23][CH2:24][C:25](=[O:26])[O:27][C:28]([CH3:29])([CH3:30])[CH3:31])[c:32]1[CH3:33]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:24][CH:23]1[CH2:22][CH2:21][N:20]([CH2:19][CH2:18][NH:17][C:15]([c:14]2[n:13][n:12]([CH:10]([CH3:9])[CH3:11])[c:32]3[c:27]2[cH:28][cH:29][cH:30][cH:31]3)=[O:16])[CH2:26][CH2:25]1' and '[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH:21][C:49](=[NH:48])[NH2:50])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:24][CH:23]1[CH2:22][CH2:21][N:20]([CH2:19][CH2:18][NH:17][C:15]([c:14]2[n:13][n:12]([CH:10]([CH3:9])[CH3:11])[c:32]3[c:27]2[cH:28][cH:29][cH:30][cH:31]3)=[O:16])[CH2:26][CH2:25]1\", \"[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH:21][C:49](=[NH:48])[NH2:50])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:24][CH:23]1[CH2:22][CH2:21][N:20]([CH2:19][CH2:18][NH:17][C:15]([c:14]2[n:13][n:12]([CH:10]([CH3:9])[CH3:11])[c:32]3[c:27]2[cH:28][cH:29][cH:30][cH:31]3)=[O:16])[CH2:26][CH2:25]1\", \"reactants\":\"Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1.[CH3:9][CH:10]([CH3:11])[n:12]1[n:13][c:14]([C:15](=[O:16])[NH:17][CH2:18][CH2:19][N:20]2[CH2:21][CH2:22][CH:23]([NH2:24])[CH2:25][CH2:26]2)[c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12\"}, {\"smile\":\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH:21][C:49](=[NH:48])[NH2:50])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1\", \"reactants\":\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH2:21])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1.[N:48]#[C:49][NH2:50]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:24][CH:23]1[CH2:22][CH2:21][N:20]([CH2:19][CH2:18][NH:17][C:15]([c:14]2[n:13][n:12]([CH:10]([CH3:9])[CH3:11])[c:32]3[c:27]2[cH:28][cH:29][cH:30][cH:31]3)=[O:16])[CH2:26][CH2:25]1' are 'Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1' and '[CH3:9][CH:10]([CH3:11])[n:12]1[n:13][c:14]([C:15](=[O:16])[NH:17][CH2:18][CH2:19][N:20]2[CH2:21][CH2:22][CH:23]([NH2:24])[CH2:25][CH2:26]2)[c:27]2[cH:28][cH:29][cH:30][cH:31][c:32]12', for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH:21][C:49](=[NH:48])[NH2:50])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1' are '[CH3:1][CH2:2][O:3][C:4](=[O:5])[c:6]1[cH:7][cH:8][c:9]([O:10][c:11]2[c:12]([F:13])[c:14]([O:15][c:16]3[cH:17][cH:18][cH:19][c:20]([NH2:21])[cH:22]3)[n:23][c:24]([O:25][c:26]3[cH:27][c:28]([C:29]#[N:30])[cH:31][cH:32][c:33]3[O:34][CH2:35][c:36]3[cH:37][cH:38][cH:39][cH:40][cH:41]3)[c:42]2[F:43])[c:44]([O:45][CH3:46])[cH:47]1' and '[N:48]#[C:49][NH2:50]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis path for '[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1' and '[CH2:1]([c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15])[N:16]=[N+:17]=[N-:18]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1\", \"[CH2:1]([c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15])[N:16]=[N+:17]=[N-:18]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1\", \"reactants\":\"Br[CH2:1][c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16].[Br:17][c:18]1[cH:19][cH:20][c:21]([N:22]2[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]2)[cH:28][cH:29]1\"}, {\"smile\":\"[CH2:1]([c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15])[N:16]=[N+:17]=[N-:18]\", \"reactants\":\"Br[CH2:1][c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15].[N-:16]=[N+:17]=[N-:18]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16])[N:25]1[CH2:24][CH2:23][N:22]([c:21]2[cH:20][cH:19][c:18]([Br:17])[cH:29][cH:28]2)[CH2:27][CH2:26]1' are 'Br[CH2:1][c:2]1[n:3]([CH2:4][CH3:5])[n:6](-[c:7]2[cH:8][cH:9][cH:10][cH:11][cH:12]2)[c:13](=[O:14])[c:15]1[Cl:16]' and '[Br:17][c:18]1[cH:19][cH:20][c:21]([N:22]2[CH2:23][CH2:24][NH:25][CH2:26][CH2:27]2)[cH:28][cH:29]1', for '[CH2:1]([c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15])[N:16]=[N+:17]=[N-:18]' are 'Br[CH2:1][c:2]1[c:3]([C:4]([O:5][CH3:6])=[O:7])[cH:8][c:9]([F:10])[cH:11][c:12]1[N+:13](=[O:14])[O-:15]' and '[N-:16]=[N+:17]=[N-:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis method of '[c:1]1([NH:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2' and '[CH:1]([CH2:2][CH3:3])([C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1)[S:26][c:25]1[cH:24][cH:23][c:22]2[n:21][n:20][c:19](-[c:18]3[c:13]([F:12])[cH:14][cH:15][cH:16][cH:17]3)[n:28]2[n:27]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2\", \"[CH:1]([CH2:2][CH3:3])([C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1)[S:26][c:25]1[cH:24][cH:23][c:22]2[n:21][n:20][c:19](-[c:18]3[c:13]([F:12])[cH:14][cH:15][cH:16][cH:17]3)[n:28]2[n:27]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2\", \"reactants\":\"O=S(=O)(O[c:1]1[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2)C(F)(F)F.[NH2:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1\"}, {\"smile\":\"[CH:1]([CH2:2][CH3:3])([C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1)[S:26][c:25]1[cH:24][cH:23][c:22]2[n:21][n:20][c:19](-[c:18]3[c:13]([F:12])[cH:14][cH:15][cH:16][cH:17]3)[n:28]2[n:27]1\", \"reactants\":\"Br[CH:1]([CH2:2][CH3:3])[C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1.[F:12][c:13]1[cH:14][cH:15][cH:16][cH:17][c:18]1-[c:19]1[n:20][n:21][c:22]2[cH:23][cH:24][c:25]([SH:26])[n:27][n:28]12\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([NH:20][CH2:21][c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2' are 'O=S(=O)(O[c:1]1[n:2][c:3]2[cH:4][cH:5][n:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][CH3:12])[c:13]2[c:14]2[c:15]1[cH:16][cH:17][n:18][cH:19]2)C(F)(F)F' and '[NH2:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1', for '[CH:1]([CH2:2][CH3:3])([C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1)[S:26][c:25]1[cH:24][cH:23][c:22]2[n:21][n:20][c:19](-[c:18]3[c:13]([F:12])[cH:14][cH:15][cH:16][cH:17]3)[n:28]2[n:27]1' are 'Br[CH:1]([CH2:2][CH3:3])[C:4](=[O:5])[N:6]1[CH2:7][CH2:8][CH2:9][CH2:10][CH2:11]1' and '[F:12][c:13]1[cH:14][cH:15][cH:16][cH:17][c:18]1-[c:19]1[n:20][n:21][c:22]2[cH:23][cH:24][c:25]([SH:26])[n:27][n:28]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[C:1](=[O:2])([O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8])[N:10]([CH3:9])[c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]' and '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8])[N:10]([CH3:9])[c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]\", \"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8])[N:10]([CH3:9])[c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]\", \"reactants\":\"Cl[C:1](=[O:2])[O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8].[CH3:9][NH:10][c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]\"}, {\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1\", \"reactants\":\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[OH:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8])[N:10]([CH3:9])[c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]' are 'Cl[C:1](=[O:2])[O:3][CH2:4][C:5]([Cl:6])([Cl:7])[Cl:8]' and '[CH3:9][NH:10][c:11]1[cH:12][cH:13][cH:14][c:15]([C:16](=[O:17])[NH:18][c:19]2[c:20]([I:21])[cH:22][c:23]([C:24]([F:25])([C:26]([F:27])([F:28])[F:29])[C:30]([F:31])([F:32])[C:33]([F:34])([F:35])[F:36])[cH:37][c:38]2[C:39]([F:40])([F:41])[F:42])[c:43]1[F:44]', for '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1' are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[OH:5][CH2:6][CH2:7][CH2:8][CH2:9][CH2:10][CH2:11][CH2:12][CH2:13][CH2:14][CH2:15][CH2:16][N:17]([CH2:18][c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1)[CH2:25][c:26]1[cH:27][cH:28][cH:29][cH:30][cH:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1)[NH:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]' and '[CH2:1]([c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1)[C:10]#[N:11]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1)[NH:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]\", \"[CH2:1]([c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1)[C:10]#[N:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1)[NH:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1.[NH2:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]\"}, {\"smile\":\"[CH2:1]([c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1)[C:10]#[N:11]\", \"reactants\":\"Br[CH2:1][c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1.[C-:10]#[N:11]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1)[NH:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]' are 'O[C:1](=[O:2])[c:3]1[cH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]2[o:11]1' and '[NH2:12][CH2:13][c:14]1[cH:15][cH:16][cH:17][c:18]2[c:19]1[C:20](=[O:21])[N:22]([CH:23]1[CH2:24][CH2:25][C:26](=[O:27])[NH:28][C:29]1=[O:30])[C:31]2=[O:32]', for '[CH2:1]([c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1)[C:10]#[N:11]' are 'Br[CH2:1][c:2]1[c:3]([F:4])[cH:5][c:6]([Br:7])[cH:8][cH:9]1' and '[C-:10]#[N:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how to synthesize '[c:1]1(/[CH:8]=[CH:9]/[c:10]2[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]2)[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1' and '[O:1]([CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1)[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(/[CH:8]=[CH:9]/[c:10]2[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]2)[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1\", \"[O:1]([CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1)[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(/[CH:8]=[CH:9]/[c:10]2[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]2)[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1\", \"reactants\":\"Br[c:1]1[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1.OB(O)/[CH:8]=[CH:9]/[c:10]1[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]1\"}, {\"smile\":\"[O:1]([CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1)[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1\", \"reactants\":\"O[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1.[OH:1][CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1(/[CH:8]=[CH:9]/[c:10]2[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]2)[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1' are 'Br[c:1]1[n:2][cH:3][cH:4][c:5]([Br:6])[cH:7]1' and 'OB(O)/[CH:8]=[CH:9]/[c:10]1[cH:11][cH:12][c:13]([F:14])[cH:15][cH:16]1', for '[O:1]([CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1)[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1' are 'O[c:22]1[cH:21][cH:20][c:19]([C:17]([CH:16]([NH:15][C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)=[O:18])[cH:24][cH:23]1' and '[OH:1][CH:2]1[CH2:3][CH2:4][CH2:5][CH2:6][CH2:7]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how to synthesize '[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])(=[O:12])[NH:35][CH2:34][CH2:33][CH2:32][CH2:31][n:30]1[c:17]([CH2:16][CH2:15][CH2:14][CH3:13])[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]21' and '[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)(=[O:25])[NH:30][CH2:29][C:27]([CH3:26])=[O:28]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])(=[O:12])[NH:35][CH2:34][CH2:33][CH2:32][CH2:31][n:30]1[c:17]([CH2:16][CH2:15][CH2:14][CH3:13])[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]21\", \"[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)(=[O:25])[NH:30][CH2:29][C:27]([CH3:26])=[O:28]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])(=[O:12])[NH:35][CH2:34][CH2:33][CH2:32][CH2:31][n:30]1[c:17]([CH2:16][CH2:15][CH2:14][CH3:13])[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]21\", \"reactants\":\"O[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])=[O:12].[CH3:13][CH2:14][CH2:15][CH2:16][c:17]1[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]2[n:30]1[CH2:31][CH2:32][CH2:33][CH2:34][NH2:35]\"}, {\"smile\":\"[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)(=[O:25])[NH:30][CH2:29][C:27]([CH3:26])=[O:28]\", \"reactants\":\"O[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)=[O:25].[CH3:26][C:27](=[O:28])[CH2:29][NH2:30]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])(=[O:12])[NH:35][CH2:34][CH2:33][CH2:32][CH2:31][n:30]1[c:17]([CH2:16][CH2:15][CH2:14][CH3:13])[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]21' are 'O[C:1]([CH2:2][n:3]1[cH:4][c:5]([CH3:6])[c:7]([OH:8])[n:9][c:10]1=[O:11])=[O:12]' and '[CH3:13][CH2:14][CH2:15][CH2:16][c:17]1[n:18][c:19]2[c:20]([NH2:21])[n:22][c:23]3[cH:24][cH:25][cH:26][n:27][c:28]3[c:29]2[n:30]1[CH2:31][CH2:32][CH2:33][CH2:34][NH2:35]', for '[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)(=[O:25])[NH:30][CH2:29][C:27]([CH3:26])=[O:28]' are 'O[C:1]([CH2:2][C@:3]1([c:4]2[cH:5][cH:6][cH:7][cH:8][cH:9]2)[CH2:10][CH2:11][N:12]([C@@H:13]([CH3:14])[c:15]2[cH:16][cH:17][c:18]([Br:19])[cH:20][cH:21]2)[C:22](=[O:23])[O:24]1)=[O:25]' and '[CH3:26][C:27](=[O:28])[CH2:29][NH2:30]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the most likely synthesis path for '[C:1](=[O:2])([c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25])[NH:26][c:27]1[n:28][cH:29][cH:30][s:31]1' and '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[O:28][c:27]1[cH:26][cH:25][cH:24][c:23]([CH:22]2[CH2:21][CH2:20][N:19]([C:17]([O:16][C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18])[CH2:31][CH2:30]2)[cH:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25])[NH:26][c:27]1[n:28][cH:29][cH:30][s:31]1\", \"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[O:28][c:27]1[cH:26][cH:25][cH:24][c:23]([CH:22]2[CH2:21][CH2:20][N:19]([C:17]([O:16][C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18])[CH2:31][CH2:30]2)[cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25])[NH:26][c:27]1[n:28][cH:29][cH:30][s:31]1\", \"reactants\":\"CCO[C:1](=[O:2])[c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25].[NH2:26][c:27]1[n:28][cH:29][cH:30][s:31]1\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[O:28][c:27]1[cH:26][cH:25][cH:24][c:23]([CH:22]2[CH2:21][CH2:20][N:19]([C:17]([O:16][C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18])[CH2:31][CH2:30]2)[cH:29]1\", \"reactants\":\"Cl[CH2:1][c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1.[CH3:12][C:13]([CH3:14])([CH3:15])[O:16][C:17](=[O:18])[N:19]1[CH2:20][CH2:21][CH:22]([c:23]2[cH:24][cH:25][cH:26][c:27]([OH:28])[cH:29]2)[CH2:30][CH2:31]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25])[NH:26][c:27]1[n:28][cH:29][cH:30][s:31]1' are 'CCO[C:1](=[O:2])[c:3]1[c:4]([C:5]([F:6])([F:7])[F:8])[n:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[c:17](-[c:18]2[cH:19][cH:20][cH:21][cH:22][cH:23]2)[c:24]1[OH:25]' and '[NH2:26][c:27]1[n:28][cH:29][cH:30][s:31]1', for '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[O:28][c:27]1[cH:26][cH:25][cH:24][c:23]([CH:22]2[CH2:21][CH2:20][N:19]([C:17]([O:16][C:13]([CH3:12])([CH3:14])[CH3:15])=[O:18])[CH2:31][CH2:30]2)[cH:29]1' are 'Cl[CH2:1][c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1' and '[CH3:12][C:13]([CH3:14])([CH3:15])[O:16][C:17](=[O:18])[N:19]1[CH2:20][CH2:21][CH:22]([c:23]2[cH:24][cH:25][cH:26][c:27]([OH:28])[cH:29]2)[CH2:30][CH2:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Outline the synthesis route for '[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)(=[O:18])[NH:19][OH:20]' and '[CH2:1]([c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1)[N:28]1[CH2:27][CH2:26][C@@H:25]([OH:24])[CH2:29]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)(=[O:18])[NH:19][OH:20]\", \"[CH2:1]([c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1)[N:28]1[CH2:27][CH2:26][C@@H:25]([OH:24])[CH2:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)(=[O:18])[NH:19][OH:20]\", \"reactants\":\"Cl[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)=[O:18].[NH2:19][OH:20]\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1)[N:28]1[CH2:27][CH2:26][C@@H:25]([OH:24])[CH2:29]1\", \"reactants\":\"Cl[CH2:1][c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1.[OH:24][C@@H:25]1[CH2:26][CH2:27][NH:28][CH2:29]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)(=[O:18])[NH:19][OH:20]' are 'Cl[C:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][CH2:7][CH2:8][CH2:9][c:10]1[cH:11][cH:12][cH:13][c:14]([O:15][CH3:16])[cH:17]1)=[O:18]' and '[NH2:19][OH:20]', for '[CH2:1]([c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1)[N:28]1[CH2:27][CH2:26][C@@H:25]([OH:24])[CH2:29]1' are 'Cl[CH2:1][c:2]1[cH:3][c:4]2[c:5](=[O:6])[c:7]([C:8](=[O:9])[NH:10][CH2:11][c:12]3[cH:13][cH:14][c:15]([Cl:16])[cH:17][cH:18]3)[cH:19][n:20]([CH3:21])[c:22]2[o:23]1' and '[OH:24][C@@H:25]1[CH2:26][CH2:27][NH:28][CH2:29]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5]1)[N:11]([CH2:10][c:9]1[cH:8][c:7](=[O:6])[nH:27][c:26]2[c:19]1[cH:20][cH:21][c:22]([F:23])[c:24]2[F:25])[c:12]1[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]1' and '[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5]1)[N:11]([CH2:10][c:9]1[cH:8][c:7](=[O:6])[nH:27][c:26]2[c:19]1[cH:20][cH:21][c:22]([F:23])[c:24]2[F:25])[c:12]1[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]1\", \"[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5]1)[N:11]([CH2:10][c:9]1[cH:8][c:7](=[O:6])[nH:27][c:26]2[c:19]1[cH:20][cH:21][c:22]([F:23])[c:24]2[F:25])[c:12]1[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]1\", \"reactants\":\"O[C:1](=[O:2])[CH:3]1[CH2:4][CH2:5]1.[O:6]=[c:7]1[cH:8][c:9]([CH2:10][NH:11][c:12]2[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]2)[c:19]2[cH:20][cH:21][c:22]([F:23])[c:24]([F:25])[c:26]2[nH:27]1\"}, {\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][o:6][n:7]1.[CH3:8][c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[C@@H:15]1[C@@H:16]([O:17][C@H:18]([CH3:19])[c:20]2[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]2)[O:34][CH2:35][C@@H:36]2[CH2:37][NH:38][CH2:39][C@@H:40]12\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([CH:3]1[CH2:4][CH2:5]1)[N:11]([CH2:10][c:9]1[cH:8][c:7](=[O:6])[nH:27][c:26]2[c:19]1[cH:20][cH:21][c:22]([F:23])[c:24]2[F:25])[c:12]1[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]1' are 'O[C:1](=[O:2])[CH:3]1[CH2:4][CH2:5]1' and '[O:6]=[c:7]1[cH:8][c:9]([CH2:10][NH:11][c:12]2[cH:13][cH:14][cH:15][c:16]([Cl:17])[cH:18]2)[c:19]2[cH:20][cH:21][c:22]([F:23])[c:24]([F:25])[c:26]2[nH:27]1', for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][o:6][n:7]1)[N:38]1[CH2:37][C@H:36]2[CH2:35][O:34][C@H:16]([O:17][C@H:18]([CH3:19])[c:20]3[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]3)[C@@H:15]([c:14]3[c:9]([CH3:8])[cH:10][cH:11][cH:12][cH:13]3)[C@@H:40]2[CH2:39]1' are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][o:6][n:7]1' and '[CH3:8][c:9]1[cH:10][cH:11][cH:12][cH:13][c:14]1[C@@H:15]1[C@@H:16]([O:17][C@H:18]([CH3:19])[c:20]2[cH:21][c:22]([C:23]([F:24])([F:25])[F:26])[cH:27][c:28]([C:29]([F:30])([F:31])[F:32])[cH:33]2)[O:34][CH2:35][C@@H:36]2[CH2:37][NH:38][CH2:39][C@@H:40]12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the possible synthesis path of '[c:1]1([O:21][CH2:20][C@H:19]([CH3:18])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29])[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1' and '[CH2:1]([CH:2]=[O:3])[NH:14][c:13]1[c:7]([CH2:6][CH2:5][CH3:4])[cH:8][cH:9][c:10]([F:11])[cH:12]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:21][CH2:20][C@H:19]([CH3:18])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29])[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1\", \"[CH2:1]([CH:2]=[O:3])[NH:14][c:13]1[c:7]([CH2:6][CH2:5][CH3:4])[cH:8][cH:9][c:10]([F:11])[cH:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:21][CH2:20][C@H:19]([CH3:18])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29])[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1\", \"reactants\":\"Cl[c:1]1[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1.[CH3:18][C@@H:19]([CH2:20][OH:21])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29]\"}, {\"smile\":\"[CH2:1]([CH:2]=[O:3])[NH:14][c:13]1[c:7]([CH2:6][CH2:5][CH3:4])[cH:8][cH:9][c:10]([F:11])[cH:12]1\", \"reactants\":\"Cl[CH2:1][CH:2]=[O:3].[CH3:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][c:10]([F:11])[cH:12][c:13]1[NH2:14]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([O:21][CH2:20][C@H:19]([CH3:18])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29])[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1' are 'Cl[c:1]1[n:2][cH:3][c:4]([N+:5](=[O:6])[O-:7])[c:8]([O:9][CH2:10][c:11]2[cH:12][cH:13][cH:14][cH:15][cH:16]2)[n:17]1' and '[CH3:18][C@@H:19]([CH2:20][OH:21])[NH:22][C:23](=[O:24])[O:25][C:26]([CH3:27])([CH3:28])[CH3:29]', for '[CH2:1]([CH:2]=[O:3])[NH:14][c:13]1[c:7]([CH2:6][CH2:5][CH3:4])[cH:8][cH:9][c:10]([F:11])[cH:12]1' are 'Cl[CH2:1][CH:2]=[O:3]' and '[CH3:4][CH2:5][CH2:6][c:7]1[cH:8][cH:9][c:10]([F:11])[cH:12][c:13]1[NH2:14]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the most probable synthesis route for '[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]' and '[CH3:1][C:2](=[O:3])[NH:17][c:16]1[s:15][cH:14][c:13](-[c:12]2[cH:11][cH:10][c:9]([CH2:8][C:6]([O:5][CH3:4])=[O:7])[s:19]2)[n:18]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]\", \"[CH3:1][C:2](=[O:3])[NH:17][c:16]1[s:15][cH:14][c:13](-[c:12]2[cH:11][cH:10][c:9]([CH2:8][C:6]([O:5][CH3:4])=[O:7])[s:19]2)[n:18]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]\", \"reactants\":\"CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31].[CH3:32][O:33][CH2:34][CH2:35][NH2:36]\"}, {\"smile\":\"[CH3:1][C:2](=[O:3])[NH:17][c:16]1[s:15][cH:14][c:13](-[c:12]2[cH:11][cH:10][c:9]([CH2:8][C:6]([O:5][CH3:4])=[O:7])[s:19]2)[n:18]1\", \"reactants\":\"CC(=O)O[C:2]([CH3:1])=[O:3].[CH3:4][O:5][C:6](=[O:7])[CH2:8][c:9]1[cH:10][cH:11][c:12](-[c:13]2[cH:14][s:15][c:16]([NH2:17])[n:18]2)[s:19]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31])[NH:36][CH2:35][CH2:34][O:33][CH3:32]' are 'CCO[C:1](=[O:2])[c:3]1[c:4]([OH:5])[c:6]2[n:7][cH:8][c:9]([CH2:10][c:11]3[cH:12][cH:13][c:14]([F:15])[cH:16][cH:17]3)[cH:18][c:19]2[n:20]([CH2:21][CH2:22][N:23]2[CH2:24][CH2:25][CH2:26][CH2:27][C:28]2=[O:29])[c:30]1=[O:31]' and '[CH3:32][O:33][CH2:34][CH2:35][NH2:36]', for '[CH3:1][C:2](=[O:3])[NH:17][c:16]1[s:15][cH:14][c:13](-[c:12]2[cH:11][cH:10][c:9]([CH2:8][C:6]([O:5][CH3:4])=[O:7])[s:19]2)[n:18]1' are 'CC(=O)O[C:2]([CH3:1])=[O:3]' and '[CH3:4][O:5][C:6](=[O:7])[CH2:8][c:9]1[cH:10][cH:11][c:12](-[c:13]2[cH:14][s:15][c:16]([NH2:17])[n:18]2)[s:19]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the best synthesis path for '[CH2:1]([CH2:2][CH2:3][N:4]([CH3:5])[CH3:6])[O:23][c:22]1[c:19]([O:20][CH3:21])[cH:18][c:17]2[n:16][cH:15][n:14](-[c:13]3[s:12][c:11]([C:9]([O:8][CH3:7])=[O:10])[c:27]([O:28][CH2:29][c:30]4[cH:31][cH:32][cH:33][cH:34][c:35]4[C:36]([F:37])([F:38])[F:39])[cH:26]3)[c:25]2[cH:24]1' and '[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][N:4]([CH3:5])[CH3:6])[O:23][c:22]1[c:19]([O:20][CH3:21])[cH:18][c:17]2[n:16][cH:15][n:14](-[c:13]3[s:12][c:11]([C:9]([O:8][CH3:7])=[O:10])[c:27]([O:28][CH2:29][c:30]4[cH:31][cH:32][cH:33][cH:34][c:35]4[C:36]([F:37])([F:38])[F:39])[cH:26]3)[c:25]2[cH:24]1\", \"[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][N:4]([CH3:5])[CH3:6])[O:23][c:22]1[c:19]([O:20][CH3:21])[cH:18][c:17]2[n:16][cH:15][n:14](-[c:13]3[s:12][c:11]([C:9]([O:8][CH3:7])=[O:10])[c:27]([O:28][CH2:29][c:30]4[cH:31][cH:32][cH:33][cH:34][c:35]4[C:36]([F:37])([F:38])[F:39])[cH:26]3)[c:25]2[cH:24]1\", \"reactants\":\"O[CH2:1][CH2:2][CH2:3][N:4]([CH3:5])[CH3:6].[CH3:7][O:8][C:9](=[O:10])[c:11]1[s:12][c:13](-[n:14]2[cH:15][n:16][c:17]3[cH:18][c:19]([O:20][CH3:21])[c:22]([OH:23])[cH:24][c:25]23)[cH:26][c:27]1[O:28][CH2:29][c:30]1[cH:31][cH:32][cH:33][cH:34][c:35]1[C:36]([F:37])([F:38])[F:39]\"}, {\"smile\":\"[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2\", \"reactants\":\"O=[C:1]1[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2.[NH4+:18]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([CH2:2][CH2:3][N:4]([CH3:5])[CH3:6])[O:23][c:22]1[c:19]([O:20][CH3:21])[cH:18][c:17]2[n:16][cH:15][n:14](-[c:13]3[s:12][c:11]([C:9]([O:8][CH3:7])=[O:10])[c:27]([O:28][CH2:29][c:30]4[cH:31][cH:32][cH:33][cH:34][c:35]4[C:36]([F:37])([F:38])[F:39])[cH:26]3)[c:25]2[cH:24]1' are 'O[CH2:1][CH2:2][CH2:3][N:4]([CH3:5])[CH3:6]' and '[CH3:7][O:8][C:9](=[O:10])[c:11]1[s:12][c:13](-[n:14]2[cH:15][n:16][c:17]3[cH:18][c:19]([O:20][CH3:21])[c:22]([OH:23])[cH:24][c:25]23)[cH:26][c:27]1[O:28][CH2:29][c:30]1[cH:31][cH:32][cH:33][cH:34][c:35]1[C:36]([F:37])([F:38])[F:39]', for '[CH:1]1([NH2:18])[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2' are 'O=[C:1]1[C:2]2([CH3:3])[c:4]3[c:5]([cH:6][cH:7][c:8]([O:9][CH3:10])[cH:11]3)[CH2:12][CH:13]1[CH2:14][CH2:15][CH2:16][CH2:17]2' and '[NH4+:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis process for '[CH3:1][N:16]1[c:15]2[c:10]([n:11][cH:12][cH:13][cH:14]2)[NH:9][c:8]2[c:3]([CH3:2])[n:4][n:5]([CH3:6])[c:7]2[C:17]1=[O:18]' and '[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][N:16]1[c:15]2[c:10]([n:11][cH:12][cH:13][cH:14]2)[NH:9][c:8]2[c:3]([CH3:2])[n:4][n:5]([CH3:6])[c:7]2[C:17]1=[O:18]\", \"[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][N:16]1[c:15]2[c:10]([n:11][cH:12][cH:13][cH:14]2)[NH:9][c:8]2[c:3]([CH3:2])[n:4][n:5]([CH3:6])[c:7]2[C:17]1=[O:18]\", \"reactants\":\"I[CH3:1].[CH3:2][c:3]1[n:4][n:5]([CH3:6])[c:7]2[c:8]1[NH:9][c:10]1[n:11][cH:12][cH:13][cH:14][c:15]1[NH:16][C:17]2=[O:18]\"}, {\"smile\":\"[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]\", \"reactants\":\"I[CH3:1].[CH3:2][CH2:3][O:4][N:5]=[CH:6][c:7]1[cH:8][c:9](-[n:10]2[c:11](=[O:12])[cH:13][c:14]([C:15]([F:16])([F:17])[F:18])[nH:19][c:20]2=[O:21])[c:22]([Cl:23])[cH:24][c:25]1[Cl:26]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH3:1][N:16]1[c:15]2[c:10]([n:11][cH:12][cH:13][cH:14]2)[NH:9][c:8]2[c:3]([CH3:2])[n:4][n:5]([CH3:6])[c:7]2[C:17]1=[O:18]' are 'I[CH3:1]' and '[CH3:2][c:3]1[n:4][n:5]([CH3:6])[c:7]2[c:8]1[NH:9][c:10]1[n:11][cH:12][cH:13][cH:14][c:15]1[NH:16][C:17]2=[O:18]', for '[CH3:1][n:19]1[c:14]([C:15]([F:16])([F:17])[F:18])[cH:13][c:11](=[O:12])[n:10](-[c:9]2[cH:8][c:7]([CH:6]=[N:5][O:4][CH2:3][CH3:2])[c:25]([Cl:26])[cH:24][c:22]2[Cl:23])[c:20]1=[O:21]' are 'I[CH3:1]' and '[CH3:2][CH2:3][O:4][N:5]=[CH:6][c:7]1[cH:8][c:9](-[n:10]2[c:11](=[O:12])[cH:13][c:14]([C:15]([F:16])([F:17])[F:18])[nH:19][c:20]2=[O:21])[c:22]([Cl:23])[cH:24][c:25]1[Cl:26]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' and '[c:1]1([O:8][CH2:9][CH2:10][c:11]2[cH:12][cH:13][s:14][cH:15]2)[cH:2][cH:3][n:4][cH:5][c:6]1[I:7]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1\", \"[c:1]1([O:8][CH2:9][CH2:10][c:11]2[cH:12][cH:13][s:14][cH:15]2)[cH:2][cH:3][n:4][cH:5][c:6]1[I:7]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1.[CH:23]#[C:24][c:25]1[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]1\"}, {\"smile\":\"[c:1]1([O:8][CH2:9][CH2:10][c:11]2[cH:12][cH:13][s:14][cH:15]2)[cH:2][cH:3][n:4][cH:5][c:6]1[I:7]\", \"reactants\":\"Cl[c:1]1[cH:2][cH:3][n:4][cH:5][c:6]1[I:7].[OH:8][CH2:9][CH2:10][c:11]1[cH:12][cH:13][s:14][cH:15]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' and '[CH:23]#[C:24][c:25]1[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]1', for '[c:1]1([O:8][CH2:9][CH2:10][c:11]2[cH:12][cH:13][s:14][cH:15]2)[cH:2][cH:3][n:4][cH:5][c:6]1[I:7]' are 'Cl[c:1]1[cH:2][cH:3][n:4][cH:5][c:6]1[I:7]' and '[OH:8][CH2:9][CH2:10][c:11]1[cH:12][cH:13][s:14][cH:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path of '[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)(=[O:28])[NH:31][CH:30]([CH3:29])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1' and '[c:1]1([NH:20][CH2:19][c:18]2[cH:17][cH:16][c:15]([O:14][CH3:13])[c:22]([O:23][CH3:24])[cH:21]2)[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)(=[O:28])[NH:31][CH:30]([CH3:29])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1\", \"[c:1]1([NH:20][CH2:19][c:18]2[cH:17][cH:16][c:15]([O:14][CH3:13])[c:22]([O:23][CH3:24])[cH:21]2)[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)(=[O:28])[NH:31][CH:30]([CH3:29])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1\", \"reactants\":\"O[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)=[O:28].[CH3:29][CH:30]([NH2:31])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1\"}, {\"smile\":\"[c:1]1([NH:20][CH2:19][c:18]2[cH:17][cH:16][c:15]([O:14][CH3:13])[c:22]([O:23][CH3:24])[cH:21]2)[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12\", \"reactants\":\"Cl[c:1]1[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12.[CH3:13][O:14][c:15]1[cH:16][cH:17][c:18]([CH2:19][NH2:20])[cH:21][c:22]1[O:23][CH3:24]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)(=[O:28])[NH:31][CH:30]([CH3:29])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1' are 'O[C:1]([c:2]1[c:3]([CH2:4][CH3:5])[n:6][c:7]([O:8][CH3:9])[c:10]([NH:11][C:12](=[O:13])[N:14]2[CH2:15][CH2:16][N:17]([c:18]3[cH:19][cH:20][cH:21][c:22]([F:23])[cH:24]3)[CH2:25][CH2:26]2)[cH:27]1)=[O:28]' and '[CH3:29][CH:30]([NH2:31])[C:32](=[O:33])[NH:34][c:35]1[cH:36][c:37]([CH2:38][OH:39])[cH:40][c:41]([NH:42][c:43]2[c:44]3[cH:45][cH:46][cH:47][cH:48][c:49]3[n:50][c:51]3[cH:52][cH:53][cH:54][cH:55][c:56]23)[cH:57]1', for '[c:1]1([NH:20][CH2:19][c:18]2[cH:17][cH:16][c:15]([O:14][CH3:13])[c:22]([O:23][CH3:24])[cH:21]2)[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12' are 'Cl[c:1]1[n:2][c:3]([Cl:4])[n:5][c:6]2[s:7][c:8]([CH3:9])[c:10]([CH3:11])[c:12]12' and '[CH3:13][O:14][c:15]1[cH:16][cH:17][c:18]([CH2:19][NH2:20])[cH:21][c:22]1[O:23][CH3:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[c:1]1(-[n:27]2[cH:26][n:25][cH:24][n:28]2)[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2' and '[CH2:1]([c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1)[O:45][CH2:44][c:43]1[c:37]([CH2:36][CH:35]=[CH2:34])[cH:38][c:39]([F:40])[cH:41][cH:42]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:27]2[cH:26][n:25][cH:24][n:28]2)[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2\", \"[CH2:1]([c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1)[O:45][CH2:44][c:43]1[c:37]([CH2:36][CH:35]=[CH2:34])[cH:38][c:39]([F:40])[cH:41][cH:42]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[n:27]2[cH:26][n:25][cH:24][n:28]2)[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2\", \"reactants\":\"Cl[c:1]1[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2.[cH:24]1[n:25][cH:26][nH:27][n:28]1\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1)[O:45][CH2:44][c:43]1[c:37]([CH2:36][CH:35]=[CH2:34])[cH:38][c:39]([F:40])[cH:41][cH:42]1\", \"reactants\":\"I[CH2:1][c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1.[CH2:34]=[CH:35][CH2:36][c:37]1[cH:38][c:39]([F:40])[cH:41][cH:42][c:43]1[CH2:44][OH:45]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1(-[n:27]2[cH:26][n:25][cH:24][n:28]2)[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2' are 'Cl[c:1]1[n:2][c:3]([NH:4][CH2:5][c:6]2[cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]([Cl:13])[cH:14]2)[c:15]2[c:16]([n:17]1)[s:18][c:19]([N+:20](=[O:21])[O-:22])[cH:23]2' and '[cH:24]1[n:25][cH:26][nH:27][n:28]1', for '[CH2:1]([c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1)[O:45][CH2:44][c:43]1[c:37]([CH2:36][CH:35]=[CH2:34])[cH:38][c:39]([F:40])[cH:41][cH:42]1' are 'I[CH2:1][c:2]1[cH:3][c:4]2[n:5][c:6]([CH3:7])[c:8]([C@H:9]([O:10][C:11]([CH3:12])([CH3:13])[CH3:14])[C:15](=[O:16])[O:17][CH2:18][CH3:19])[c:20]([N:21]3[CH2:22][CH2:23][C:24]([O:25][CH2:26][CH:27]=[CH2:28])([CH3:29])[CH2:30][CH2:31]3)[n:32]2[n:33]1' and '[CH2:34]=[CH:35][CH2:36][c:37]1[cH:38][c:39]([F:40])[cH:41][cH:42][c:43]1[CH2:44][OH:45]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the synthesis method for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1)[NH:25][c:24]1[n:23][c:22]2[c:15](-[c:16]3[cH:17][cH:18][cH:19][cH:20][cH:21]3)[cH:14][cH:13][c:12]([O:11][CH3:10])[n:27]2[n:26]1' and '[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[O:27][C@H:26]1[CH2:25][CH2:24][C@H:23]([c:22]2[cH:21][cH:20][c:19]([CH2:18][N:14]([CH2:13][CH:12]=[CH2:11])[CH2:15][CH:16]=[CH2:17])[cH:31][cH:30]2)[CH2:29][CH2:28]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1)[NH:25][c:24]1[n:23][c:22]2[c:15](-[c:16]3[cH:17][cH:18][cH:19][cH:20][cH:21]3)[cH:14][cH:13][c:12]([O:11][CH3:10])[n:27]2[n:26]1\", \"[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[O:27][C@H:26]1[CH2:25][CH2:24][C@H:23]([c:22]2[cH:21][cH:20][c:19]([CH2:18][N:14]([CH2:13][CH:12]=[CH2:11])[CH2:15][CH:16]=[CH2:17])[cH:31][cH:30]2)[CH2:29][CH2:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1)[NH:25][c:24]1[n:23][c:22]2[c:15](-[c:16]3[cH:17][cH:18][cH:19][cH:20][cH:21]3)[cH:14][cH:13][c:12]([O:11][CH3:10])[n:27]2[n:26]1\", \"reactants\":\"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1.[CH3:10][O:11][c:12]1[cH:13][cH:14][c:15](-[c:16]2[cH:17][cH:18][cH:19][cH:20][cH:21]2)[c:22]2[n:23][c:24]([NH2:25])[n:26][n:27]12\"}, {\"smile\":\"[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[O:27][C@H:26]1[CH2:25][CH2:24][C@H:23]([c:22]2[cH:21][cH:20][c:19]([CH2:18][N:14]([CH2:13][CH:12]=[CH2:11])[CH2:15][CH:16]=[CH2:17])[cH:31][cH:30]2)[CH2:29][CH2:28]1\", \"reactants\":\"O[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1.[CH2:11]=[CH:12][CH2:13][N:14]([CH2:15][CH:16]=[CH2:17])[CH2:18][c:19]1[cH:20][cH:21][c:22]([C@H:23]2[CH2:24][CH2:25][C@H:26]([OH:27])[CH2:28][CH2:29]2)[cH:30][cH:31]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1)[NH:25][c:24]1[n:23][c:22]2[c:15](-[c:16]3[cH:17][cH:18][cH:19][cH:20][cH:21]3)[cH:14][cH:13][c:12]([O:11][CH3:10])[n:27]2[n:26]1' are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][c:7]([F:8])[cH:9]1' and '[CH3:10][O:11][c:12]1[cH:13][cH:14][c:15](-[c:16]2[cH:17][cH:18][cH:19][cH:20][cH:21]2)[c:22]2[n:23][c:24]([NH2:25])[n:26][n:27]12', for '[C:1](=[O:2])([CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1)[O:27][C@H:26]1[CH2:25][CH2:24][C@H:23]([c:22]2[cH:21][cH:20][c:19]([CH2:18][N:14]([CH2:13][CH:12]=[CH2:11])[CH2:15][CH:16]=[CH2:17])[cH:31][cH:30]2)[CH2:29][CH2:28]1' are 'O[C:1](=[O:2])[CH2:3][c:4]1[cH:5][cH:6][c:7]([Cl:8])[cH:9][cH:10]1' and '[CH2:11]=[CH:12][CH2:13][N:14]([CH2:15][CH:16]=[CH2:17])[CH2:18][c:19]1[cH:20][cH:21][c:22]([C@H:23]2[CH2:24][CH2:25][C@H:26]([OH:27])[CH2:28][CH2:29]2)[cH:30][cH:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '[c:1]1([O:26][CH2:27][CH:28]2[CH2:29][NH:30][CH2:31][CH2:32][O:33]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1' and '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:26][CH2:27][CH:28]2[CH2:29][NH:30][CH2:31][CH2:32][O:33]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1\", \"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:26][CH2:27][CH:28]2[CH2:29][NH:30][CH2:31][CH2:32][O:33]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1\", \"reactants\":\"F[c:1]1[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1.[OH:26][CH2:27][CH:28]1[CH2:29][NH:30][CH2:31][CH2:32][O:33]1\"}, {\"smile\":\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]\", \"reactants\":\"CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10].[Mg+][CH3:11]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([O:26][CH2:27][CH:28]2[CH2:29][NH:30][CH2:31][CH2:32][O:33]2)[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1' are 'F[c:1]1[cH:2][cH:3][c:4](-[c:5]2[o:6][n:7][c:8](-[c:9]3[cH:10][cH:11][c:12]([O:13][CH:14]([CH3:15])[CH3:16])[c:17]([C:18]([F:19])([F:20])[F:21])[cH:22]3)[n:23]2)[cH:24][cH:25]1' and '[OH:26][CH2:27][CH:28]1[CH2:29][NH:30][CH2:31][CH2:32][O:33]1', for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]' are 'CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10]' and '[Mg+][CH3:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze how '[c:1]1([NH:13][CH2:14][CH2:15][CH2:16][N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2' and '[C:1]1(=[N:10][OH:11])[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:13][CH2:14][CH2:15][CH2:16][N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2\", \"[C:1]1(=[N:10][OH:11])[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:13][CH2:14][CH2:15][CH2:16][N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2\", \"reactants\":\"Cl[c:1]1[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2.[NH2:13][CH2:14][CH2:15][CH2:16][N:17]1[CH2:18][CH2:19][N:20]([CH:21]([c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[c:28]2[cH:29][cH:30][cH:31][cH:32][cH:33]2)[CH2:34][CH2:35]1\"}, {\"smile\":\"[C:1]1(=[N:10][OH:11])[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21\", \"reactants\":\"O=[C:1]1[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21.[NH2:10][OH:11]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([NH:13][CH2:14][CH2:15][CH2:16][N:17]2[CH2:18][CH2:19][N:20]([CH:21]([c:22]3[cH:23][cH:24][cH:25][cH:26][cH:27]3)[c:28]3[cH:29][cH:30][cH:31][cH:32][cH:33]3)[CH2:34][CH2:35]2)[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2' are 'Cl[c:1]1[n:2][c:3](=[O:4])[n:5]([CH3:6])[c:7]2[c:8]1[cH:9][cH:10][cH:11][cH:12]2' and '[NH2:13][CH2:14][CH2:15][CH2:16][N:17]1[CH2:18][CH2:19][N:20]([CH:21]([c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[c:28]2[cH:29][cH:30][cH:31][cH:32][cH:33]2)[CH2:34][CH2:35]1', for '[C:1]1(=[N:10][OH:11])[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21' are 'O=[C:1]1[CH2:2][CH2:3][c:4]2[cH:5][cH:6][cH:7][cH:8][c:9]21' and '[NH2:10][OH:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis route for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:25]1[C:9]([CH3:8])=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@H:18]1[c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1' and '[c:1]1([NH:27][c:26]2[c:17]([C:15]([O:14][CH2:13][CH3:12])=[O:16])[nH:18][c:19]3[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]32)[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:25]1[C:9]([CH3:8])=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@H:18]1[c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1\", \"[c:1]1([NH:27][c:26]2[c:17]([C:15]([O:14][CH2:13][CH3:12])=[O:16])[nH:18][c:19]3[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]32)[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:25]1[C:9]([CH3:8])=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@H:18]1[c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1\", \"reactants\":\"CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7].[CH3:8][C:9]1=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@@H:18]([c:19]2[cH:20][cH:21][cH:22][cH:23][cH:24]2)[NH:25]1\"}, {\"smile\":\"[c:1]1([NH:27][c:26]2[c:17]([C:15]([O:14][CH2:13][CH3:12])=[O:16])[nH:18][c:19]3[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]32)[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11]\", \"reactants\":\"O=S(=O)(O[c:1]1[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11])C(F)(F)F.[CH3:12][CH2:13][O:14][C:15](=[O:16])[c:17]1[nH:18][c:19]2[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]2[c:26]1[NH2:27]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH3:1][C:2]([CH3:3])([CH3:4])[O:5][C:6](=[O:7])[N:25]1[C:9]([CH3:8])=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@H:18]1[c:19]1[cH:20][cH:21][cH:22][cH:23][cH:24]1' are 'CC(C)(C)OC(=O)O[C:6]([O:5][C:2]([CH3:1])([CH3:3])[CH3:4])=[O:7]' and '[CH3:8][C:9]1=[N:10][C@@H:11]([c:12]2[cH:13][cH:14][cH:15][cH:16][cH:17]2)[C@@H:18]([c:19]2[cH:20][cH:21][cH:22][cH:23][cH:24]2)[NH:25]1', for '[c:1]1([NH:27][c:26]2[c:17]([C:15]([O:14][CH2:13][CH3:12])=[O:16])[nH:18][c:19]3[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]32)[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11]' are 'O=S(=O)(O[c:1]1[cH:2][cH:3][c:4]([Si:5]([CH3:6])([CH3:7])[CH3:8])[cH:9][c:10]1[F:11])C(F)(F)F' and '[CH3:12][CH2:13][O:14][C:15](=[O:16])[c:17]1[nH:18][c:19]2[c:20]([Cl:21])[cH:22][n:23][cH:24][c:25]2[c:26]1[NH2:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis steps for '[O:1]=[S:6]([CH2:5][CH2:4][O:3][CH3:2])[c:7]1[n:8][cH:9][nH:10][n:11]1' and '[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[O:1]=[S:6]([CH2:5][CH2:4][O:3][CH3:2])[c:7]1[n:8][cH:9][nH:10][n:11]1\", \"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[O:1]=[S:6]([CH2:5][CH2:4][O:3][CH3:2])[c:7]1[n:8][cH:9][nH:10][n:11]1\", \"reactants\":\"O[OH:1].[CH3:2][O:3][CH2:4][CH2:5][S:6][c:7]1[n:8][cH:9][nH:10][n:11]1\"}, {\"smile\":\"[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1\", \"reactants\":\"CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1.[CH3:8][c:9]1[c:10]([N:11]([CH2:12][c:13]2[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]2)[CH2:21][c:22]2[cH:23][cH:24][c:25]([O:26][c:27]3[cH:28][cH:29][cH:30][c:31]([OH:32])[cH:33]3)[cH:34][cH:35]2)[cH:36][cH:37][cH:38][c:39]1[N+:40](=[O:41])[O-:42]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[O:1]=[S:6]([CH2:5][CH2:4][O:3][CH3:2])[c:7]1[n:8][cH:9][nH:10][n:11]1' are 'O[OH:1]' and '[CH3:2][O:3][CH2:4][CH2:5][S:6][c:7]1[n:8][cH:9][nH:10][n:11]1', for '[CH2:1]([CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1)[O:32][c:31]1[cH:30][cH:29][cH:28][c:27]([O:26][c:25]2[cH:24][cH:23][c:22]([CH2:21][N:11]([c:10]3[c:9]([CH3:8])[c:39]([N+:40](=[O:41])[O-:42])[cH:38][cH:37][cH:36]3)[CH2:12][c:13]3[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]3)[cH:35][cH:34]2)[cH:33]1' are 'CS(=O)(=O)O[CH2:1][CH2:2][c:3]1[cH:4][cH:5][cH:6][s:7]1' and '[CH3:8][c:9]1[c:10]([N:11]([CH2:12][c:13]2[cH:14][cH:15][c:16]([C:17]#[N:18])[cH:19][cH:20]2)[CH2:21][c:22]2[cH:23][cH:24][c:25]([O:26][c:27]3[cH:28][cH:29][cH:30][c:31]([OH:32])[cH:33]3)[cH:34][cH:35]2)[cH:36][cH:37][cH:38][c:39]1[N+:40](=[O:41])[O-:42]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the likely synthesis procedure for '[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1)[O:24][c:23]1[c:18]([CH:19]2[CH2:20][CH2:21][CH2:22]2)[cH:17][cH:16][c:15](-[c:14]2[n:13][cH:12][c:11]([NH2:10])[n:28][cH:27]2)[c:25]1[F:26]' and '[CH3:1][O:25][CH2:24][C@H:23]1[N:20]([c:19]2[n:18]3[c:14]([n:13][c:12]4[c:26]2[CH2:27][CH2:28][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11]4)[cH:15][cH:16][n:17]3)[CH2:21][CH2:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1)[O:24][c:23]1[c:18]([CH:19]2[CH2:20][CH2:21][CH2:22]2)[cH:17][cH:16][c:15](-[c:14]2[n:13][cH:12][c:11]([NH2:10])[n:28][cH:27]2)[c:25]1[F:26]\", \"[CH3:1][O:25][CH2:24][C@H:23]1[N:20]([c:19]2[n:18]3[c:14]([n:13][c:12]4[c:26]2[CH2:27][CH2:28][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11]4)[cH:15][cH:16][n:17]3)[CH2:21][CH2:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1)[O:24][c:23]1[c:18]([CH:19]2[CH2:20][CH2:21][CH2:22]2)[cH:17][cH:16][c:15](-[c:14]2[n:13][cH:12][c:11]([NH2:10])[n:28][cH:27]2)[c:25]1[F:26]\", \"reactants\":\"Br[CH2:1][c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1.[NH2:10][c:11]1[cH:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([CH:19]3[CH2:20][CH2:21][CH2:22]3)[c:23]([OH:24])[c:25]2[F:26])[cH:27][n:28]1\"}, {\"smile\":\"[CH3:1][O:25][CH2:24][C@H:23]1[N:20]([c:19]2[n:18]3[c:14]([n:13][c:12]4[c:26]2[CH2:27][CH2:28][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11]4)[cH:15][cH:16][n:17]3)[CH2:21][CH2:22]1\", \"reactants\":\"I[CH3:1].[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][c:12]2[n:13][c:14]3[cH:15][cH:16][n:17][n:18]3[c:19]([N:20]3[CH2:21][CH2:22][C@H:23]3[CH2:24][OH:25])[c:26]2[CH2:27][CH2:28]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1)[O:24][c:23]1[c:18]([CH:19]2[CH2:20][CH2:21][CH2:22]2)[cH:17][cH:16][c:15](-[c:14]2[n:13][cH:12][c:11]([NH2:10])[n:28][cH:27]2)[c:25]1[F:26]' are 'Br[CH2:1][c:2]1[c:3]([Cl:4])[cH:5][cH:6][c:7]([F:8])[cH:9]1' and '[NH2:10][c:11]1[cH:12][n:13][c:14](-[c:15]2[cH:16][cH:17][c:18]([CH:19]3[CH2:20][CH2:21][CH2:22]3)[c:23]([OH:24])[c:25]2[F:26])[cH:27][n:28]1', for '[CH3:1][O:25][CH2:24][C@H:23]1[N:20]([c:19]2[n:18]3[c:14]([n:13][c:12]4[c:26]2[CH2:27][CH2:28][N:9]([C:7]([O:6][C:3]([CH3:2])([CH3:4])[CH3:5])=[O:8])[CH2:10][CH2:11]4)[cH:15][cH:16][n:17]3)[CH2:21][CH2:22]1' are 'I[CH3:1]' and '[CH3:2][C:3]([CH3:4])([CH3:5])[O:6][C:7](=[O:8])[N:9]1[CH2:10][CH2:11][c:12]2[n:13][c:14]3[cH:15][cH:16][n:17][n:18]3[c:19]([N:20]3[CH2:21][CH2:22][C@H:23]3[CH2:24][OH:25])[c:26]2[CH2:27][CH2:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]([OH:9])[CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17])[cH:10][o:11]1' and '[c:1]1([S:23][c:22]2[c:17]([C:15](=[O:14])[OH:16])[cH:18][cH:19][cH:20][cH:21]2)[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]([OH:9])[CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17])[cH:10][o:11]1\", \"[c:1]1([S:23][c:22]2[c:17]([C:15](=[O:14])[OH:16])[cH:18][cH:19][cH:20][cH:21]2)[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]([OH:9])[CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17])[cH:10][o:11]1\", \"reactants\":\"[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]=[O:9])[cH:10][o:11]1.[Mg+][CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17]\"}, {\"smile\":\"[c:1]1([S:23][c:22]2[c:17]([C:15](=[O:14])[OH:16])[cH:18][cH:19][cH:20][cH:21]2)[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2\", \"reactants\":\"Br[c:1]1[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2.[O:14]=[C:15]([OH:16])[c:17]1[cH:18][cH:19][cH:20][cH:21][c:22]1[SH:23]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]([OH:9])[CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17])[cH:10][o:11]1' are '[CH3:1][Si:2]([CH3:3])([CH3:4])[c:5]1[cH:6][c:7]([CH:8]=[O:9])[cH:10][o:11]1' and '[Mg+][CH2:12][CH2:13][CH:14]=[C:15]([CH3:16])[CH3:17]', for '[c:1]1([S:23][c:22]2[c:17]([C:15](=[O:14])[OH:16])[cH:18][cH:19][cH:20][cH:21]2)[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2' are 'Br[c:1]1[c:2]([C:3](=[O:4])[OH:5])[c:6]2[c:7]([cH:8][cH:9]1)[cH:10][cH:11][cH:12][cH:13]2' and '[O:14]=[C:15]([OH:16])[c:17]1[cH:18][cH:19][cH:20][cH:21][c:22]1[SH:23]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[c:1]1(-[c:30]2[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]2)[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1' and '[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:30]2[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]2)[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1\", \"[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1(-[c:30]2[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]2)[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1\", \"reactants\":\"O=S(=O)(O[c:1]1[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1)C(F)(F)F.OB(O)[c:30]1[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]1\"}, {\"smile\":\"[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1\", \"reactants\":\"Cl[c:1]1[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1.[CH3:29][CH2:30][c:31]1[n:32][c:33]2[cH:34][cH:35][cH:36][cH:37][c:38]2[nH:39]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1(-[c:30]2[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]2)[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1' are 'O=S(=O)(O[c:1]1[cH:2][c:3]2[c:4]([C:5](=[O:6])[O:7][CH2:8][CH3:9])[c:10]([CH2:11][C:12]([O:13][CH2:14][CH3:15])=[O:16])[n:17](-[c:18]3[cH:19][cH:20][c:21]([CH:22]([CH3:23])[CH3:24])[cH:25][cH:26]3)[c:27]2[cH:28][cH:29]1)C(F)(F)F' and 'OB(O)[c:30]1[cH:31][cH:32][c:33]([C:34]([F:35])([F:36])[F:37])[cH:38][cH:39]1', for '[c:1]1(-[n:39]2[c:31]([CH2:30][CH3:29])[n:32][c:33]3[cH:34][cH:35][cH:36][cH:37][c:38]32)[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1' are 'Cl[c:1]1[n:2][c:3]([N:4]2[CH2:5][CH2:6][O:7][CH2:8][CH2:9]2)[c:10]2[n:11][c:12]([CH:13]=[C:14]3[CH2:15][N:16]([C:17](=[O:18])[O:19][C:20]([CH3:21])([CH3:22])[CH3:23])[CH2:24]3)[n:25]([CH3:26])[c:27]2[n:28]1' and '[CH3:29][CH2:30][c:31]1[n:32][c:33]2[cH:34][cH:35][cH:36][cH:37][c:38]2[nH:39]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis steps for '[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])(=[O:10])[NH:22][CH2:21][c:20]1[cH:19][cH:18][cH:17][c:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH3:11])[cH:23]1' and '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[NH:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])(=[O:10])[NH:22][CH2:21][c:20]1[cH:19][cH:18][cH:17][c:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH3:11])[cH:23]1\", \"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[NH:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])(=[O:10])[NH:22][CH2:21][c:20]1[cH:19][cH:18][cH:17][c:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH3:11])[cH:23]1\", \"reactants\":\"O[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])=[O:10].[CH3:11][CH2:12][CH2:13][CH2:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([CH2:21][NH2:22])[cH:23]1\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[NH:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1.[NH2:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])(=[O:10])[NH:22][CH2:21][c:20]1[cH:19][cH:18][cH:17][c:16]([CH2:15][CH2:14][CH2:13][CH2:12][CH3:11])[cH:23]1' are 'O[C:1]([c:2]1[cH:3][cH:4][c:5]([CH3:6])[n:7][c:8]1[NH2:9])=[O:10]' and '[CH3:11][CH2:12][CH2:13][CH2:14][CH2:15][c:16]1[cH:17][cH:18][cH:19][c:20]([CH2:21][NH2:22])[cH:23]1', for '[CH2:1]([c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1)[NH:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1' are 'O=[CH:1][c:2]1[cH:3][cH:4][c:5]([S:6]([CH3:7])(=[O:8])=[O:9])[cH:10][cH:11]1' and '[NH2:12][c:13]1[cH:14][cH:15][c:16](-[c:17]2[c:18]([NH2:19])[n:20][c:21]([NH2:22])[n:23][c:24]2[CH:25]2[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:30][cH:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis steps for '[CH2:1]([CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])[O:29][c:28]1[n:27][c:26]2[n:12]([CH:13]([c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:11][c:10]([C:9]#[N:8])[c:38]2[c:31](-[c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[cH:30]1' and '[CH2:1]([c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])[O:29][c:28]1[n:27][c:26]2[n:12]([CH:13]([c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:11][c:10]([C:9]#[N:8])[c:38]2[c:31](-[c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[cH:30]1\", \"[CH2:1]([c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])[O:29][c:28]1[n:27][c:26]2[n:12]([CH:13]([c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:11][c:10]([C:9]#[N:8])[c:38]2[c:31](-[c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[cH:30]1\", \"reactants\":\"Br[CH2:1][CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7].[N:8]#[C:9][c:10]1[cH:11][n:12]([CH:13]([c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[c:20]2[cH:21][cH:22][cH:23][cH:24][cH:25]2)[c:26]2[n:27][c:28]([OH:29])[cH:30][c:31](-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[c:38]12\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1\", \"reactants\":\"Br[CH2:1][c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13].[NH2:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])[O:29][c:28]1[n:27][c:26]2[n:12]([CH:13]([c:14]3[cH:15][cH:16][cH:17][cH:18][cH:19]3)[c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:11][c:10]([C:9]#[N:8])[c:38]2[c:31](-[c:32]2[cH:33][cH:34][cH:35][cH:36][cH:37]2)[cH:30]1' are 'Br[CH2:1][CH2:2][N:3]([CH2:4][CH3:5])[CH2:6][CH3:7]' and '[N:8]#[C:9][c:10]1[cH:11][n:12]([CH:13]([c:14]2[cH:15][cH:16][cH:17][cH:18][cH:19]2)[c:20]2[cH:21][cH:22][cH:23][cH:24][cH:25]2)[c:26]2[n:27][c:28]([OH:29])[cH:30][c:31](-[c:32]3[cH:33][cH:34][cH:35][cH:36][cH:37]3)[c:38]12', for '[CH2:1]([c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13])[NH:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1' are 'Br[CH2:1][c:2]1[cH:3][c:4](=[O:5])[nH:6][c:7]2[c:8]1[cH:9][cH:10][cH:11][c:12]2[F:13]' and '[NH2:14][c:15]1[cH:16][cH:17][cH:18][c:19]([Cl:20])[cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis method for '[CH2:1]([C:2]([Cl:3])([Cl:4])[Cl:5])[O:28][C:26]([CH:9]([O:8][CH2:7][CH3:6])[CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)=[O:27]' and '[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:35]1[CH2:34][CH2:33][CH:32]([NH:31][C:29]([c:28]2[cH:27][cH:26][cH:25][c:24]([O:23][C:20]([C:18]([O:17][CH2:16][CH3:15])=[O:19])([CH3:21])[CH3:22])[cH:38]2)=[O:30])[CH2:37][CH2:36]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]([Cl:3])([Cl:4])[Cl:5])[O:28][C:26]([CH:9]([O:8][CH2:7][CH3:6])[CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)=[O:27]\", \"[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:35]1[CH2:34][CH2:33][CH:32]([NH:31][C:29]([c:28]2[cH:27][cH:26][cH:25][c:24]([O:23][C:20]([C:18]([O:17][CH2:16][CH3:15])=[O:19])([CH3:21])[CH3:22])[cH:38]2)=[O:30])[CH2:37][CH2:36]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]([Cl:3])([Cl:4])[Cl:5])[O:28][C:26]([CH:9]([O:8][CH2:7][CH3:6])[CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)=[O:27]\", \"reactants\":\"O[CH2:1][C:2]([Cl:3])([Cl:4])[Cl:5].[CH3:6][CH2:7][O:8][CH:9]([CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)[C:26](=[O:27])[OH:28]\"}, {\"smile\":\"[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:35]1[CH2:34][CH2:33][CH:32]([NH:31][C:29]([c:28]2[cH:27][cH:26][cH:25][c:24]([O:23][C:20]([C:18]([O:17][CH2:16][CH3:15])=[O:19])([CH3:21])[CH3:22])[cH:38]2)=[O:30])[CH2:37][CH2:36]1\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1.[CH3:15][CH2:16][O:17][C:18](=[O:19])[C:20]([CH3:21])([CH3:22])[O:23][c:24]1[cH:25][cH:26][cH:27][c:28]([C:29](=[O:30])[NH:31][CH:32]2[CH2:33][CH2:34][NH:35][CH2:36][CH2:37]2)[cH:38]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([C:2]([Cl:3])([Cl:4])[Cl:5])[O:28][C:26]([CH:9]([O:8][CH2:7][CH3:6])[CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)=[O:27]' are 'O[CH2:1][C:2]([Cl:3])([Cl:4])[Cl:5]' and '[CH3:6][CH2:7][O:8][CH:9]([CH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]([CH3:16])([CH3:17])[O:18][SiH2:19][C:20]([CH3:21])([CH3:22])[CH3:23])[cH:24][cH:25]1)[C:26](=[O:27])[OH:28]', for '[CH2:1]([c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1)[N:35]1[CH2:34][CH2:33][CH:32]([NH:31][C:29]([c:28]2[cH:27][cH:26][cH:25][c:24]([O:23][C:20]([C:18]([O:17][CH2:16][CH3:15])=[O:19])([CH3:21])[CH3:22])[cH:38]2)=[O:30])[CH2:37][CH2:36]1' are 'O=[CH:1][c:2]1[cH:3][c:4]([O:5][CH2:6][CH3:7])[c:8]([F:9])[c:10]([O:11][CH2:12][CH3:13])[cH:14]1' and '[CH3:15][CH2:16][O:17][C:18](=[O:19])[C:20]([CH3:21])([CH3:22])[O:23][c:24]1[cH:25][cH:26][cH:27][c:28]([C:29](=[O:30])[NH:31][CH:32]2[CH2:33][CH2:34][NH:35][CH2:36][CH2:37]2)[cH:38]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH:9][C:22](=[O:21])[NH:23][C:24](=[O:25])[c:26]2[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]2[F:33])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20]' and '[c:1]1(-[c:14]2[cH:15][cH:16][c:17](-[c:18]3[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]3)[cH:29][c:30]2[F:31])[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]1[Cl:13]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH:9][C:22](=[O:21])[NH:23][C:24](=[O:25])[c:26]2[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]2[F:33])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20]\", \"[c:1]1(-[c:14]2[cH:15][cH:16][c:17](-[c:18]3[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]3)[cH:29][c:30]2[F:31])[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]1[Cl:13]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH:9][C:22](=[O:21])[NH:23][C:24](=[O:25])[c:26]2[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]2[F:33])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20]\", \"reactants\":\"[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH2:9])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20].[O:21]=[C:22]=[N:23][C:24](=[O:25])[c:26]1[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]1[F:33]\"}, {\"smile\":\"[c:1]1(-[c:14]2[cH:15][cH:16][c:17](-[c:18]3[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]3)[cH:29][c:30]2[F:31])[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]1[Cl:13]\", \"reactants\":\"CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]2[Cl:13])OC1(C)C.O=S(=O)(O[c:14]1[cH:15][cH:16][c:17](-[c:18]2[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]2)[cH:29][c:30]1[F:31])C(F)(F)F\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH:9][C:22](=[O:21])[NH:23][C:24](=[O:25])[c:26]2[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]2[F:33])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20]' are '[CH3:1][CH:2]([O:3][c:4]1[c:5]([Cl:6])[cH:7][c:8]([NH2:9])[c:10]([F:11])[c:12]1[Cl:13])[C:14]([F:15])([F:16])[C:17]([F:18])([F:19])[F:20]' and '[O:21]=[C:22]=[N:23][C:24](=[O:25])[c:26]1[c:27]([F:28])[cH:29][cH:30][cH:31][c:32]1[F:33]', for '[c:1]1(-[c:14]2[cH:15][cH:16][c:17](-[c:18]3[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]3)[cH:29][c:30]2[F:31])[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]1[Cl:13]' are 'CC1(C)OB([c:1]2[cH:2][cH:3][c:4]([CH:5]([C:6]([O:7][CH3:8])=[O:9])[CH3:10])[cH:11][c:12]2[Cl:13])OC1(C)C' and 'O=S(=O)(O[c:14]1[cH:15][cH:16][c:17](-[c:18]2[c:19]([CH3:20])[n:21][c:22]([CH3:23])[c:24]([C:25]([NH2:26])=[O:27])[n:28]2)[cH:29][c:30]1[F:31])C(F)(F)F'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the synthesis method of '[c:1]1([O:34][CH3:33])[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1' and '[c:1]1([N:30]2[S:24](=[O:23])(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:34][CH3:33])[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1\", \"[c:1]1([N:30]2[S:24](=[O:23])(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:34][CH3:33])[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1\", \"reactants\":\"Br[c:1]1[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1.[CH3:33][OH:34]\"}, {\"smile\":\"[c:1]1([N:30]2[S:24](=[O:23])(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1.[O:23]=[S:24]1(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29][NH:30]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([O:34][CH3:33])[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1' are 'Br[c:1]1[n:2][c:3]2[n:4][c:5](-[c:6]3[cH:7][c:8]([O:9][c:10]4[cH:11][cH:12][c:13]([S:14]([CH3:15])(=[O:16])=[O:17])[cH:18][cH:19]4)[cH:20][c:21]([O:22][C@H:23]([CH2:24][O:25][CH3:26])[CH3:27])[cH:28]3)[nH:29][c:30]2[n:31][cH:32]1' and '[CH3:33][OH:34]', for '[c:1]1([N:30]2[S:24](=[O:23])(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29]2)[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1' are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([N:6]2[CH2:7][CH2:8][N:9]([c:10]3[n:11][cH:12][c:13]([CH3:14])[cH:15][c:16]3[CH3:17])[CH2:18][CH2:19]2)=[O:20])[cH:21][n:22]1' and '[O:23]=[S:24]1(=[O:25])[CH2:26][CH2:27][CH2:28][CH2:29][NH:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[CH:1]([CH2:2][CH3:3])([CH2:4][CH3:5])[n:21]1[c:15]2[cH:14][cH:13][cH:12][c:11](-[c:10]3[cH:9][cH:8][c:7]([CH3:6])[cH:24][c:22]3[CH3:23])[c:16]2[c:17](=[O:18])[n:19]1[CH3:20]' and '[C:1]([CH3:2])(=[O:3])[NH:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1]([CH2:2][CH3:3])([CH2:4][CH3:5])[n:21]1[c:15]2[cH:14][cH:13][cH:12][c:11](-[c:10]3[cH:9][cH:8][c:7]([CH3:6])[cH:24][c:22]3[CH3:23])[c:16]2[c:17](=[O:18])[n:19]1[CH3:20]\", \"[C:1]([CH3:2])(=[O:3])[NH:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH:1]([CH2:2][CH3:3])([CH2:4][CH3:5])[n:21]1[c:15]2[cH:14][cH:13][cH:12][c:11](-[c:10]3[cH:9][cH:8][c:7]([CH3:6])[cH:24][c:22]3[CH3:23])[c:16]2[c:17](=[O:18])[n:19]1[CH3:20]\", \"reactants\":\"Br[CH:1]([CH2:2][CH3:3])[CH2:4][CH3:5].[CH3:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][c:15]3[c:16]2[c:17](=[O:18])[n:19]([CH3:20])[nH:21]3)[c:22]([CH3:23])[cH:24]1\"}, {\"smile\":\"[C:1]([CH3:2])(=[O:3])[NH:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1\", \"reactants\":\"Cl[C:1]([CH3:2])=[O:3].[NH2:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH:1]([CH2:2][CH3:3])([CH2:4][CH3:5])[n:21]1[c:15]2[cH:14][cH:13][cH:12][c:11](-[c:10]3[cH:9][cH:8][c:7]([CH3:6])[cH:24][c:22]3[CH3:23])[c:16]2[c:17](=[O:18])[n:19]1[CH3:20]' are 'Br[CH:1]([CH2:2][CH3:3])[CH2:4][CH3:5]' and '[CH3:6][c:7]1[cH:8][cH:9][c:10](-[c:11]2[cH:12][cH:13][cH:14][c:15]3[c:16]2[c:17](=[O:18])[n:19]([CH3:20])[nH:21]3)[c:22]([CH3:23])[cH:24]1', for '[C:1]([CH3:2])(=[O:3])[NH:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1' are 'Cl[C:1]([CH3:2])=[O:3]' and '[NH2:4][c:5]1[cH:6][cH:7][cH:8][c:9]([NH2:10])[n:11]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[c:1]1([C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28])[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1' and '[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:24]1[C:22](=[O:23])[N:21]([CH2:20][C:18]([N:17]([c:16]2[cH:15][cH:14][c:13]([O:12][CH3:11])[n:46][cH:45]2)[CH:42]([CH3:43])[CH3:44])=[O:19])[c:41]2[c:36]([cH:37][cH:38][cH:39][cH:40]2)-[n:35]2[c:25]1[n:26][n:27][c:28]2-[c:29]1[cH:30][cH:31][cH:32][cH:33][cH:34]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28])[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1\", \"[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:24]1[C:22](=[O:23])[N:21]([CH2:20][C:18]([N:17]([c:16]2[cH:15][cH:14][c:13]([O:12][CH3:11])[n:46][cH:45]2)[CH:42]([CH3:43])[CH3:44])=[O:19])[c:41]2[c:36]([cH:37][cH:38][cH:39][cH:40]2)-[n:35]2[c:25]1[n:26][n:27][c:28]2-[c:29]1[cH:30][cH:31][cH:32][cH:33][cH:34]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28])[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1.CC(C)OB(OC(C)C)[C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28]\"}, {\"smile\":\"[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:24]1[C:22](=[O:23])[N:21]([CH2:20][C:18]([N:17]([c:16]2[cH:15][cH:14][c:13]([O:12][CH3:11])[n:46][cH:45]2)[CH:42]([CH3:43])[CH3:44])=[O:19])[c:41]2[c:36]([cH:37][cH:38][cH:39][cH:40]2)-[n:35]2[c:25]1[n:26][n:27][c:28]2-[c:29]1[cH:30][cH:31][cH:32][cH:33][cH:34]1\", \"reactants\":\"O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12.[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]([N:17]([C:18](=[O:19])[CH2:20][N:21]2[C:22](=[O:23])[CH2:24][c:25]3[n:26][n:27][c:28](-[c:29]4[cH:30][cH:31][cH:32][cH:33][cH:34]4)[n:35]3-[c:36]3[cH:37][cH:38][cH:39][cH:40][c:41]32)[CH:42]([CH3:43])[CH3:44])[cH:45][n:46]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28])[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1' are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][n:22]1' and 'CC(C)OB(OC(C)C)[C:23]#[C:24][C:25]([CH3:26])([CH3:27])[CH3:28]', for '[CH:1]([c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12)=[C:24]1[C:22](=[O:23])[N:21]([CH2:20][C:18]([N:17]([c:16]2[cH:15][cH:14][c:13]([O:12][CH3:11])[n:46][cH:45]2)[CH:42]([CH3:43])[CH3:44])=[O:19])[c:41]2[c:36]([cH:37][cH:38][cH:39][cH:40]2)-[n:35]2[c:25]1[n:26][n:27][c:28]2-[c:29]1[cH:30][cH:31][cH:32][cH:33][cH:34]1' are 'O=[CH:1][c:2]1[cH:3][nH:4][c:5]2[cH:6][cH:7][cH:8][cH:9][c:10]12' and '[CH3:11][O:12][c:13]1[cH:14][cH:15][c:16]([N:17]([C:18](=[O:19])[CH2:20][N:21]2[C:22](=[O:23])[CH2:24][c:25]3[n:26][n:27][c:28](-[c:29]4[cH:30][cH:31][cH:32][cH:33][cH:34]4)[n:35]3-[c:36]3[cH:37][cH:38][cH:39][cH:40][c:41]32)[CH:42]([CH3:43])[CH3:44])[cH:45][n:46]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis method for '[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' and '[c:1]1([N:31]2[CH2:30][CH2:29][C@H:28]([N:26]([CH3:25])[CH3:27])[CH2:32]2)[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1\", \"[c:1]1([N:31]2[CH2:30][CH2:29][C@H:28]([N:26]([CH3:25])[CH3:27])[CH2:32]2)[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1\", \"reactants\":\"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1.[CH:23]#[C:24][c:25]1[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]1\"}, {\"smile\":\"[c:1]1([N:31]2[CH2:30][CH2:29][C@H:28]([N:26]([CH3:25])[CH3:27])[CH2:32]2)[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12\", \"reactants\":\"F[c:1]1[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12.[CH3:25][N:26]([CH3:27])[C@H:28]1[CH2:29][CH2:30][NH:31][CH2:32]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([C:23]#[C:24][c:25]2[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]2)[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([NH:6][S:7]([c:8]2[c:9]([S:10]([NH2:11])(=[O:12])=[O:13])[cH:14][cH:15][cH:16][cH:17]2)(=[O:18])=[O:19])=[O:20])[cH:21][cH:22]1' and '[CH:23]#[C:24][c:25]1[cH:26][cH:27][c:28]([Cl:29])[cH:30][cH:31]1', for '[c:1]1([N:31]2[CH2:30][CH2:29][C@H:28]([N:26]([CH3:25])[CH3:27])[CH2:32]2)[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12' are 'F[c:1]1[cH:2][cH:3][cH:4][c:5]2[n:6][c:7]([CH2:8][N:9]3[CH2:10][CH2:11][CH2:12][C@H:13]4[CH2:14][CH2:15][c:16]5[cH:17][cH:18][cH:19][n:20][c:21]5[C@@H:22]34)[cH:23][n:24]12' and '[CH3:25][N:26]([CH3:27])[C@H:28]1[CH2:29][CH2:30][NH:31][CH2:32]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])(=[O:18])[N:20]([CH3:19])[CH2:21][CH2:22][NH:23][CH3:24]' and '[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]2)[c:2]([C:3]([O:4][CH2:5][CH3:6])=[O:7])[s:18][c:16]([CH3:15])[n:17]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])(=[O:18])[N:20]([CH3:19])[CH2:21][CH2:22][NH:23][CH3:24]\", \"[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]2)[c:2]([C:3]([O:4][CH2:5][CH3:6])=[O:7])[s:18][c:16]([CH3:15])[n:17]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])(=[O:18])[N:20]([CH3:19])[CH2:21][CH2:22][NH:23][CH3:24]\", \"reactants\":\"Cl[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])=[O:18].[CH3:19][NH:20][CH2:21][CH2:22][NH:23][CH3:24]\"}, {\"smile\":\"[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]2)[c:2]([C:3]([O:4][CH2:5][CH3:6])=[O:7])[s:18][c:16]([CH3:15])[n:17]1\", \"reactants\":\"O=[C:1]([CH:2](Br)[C:3]([O:4][CH2:5][CH3:6])=[O:7])[c:8]1[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]1.[CH3:15][C:16]([NH2:17])=[S:18]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])(=[O:18])[N:20]([CH3:19])[CH2:21][CH2:22][NH:23][CH3:24]' are 'Cl[S:1]([c:2]1[cH:3][cH:4][c:5](-[c:6]2[c:7]([CH3:8])[n:9][c:10]([NH:11][C:12]([CH3:13])=[O:14])[s:15]2)[s:16]1)(=[O:17])=[O:18]' and '[CH3:19][NH:20][CH2:21][CH2:22][NH:23][CH3:24]', for '[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]2)[c:2]([C:3]([O:4][CH2:5][CH3:6])=[O:7])[s:18][c:16]([CH3:15])[n:17]1' are 'O=[C:1]([CH:2](Br)[C:3]([O:4][CH2:5][CH3:6])=[O:7])[c:8]1[cH:9][cH:10][c:11]([CH3:12])[cH:13][cH:14]1' and '[CH3:15][C:16]([NH2:17])=[S:18]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the synthesis strategy for '[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1' and '[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH:8][C:11](=[O:10])[NH:12][S:13](=[O:14])(=[O:15])[c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2-[c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:9]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1\", \"[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH:8][C:11](=[O:10])[NH:12][S:13](=[O:14])(=[O:15])[c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2-[c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:9]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1\", \"reactants\":\"Cl[c:1]1[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1.[CH3:23][NH:24][C:25](=[O:26])[NH:27][CH2:28][C:29]#[C:30][c:31]1[cH:32][c:33]([Cl:34])[c:35]([NH2:36])[c:37]2[c:38]1[O:39][CH2:40][O:41]2\"}, {\"smile\":\"[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH:8][C:11](=[O:10])[NH:12][S:13](=[O:14])(=[O:15])[c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2-[c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:9]1\", \"reactants\":\"[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH2:8])[n:9]1.[O:10]=[C:11]=[N:12][S:13](=[O:14])(=[O:15])[c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1-[c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([NH:36][c:35]2[c:33]([Cl:34])[cH:32][c:31]([C:30]#[C:29][CH2:28][NH:27][C:25]([NH:24][CH3:23])=[O:26])[c:38]3[c:37]2[O:41][CH2:40][O:39]3)[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1' are 'Cl[c:1]1[c:2]2[cH:3][c:4]([O:5][CH3:6])[c:7]([O:8][CH2:9][CH2:10][CH2:11][N:12]3[CH2:13][CH2:14][O:15][CH2:16][CH2:17]3)[cH:18][c:19]2[n:20][cH:21][n:22]1' and '[CH3:23][NH:24][C:25](=[O:26])[NH:27][CH2:28][C:29]#[C:30][c:31]1[cH:32][c:33]([Cl:34])[c:35]([NH2:36])[c:37]2[c:38]1[O:39][CH2:40][O:41]2', for '[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH:8][C:11](=[O:10])[NH:12][S:13](=[O:14])(=[O:15])[c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2-[c:22]2[cH:23][cH:24][cH:25][cH:26][cH:27]2)[n:9]1' are '[CH3:1][c:2]1[cH:3][c:4]([CH3:5])[n:6][c:7]([NH2:8])[n:9]1' and '[O:10]=[C:11]=[N:12][S:13](=[O:14])(=[O:15])[c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1-[c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis route for '[CH2:1]([CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18])[N:25]([CH2:24][CH2:23][CH2:22][NH:21][C:20](=[O:19])[C:36]([F:37])([F:38])[F:39])[CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35]' and '[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18])[N:25]([CH2:24][CH2:23][CH2:22][NH:21][C:20](=[O:19])[C:36]([F:37])([F:38])[F:39])[CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35]\", \"[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18])[N:25]([CH2:24][CH2:23][CH2:22][NH:21][C:20](=[O:19])[C:36]([F:37])([F:38])[F:39])[CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35]\", \"reactants\":\"Br[CH2:1][CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18].[O:19]=[C:20]([NH:21][CH2:22][CH2:23][CH2:24][NH:25][CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35])[C:36]([F:37])([F:38])[F:39]\"}, {\"smile\":\"[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\", \"reactants\":\"O[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)=[O:9].[NH2:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18])[N:25]([CH2:24][CH2:23][CH2:22][NH:21][C:20](=[O:19])[C:36]([F:37])([F:38])[F:39])[CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35]' are 'Br[CH2:1][CH2:2][CH2:3][N:4]([CH2:5][C:6]([O:7][C:8]([CH3:9])([CH3:10])[CH3:11])=[O:12])[C:13](=[O:14])[C:15]([F:16])([F:17])[F:18]' and '[O:19]=[C:20]([NH:21][CH2:22][CH2:23][CH2:24][NH:25][CH2:26][CH2:27][CH2:28][NH:29][C:30](=[O:31])[C:32]([F:33])([F:34])[F:35])[C:36]([F:37])([F:38])[F:39]', for '[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)(=[O:9])[NH:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1' are 'O[C:1]([c:2]1[c:3]([NH2:4])[n:5][cH:6][cH:7][cH:8]1)=[O:9]' and '[NH2:10][CH2:11][c:12]1[cH:13][cH:14][c:15]([O:16][c:17]2[cH:18][cH:19][cH:20][c:21]([F:22])[cH:23]2)[cH:24][cH:25]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the synthesis path of '[c:1]1([NH:10][c:11]2[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]2[C:18]([F:19])([F:20])[F:21])[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1' and '[c:1]1(-[c:7]2[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]2)[cH:2][cH:3][cH:4][cH:5][n:6]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH:10][c:11]2[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]2[C:18]([F:19])([F:20])[F:21])[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1\", \"[c:1]1(-[c:7]2[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]2)[cH:2][cH:3][cH:4][cH:5][n:6]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([NH:10][c:11]2[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]2[C:18]([F:19])([F:20])[F:21])[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1\", \"reactants\":\"Br[c:1]1[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1.[NH2:10][c:11]1[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]1[C:18]([F:19])([F:20])[F:21]\"}, {\"smile\":\"[c:1]1(-[c:7]2[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]2)[cH:2][cH:3][cH:4][cH:5][n:6]1\", \"reactants\":\"CCCC[Sn](CCCC)(CCCC)[c:1]1[cH:2][cH:3][cH:4][cH:5][n:6]1.Cl[c:7]1[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([NH:10][c:11]2[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]2[C:18]([F:19])([F:20])[F:21])[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1' are 'Br[c:1]1[c:2]([C:3]([NH:4][CH3:5])=[O:6])[n:7][cH:8][s:9]1' and '[NH2:10][c:11]1[cH:12][c:13]([Cl:14])[n:15][cH:16][c:17]1[C:18]([F:19])([F:20])[F:21]', for '[c:1]1(-[c:7]2[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]2)[cH:2][cH:3][cH:4][cH:5][n:6]1' are 'CCCC[Sn](CCCC)(CCCC)[c:1]1[cH:2][cH:3][cH:4][cH:5][n:6]1' and 'Cl[c:7]1[cH:8][cH:9][c:10]([O:11][CH3:12])[n:13][n:14]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how '[CH2:1]1[CH2:2][CH2:3][N:4]([C:6](=[O:7])[c:8]2[cH:9][c:10]3[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][CH2:20][CH2:21]4)[cH:22][n:23][c:24]3[nH:25]2)[CH2:5]1' and '[CH:1]([CH3:2])([CH3:3])[O:6][C:5](=[O:4])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]1[CH2:2][CH2:3][N:4]([C:6](=[O:7])[c:8]2[cH:9][c:10]3[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][CH2:20][CH2:21]4)[cH:22][n:23][c:24]3[nH:25]2)[CH2:5]1\", \"[CH:1]([CH3:2])([CH3:3])[O:6][C:5](=[O:4])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]1[CH2:2][CH2:3][N:4]([C:6](=[O:7])[c:8]2[cH:9][c:10]3[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][CH2:20][CH2:21]4)[cH:22][n:23][c:24]3[nH:25]2)[CH2:5]1\", \"reactants\":\"[CH2:1]1[CH2:2][CH2:3][NH:4][CH2:5]1.[O-][C:6](=[O:7])[c:8]1[cH:9][c:10]2[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]3[CH2:18][CH2:19][CH2:20][CH2:21]3)[cH:22][n:23][c:24]2[nH:25]1\"}, {\"smile\":\"[CH:1]([CH3:2])([CH3:3])[O:6][C:5](=[O:4])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1\", \"reactants\":\"O[CH:1]([CH3:2])[CH3:3].[O:4]=[C:5]([OH:6])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]1[CH2:2][CH2:3][N:4]([C:6](=[O:7])[c:8]2[cH:9][c:10]3[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][CH2:20][CH2:21]4)[cH:22][n:23][c:24]3[nH:25]2)[CH2:5]1' are '[CH2:1]1[CH2:2][CH2:3][NH:4][CH2:5]1' and '[O-][C:6](=[O:7])[c:8]1[cH:9][c:10]2[cH:11][c:12]([O:13][CH2:14][CH2:15][CH2:16][N:17]3[CH2:18][CH2:19][CH2:20][CH2:21]3)[cH:22][n:23][c:24]2[nH:25]1', for '[CH:1]([CH3:2])([CH3:3])[O:6][C:5](=[O:4])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1' are 'O[CH:1]([CH3:2])[CH3:3]' and '[O:4]=[C:5]([OH:6])[c:7]1[cH:8][cH:9][c:10]([I:11])[cH:12][cH:13]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis procedure for '[CH2:1]([C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12])[O:30][C:28]([C@H:16]([CH:14]([CH3:13])[CH3:15])[NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)=[O:29]' and '[CH2:1]([CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22])[NH:24][CH3:23]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12])[O:30][C:28]([C@H:16]([CH:14]([CH3:13])[CH3:15])[NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)=[O:29]\", \"[CH2:1]([CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22])[NH:24][CH3:23]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12])[O:30][C:28]([C@H:16]([CH:14]([CH3:13])[CH3:15])[NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)=[O:29]\", \"reactants\":\"O[CH2:1][C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12].[CH3:13][CH:14]([CH3:15])[C@H:16]([NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)[C:28](=[O:29])[OH:30]\"}, {\"smile\":\"[CH2:1]([CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22])[NH:24][CH3:23]\", \"reactants\":\"Cl[CH2:1][CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22].[CH3:23][NH2:24]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12])[O:30][C:28]([C@H:16]([CH:14]([CH3:13])[CH3:15])[NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)=[O:29]' are 'O[CH2:1][C:2]([CH3:3])([CH3:4])[NH:5][C:6](=[O:7])[O:8][C:9]([CH3:10])([CH3:11])[CH3:12]' and '[CH3:13][CH:14]([CH3:15])[C@H:16]([NH:17][C:18](=[O:19])[O:20][CH2:21][c:22]1[cH:23][cH:24][cH:25][cH:26][cH:27]1)[C:28](=[O:29])[OH:30]', for '[CH2:1]([CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22])[NH:24][CH3:23]' are 'Cl[CH2:1][CH2:2][CH2:3][N:4]1[CH2:5][c:6]2[c:7]([cH:8][cH:9][cH:10][cH:11]2)[N:12]([c:13]2[cH:14][cH:15][c:16]([CH3:17])[cH:18][cH:19]2)[S:20]1(=[O:21])=[O:22]' and '[CH3:23][NH2:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest the synthesis pathway for '[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]' and '[C:1]([CH3:2])(=[O:3])[O:7][CH2:6][CH:5]([CH3:4])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]\", \"[C:1]([CH3:2])(=[O:3])[O:7][CH2:6][CH:5]([CH3:4])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]\", \"reactants\":\"O[C@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])[CH3:9].[CH3:10][O:11][C:12](=[O:13])/[CH:14]=[CH:15]/[C:16](=[O:17])[OH:18]\"}, {\"smile\":\"[C:1]([CH3:2])(=[O:3])[O:7][CH2:6][CH:5]([CH3:4])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]\", \"reactants\":\"Cl[C:1]([CH3:2])=[O:3].[CH3:4][CH:5]([CH2:6][OH:7])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[C@@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])([CH3:9])[O:18][C:16](/[CH:15]=[CH:14]/[C:12]([O:11][CH3:10])=[O:13])=[O:17]' are 'O[C@H:1]([C:2]([N:3]([CH2:4][CH3:5])[CH2:6][CH3:7])=[O:8])[CH3:9]' and '[CH3:10][O:11][C:12](=[O:13])/[CH:14]=[CH:15]/[C:16](=[O:17])[OH:18]', for '[C:1]([CH3:2])(=[O:3])[O:7][CH2:6][CH:5]([CH3:4])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]' are 'Cl[C:1]([CH3:2])=[O:3]' and '[CH3:4][CH:5]([CH2:6][OH:7])[c:8]1[cH:9][cH:10][c:11]2[c:12]([cH:13]1)[cH:14][cH:15][c:16]1[cH:17][cH:18][cH:19][cH:20][c:21]1[c:22]2=[O:23]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Suggest a synthesis path for '[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1' and '[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10])[O:20][c:19]1[cH:18][cH:17][c:16]([C:13]([CH2:12][CH3:11])([CH2:14][CH3:15])[c:24]2[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]3([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]3)[c:36]([CH3:37])[cH:38]2)[cH:23][c:21]1[CH3:22]'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1\", \"[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10])[O:20][c:19]1[cH:18][cH:17][c:16]([C:13]([CH2:12][CH3:11])([CH2:14][CH3:15])[c:24]2[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]3([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]3)[c:36]([CH3:37])[cH:38]2)[cH:23][c:21]1[CH3:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1\", \"reactants\":\"Cl[c:1]1[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1.[CH3:11][O:12][C:13](=[O:14])[CH2:15][OH:16]\"}, {\"smile\":\"[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10])[O:20][c:19]1[cH:18][cH:17][c:16]([C:13]([CH2:12][CH3:11])([CH2:14][CH3:15])[c:24]2[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]3([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]3)[c:36]([CH3:37])[cH:38]2)[cH:23][c:21]1[CH3:22]\", \"reactants\":\"Br[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10].[CH3:11][CH2:12][C:13]([CH2:14][CH3:15])([c:16]1[cH:17][cH:18][c:19]([OH:20])[c:21]([CH3:22])[cH:23]1)[c:24]1[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]2([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]2)[c:36]([CH3:37])[cH:38]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([O:16][CH2:15][C:13]([O:12][CH3:11])=[O:14])[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1' are 'Cl[c:1]1[c:2]([I:3])[c:4]([NH2:5])[n:6][c:7]([S:8][CH3:9])[n:10]1' and '[CH3:11][O:12][C:13](=[O:14])[CH2:15][OH:16]', for '[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10])[O:20][c:19]1[cH:18][cH:17][c:16]([C:13]([CH2:12][CH3:11])([CH2:14][CH3:15])[c:24]2[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]3([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]3)[c:36]([CH3:37])[cH:38]2)[cH:23][c:21]1[CH3:22]' are 'Br[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][C:6]([O:7][CH2:8][CH3:9])=[O:10]' and '[CH3:11][CH2:12][C:13]([CH2:14][CH3:15])([c:16]1[cH:17][cH:18][c:19]([OH:20])[c:21]([CH3:22])[cH:23]1)[c:24]1[cH:25][cH:26][c:27](/[CH:28]=[CH:29]/[C:30]2([OH:31])[CH2:32][CH2:33][CH2:34][CH2:35]2)[c:36]([CH3:37])[cH:38]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how '[c:1]1([C:2]([O:3][CH2:4][CH3:5])=[O:6])[cH:7][c:8](-[c:9]2[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]2)[n:22](-[c:21]2[cH:20][cH:19][c:18]([O:17][CH3:16])[n:25][cH:24]2)[n:23]1' and '[C:1]([C:2]#[CH:3])(=[O:4])[O:10][CH:9]1[CH2:8][CH2:7][N:6]([CH3:5])[C:20](=[O:21])[N:11]1[c:12]1[n:13][n:14][c:15]([CH:16]2[CH2:17][CH2:18]2)[s:19]1' synthesizes. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([C:2]([O:3][CH2:4][CH3:5])=[O:6])[cH:7][c:8](-[c:9]2[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]2)[n:22](-[c:21]2[cH:20][cH:19][c:18]([O:17][CH3:16])[n:25][cH:24]2)[n:23]1\", \"[C:1]([C:2]#[CH:3])(=[O:4])[O:10][CH:9]1[CH2:8][CH2:7][N:6]([CH3:5])[C:20](=[O:21])[N:11]1[c:12]1[n:13][n:14][c:15]([CH:16]2[CH2:17][CH2:18]2)[s:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[c:1]1([C:2]([O:3][CH2:4][CH3:5])=[O:6])[cH:7][c:8](-[c:9]2[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]2)[n:22](-[c:21]2[cH:20][cH:19][c:18]([O:17][CH3:16])[n:25][cH:24]2)[n:23]1\", \"reactants\":\"O=[C:1]([C:2]([O:3][CH2:4][CH3:5])=[O:6])[CH2:7][C:8](=O)[c:9]1[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]1.[CH3:16][O:17][c:18]1[cH:19][cH:20][c:21]([NH:22][NH2:23])[cH:24][n:25]1\"}, {\"smile\":\"[C:1]([C:2]#[CH:3])(=[O:4])[O:10][CH:9]1[CH2:8][CH2:7][N:6]([CH3:5])[C:20](=[O:21])[N:11]1[c:12]1[n:13][n:14][c:15]([CH:16]2[CH2:17][CH2:18]2)[s:19]1\", \"reactants\":\"Cl[C:1]([C:2]#[CH:3])=[O:4].[CH3:5][N:6]1[CH2:7][CH2:8][CH:9]([OH:10])[N:11]([c:12]2[n:13][n:14][c:15]([CH:16]3[CH2:17][CH2:18]3)[s:19]2)[C:20]1=[O:21]\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[c:1]1([C:2]([O:3][CH2:4][CH3:5])=[O:6])[cH:7][c:8](-[c:9]2[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]2)[n:22](-[c:21]2[cH:20][cH:19][c:18]([O:17][CH3:16])[n:25][cH:24]2)[n:23]1' are 'O=[C:1]([C:2]([O:3][CH2:4][CH3:5])=[O:6])[CH2:7][C:8](=O)[c:9]1[cH:10][cH:11][cH:12][c:13]([CH3:14])[cH:15]1' and '[CH3:16][O:17][c:18]1[cH:19][cH:20][c:21]([NH:22][NH2:23])[cH:24][n:25]1', for '[C:1]([C:2]#[CH:3])(=[O:4])[O:10][CH:9]1[CH2:8][CH2:7][N:6]([CH3:5])[C:20](=[O:21])[N:11]1[c:12]1[n:13][n:14][c:15]([CH:16]2[CH2:17][CH2:18]2)[s:19]1' are 'Cl[C:1]([C:2]#[CH:3])=[O:4]' and '[CH3:5][N:6]1[CH2:7][CH2:8][CH:9]([OH:10])[N:11]([c:12]2[n:13][n:14][c:15]([CH:16]3[CH2:17][CH2:18]3)[s:19]2)[C:20]1=[O:21]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the synthesis method of '[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1' and '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:23][CH2:22][C@H:21]1[CH2:20][CH2:19][C@H:18]([NH:17][c:16]2[c:8]([N+:6](=[O:5])[O-:7])[cH:9][n:10][c:11]3[cH:12][cH:13][s:14][c:15]32)[CH2:25][CH2:24]1'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1\", \"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:23][CH2:22][C@H:21]1[CH2:20][CH2:19][C@H:18]([NH:17][c:16]2[c:8]([N+:6](=[O:5])[O-:7])[cH:9][n:10][c:11]3[cH:12][cH:13][s:14][c:15]32)[CH2:25][CH2:24]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the possible precursors of drug molecules that could react and transform into the given molecules\", \"result\":[{\"smile\":\"[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1\", \"reactants\":\"Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1.[SH:20][c:21]1[n:22][cH:23][nH:24][n:25]1\"}, {\"smile\":\"[S:1]([CH3:2])(=[O:3])(=[O:4])[O:23][CH2:22][C@H:21]1[CH2:20][CH2:19][C@H:18]([NH:17][c:16]2[c:8]([N+:6](=[O:5])[O-:7])[cH:9][n:10][c:11]3[cH:12][cH:13][s:14][c:15]32)[CH2:25][CH2:24]1\", \"reactants\":\"Cl[S:1]([CH3:2])(=[O:3])=[O:4].[O:5]=[N+:6]([O-:7])[c:8]1[cH:9][n:10][c:11]2[cH:12][cH:13][s:14][c:15]2[c:16]1[NH:17][C@H:18]1[CH2:19][CH2:20][C@H:21]([CH2:22][OH:23])[CH2:24][CH2:25]1\"}]} MemoryPool(arguments=dict_keys(['user_smiles','drug_synthetic_pathway_result']))"
            },
            {
                "from": "gpt",
                "value": "The possible precursor reactants for '[CH2:1]([C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1)[S:20][c:21]1[n:22][cH:23][nH:24][n:25]1' are 'Br[CH2:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][c:10]2[c:11]([F:12])[cH:13][cH:14][cH:15][c:16]2[F:17])[cH:18][cH:19]1' and '[SH:20][c:21]1[n:22][cH:23][nH:24][n:25]1', for '[S:1]([CH3:2])(=[O:3])(=[O:4])[O:23][CH2:22][C@H:21]1[CH2:20][CH2:19][C@H:18]([NH:17][c:16]2[c:8]([N+:6](=[O:5])[O-:7])[cH:9][n:10][c:11]3[cH:12][cH:13][s:14][c:15]32)[CH2:25][CH2:24]1' are 'Cl[S:1]([CH3:2])(=[O:3])=[O:4]' and '[O:5]=[N+:6]([O-:7])[c:8]1[cH:9][n:10][c:11]2[cH:12][cH:13][s:14][c:15]2[c:16]1[NH:17][C@H:18]1[CH2:19][CH2:20][C@H:21]([CH2:22][OH:23])[CH2:24][CH2:25]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drugs and the target protein (target_seq1) will interact. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"(target_seq1)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"(target_seq1)\", \"result\": [{\"smile\": \"(drug_smile1)\", \"value\": (value1)}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug (drug_smile1) is less likely to interact with the protein (target_seq1)."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict whether the drug (drug_smile1), (drug_smile2) and the target protein will interact. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"(drug_smile1)\", \"(drug_smile2)\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"(target_seq1)\", \"result\": [{\"smile\": \"(drug_smile1)\", \"value\": (value1)}, {\"smile\": \"(drug_smile2)\", \"value\": (value2)}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug (drug_smile1) is less likely to interact with the protein (target_seq1)."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug will interact with the target protein 'MAPVYEGMASHVQVFSPHTLQSSAFCSVKKLKIEPSSNWDMTGYGSHSKVYSQSKNIPLSQPATTTVSTSLPVPNPSLPYEQTIVFPGSTGHIVVTSASSTSVTGQVLGGPHNLMRRSTVSLLDTYQKCGLKRKSEEIENTSSVQIIEEHPPMIQNNASGATVATATTSTATSKNSGSNSEGDYQLVQHEVLCSMTNTYEVLEFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTESADDYNFVRAYECFQHKNHTCLVFEMLEQNLYDFLKQNKFSPLPLKYIRPVLQQVATALMKLKSLGLIHADLKPENIMLVDPSRQPYRVKVIDFGSASHVSKAVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGASEYDQIRYISQTQGLPAEYLLSAGTKTTRFFNRDTDSPYPLWRLKTPDDHEAETGIKSKEARKYIFNCLDDMAQVNMTTDLEGSDMLVEKADRREFIDLLKKMLTIDADKRITPIETLNHPFVTMTHLLDFPHSTHVKSCFQNMEICKRRVNMYDTVNQSKTPFITHVAPSTSTNLTMTFNNQLTTVHNQAPSSTSATISLANPEVSILNYPSTLYQPSAASMAAVAQRSMPLQTGTAQICARPDPFQQALIVCPPGFQGLQASPSKHAGYSVRMENAVPIVTQAPGAQPLQIQPGLLAQQAWPSGTQQILLPPAWQQLTGVATHTSVQHATVIPETMAGTQQLADWRNTHAHGSHYNPIMQQPALLTGHVTLPAAQPLNVGVAHVMRQQPTSTTSSRKSKQHQSSVRNVSTCEVSSSQAISSPQRSKRVKENTPPRCAMVHSSPACSTSVTCGWGDVASSTTRERQRQTIVIPDTPSPTVSVITISSDTDEEEEQKHAPTSTVSKQRKNVISCVTVHDSPYSDSSSNTSPYSVQQRAGHNNANAFDTKGSLENHCTGNPRTIIVPPLKTQASEVLVECDSLVPVNTSHHSSSYKSKSSSNVTSTSGHSSGSSSGAITYRQQRPGPHFQQQQPLNLSQAQQHITTDRTGSHRRQQAYITPTMAQAPYSFPHNSPSHGTVHPHLAAAAAAAHLPTQPHLYTYTAPAALGSTGTVAHLVASQGSARHTVQHTAYPASIVHQVPVSMGPRVLPSPTIHPSQYPAQFAHQTYISASPASTVYTGYPLSPAKVNQYPYI'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPVYEGMASHVQVFSPHTLQSSAFCSVKKLKIEPSSNWDMTGYGSHSKVYSQSKNIPLSQPATTTVSTSLPVPNPSLPYEQTIVFPGSTGHIVVTSASSTSVTGQVLGGPHNLMRRSTVSLLDTYQKCGLKRKSEEIENTSSVQIIEEHPPMIQNNASGATVATATTSTATSKNSGSNSEGDYQLVQHEVLCSMTNTYEVLEFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTESADDYNFVRAYECFQHKNHTCLVFEMLEQNLYDFLKQNKFSPLPLKYIRPVLQQVATALMKLKSLGLIHADLKPENIMLVDPSRQPYRVKVIDFGSASHVSKAVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGASEYDQIRYISQTQGLPAEYLLSAGTKTTRFFNRDTDSPYPLWRLKTPDDHEAETGIKSKEARKYIFNCLDDMAQVNMTTDLEGSDMLVEKADRREFIDLLKKMLTIDADKRITPIETLNHPFVTMTHLLDFPHSTHVKSCFQNMEICKRRVNMYDTVNQSKTPFITHVAPSTSTNLTMTFNNQLTTVHNQAPSSTSATISLANPEVSILNYPSTLYQPSAASMAAVAQRSMPLQTGTAQICARPDPFQQALIVCPPGFQGLQASPSKHAGYSVRMENAVPIVTQAPGAQPLQIQPGLLAQQAWPSGTQQILLPPAWQQLTGVATHTSVQHATVIPETMAGTQQLADWRNTHAHGSHYNPIMQQPALLTGHVTLPAAQPLNVGVAHVMRQQPTSTTSSRKSKQHQSSVRNVSTCEVSSSQAISSPQRSKRVKENTPPRCAMVHSSPACSTSVTCGWGDVASSTTRERQRQTIVIPDTPSPTVSVITISSDTDEEEEQKHAPTSTVSKQRKNVISCVTVHDSPYSDSSSNTSPYSVQQRAGHNNANAFDTKGSLENHCTGNPRTIIVPPLKTQASEVLVECDSLVPVNTSHHSSSYKSKSSSNVTSTSGHSSGSSSGAITYRQQRPGPHFQQQQPLNLSQAQQHITTDRTGSHRRQQAYITPTMAQAPYSFPHNSPSHGTVHPHLAAAAAAAHLPTQPHLYTYTAPAALGSTGTVAHLVASQGSARHTVQHTAYPASIVHQVPVSMGPRVLPSPTIHPSQYPAQFAHQTYISASPASTVYTGYPLSPAKVNQYPYI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPVYEGMASHVQVFSPHTLQSSAFCSVKKLKIEPSSNWDMTGYGSHSKVYSQSKNIPLSQPATTTVSTSLPVPNPSLPYEQTIVFPGSTGHIVVTSASSTSVTGQVLGGPHNLMRRSTVSLLDTYQKCGLKRKSEEIENTSSVQIIEEHPPMIQNNASGATVATATTSTATSKNSGSNSEGDYQLVQHEVLCSMTNTYEVLEFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTESADDYNFVRAYECFQHKNHTCLVFEMLEQNLYDFLKQNKFSPLPLKYIRPVLQQVATALMKLKSLGLIHADLKPENIMLVDPSRQPYRVKVIDFGSASHVSKAVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGASEYDQIRYISQTQGLPAEYLLSAGTKTTRFFNRDTDSPYPLWRLKTPDDHEAETGIKSKEARKYIFNCLDDMAQVNMTTDLEGSDMLVEKADRREFIDLLKKMLTIDADKRITPIETLNHPFVTMTHLLDFPHSTHVKSCFQNMEICKRRVNMYDTVNQSKTPFITHVAPSTSTNLTMTFNNQLTTVHNQAPSSTSATISLANPEVSILNYPSTLYQPSAASMAAVAQRSMPLQTGTAQICARPDPFQQALIVCPPGFQGLQASPSKHAGYSVRMENAVPIVTQAPGAQPLQIQPGLLAQQAWPSGTQQILLPPAWQQLTGVATHTSVQHATVIPETMAGTQQLADWRNTHAHGSHYNPIMQQPALLTGHVTLPAAQPLNVGVAHVMRQQPTSTTSSRKSKQHQSSVRNVSTCEVSSSQAISSPQRSKRVKENTPPRCAMVHSSPACSTSVTCGWGDVASSTTRERQRQTIVIPDTPSPTVSVITISSDTDEEEEQKHAPTSTVSKQRKNVISCVTVHDSPYSDSSSNTSPYSVQQRAGHNNANAFDTKGSLENHCTGNPRTIIVPPLKTQASEVLVECDSLVPVNTSHHSSSYKSKSSSNVTSTSGHSSGSSSGAITYRQQRPGPHFQQQQPLNLSQAQQHITTDRTGSHRRQQAYITPTMAQAPYSFPHNSPSHGTVHPHLAAAAAAAHLPTQPHLYTYTAPAALGSTGTVAHLVASQGSARHTVQHTAYPASIVHQVPVSMGPRVLPSPTIHPSQYPAQFAHQTYISASPASTVYTGYPLSPAKVNQYPYI\", \"result\": [{\"smile\": \"CC(=O)Nc1ccc2ccn(-c3cc(NC4CC4)n4ncc(C#N)c4n3)c2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(=O)Nc1ccc2ccn(-c3cc(NC4CC4)n4ncc(C#N)c4n3)c2c1 is less likely to interact with the protein MAPVYEGMASHVQVFSPHTLQSSAFCSVKKLKIEPSSNWDMTGYGSHSKVYSQSKNIPLSQPATTTVSTSLPVPNPSLPYEQTIVFPGSTGHIVVTSASSTSVTGQVLGGPHNLMRRSTVSLLDTYQKCGLKRKSEEIENTSSVQIIEEHPPMIQNNASGATVATATTSTATSKNSGSNSEGDYQLVQHEVLCSMTNTYEVLEFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTESADDYNFVRAYECFQHKNHTCLVFEMLEQNLYDFLKQNKFSPLPLKYIRPVLQQVATALMKLKSLGLIHADLKPENIMLVDPSRQPYRVKVIDFGSASHVSKAVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGASEYDQIRYISQTQGLPAEYLLSAGTKTTRFFNRDTDSPYPLWRLKTPDDHEAETGIKSKEARKYIFNCLDDMAQVNMTTDLEGSDMLVEKADRREFIDLLKKMLTIDADKRITPIETLNHPFVTMTHLLDFPHSTHVKSCFQNMEICKRRVNMYDTVNQSKTPFITHVAPSTSTNLTMTFNNQLTTVHNQAPSSTSATISLANPEVSILNYPSTLYQPSAASMAAVAQRSMPLQTGTAQICARPDPFQQALIVCPPGFQGLQASPSKHAGYSVRMENAVPIVTQAPGAQPLQIQPGLLAQQAWPSGTQQILLPPAWQQLTGVATHTSVQHATVIPETMAGTQQLADWRNTHAHGSHYNPIMQQPALLTGHVTLPAAQPLNVGVAHVMRQQPTSTTSSRKSKQHQSSVRNVSTCEVSSSQAISSPQRSKRVKENTPPRCAMVHSSPACSTSVTCGWGDVASSTTRERQRQTIVIPDTPSPTVSVITISSDTDEEEEQKHAPTSTVSKQRKNVISCVTVHDSPYSDSSSNTSPYSVQQRAGHNNANAFDTKGSLENHCTGNPRTIIVPPLKTQASEVLVECDSLVPVNTSHHSSSYKSKSSSNVTSTSGHSSGSSSGAITYRQQRPGPHFQQQQPLNLSQAQQHITTDRTGSHRRQQAYITPTMAQAPYSFPHNSPSHGTVHPHLAAAAAAAHLPTQPHLYTYTAPAALGSTGTVAHLVASQGSARHTVQHTAYPASIVHQVPVSMGPRVLPSPTIHPSQYPAQFAHQTYISASPASTVYTGYPLSPAKVNQYPYI."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the prediction for the drug's interaction with the target protein 'MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\", \"result\": [{\"smile\": \"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4 is less likely to interact with the protein MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug interact with the target protein 'MLSSTAMYSAPGRDLGMEPHRAAGPLQLRFSPYVFNGGTILAIAGEDFAIVASDTRLSEGFSIHTRDSPKCYKLTDKTVIGCSGFHGDCLTLTKIIEARLKMYKHSNNKAMTTGAIAAMLSTILYSRRFFPYYVYNIIGGLDEEGKGAVYSFDPVGSYQRDSFKAGGSASAMLQPLLDNQVGFKNMQNVEHVPLSLDRAMRLVKDVFISAAERDVYTGDALRICIVTKEGIREETVSLRKD', based on your analysis? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLSSTAMYSAPGRDLGMEPHRAAGPLQLRFSPYVFNGGTILAIAGEDFAIVASDTRLSEGFSIHTRDSPKCYKLTDKTVIGCSGFHGDCLTLTKIIEARLKMYKHSNNKAMTTGAIAAMLSTILYSRRFFPYYVYNIIGGLDEEGKGAVYSFDPVGSYQRDSFKAGGSASAMLQPLLDNQVGFKNMQNVEHVPLSLDRAMRLVKDVFISAAERDVYTGDALRICIVTKEGIREETVSLRKD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLSSTAMYSAPGRDLGMEPHRAAGPLQLRFSPYVFNGGTILAIAGEDFAIVASDTRLSEGFSIHTRDSPKCYKLTDKTVIGCSGFHGDCLTLTKIIEARLKMYKHSNNKAMTTGAIAAMLSTILYSRRFFPYYVYNIIGGLDEEGKGAVYSFDPVGSYQRDSFKAGGSASAMLQPLLDNQVGFKNMQNVEHVPLSLDRAMRLVKDVFISAAERDVYTGDALRICIVTKEGIREETVSLRKD\", \"result\": [{\"smile\": \"CC[C@H](C)[C@H](NC(=S)Nc1cccc(C(F)(F)F)c1)C(=O)N[C@H](Cc1ccccc1)C(=O)Nc1ccc(Cl)c(Cl)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC[C@H](C)[C@H](NC(=S)Nc1cccc(C(F)(F)F)c1)C(=O)N[C@H](Cc1ccccc1)C(=O)Nc1ccc(Cl)c(Cl)c1 is less likely to interact with the protein MLSSTAMYSAPGRDLGMEPHRAAGPLQLRFSPYVFNGGTILAIAGEDFAIVASDTRLSEGFSIHTRDSPKCYKLTDKTVIGCSGFHGDCLTLTKIIEARLKMYKHSNNKAMTTGAIAAMLSTILYSRRFFPYYVYNIIGGLDEEGKGAVYSFDPVGSYQRDSFKAGGSASAMLQPLLDNQVGFKNMQNVEHVPLSLDRAMRLVKDVFISAAERDVYTGDALRICIVTKEGIREETVSLRKD."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug is likely to interact with the target protein 'MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDSSKPAVYNYPEGAAYEFNAAAAANAQVYGQTGLPYGPGSEAAAFGSNGLGGFPPLNSVSPSPLMLLHPPPQLSPFLQPHGQQVPYYLENEPSGYTVREAGPPAFYRPNSDNRRQGGRERLASTNDKGSMAMESAKETRYCAVCNDYASGYHYGVWSCEGCKAFFKRSIQGHNDYMCPATNQCTIDKNRRKSCQACRLRKCYEVGMMKGGIRKDRRGGRMLKHKRQRDDGEGRGEVGSAGDMRAANLWPSPLMIKRSKKNSLALSLTADQMVSALLDAEPPILYSEYDPTRPFSEASMMGLLTNLADRELVHMINWAKRVPGFVDLTLHDQVHLLECAWLEILMIGLVWRSMEHPGKLLFAPNLLLDRNQGKCVEGMVEIFDMLLATSSRFRMMNLQGEEFVCLKSIILLNSGVYTFLSSTLKSLEEKDHIHRVLDKITDTLIHLMAKAGLTLQQQHQRLAQLLLILSHIRHMSNKGMEHLYSMKCKNVVPLYDLLLEMLDAHRLHAPTSRGGASVEETDQSHLATAGSTSSHSLQKYYITGEAEGFPATV'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDSSKPAVYNYPEGAAYEFNAAAAANAQVYGQTGLPYGPGSEAAAFGSNGLGGFPPLNSVSPSPLMLLHPPPQLSPFLQPHGQQVPYYLENEPSGYTVREAGPPAFYRPNSDNRRQGGRERLASTNDKGSMAMESAKETRYCAVCNDYASGYHYGVWSCEGCKAFFKRSIQGHNDYMCPATNQCTIDKNRRKSCQACRLRKCYEVGMMKGGIRKDRRGGRMLKHKRQRDDGEGRGEVGSAGDMRAANLWPSPLMIKRSKKNSLALSLTADQMVSALLDAEPPILYSEYDPTRPFSEASMMGLLTNLADRELVHMINWAKRVPGFVDLTLHDQVHLLECAWLEILMIGLVWRSMEHPGKLLFAPNLLLDRNQGKCVEGMVEIFDMLLATSSRFRMMNLQGEEFVCLKSIILLNSGVYTFLSSTLKSLEEKDHIHRVLDKITDTLIHLMAKAGLTLQQQHQRLAQLLLILSHIRHMSNKGMEHLYSMKCKNVVPLYDLLLEMLDAHRLHAPTSRGGASVEETDQSHLATAGSTSSHSLQKYYITGEAEGFPATV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDSSKPAVYNYPEGAAYEFNAAAAANAQVYGQTGLPYGPGSEAAAFGSNGLGGFPPLNSVSPSPLMLLHPPPQLSPFLQPHGQQVPYYLENEPSGYTVREAGPPAFYRPNSDNRRQGGRERLASTNDKGSMAMESAKETRYCAVCNDYASGYHYGVWSCEGCKAFFKRSIQGHNDYMCPATNQCTIDKNRRKSCQACRLRKCYEVGMMKGGIRKDRRGGRMLKHKRQRDDGEGRGEVGSAGDMRAANLWPSPLMIKRSKKNSLALSLTADQMVSALLDAEPPILYSEYDPTRPFSEASMMGLLTNLADRELVHMINWAKRVPGFVDLTLHDQVHLLECAWLEILMIGLVWRSMEHPGKLLFAPNLLLDRNQGKCVEGMVEIFDMLLATSSRFRMMNLQGEEFVCLKSIILLNSGVYTFLSSTLKSLEEKDHIHRVLDKITDTLIHLMAKAGLTLQQQHQRLAQLLLILSHIRHMSNKGMEHLYSMKCKNVVPLYDLLLEMLDAHRLHAPTSRGGASVEETDQSHLATAGSTSSHSLQKYYITGEAEGFPATV\", \"result\": [{\"smile\": \"CC(C)CN1[C@H](C)Cc2cc(O)ccc2[C@H]1c1ccc(/C=C/C(=O)O)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(C)CN1[C@H](C)Cc2cc(O)ccc2[C@H]1c1ccc(/C=C/C(=O)O)cc1 is less likely to interact with the protein MTMTLHTKASGMALLHQIQGNELEPLNRPQLKIPLERPLGEVYLDSSKPAVYNYPEGAAYEFNAAAAANAQVYGQTGLPYGPGSEAAAFGSNGLGGFPPLNSVSPSPLMLLHPPPQLSPFLQPHGQQVPYYLENEPSGYTVREAGPPAFYRPNSDNRRQGGRERLASTNDKGSMAMESAKETRYCAVCNDYASGYHYGVWSCEGCKAFFKRSIQGHNDYMCPATNQCTIDKNRRKSCQACRLRKCYEVGMMKGGIRKDRRGGRMLKHKRQRDDGEGRGEVGSAGDMRAANLWPSPLMIKRSKKNSLALSLTADQMVSALLDAEPPILYSEYDPTRPFSEASMMGLLTNLADRELVHMINWAKRVPGFVDLTLHDQVHLLECAWLEILMIGLVWRSMEHPGKLLFAPNLLLDRNQGKCVEGMVEIFDMLLATSSRFRMMNLQGEEFVCLKSIILLNSGVYTFLSSTLKSLEEKDHIHRVLDKITDTLIHLMAKAGLTLQQQHQRLAQLLLILSHIRHMSNKGMEHLYSMKCKNVVPLYDLLLEMLDAHRLHAPTSRGGASVEETDQSHLATAGSTSSHSLQKYYITGEAEGFPATV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you expect the drug to have an interaction with the target protein 'MGVLCFLASFWLALVGGAIADNAERYSANLSSHVEDFTPFPGTEFNFLGTTLQPPNLALPSNGSMHGYCPQQTKITTAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFDHNDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLITAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEQHRTCMLNATTKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCHQSKSLMTSVPMNGTSIQWKNQEQNHNTERSSHKDSMN'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGVLCFLASFWLALVGGAIADNAERYSANLSSHVEDFTPFPGTEFNFLGTTLQPPNLALPSNGSMHGYCPQQTKITTAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFDHNDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLITAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEQHRTCMLNATTKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCHQSKSLMTSVPMNGTSIQWKNQEQNHNTERSSHKDSMN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGVLCFLASFWLALVGGAIADNAERYSANLSSHVEDFTPFPGTEFNFLGTTLQPPNLALPSNGSMHGYCPQQTKITTAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFDHNDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLITAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEQHRTCMLNATTKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCHQSKSLMTSVPMNGTSIQWKNQEQNHNTERSSHKDSMN\", \"result\": [{\"smile\": \"Cc1oc([C@@H](Cc2cn(C)c3ccccc23)NC(=O)[C@H](CC(C)C)NC(=O)N2CCCc3ccccc32)nc1C(=O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug Cc1oc([C@@H](Cc2cn(C)c3ccccc23)NC(=O)[C@H](CC(C)C)NC(=O)N2CCCc3ccccc32)nc1C(=O)O is less likely to interact with the protein MGVLCFLASFWLALVGGAIADNAERYSANLSSHVEDFTPFPGTEFNFLGTTLQPPNLALPSNGSMHGYCPQQTKITTAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFDHNDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLITAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEQHRTCMLNATTKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCHQSKSLMTSVPMNGTSIQWKNQEQNHNTERSSHKDSMN."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine if the drug will interact with the target protein 'MAAAQTSPAATLVDVLTKPWSLDQTDSYMSTFVPLSYKIMIGYLVTIYFGQKLMAHRKPFDLQNTLALWNFGFSLFSGIAAYKLIPELFGVFMKDGFVASYCQNENYYTDASTGFWGWAFVMSKAPELGDTMFLVLRKKPVIFMHWYHHALTFVYAVVTYSEHQAWARWSLALNLAVHTVMYFYFAVRALNIQTPRPVAKFITTIQIVQFVISCYIFGHLVFIKSADSVPGCAVSWNVLSIGGLMYISYLFLFAKFFYKAYIQKRSPTKTSKQE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAAAQTSPAATLVDVLTKPWSLDQTDSYMSTFVPLSYKIMIGYLVTIYFGQKLMAHRKPFDLQNTLALWNFGFSLFSGIAAYKLIPELFGVFMKDGFVASYCQNENYYTDASTGFWGWAFVMSKAPELGDTMFLVLRKKPVIFMHWYHHALTFVYAVVTYSEHQAWARWSLALNLAVHTVMYFYFAVRALNIQTPRPVAKFITTIQIVQFVISCYIFGHLVFIKSADSVPGCAVSWNVLSIGGLMYISYLFLFAKFFYKAYIQKRSPTKTSKQE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAAQTSPAATLVDVLTKPWSLDQTDSYMSTFVPLSYKIMIGYLVTIYFGQKLMAHRKPFDLQNTLALWNFGFSLFSGIAAYKLIPELFGVFMKDGFVASYCQNENYYTDASTGFWGWAFVMSKAPELGDTMFLVLRKKPVIFMHWYHHALTFVYAVVTYSEHQAWARWSLALNLAVHTVMYFYFAVRALNIQTPRPVAKFITTIQIVQFVISCYIFGHLVFIKSADSVPGCAVSWNVLSIGGLMYISYLFLFAKFFYKAYIQKRSPTKTSKQE\", \"result\": [{\"smile\": \"C(CC(=O)C(=O)O)CC(=O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C(CC(=O)C(=O)O)CC(=O)O is less likely to interact with the protein MAAAQTSPAATLVDVLTKPWSLDQTDSYMSTFVPLSYKIMIGYLVTIYFGQKLMAHRKPFDLQNTLALWNFGFSLFSGIAAYKLIPELFGVFMKDGFVASYCQNENYYTDASTGFWGWAFVMSKAPELGDTMFLVLRKKPVIFMHWYHHALTFVYAVVTYSEHQAWARWSLALNLAVHTVMYFYFAVRALNIQTPRPVAKFITTIQIVQFVISCYIFGHLVFIKSADSVPGCAVSWNVLSIGGLMYISYLFLFAKFFYKAYIQKRSPTKTSKQE."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood that the drug will engage with the target protein 'MREIVHLQAGQCGNQIGAKFWEVISDEHGIDPTGTYHGDSDLQLERINVYYNEATGGNYVPRAVLVDLEPGTMDSVRSGPFGQIFRPDNFVFGQSGAGNNWAKGHYTEGAELVDAVLDVVRKEAESCDCLQGFQLTHSLGGGTGSGMGTLLISKIREEFPDRIMNTFSVVPSPKVSDTVVEPYNATLSVHQLVENTDETYCIDNEALYDICFRTLKLTTPTYGDLNHLVSATMSGVTTCLRFPGQLNADLRKLAVNMVPFPRLHFFMPGFAPLTSRGSQQYRALTVPELTQQMFDAKNMMAACDPRHGRYLTVAAVFRGRMSMKEVDEQMLSVQSKNSSYFVEWIPNNVKTAVCDIPPRGLKMAATFIGNSTAIQELFKRISEQFTAMFRRKAFLHWYTGEGMDEMEFTEAESNMNDLVSEYQQYQDATAEEGEFEEEAEEEVA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MREIVHLQAGQCGNQIGAKFWEVISDEHGIDPTGTYHGDSDLQLERINVYYNEATGGNYVPRAVLVDLEPGTMDSVRSGPFGQIFRPDNFVFGQSGAGNNWAKGHYTEGAELVDAVLDVVRKEAESCDCLQGFQLTHSLGGGTGSGMGTLLISKIREEFPDRIMNTFSVVPSPKVSDTVVEPYNATLSVHQLVENTDETYCIDNEALYDICFRTLKLTTPTYGDLNHLVSATMSGVTTCLRFPGQLNADLRKLAVNMVPFPRLHFFMPGFAPLTSRGSQQYRALTVPELTQQMFDAKNMMAACDPRHGRYLTVAAVFRGRMSMKEVDEQMLSVQSKNSSYFVEWIPNNVKTAVCDIPPRGLKMAATFIGNSTAIQELFKRISEQFTAMFRRKAFLHWYTGEGMDEMEFTEAESNMNDLVSEYQQYQDATAEEGEFEEEAEEEVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MREIVHLQAGQCGNQIGAKFWEVISDEHGIDPTGTYHGDSDLQLERINVYYNEATGGNYVPRAVLVDLEPGTMDSVRSGPFGQIFRPDNFVFGQSGAGNNWAKGHYTEGAELVDAVLDVVRKEAESCDCLQGFQLTHSLGGGTGSGMGTLLISKIREEFPDRIMNTFSVVPSPKVSDTVVEPYNATLSVHQLVENTDETYCIDNEALYDICFRTLKLTTPTYGDLNHLVSATMSGVTTCLRFPGQLNADLRKLAVNMVPFPRLHFFMPGFAPLTSRGSQQYRALTVPELTQQMFDAKNMMAACDPRHGRYLTVAAVFRGRMSMKEVDEQMLSVQSKNSSYFVEWIPNNVKTAVCDIPPRGLKMAATFIGNSTAIQELFKRISEQFTAMFRRKAFLHWYTGEGMDEMEFTEAESNMNDLVSEYQQYQDATAEEGEFEEEAEEEVA\", \"result\": [{\"smile\": \"C1=CC=C(C=C1)C(=O)NC(CCCN=C(N)N)C(=O)N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=CC=C(C=C1)C(=O)NC(CCCN=C(N)N)C(=O)N is less likely to interact with the protein MREIVHLQAGQCGNQIGAKFWEVISDEHGIDPTGTYHGDSDLQLERINVYYNEATGGNYVPRAVLVDLEPGTMDSVRSGPFGQIFRPDNFVFGQSGAGNNWAKGHYTEGAELVDAVLDVVRKEAESCDCLQGFQLTHSLGGGTGSGMGTLLISKIREEFPDRIMNTFSVVPSPKVSDTVVEPYNATLSVHQLVENTDETYCIDNEALYDICFRTLKLTTPTYGDLNHLVSATMSGVTTCLRFPGQLNADLRKLAVNMVPFPRLHFFMPGFAPLTSRGSQQYRALTVPELTQQMFDAKNMMAACDPRHGRYLTVAAVFRGRMSMKEVDEQMLSVQSKNSSYFVEWIPNNVKTAVCDIPPRGLKMAATFIGNSTAIQELFKRISEQFTAMFRRKAFLHWYTGEGMDEMEFTEAESNMNDLVSEYQQYQDATAEEGEFEEEAEEEVA."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if there will be an interaction between the drug and the target protein 'MAEDKSKRDSIEMSMKGCQTNNGFVHNEDILEQTPDPGSSTDNLKHSTRGILGSQEPDFKGVQPYAGMPKEVLFQFSGQARYRIPREILFWLTVASVLVLIAATIAIIALSPKCLDWWQEGPMYQIYPRSFKDSNKDGNGDLKGIQDKLDYITALNIKTVWITSFYKSSLKDFRYGVEDFREVDPIFGTMEDFENLVAAIHDKGLKLIIDFIPNHTSDKHIWFQLSRTRTGKYTDYYIWHDCTHENGKTIPPNNWLSVYGNSSWHFDEVRNQCYFHQFMKEQPDLNFRNPDVQEEIKEILRFWLTKGVDGFSLDAVKFLLEAKHLRDEIQVNKTQIPDTVTQYSELYHDFTTTQVGMHDIVRSFRQTMDQYSTEPGRYRFMGTEAYAESIDRTVMYYGLPFIQEADFPFNNYLSMLDTVSGNSVYEVITSWMENMPEGKWPNWMIGGPDSSRLTSRLGNQYVNVMNMLLFTLPGTPITYYGEEIGMGNIVAANLNESYDINTLRSKSPMQWDNSSNAGFSEASNTWLPTNSDYHTVNVDVQKTQPRSALKLYQDLSLLHANELLLNRGWFCHLRNDSHYVVYTRELDGIDRIFIVVLNFGESTLLNLHNMISGLPAKMRIRLSTNSADKGSKVDTSGIFLDKGEGLIFEHNTKNLLHRQTAFRDRCFVSNRACYSSVLNILYTSC'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAEDKSKRDSIEMSMKGCQTNNGFVHNEDILEQTPDPGSSTDNLKHSTRGILGSQEPDFKGVQPYAGMPKEVLFQFSGQARYRIPREILFWLTVASVLVLIAATIAIIALSPKCLDWWQEGPMYQIYPRSFKDSNKDGNGDLKGIQDKLDYITALNIKTVWITSFYKSSLKDFRYGVEDFREVDPIFGTMEDFENLVAAIHDKGLKLIIDFIPNHTSDKHIWFQLSRTRTGKYTDYYIWHDCTHENGKTIPPNNWLSVYGNSSWHFDEVRNQCYFHQFMKEQPDLNFRNPDVQEEIKEILRFWLTKGVDGFSLDAVKFLLEAKHLRDEIQVNKTQIPDTVTQYSELYHDFTTTQVGMHDIVRSFRQTMDQYSTEPGRYRFMGTEAYAESIDRTVMYYGLPFIQEADFPFNNYLSMLDTVSGNSVYEVITSWMENMPEGKWPNWMIGGPDSSRLTSRLGNQYVNVMNMLLFTLPGTPITYYGEEIGMGNIVAANLNESYDINTLRSKSPMQWDNSSNAGFSEASNTWLPTNSDYHTVNVDVQKTQPRSALKLYQDLSLLHANELLLNRGWFCHLRNDSHYVVYTRELDGIDRIFIVVLNFGESTLLNLHNMISGLPAKMRIRLSTNSADKGSKVDTSGIFLDKGEGLIFEHNTKNLLHRQTAFRDRCFVSNRACYSSVLNILYTSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAEDKSKRDSIEMSMKGCQTNNGFVHNEDILEQTPDPGSSTDNLKHSTRGILGSQEPDFKGVQPYAGMPKEVLFQFSGQARYRIPREILFWLTVASVLVLIAATIAIIALSPKCLDWWQEGPMYQIYPRSFKDSNKDGNGDLKGIQDKLDYITALNIKTVWITSFYKSSLKDFRYGVEDFREVDPIFGTMEDFENLVAAIHDKGLKLIIDFIPNHTSDKHIWFQLSRTRTGKYTDYYIWHDCTHENGKTIPPNNWLSVYGNSSWHFDEVRNQCYFHQFMKEQPDLNFRNPDVQEEIKEILRFWLTKGVDGFSLDAVKFLLEAKHLRDEIQVNKTQIPDTVTQYSELYHDFTTTQVGMHDIVRSFRQTMDQYSTEPGRYRFMGTEAYAESIDRTVMYYGLPFIQEADFPFNNYLSMLDTVSGNSVYEVITSWMENMPEGKWPNWMIGGPDSSRLTSRLGNQYVNVMNMLLFTLPGTPITYYGEEIGMGNIVAANLNESYDINTLRSKSPMQWDNSSNAGFSEASNTWLPTNSDYHTVNVDVQKTQPRSALKLYQDLSLLHANELLLNRGWFCHLRNDSHYVVYTRELDGIDRIFIVVLNFGESTLLNLHNMISGLPAKMRIRLSTNSADKGSKVDTSGIFLDKGEGLIFEHNTKNLLHRQTAFRDRCFVSNRACYSSVLNILYTSC\", \"result\": [{\"smile\": \"C[C@@H]1C/C=C/C=C/[C@@H]([C@@H](C[C@@H]([C@@H]([C@H]([C@@H](CC(=O)O1)OC(=O)C)OC)O[C@H]2[C@@H]([C@H]([C@@H]([C@H](O2)C)O[C@H]3C[C@@]([C@H]([C@@H](O3)C)OC(=O)CC(C)C)(C)O)N(C)C)O)CC=O)C)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C[C@@H]1C/C=C/C=C/[C@@H]([C@@H](C[C@@H]([C@@H]([C@H]([C@@H](CC(=O)O1)OC(=O)C)OC)O[C@H]2[C@@H]([C@H]([C@@H]([C@H](O2)C)O[C@H]3C[C@@]([C@H]([C@@H](O3)C)OC(=O)CC(C)C)(C)O)N(C)C)O)CC=O)C)O is less likely to interact with the protein MAEDKSKRDSIEMSMKGCQTNNGFVHNEDILEQTPDPGSSTDNLKHSTRGILGSQEPDFKGVQPYAGMPKEVLFQFSGQARYRIPREILFWLTVASVLVLIAATIAIIALSPKCLDWWQEGPMYQIYPRSFKDSNKDGNGDLKGIQDKLDYITALNIKTVWITSFYKSSLKDFRYGVEDFREVDPIFGTMEDFENLVAAIHDKGLKLIIDFIPNHTSDKHIWFQLSRTRTGKYTDYYIWHDCTHENGKTIPPNNWLSVYGNSSWHFDEVRNQCYFHQFMKEQPDLNFRNPDVQEEIKEILRFWLTKGVDGFSLDAVKFLLEAKHLRDEIQVNKTQIPDTVTQYSELYHDFTTTQVGMHDIVRSFRQTMDQYSTEPGRYRFMGTEAYAESIDRTVMYYGLPFIQEADFPFNNYLSMLDTVSGNSVYEVITSWMENMPEGKWPNWMIGGPDSSRLTSRLGNQYVNVMNMLLFTLPGTPITYYGEEIGMGNIVAANLNESYDINTLRSKSPMQWDNSSNAGFSEASNTWLPTNSDYHTVNVDVQKTQPRSALKLYQDLSLLHANELLLNRGWFCHLRNDSHYVVYTRELDGIDRIFIVVLNFGESTLLNLHNMISGLPAKMRIRLSTNSADKGSKVDTSGIFLDKGEGLIFEHNTKNLLHRQTAFRDRCFVSNRACYSSVLNILYTSC."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the drug can interact with the target protein 'MYFNEEKAKRIGERFEGGKLAKKVHKSIEQLKRHDPDVQISTEPTKYLLVSNSSILCGVSLEELEEIFLPLDELAEFIVYPNKRSYSFVQCSSIEKSIQVRTELHGLIPPSLKNSHQPFAISYVENLPEATKCEDFRPANLKIIEEYVSSDLEKELVDLVTNHPSVQSLKHRAVVHFGHVFDYSTNSASEWKEADPIPPVINSLIDRLISDKYITERPDQVTANVYESGHGIPSHYDTHSAFDDPIVSISLLSDVVMEFKDGANSARIAPVLLKARSLCLIQGESRYRWKHGIVNRKYDVDPRTNRVVPRQTRVSLTLRKIRRKPCECEWKEFCDWDRKGEMSVPSNEDLALKLENSYVSDVYENIASHFDETRHSSWKAVKQFINEIPRGSVMYDVGCGNGKYLIPKDGLLKIGCDMCMGLCDIARKKDCHVARCDALALPFRYESADAAISIAVLHHIATFERRKRLIEELLRVVKPGSKICVTVWSMDQSQSEYAKMRGNKDDVAAAPAVSSEETQTTNRLKVHDGKDFEQQDVLVPWTIDQKGETFLRYYHVFREGEAEKLIESVQGCKLISVEKEQGNYIIIAKKI'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MYFNEEKAKRIGERFEGGKLAKKVHKSIEQLKRHDPDVQISTEPTKYLLVSNSSILCGVSLEELEEIFLPLDELAEFIVYPNKRSYSFVQCSSIEKSIQVRTELHGLIPPSLKNSHQPFAISYVENLPEATKCEDFRPANLKIIEEYVSSDLEKELVDLVTNHPSVQSLKHRAVVHFGHVFDYSTNSASEWKEADPIPPVINSLIDRLISDKYITERPDQVTANVYESGHGIPSHYDTHSAFDDPIVSISLLSDVVMEFKDGANSARIAPVLLKARSLCLIQGESRYRWKHGIVNRKYDVDPRTNRVVPRQTRVSLTLRKIRRKPCECEWKEFCDWDRKGEMSVPSNEDLALKLENSYVSDVYENIASHFDETRHSSWKAVKQFINEIPRGSVMYDVGCGNGKYLIPKDGLLKIGCDMCMGLCDIARKKDCHVARCDALALPFRYESADAAISIAVLHHIATFERRKRLIEELLRVVKPGSKICVTVWSMDQSQSEYAKMRGNKDDVAAAPAVSSEETQTTNRLKVHDGKDFEQQDVLVPWTIDQKGETFLRYYHVFREGEAEKLIESVQGCKLISVEKEQGNYIIIAKKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MYFNEEKAKRIGERFEGGKLAKKVHKSIEQLKRHDPDVQISTEPTKYLLVSNSSILCGVSLEELEEIFLPLDELAEFIVYPNKRSYSFVQCSSIEKSIQVRTELHGLIPPSLKNSHQPFAISYVENLPEATKCEDFRPANLKIIEEYVSSDLEKELVDLVTNHPSVQSLKHRAVVHFGHVFDYSTNSASEWKEADPIPPVINSLIDRLISDKYITERPDQVTANVYESGHGIPSHYDTHSAFDDPIVSISLLSDVVMEFKDGANSARIAPVLLKARSLCLIQGESRYRWKHGIVNRKYDVDPRTNRVVPRQTRVSLTLRKIRRKPCECEWKEFCDWDRKGEMSVPSNEDLALKLENSYVSDVYENIASHFDETRHSSWKAVKQFINEIPRGSVMYDVGCGNGKYLIPKDGLLKIGCDMCMGLCDIARKKDCHVARCDALALPFRYESADAAISIAVLHHIATFERRKRLIEELLRVVKPGSKICVTVWSMDQSQSEYAKMRGNKDDVAAAPAVSSEETQTTNRLKVHDGKDFEQQDVLVPWTIDQKGETFLRYYHVFREGEAEKLIESVQGCKLISVEKEQGNYIIIAKKI\", \"result\": [{\"smile\": \"[Mg+2]\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [Mg+2] is less likely to interact with the protein MYFNEEKAKRIGERFEGGKLAKKVHKSIEQLKRHDPDVQISTEPTKYLLVSNSSILCGVSLEELEEIFLPLDELAEFIVYPNKRSYSFVQCSSIEKSIQVRTELHGLIPPSLKNSHQPFAISYVENLPEATKCEDFRPANLKIIEEYVSSDLEKELVDLVTNHPSVQSLKHRAVVHFGHVFDYSTNSASEWKEADPIPPVINSLIDRLISDKYITERPDQVTANVYESGHGIPSHYDTHSAFDDPIVSISLLSDVVMEFKDGANSARIAPVLLKARSLCLIQGESRYRWKHGIVNRKYDVDPRTNRVVPRQTRVSLTLRKIRRKPCECEWKEFCDWDRKGEMSVPSNEDLALKLENSYVSDVYENIASHFDETRHSSWKAVKQFINEIPRGSVMYDVGCGNGKYLIPKDGLLKIGCDMCMGLCDIARKKDCHVARCDALALPFRYESADAAISIAVLHHIATFERRKRLIEELLRVVKPGSKICVTVWSMDQSQSEYAKMRGNKDDVAAAPAVSSEETQTTNRLKVHDGKDFEQQDVLVPWTIDQKGETFLRYYHVFREGEAEKLIESVQGCKLISVEKEQGNYIIIAKKI."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a chance the drug will interact with the target protein 'MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDRAWSEHRRHFPNCFFVLGRNLNIRSE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDRAWSEHRRHFPNCFFVLGRNLNIRSE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDRAWSEHRRHFPNCFFVLGRNLNIRSE\", \"result\": [{\"smile\": \"CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2cccc3c2cc(C#N)n3-c2ccccc2C#N)c2ccccc2O[C@H]1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2cccc3c2cc(C#N)n3-c2ccccc2C#N)c2ccccc2O[C@H]1C is less likely to interact with the protein MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDRAWSEHRRHFPNCFFVLGRNLNIRSE."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction between the drug and the target protein 'MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY\", \"result\": [{\"smile\": \"COc1ccc(C(=O)c2c(C)n(Cc3cccc(O[C@@H](C)C(=O)O)c3)c3nc(C)ccc23)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1ccc(C(=O)c2c(C)n(Cc3cccc(O[C@@H](C)C(=O)O)c3)c3nc(C)ccc23)cc1 is less likely to interact with the protein MGETLGDSPIDPESDSFTDTLSANISQEMTMVDTEMPFWPTNFGISSVDLSVMEDHSHSFDIKPFTTVDFSSISTPHYEDIPFTRTDPVVADYKYDLKLQEYQSAIKVEPASPPYYSEKTQLYNKPHEEPSNSLMAIECRVCGDKASGFHYGVHACEGCKGFFRRTIRLKLIYDRCDLNCRIHKKSRNKCQYCRFQKCLAVGMSHNAIRFGRMPQAEKEKLLAEISSDIDQLNPESADLRALAKHLYDSYIKSFPLTKAKARAILTGKTTDKSPFVIYDMNSLMMGEDKIKFKHITPLQEQSKEVAIRIFQGCQFRSVEAVQEITEYAKSIPGFVNLDLNDQVTLLKYGVHEIIYTMLASLMNKDGVLISEGQGFMTREFLKSLRKPFGDFMEPKFEFAVKFNALELDDSDLAIFIAVIILSGDRPGLLNVKPIEDIQDNLLQALELQLKLNHPESSQLFAKLLQKMTDLRQIVTEHVQLLQVIKKTETDMSLHPLLQEIYKDLY."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug is expected to interact with the target protein 'MGTQDPGNMGTGVPASEQISCAKEDPQVYCPEETGGTKDVQVTDCKSPEDSRPPKETDCCNPEDSGQLMVSYEGKAMGYQVPPFGWRICLAHEFTEKRKPFQANNVSLSNMIKHIGMGLRYLQWWYRKTHVEKKTPFIDMINSVPLRQIYGCPLGGIGGGTITRGWRGQFCRWQLNPGMYQHRTVIADQFTVCLRREGQTVYQQVLSLERPSVLRSWNWGLCGYFAFYHALYPRAWTVYQLPGQNVTLTCRQITPILPHDYQDSSLPVGVFVWDVENEGDEALDVSIMFSMRNGLGGGDDAPGGLWNEPFCLERSGETVRGLLLHHPTLPNPYTMAVAARVTAATTVTHITAFDPDSTGQQVWQDLLQDGQLDSPTGQSTPTQKGVGIAGAVCVSSKLRPRGQCRLEFSLAWDMPRIMFGAKGQVHYRRYTRFFGQDGDAAPALSHYALCRYAEWEERISAWQSPVLDDRSLPAWYKSALFNELYFLADGGTVWLEVLEDSLPEELGRNMCHLRPTLRDYGRFGYLEGQEYRMYNTYDVHFYASFALIMLWPKLELSLQYDMALATLREDLTRRRYLMSGVMAPVKRRNVIPHDIGDPDDEPWLRVNAYLIHDTADWKDLNLKFVLQVYRDYYLTGDQNFLKDMWPVCLAVMESEMKFDKDHDGLIENGGYADQTYDGWVTTGPSAYCGGLWLAAVAVMVQMAALCGAQDIQDKFSSILSRGQEAYERLLWNGRYYNYDSSSRPQSRSVMSDQCAGQWFLKACGLGEGDTEVFPTQHVVRALQTIFELNVQAFAGGAMGAVNGMQPHGVPDKSSVQSDEVWVGVVYGLAATMIQEGLTWEGFQTAEGCYRTVWERLGLAFQTPEAYCQQRVFRSLAYMRPLSIWAMQLALQQQQHKKASWPKVKQGTGLRTGPMFGPKEAMANLSPE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGTQDPGNMGTGVPASEQISCAKEDPQVYCPEETGGTKDVQVTDCKSPEDSRPPKETDCCNPEDSGQLMVSYEGKAMGYQVPPFGWRICLAHEFTEKRKPFQANNVSLSNMIKHIGMGLRYLQWWYRKTHVEKKTPFIDMINSVPLRQIYGCPLGGIGGGTITRGWRGQFCRWQLNPGMYQHRTVIADQFTVCLRREGQTVYQQVLSLERPSVLRSWNWGLCGYFAFYHALYPRAWTVYQLPGQNVTLTCRQITPILPHDYQDSSLPVGVFVWDVENEGDEALDVSIMFSMRNGLGGGDDAPGGLWNEPFCLERSGETVRGLLLHHPTLPNPYTMAVAARVTAATTVTHITAFDPDSTGQQVWQDLLQDGQLDSPTGQSTPTQKGVGIAGAVCVSSKLRPRGQCRLEFSLAWDMPRIMFGAKGQVHYRRYTRFFGQDGDAAPALSHYALCRYAEWEERISAWQSPVLDDRSLPAWYKSALFNELYFLADGGTVWLEVLEDSLPEELGRNMCHLRPTLRDYGRFGYLEGQEYRMYNTYDVHFYASFALIMLWPKLELSLQYDMALATLREDLTRRRYLMSGVMAPVKRRNVIPHDIGDPDDEPWLRVNAYLIHDTADWKDLNLKFVLQVYRDYYLTGDQNFLKDMWPVCLAVMESEMKFDKDHDGLIENGGYADQTYDGWVTTGPSAYCGGLWLAAVAVMVQMAALCGAQDIQDKFSSILSRGQEAYERLLWNGRYYNYDSSSRPQSRSVMSDQCAGQWFLKACGLGEGDTEVFPTQHVVRALQTIFELNVQAFAGGAMGAVNGMQPHGVPDKSSVQSDEVWVGVVYGLAATMIQEGLTWEGFQTAEGCYRTVWERLGLAFQTPEAYCQQRVFRSLAYMRPLSIWAMQLALQQQQHKKASWPKVKQGTGLRTGPMFGPKEAMANLSPE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTQDPGNMGTGVPASEQISCAKEDPQVYCPEETGGTKDVQVTDCKSPEDSRPPKETDCCNPEDSGQLMVSYEGKAMGYQVPPFGWRICLAHEFTEKRKPFQANNVSLSNMIKHIGMGLRYLQWWYRKTHVEKKTPFIDMINSVPLRQIYGCPLGGIGGGTITRGWRGQFCRWQLNPGMYQHRTVIADQFTVCLRREGQTVYQQVLSLERPSVLRSWNWGLCGYFAFYHALYPRAWTVYQLPGQNVTLTCRQITPILPHDYQDSSLPVGVFVWDVENEGDEALDVSIMFSMRNGLGGGDDAPGGLWNEPFCLERSGETVRGLLLHHPTLPNPYTMAVAARVTAATTVTHITAFDPDSTGQQVWQDLLQDGQLDSPTGQSTPTQKGVGIAGAVCVSSKLRPRGQCRLEFSLAWDMPRIMFGAKGQVHYRRYTRFFGQDGDAAPALSHYALCRYAEWEERISAWQSPVLDDRSLPAWYKSALFNELYFLADGGTVWLEVLEDSLPEELGRNMCHLRPTLRDYGRFGYLEGQEYRMYNTYDVHFYASFALIMLWPKLELSLQYDMALATLREDLTRRRYLMSGVMAPVKRRNVIPHDIGDPDDEPWLRVNAYLIHDTADWKDLNLKFVLQVYRDYYLTGDQNFLKDMWPVCLAVMESEMKFDKDHDGLIENGGYADQTYDGWVTTGPSAYCGGLWLAAVAVMVQMAALCGAQDIQDKFSSILSRGQEAYERLLWNGRYYNYDSSSRPQSRSVMSDQCAGQWFLKACGLGEGDTEVFPTQHVVRALQTIFELNVQAFAGGAMGAVNGMQPHGVPDKSSVQSDEVWVGVVYGLAATMIQEGLTWEGFQTAEGCYRTVWERLGLAFQTPEAYCQQRVFRSLAYMRPLSIWAMQLALQQQQHKKASWPKVKQGTGLRTGPMFGPKEAMANLSPE\", \"result\": [{\"smile\": \"OC[C@H]1[C@@H](O)[C@H](O)[C@@H](O)CN1CC(F)(F)CCCOCc1ccc(-c2ccccc2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug OC[C@H]1[C@@H](O)[C@H](O)[C@@H](O)CN1CC(F)(F)CCCOCc1ccc(-c2ccccc2)cc1 is less likely to interact with the protein MGTQDPGNMGTGVPASEQISCAKEDPQVYCPEETGGTKDVQVTDCKSPEDSRPPKETDCCNPEDSGQLMVSYEGKAMGYQVPPFGWRICLAHEFTEKRKPFQANNVSLSNMIKHIGMGLRYLQWWYRKTHVEKKTPFIDMINSVPLRQIYGCPLGGIGGGTITRGWRGQFCRWQLNPGMYQHRTVIADQFTVCLRREGQTVYQQVLSLERPSVLRSWNWGLCGYFAFYHALYPRAWTVYQLPGQNVTLTCRQITPILPHDYQDSSLPVGVFVWDVENEGDEALDVSIMFSMRNGLGGGDDAPGGLWNEPFCLERSGETVRGLLLHHPTLPNPYTMAVAARVTAATTVTHITAFDPDSTGQQVWQDLLQDGQLDSPTGQSTPTQKGVGIAGAVCVSSKLRPRGQCRLEFSLAWDMPRIMFGAKGQVHYRRYTRFFGQDGDAAPALSHYALCRYAEWEERISAWQSPVLDDRSLPAWYKSALFNELYFLADGGTVWLEVLEDSLPEELGRNMCHLRPTLRDYGRFGYLEGQEYRMYNTYDVHFYASFALIMLWPKLELSLQYDMALATLREDLTRRRYLMSGVMAPVKRRNVIPHDIGDPDDEPWLRVNAYLIHDTADWKDLNLKFVLQVYRDYYLTGDQNFLKDMWPVCLAVMESEMKFDKDHDGLIENGGYADQTYDGWVTTGPSAYCGGLWLAAVAVMVQMAALCGAQDIQDKFSSILSRGQEAYERLLWNGRYYNYDSSSRPQSRSVMSDQCAGQWFLKACGLGEGDTEVFPTQHVVRALQTIFELNVQAFAGGAMGAVNGMQPHGVPDKSSVQSDEVWVGVVYGLAATMIQEGLTWEGFQTAEGCYRTVWERLGLAFQTPEAYCQQRVFRSLAYMRPLSIWAMQLALQQQQHKKASWPKVKQGTGLRTGPMFGPKEAMANLSPE."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction regarding the drug's interaction with the target protein 'MTTQLRVVHLLPLLLACFVQTSPKQEKMKMDCHKDEKGTIYDYEAIALNKNEYVSFKQYVGKHILFVNVATYCGLTAQYPELNALQEELKPYGLVVLGFPCNQFGKQEPGDNKEILPGLKYVRPGGGFVPSFQLFEKGDVNGEKEQKVFSFLKHSCPHPSEILGTFKSISWDPVKVHDIRWNFEKFLVGPDGIPVMRWSHRATVSSVKTDILAYLKQFKTK'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTTQLRVVHLLPLLLACFVQTSPKQEKMKMDCHKDEKGTIYDYEAIALNKNEYVSFKQYVGKHILFVNVATYCGLTAQYPELNALQEELKPYGLVVLGFPCNQFGKQEPGDNKEILPGLKYVRPGGGFVPSFQLFEKGDVNGEKEQKVFSFLKHSCPHPSEILGTFKSISWDPVKVHDIRWNFEKFLVGPDGIPVMRWSHRATVSSVKTDILAYLKQFKTK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTTQLRVVHLLPLLLACFVQTSPKQEKMKMDCHKDEKGTIYDYEAIALNKNEYVSFKQYVGKHILFVNVATYCGLTAQYPELNALQEELKPYGLVVLGFPCNQFGKQEPGDNKEILPGLKYVRPGGGFVPSFQLFEKGDVNGEKEQKVFSFLKHSCPHPSEILGTFKSISWDPVKVHDIRWNFEKFLVGPDGIPVMRWSHRATVSSVKTDILAYLKQFKTK\", \"result\": [{\"smile\": \"N[C@@H](CCC(=O)N[C@@H](CS)C(=O)NCC(O)=O)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug N[C@@H](CCC(=O)N[C@@H](CS)C(=O)NCC(O)=O)C(O)=O is less likely to interact with the protein MTTQLRVVHLLPLLLACFVQTSPKQEKMKMDCHKDEKGTIYDYEAIALNKNEYVSFKQYVGKHILFVNVATYCGLTAQYPELNALQEELKPYGLVVLGFPCNQFGKQEPGDNKEILPGLKYVRPGGGFVPSFQLFEKGDVNGEKEQKVFSFLKHSCPHPSEILGTFKSISWDPVKVHDIRWNFEKFLVGPDGIPVMRWSHRATVSSVKTDILAYLKQFKTK."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you assess the possibility of the drug interacting with the target protein 'MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C is less likely to interact with the protein MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think the drug will have any interaction with the target protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": [{\"smile\": \"C[C@H](Nc1nc(Nc2cc(C3CC3)n[nH]2)cnc1C#N)c1ccc(F)cn1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C[C@H](Nc1nc(Nc2cc(C3CC3)n[nH]2)cnc1C#N)c1ccc(F)cn1 is less likely to interact with the protein MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide insights on whether the drug will interact with the target protein 'MASPALAAALAVAAAAGPNASGAGERGSGGVANASGASWGPPRGQYSAGAVAGLAAVVGFLIVFTVVGNVLVVIAVLTSRALRAPQNLFLVSLASADILVATLVMPFSLANELMAYWYFGQVWCGVYLALDVLFCTSSIVHLCAISLDRYWSVTQAVEYNLKRTPRRVKATIVAVWLISAVISFPPLVSLYRQPDGAAYPQCGLNDETWYILSSCIGSFFAPCLIMGLVYARIYRVAKLRTRTLSEKRAPVGPDGASPTTENGLGAAAGAGENGHCAPPPADVEPDESSAAAERRRRRGALRRGGRRRAGAEGGAGGADGQGAGPGAAESGALTASRSPGPGGRLSRASSRSVEFFLSRRRRARSSVCRRKVAQAREKRFTFVLAVVMGVFVLCWFPFFFSYSLYGICREACQVPGPLFKFFFWIGYCNSSLNPVIYTVFNQDFRRSFKHILFRRRRRGFRQ'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASPALAAALAVAAAAGPNASGAGERGSGGVANASGASWGPPRGQYSAGAVAGLAAVVGFLIVFTVVGNVLVVIAVLTSRALRAPQNLFLVSLASADILVATLVMPFSLANELMAYWYFGQVWCGVYLALDVLFCTSSIVHLCAISLDRYWSVTQAVEYNLKRTPRRVKATIVAVWLISAVISFPPLVSLYRQPDGAAYPQCGLNDETWYILSSCIGSFFAPCLIMGLVYARIYRVAKLRTRTLSEKRAPVGPDGASPTTENGLGAAAGAGENGHCAPPPADVEPDESSAAAERRRRRGALRRGGRRRAGAEGGAGGADGQGAGPGAAESGALTASRSPGPGGRLSRASSRSVEFFLSRRRRARSSVCRRKVAQAREKRFTFVLAVVMGVFVLCWFPFFFSYSLYGICREACQVPGPLFKFFFWIGYCNSSLNPVIYTVFNQDFRRSFKHILFRRRRRGFRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASPALAAALAVAAAAGPNASGAGERGSGGVANASGASWGPPRGQYSAGAVAGLAAVVGFLIVFTVVGNVLVVIAVLTSRALRAPQNLFLVSLASADILVATLVMPFSLANELMAYWYFGQVWCGVYLALDVLFCTSSIVHLCAISLDRYWSVTQAVEYNLKRTPRRVKATIVAVWLISAVISFPPLVSLYRQPDGAAYPQCGLNDETWYILSSCIGSFFAPCLIMGLVYARIYRVAKLRTRTLSEKRAPVGPDGASPTTENGLGAAAGAGENGHCAPPPADVEPDESSAAAERRRRRGALRRGGRRRAGAEGGAGGADGQGAGPGAAESGALTASRSPGPGGRLSRASSRSVEFFLSRRRRARSSVCRRKVAQAREKRFTFVLAVVMGVFVLCWFPFFFSYSLYGICREACQVPGPLFKFFFWIGYCNSSLNPVIYTVFNQDFRRSFKHILFRRRRRGFRQ\", \"result\": [{\"smile\": \"CNCCCN1C2=CC=CC=C2CCC2=CC=CC=C12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CNCCCN1C2=CC=CC=C2CCC2=CC=CC=C12 is less likely to interact with the protein MASPALAAALAVAAAAGPNASGAGERGSGGVANASGASWGPPRGQYSAGAVAGLAAVVGFLIVFTVVGNVLVVIAVLTSRALRAPQNLFLVSLASADILVATLVMPFSLANELMAYWYFGQVWCGVYLALDVLFCTSSIVHLCAISLDRYWSVTQAVEYNLKRTPRRVKATIVAVWLISAVISFPPLVSLYRQPDGAAYPQCGLNDETWYILSSCIGSFFAPCLIMGLVYARIYRVAKLRTRTLSEKRAPVGPDGASPTTENGLGAAAGAGENGHCAPPPADVEPDESSAAAERRRRRGALRRGGRRRAGAEGGAGGADGQGAGPGAAESGALTASRSPGPGGRLSRASSRSVEFFLSRRRRARSSVCRRKVAQAREKRFTFVLAVVMGVFVLCWFPFFFSYSLYGICREACQVPGPLFKFFFWIGYCNSSLNPVIYTVFNQDFRRSFKHILFRRRRRGFRQ."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your take on the drug's potential interaction with the target protein 'MPGAGDGGKAPARWLGTGLLGLFLLPVTLSLEVSVGKATDIYAVNGTEILLPCTFSSCFGFEDLHFRWTYNSSDAFKILIEGTVKNEKSDPKVTLKDDDRITLVGSTKEKMNNISIVLRDLEFSDTGKYTCHVKNPKENNLQHHATIFLQVVDRLEEVDNTVTLIILAVVGGVIGLLILILLIKKLIIFILKKTREKKKECLVSSSGNDNTENGLPGSKAEEKPPSKV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPGAGDGGKAPARWLGTGLLGLFLLPVTLSLEVSVGKATDIYAVNGTEILLPCTFSSCFGFEDLHFRWTYNSSDAFKILIEGTVKNEKSDPKVTLKDDDRITLVGSTKEKMNNISIVLRDLEFSDTGKYTCHVKNPKENNLQHHATIFLQVVDRLEEVDNTVTLIILAVVGGVIGLLILILLIKKLIIFILKKTREKKKECLVSSSGNDNTENGLPGSKAEEKPPSKV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPGAGDGGKAPARWLGTGLLGLFLLPVTLSLEVSVGKATDIYAVNGTEILLPCTFSSCFGFEDLHFRWTYNSSDAFKILIEGTVKNEKSDPKVTLKDDDRITLVGSTKEKMNNISIVLRDLEFSDTGKYTCHVKNPKENNLQHHATIFLQVVDRLEEVDNTVTLIILAVVGGVIGLLILILLIKKLIIFILKKTREKKKECLVSSSGNDNTENGLPGSKAEEKPPSKV\", \"result\": [{\"smile\": \"[H][C@]1(CO)O[C@@]([H])(N2C=CC(O)=NC2=O)[C@]([H])(O)[C@]1([H])OP(O)(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [H][C@]1(CO)O[C@@]([H])(N2C=CC(O)=NC2=O)[C@]([H])(O)[C@]1([H])OP(O)(O)=O is less likely to interact with the protein MPGAGDGGKAPARWLGTGLLGLFLLPVTLSLEVSVGKATDIYAVNGTEILLPCTFSSCFGFEDLHFRWTYNSSDAFKILIEGTVKNEKSDPKVTLKDDDRITLVGSTKEKMNNISIVLRDLEFSDTGKYTCHVKNPKENNLQHHATIFLQVVDRLEEVDNTVTLIILAVVGGVIGLLILILLIKKLIIFILKKTREKKKECLVSSSGNDNTENGLPGSKAEEKPPSKV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the chances of the drug interacting with the target protein 'MAWRQLRKRALDAVIILGGGGLLFTSYLTATGDDHFYAEYLMPGLQRLLDPESAHRLAVRVTSLGLLPRATFQDSDMLEVKVLGHKFRNPVGIAAGFDKNGEAVDGLYKLGFGFVEVGSVTPQPQEGNPRPRVFRLPEDQAVINRYGFNSHGLSVVEHRLRARQQKQAQLTADGLPLGINLGKNKTSEDAAADYAEGVRTLGPLADYLVVNVSSPNTAGLRSLQGKTELRHLLSKVLQERDALKGTRKPAVLVKIAPDLTAQDKEDIASVARELGIDGLIVTNTTVSRPVGLQGALRSETGGLSGKPLRDLSTQTIREMYALTQGRIPIIGVGGVSSGQDALEKIQAGASLVQLYTALIFLGPPVVVRVKRELEALLKERGFTTVTDAIGADHRR'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAWRQLRKRALDAVIILGGGGLLFTSYLTATGDDHFYAEYLMPGLQRLLDPESAHRLAVRVTSLGLLPRATFQDSDMLEVKVLGHKFRNPVGIAAGFDKNGEAVDGLYKLGFGFVEVGSVTPQPQEGNPRPRVFRLPEDQAVINRYGFNSHGLSVVEHRLRARQQKQAQLTADGLPLGINLGKNKTSEDAAADYAEGVRTLGPLADYLVVNVSSPNTAGLRSLQGKTELRHLLSKVLQERDALKGTRKPAVLVKIAPDLTAQDKEDIASVARELGIDGLIVTNTTVSRPVGLQGALRSETGGLSGKPLRDLSTQTIREMYALTQGRIPIIGVGGVSSGQDALEKIQAGASLVQLYTALIFLGPPVVVRVKRELEALLKERGFTTVTDAIGADHRR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAWRQLRKRALDAVIILGGGGLLFTSYLTATGDDHFYAEYLMPGLQRLLDPESAHRLAVRVTSLGLLPRATFQDSDMLEVKVLGHKFRNPVGIAAGFDKNGEAVDGLYKLGFGFVEVGSVTPQPQEGNPRPRVFRLPEDQAVINRYGFNSHGLSVVEHRLRARQQKQAQLTADGLPLGINLGKNKTSEDAAADYAEGVRTLGPLADYLVVNVSSPNTAGLRSLQGKTELRHLLSKVLQERDALKGTRKPAVLVKIAPDLTAQDKEDIASVARELGIDGLIVTNTTVSRPVGLQGALRSETGGLSGKPLRDLSTQTIREMYALTQGRIPIIGVGGVSSGQDALEKIQAGASLVQLYTALIFLGPPVVVRVKRELEALLKERGFTTVTDAIGADHRR\", \"result\": [{\"smile\": \"CC(=O)C(C#N)C(=O)Nc1ccc(C(F)(F)F)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(=O)C(C#N)C(=O)Nc1ccc(C(F)(F)F)cc1 is less likely to interact with the protein MAWRQLRKRALDAVIILGGGGLLFTSYLTATGDDHFYAEYLMPGLQRLLDPESAHRLAVRVTSLGLLPRATFQDSDMLEVKVLGHKFRNPVGIAAGFDKNGEAVDGLYKLGFGFVEVGSVTPQPQEGNPRPRVFRLPEDQAVINRYGFNSHGLSVVEHRLRARQQKQAQLTADGLPLGINLGKNKTSEDAAADYAEGVRTLGPLADYLVVNVSSPNTAGLRSLQGKTELRHLLSKVLQERDALKGTRKPAVLVKIAPDLTAQDKEDIASVARELGIDGLIVTNTTVSRPVGLQGALRSETGGLSGKPLRDLSTQTIREMYALTQGRIPIIGVGGVSSGQDALEKIQAGASLVQLYTALIFLGPPVVVRVKRELEALLKERGFTTVTDAIGADHRR."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug likely interact with the target protein 'MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC\", \"result\": [{\"smile\": \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1 is less likely to interact with the protein MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug and the target protein 'MGPGVLLLLLVATAWHGQGIPVIEPSVPELVVKPGATVTLRCVGNGSVEWDGPPSPHWTLYSDGSSSILSTNNATFQNTGTYRCTEPGDPLGGSAAIHLYVKDPARPWNVLAQEVVVFEDQDALLPCLLTDPVLEAGVSLVRVRGRPLMRHTNYSFSPWHGFTIHRAKFIQSQDYQCSALMGGRKVMSISIRLKVQKVIPGPPALTLVPAELVRIRGEAAQIVCSASSVDVNFDVFLQHNNTKLAIPQQSDFHNNRYQKVLTLNLDQVDFQHAGNYSCVASNVQGKHSTSMFFRVVESAYLNLSSEQNLIQEVTVGEGLNLKVMVEAYPGLQGFNWTYLGPFSDHQPEPKLANATTKDTYRHTFTLSLPRLKPSEAGRYSFLARNPGGWRALTFELTLRYPPEVSVIWTFINGSGTLLCAASGYPQPNVTWLQCSGHTDRCDEAQVLQVWDDPYPEVLSQEPFHKVTVQSLLTVETLEHNQTYECRAHNSVGSGSWAFIPISAGAHTHPPDEFLFTPVVVACMSIMALLLLLLLLLLYKYKQKPKYQVRWKIIESYEGNSYTFIDPTQLPYNEKWEFPRNNLQFGKTLGAGAFGKVVEATAFGLGKEDAVLKVAVKMLKSTAHADEKEALMSELKIMSHLGQHENIVNLLGACTHGGPVLVITEYCCYGDLLNFLRRKAEAMLGPSLSPGQDPEGGVDYKNIHLEKKYVRRDSGFSSQGVDTYVEMRPVSTSSNDSFSEQDLDKEDGRPLELRDLLHFSSQVAQGMAFLASKNCIHRDVAARNVLLTNGHVAKIGDFGLARDIMNDSNYIVKGNARLPVKWMAPESIFDCVYTVQSDVWSYGILLWEIFSLGLNPYPGILVNSKFYKLVKDGYQMAQPAFAPKNIYSIMQACWALEPTHRPTFQQICSFLQEQAQEDRRERDYTNLPSSSRSGGSGSSSSELEEESSSEHLTCCEQGDIAQPLLQPNNYQFC'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGPGVLLLLLVATAWHGQGIPVIEPSVPELVVKPGATVTLRCVGNGSVEWDGPPSPHWTLYSDGSSSILSTNNATFQNTGTYRCTEPGDPLGGSAAIHLYVKDPARPWNVLAQEVVVFEDQDALLPCLLTDPVLEAGVSLVRVRGRPLMRHTNYSFSPWHGFTIHRAKFIQSQDYQCSALMGGRKVMSISIRLKVQKVIPGPPALTLVPAELVRIRGEAAQIVCSASSVDVNFDVFLQHNNTKLAIPQQSDFHNNRYQKVLTLNLDQVDFQHAGNYSCVASNVQGKHSTSMFFRVVESAYLNLSSEQNLIQEVTVGEGLNLKVMVEAYPGLQGFNWTYLGPFSDHQPEPKLANATTKDTYRHTFTLSLPRLKPSEAGRYSFLARNPGGWRALTFELTLRYPPEVSVIWTFINGSGTLLCAASGYPQPNVTWLQCSGHTDRCDEAQVLQVWDDPYPEVLSQEPFHKVTVQSLLTVETLEHNQTYECRAHNSVGSGSWAFIPISAGAHTHPPDEFLFTPVVVACMSIMALLLLLLLLLLYKYKQKPKYQVRWKIIESYEGNSYTFIDPTQLPYNEKWEFPRNNLQFGKTLGAGAFGKVVEATAFGLGKEDAVLKVAVKMLKSTAHADEKEALMSELKIMSHLGQHENIVNLLGACTHGGPVLVITEYCCYGDLLNFLRRKAEAMLGPSLSPGQDPEGGVDYKNIHLEKKYVRRDSGFSSQGVDTYVEMRPVSTSSNDSFSEQDLDKEDGRPLELRDLLHFSSQVAQGMAFLASKNCIHRDVAARNVLLTNGHVAKIGDFGLARDIMNDSNYIVKGNARLPVKWMAPESIFDCVYTVQSDVWSYGILLWEIFSLGLNPYPGILVNSKFYKLVKDGYQMAQPAFAPKNIYSIMQACWALEPTHRPTFQQICSFLQEQAQEDRRERDYTNLPSSSRSGGSGSSSSELEEESSSEHLTCCEQGDIAQPLLQPNNYQFC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGPGVLLLLLVATAWHGQGIPVIEPSVPELVVKPGATVTLRCVGNGSVEWDGPPSPHWTLYSDGSSSILSTNNATFQNTGTYRCTEPGDPLGGSAAIHLYVKDPARPWNVLAQEVVVFEDQDALLPCLLTDPVLEAGVSLVRVRGRPLMRHTNYSFSPWHGFTIHRAKFIQSQDYQCSALMGGRKVMSISIRLKVQKVIPGPPALTLVPAELVRIRGEAAQIVCSASSVDVNFDVFLQHNNTKLAIPQQSDFHNNRYQKVLTLNLDQVDFQHAGNYSCVASNVQGKHSTSMFFRVVESAYLNLSSEQNLIQEVTVGEGLNLKVMVEAYPGLQGFNWTYLGPFSDHQPEPKLANATTKDTYRHTFTLSLPRLKPSEAGRYSFLARNPGGWRALTFELTLRYPPEVSVIWTFINGSGTLLCAASGYPQPNVTWLQCSGHTDRCDEAQVLQVWDDPYPEVLSQEPFHKVTVQSLLTVETLEHNQTYECRAHNSVGSGSWAFIPISAGAHTHPPDEFLFTPVVVACMSIMALLLLLLLLLLYKYKQKPKYQVRWKIIESYEGNSYTFIDPTQLPYNEKWEFPRNNLQFGKTLGAGAFGKVVEATAFGLGKEDAVLKVAVKMLKSTAHADEKEALMSELKIMSHLGQHENIVNLLGACTHGGPVLVITEYCCYGDLLNFLRRKAEAMLGPSLSPGQDPEGGVDYKNIHLEKKYVRRDSGFSSQGVDTYVEMRPVSTSSNDSFSEQDLDKEDGRPLELRDLLHFSSQVAQGMAFLASKNCIHRDVAARNVLLTNGHVAKIGDFGLARDIMNDSNYIVKGNARLPVKWMAPESIFDCVYTVQSDVWSYGILLWEIFSLGLNPYPGILVNSKFYKLVKDGYQMAQPAFAPKNIYSIMQACWALEPTHRPTFQQICSFLQEQAQEDRRERDYTNLPSSSRSGGSGSSSSELEEESSSEHLTCCEQGDIAQPLLQPNNYQFC\", \"result\": [{\"smile\": \"CC(C)Cn1c2ccc(Nc3ncccn3)cc2c2c3c(c4c(c21)CCc1nn(C)cc1-4)C(=O)NC3\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(C)Cn1c2ccc(Nc3ncccn3)cc2c2c3c(c4c(c21)CCc1nn(C)cc1-4)C(=O)NC3 is less likely to interact with the protein MGPGVLLLLLVATAWHGQGIPVIEPSVPELVVKPGATVTLRCVGNGSVEWDGPPSPHWTLYSDGSSSILSTNNATFQNTGTYRCTEPGDPLGGSAAIHLYVKDPARPWNVLAQEVVVFEDQDALLPCLLTDPVLEAGVSLVRVRGRPLMRHTNYSFSPWHGFTIHRAKFIQSQDYQCSALMGGRKVMSISIRLKVQKVIPGPPALTLVPAELVRIRGEAAQIVCSASSVDVNFDVFLQHNNTKLAIPQQSDFHNNRYQKVLTLNLDQVDFQHAGNYSCVASNVQGKHSTSMFFRVVESAYLNLSSEQNLIQEVTVGEGLNLKVMVEAYPGLQGFNWTYLGPFSDHQPEPKLANATTKDTYRHTFTLSLPRLKPSEAGRYSFLARNPGGWRALTFELTLRYPPEVSVIWTFINGSGTLLCAASGYPQPNVTWLQCSGHTDRCDEAQVLQVWDDPYPEVLSQEPFHKVTVQSLLTVETLEHNQTYECRAHNSVGSGSWAFIPISAGAHTHPPDEFLFTPVVVACMSIMALLLLLLLLLLYKYKQKPKYQVRWKIIESYEGNSYTFIDPTQLPYNEKWEFPRNNLQFGKTLGAGAFGKVVEATAFGLGKEDAVLKVAVKMLKSTAHADEKEALMSELKIMSHLGQHENIVNLLGACTHGGPVLVITEYCCYGDLLNFLRRKAEAMLGPSLSPGQDPEGGVDYKNIHLEKKYVRRDSGFSSQGVDTYVEMRPVSTSSNDSFSEQDLDKEDGRPLELRDLLHFSSQVAQGMAFLASKNCIHRDVAARNVLLTNGHVAKIGDFGLARDIMNDSNYIVKGNARLPVKWMAPESIFDCVYTVQSDVWSYGILLWEIFSLGLNPYPGILVNSKFYKLVKDGYQMAQPAFAPKNIYSIMQACWALEPTHRPTFQQICSFLQEQAQEDRRERDYTNLPSSSRSGGSGSSSSELEEESSSEHLTCCEQGDIAQPLLQPNNYQFC."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you see the drug interacting with the target protein 'MKFFLLLFTIGFCWAQYSPNTQQGRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIHNPFRPWWERYQPVSYKLCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMSGNAVSAGTSSTCGSYFNPGSRDFPAVPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLVGLLDLALEKDYVRSKIAEYMNHLIDIGVAGFRLDASKHMWPGDIKAILDKLHNLNSNWFPAGSKPFIYQEVIDLGGEPIKSSDYFGNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFMPSDRALVFVDNHDNQRGHGAGGASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRQFQNGNDVNDWVGPPNNNGVIKEVTINPDTTCGNDWVCEHRWRQIRNMVNFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIVFNNDDWTFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFIAIHAESKL'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKFFLLLFTIGFCWAQYSPNTQQGRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIHNPFRPWWERYQPVSYKLCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMSGNAVSAGTSSTCGSYFNPGSRDFPAVPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLVGLLDLALEKDYVRSKIAEYMNHLIDIGVAGFRLDASKHMWPGDIKAILDKLHNLNSNWFPAGSKPFIYQEVIDLGGEPIKSSDYFGNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFMPSDRALVFVDNHDNQRGHGAGGASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRQFQNGNDVNDWVGPPNNNGVIKEVTINPDTTCGNDWVCEHRWRQIRNMVNFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIVFNNDDWTFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFIAIHAESKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKFFLLLFTIGFCWAQYSPNTQQGRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIHNPFRPWWERYQPVSYKLCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMSGNAVSAGTSSTCGSYFNPGSRDFPAVPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLVGLLDLALEKDYVRSKIAEYMNHLIDIGVAGFRLDASKHMWPGDIKAILDKLHNLNSNWFPAGSKPFIYQEVIDLGGEPIKSSDYFGNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFMPSDRALVFVDNHDNQRGHGAGGASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRQFQNGNDVNDWVGPPNNNGVIKEVTINPDTTCGNDWVCEHRWRQIRNMVNFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIVFNNDDWTFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFIAIHAESKL\", \"result\": [{\"smile\": \"OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug OC[C@H]1O[C@@H](O)[C@H](O)[C@@H](O)[C@@H]1O is less likely to interact with the protein MKFFLLLFTIGFCWAQYSPNTQQGRTSIVHLFEWRWVDIALECERYLAPKGFGGVQVSPPNENVAIHNPFRPWWERYQPVSYKLCTRSGNEDEFRNMVTRCNNVGVRIYVDAVINHMSGNAVSAGTSSTCGSYFNPGSRDFPAVPYSGWDFNDGKCKTGSGDIENYNDATQVRDCRLVGLLDLALEKDYVRSKIAEYMNHLIDIGVAGFRLDASKHMWPGDIKAILDKLHNLNSNWFPAGSKPFIYQEVIDLGGEPIKSSDYFGNGRVTEFKYGAKLGTVIRKWNGEKMSYLKNWGEGWGFMPSDRALVFVDNHDNQRGHGAGGASILTFWDARLYKMAVGFMLAHPYGFTRVMSSYRWPRQFQNGNDVNDWVGPPNNNGVIKEVTINPDTTCGNDWVCEHRWRQIRNMVNFRNVVDGQPFTNWYDNGSNQVAFGRGNRGFIVFNNDDWTFSLTLQTGLPAGTYCDVISGDKINGNCTGIKIYVSDDGKAHFSISNSAEDPFIAIHAESKL."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the drug's interaction with the target protein 'MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE\", \"result\": [{\"smile\": \"CN1C2=C(C=C(C=C2)NS(=O)(=O)C3=CC=CC=C3)N=C1CC4=CC=C(C=C4)C(=N)N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN1C2=C(C=C(C=C2)NS(=O)(=O)C3=CC=CC=C3)N=C1CC4=CC=C(C=C4)C(=N)N is less likely to interact with the protein MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the likelihood of interaction between the drug and the target protein 'MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM\", \"result\": [{\"smile\": \"N#Cc1cnc2ccc(-c3c(-c4ccc(F)cc4)ncn3C3CNC3)nn12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug N#Cc1cnc2ccc(-c3c(-c4ccc(F)cc4)ncn3C3CNC3)nn12 is less likely to interact with the protein MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the drug with the target protein 'MFLTRSEYDRGVNTFSPEGRLFQVEYAIEAIKLGSTAIGIQTSEGVCLAVEKRITSPLMEPSSIEKIVEIDAHIGCAMSGLIADAKTLIDKARVETQNHWFTYNETMTVESVTQAVSNLALQFGEEDADPGAMSRPFGVALLFGGVDEKGPQLFHMDPSGTFVQCDARAIGSASEGAQSSLQEVYHKSMTLKEAIKSSLIILKQVMEEKLNATNIELATVQPGQNFHMFTKEELEEVIKDI'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MFLTRSEYDRGVNTFSPEGRLFQVEYAIEAIKLGSTAIGIQTSEGVCLAVEKRITSPLMEPSSIEKIVEIDAHIGCAMSGLIADAKTLIDKARVETQNHWFTYNETMTVESVTQAVSNLALQFGEEDADPGAMSRPFGVALLFGGVDEKGPQLFHMDPSGTFVQCDARAIGSASEGAQSSLQEVYHKSMTLKEAIKSSLIILKQVMEEKLNATNIELATVQPGQNFHMFTKEELEEVIKDI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFLTRSEYDRGVNTFSPEGRLFQVEYAIEAIKLGSTAIGIQTSEGVCLAVEKRITSPLMEPSSIEKIVEIDAHIGCAMSGLIADAKTLIDKARVETQNHWFTYNETMTVESVTQAVSNLALQFGEEDADPGAMSRPFGVALLFGGVDEKGPQLFHMDPSGTFVQCDARAIGSASEGAQSSLQEVYHKSMTLKEAIKSSLIILKQVMEEKLNATNIELATVQPGQNFHMFTKEELEEVIKDI\", \"result\": [{\"smile\": \"CCOC1=CC=C(OCC)C(NC(=O)C2=CC(C)=C(OCCN)C(C)=C2)=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCOC1=CC=C(OCC)C(NC(=O)C2=CC(C)=C(OCCN)C(C)=C2)=C1 is less likely to interact with the protein MFLTRSEYDRGVNTFSPEGRLFQVEYAIEAIKLGSTAIGIQTSEGVCLAVEKRITSPLMEPSSIEKIVEIDAHIGCAMSGLIADAKTLIDKARVETQNHWFTYNETMTVESVTQAVSNLALQFGEEDADPGAMSRPFGVALLFGGVDEKGPQLFHMDPSGTFVQCDARAIGSASEGAQSSLQEVYHKSMTLKEAIKSSLIILKQVMEEKLNATNIELATVQPGQNFHMFTKEELEEVIKDI."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you foresee any interaction between the drug and the target protein 'MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\", \"result\": [{\"smile\": \"CC1=C(C(=CC=C1)Cl)NC(=O)C2=CN=C(S2)NC3=NC(=NC(=C3)N4CCN(CC4)CCO)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1=C(C(=CC=C1)Cl)NC(=O)C2=CN=C(S2)NC3=NC(=NC(=C3)N4CCN(CC4)CCO)C is less likely to interact with the protein MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug will engage with the target protein 'MGPEALSSLLLLLLVASGDADMKGHFDPAKCRYALGMQDRTIPDSDISASSSWSDSTAARHSRLESSDGDGAWCPAGSVFPKEEEYLQVDLQRLHLVALVGTQGRHAGGLGKEFSRSYRLRYSRDGRRWMGWKDRWGQEVISGNEDPEGVVLKDLGPPMVARLVRFYPRADRVMSVCLRVELYGCLWRDGLLSYTAPVGQTMYLSEAVYLNDSTYDGHTVGGLQYGGLGQLADGVVGLDDFRKSQELRVWPGYDYVGWSNHSFSSGYVEMEFEFDRLRAFQAMQVHCNNMHTLGARLPGGVECRFRRGPAMAWEGEPMRHNLGGNLGDPRARAVSVPLGGRVARFLQCRFLFAGPWLLFSEISFISDVVNNSSPALGGTFPPAPWWPPGPPPTNFSSLELEPRGQQPVAKAEGSPTAILIGCLVAIILLLLLIIALMLWRLHWRRLLSKAERRVLEEELTVHLSVPGDTILINNRPGPREPPPYQEPRPRGNPPHSAPCVPNGSALLLSNPAYRLLLATYARPPRGPGPPTPAWAKPTNTQAYSGDYMEPEKPGAPLLPPPPQNSVPHYAEADIVTLQGVTGGNTYAVPALPPGAVGDGPPRVDFPRSRLRFKEKLGEGQFGEVHLCEVDSPQDLVSLDFPLNVRKGHPLLVAVKILRPDATKNARNDFLKEVKIMSRLKDPNIIRLLGVCVQDDPLCMITDYMENGDLNQFLSAHQLEDKAAEGAPGDGQAAQGPTISYPMLLHVAAQIASGMRYLATLNFVHRDLATRNCLVGENFTIKIADFGMSRNLYAGDYYRVQGRAVLPIRWMAWECILMGKFTTASDVWAFGVTLWEVLMLCRAQPFGQLTDEQVIENAGEFFRDQGRQVYLSRPPACPQGLYELMLRCWSRESEQRPPFSQLHRFLAEDALNTV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGPEALSSLLLLLLVASGDADMKGHFDPAKCRYALGMQDRTIPDSDISASSSWSDSTAARHSRLESSDGDGAWCPAGSVFPKEEEYLQVDLQRLHLVALVGTQGRHAGGLGKEFSRSYRLRYSRDGRRWMGWKDRWGQEVISGNEDPEGVVLKDLGPPMVARLVRFYPRADRVMSVCLRVELYGCLWRDGLLSYTAPVGQTMYLSEAVYLNDSTYDGHTVGGLQYGGLGQLADGVVGLDDFRKSQELRVWPGYDYVGWSNHSFSSGYVEMEFEFDRLRAFQAMQVHCNNMHTLGARLPGGVECRFRRGPAMAWEGEPMRHNLGGNLGDPRARAVSVPLGGRVARFLQCRFLFAGPWLLFSEISFISDVVNNSSPALGGTFPPAPWWPPGPPPTNFSSLELEPRGQQPVAKAEGSPTAILIGCLVAIILLLLLIIALMLWRLHWRRLLSKAERRVLEEELTVHLSVPGDTILINNRPGPREPPPYQEPRPRGNPPHSAPCVPNGSALLLSNPAYRLLLATYARPPRGPGPPTPAWAKPTNTQAYSGDYMEPEKPGAPLLPPPPQNSVPHYAEADIVTLQGVTGGNTYAVPALPPGAVGDGPPRVDFPRSRLRFKEKLGEGQFGEVHLCEVDSPQDLVSLDFPLNVRKGHPLLVAVKILRPDATKNARNDFLKEVKIMSRLKDPNIIRLLGVCVQDDPLCMITDYMENGDLNQFLSAHQLEDKAAEGAPGDGQAAQGPTISYPMLLHVAAQIASGMRYLATLNFVHRDLATRNCLVGENFTIKIADFGMSRNLYAGDYYRVQGRAVLPIRWMAWECILMGKFTTASDVWAFGVTLWEVLMLCRAQPFGQLTDEQVIENAGEFFRDQGRQVYLSRPPACPQGLYELMLRCWSRESEQRPPFSQLHRFLAEDALNTV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGPEALSSLLLLLLVASGDADMKGHFDPAKCRYALGMQDRTIPDSDISASSSWSDSTAARHSRLESSDGDGAWCPAGSVFPKEEEYLQVDLQRLHLVALVGTQGRHAGGLGKEFSRSYRLRYSRDGRRWMGWKDRWGQEVISGNEDPEGVVLKDLGPPMVARLVRFYPRADRVMSVCLRVELYGCLWRDGLLSYTAPVGQTMYLSEAVYLNDSTYDGHTVGGLQYGGLGQLADGVVGLDDFRKSQELRVWPGYDYVGWSNHSFSSGYVEMEFEFDRLRAFQAMQVHCNNMHTLGARLPGGVECRFRRGPAMAWEGEPMRHNLGGNLGDPRARAVSVPLGGRVARFLQCRFLFAGPWLLFSEISFISDVVNNSSPALGGTFPPAPWWPPGPPPTNFSSLELEPRGQQPVAKAEGSPTAILIGCLVAIILLLLLIIALMLWRLHWRRLLSKAERRVLEEELTVHLSVPGDTILINNRPGPREPPPYQEPRPRGNPPHSAPCVPNGSALLLSNPAYRLLLATYARPPRGPGPPTPAWAKPTNTQAYSGDYMEPEKPGAPLLPPPPQNSVPHYAEADIVTLQGVTGGNTYAVPALPPGAVGDGPPRVDFPRSRLRFKEKLGEGQFGEVHLCEVDSPQDLVSLDFPLNVRKGHPLLVAVKILRPDATKNARNDFLKEVKIMSRLKDPNIIRLLGVCVQDDPLCMITDYMENGDLNQFLSAHQLEDKAAEGAPGDGQAAQGPTISYPMLLHVAAQIASGMRYLATLNFVHRDLATRNCLVGENFTIKIADFGMSRNLYAGDYYRVQGRAVLPIRWMAWECILMGKFTTASDVWAFGVTLWEVLMLCRAQPFGQLTDEQVIENAGEFFRDQGRQVYLSRPPACPQGLYELMLRCWSRESEQRPPFSQLHRFLAEDALNTV\", \"result\": [{\"smile\": \"CCCNS(=O)(=O)NC1=C(C(OCCOC2=NC=C(Br)C=N2)=NC=N1)C1=CC=C(Br)C=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCCNS(=O)(=O)NC1=C(C(OCCOC2=NC=C(Br)C=N2)=NC=N1)C1=CC=C(Br)C=C1 is less likely to interact with the protein MGPEALSSLLLLLLVASGDADMKGHFDPAKCRYALGMQDRTIPDSDISASSSWSDSTAARHSRLESSDGDGAWCPAGSVFPKEEEYLQVDLQRLHLVALVGTQGRHAGGLGKEFSRSYRLRYSRDGRRWMGWKDRWGQEVISGNEDPEGVVLKDLGPPMVARLVRFYPRADRVMSVCLRVELYGCLWRDGLLSYTAPVGQTMYLSEAVYLNDSTYDGHTVGGLQYGGLGQLADGVVGLDDFRKSQELRVWPGYDYVGWSNHSFSSGYVEMEFEFDRLRAFQAMQVHCNNMHTLGARLPGGVECRFRRGPAMAWEGEPMRHNLGGNLGDPRARAVSVPLGGRVARFLQCRFLFAGPWLLFSEISFISDVVNNSSPALGGTFPPAPWWPPGPPPTNFSSLELEPRGQQPVAKAEGSPTAILIGCLVAIILLLLLIIALMLWRLHWRRLLSKAERRVLEEELTVHLSVPGDTILINNRPGPREPPPYQEPRPRGNPPHSAPCVPNGSALLLSNPAYRLLLATYARPPRGPGPPTPAWAKPTNTQAYSGDYMEPEKPGAPLLPPPPQNSVPHYAEADIVTLQGVTGGNTYAVPALPPGAVGDGPPRVDFPRSRLRFKEKLGEGQFGEVHLCEVDSPQDLVSLDFPLNVRKGHPLLVAVKILRPDATKNARNDFLKEVKIMSRLKDPNIIRLLGVCVQDDPLCMITDYMENGDLNQFLSAHQLEDKAAEGAPGDGQAAQGPTISYPMLLHVAAQIASGMRYLATLNFVHRDLATRNCLVGENFTIKIADFGMSRNLYAGDYYRVQGRAVLPIRWMAWECILMGKFTTASDVWAFGVTLWEVLMLCRAQPFGQLTDEQVIENAGEFFRDQGRQVYLSRPPACPQGLYELMLRCWSRESEQRPPFSQLHRFLAEDALNTV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome for the drug interacting with the target protein 'MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS\", \"result\": [{\"smile\": \"COCC(F)(F)CN1[C@H](c2ccc(NC3CN(CCCF)C3)cn2)c2ccc3[nH]ncc3c2C[C@H]1C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COCC(F)(F)CN1[C@H](c2ccc(NC3CN(CCCF)C3)cn2)c2ccc3[nH]ncc3c2C[C@H]1C is less likely to interact with the protein MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug interact with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR', based on your evaluation? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CCC(NCCCC[C@@H](Cc1cc(C)c(F)c(C)c1)C(=O)NO)c1ccc(F)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCC(NCCCC[C@@H](Cc1cc(C)c(F)c(C)c1)C(=O)NO)c1ccc(F)cc1 is less likely to interact with the protein MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug and the target protein 'MSDPAGNRPKTEKELKKEAEKAAKLAKFEEKQKKLAEKKAAASDKPVKEAKAKKEQTVEAAEPVDQTPTGQRKKIDGEIPAAYFPGYVESGWYSWWEKEGFFKPEYIDKLNPGSNPADSFTVCIPPPNVTGNLHVGHALATTVEDTITRFNRMHGKRTLFNPGCDHAGIATQVVVEKRLKRERGLTRHDLGRDRFNQEVWHWKNEKGDVIYDQFRKLGASVDWDRAVFTMDPKMCRAVTEAFIRMHESGTIYRSNRLVNWSCALRSAISDIEVDKKELTGSTLIAVPGYDKKIEFGVLNSFAYKIQGSDEEIVVSTTRIETMLGDSGVAVHPDDQRYKHLVGKQCIHPFIPTRNLPIFADSFVEMEFGTGAVKITPAHDHNDYEVGIRQNLPFHNCITDDGLISQGCGEFSGMKRFDARTAVIEALKEKGLYRGKEDNPMVVPTCSRSKDVIEPILKPQWYVKCAHMAEKAVAAVANGDLQIIPEFHKATWNRWLESSRDWCISRQLWWGHRIPAYYISFADGREQPLPEENYWVSARTEQEALAKAAQKFQVPEAEILLKWDEDVLDTWFSSGMWPFAVFGWPDATKDMDLFFPGAVLETGHDILFFWVARMVFMAQELTGKLPFKEILLHAMIRDAHGRKMSKSLGNVIDPLDVIRGISLNDLQAQLLGGNLDEKEIAVAKEGQARDYPDGIPECGVDALRFALLSYTSQGRDINLDVLRVHGYRKFCNKLWQVVRFALARISDKPEQKPTFEINLKSATPTDLWILSRLAKAVKETNEALKAYNFTQATTVTYNFWLYDFCDVYVETIKPVLYGDNTTLRQVAISVLHKCIDTGLRLISPLMPFISEELWQRMPRLDDSDYTSPSIIVAQYPLTQKYEKYQNEKLEAAFEFAQELIGKVRSLRADYDLKKTKITMQILSETPEDESMLNDISAVITTLTFSEKVSILNKCESDKIEKGSAHIACGGRCQVYINLTGIIDVPKEIEKLGAKLQKNQISVKKIGDIQSSADYEQKVPVDIRALDQEKKATLEKEIENITAAIAQLKALN'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSDPAGNRPKTEKELKKEAEKAAKLAKFEEKQKKLAEKKAAASDKPVKEAKAKKEQTVEAAEPVDQTPTGQRKKIDGEIPAAYFPGYVESGWYSWWEKEGFFKPEYIDKLNPGSNPADSFTVCIPPPNVTGNLHVGHALATTVEDTITRFNRMHGKRTLFNPGCDHAGIATQVVVEKRLKRERGLTRHDLGRDRFNQEVWHWKNEKGDVIYDQFRKLGASVDWDRAVFTMDPKMCRAVTEAFIRMHESGTIYRSNRLVNWSCALRSAISDIEVDKKELTGSTLIAVPGYDKKIEFGVLNSFAYKIQGSDEEIVVSTTRIETMLGDSGVAVHPDDQRYKHLVGKQCIHPFIPTRNLPIFADSFVEMEFGTGAVKITPAHDHNDYEVGIRQNLPFHNCITDDGLISQGCGEFSGMKRFDARTAVIEALKEKGLYRGKEDNPMVVPTCSRSKDVIEPILKPQWYVKCAHMAEKAVAAVANGDLQIIPEFHKATWNRWLESSRDWCISRQLWWGHRIPAYYISFADGREQPLPEENYWVSARTEQEALAKAAQKFQVPEAEILLKWDEDVLDTWFSSGMWPFAVFGWPDATKDMDLFFPGAVLETGHDILFFWVARMVFMAQELTGKLPFKEILLHAMIRDAHGRKMSKSLGNVIDPLDVIRGISLNDLQAQLLGGNLDEKEIAVAKEGQARDYPDGIPECGVDALRFALLSYTSQGRDINLDVLRVHGYRKFCNKLWQVVRFALARISDKPEQKPTFEINLKSATPTDLWILSRLAKAVKETNEALKAYNFTQATTVTYNFWLYDFCDVYVETIKPVLYGDNTTLRQVAISVLHKCIDTGLRLISPLMPFISEELWQRMPRLDDSDYTSPSIIVAQYPLTQKYEKYQNEKLEAAFEFAQELIGKVRSLRADYDLKKTKITMQILSETPEDESMLNDISAVITTLTFSEKVSILNKCESDKIEKGSAHIACGGRCQVYINLTGIIDVPKEIEKLGAKLQKNQISVKKIGDIQSSADYEQKVPVDIRALDQEKKATLEKEIENITAAIAQLKALN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSDPAGNRPKTEKELKKEAEKAAKLAKFEEKQKKLAEKKAAASDKPVKEAKAKKEQTVEAAEPVDQTPTGQRKKIDGEIPAAYFPGYVESGWYSWWEKEGFFKPEYIDKLNPGSNPADSFTVCIPPPNVTGNLHVGHALATTVEDTITRFNRMHGKRTLFNPGCDHAGIATQVVVEKRLKRERGLTRHDLGRDRFNQEVWHWKNEKGDVIYDQFRKLGASVDWDRAVFTMDPKMCRAVTEAFIRMHESGTIYRSNRLVNWSCALRSAISDIEVDKKELTGSTLIAVPGYDKKIEFGVLNSFAYKIQGSDEEIVVSTTRIETMLGDSGVAVHPDDQRYKHLVGKQCIHPFIPTRNLPIFADSFVEMEFGTGAVKITPAHDHNDYEVGIRQNLPFHNCITDDGLISQGCGEFSGMKRFDARTAVIEALKEKGLYRGKEDNPMVVPTCSRSKDVIEPILKPQWYVKCAHMAEKAVAAVANGDLQIIPEFHKATWNRWLESSRDWCISRQLWWGHRIPAYYISFADGREQPLPEENYWVSARTEQEALAKAAQKFQVPEAEILLKWDEDVLDTWFSSGMWPFAVFGWPDATKDMDLFFPGAVLETGHDILFFWVARMVFMAQELTGKLPFKEILLHAMIRDAHGRKMSKSLGNVIDPLDVIRGISLNDLQAQLLGGNLDEKEIAVAKEGQARDYPDGIPECGVDALRFALLSYTSQGRDINLDVLRVHGYRKFCNKLWQVVRFALARISDKPEQKPTFEINLKSATPTDLWILSRLAKAVKETNEALKAYNFTQATTVTYNFWLYDFCDVYVETIKPVLYGDNTTLRQVAISVLHKCIDTGLRLISPLMPFISEELWQRMPRLDDSDYTSPSIIVAQYPLTQKYEKYQNEKLEAAFEFAQELIGKVRSLRADYDLKKTKITMQILSETPEDESMLNDISAVITTLTFSEKVSILNKCESDKIEKGSAHIACGGRCQVYINLTGIIDVPKEIEKLGAKLQKNQISVKKIGDIQSSADYEQKVPVDIRALDQEKKATLEKEIENITAAIAQLKALN\", \"result\": [{\"smile\": \"CC(C(=O)O)C(=O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(C(=O)O)C(=O)O is less likely to interact with the protein MSDPAGNRPKTEKELKKEAEKAAKLAKFEEKQKKLAEKKAAASDKPVKEAKAKKEQTVEAAEPVDQTPTGQRKKIDGEIPAAYFPGYVESGWYSWWEKEGFFKPEYIDKLNPGSNPADSFTVCIPPPNVTGNLHVGHALATTVEDTITRFNRMHGKRTLFNPGCDHAGIATQVVVEKRLKRERGLTRHDLGRDRFNQEVWHWKNEKGDVIYDQFRKLGASVDWDRAVFTMDPKMCRAVTEAFIRMHESGTIYRSNRLVNWSCALRSAISDIEVDKKELTGSTLIAVPGYDKKIEFGVLNSFAYKIQGSDEEIVVSTTRIETMLGDSGVAVHPDDQRYKHLVGKQCIHPFIPTRNLPIFADSFVEMEFGTGAVKITPAHDHNDYEVGIRQNLPFHNCITDDGLISQGCGEFSGMKRFDARTAVIEALKEKGLYRGKEDNPMVVPTCSRSKDVIEPILKPQWYVKCAHMAEKAVAAVANGDLQIIPEFHKATWNRWLESSRDWCISRQLWWGHRIPAYYISFADGREQPLPEENYWVSARTEQEALAKAAQKFQVPEAEILLKWDEDVLDTWFSSGMWPFAVFGWPDATKDMDLFFPGAVLETGHDILFFWVARMVFMAQELTGKLPFKEILLHAMIRDAHGRKMSKSLGNVIDPLDVIRGISLNDLQAQLLGGNLDEKEIAVAKEGQARDYPDGIPECGVDALRFALLSYTSQGRDINLDVLRVHGYRKFCNKLWQVVRFALARISDKPEQKPTFEINLKSATPTDLWILSRLAKAVKETNEALKAYNFTQATTVTYNFWLYDFCDVYVETIKPVLYGDNTTLRQVAISVLHKCIDTGLRLISPLMPFISEELWQRMPRLDDSDYTSPSIIVAQYPLTQKYEKYQNEKLEAAFEFAQELIGKVRSLRADYDLKKTKITMQILSETPEDESMLNDISAVITTLTFSEKVSILNKCESDKIEKGSAHIACGGRCQVYINLTGIIDVPKEIEKLGAKLQKNQISVKKIGDIQSSADYEQKVPVDIRALDQEKKATLEKEIENITAAIAQLKALN."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely do you think it is that the drug will interact with the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC2=NC=CC(=N2)N(C)C3=CC4=NN(C(=C4C=C3)C)C)S(=O)(=O)N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1=C(C=C(C=C1)NC2=NC=CC(=N2)N(C)C3=CC4=NN(C(=C4C=C3)C)C)S(=O)(=O)N is less likely to interact with the protein MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug's interaction with the target protein 'MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\", \"result\": [{\"smile\": \"CN1C2=C(C=C(C=C2)OC3=CC(=NC=C3)C4=NC=C(N4)C(F)(F)F)N=C1NC5=CC=C(C=C5)C(F)(F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN1C2=C(C=C(C=C2)OC3=CC(=NC=C3)C4=NC=C(N4)C(F)(F)F)N=C1NC5=CC=C(C=C5)C(F)(F)F is less likely to interact with the protein MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction on whether the drug will interact with the target protein 'MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV\", \"result\": [{\"smile\": \"[H][C@]12CN(C)CCN1C1=C(CC3=CC=CC=C23)C=CC=N1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [H][C@]12CN(C)CCN1C1=C(CC3=CC=CC=C23)C=CC=N1 is less likely to interact with the protein MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the chance that the drug interacts with the target protein 'MKIAVIGAGVTGLAAAARIASQGHEVTIFEKNNNVGGRMNQLKKDGFTFDMGPTIVMMPDVYKDVFTACGKNYEDYIELRQLRYIYDVYFDHDDRITVPTDLAELQQMLESIEPGSTHGFMSFLTDVYKKYEIARRYFLERTYRKPSDFYNMTSLVQGAKLKTLNHADQLIEHYIDNEKIQKLLAFQTLYIGIDPKRGPSLYSIIPMIEMMFGVHFIKGGMYGMAQGLAQLNKDLGVNIELNAEIEQIIIDPKFKRADAIKVNGDIRKFDKILCTADFPSVAESLMPDFAPIKKYPPHKIADLDYSCSAFLMYIGIDIDVTDQVRLHNVIFSDDFRGNIEEIFEGRLSYDPSIYVYVPAVADKSLAPEGKTGIYVLMPTPELKTGSGIDWSDEALTQQIKEIIYRKLATIEVFEDIKSHIVSETIFTPNDFEQTYHAKFGSAFGLMPTLAQSNYYRPQNVSRDYKDLYFAGASTHPGAGVPIVLTSAKITVDEMIKDIERGV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKIAVIGAGVTGLAAAARIASQGHEVTIFEKNNNVGGRMNQLKKDGFTFDMGPTIVMMPDVYKDVFTACGKNYEDYIELRQLRYIYDVYFDHDDRITVPTDLAELQQMLESIEPGSTHGFMSFLTDVYKKYEIARRYFLERTYRKPSDFYNMTSLVQGAKLKTLNHADQLIEHYIDNEKIQKLLAFQTLYIGIDPKRGPSLYSIIPMIEMMFGVHFIKGGMYGMAQGLAQLNKDLGVNIELNAEIEQIIIDPKFKRADAIKVNGDIRKFDKILCTADFPSVAESLMPDFAPIKKYPPHKIADLDYSCSAFLMYIGIDIDVTDQVRLHNVIFSDDFRGNIEEIFEGRLSYDPSIYVYVPAVADKSLAPEGKTGIYVLMPTPELKTGSGIDWSDEALTQQIKEIIYRKLATIEVFEDIKSHIVSETIFTPNDFEQTYHAKFGSAFGLMPTLAQSNYYRPQNVSRDYKDLYFAGASTHPGAGVPIVLTSAKITVDEMIKDIERGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKIAVIGAGVTGLAAAARIASQGHEVTIFEKNNNVGGRMNQLKKDGFTFDMGPTIVMMPDVYKDVFTACGKNYEDYIELRQLRYIYDVYFDHDDRITVPTDLAELQQMLESIEPGSTHGFMSFLTDVYKKYEIARRYFLERTYRKPSDFYNMTSLVQGAKLKTLNHADQLIEHYIDNEKIQKLLAFQTLYIGIDPKRGPSLYSIIPMIEMMFGVHFIKGGMYGMAQGLAQLNKDLGVNIELNAEIEQIIIDPKFKRADAIKVNGDIRKFDKILCTADFPSVAESLMPDFAPIKKYPPHKIADLDYSCSAFLMYIGIDIDVTDQVRLHNVIFSDDFRGNIEEIFEGRLSYDPSIYVYVPAVADKSLAPEGKTGIYVLMPTPELKTGSGIDWSDEALTQQIKEIIYRKLATIEVFEDIKSHIVSETIFTPNDFEQTYHAKFGSAFGLMPTLAQSNYYRPQNVSRDYKDLYFAGASTHPGAGVPIVLTSAKITVDEMIKDIERGV\", \"result\": [{\"smile\": \"CN(C/C=C/c1ccc(-c2ccccc2)cc1)Cc1cccc2cc[nH]c12.Cl\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CN(C/C=C/c1ccc(-c2ccccc2)cc1)Cc1cccc2cc[nH]c12.Cl is less likely to interact with the protein MKIAVIGAGVTGLAAAARIASQGHEVTIFEKNNNVGGRMNQLKKDGFTFDMGPTIVMMPDVYKDVFTACGKNYEDYIELRQLRYIYDVYFDHDDRITVPTDLAELQQMLESIEPGSTHGFMSFLTDVYKKYEIARRYFLERTYRKPSDFYNMTSLVQGAKLKTLNHADQLIEHYIDNEKIQKLLAFQTLYIGIDPKRGPSLYSIIPMIEMMFGVHFIKGGMYGMAQGLAQLNKDLGVNIELNAEIEQIIIDPKFKRADAIKVNGDIRKFDKILCTADFPSVAESLMPDFAPIKKYPPHKIADLDYSCSAFLMYIGIDIDVTDQVRLHNVIFSDDFRGNIEEIFEGRLSYDPSIYVYVPAVADKSLAPEGKTGIYVLMPTPELKTGSGIDWSDEALTQQIKEIIYRKLATIEVFEDIKSHIVSETIFTPNDFEQTYHAKFGSAFGLMPTLAQSNYYRPQNVSRDYKDLYFAGASTHPGAGVPIVLTSAKITVDEMIKDIERGV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction expected between the drug and the target protein 'MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV\", \"result\": [{\"smile\": \"CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F is less likely to interact with the protein MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge whether the drug will interact with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1 is less likely to interact with the protein MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug and the target protein 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\", \"result\": [{\"smile\": \"COc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5c(cnn5C)c4)n3)c3ccccc23)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug COc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5c(cnn5C)c4)n3)c3ccccc23)cc1 is less likely to interact with the protein MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of interaction between the drug and the target protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": [{\"smile\": \"C[C@H](O)C(=O)N1CCC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C[C@H](O)C(=O)N1CCC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1 is less likely to interact with the protein MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug possibly engage with the target protein 'MAFLASGPYLTHQQKVLRLYKRALRHLESWCVQRDKYRYFACLMRARFEEHKNEKDMAKATQLLKEAEEEFWYRQHPQPYIFPDSPGGTSYERYDCYKVPEWCLDDWHPSEKAMYPDYFAKREQWKKLRRESWEREVKQLQEETPPGGPLTEALPPARKEGDLPPLWWYIVTRPRERPM'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAFLASGPYLTHQQKVLRLYKRALRHLESWCVQRDKYRYFACLMRARFEEHKNEKDMAKATQLLKEAEEEFWYRQHPQPYIFPDSPGGTSYERYDCYKVPEWCLDDWHPSEKAMYPDYFAKREQWKKLRRESWEREVKQLQEETPPGGPLTEALPPARKEGDLPPLWWYIVTRPRERPM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAFLASGPYLTHQQKVLRLYKRALRHLESWCVQRDKYRYFACLMRARFEEHKNEKDMAKATQLLKEAEEEFWYRQHPQPYIFPDSPGGTSYERYDCYKVPEWCLDDWHPSEKAMYPDYFAKREQWKKLRRESWEREVKQLQEETPPGGPLTEALPPARKEGDLPPLWWYIVTRPRERPM\", \"result\": [{\"smile\": \"CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12 is less likely to interact with the protein MAFLASGPYLTHQQKVLRLYKRALRHLESWCVQRDKYRYFACLMRARFEEHKNEKDMAKATQLLKEAEEEFWYRQHPQPYIFPDSPGGTSYERYDCYKVPEWCLDDWHPSEKAMYPDYFAKREQWKKLRRESWEREVKQLQEETPPGGPLTEALPPARKEGDLPPLWWYIVTRPRERPM."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be an interaction of the drug with the target protein 'MDSPCQPQPLSQALPQLPGSSSEPLEPEPGRARMGVESYLPCPLLPSYHCPGVPSEASAGSGTPRATATSTTASPLRDGFGGQDGGELRPLQSEGAAALVTKGCQRLAAQGARPEAPKRKWAEDGGDAPSPSKRPWARQENQEAEREGGMSCSCSSGSGEASAGLMEEALPSAPERLALDYIVPCMRYYGICVKDSFLGAALGGRVLAEVEALKRGGRLRDGQLVSQRAIPPRSIRGDQIAWVEGHEPGCRSIGALMAHVDAVIRHCAGRLGSYVINGRTKAMVACYPGNGLGYVRHVDNPHGDGRCITCIYYLNQNWDVKVHGGLLQIFPEGRPVVANIEPLFDRLLIFWSDRRNPHEVKPAYATRYAITVWYFDAKERAAAKDKYQLASGQKGVQVPVSQPPTPT'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDSPCQPQPLSQALPQLPGSSSEPLEPEPGRARMGVESYLPCPLLPSYHCPGVPSEASAGSGTPRATATSTTASPLRDGFGGQDGGELRPLQSEGAAALVTKGCQRLAAQGARPEAPKRKWAEDGGDAPSPSKRPWARQENQEAEREGGMSCSCSSGSGEASAGLMEEALPSAPERLALDYIVPCMRYYGICVKDSFLGAALGGRVLAEVEALKRGGRLRDGQLVSQRAIPPRSIRGDQIAWVEGHEPGCRSIGALMAHVDAVIRHCAGRLGSYVINGRTKAMVACYPGNGLGYVRHVDNPHGDGRCITCIYYLNQNWDVKVHGGLLQIFPEGRPVVANIEPLFDRLLIFWSDRRNPHEVKPAYATRYAITVWYFDAKERAAAKDKYQLASGQKGVQVPVSQPPTPT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSPCQPQPLSQALPQLPGSSSEPLEPEPGRARMGVESYLPCPLLPSYHCPGVPSEASAGSGTPRATATSTTASPLRDGFGGQDGGELRPLQSEGAAALVTKGCQRLAAQGARPEAPKRKWAEDGGDAPSPSKRPWARQENQEAEREGGMSCSCSSGSGEASAGLMEEALPSAPERLALDYIVPCMRYYGICVKDSFLGAALGGRVLAEVEALKRGGRLRDGQLVSQRAIPPRSIRGDQIAWVEGHEPGCRSIGALMAHVDAVIRHCAGRLGSYVINGRTKAMVACYPGNGLGYVRHVDNPHGDGRCITCIYYLNQNWDVKVHGGLLQIFPEGRPVVANIEPLFDRLLIFWSDRRNPHEVKPAYATRYAITVWYFDAKERAAAKDKYQLASGQKGVQVPVSQPPTPT\", \"result\": [{\"smile\": \"CCCCCCCCCCCCCCCCCC(=O)OC[C@H](CO[P@@](O)(=O)OCC(O)CO[P@](O)(=O)OC[C@@H](COC(=O)CCCCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCCCC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCCCCCCCCCCCCCCCCC(=O)OC[C@H](CO[P@@](O)(=O)OCC(O)CO[P@](O)(=O)OC[C@@H](COC(=O)CCCCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCCCC is less likely to interact with the protein MDSPCQPQPLSQALPQLPGSSSEPLEPEPGRARMGVESYLPCPLLPSYHCPGVPSEASAGSGTPRATATSTTASPLRDGFGGQDGGELRPLQSEGAAALVTKGCQRLAAQGARPEAPKRKWAEDGGDAPSPSKRPWARQENQEAEREGGMSCSCSSGSGEASAGLMEEALPSAPERLALDYIVPCMRYYGICVKDSFLGAALGGRVLAEVEALKRGGRLRDGQLVSQRAIPPRSIRGDQIAWVEGHEPGCRSIGALMAHVDAVIRHCAGRLGSYVINGRTKAMVACYPGNGLGYVRHVDNPHGDGRCITCIYYLNQNWDVKVHGGLLQIFPEGRPVVANIEPLFDRLLIFWSDRRNPHEVKPAYATRYAITVWYFDAKERAAAKDKYQLASGQKGVQVPVSQPPTPT."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug is likely to interact with the target protein 'MDEPSPLAQPLELNQHSRFIIGSVSEDNSEDEISNLVKLDLLEEKEGSLSPASVGSDTLSDLGISSLQDGLALHIRSSMSGLHLVKQGRDRKKIDSQRDFTVASPAEFVTRFGGNKVIEKVLIANNGIAAVKCMRSIRRWSYEMFRNERAIRFVVMVTPEDLKANAEYIKMADHYVPVPGGPNNNNYANVELILDIAKRIPVQAVWAGWGHASENPKLPELLLKNGIAFMGPPSQAMWALGDKIASSIVAQTAGIPTLPWSGSGLRVDWQENDFSKRILNVPQELYEKGYVKDVDDGLQAAEEVGYPVMIKASEGGGGKGIRKVNNADDFPNLFRQVQAEVPGSPIFVMRLAKQSRHLEVQILADQYGNAISLFGRDCSVQRRHQKIIEEAPATIATPAVFEHMEQCAVKLAKMVGYVSAGTVEYLYSQDGSFYFLELNPRLQVEHPCTEMVADVNLPAAQLQIAMGIPLYRIKDIRMMYGVSPWGDSPIDFEDSAHVPCPRGHVIAARITSENPDEGFKPSSGTVQELNFRSNKNVWGYFSVAAAGGLHEFADSQFGHCFSWGENREEAISNMVVALKELSIRGDFRTTVEYLIKLLETESFQMNRIDTGWLDRLIAEKVQAERPDTMLGVVCGALHVADVSLRNSVSNFLHSLERGQVLPAHTLLNTVDVELIYEGVKYVLKVTRQSPNSYVVIMNGSCVEVDVHRLSDGGLLLSYDGSSYTTYMKEEVDRYRITIGNKTCVFEKENDPSVMRSPSAGKLIQYIVEDGGHVFAGQCYAEIEVMKMVMTLTAVESGCIHYVKRPGAALDPGCVLAKMQLDNPSKVQQAELHTGSLPRIQSTALRGEKLHRVFHYVLDNLVNVMNGYCLPDPFFSSKVKDWVERLMKTLRDPSLPLLELQDIMTSVSGRIPPNVEKSIKKEMAQYASNITSVLCQFPSQQIANILDSHAATLNRKSEREVFFMNTQSIVQLVQRYRSGIRGHMKAVVMDLLRQYLRVETQFQNGHYDKCVFALREENKSDMNTVLNYIFSHAQVTKKNLLVTMLIDQLCGRDPTLTDELLNILTELTQLSKTTNAKVALRARQVLIASHLPSYELRHNQVESIFLSAIDMYGHQFCIENLQKLILSETSIFDVLPNFFYHSNQVVRMAALEVYVRRAYIAYELNSVQHRQLKDNTCVVEFQFMLPTSHPNRGNIPTLNRMSFSSNLNHYGMTHVASVSDVLLDNSFTPPCQRMGGMVSFRTFEDFVRIFDEVMGCFSDSPPQSPTFPEAGHTSLYDEDKVPRDEPIHILNVAIKTDCDIEDDRLAAMFREFTQQNKATLVDHGIRRLTFLVAQKDFRKQVNYEVDRRFHREFPKFFTFRARDKFEEDRIYRHLEPALAFQLELNRMRNFDLTAIPCANHKMHLYLGAAKVEVGTEVTDYRFFVRAIIRHSDLVTKEASFEYLQNEGERLLLEAMDELEVAFNNTNVRTDCNHIFLNFVPTVIMDPSKIEESVRSMVMRYGSRLWKLRVLQAELKINIRLTPTGKAIPIRLFLTNESGYYLDISLYKEVTDSRTAQIMFQAYGDKQGPLHGMLINTPYVTKDLLQSKRFQAQSLGTTYIYDIPEMFRQSLIKLWESMSTQAFLPSPPLPSDMLTYTELVLDDQGQLVHMNRLPGGNEIGMVAWKMTFKSPEYPEGRDIIVIGNDITYRIGSFGPQEDLLFLRASELARAEGIPRIYVSANSGARIGLAEEIRHMFHVAWVDPEDPYKGYRYLYLTPQDYKRVSALNSVHCEHVEDEGESRYKITDIIGKEEGIGPENLRGSGMIAGESSLAYNEIITISLVTCRAIGIGAYLVRLGQRTIQVENSHLILTGAGALNKVLGREVYTSNNQLGGIQIMHNNGVTHCTVCDDFEGVFTVLHWLSYMPKSVHSSVPLLNSKDPIDRIIEFVPTKTPYDPRWMLAGRPHPTQKGQWLSGFFDYGSFSEIMQPWAQTVVVGRARLGGIPVGVVAVETRTVELSIPADPANLDSEAKIIQQAGQVWFPDSAFKTYQAIKDFNREGLPLMVFANWRGFSGGMKDMYDQVLKFGAYIVDGLRECCQPVLVYIPPQAELRGGSWVVIDSSINPRHMEMYADRESRGSVLEPEGTVEIKFRRKDLVKTMRRVDPVYIHLAERLGTPELSTAERKELENKLKEREEFLIPIYHQVAVQFADLHDTPGRMQEKGVISDILDWKTSRTFFYWRLRRLLLEDLVKKKIHNANPELTDGQIQAMLRRWFVEVEGTVKAYVWDNNKDLAEWLEKQLTEEDGVHSVIEENIKCISRDYVLKQIRSLVQANPEVAMDSIIHMTQHISPTQRAEVIRILSTMDSPST'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDEPSPLAQPLELNQHSRFIIGSVSEDNSEDEISNLVKLDLLEEKEGSLSPASVGSDTLSDLGISSLQDGLALHIRSSMSGLHLVKQGRDRKKIDSQRDFTVASPAEFVTRFGGNKVIEKVLIANNGIAAVKCMRSIRRWSYEMFRNERAIRFVVMVTPEDLKANAEYIKMADHYVPVPGGPNNNNYANVELILDIAKRIPVQAVWAGWGHASENPKLPELLLKNGIAFMGPPSQAMWALGDKIASSIVAQTAGIPTLPWSGSGLRVDWQENDFSKRILNVPQELYEKGYVKDVDDGLQAAEEVGYPVMIKASEGGGGKGIRKVNNADDFPNLFRQVQAEVPGSPIFVMRLAKQSRHLEVQILADQYGNAISLFGRDCSVQRRHQKIIEEAPATIATPAVFEHMEQCAVKLAKMVGYVSAGTVEYLYSQDGSFYFLELNPRLQVEHPCTEMVADVNLPAAQLQIAMGIPLYRIKDIRMMYGVSPWGDSPIDFEDSAHVPCPRGHVIAARITSENPDEGFKPSSGTVQELNFRSNKNVWGYFSVAAAGGLHEFADSQFGHCFSWGENREEAISNMVVALKELSIRGDFRTTVEYLIKLLETESFQMNRIDTGWLDRLIAEKVQAERPDTMLGVVCGALHVADVSLRNSVSNFLHSLERGQVLPAHTLLNTVDVELIYEGVKYVLKVTRQSPNSYVVIMNGSCVEVDVHRLSDGGLLLSYDGSSYTTYMKEEVDRYRITIGNKTCVFEKENDPSVMRSPSAGKLIQYIVEDGGHVFAGQCYAEIEVMKMVMTLTAVESGCIHYVKRPGAALDPGCVLAKMQLDNPSKVQQAELHTGSLPRIQSTALRGEKLHRVFHYVLDNLVNVMNGYCLPDPFFSSKVKDWVERLMKTLRDPSLPLLELQDIMTSVSGRIPPNVEKSIKKEMAQYASNITSVLCQFPSQQIANILDSHAATLNRKSEREVFFMNTQSIVQLVQRYRSGIRGHMKAVVMDLLRQYLRVETQFQNGHYDKCVFALREENKSDMNTVLNYIFSHAQVTKKNLLVTMLIDQLCGRDPTLTDELLNILTELTQLSKTTNAKVALRARQVLIASHLPSYELRHNQVESIFLSAIDMYGHQFCIENLQKLILSETSIFDVLPNFFYHSNQVVRMAALEVYVRRAYIAYELNSVQHRQLKDNTCVVEFQFMLPTSHPNRGNIPTLNRMSFSSNLNHYGMTHVASVSDVLLDNSFTPPCQRMGGMVSFRTFEDFVRIFDEVMGCFSDSPPQSPTFPEAGHTSLYDEDKVPRDEPIHILNVAIKTDCDIEDDRLAAMFREFTQQNKATLVDHGIRRLTFLVAQKDFRKQVNYEVDRRFHREFPKFFTFRARDKFEEDRIYRHLEPALAFQLELNRMRNFDLTAIPCANHKMHLYLGAAKVEVGTEVTDYRFFVRAIIRHSDLVTKEASFEYLQNEGERLLLEAMDELEVAFNNTNVRTDCNHIFLNFVPTVIMDPSKIEESVRSMVMRYGSRLWKLRVLQAELKINIRLTPTGKAIPIRLFLTNESGYYLDISLYKEVTDSRTAQIMFQAYGDKQGPLHGMLINTPYVTKDLLQSKRFQAQSLGTTYIYDIPEMFRQSLIKLWESMSTQAFLPSPPLPSDMLTYTELVLDDQGQLVHMNRLPGGNEIGMVAWKMTFKSPEYPEGRDIIVIGNDITYRIGSFGPQEDLLFLRASELARAEGIPRIYVSANSGARIGLAEEIRHMFHVAWVDPEDPYKGYRYLYLTPQDYKRVSALNSVHCEHVEDEGESRYKITDIIGKEEGIGPENLRGSGMIAGESSLAYNEIITISLVTCRAIGIGAYLVRLGQRTIQVENSHLILTGAGALNKVLGREVYTSNNQLGGIQIMHNNGVTHCTVCDDFEGVFTVLHWLSYMPKSVHSSVPLLNSKDPIDRIIEFVPTKTPYDPRWMLAGRPHPTQKGQWLSGFFDYGSFSEIMQPWAQTVVVGRARLGGIPVGVVAVETRTVELSIPADPANLDSEAKIIQQAGQVWFPDSAFKTYQAIKDFNREGLPLMVFANWRGFSGGMKDMYDQVLKFGAYIVDGLRECCQPVLVYIPPQAELRGGSWVVIDSSINPRHMEMYADRESRGSVLEPEGTVEIKFRRKDLVKTMRRVDPVYIHLAERLGTPELSTAERKELENKLKEREEFLIPIYHQVAVQFADLHDTPGRMQEKGVISDILDWKTSRTFFYWRLRRLLLEDLVKKKIHNANPELTDGQIQAMLRRWFVEVEGTVKAYVWDNNKDLAEWLEKQLTEEDGVHSVIEENIKCISRDYVLKQIRSLVQANPEVAMDSIIHMTQHISPTQRAEVIRILSTMDSPST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDEPSPLAQPLELNQHSRFIIGSVSEDNSEDEISNLVKLDLLEEKEGSLSPASVGSDTLSDLGISSLQDGLALHIRSSMSGLHLVKQGRDRKKIDSQRDFTVASPAEFVTRFGGNKVIEKVLIANNGIAAVKCMRSIRRWSYEMFRNERAIRFVVMVTPEDLKANAEYIKMADHYVPVPGGPNNNNYANVELILDIAKRIPVQAVWAGWGHASENPKLPELLLKNGIAFMGPPSQAMWALGDKIASSIVAQTAGIPTLPWSGSGLRVDWQENDFSKRILNVPQELYEKGYVKDVDDGLQAAEEVGYPVMIKASEGGGGKGIRKVNNADDFPNLFRQVQAEVPGSPIFVMRLAKQSRHLEVQILADQYGNAISLFGRDCSVQRRHQKIIEEAPATIATPAVFEHMEQCAVKLAKMVGYVSAGTVEYLYSQDGSFYFLELNPRLQVEHPCTEMVADVNLPAAQLQIAMGIPLYRIKDIRMMYGVSPWGDSPIDFEDSAHVPCPRGHVIAARITSENPDEGFKPSSGTVQELNFRSNKNVWGYFSVAAAGGLHEFADSQFGHCFSWGENREEAISNMVVALKELSIRGDFRTTVEYLIKLLETESFQMNRIDTGWLDRLIAEKVQAERPDTMLGVVCGALHVADVSLRNSVSNFLHSLERGQVLPAHTLLNTVDVELIYEGVKYVLKVTRQSPNSYVVIMNGSCVEVDVHRLSDGGLLLSYDGSSYTTYMKEEVDRYRITIGNKTCVFEKENDPSVMRSPSAGKLIQYIVEDGGHVFAGQCYAEIEVMKMVMTLTAVESGCIHYVKRPGAALDPGCVLAKMQLDNPSKVQQAELHTGSLPRIQSTALRGEKLHRVFHYVLDNLVNVMNGYCLPDPFFSSKVKDWVERLMKTLRDPSLPLLELQDIMTSVSGRIPPNVEKSIKKEMAQYASNITSVLCQFPSQQIANILDSHAATLNRKSEREVFFMNTQSIVQLVQRYRSGIRGHMKAVVMDLLRQYLRVETQFQNGHYDKCVFALREENKSDMNTVLNYIFSHAQVTKKNLLVTMLIDQLCGRDPTLTDELLNILTELTQLSKTTNAKVALRARQVLIASHLPSYELRHNQVESIFLSAIDMYGHQFCIENLQKLILSETSIFDVLPNFFYHSNQVVRMAALEVYVRRAYIAYELNSVQHRQLKDNTCVVEFQFMLPTSHPNRGNIPTLNRMSFSSNLNHYGMTHVASVSDVLLDNSFTPPCQRMGGMVSFRTFEDFVRIFDEVMGCFSDSPPQSPTFPEAGHTSLYDEDKVPRDEPIHILNVAIKTDCDIEDDRLAAMFREFTQQNKATLVDHGIRRLTFLVAQKDFRKQVNYEVDRRFHREFPKFFTFRARDKFEEDRIYRHLEPALAFQLELNRMRNFDLTAIPCANHKMHLYLGAAKVEVGTEVTDYRFFVRAIIRHSDLVTKEASFEYLQNEGERLLLEAMDELEVAFNNTNVRTDCNHIFLNFVPTVIMDPSKIEESVRSMVMRYGSRLWKLRVLQAELKINIRLTPTGKAIPIRLFLTNESGYYLDISLYKEVTDSRTAQIMFQAYGDKQGPLHGMLINTPYVTKDLLQSKRFQAQSLGTTYIYDIPEMFRQSLIKLWESMSTQAFLPSPPLPSDMLTYTELVLDDQGQLVHMNRLPGGNEIGMVAWKMTFKSPEYPEGRDIIVIGNDITYRIGSFGPQEDLLFLRASELARAEGIPRIYVSANSGARIGLAEEIRHMFHVAWVDPEDPYKGYRYLYLTPQDYKRVSALNSVHCEHVEDEGESRYKITDIIGKEEGIGPENLRGSGMIAGESSLAYNEIITISLVTCRAIGIGAYLVRLGQRTIQVENSHLILTGAGALNKVLGREVYTSNNQLGGIQIMHNNGVTHCTVCDDFEGVFTVLHWLSYMPKSVHSSVPLLNSKDPIDRIIEFVPTKTPYDPRWMLAGRPHPTQKGQWLSGFFDYGSFSEIMQPWAQTVVVGRARLGGIPVGVVAVETRTVELSIPADPANLDSEAKIIQQAGQVWFPDSAFKTYQAIKDFNREGLPLMVFANWRGFSGGMKDMYDQVLKFGAYIVDGLRECCQPVLVYIPPQAELRGGSWVVIDSSINPRHMEMYADRESRGSVLEPEGTVEIKFRRKDLVKTMRRVDPVYIHLAERLGTPELSTAERKELENKLKEREEFLIPIYHQVAVQFADLHDTPGRMQEKGVISDILDWKTSRTFFYWRLRRLLLEDLVKKKIHNANPELTDGQIQAMLRRWFVEVEGTVKAYVWDNNKDLAEWLEKQLTEEDGVHSVIEENIKCISRDYVLKQIRSLVQANPEVAMDSIIHMTQHISPTQRAEVIRILSTMDSPST\", \"result\": [{\"smile\": \"CCC(=O)NC(C)C#Cc1cnc(Oc2ccc(OC(C)C)cc2)s1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCC(=O)NC(C)C#Cc1cnc(Oc2ccc(OC(C)C)cc2)s1 is less likely to interact with the protein MDEPSPLAQPLELNQHSRFIIGSVSEDNSEDEISNLVKLDLLEEKEGSLSPASVGSDTLSDLGISSLQDGLALHIRSSMSGLHLVKQGRDRKKIDSQRDFTVASPAEFVTRFGGNKVIEKVLIANNGIAAVKCMRSIRRWSYEMFRNERAIRFVVMVTPEDLKANAEYIKMADHYVPVPGGPNNNNYANVELILDIAKRIPVQAVWAGWGHASENPKLPELLLKNGIAFMGPPSQAMWALGDKIASSIVAQTAGIPTLPWSGSGLRVDWQENDFSKRILNVPQELYEKGYVKDVDDGLQAAEEVGYPVMIKASEGGGGKGIRKVNNADDFPNLFRQVQAEVPGSPIFVMRLAKQSRHLEVQILADQYGNAISLFGRDCSVQRRHQKIIEEAPATIATPAVFEHMEQCAVKLAKMVGYVSAGTVEYLYSQDGSFYFLELNPRLQVEHPCTEMVADVNLPAAQLQIAMGIPLYRIKDIRMMYGVSPWGDSPIDFEDSAHVPCPRGHVIAARITSENPDEGFKPSSGTVQELNFRSNKNVWGYFSVAAAGGLHEFADSQFGHCFSWGENREEAISNMVVALKELSIRGDFRTTVEYLIKLLETESFQMNRIDTGWLDRLIAEKVQAERPDTMLGVVCGALHVADVSLRNSVSNFLHSLERGQVLPAHTLLNTVDVELIYEGVKYVLKVTRQSPNSYVVIMNGSCVEVDVHRLSDGGLLLSYDGSSYTTYMKEEVDRYRITIGNKTCVFEKENDPSVMRSPSAGKLIQYIVEDGGHVFAGQCYAEIEVMKMVMTLTAVESGCIHYVKRPGAALDPGCVLAKMQLDNPSKVQQAELHTGSLPRIQSTALRGEKLHRVFHYVLDNLVNVMNGYCLPDPFFSSKVKDWVERLMKTLRDPSLPLLELQDIMTSVSGRIPPNVEKSIKKEMAQYASNITSVLCQFPSQQIANILDSHAATLNRKSEREVFFMNTQSIVQLVQRYRSGIRGHMKAVVMDLLRQYLRVETQFQNGHYDKCVFALREENKSDMNTVLNYIFSHAQVTKKNLLVTMLIDQLCGRDPTLTDELLNILTELTQLSKTTNAKVALRARQVLIASHLPSYELRHNQVESIFLSAIDMYGHQFCIENLQKLILSETSIFDVLPNFFYHSNQVVRMAALEVYVRRAYIAYELNSVQHRQLKDNTCVVEFQFMLPTSHPNRGNIPTLNRMSFSSNLNHYGMTHVASVSDVLLDNSFTPPCQRMGGMVSFRTFEDFVRIFDEVMGCFSDSPPQSPTFPEAGHTSLYDEDKVPRDEPIHILNVAIKTDCDIEDDRLAAMFREFTQQNKATLVDHGIRRLTFLVAQKDFRKQVNYEVDRRFHREFPKFFTFRARDKFEEDRIYRHLEPALAFQLELNRMRNFDLTAIPCANHKMHLYLGAAKVEVGTEVTDYRFFVRAIIRHSDLVTKEASFEYLQNEGERLLLEAMDELEVAFNNTNVRTDCNHIFLNFVPTVIMDPSKIEESVRSMVMRYGSRLWKLRVLQAELKINIRLTPTGKAIPIRLFLTNESGYYLDISLYKEVTDSRTAQIMFQAYGDKQGPLHGMLINTPYVTKDLLQSKRFQAQSLGTTYIYDIPEMFRQSLIKLWESMSTQAFLPSPPLPSDMLTYTELVLDDQGQLVHMNRLPGGNEIGMVAWKMTFKSPEYPEGRDIIVIGNDITYRIGSFGPQEDLLFLRASELARAEGIPRIYVSANSGARIGLAEEIRHMFHVAWVDPEDPYKGYRYLYLTPQDYKRVSALNSVHCEHVEDEGESRYKITDIIGKEEGIGPENLRGSGMIAGESSLAYNEIITISLVTCRAIGIGAYLVRLGQRTIQVENSHLILTGAGALNKVLGREVYTSNNQLGGIQIMHNNGVTHCTVCDDFEGVFTVLHWLSYMPKSVHSSVPLLNSKDPIDRIIEFVPTKTPYDPRWMLAGRPHPTQKGQWLSGFFDYGSFSEIMQPWAQTVVVGRARLGGIPVGVVAVETRTVELSIPADPANLDSEAKIIQQAGQVWFPDSAFKTYQAIKDFNREGLPLMVFANWRGFSGGMKDMYDQVLKFGAYIVDGLRECCQPVLVYIPPQAELRGGSWVVIDSSINPRHMEMYADRESRGSVLEPEGTVEIKFRRKDLVKTMRRVDPVYIHLAERLGTPELSTAERKELENKLKEREEFLIPIYHQVAVQFADLHDTPGRMQEKGVISDILDWKTSRTFFYWRLRRLLLEDLVKKKIHNANPELTDGQIQAMLRRWFVEVEGTVKAYVWDNNKDLAEWLEKQLTEEDGVHSVIEENIKCISRDYVLKQIRSLVQANPEVAMDSIIHMTQHISPTQRAEVIRILSTMDSPST."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the probability that the drug will have an interaction with the target protein 'MLFPISMSATEFLLASVIFCLVFWVIRASRPQVPKGLKNPPGPWGWPLIGHMLTLGKNPHLALSRMSQQYGDVLQIRIGSTPVVVLSGLDTIRQALVRQGDDFKGRPDLYTFTLISNGQSMSFSPDSGPVWAARRRLAQNGLKSFSIASDPASSTSCYLEEHVSKEAEVLISTLQELMAGPGHFNPYRYVVVSVTNVICAICFGRRYDHNHQELLSLVNLNNNFGEVVGSGNPADFIPILRYLPNPSLNAFKDLNEKFYSFMQKMVKEHYKTFEKGHIRDITDSLIEHCQEKQLDENANVQLSDEKIINIVLDLFGAGFDTVTTAISWSLMYLVMNPRVQRKIQEELDTVIGRSRRPRLSDRSHLPYMEAFILETFRHSSFVPFTIPHSTTRDTSLKGFYIPKGRCVFVNQWQINHDQKLWVNPSEFLPERFLTPDGAIDKVLSEKVIIFGMGKRKCIGETIARWEVFLFLAILLQRVEFSVPLGVKVDMTPIYGLTMKHACCEHFQMQLRS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLFPISMSATEFLLASVIFCLVFWVIRASRPQVPKGLKNPPGPWGWPLIGHMLTLGKNPHLALSRMSQQYGDVLQIRIGSTPVVVLSGLDTIRQALVRQGDDFKGRPDLYTFTLISNGQSMSFSPDSGPVWAARRRLAQNGLKSFSIASDPASSTSCYLEEHVSKEAEVLISTLQELMAGPGHFNPYRYVVVSVTNVICAICFGRRYDHNHQELLSLVNLNNNFGEVVGSGNPADFIPILRYLPNPSLNAFKDLNEKFYSFMQKMVKEHYKTFEKGHIRDITDSLIEHCQEKQLDENANVQLSDEKIINIVLDLFGAGFDTVTTAISWSLMYLVMNPRVQRKIQEELDTVIGRSRRPRLSDRSHLPYMEAFILETFRHSSFVPFTIPHSTTRDTSLKGFYIPKGRCVFVNQWQINHDQKLWVNPSEFLPERFLTPDGAIDKVLSEKVIIFGMGKRKCIGETIARWEVFLFLAILLQRVEFSVPLGVKVDMTPIYGLTMKHACCEHFQMQLRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLFPISMSATEFLLASVIFCLVFWVIRASRPQVPKGLKNPPGPWGWPLIGHMLTLGKNPHLALSRMSQQYGDVLQIRIGSTPVVVLSGLDTIRQALVRQGDDFKGRPDLYTFTLISNGQSMSFSPDSGPVWAARRRLAQNGLKSFSIASDPASSTSCYLEEHVSKEAEVLISTLQELMAGPGHFNPYRYVVVSVTNVICAICFGRRYDHNHQELLSLVNLNNNFGEVVGSGNPADFIPILRYLPNPSLNAFKDLNEKFYSFMQKMVKEHYKTFEKGHIRDITDSLIEHCQEKQLDENANVQLSDEKIINIVLDLFGAGFDTVTTAISWSLMYLVMNPRVQRKIQEELDTVIGRSRRPRLSDRSHLPYMEAFILETFRHSSFVPFTIPHSTTRDTSLKGFYIPKGRCVFVNQWQINHDQKLWVNPSEFLPERFLTPDGAIDKVLSEKVIIFGMGKRKCIGETIARWEVFLFLAILLQRVEFSVPLGVKVDMTPIYGLTMKHACCEHFQMQLRS\", \"result\": [{\"smile\": \"[H][C@@]12CC[C@H](O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [H][C@@]12CC[C@H](O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C is less likely to interact with the protein MLFPISMSATEFLLASVIFCLVFWVIRASRPQVPKGLKNPPGPWGWPLIGHMLTLGKNPHLALSRMSQQYGDVLQIRIGSTPVVVLSGLDTIRQALVRQGDDFKGRPDLYTFTLISNGQSMSFSPDSGPVWAARRRLAQNGLKSFSIASDPASSTSCYLEEHVSKEAEVLISTLQELMAGPGHFNPYRYVVVSVTNVICAICFGRRYDHNHQELLSLVNLNNNFGEVVGSGNPADFIPILRYLPNPSLNAFKDLNEKFYSFMQKMVKEHYKTFEKGHIRDITDSLIEHCQEKQLDENANVQLSDEKIINIVLDLFGAGFDTVTTAISWSLMYLVMNPRVQRKIQEELDTVIGRSRRPRLSDRSHLPYMEAFILETFRHSSFVPFTIPHSTTRDTSLKGFYIPKGRCVFVNQWQINHDQKLWVNPSEFLPERFLTPDGAIDKVLSEKVIIFGMGKRKCIGETIARWEVFLFLAILLQRVEFSVPLGVKVDMTPIYGLTMKHACCEHFQMQLRS."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug interacting with the target protein 'MPLAFCGSENHSAAYRVDQGVLNNGCFVDALNVVPHVFLLFITFPILFIGWGSQSSKVHIHHSTWLHFPGHNLRWILTFMLLFVLVCEIAEGILSDGVTESHHLHLYMPAGMAFMAAVTSVVYYHNIETSNFPKLLIALLVYWTLAFITKTIKFVKFLDHAIGFSQLRFCLTGLLVILYGMLLLVEVNVIRVRRYIFFKTPREVKPPEDLQDLGVRFLQPFVNLLSKGTYWWMNAFIKTAHKKPIDLRAIGKLPIAMRALTNYQRLCEAFDAQVRKDIQGTQGARAIWQALSHAFGRRLVLSSTFRILADLLGFAGPLCIFGIVDHLGKENDVFQPKTQFLGVYFVSSQEFLANAYVLAVLLFLALLLQRTFLQASYYVAIETGINLRGAIQTKIYNKIMHLSTSNLSMGEMTAGQICNLVAIDTNQLMWFFFLCPNLWAMPVQIIVGVILLYYILGVSALIGAAVIILLAPVQYFVATKLSQAQRSTLEYSNERLKQTNEMLRGIKLLKLYAWENIFRTRVETTRRKEMTSLRAFAIYTSISIFMNTAIPIAAVLITFVGHVSFFKEADFSPSVAFASLSLFHILVTPLFLLSSVVRSTVKALVSVQKLSEFLSSAEIREEQCAPHEPTPQGPASKYQAVPLRVVNRKRPAREDCRGLTGPLQSLVPSADGDADNCCVQIMGGYFTWTPDGIPTLSNITIRIPRGQLTMIVGQVGCGKSSLLLAALGEMQKVSGAVFWSSLPDSEIGEDPSPERETATDLDIRKRGPVAYASQKPWLLNATVEENIIFESPFNKQRYKMVIEACSLQPDIDILPHGDQTQIGERGINLSGGQRQRISVARALYQHANVVFLDDPFSALDIHLSDHLMQAGILELLRDDKRTVVLVTHKLQYLPHADWIIAMKDGTIQREGTLKDFQRSECQLFEHWKTLMNRQDQELEKETVTERKATEPPQGLSRAMSSRDGLLQDEEEEEEEAAESEEDDNLSSMLHQRAEIPWRACAKYLSSAGILLLSLLVFSQLLKHMVLVAIDYWLAKWTDSALTLTPAARNCSLSQECTLDQTVYAMVFTVLCSLGIVLCLVTSVTVEWTGLKVAKRLHRSLLNRIILAPMRFFETTPLGSILNRFSSDCNTIDQHIPSTLECLSRSTLLCVSALAVISYVTPVFLVALLPLAIVCYFIQKYFRVASRDLQQLDDTTQLPLLSHFAETVEGLTTIRAFRYEARFQQKLLEYTDSNNIASLFLTAANRWLEVRMEYIGACVVLIAAVTSISNSLHRELSAGLVGLGLTYALMVSNYLNWMVRNLADMELQLGAVKRIHGLLKTEAESYEGLLAPSLIPKNWPDQGKIQIQNLSVRYDSSLKPVLKHVNALIAPGQKIGICGRTGSGKSSFSLAFFRMVDTFEGHIIIDGIDIAKLPLHTLRSRLSIILQDPVLFSGTIRFNLDPERKCSDSTLWEALEIAQLKLVVKALPGGLDAIITEGGENFSQGQRQLFCLARAFVRKTSIFIMDEATASIDMATENILQKVVMTAFADRTVVTIAHRVHTILSADLVIVLKRGAILEFDKPEKLLSRKDSVFASFVRADK'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPLAFCGSENHSAAYRVDQGVLNNGCFVDALNVVPHVFLLFITFPILFIGWGSQSSKVHIHHSTWLHFPGHNLRWILTFMLLFVLVCEIAEGILSDGVTESHHLHLYMPAGMAFMAAVTSVVYYHNIETSNFPKLLIALLVYWTLAFITKTIKFVKFLDHAIGFSQLRFCLTGLLVILYGMLLLVEVNVIRVRRYIFFKTPREVKPPEDLQDLGVRFLQPFVNLLSKGTYWWMNAFIKTAHKKPIDLRAIGKLPIAMRALTNYQRLCEAFDAQVRKDIQGTQGARAIWQALSHAFGRRLVLSSTFRILADLLGFAGPLCIFGIVDHLGKENDVFQPKTQFLGVYFVSSQEFLANAYVLAVLLFLALLLQRTFLQASYYVAIETGINLRGAIQTKIYNKIMHLSTSNLSMGEMTAGQICNLVAIDTNQLMWFFFLCPNLWAMPVQIIVGVILLYYILGVSALIGAAVIILLAPVQYFVATKLSQAQRSTLEYSNERLKQTNEMLRGIKLLKLYAWENIFRTRVETTRRKEMTSLRAFAIYTSISIFMNTAIPIAAVLITFVGHVSFFKEADFSPSVAFASLSLFHILVTPLFLLSSVVRSTVKALVSVQKLSEFLSSAEIREEQCAPHEPTPQGPASKYQAVPLRVVNRKRPAREDCRGLTGPLQSLVPSADGDADNCCVQIMGGYFTWTPDGIPTLSNITIRIPRGQLTMIVGQVGCGKSSLLLAALGEMQKVSGAVFWSSLPDSEIGEDPSPERETATDLDIRKRGPVAYASQKPWLLNATVEENIIFESPFNKQRYKMVIEACSLQPDIDILPHGDQTQIGERGINLSGGQRQRISVARALYQHANVVFLDDPFSALDIHLSDHLMQAGILELLRDDKRTVVLVTHKLQYLPHADWIIAMKDGTIQREGTLKDFQRSECQLFEHWKTLMNRQDQELEKETVTERKATEPPQGLSRAMSSRDGLLQDEEEEEEEAAESEEDDNLSSMLHQRAEIPWRACAKYLSSAGILLLSLLVFSQLLKHMVLVAIDYWLAKWTDSALTLTPAARNCSLSQECTLDQTVYAMVFTVLCSLGIVLCLVTSVTVEWTGLKVAKRLHRSLLNRIILAPMRFFETTPLGSILNRFSSDCNTIDQHIPSTLECLSRSTLLCVSALAVISYVTPVFLVALLPLAIVCYFIQKYFRVASRDLQQLDDTTQLPLLSHFAETVEGLTTIRAFRYEARFQQKLLEYTDSNNIASLFLTAANRWLEVRMEYIGACVVLIAAVTSISNSLHRELSAGLVGLGLTYALMVSNYLNWMVRNLADMELQLGAVKRIHGLLKTEAESYEGLLAPSLIPKNWPDQGKIQIQNLSVRYDSSLKPVLKHVNALIAPGQKIGICGRTGSGKSSFSLAFFRMVDTFEGHIIIDGIDIAKLPLHTLRSRLSIILQDPVLFSGTIRFNLDPERKCSDSTLWEALEIAQLKLVVKALPGGLDAIITEGGENFSQGQRQLFCLARAFVRKTSIFIMDEATASIDMATENILQKVVMTAFADRTVVTIAHRVHTILSADLVIVLKRGAILEFDKPEKLLSRKDSVFASFVRADK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPLAFCGSENHSAAYRVDQGVLNNGCFVDALNVVPHVFLLFITFPILFIGWGSQSSKVHIHHSTWLHFPGHNLRWILTFMLLFVLVCEIAEGILSDGVTESHHLHLYMPAGMAFMAAVTSVVYYHNIETSNFPKLLIALLVYWTLAFITKTIKFVKFLDHAIGFSQLRFCLTGLLVILYGMLLLVEVNVIRVRRYIFFKTPREVKPPEDLQDLGVRFLQPFVNLLSKGTYWWMNAFIKTAHKKPIDLRAIGKLPIAMRALTNYQRLCEAFDAQVRKDIQGTQGARAIWQALSHAFGRRLVLSSTFRILADLLGFAGPLCIFGIVDHLGKENDVFQPKTQFLGVYFVSSQEFLANAYVLAVLLFLALLLQRTFLQASYYVAIETGINLRGAIQTKIYNKIMHLSTSNLSMGEMTAGQICNLVAIDTNQLMWFFFLCPNLWAMPVQIIVGVILLYYILGVSALIGAAVIILLAPVQYFVATKLSQAQRSTLEYSNERLKQTNEMLRGIKLLKLYAWENIFRTRVETTRRKEMTSLRAFAIYTSISIFMNTAIPIAAVLITFVGHVSFFKEADFSPSVAFASLSLFHILVTPLFLLSSVVRSTVKALVSVQKLSEFLSSAEIREEQCAPHEPTPQGPASKYQAVPLRVVNRKRPAREDCRGLTGPLQSLVPSADGDADNCCVQIMGGYFTWTPDGIPTLSNITIRIPRGQLTMIVGQVGCGKSSLLLAALGEMQKVSGAVFWSSLPDSEIGEDPSPERETATDLDIRKRGPVAYASQKPWLLNATVEENIIFESPFNKQRYKMVIEACSLQPDIDILPHGDQTQIGERGINLSGGQRQRISVARALYQHANVVFLDDPFSALDIHLSDHLMQAGILELLRDDKRTVVLVTHKLQYLPHADWIIAMKDGTIQREGTLKDFQRSECQLFEHWKTLMNRQDQELEKETVTERKATEPPQGLSRAMSSRDGLLQDEEEEEEEAAESEEDDNLSSMLHQRAEIPWRACAKYLSSAGILLLSLLVFSQLLKHMVLVAIDYWLAKWTDSALTLTPAARNCSLSQECTLDQTVYAMVFTVLCSLGIVLCLVTSVTVEWTGLKVAKRLHRSLLNRIILAPMRFFETTPLGSILNRFSSDCNTIDQHIPSTLECLSRSTLLCVSALAVISYVTPVFLVALLPLAIVCYFIQKYFRVASRDLQQLDDTTQLPLLSHFAETVEGLTTIRAFRYEARFQQKLLEYTDSNNIASLFLTAANRWLEVRMEYIGACVVLIAAVTSISNSLHRELSAGLVGLGLTYALMVSNYLNWMVRNLADMELQLGAVKRIHGLLKTEAESYEGLLAPSLIPKNWPDQGKIQIQNLSVRYDSSLKPVLKHVNALIAPGQKIGICGRTGSGKSSFSLAFFRMVDTFEGHIIIDGIDIAKLPLHTLRSRLSIILQDPVLFSGTIRFNLDPERKCSDSTLWEALEIAQLKLVVKALPGGLDAIITEGGENFSQGQRQLFCLARAFVRKTSIFIMDEATASIDMATENILQKVVMTAFADRTVVTIAHRVHTILSADLVIVLKRGAILEFDKPEKLLSRKDSVFASFVRADK\", \"result\": [{\"smile\": \"[H][C@@](CC(=O)N1C[C@@]2([H])CCCC[C@@]2([H])C1)(CC1=CC=CC=C1)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug [H][C@@](CC(=O)N1C[C@@]2([H])CCCC[C@@]2([H])C1)(CC1=CC=CC=C1)C(O)=O is less likely to interact with the protein MPLAFCGSENHSAAYRVDQGVLNNGCFVDALNVVPHVFLLFITFPILFIGWGSQSSKVHIHHSTWLHFPGHNLRWILTFMLLFVLVCEIAEGILSDGVTESHHLHLYMPAGMAFMAAVTSVVYYHNIETSNFPKLLIALLVYWTLAFITKTIKFVKFLDHAIGFSQLRFCLTGLLVILYGMLLLVEVNVIRVRRYIFFKTPREVKPPEDLQDLGVRFLQPFVNLLSKGTYWWMNAFIKTAHKKPIDLRAIGKLPIAMRALTNYQRLCEAFDAQVRKDIQGTQGARAIWQALSHAFGRRLVLSSTFRILADLLGFAGPLCIFGIVDHLGKENDVFQPKTQFLGVYFVSSQEFLANAYVLAVLLFLALLLQRTFLQASYYVAIETGINLRGAIQTKIYNKIMHLSTSNLSMGEMTAGQICNLVAIDTNQLMWFFFLCPNLWAMPVQIIVGVILLYYILGVSALIGAAVIILLAPVQYFVATKLSQAQRSTLEYSNERLKQTNEMLRGIKLLKLYAWENIFRTRVETTRRKEMTSLRAFAIYTSISIFMNTAIPIAAVLITFVGHVSFFKEADFSPSVAFASLSLFHILVTPLFLLSSVVRSTVKALVSVQKLSEFLSSAEIREEQCAPHEPTPQGPASKYQAVPLRVVNRKRPAREDCRGLTGPLQSLVPSADGDADNCCVQIMGGYFTWTPDGIPTLSNITIRIPRGQLTMIVGQVGCGKSSLLLAALGEMQKVSGAVFWSSLPDSEIGEDPSPERETATDLDIRKRGPVAYASQKPWLLNATVEENIIFESPFNKQRYKMVIEACSLQPDIDILPHGDQTQIGERGINLSGGQRQRISVARALYQHANVVFLDDPFSALDIHLSDHLMQAGILELLRDDKRTVVLVTHKLQYLPHADWIIAMKDGTIQREGTLKDFQRSECQLFEHWKTLMNRQDQELEKETVTERKATEPPQGLSRAMSSRDGLLQDEEEEEEEAAESEEDDNLSSMLHQRAEIPWRACAKYLSSAGILLLSLLVFSQLLKHMVLVAIDYWLAKWTDSALTLTPAARNCSLSQECTLDQTVYAMVFTVLCSLGIVLCLVTSVTVEWTGLKVAKRLHRSLLNRIILAPMRFFETTPLGSILNRFSSDCNTIDQHIPSTLECLSRSTLLCVSALAVISYVTPVFLVALLPLAIVCYFIQKYFRVASRDLQQLDDTTQLPLLSHFAETVEGLTTIRAFRYEARFQQKLLEYTDSNNIASLFLTAANRWLEVRMEYIGACVVLIAAVTSISNSLHRELSAGLVGLGLTYALMVSNYLNWMVRNLADMELQLGAVKRIHGLLKTEAESYEGLLAPSLIPKNWPDQGKIQIQNLSVRYDSSLKPVLKHVNALIAPGQKIGICGRTGSGKSSFSLAFFRMVDTFEGHIIIDGIDIAKLPLHTLRSRLSIILQDPVLFSGTIRFNLDPERKCSDSTLWEALEIAQLKLVVKALPGGLDAIITEGGENFSQGQRQLFCLARAFVRKTSIFIMDEATASIDMATENILQKVVMTAFADRTVVTIAHRVHTILSADLVIVLKRGAILEFDKPEKLLSRKDSVFASFVRADK."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug and target protein 'MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL' interact, based on your prediction? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL\", \"result\": [{\"smile\": \"CS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)nc(NC3CCCCC3)n2)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)nc(NC3CCCCC3)n2)cc1 is less likely to interact with the protein MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the likelihood of interaction between the drug and the target protein 'MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV\", \"result\": [{\"smile\": \"N[C@@H](CC1=CC=C(O)C(=C1)[N+]([O-])=O)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug N[C@@H](CC1=CC=C(O)C(=C1)[N+]([O-])=O)C(O)=O is less likely to interact with the protein MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the drug's potential to interact with the target protein 'MLKAKKESNYTKGIVPVLITIFLLITGYALYKPLPEGFTISKWDRAVMHVVEPALRVAYYYPSRMFTKPSNMVQWTRGALNTLSKTLGLLVNTHGQVDIKWHKWNETPVKVYRPTNNKTSTDGAVLFIHGGGFALGNVDMYDSLVKRMAYEMKTLFISIEYRLSPETVFPGGILDCEAAIDHFFDFGAVQFGVNTSKVVIMGDSAGGNLATVIAQRRAARNSFPKLAGQVLIYPLLQMADMQTVSYRYFHSRLRGYALVDPESVAYYYMFYAGIDMDEKAYLVPSVISNGHVANHLQSEASEIMSYKSVIEMKHNYKNHSITERWEVSQSYEAQDLMEPFLTNPDFSPLMRKDLSNLPPTMVITCEFDILRDEGLIYGERLKVSGVPTTTIHYENGFHAMLNFHSELDEASKSVDDIEQWTLNAINNA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLKAKKESNYTKGIVPVLITIFLLITGYALYKPLPEGFTISKWDRAVMHVVEPALRVAYYYPSRMFTKPSNMVQWTRGALNTLSKTLGLLVNTHGQVDIKWHKWNETPVKVYRPTNNKTSTDGAVLFIHGGGFALGNVDMYDSLVKRMAYEMKTLFISIEYRLSPETVFPGGILDCEAAIDHFFDFGAVQFGVNTSKVVIMGDSAGGNLATVIAQRRAARNSFPKLAGQVLIYPLLQMADMQTVSYRYFHSRLRGYALVDPESVAYYYMFYAGIDMDEKAYLVPSVISNGHVANHLQSEASEIMSYKSVIEMKHNYKNHSITERWEVSQSYEAQDLMEPFLTNPDFSPLMRKDLSNLPPTMVITCEFDILRDEGLIYGERLKVSGVPTTTIHYENGFHAMLNFHSELDEASKSVDDIEQWTLNAINNA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLKAKKESNYTKGIVPVLITIFLLITGYALYKPLPEGFTISKWDRAVMHVVEPALRVAYYYPSRMFTKPSNMVQWTRGALNTLSKTLGLLVNTHGQVDIKWHKWNETPVKVYRPTNNKTSTDGAVLFIHGGGFALGNVDMYDSLVKRMAYEMKTLFISIEYRLSPETVFPGGILDCEAAIDHFFDFGAVQFGVNTSKVVIMGDSAGGNLATVIAQRRAARNSFPKLAGQVLIYPLLQMADMQTVSYRYFHSRLRGYALVDPESVAYYYMFYAGIDMDEKAYLVPSVISNGHVANHLQSEASEIMSYKSVIEMKHNYKNHSITERWEVSQSYEAQDLMEPFLTNPDFSPLMRKDLSNLPPTMVITCEFDILRDEGLIYGERLKVSGVPTTTIHYENGFHAMLNFHSELDEASKSVDDIEQWTLNAINNA\", \"result\": [{\"smile\": \"C1=C(C=C(C(=C1[N+](=O)[O-])Cl)[N+](=O)[O-])[N+](=O)[O-]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug C1=C(C=C(C(=C1[N+](=O)[O-])Cl)[N+](=O)[O-])[N+](=O)[O-] is less likely to interact with the protein MLKAKKESNYTKGIVPVLITIFLLITGYALYKPLPEGFTISKWDRAVMHVVEPALRVAYYYPSRMFTKPSNMVQWTRGALNTLSKTLGLLVNTHGQVDIKWHKWNETPVKVYRPTNNKTSTDGAVLFIHGGGFALGNVDMYDSLVKRMAYEMKTLFISIEYRLSPETVFPGGILDCEAAIDHFFDFGAVQFGVNTSKVVIMGDSAGGNLATVIAQRRAARNSFPKLAGQVLIYPLLQMADMQTVSYRYFHSRLRGYALVDPESVAYYYMFYAGIDMDEKAYLVPSVISNGHVANHLQSEASEIMSYKSVIEMKHNYKNHSITERWEVSQSYEAQDLMEPFLTNPDFSPLMRKDLSNLPPTMVITCEFDILRDEGLIYGERLKVSGVPTTTIHYENGFHAMLNFHSELDEASKSVDDIEQWTLNAINNA."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the drug will interact with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1 is less likely to interact with the protein MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the drug's interaction with the target protein 'MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C is less likely to interact with the protein MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a possibility of the drug interacting with the target protein 'MDSKHQCVKLNDGHFMPVLGFGTYAPPEVPRSKALEVTKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWSTFHRPELVRPALENSLKKAQLDYVDLYLIHSPMSLKPGEELSPTDENGKVIFDIVDLCTTWEAMEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNRSKLLDFCKSKDIVLVAYSALGSQRDKRWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTAEDMKAIDGLDRNLHYFNSDSFASHPNYPYSDEY'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDSKHQCVKLNDGHFMPVLGFGTYAPPEVPRSKALEVTKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWSTFHRPELVRPALENSLKKAQLDYVDLYLIHSPMSLKPGEELSPTDENGKVIFDIVDLCTTWEAMEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNRSKLLDFCKSKDIVLVAYSALGSQRDKRWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTAEDMKAIDGLDRNLHYFNSDSFASHPNYPYSDEY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSKHQCVKLNDGHFMPVLGFGTYAPPEVPRSKALEVTKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWSTFHRPELVRPALENSLKKAQLDYVDLYLIHSPMSLKPGEELSPTDENGKVIFDIVDLCTTWEAMEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNRSKLLDFCKSKDIVLVAYSALGSQRDKRWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTAEDMKAIDGLDRNLHYFNSDSFASHPNYPYSDEY\", \"result\": [{\"smile\": \"CC1Cc2ccccc2CN1S(=O)(=O)c1cccc(C(=O)O)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CC1Cc2ccccc2CN1S(=O)(=O)c1cccc(C(=O)O)c1 is less likely to interact with the protein MDSKHQCVKLNDGHFMPVLGFGTYAPPEVPRSKALEVTKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWSTFHRPELVRPALENSLKKAQLDYVDLYLIHSPMSLKPGEELSPTDENGKVIFDIVDLCTTWEAMEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNRSKLLDFCKSKDIVLVAYSALGSQRDKRWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTAEDMKAIDGLDRNLHYFNSDSFASHPNYPYSDEY."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the drug is likely to engage with the target protein 'MLRVPLCTPLPLLALLQLLGAAHGIYNVTQRTFKLDYSRDRFLKDGQPFRYISGSIHYFRIPRFYWEDRLLKMKMAGLNAIQMYVPWNFHEPQPGQYEFSGDRDVEHFIQLAHELGLLVILRPGPYICAEWDMGGLPAWLLEKQSIVLRSSDPDYLVAVDKWLAVLLPKMKPLLYQNGGPIITVQVENEYGSYFACDYDYLRFLVHRFRYHLGNDVILFTTDGASEKMLKCGTLQDLYATVDFGTGNNITQAFLVQRKFEPKGPLINSEFYTGWLDHWGKPHSTVKTKTLATSLYNLLARGANVNLYMFIGGTNFAYWNGANTPYEPQPTSYDYDAPLSEAGDLTKKYFALREVIQMFKEVPEGPIPPSTPKFAYGKVALRKFKTVAEALGILCPNGPVKSLYPLTFTQVKQYFGYVLYRTTLPQDCSNPKPIFSSPFNGVRDRAYVSVDGVPQGILDRNLMTALNIRGKAGATLDILVENMGRVNYGRFINDFKGLISNMTINSTVLTNWTVFPLNTEAMVRNHLWGREASDEGHLDGRSTSNSSDLILPTFYVGNFSIPSGIPDLPQDTFIQFPGWSKGQVWINGFNLGRYWPTMGPQKTLFVPRNILTTSAPNNITVLELEFAPCSEGTPELCTVEFVDTPVIS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLRVPLCTPLPLLALLQLLGAAHGIYNVTQRTFKLDYSRDRFLKDGQPFRYISGSIHYFRIPRFYWEDRLLKMKMAGLNAIQMYVPWNFHEPQPGQYEFSGDRDVEHFIQLAHELGLLVILRPGPYICAEWDMGGLPAWLLEKQSIVLRSSDPDYLVAVDKWLAVLLPKMKPLLYQNGGPIITVQVENEYGSYFACDYDYLRFLVHRFRYHLGNDVILFTTDGASEKMLKCGTLQDLYATVDFGTGNNITQAFLVQRKFEPKGPLINSEFYTGWLDHWGKPHSTVKTKTLATSLYNLLARGANVNLYMFIGGTNFAYWNGANTPYEPQPTSYDYDAPLSEAGDLTKKYFALREVIQMFKEVPEGPIPPSTPKFAYGKVALRKFKTVAEALGILCPNGPVKSLYPLTFTQVKQYFGYVLYRTTLPQDCSNPKPIFSSPFNGVRDRAYVSVDGVPQGILDRNLMTALNIRGKAGATLDILVENMGRVNYGRFINDFKGLISNMTINSTVLTNWTVFPLNTEAMVRNHLWGREASDEGHLDGRSTSNSSDLILPTFYVGNFSIPSGIPDLPQDTFIQFPGWSKGQVWINGFNLGRYWPTMGPQKTLFVPRNILTTSAPNNITVLELEFAPCSEGTPELCTVEFVDTPVIS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLRVPLCTPLPLLALLQLLGAAHGIYNVTQRTFKLDYSRDRFLKDGQPFRYISGSIHYFRIPRFYWEDRLLKMKMAGLNAIQMYVPWNFHEPQPGQYEFSGDRDVEHFIQLAHELGLLVILRPGPYICAEWDMGGLPAWLLEKQSIVLRSSDPDYLVAVDKWLAVLLPKMKPLLYQNGGPIITVQVENEYGSYFACDYDYLRFLVHRFRYHLGNDVILFTTDGASEKMLKCGTLQDLYATVDFGTGNNITQAFLVQRKFEPKGPLINSEFYTGWLDHWGKPHSTVKTKTLATSLYNLLARGANVNLYMFIGGTNFAYWNGANTPYEPQPTSYDYDAPLSEAGDLTKKYFALREVIQMFKEVPEGPIPPSTPKFAYGKVALRKFKTVAEALGILCPNGPVKSLYPLTFTQVKQYFGYVLYRTTLPQDCSNPKPIFSSPFNGVRDRAYVSVDGVPQGILDRNLMTALNIRGKAGATLDILVENMGRVNYGRFINDFKGLISNMTINSTVLTNWTVFPLNTEAMVRNHLWGREASDEGHLDGRSTSNSSDLILPTFYVGNFSIPSGIPDLPQDTFIQFPGWSKGQVWINGFNLGRYWPTMGPQKTLFVPRNILTTSAPNNITVLELEFAPCSEGTPELCTVEFVDTPVIS\", \"result\": [{\"smile\": \"CCCCCCCCOCCCCCN1C[C@H](O)[C@@H](O)[C@H](O)[C@@H]1CO\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug CCCCCCCCOCCCCCN1C[C@H](O)[C@@H](O)[C@H](O)[C@@H]1CO is less likely to interact with the protein MLRVPLCTPLPLLALLQLLGAAHGIYNVTQRTFKLDYSRDRFLKDGQPFRYISGSIHYFRIPRFYWEDRLLKMKMAGLNAIQMYVPWNFHEPQPGQYEFSGDRDVEHFIQLAHELGLLVILRPGPYICAEWDMGGLPAWLLEKQSIVLRSSDPDYLVAVDKWLAVLLPKMKPLLYQNGGPIITVQVENEYGSYFACDYDYLRFLVHRFRYHLGNDVILFTTDGASEKMLKCGTLQDLYATVDFGTGNNITQAFLVQRKFEPKGPLINSEFYTGWLDHWGKPHSTVKTKTLATSLYNLLARGANVNLYMFIGGTNFAYWNGANTPYEPQPTSYDYDAPLSEAGDLTKKYFALREVIQMFKEVPEGPIPPSTPKFAYGKVALRKFKTVAEALGILCPNGPVKSLYPLTFTQVKQYFGYVLYRTTLPQDCSNPKPIFSSPFNGVRDRAYVSVDGVPQGILDRNLMTALNIRGKAGATLDILVENMGRVNYGRFINDFKGLISNMTINSTVLTNWTVFPLNTEAMVRNHLWGREASDEGHLDGRSTSNSSDLILPTFYVGNFSIPSGIPDLPQDTFIQFPGWSKGQVWINGFNLGRYWPTMGPQKTLFVPRNILTTSAPNNITVLELEFAPCSEGTPELCTVEFVDTPVIS."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you predict about the interaction of the drug with the target protein 'MADVFPGNDSTASQDVANRFARKGALRQKNVHEVKDHKFIARFFKQPTFCSHCTDFIWGFGKQGFQCQVCCFVVHKRCHEFVTFSCPGADKGPDTDDPRSKHKFKIHTYGSPTFCDHCGSLLYGLIHQGMKCDTCDMNVHKQCVINVPSLCGMDHTEKRGRIYLKAEVADEKLHVTVRDAKNLIPMDPNGLSDPYVKLKLIPDPKNESKQKTKTIRSTLNPQWNESFTFKLKPSDKDRRLSVEIWDWDRTTRNDFMGSLSFGVSELMKMPASGWYKLLNQEEGEYYNVPIPEGDEEGNMELRQKFEKAKLGPAGNKVISPSEDRKQPSNNLDRVKLTDFNFLMVLGKGSFGKVMLADRKGTEELYAIKILKKDVVIQDDDVECTMVEKRVLALLDKPPFLTQLHSCFQTVDRLYFVMEYVNGGDLMYHIQQVGKFKEPQAVFYAAEISIGLFFLHKRGIIYRDLKLDNVMLDSEGHIKIADFGMCKEHMMDGVTTRTFCGTPDYIAPEIIAYQPYGKSVDWWAYGVLLYEMLAGQPPFDGEDEDELFQSIMEHNVSYPKSLSKEAVSVCKGLMTKHPAKRLGCGPEGERDVREHAFFRRIDWEKLENREIQPPFKPKVCGKGAENFDKFFTRGQPVLTPPDQLVIANIDQSDFEGFSYVNPQFVHPILQSAV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MADVFPGNDSTASQDVANRFARKGALRQKNVHEVKDHKFIARFFKQPTFCSHCTDFIWGFGKQGFQCQVCCFVVHKRCHEFVTFSCPGADKGPDTDDPRSKHKFKIHTYGSPTFCDHCGSLLYGLIHQGMKCDTCDMNVHKQCVINVPSLCGMDHTEKRGRIYLKAEVADEKLHVTVRDAKNLIPMDPNGLSDPYVKLKLIPDPKNESKQKTKTIRSTLNPQWNESFTFKLKPSDKDRRLSVEIWDWDRTTRNDFMGSLSFGVSELMKMPASGWYKLLNQEEGEYYNVPIPEGDEEGNMELRQKFEKAKLGPAGNKVISPSEDRKQPSNNLDRVKLTDFNFLMVLGKGSFGKVMLADRKGTEELYAIKILKKDVVIQDDDVECTMVEKRVLALLDKPPFLTQLHSCFQTVDRLYFVMEYVNGGDLMYHIQQVGKFKEPQAVFYAAEISIGLFFLHKRGIIYRDLKLDNVMLDSEGHIKIADFGMCKEHMMDGVTTRTFCGTPDYIAPEIIAYQPYGKSVDWWAYGVLLYEMLAGQPPFDGEDEDELFQSIMEHNVSYPKSLSKEAVSVCKGLMTKHPAKRLGCGPEGERDVREHAFFRRIDWEKLENREIQPPFKPKVCGKGAENFDKFFTRGQPVLTPPDQLVIANIDQSDFEGFSYVNPQFVHPILQSAV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MADVFPGNDSTASQDVANRFARKGALRQKNVHEVKDHKFIARFFKQPTFCSHCTDFIWGFGKQGFQCQVCCFVVHKRCHEFVTFSCPGADKGPDTDDPRSKHKFKIHTYGSPTFCDHCGSLLYGLIHQGMKCDTCDMNVHKQCVINVPSLCGMDHTEKRGRIYLKAEVADEKLHVTVRDAKNLIPMDPNGLSDPYVKLKLIPDPKNESKQKTKTIRSTLNPQWNESFTFKLKPSDKDRRLSVEIWDWDRTTRNDFMGSLSFGVSELMKMPASGWYKLLNQEEGEYYNVPIPEGDEEGNMELRQKFEKAKLGPAGNKVISPSEDRKQPSNNLDRVKLTDFNFLMVLGKGSFGKVMLADRKGTEELYAIKILKKDVVIQDDDVECTMVEKRVLALLDKPPFLTQLHSCFQTVDRLYFVMEYVNGGDLMYHIQQVGKFKEPQAVFYAAEISIGLFFLHKRGIIYRDLKLDNVMLDSEGHIKIADFGMCKEHMMDGVTTRTFCGTPDYIAPEIIAYQPYGKSVDWWAYGVLLYEMLAGQPPFDGEDEDELFQSIMEHNVSYPKSLSKEAVSVCKGLMTKHPAKRLGCGPEGERDVREHAFFRRIDWEKLENREIQPPFKPKVCGKGAENFDKFFTRGQPVLTPPDQLVIANIDQSDFEGFSYVNPQFVHPILQSAV\", \"result\": [{\"smile\": \"O=C(Nc1cccc(-c2ccnc3cc(-c4ccncc4)nn23)c1)c1cccc(C(F)(F)F)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug O=C(Nc1cccc(-c2ccnc3cc(-c4ccncc4)nn23)c1)c1cccc(C(F)(F)F)c1 is less likely to interact with the protein MADVFPGNDSTASQDVANRFARKGALRQKNVHEVKDHKFIARFFKQPTFCSHCTDFIWGFGKQGFQCQVCCFVVHKRCHEFVTFSCPGADKGPDTDDPRSKHKFKIHTYGSPTFCDHCGSLLYGLIHQGMKCDTCDMNVHKQCVINVPSLCGMDHTEKRGRIYLKAEVADEKLHVTVRDAKNLIPMDPNGLSDPYVKLKLIPDPKNESKQKTKTIRSTLNPQWNESFTFKLKPSDKDRRLSVEIWDWDRTTRNDFMGSLSFGVSELMKMPASGWYKLLNQEEGEYYNVPIPEGDEEGNMELRQKFEKAKLGPAGNKVISPSEDRKQPSNNLDRVKLTDFNFLMVLGKGSFGKVMLADRKGTEELYAIKILKKDVVIQDDDVECTMVEKRVLALLDKPPFLTQLHSCFQTVDRLYFVMEYVNGGDLMYHIQQVGKFKEPQAVFYAAEISIGLFFLHKRGIIYRDLKLDNVMLDSEGHIKIADFGMCKEHMMDGVTTRTFCGTPDYIAPEIIAYQPYGKSVDWWAYGVLLYEMLAGQPPFDGEDEDELFQSIMEHNVSYPKSLSKEAVSVCKGLMTKHPAKRLGCGPEGERDVREHAFFRRIDWEKLENREIQPPFKPKVCGKGAENFDKFFTRGQPVLTPPDQLVIANIDQSDFEGFSYVNPQFVHPILQSAV."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there likely be an interaction between the drug 'CC(C)n1c(O)cn(-c2ccc(Cc3cc([C@@H]4O[C@H](CO)[C@@H](O)[C@H](O)[C@H]4O)ccc3Cl)cc2)c1=O', 'OC[C@H]1O[C@@H](Oc2[nH]nc(C(F)(F)F)c2Cc2ccccc2OCc2ccccc2)[C@H](O)[C@@H](O)[C@@H]1O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)n1c(O)cn(-c2ccc(Cc3cc([C@@H]4O[C@H](CO)[C@@H](O)[C@H](O)[C@H]4O)ccc3Cl)cc2)c1=O\", \"OC[C@H]1O[C@@H](Oc2[nH]nc(C(F)(F)F)c2Cc2ccccc2OCc2ccccc2)[C@H](O)[C@@H](O)[C@@H]1O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEEHTEAGSAPEMGAQKALIDNPADILVIAAYFLLVIGVGLWSMCRTNRGTVGGYFLAGRSMVWWPVGASLFASNIGSGHFVGLAGTGAASGLAVAGFEWNALFVVLLLGWLFAPVYLTAGVITMPQYLRKRFGGRRIRLYLSVLSLFLYIFTKISVDMFSGAVFIQQALGWNIYASVIALLGITMIYTVTGGLAALMYTDTVQTFVILGGACILMGYAFHEVGGYSGLFDKYLGAATSLTVSEDPAVGNISSFCYRPRPDSYHLLRHPVTGDLPWPALLLGLTIVSGWYWCSDQVIVQRCLAGKSLTHIKAGCILCGYLKLTPMFLMVMPGMISRILYPDEVACVVPEVCRRVCGTEVGCSNIAYPRLVVKLMPNGLRGLMLAVMLAALMSSLASIFNSSSTLFTMDIYTRLRPRAGDRELLLVGRLWVVFIVVVSVAWLPVVQAAQGGQLFDYIQAVSSYLAPPVSAVFVLALFVPRVNEQGAFWGLIGGLLMGLARLIPEFSFGSGSCVQPSACPAFLCGVHYLYFAIVLFFCSGLLTLTVSLCTAPIPRKHLHRLVFSLRHSKEEREDLDADEQQGSSLPVQNGCPESAMEMNEPQAPAPSLFRQCLLWFCGMSRGGVGSPPPLTQEEAAAAARRLEDISEDPSWARVVNLNALLMMAVAVFLWGFYA\", \"result\": [{\"smile\": \"CC(C)n1c(O)cn(-c2ccc(Cc3cc([C@@H]4O[C@H](CO)[C@@H](O)[C@H](O)[C@H]4O)ccc3Cl)cc2)c1=O\", \"value\": 1.0}, {\"smile\": \"OC[C@H]1O[C@@H](Oc2[nH]nc(C(F)(F)F)c2Cc2ccccc2OCc2ccccc2)[C@H](O)[C@@H](O)[C@@H]1O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CC(C)n1c(O)cn(-c2ccc(Cc3cc([C@@H]4O[C@H](CO)[C@@H](O)[C@H](O)[C@H]4O)ccc3Cl)cc2)c1=O' can interact with the protein, while the second one 'OC[C@H]1O[C@@H](Oc2[nH]nc(C(F)(F)F)c2Cc2ccccc2OCc2ccccc2)[C@H](O)[C@@H](O)[C@@H]1O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess if the drug 'CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1', 'N[C@@H](CCCNC(=O)CP(O)(O)=O)C(O)=O' will engage with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\", \"N[C@@H](CCCNC(=O)CP(O)(O)=O)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\", \"result\": [{\"smile\": \"CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\", \"value\": 0.0}, {\"smile\": \"N[C@@H](CCCNC(=O)CP(O)(O)=O)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1' nor 'N[C@@H](CCCNC(=O)CP(O)(O)=O)C(O)=O' can interact with the protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'CCCCC\\C=C\\CC(C\\C=C\\CCCCC)(P(O)(O)=O)P(O)(O)=O', 'O=P(O)(O)C(O)(Cn1ccnc1)P(=O)(O)O' will interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCC\\C=C\\CC(C\\C=C\\CCCCC)(P(O)(O)=O)P(O)(O)=O\", \"O=P(O)(O)C(O)(Cn1ccnc1)P(=O)(O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEKTQETVQRILLEPYKYLLQLPGKQVRTKLSQAFNHWLKVPEDKLQIIIEVTEMLHNASLLIDDIEDNSKLRRGFPVAHSIYGIPSVINSANYVYFLGLEKVLTLDHPDAVKLFTRQLLELHQGQGLDIYWRDNYTCPTEEEYKAMVLQKTGGLFGLAVGLMQLFSDYKEDLKPLLNTLGLFFQIRDDYANLHSKEYSENKSFCEDLTEGKFSFPTIHAIWSRPESTQVQNILRQRTENIDIKKYCVHYLEDVGSFEYTRNTLKELEAKAYKQIDARGGNPELVALVKHLSKMFKEENE\", \"result\": [{\"smile\": \"CCCCC\\C=C\\CC(C\\C=C\\CCCCC)(P(O)(O)=O)P(O)(O)=O\", \"value\": 1.0}, {\"smile\": \"O=P(O)(O)C(O)(Cn1ccnc1)P(=O)(O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CCCCC\\C=C\\CC(C\\C=C\\CCCCC)(P(O)(O)=O)P(O)(O)=O' can interact with the protein, while the second one 'O=P(O)(O)C(O)(Cn1ccnc1)P(=O)(O)O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug 'CC(O)C(=O)N1CC[C@@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1', 'COc1cc(Nc2ncnc(-c3ccc(O[C@H]4CCN(C(=O)CO)CC4(F)F)c(C#N)c3)n2)ccc1N1CCN(C2COC2)CC1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(O)C(=O)N1CC[C@@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1\", \"COc1cc(Nc2ncnc(-c3ccc(O[C@H]4CCN(C(=O)CO)CC4(F)F)c(C#N)c3)n2)ccc1N1CCN(C2COC2)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL\", \"result\": [{\"smile\": \"CC(O)C(=O)N1CC[C@@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1\", \"value\": 1.0}, {\"smile\": \"COc1cc(Nc2ncnc(-c3ccc(O[C@H]4CCN(C(=O)CO)CC4(F)F)c(C#N)c3)n2)ccc1N1CCN(C2COC2)CC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC(O)C(=O)N1CC[C@@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)C1' and 'COc1cc(Nc2ncnc(-c3ccc(O[C@H]4CCN(C(=O)CO)CC4(F)F)c(C#N)c3)n2)ccc1N1CCN(C2COC2)CC1' can interact with the protein 'MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O', 'OB(O)CCC1=CC=CC=C1' probably interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O\", \"OB(O)CCC1=CC=CC=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTQNKLKLCSKANVYTEVPDGGWGWAVAVSFFFVEVFTYGIIKTFGVFFNDLMDSFNESNSRISWIISICVFVLTFSAPLATVLSNRFGHRLVVMLGGLLVSTGMVAASFSQEVSHMYVAIGIISGLGYCFSFLPTVTILSQYFGKRRSIVTAVASTGECFAVFAFAPAIMALKERIGWRYSLLFVGLLQLNIVIFGALLRPIFIRGPASPKIVIQENRKEAQYMLENEKTRTSIDSIDSGVELTTSPKNVPTHTNLELEPKADMQQVLVKTSPRPSEKKAPLLDFSILKEKSFICYALFGLFATLGFFAPSLYIIPLGISLGIDQDRAAFLLSTMAIAEVFGRIGAGFVLNREPIRKIYIELICVILLTVSLFAFTFATEFWGLMSCSIFFGFMVGTIGGTHIPLLAEDDVVGIEKMSSAAGVYIFIQSIAGLAGPPLAGLLVDQSKIYSRAFYSCAAGMALAAVCLALVRPCKMGLCQHHHSGETKVVSHRGKTLQDIPEDFLEMDLAKNEHRVHVQMEPV\", \"result\": [{\"smile\": \"OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O\", \"value\": 0.0}, {\"smile\": \"OB(O)CCC1=CC=CC=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O' nor 'OB(O)CCC1=CC=CC=C1' can interact with the protein 'MTQNKLKLCSKANVYTEVPDGGWGWAVAVSFFFVEVFTYGIIKTFGVFFNDLMDSFNESNSRISWIISICVFVLTFSAPLATVLSNRFGHRLVVMLGGLLVSTGMVAASFSQEVSHMYVAIGIISGLGYCFSFLPTVTILSQYFGKRRSIVTAVASTGECFAVFAFAPAIMALKERIGWRYSLLFVGLLQLNIVIFGALLRPIFIRGPASPKIVIQENRKEAQYMLENEKTRTSIDSIDSGVELTTSPKNVPTHTNLELEPKADMQQVLVKTSPRPSEKKAPLLDFSILKEKSFICYALFGLFATLGFFAPSLYIIPLGISLGIDQDRAAFLLSTMAIAEVFGRIGAGFVLNREPIRKIYIELICVILLTVSLFAFTFATEFWGLMSCSIFFGFMVGTIGGTHIPLLAEDDVVGIEKMSSAAGVYIFIQSIAGLAGPPLAGLLVDQSKIYSRAFYSCAAGMALAAVCLALVRPCKMGLCQHHHSGETKVVSHRGKTLQDIPEDFLEMDLAKNEHRVHVQMEPV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'C1CN(CCN1)C(=O)C2=CC=C(C=C2)C=CC3=NNC4=CC=CC=C43', 'Cn1nc(C2CCN(C(=O)C(C)(C)O)CC2)nc1-c1cnc(N)c(-c2nnc(C(C)(C)C)o2)n1''s interaction potential with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1CN(CCN1)C(=O)C2=CC=C(C=C2)C=CC3=NNC4=CC=CC=C43\", \"Cn1nc(C2CCN(C(=O)C(C)(C)O)CC2)nc1-c1cnc(N)c(-c2nnc(C(C)(C)C)o2)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"C1CN(CCN1)C(=O)C2=CC=C(C=C2)C=CC3=NNC4=CC=CC=C43\", \"value\": 0.0}, {\"smile\": \"Cn1nc(C2CCN(C(=O)C(C)(C)O)CC2)nc1-c1cnc(N)c(-c2nnc(C(C)(C)C)o2)n1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'Cn1nc(C2CCN(C(=O)C(C)(C)O)CC2)nc1-c1cnc(N)c(-c2nnc(C(C)(C)C)o2)n1' can interact with the protein, while the first one 'C1CN(CCN1)C(=O)C2=CC=C(C=C2)C=CC3=NNC4=CC=CC=C43' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the chances of the drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)OCC4=CC=CC=N4)Cl)C#N)NC(=O)C=CCN(C)C', 'C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl' interacting with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)OCC4=CC=CC=N4)Cl)C#N)NC(=O)C=CCN(C)C\", \"C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNRYTTMRQLGDGTYGSVLMGKSNESGELVAIKRMKRKFYSWDECMNLREVKSLKKLNHANVIKLKEVIRENDHLYFIFEYMKENLYQLMKDRNKLFPESVIRNIMYQILQGLAFIHKHGFFHRDMKPENLLCMGPELVKIADFGLARELRSQPPYTDYVSTRWYRAPEVLLRSSVYSSPIDVWAVGSIMAELYMLRPLFPGTSEVDEIFKICQVLGTPKKSDWPEGYQLASSMNFRFPQCVPINLKTLIPNASNEAIQLMTEMLNWDPKKRPTASQALKHPYFQVGQVLGPSSNHLESKQSLNKQLQPLESKPSLVEVEPKPLPDIIDQVVGQPQPKTSQQPLQPIQPPQNLSVQQPPKQQSQEKPPQTLFPSIVKNMPTKPNGTLSHKSGRRRWGQTIFKSGDSWEELEDYDFGASHSKKPSMGVFKEKRKKDSPFRLPEPVPSGSNHSTGENKSLPAVTSLKSDSELSTAPTSKQYYLKQSRYLPGVNPKKVSLIASGKEINPHTWSNQLFPKSLGPVGAELAFKRSNAGNLGSYATYNQSGYIPSFLKKEVQSAGQRIHLAPLNATASEYTWNTKTGRGQFSGRTYNPTAKNLNIVNRAQPIPSVHGRTDWVAKYGGHR\", \"result\": [{\"smile\": \"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)OCC4=CC=CC=N4)Cl)C#N)NC(=O)C=CCN(C)C\", \"value\": 0.0}, {\"smile\": \"C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)OCC4=CC=CC=N4)Cl)C#N)NC(=O)C=CCN(C)C' nor 'C1CC1CONC(=O)C2=C(C(=C(C=C2)F)F)NC3=C(C=C(C=C3)I)Cl' can interact with the protein 'MNRYTTMRQLGDGTYGSVLMGKSNESGELVAIKRMKRKFYSWDECMNLREVKSLKKLNHANVIKLKEVIRENDHLYFIFEYMKENLYQLMKDRNKLFPESVIRNIMYQILQGLAFIHKHGFFHRDMKPENLLCMGPELVKIADFGLARELRSQPPYTDYVSTRWYRAPEVLLRSSVYSSPIDVWAVGSIMAELYMLRPLFPGTSEVDEIFKICQVLGTPKKSDWPEGYQLASSMNFRFPQCVPINLKTLIPNASNEAIQLMTEMLNWDPKKRPTASQALKHPYFQVGQVLGPSSNHLESKQSLNKQLQPLESKPSLVEVEPKPLPDIIDQVVGQPQPKTSQQPLQPIQPPQNLSVQQPPKQQSQEKPPQTLFPSIVKNMPTKPNGTLSHKSGRRRWGQTIFKSGDSWEELEDYDFGASHSKKPSMGVFKEKRKKDSPFRLPEPVPSGSNHSTGENKSLPAVTSLKSDSELSTAPTSKQYYLKQSRYLPGVNPKKVSLIASGKEINPHTWSNQLFPKSLGPVGAELAFKRSNAGNLGSYATYNQSGYIPSFLKKEVQSAGQRIHLAPLNATASEYTWNTKTGRGQFSGRTYNPTAKNLNIVNRAQPIPSVHGRTDWVAKYGGHR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you expect any interaction between the drug 'CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc(Br)cc1', 'CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc2ccccc2c1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc(Br)cc1\", \"CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc2ccccc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQKPVCLVVAMTPKRGIGINNGLPWPHLTTDFKHFSRVTKTTPEEASRLNGWLPRKFAKTGDSGLPSPSVGKRFNAVVMGRKTWESMPRKFRPLVDRLNIVVSSSLKEEDIAAEKPQAEGQQRVRVCASLPAALSLLEEEYKDSVDQIFVVGGAGLYEAALSLGVASHLYITRVAREFPCDVFFPAFPGDDILSNKSTAAQAAAPAESVFVPFCPELGREKDNEATYRPIFISKTFSDNGVPYDFVVLEKRRKTDDAATAEPSNAMSSLTSTRETTPVHGLQAPSSAAAIAPVLAWMDEEDRKKREQKELIRAVPHVHFRGHEEFQYLDLIADIINNGRTMDDRTGVGVISKFGCTMRYSLDQAFPLLTTKRVFWKGVLEELLWFIRGDTNANHLSEKGVKIWDKNVTREFLDSRNLPHREVGDIGPGYGFQWRHFGAAYKDMHTDYTGQGVDQLKNVIQMLRTNPTDRRMLMTAWNPAALDEMALPPCHLLCQFYVNDQKELSCIMYQRSCDVGLGVPFNIASYSLLTLMVAHVCNLKPKEFIHFMGNTHVYTNHVEALKEQLRREPRPFPIVNILNKERIKEIDDFTAEDFEVVGYVPHGRIQMEMAV\", \"result\": [{\"smile\": \"CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc(Br)cc1\", \"value\": 1.0}, {\"smile\": \"CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc2ccccc2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc(Br)cc1' and 'CCc1sc2nc(N)[nH]c(=O)c2c1Sc1ccc2ccccc2c1' can interact with the protein 'MQKPVCLVVAMTPKRGIGINNGLPWPHLTTDFKHFSRVTKTTPEEASRLNGWLPRKFAKTGDSGLPSPSVGKRFNAVVMGRKTWESMPRKFRPLVDRLNIVVSSSLKEEDIAAEKPQAEGQQRVRVCASLPAALSLLEEEYKDSVDQIFVVGGAGLYEAALSLGVASHLYITRVAREFPCDVFFPAFPGDDILSNKSTAAQAAAPAESVFVPFCPELGREKDNEATYRPIFISKTFSDNGVPYDFVVLEKRRKTDDAATAEPSNAMSSLTSTRETTPVHGLQAPSSAAAIAPVLAWMDEEDRKKREQKELIRAVPHVHFRGHEEFQYLDLIADIINNGRTMDDRTGVGVISKFGCTMRYSLDQAFPLLTTKRVFWKGVLEELLWFIRGDTNANHLSEKGVKIWDKNVTREFLDSRNLPHREVGDIGPGYGFQWRHFGAAYKDMHTDYTGQGVDQLKNVIQMLRTNPTDRRMLMTAWNPAALDEMALPPCHLLCQFYVNDQKELSCIMYQRSCDVGLGVPFNIASYSLLTLMVAHVCNLKPKEFIHFMGNTHVYTNHVEALKEQLRREPRPFPIVNILNKERIKEIDDFTAEDFEVVGYVPHGRIQMEMAV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted interaction outcome for the drug 'O=C(NC[C@@H]1CCCN1)c1ccc(-c2cnc3ccc(NCC4CC4)nn23)cc1', 'O=Nc1c(-c2c(O)[nH]c3ccccc23)[nH]c2ccccc12' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC[C@@H]1CCCN1)c1ccc(-c2cnc3ccc(NCC4CC4)nn23)cc1\", \"O=Nc1c(-c2c(O)[nH]c3ccccc23)[nH]c2ccccc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPDRPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYSRAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDELRDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASTPTNATAASDANTGDRGQTNNAASASASNST\", \"result\": [{\"smile\": \"O=C(NC[C@@H]1CCCN1)c1ccc(-c2cnc3ccc(NCC4CC4)nn23)cc1\", \"value\": 0.0}, {\"smile\": \"O=Nc1c(-c2c(O)[nH]c3ccccc23)[nH]c2ccccc12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'O=Nc1c(-c2c(O)[nH]c3ccccc23)[nH]c2ccccc12' can interact with the protein, while the first one 'O=C(NC[C@@H]1CCCN1)c1ccc(-c2cnc3ccc(NCC4CC4)nn23)cc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you view the possibility of the drug 'C[C@@H]1C[C@@H]2[C@H]([C@@H]([C@]3([C@H]1C=CC3=O)C)OC(=O)C)C(=C)C(=O)O2', 'Cc1ccc(Sc2cncc3sc(C(N)=O)cc23)cc1' interacting with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H]1C[C@@H]2[C@H]([C@@H]([C@]3([C@H]1C=CC3=O)C)OC(=O)C)C(=C)C(=O)O2\", \"Cc1ccc(Sc2cncc3sc(C(N)=O)cc23)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MIASQFLSALTLVLLIKESGAWSYNTSTEAMTYDEASAYCQQRYTHLVAIQNKEEIEYLNSILSYSPSYYWIGIRKVNNVWVWVGTQKPLTEEAKNWAPGEPNNRQKDEDCVEIYIKREKDVGMWNDERCSKKKLALCYTAACTNTSCSGHGECVETINNYTCKCDPGFSGLKCEQIVNCTALESPEHGSLVCSHPLGNFSYNSSCSISCDRGYLPSSMETMQCMSSGEWSAPIPACNVVECDAVTNPANGFVECFQNPGSFPWNTTCTFDCEEGFELMGAQSLQCTSSGNWDNEKPTCKAVTCRAVRQPQNGSVRCSHSPAGEFTFKSSCNFTCEEGFMLQGPAQVECTTQGQWTQQIPVCEAFQCTALSNPERGYMNCLPSASGSFRYGSSCEFSCEQGFVLKGSKRLQCGPTGEWDNEKPTCEAVRCDAVHQPPKGLVRCAHSPIGEFTYKSSCAFSCEEGFELHGSTQLECTSQGQWTEEVPSCQVVKCSSLAVPGKINMSCSGEPVFGTVCKFACPEGWTLNGSAARTCGATGHWSGLLPTCEAPTESNIPLVAGLSAAGLSLLTLAPFLLWLRKCLRKAKKFVPASSCQSLESDGSYQKPSYIL\", \"result\": [{\"smile\": \"C[C@@H]1C[C@@H]2[C@H]([C@@H]([C@]3([C@H]1C=CC3=O)C)OC(=O)C)C(=C)C(=O)O2\", \"value\": 0.0}, {\"smile\": \"Cc1ccc(Sc2cncc3sc(C(N)=O)cc23)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'Cc1ccc(Sc2cncc3sc(C(N)=O)cc23)cc1' can interact with the protein, while the first one 'C[C@@H]1C[C@@H]2[C@H]([C@@H]([C@]3([C@H]1C=CC3=O)C)OC(=O)C)C(=C)C(=O)O2' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your prediction about the interaction of the drug 'NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(O)(O)=O)[C@@H](OP(O)(O)=O)[C@H]1O', '[H][C@](N)(CC1=CC=CC=C1)[C@]([H])(O)CNS(=O)(=O)C1=C(C)C=C(OC)C(C)=C1C' with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(O)(O)=O)[C@@H](OP(O)(O)=O)[C@H]1O\", \"[H][C@](N)(CC1=CC=CC=C1)[C@]([H])(O)CNS(=O)(=O)C1=C(C)C=C(OC)C(C)=C1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVDPVGFAEAWKAQFPDSEPPRMELRSVGDIEQELERCKASIRRLEQEVNQERFRMIYLQTLLAKEKKSYDRQRWGFRRAAQAPDGASEPRASASRPQPAPADGADPPPAEEPEARPDGEGSPGKARPGTARRPGAAASGERDDRGPPASVAALRSNFERIRKGHGQPGADAEKPFYVNVEFHHERGLVKVNDKEVSDRISSLGSQAMQMERKKSQHGAGSSVGDASRPPYRGRSSESSCGVDGDYEDAELNPRFLKDNLIDANGGSRPPWPPLEYQPYQSIYVGGMMEGEGKGPLLRSQSTSEQEKRLTWPRRSYSPRSFEDCGGGYTPDCSSNENLTSSEEDFSSGQSSRVSPSPTTYRMFRDKSRSPSQNSQQSFDSSSPPTPQCHKRHRHCPVVVSEATIVGVRKTGQIWPNDGEGAFHGDADGSFGTPPGYGCAADRAEEQRRHQDGLPYIDDSPSSSPHLSSKGRGSRDALVSGALESTKASELDLEKGLEMRKWVLSGILASEETYLSHLEALLLPMKPLKAAATTSQPVLTSQQIETIFFKVPELYEIHKEFYDGLFPRVQQWSHQQRVGDLFQKLASQLGVYRAFVDNYGVAMEMAEKCCQANAQFAEISENLRARSNKDAKDPTTKNSLETLLYKPVDRVTRSTLVLHDLLKHTPASHPDHPLLQDALRISQNFLSSINEEITPRRQSMTVKKGEHRQLLKDSFMVELVEGARKLRHVFLFTDLLLCTKLKKQSGGKTQQYDCKWYIPLTDLSFQMVDELEAVPNIPLVPDEELDALKIKISQIKNDIQREKRANKGSKATERLKKKLSEQESLLLLMSPSMAFRVHSRNGKSYTFLISSDYERAEWRENIREQQKKCFRSFSLTSVELQMLTNSCVKLQTVHSIPLTINKEDDESPGLYGFLNVIVHSATGFKQSSNLYCTLEVDSFGYFVNKAKTRVYRDTAEPNWNEEFEIELEGSQTLRILCYEKCYNKTKIPKEDGESTDRLMGKGQVQLDPQALQDRDWQRTVIAMNGIEVKLSVKFNSREFSLKRMPSRKQTGVFGVKIAVVTKRERSKVPYIVRQCVEEIERRGMEEVGIYRVSGVATDIQALKAAFDVNNKDVSVMMSEMDVNAIAGTLKLYFRELPEPLFTDEFYPNFAEGIALSDPVAKESCMLNLLLSLPEANLLTFLFLLDHLKRVAEKEAVNKMSLHNLATVFGPTLLRPSEKESKLPANPSQPITMTDSWSLEVMSQVQVLLYFLQLEAIPAPDSKRQSILFSTEV\", \"result\": [{\"smile\": \"NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(O)(O)=O)[C@@H](OP(O)(O)=O)[C@H]1O\", \"value\": 0.0}, {\"smile\": \"[H][C@](N)(CC1=CC=CC=C1)[C@]([H])(O)CNS(=O)(=O)C1=C(C)C=C(OC)C(C)=C1C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'NC1=NC=NC2=C1N=CN2[C@@H]1O[C@H](COP(O)(O)=O)[C@@H](OP(O)(O)=O)[C@H]1O' nor '[H][C@](N)(CC1=CC=CC=C1)[C@]([H])(O)CNS(=O)(=O)C1=C(C)C=C(OC)C(C)=C1C' can interact with the protein 'MVDPVGFAEAWKAQFPDSEPPRMELRSVGDIEQELERCKASIRRLEQEVNQERFRMIYLQTLLAKEKKSYDRQRWGFRRAAQAPDGASEPRASASRPQPAPADGADPPPAEEPEARPDGEGSPGKARPGTARRPGAAASGERDDRGPPASVAALRSNFERIRKGHGQPGADAEKPFYVNVEFHHERGLVKVNDKEVSDRISSLGSQAMQMERKKSQHGAGSSVGDASRPPYRGRSSESSCGVDGDYEDAELNPRFLKDNLIDANGGSRPPWPPLEYQPYQSIYVGGMMEGEGKGPLLRSQSTSEQEKRLTWPRRSYSPRSFEDCGGGYTPDCSSNENLTSSEEDFSSGQSSRVSPSPTTYRMFRDKSRSPSQNSQQSFDSSSPPTPQCHKRHRHCPVVVSEATIVGVRKTGQIWPNDGEGAFHGDADGSFGTPPGYGCAADRAEEQRRHQDGLPYIDDSPSSSPHLSSKGRGSRDALVSGALESTKASELDLEKGLEMRKWVLSGILASEETYLSHLEALLLPMKPLKAAATTSQPVLTSQQIETIFFKVPELYEIHKEFYDGLFPRVQQWSHQQRVGDLFQKLASQLGVYRAFVDNYGVAMEMAEKCCQANAQFAEISENLRARSNKDAKDPTTKNSLETLLYKPVDRVTRSTLVLHDLLKHTPASHPDHPLLQDALRISQNFLSSINEEITPRRQSMTVKKGEHRQLLKDSFMVELVEGARKLRHVFLFTDLLLCTKLKKQSGGKTQQYDCKWYIPLTDLSFQMVDELEAVPNIPLVPDEELDALKIKISQIKNDIQREKRANKGSKATERLKKKLSEQESLLLLMSPSMAFRVHSRNGKSYTFLISSDYERAEWRENIREQQKKCFRSFSLTSVELQMLTNSCVKLQTVHSIPLTINKEDDESPGLYGFLNVIVHSATGFKQSSNLYCTLEVDSFGYFVNKAKTRVYRDTAEPNWNEEFEIELEGSQTLRILCYEKCYNKTKIPKEDGESTDRLMGKGQVQLDPQALQDRDWQRTVIAMNGIEVKLSVKFNSREFSLKRMPSRKQTGVFGVKIAVVTKRERSKVPYIVRQCVEEIERRGMEEVGIYRVSGVATDIQALKAAFDVNNKDVSVMMSEMDVNAIAGTLKLYFRELPEPLFTDEFYPNFAEGIALSDPVAKESCMLNLLLSLPEANLLTFLFLLDHLKRVAEKEAVNKMSLHNLATVFGPTLLRPSEKESKLPANPSQPITMTDSWSLEVMSQVQVLLYFLQLEAIPAPDSKRQSILFSTEV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[Cl-].[K+]', 'CCC1=C(O)C=C(O)C(=C1)C1=NNC=C1C1=CNC=N1' have a chance to interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[Cl-].[K+]\", \"CCC1=C(O)C=C(O)C(=C1)C1=NNC=C1C1=CNC=N1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPHFTVVPVDGPRRGDYDNLEGLSWVDYGERAELDDSDGHGNHRESSPFLSPLEASRGIDYYDRNLALFEEELDIRPKVSSLLGKLVSYTNLTQGAKEHEEAESGEGTRRRAAEAPSMGTLMGVYLPCLQNIFGVILFLRLTWMVGTAGVLQALLIVLICCCCTLLTAISMSAIATNGVVPAGGSYFMISRSLGPEFGGAVGLCFYLGTTFAAAMYILGAIEILLTYIAPPAAIFYPSGAHDTSNATLNNMRVYGTIFLTFMTLVVFVGVKYVNKFASLFLACVIISILSIYAGGIKSIFDPPVFPVCMLGNRTLSRDQFDICAKTAVVDNETVATQLWSFFCHSPNLTTDSCDPYFMLNNVTEIPGIPGAAAGVLQENLWSAYLEKGDIVEKHGLPSADAPSLKESLPLYVVADIATSFTVLVGIFFPSVTGIMAGSNRSGDLRDAQKSIPVGTILAIITTSLVYFSSVVLFGACIEGVVLRDKYGDGVSRNLVVGTLAWPSPWVIVIGSFFSTCGAGLQSLTGAPRLLQAIAKDNIIPFLRVFGHGKVNGEPTWALLLTALIAELGILIASLDMVAPILSMFFLMCYLFVNLACAVQTLLRTPNWRPRFKYYHWALSFLGMSLCLALMFVSSWYYALVAMLIAGMIYKYIEYQGAEKEWGDGIRGLSLSAARYALLRLEEGPPHTKNWRPQLLVLLKLDEDLHVKYPRLLTFASQLKAGKGLTIVGSVIQGSFLESYGEAQAAEQTIKNMMEIEKVKGFCQVVVASKVREGLAHLIQSCGLGGMRHNSVVLGWPYGWRQSEDPRAWKTFIDTVRCTTAAHLALLVPKNIAFYPSNHERYLEGHIDVWWIVHDGGMLMLLPFLLRQHKVWRKCRMRIFTVAQMDDNSIQMKKDLAVFLYHLRLEAEVEVVEMHNSDISAYTYERTLMMEQRSQMLRQMRLTKTEREREAQLVKDRHSALRLESLYSDEEDESAVGADKIQMTWTRDKYMTETWDPSHAPDNFRELVHIKPDQSNVRRMHTAVKLNEVIVTRSHDARLVLLNMPGPPRNSEGDENYMEFLEVLTEGLERVLLVRGGGREVITIYS\", \"result\": [{\"smile\": \"[Cl-].[K+]\", \"value\": 1.0}, {\"smile\": \"CCC1=C(O)C=C(O)C(=C1)C1=NNC=C1C1=CNC=N1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug '[Cl-].[K+]' can interact with the protein, while the second one 'CCC1=C(O)C=C(O)C(=C1)C1=NNC=C1C1=CNC=N1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast for whether the drug 'CCCC[C@H](NC(=O)OC1CC2CCC1C2)C(=O)C(=O)N[C@H](C)c1ccccc1', 'N#CCNC(=O)C1(NC(=O)c2ccc(N3CCOCC3)cc2)CCCCC1' will interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCC[C@H](NC(=O)OC1CC2CCC1C2)C(=O)C(=O)N[C@H](C)c1ccccc1\", \"N#CCNC(=O)C1(NC(=O)c2ccc(N3CCOCC3)cc2)CCCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI\", \"result\": [{\"smile\": \"CCCC[C@H](NC(=O)OC1CC2CCC1C2)C(=O)C(=O)N[C@H](C)c1ccccc1\", \"value\": 1.0}, {\"smile\": \"N#CCNC(=O)C1(NC(=O)c2ccc(N3CCOCC3)cc2)CCCCC1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CCCC[C@H](NC(=O)OC1CC2CCC1C2)C(=O)C(=O)N[C@H](C)c1ccccc1' can interact with the protein, while the second one 'N#CCNC(=O)C1(NC(=O)c2ccc(N3CCOCC3)cc2)CCCCC1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the drug 'CCn1cnc2c(N3CCCC3=O)cc(C(=O)N[C@@H](Cc3ccccc3)[C@H](O)CNCc3cccc(C(F)(F)F)c3)cc21', 'COc1cnc(C(=O)Nc2ccc3c(c2)C2(CCSC(N)=N2)c2cc(C4=CCCOC4)ncc2O3)cn1''s potential to interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCn1cnc2c(N3CCCC3=O)cc(C(=O)N[C@@H](Cc3ccccc3)[C@H](O)CNCc3cccc(C(F)(F)F)c3)cc21\", \"COc1cnc(C(=O)Nc2ccc3c(c2)C2(CCSC(N)=N2)c2cc(C4=CCCOC4)ncc2O3)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\", \"result\": [{\"smile\": \"CCn1cnc2c(N3CCCC3=O)cc(C(=O)N[C@@H](Cc3ccccc3)[C@H](O)CNCc3cccc(C(F)(F)F)c3)cc21\", \"value\": 0.0}, {\"smile\": \"COc1cnc(C(=O)Nc2ccc3c(c2)C2(CCSC(N)=N2)c2cc(C4=CCCOC4)ncc2O3)cn1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCn1cnc2c(N3CCCC3=O)cc(C(=O)N[C@@H](Cc3ccccc3)[C@H](O)CNCc3cccc(C(F)(F)F)c3)cc21' nor 'COc1cnc(C(=O)Nc2ccc3c(c2)C2(CCSC(N)=N2)c2cc(C4=CCCOC4)ncc2O3)cn1' can interact with the protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it for the drug 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4', 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and target protein to have an interaction? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\", \"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV\", \"result\": [{\"smile\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4\", \"value\": 0.0}, {\"smile\": \"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4' nor 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' can interact with the protein 'MKRSEKPEGYRQMRPKTFPASNYTVSSRQMLQEIRESLRNLSKPSDAAKAEHNMSKMSTEDPRQVRNPPKFGTHHKALQEIRNSLLPFANETNSSRSTSEVNPQMLQDLQAAGFDEDMVIQALQKTNNRSIEAAIEFISKMSYQDPRREQMAAAAARPINASMKPGNVQQSVNRKQSWKGSKESLVPQRHGPPLGESVAYHSESPNSQTDVGRPLSGSGISAFVQAHPSNGQRVNPPPPPQVRSVTPPPPPRGQTPPPRGTTPPPPSWEPNSQTKRYSGNMEYVISRISPVPPGAWQEGYPPPPLNTSPMNPPNQGQRGISSVPVGRQPIIMQSSSKFNFPSGRPGMQNGTGQTDFMIHQNVVPAGTVNRQPPPPYPLTAANGQSPSALQTGGSAAPSSYTNGSIPQSMMVPNRNSHNMELYNISVPGLQTNWPQSSSAPAQSSPSSGHEIPTWQPNIPVRSNSFNNPLGNRASHSANSQPSATTVTAITPAPIQQPVKSMRVLKPELQTALAPTHPSWIPQPIQTVQPSPFPEGTASNVTVMPPVAEAPNYQGPPPPYPKHLLHQNPSVPPYESISKPSKEDQPSLPKEDESEKSYENVDSGDKEKKQITTSPITVRKNKKDEERRESRIQSYSPQAFKFFMEQHVENVLKSHQQRLHRKKQLENEMMRVGLSQDAQDQMRKMLCQKESNYIRLKRAKMDKSMFVKIKTLGIGAFGEVCLARKVDTKALYATKTLRKKDVLLRNQVAHVKAERDILAEADNEWVVRLYYSFQDKDNLYFVMDYIPGGDMMSLLIRMGIFPESLARFYIAELTCAVESVHKMGFIHRDIKPDNILIDRDGHIKLTDFGLCTGFRWTHDSKYYQSGDHPRQDSMDFSNEWGDPSSCRCGDRLKPLERRAARQHQRCLAHSLVGTPNYIAPEVLLRTGYTQLCDWWSVGVILFEMLVGQPPFLAQTPLETQMKVINWQTSLHIPPQAKLSPEASDLIIKLCRGPEDRLGKNGADEIKAHPFFKTIDFSSDLRQQSASYIPKITHPTDTSNFDPVDPDKLWSDDNEEENVNDTLNGWYKNGKHPEHAFYEFTFRRFFDDNGYPYNYPKPIEYEYINSQGSEQQSDEDDQNTGSEIKNRDLVYV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline your prediction on the drug 'CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C', 'C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O''s interaction with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C\", \"C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C\", \"value\": 0.0}, {\"smile\": \"C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCN(CC)CCNC(=O)C1=C(NC(=C1C)C=C2C3=C(C=CC(=C3)F)NC2=O)C' nor 'C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O' can interact with the protein 'MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on whether the drug 'CC1=CC=CC=C1O', 'CCCC(=O)OCOC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC(Cl)=C1Cl)C(=O)OC' will interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CC=CC=C1O\", \"CCCC(=O)OCOC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC(Cl)=C1Cl)C(=O)OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEPSSPQDEGLRKKQPKKPVPEILPRPPRALFCLTLENPLRKACISIVEWKPFETIILLTIFANCVALAVYLPMPEDDNNSLNLGLEKLEYFFLIVFSIEAAMKIIAYGFLFHQDAYLRSGWNVLDFTIVFLGVFTVILEQVNVIQSHTAPMSSKGAGLDVKALRAFRVLRPLRLVSGVPSLQVVLNSIFKAMLPLFHIALLVLFMVIIYAIIGLELFKGKMHKTCYFIGTDIVATVENEEPSPCARTGSGRRCTINGSECRGGWPGPNHGITHFDNFGFSMLTVYQCITMEGWTDVLYWVNDAIGNEWPWIYFVTLILLGSFFILNLVLGVLSGEFTKEREKAKSRGTFQKLREKQQLDEDLRGYMSWITQGEVMDVEDFREGKLSLDEGGSDTESLYEIAGLNKIIQFIRHWRQWNRIFRWKCHDIVKSKVFYWLVILIVALNTLSIASEHHNQPLWLTRLQDIANRVLLSLFTTEMLMKMYGLGLRQYFMSIFNRFDCFVVCSGILEILLVESGAMTPLGISVLRCIRLLRIFKITKYWTSLSNLVASLLNSIRSIASLLLLLFLFIVIFALLGMQLFGGRYDFEDTEVRRSNFDNFPQALISVFQVLTGEDWTSMMYNGIMAYGGPSYPGMLVCIYFIILFVCGNYILLNVFLAIAVDNLAEAESLTSAQKAKAEEKKRRKMSKGLPDKSEEEKSTMAKKLEQKPKGEGIPTTAKLKIDEFESNVNEVKDPYPSADFPGDDEEDEPEIPLSPRPRPLAELQLKEKAVPIPEASSFFIFSPTNKIRVLCHRIVNATWFTNFILLFILLSSAALAAEDPIRADSMRNQILKHFDIGFTSVFTVEIVLKMTTYGAFLHKGSFCRNYFNMLDLLVVAVSLISMGLESSAISVVKILRVLRVLRPLRAINRAKGLKHVVQCMFVAISTIGNIVLVTTLLQFMFACIGVQLFKGKFFRCTDLSKMTEEECRGYYYVYKDGDPMQIELRHREWVHSDFHFDNVLSAMMSLFTVSTFEGWPQLLYKAIDSNAEDVGPIYNNRVEMAIFFIIYIILIAFFMMNIFVGFVIVTFQEQGETEYKNCELDKNQRQCVQYALKARPLRCYIPKNPYQYQVWYIVTSSYFEYLMFALIMLNTICLGMQHYNQSEQMNHISDILNVAFTIIFTLEMILKLMAFKARGYFGDPWNVFDFLIVIGSIIDVILSEIDTFLASSGGLYCLGGGCGNVDPDESARISSAFFRLFRVMRLIKLLSRAEGVRTLLWTFIKSFQALPYVALLIVMLFFIYAVIGMQMFGKIALVDGTQINRNNNFQTFPQAVLLLFRCATGEAWQEILLACSYGKLCDPESDYAPGEEYTCGTNFAYYYFISFYMLCAFLVINLFVAVIMDNFDYLTRDWSILGPHHLDEFKAIWAEYDPEAKGRIKHLDVVTLLRRIQPPLGFGKFCPHRVACKRLVGMNMPLNSDGTVTFNATLFALVRTALKIKTEGNFEQANEELRAIIKKIWKRTSMKLLDQVIPPIGDDEVTVGKFYATFLIQEHFRKFMKRQEEYYGYRPKKDIVQIQAGLRTIEEEAAPEICRTVSGDLAAEEELERAMVEAAMEEGIFRRTGGLFGQVDNFLERTNSLPPVMANQRPLQFAEIEMEEMESPVFLEDFPQDPRTNPLARANTNNANANVAYGNSNHSNSHVFSSVHYEREFPEETETPATRGRALGQPCRVLGPHSKPCVEMLKGLLTQRAMPRGQAPPAPCQCPRVESSMPEDRKSSTPGSLHEETPHSRSTRENTSRCSAPATALLIQKALVRGGLGTLAADANFIMATGQALADACQMEPEEVEIMATELLKGREAPEGMASSLGCLNLGSSLGSLDQHQGSQETLIPPRL\", \"result\": [{\"smile\": \"CC1=CC=CC=C1O\", \"value\": 0.0}, {\"smile\": \"CCCC(=O)OCOC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC(Cl)=C1Cl)C(=O)OC\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CCCC(=O)OCOC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC(Cl)=C1Cl)C(=O)OC' can interact with the protein, while the first one 'CC1=CC=CC=C1O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the probability of interaction between the drug 'OCC1CCN(c2ncncc2Oc2ccc(Nc3nc4ccccc4[nH]3)cc2)CC1', '[H][C@]1(CNC(=O)C1)C1=CC(OC2CCCC2)=C(OC)C=C1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OCC1CCN(c2ncncc2Oc2ccc(Nc3nc4ccccc4[nH]3)cc2)CC1\", \"[H][C@]1(CNC(=O)C1)C1=CC(OC2CCCC2)=C(OC)C=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT\", \"result\": [{\"smile\": \"OCC1CCN(c2ncncc2Oc2ccc(Nc3nc4ccccc4[nH]3)cc2)CC1\", \"value\": 0.0}, {\"smile\": \"[H][C@]1(CNC(=O)C1)C1=CC(OC2CCCC2)=C(OC)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug '[H][C@]1(CNC(=O)C1)C1=CC(OC2CCCC2)=C(OC)C=C1' can interact with the protein, while the first one 'OCC1CCN(c2ncncc2Oc2ccc(Nc3nc4ccccc4[nH]3)cc2)CC1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood of the drug 'ClC1=CC=C(S1)\\C=C\\S(=O)(=O)N1CCN(CC2=CC3=C(N2)C=CN=C3)C(=O)C1', 'N[C@@H](CC1=CN=CN1)C(O)=O' engaging with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"ClC1=CC=C(S1)\\C=C\\S(=O)(=O)N1CCN(CC2=CC3=C(N2)C=CN=C3)C(=O)C1\", \"N[C@@H](CC1=CN=CN1)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPRYTVHVRGEWLAVPCQDAQLTVGWLGREAVRRYIKNKPDNGGFTSVDDAHFLVRRCKGLGLLDNEDRLEVALENNEFVEVVIEGDAMSPDFIPSQPEGVYLYSKYREPEKYIELDGDRLTTEDLVNLGKGRYKIKLTPTAEKRVQKSREVIDSIIKEKTVVYGITTGFGKFARTVIPINKLQELQVNLVRSHSSGVGKPLSPERCRMLLALRINVLAKGYSGISLETLKQVIEMFNASCLPYVPEKGTVGASGDLAPLSHLALGLVGEGKMWSPKSGWADAKYVLEAHGLKPVILKPKEGLALINGTQMITSLGCEAVERASAIARQADIVAALTLEVLKGTTKAFDTDIHALRPHRGQIEVAFRFRSLLDSDHHPSEIAESHRFCDRVQDAYTLRCCPQVHGVVNDTIAFVKNIITTELNSATDNPMVFANRGETVSGGNFHGEYPAKALDYLAIGIHELAAISERRIERLCNPSLSELPAFLVAEGGLNSGFMIAHCTAAALVSENKALCHPSSVDSLSTSAATEDHVSMGGWAARKALRVIEHVEQVLAIELLAACQGIEFLRPLKTTTPLEKVYDLVRSVVRPWIKDRFMAPDIEAAHRLLLEQKVWEVAAPYIEKYRMEHIPESRPLSPTAFSLQFLHKKSTKIPESEDL\", \"result\": [{\"smile\": \"ClC1=CC=C(S1)\\C=C\\S(=O)(=O)N1CCN(CC2=CC3=C(N2)C=CN=C3)C(=O)C1\", \"value\": 0.0}, {\"smile\": \"N[C@@H](CC1=CN=CN1)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'N[C@@H](CC1=CN=CN1)C(O)=O' can interact with the protein, while the first one 'ClC1=CC=C(S1)\\C=C\\S(=O)(=O)N1CCN(CC2=CC3=C(N2)C=CN=C3)C(=O)C1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you rate the chances of interaction between the drug 'C(=O)C(Cl)Cl', 'C1CC(=O)NC1C(=O)O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(=O)C(Cl)Cl\", \"C1CC(=O)NC1C(=O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSSAAKNFYQVVKSAPKGRFKGIKRDYTVEDVLKLRGSIDIDYTLATRGANKLWQLLHTEPFVPALGAQTGNQAVQMVRAGLKAIYLSGWQVAADANSAGDMYPDQSLYPANSGPELAKRINRSLRRADQIEACEAEDYLAQRDWYAPIVADAEAGFGGALNCFELMKAYIEAGAAGVHYEDQLGSEKKCGHMGGKVLIPTAQHIRHLNASRLAADVCGVPTIIVARTDAESSRLLTSDIDPRDHPYIDYEAGRTIEGFYRLKDSTAIQYCIDRAIQYAPYTDLIWMETSHPTIADAREFAEGVHKQYPDKMFAYNCSPSFNWKKHLSPSQMEKFQKELGAMGFKYQFITLAGYHANSYSMFDLARNYKEKGMLAYSGLQEGEFAAEKHGYTAVKHQREVGTGYFDAVSRAVTGGLSSTTALSGSTEEAQFQTAVASQDEEILSLTAQNVAGDEKILTPDALRFLHDLNTEFNPRRLRLLSKRNQVQADINNSLWFPDFNKETEVLRSDQGWKGAEIPRDLQDRRVEITGPTDRKMVINAMNSGANVFMADFEDSNSPTWRNQLEGQINLYDAVRNNISYTHPTTKKEYTLNEKHAVLKVRPRGWHLPEKHVLIHNQPTSGSLFDFGLFVFHNAKALIAQGSGPYFYLPKLQSAEEAQLWADVFKYTEDKLGLARGTIKCTVLIEHLLASFQLHEIIHALKDNIVGLNCGRWDYIFSYIKTFQNHRKFLLPDRFQIGMTAPFMRNYSLEVIKACHLRGIHAMGGMAAQIPIKHDQVANDKAFALVRADKEREATDGHDGTWVAHPGLVPLAKRVFDQMMPKPNQISKNLTRANCTKEDLTVIPEGTRTEAGFRHNISVTLGYLDSWLRGTGCVPLYNLMEDAATAEISRAQLWQWLHHDAKLEDGRTIDAGLVKQTIAAETERRLIRAGSVVNRIPEAADLLEKFVTEEKMSDFLTTDAYDRLVSEGY\", \"result\": [{\"smile\": \"C(=O)C(Cl)Cl\", \"value\": 0.0}, {\"smile\": \"C1CC(=O)NC1C(=O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C(=O)C(Cl)Cl' nor 'C1CC(=O)NC1C(=O)O' can interact with the protein 'MSSAAKNFYQVVKSAPKGRFKGIKRDYTVEDVLKLRGSIDIDYTLATRGANKLWQLLHTEPFVPALGAQTGNQAVQMVRAGLKAIYLSGWQVAADANSAGDMYPDQSLYPANSGPELAKRINRSLRRADQIEACEAEDYLAQRDWYAPIVADAEAGFGGALNCFELMKAYIEAGAAGVHYEDQLGSEKKCGHMGGKVLIPTAQHIRHLNASRLAADVCGVPTIIVARTDAESSRLLTSDIDPRDHPYIDYEAGRTIEGFYRLKDSTAIQYCIDRAIQYAPYTDLIWMETSHPTIADAREFAEGVHKQYPDKMFAYNCSPSFNWKKHLSPSQMEKFQKELGAMGFKYQFITLAGYHANSYSMFDLARNYKEKGMLAYSGLQEGEFAAEKHGYTAVKHQREVGTGYFDAVSRAVTGGLSSTTALSGSTEEAQFQTAVASQDEEILSLTAQNVAGDEKILTPDALRFLHDLNTEFNPRRLRLLSKRNQVQADINNSLWFPDFNKETEVLRSDQGWKGAEIPRDLQDRRVEITGPTDRKMVINAMNSGANVFMADFEDSNSPTWRNQLEGQINLYDAVRNNISYTHPTTKKEYTLNEKHAVLKVRPRGWHLPEKHVLIHNQPTSGSLFDFGLFVFHNAKALIAQGSGPYFYLPKLQSAEEAQLWADVFKYTEDKLGLARGTIKCTVLIEHLLASFQLHEIIHALKDNIVGLNCGRWDYIFSYIKTFQNHRKFLLPDRFQIGMTAPFMRNYSLEVIKACHLRGIHAMGGMAAQIPIKHDQVANDKAFALVRADKEREATDGHDGTWVAHPGLVPLAKRVFDQMMPKPNQISKNLTRANCTKEDLTVIPEGTRTEAGFRHNISVTLGYLDSWLRGTGCVPLYNLMEDAATAEISRAQLWQWLHHDAKLEDGRTIDAGLVKQTIAAETERRLIRAGSVVNRIPEAADLLEKFVTEEKMSDFLTTDAYDRLVSEGY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an expected interaction between the drug 'N[C@@H](CCC(=O)N[C@@H](C[S@](O)=O)C(=O)NCC(O)=O)C(O)=O', 'CC(C)[C@H](NC(=O)N(C)CC1=CSC(=N1)C(C)C)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)OCC1=CN=CS1)CC1=CC=CC=C1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CCC(=O)N[C@@H](C[S@](O)=O)C(=O)NCC(O)=O)C(O)=O\", \"CC(C)[C@H](NC(=O)N(C)CC1=CSC(=N1)C(C)C)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)OCC1=CN=CS1)CC1=CC=CC=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGPLPRTVELFYDVLSPYSWLGFEILCRYQNIWNINLQLRPSLITGIMKDSGNKPPGLLPRKGLYMANDLKLLRHHLQIPIHFPKDFLSVMLEKGSLSAMRFLTAVNLEHPEMLEKASRELWMRVWSRNEDITEPQSILAAAEKAGMSAEQAQGLLEKIATPKVKNQLKETTEAACRYGAFGLPITVAHVDGQTHMLFGSDRMELLAHLLGEKWMGPIPPAVNARL\", \"result\": [{\"smile\": \"N[C@@H](CCC(=O)N[C@@H](C[S@](O)=O)C(=O)NCC(O)=O)C(O)=O\", \"value\": 1.0}, {\"smile\": \"CC(C)[C@H](NC(=O)N(C)CC1=CSC(=N1)C(C)C)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)OCC1=CN=CS1)CC1=CC=CC=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'N[C@@H](CCC(=O)N[C@@H](C[S@](O)=O)C(=O)NCC(O)=O)C(O)=O' can interact with the protein, while the second one 'CC(C)[C@H](NC(=O)N(C)CC1=CSC(=N1)C(C)C)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)OCC1=CN=CS1)CC1=CC=CC=C1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate whether the drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5', 'O=C(NO)c1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1' will likely interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\", \"O=C(NO)c1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\", \"value\": 0.0}, {\"smile\": \"O=C(NO)c1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'O=C(NO)c1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1' can interact with the protein, while the first one 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N', 'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N\", \"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N\", \"value\": 0.0}, {\"smile\": \"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC' can interact with the protein, while the first one 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@H](O)C=C[C@H]3[C@H]1C5', 'C[C@@H]1NC(=O)[C@@H](NC(=O)[C@@H](N)Cc2ccc(O)cc2)C(C)(C)SSC(C)(C)[C@@H](C(O)O)NC(=O)[C@H](Cc2ccccc2)NC1=O''s interaction outcome with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@H](O)C=C[C@H]3[C@H]1C5\", \"C[C@@H]1NC(=O)[C@@H](NC(=O)[C@@H](N)Cc2ccc(O)cc2)C(C)(C)SSC(C)(C)[C@@H](C(O)O)NC(=O)[C@H](Cc2ccccc2)NC1=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"YTKMKTATNIYIFNLALADALATSTLPFQSVNYLMGTWPFGTILCKIVISIDYYNMFTSIFTLCTMSVDRYIAVCHPVKALDFRTPRNAKTVNVCNWI\", \"result\": [{\"smile\": \"CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@H](O)C=C[C@H]3[C@H]1C5\", \"value\": 1.0}, {\"smile\": \"C[C@@H]1NC(=O)[C@@H](NC(=O)[C@@H](N)Cc2ccc(O)cc2)C(C)(C)SSC(C)(C)[C@@H](C(O)O)NC(=O)[C@H](Cc2ccccc2)NC1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CN1CC[C@]23c4c5ccc(O)c4O[C@H]2[C@@H](O)C=C[C@H]3[C@H]1C5' can interact with the protein, while the second one 'C[C@@H]1NC(=O)[C@@H](NC(=O)[C@@H](N)Cc2ccc(O)cc2)C(C)(C)SSC(C)(C)[C@@H](C(O)O)NC(=O)[C@H](Cc2ccccc2)NC1=O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CS(=O)(=O)CC(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)[C@H](F)C1', 'N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1O[C@@H]1CCCN(C(=O)CO)C1' interact with the target protein according to your assessment? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)(=O)CC(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)[C@H](F)C1\", \"N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1O[C@@H]1CCCN(C(=O)CO)C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\", \"result\": [{\"smile\": \"CS(=O)(=O)CC(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)[C@H](F)C1\", \"value\": 1.0}, {\"smile\": \"N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1O[C@@H]1CCCN(C(=O)CO)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CS(=O)(=O)CC(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCN(C6COC6)CC5)cc4)n3)cc2C#N)[C@H](F)C1' can interact with the protein, while the second one 'N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1O[C@@H]1CCCN(C(=O)CO)C1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected likelihood of the drug 'O=c1[nH]nc(-c2ccnc(NC3CCCCC3)c2)n1-c1ccc2ccccc2c1', 'C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O' engaging with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1[nH]nc(-c2ccnc(NC3CCCCC3)c2)n1-c1ccc2ccccc2c1\", \"C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSDSKCDSQFYSVQVADSTFTVLKRYQQLKPIGSGAQGIVCAAFDTVLGINVAVKKLSRPFQNQTHAKRAYRELVLLKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIHMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTACTNFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGELVKGCVIFQGTDHIDQWNKVIEQLGTPSAEFMKKLQPTVRNYVENRPKYPGIKFEELFPDWIFPSESERDKIKTSQARDLLSKMLVIDPDKRISVDEALRHPYITVWYDPAEAEAPPPQIYDAQLEEREHAIEEWKELIYKEVMDWEERSKNGVVKDQPSDAAVSSNATPSQSSSINDISSMSTEQTLASDTDSSLDASTGPLEGCR\", \"result\": [{\"smile\": \"O=c1[nH]nc(-c2ccnc(NC3CCCCC3)c2)n1-c1ccc2ccccc2c1\", \"value\": 1.0}, {\"smile\": \"C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'O=c1[nH]nc(-c2ccnc(NC3CCCCC3)c2)n1-c1ccc2ccccc2c1' can interact with the protein, while the second one 'C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the drug 'CN1C=NC2=C1C(=O)N(C)C(=O)N2C', 'O=C(/C=C/c1ccc2c(c1)OCO2)c1cccnc1' will engage with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C=NC2=C1C(=O)N(C)C(=O)N2C\", \"O=C(/C=C/c1ccc2c(c1)OCO2)c1cccnc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CN1C=NC2=C1C(=O)N(C)C(=O)N2C\", \"value\": 1.0}, {\"smile\": \"O=C(/C=C/c1ccc2c(c1)OCO2)c1cccnc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CN1C=NC2=C1C(=O)N(C)C(=O)N2C' can interact with the protein, while the second one 'O=C(/C=C/c1ccc2c(c1)OCO2)c1cccnc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the interaction potential between the drug 'CSCC1C(C(C(O1)N2C=NC3=C2N=CN=C3N)O)O', 'CC(=O)[O-].CC(=O)[O-].[Ni+2]' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CSCC1C(C(C(O1)N2C=NC3=C2N=CN=C3N)O)O\", \"CC(=O)[O-].CC(=O)[O-].[Ni+2]\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSANKELQQGYSDYSAASDQAALLNRLITAMQKGELTDELLEHIPELGDAKKLNDKLKYRKSILEKSIAEQAAKNKKNGVKATSTSSPSSSTSAPAEKKAKKDGKTGGAPPKQAKKVDYVTVEDYGSSIFGRLQSLFKKAIEDAFPGLDVPLLLAETPNPQFGDYQCNSAMPIAAKLKANKINKRPGDVAKEIQAKLPTKIDFVEKIDVMPAGFINIFLNTDYLRRQISLLASEGVKLPKLTRKRVLVDFSSPNIAKEMHVGHLRSTIIGDSICRLFEAVGFDVLRVNHIGDWGTQFGMLIAHLYDRFPDFLKKLPDISDLQAFYKESKKRFDEDEQFKKRAYEYVVKLQSHDGDIVKAWNTICDVSKKYNQIVYNYLDIKIKDVGESFYQDKMIELVKWVKTNKPDMLREEDGRQIMFPTGCDIPLTVVKSDGGFTYDTSDLAALKYRMLEEKCDWNIYVVDSGQSLHLETVYAAGRDFGWYDESIQRVEHVAFGLVLGDDKKKFKTRSGETVRLLDLLSEGVKRATEKLIEKGRETAMSEEQLVAARDAVAFGCVKYADLSHTRTQDYVFSFDRMLEDRGNTAVYLLYAYTRIQSIFEKDEVKNVDLVKYIASTPTLPLDHPGEFKLAKQLLKLSDCVLLVLDSLMLHQMCDYVYQLATLFHDFYNECYVIENKEGEKPFVHMHRLALCDVTRKVMSTCFKILGLREVNKM\", \"result\": [{\"smile\": \"CSCC1C(C(C(O1)N2C=NC3=C2N=CN=C3N)O)O\", \"value\": 0.0}, {\"smile\": \"CC(=O)[O-].CC(=O)[O-].[Ni+2]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CSCC1C(C(C(O1)N2C=NC3=C2N=CN=C3N)O)O' nor 'CC(=O)[O-].CC(=O)[O-].[Ni+2]' can interact with the protein 'MSANKELQQGYSDYSAASDQAALLNRLITAMQKGELTDELLEHIPELGDAKKLNDKLKYRKSILEKSIAEQAAKNKKNGVKATSTSSPSSSTSAPAEKKAKKDGKTGGAPPKQAKKVDYVTVEDYGSSIFGRLQSLFKKAIEDAFPGLDVPLLLAETPNPQFGDYQCNSAMPIAAKLKANKINKRPGDVAKEIQAKLPTKIDFVEKIDVMPAGFINIFLNTDYLRRQISLLASEGVKLPKLTRKRVLVDFSSPNIAKEMHVGHLRSTIIGDSICRLFEAVGFDVLRVNHIGDWGTQFGMLIAHLYDRFPDFLKKLPDISDLQAFYKESKKRFDEDEQFKKRAYEYVVKLQSHDGDIVKAWNTICDVSKKYNQIVYNYLDIKIKDVGESFYQDKMIELVKWVKTNKPDMLREEDGRQIMFPTGCDIPLTVVKSDGGFTYDTSDLAALKYRMLEEKCDWNIYVVDSGQSLHLETVYAAGRDFGWYDESIQRVEHVAFGLVLGDDKKKFKTRSGETVRLLDLLSEGVKRATEKLIEKGRETAMSEEQLVAARDAVAFGCVKYADLSHTRTQDYVFSFDRMLEDRGNTAVYLLYAYTRIQSIFEKDEVKNVDLVKYIASTPTLPLDHPGEFKLAKQLLKLSDCVLLVLDSLMLHQMCDYVYQLATLFHDFYNECYVIENKEGEKPFVHMHRLALCDVTRKVMSTCFKILGLREVNKM'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction probability for the drug '[60Fe]', 'COC1=C(C=C(C=C1)C1=CC2=C(C=C1)C=C(C=C2)C(O)=O)C12CC3CC(CC(C3)C1)C2' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[60Fe]\", \"COC1=C(C=C(C=C1)C1=CC2=C(C=C1)C=C(C=C2)C(O)=O)C12CC3CC(CC(C3)C1)C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTTSGHACPVPAVNGHMTHYPATPYPLLFPPVIGGLSLPPLHGLHGHPPPSGCSTPSPATIETQSTSSEELVPSPPSPLPPPRVYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMIYTCHRDKNCVINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKETSKQECTESYEMTAELDDLTEKIRKAHQETFPSLCQLGKYTTNSSADHRVRLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFTFANQLLPLEMDDTETGLLSAICLICGDRQDLEEPTKVDKLQEPLLEALKIYIRKRRPSKPHMFPKILMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGHEPLTPSSSGNTAEHSPSISPSSVENSGVSQSPLVQ\", \"result\": [{\"smile\": \"[60Fe]\", \"value\": 0.0}, {\"smile\": \"COC1=C(C=C(C=C1)C1=CC2=C(C=C1)C=C(C=C2)C(O)=O)C12CC3CC(CC(C3)C1)C2\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'COC1=C(C=C(C=C1)C1=CC2=C(C=C1)C=C(C=C2)C(O)=O)C12CC3CC(CC(C3)C1)C2' can interact with the protein, while the first one '[60Fe]' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'C[C@]12CC[C@H]3[C@@H]([C@@H]1CC[C@@H]2O)CCC4=C3C=CC(=C4)O', 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C=C/C=C(\\C)/C=C/C=C(\\C)/C=C/C2=C(C[C@H](CC2(C)C)O)C)/C)/C' is expected to interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@]12CC[C@H]3[C@@H]([C@@H]1CC[C@@H]2O)CCC4=C3C=CC(=C4)O\", \"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C=C/C=C(\\C)/C=C/C=C(\\C)/C=C/C2=C(C[C@H](CC2(C)C)O)C)/C)/C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPNSEPASLLELFNSIATQGELVRSLKAGNASKDEIDSAVKMLVSLKMSYKAAAGEDYKADCPPGNPAPTSNHGPDATEAEEDFVDPWTVQTSSAKGIDYDKLIVRFGSSKIDKELINRIERATGQRPHHFLRRGIFFSHRDMNQVLDAYENKKPFYLYTGRGPSSEAMHVGHLIPFIFTKWLQDVFNVPLVIQMTDDEKYLWKDLTLDQAYSYAVENAKDIIACGFDINKTFIFSDLDYMGMSSGFYKNVVKIQKHVTFNQVKGIFGFTDSDCIGKISFPAIQAAPSFSNSFPQIFRDRTDIQCLIPCAIDQDPYFRMTRDVAPRIGYPKPALLHSTFFPALQGAQTKMSASDPNSSIFLTDTAKQIKTKVNKHAFSGGRDTIEEHRQFGGNCDVDVSFMYLTFFLEDDDKLEQIRKDYTSGAMLTGELKKALIEVLQPLIAEHQARRKEVTDEIVKEFMTPRKLSFDFQ\", \"result\": [{\"smile\": \"C[C@]12CC[C@H]3[C@@H]([C@@H]1CC[C@@H]2O)CCC4=C3C=CC(=C4)O\", \"value\": 0.0}, {\"smile\": \"CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C=C/C=C(\\C)/C=C/C=C(\\C)/C=C/C2=C(C[C@H](CC2(C)C)O)C)/C)/C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C[C@]12CC[C@H]3[C@@H]([C@@H]1CC[C@@H]2O)CCC4=C3C=CC(=C4)O' nor 'CC1=C(C(CCC1)(C)C)/C=C/C(=C/C=C/C(=C/C=C/C=C(\\C)/C=C/C=C(\\C)/C=C/C2=C(C[C@H](CC2(C)C)O)C)/C)/C' can interact with the protein 'MPNSEPASLLELFNSIATQGELVRSLKAGNASKDEIDSAVKMLVSLKMSYKAAAGEDYKADCPPGNPAPTSNHGPDATEAEEDFVDPWTVQTSSAKGIDYDKLIVRFGSSKIDKELINRIERATGQRPHHFLRRGIFFSHRDMNQVLDAYENKKPFYLYTGRGPSSEAMHVGHLIPFIFTKWLQDVFNVPLVIQMTDDEKYLWKDLTLDQAYSYAVENAKDIIACGFDINKTFIFSDLDYMGMSSGFYKNVVKIQKHVTFNQVKGIFGFTDSDCIGKISFPAIQAAPSFSNSFPQIFRDRTDIQCLIPCAIDQDPYFRMTRDVAPRIGYPKPALLHSTFFPALQGAQTKMSASDPNSSIFLTDTAKQIKTKVNKHAFSGGRDTIEEHRQFGGNCDVDVSFMYLTFFLEDDDKLEQIRKDYTSGAMLTGELKKALIEVLQPLIAEHQARRKEVTDEIVKEFMTPRKLSFDFQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely do you think the drug 'Cc1nc2sc(-c3ccc(S(C)(=O)=O)cc3)c(-c3ccccc3)n2n1', 'CS(=O)(=O)c1ccc(-n2nc(C(F)(F)F)cc2-c2ccc(F)cc2)cc1' will engage with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc2sc(-c3ccc(S(C)(=O)=O)cc3)c(-c3ccccc3)n2n1\", \"CS(=O)(=O)c1ccc(-n2nc(C(F)(F)F)cc2-c2ccc(F)cc2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\", \"result\": [{\"smile\": \"Cc1nc2sc(-c3ccc(S(C)(=O)=O)cc3)c(-c3ccccc3)n2n1\", \"value\": 1.0}, {\"smile\": \"CS(=O)(=O)c1ccc(-n2nc(C(F)(F)F)cc2-c2ccc(F)cc2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'Cc1nc2sc(-c3ccc(S(C)(=O)=O)cc3)c(-c3ccccc3)n2n1' and 'CS(=O)(=O)c1ccc(-n2nc(C(F)(F)F)cc2-c2ccc(F)cc2)cc1' can interact with the protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'O=c1c(C2=Nc3ccccc3S(=O)(=O)N2)c(O)c2cc(F)ccc2n1CCC1CC1', 'IC1=CC=CC(CNC2=C3CCCCC3=NC3=C2C=CC=C3)=C1' likely form an interaction with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1c(C2=Nc3ccccc3S(=O)(=O)N2)c(O)c2cc(F)ccc2n1CCC1CC1\", \"IC1=CC=CC(CNC2=C3CCCCC3=NC3=C2C=CC=C3)=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS\", \"result\": [{\"smile\": \"O=c1c(C2=Nc3ccccc3S(=O)(=O)N2)c(O)c2cc(F)ccc2n1CCC1CC1\", \"value\": 0.0}, {\"smile\": \"IC1=CC=CC(CNC2=C3CCCCC3=NC3=C2C=CC=C3)=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'O=c1c(C2=Nc3ccccc3S(=O)(=O)N2)c(O)c2cc(F)ccc2n1CCC1CC1' nor 'IC1=CC=CC(CNC2=C3CCCCC3=NC3=C2C=CC=C3)=C1' can interact with the protein 'MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected interaction between the drug 'O=C(NCc1nc2ccccc2[nH]1)NC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1', 'CC(C)Oc1ncc([C@]2(O)CC[C@H](N3CC(NC(=O)CNC(=O)c4cccc(C(F)(F)F)c4)C3)CC2)s1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NCc1nc2ccccc2[nH]1)NC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1\", \"CC(C)Oc1ncc([C@]2(O)CC[C@H](N3CC(NC(=O)CNC(=O)c4cccc(C(F)(F)F)c4)C3)CC2)s1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS\", \"result\": [{\"smile\": \"O=C(NCc1nc2ccccc2[nH]1)NC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1\", \"value\": 0.0}, {\"smile\": \"CC(C)Oc1ncc([C@]2(O)CC[C@H](N3CC(NC(=O)CNC(=O)c4cccc(C(F)(F)F)c4)C3)CC2)s1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'O=C(NCc1nc2ccccc2[nH]1)NC1CCN(Cc2ccn(-c3ccc(C(F)(F)F)cc3)c2)CC1' nor 'CC(C)Oc1ncc([C@]2(O)CC[C@H](N3CC(NC(=O)CNC(=O)c4cccc(C(F)(F)F)c4)C3)CC2)s1' can interact with the protein 'MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug 'CC(C)N1CCN(CC1)C2=CC=C(C=C2)OC[C@H]3CO[C@](O3)(CN4C=NC=N4)C5=C(C=C(C=C5)Cl)Cl', 'CC(CCC1=CC=C(C=C1)O)NCCC2=CC(=C(C=C2)O)O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1CCN(CC1)C2=CC=C(C=C2)OC[C@H]3CO[C@](O3)(CN4C=NC=N4)C5=C(C=C(C=C5)Cl)Cl\", \"CC(CCC1=CC=C(C=C1)O)NCCC2=CC(=C(C=C2)O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNDLQWPLLGLFSVLIILALFGNLLVCAAILWDRSLRKQPENLFLVSLAVSDLLVSVLVMLFAAVNDILGYWPFGQFYCQFWISFDITTCTASILNLCAISLDRYWHISRPMVYIRYCNRRRINYVIVLVWLISAGIGAAPLGFGFGSKVTINNLTGLPVCEMRLPLPYAIGSSMVSFFLPAMVMVILYTKLYLYARKHVRSIKTQLQQATSFLIMQLASEKIREVTAATLKGEALLPPDSPATERTTMTVSRHYSRRSTTTTTTATPRRGDKKTSSQNKVESIRTSIFSKLNFLCPTRFKNQRSPQDPHTPAAHNRSNISDQKARLTLGVIMGTFLVCWLPFFTVNILRAWLPEIFSSKTIMAVTWLGYANSSANPLIYSIFNRDFRRAFKKIIVRVFGCCWEEPDLNKSISSRYAAPDNIERRRSCTRSSESAHDNNNDANATRLNLLSNNNEETIPE\", \"result\": [{\"smile\": \"CC(C)N1CCN(CC1)C2=CC=C(C=C2)OC[C@H]3CO[C@](O3)(CN4C=NC=N4)C5=C(C=C(C=C5)Cl)Cl\", \"value\": 1.0}, {\"smile\": \"CC(CCC1=CC=C(C=C1)O)NCCC2=CC(=C(C=C2)O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC(C)N1CCN(CC1)C2=CC=C(C=C2)OC[C@H]3CO[C@](O3)(CN4C=NC=N4)C5=C(C=C(C=C5)Cl)Cl' and 'CC(CCC1=CC=C(C=C1)O)NCCC2=CC(=C(C=C2)O)O' can interact with the protein 'MNDLQWPLLGLFSVLIILALFGNLLVCAAILWDRSLRKQPENLFLVSLAVSDLLVSVLVMLFAAVNDILGYWPFGQFYCQFWISFDITTCTASILNLCAISLDRYWHISRPMVYIRYCNRRRINYVIVLVWLISAGIGAAPLGFGFGSKVTINNLTGLPVCEMRLPLPYAIGSSMVSFFLPAMVMVILYTKLYLYARKHVRSIKTQLQQATSFLIMQLASEKIREVTAATLKGEALLPPDSPATERTTMTVSRHYSRRSTTTTTTATPRRGDKKTSSQNKVESIRTSIFSKLNFLCPTRFKNQRSPQDPHTPAAHNRSNISDQKARLTLGVIMGTFLVCWLPFFTVNILRAWLPEIFSSKTIMAVTWLGYANSSANPLIYSIFNRDFRRAFKKIIVRVFGCCWEEPDLNKSISSRYAAPDNIERRRSCTRSSESAHDNNNDANATRLNLLSNNNEETIPE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be any interaction between the drug '[Zn]', '[Mg+2]' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[Zn]\", \"[Mg+2]\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPPLVIKNGTVVNEDGMFKADVLVRNGIIVEVSPNITALPDTEVIDATDRLVIPGGIDPHTHMQMPYMGEVTKDDFLKGTEAAVAGGTTMIIDFCCPDHRNGESLIAGYNRWRSWADPKVCCDYGLSVAITMWRPETAEQMAIITSPEFGVNSFKFYMAYENTLMVRDDELFRGMQECAKLRALARVHCENGSVIKEKEIDLLAKGVTGPEGHTQSRPEEIEAEATNRACVLAAQANCPVYIVHVMTKGAASAISHHRAQGSIVFGEPIAAGLALDGSHYYNEDWLHAARYVMSPPLSRDPTTPELLMKLLAAGELHLTGTDNCTYDCRQKSLGKGNFTKIPNGINGVEDRMSVVWEKGVHSGIIDPMRYVSITSSTAAKIFNIYPRKGRIAVGSDADIVIFNPNATRTISKDTHHHNLDFNIFEGINCHGVAEVTISRGRIVWAHGKLQTVPGSGKFIPLLANSPFVFSTHEKREQKIQPRIVERLE\", \"result\": [{\"smile\": \"[Zn]\", \"value\": 1.0}, {\"smile\": \"[Mg+2]\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs '[Zn]' and '[Mg+2]' can interact with the protein 'MSPPLVIKNGTVVNEDGMFKADVLVRNGIIVEVSPNITALPDTEVIDATDRLVIPGGIDPHTHMQMPYMGEVTKDDFLKGTEAAVAGGTTMIIDFCCPDHRNGESLIAGYNRWRSWADPKVCCDYGLSVAITMWRPETAEQMAIITSPEFGVNSFKFYMAYENTLMVRDDELFRGMQECAKLRALARVHCENGSVIKEKEIDLLAKGVTGPEGHTQSRPEEIEAEATNRACVLAAQANCPVYIVHVMTKGAASAISHHRAQGSIVFGEPIAAGLALDGSHYYNEDWLHAARYVMSPPLSRDPTTPELLMKLLAAGELHLTGTDNCTYDCRQKSLGKGNFTKIPNGINGVEDRMSVVWEKGVHSGIIDPMRYVSITSSTAAKIFNIYPRKGRIAVGSDADIVIFNPNATRTISKDTHHHNLDFNIFEGINCHGVAEVTISRGRIVWAHGKLQTVPGSGKFIPLLANSPFVFSTHEKREQKIQPRIVERLE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you view the chances of the drug 'N[C@@H](C\\N=C(/N)NO)C(O)=O', 'CC(C)OP(O)(O)=O' interacting with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](C\\N=C(/N)NO)C(O)=O\", \"CC(C)OP(O)(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDDKAFTKELDQWVEQLNECKQLNENQVRTLCEKAKEILTKESNVQEVRCPVTVCGDVHGQFHDLMELFRIGGKSPDTNYLFMGDYVDRGYYSVETVTLLVALKVRYPERITILRGNHESRQITQVYGFYDECLRKYGNANVWKYFTDLFDYLPLTALVDGQIFCLHGGLSPSIDTLDHIRALDRLQEVPHEGPMCDLLWSDPDDRGGWGISPRGAGYTFGQDISETFNHANGLTLVSRAHQLVMEGYNWCHDRNVVTIFSAPNYCYRCGNQAAIMELDDTLKYSFLQFDPAPRRGEPHVTRRTPDYFL\", \"result\": [{\"smile\": \"N[C@@H](C\\N=C(/N)NO)C(O)=O\", \"value\": 0.0}, {\"smile\": \"CC(C)OP(O)(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'N[C@@H](C\\N=C(/N)NO)C(O)=O' nor 'CC(C)OP(O)(O)=O' can interact with the protein 'MDDKAFTKELDQWVEQLNECKQLNENQVRTLCEKAKEILTKESNVQEVRCPVTVCGDVHGQFHDLMELFRIGGKSPDTNYLFMGDYVDRGYYSVETVTLLVALKVRYPERITILRGNHESRQITQVYGFYDECLRKYGNANVWKYFTDLFDYLPLTALVDGQIFCLHGGLSPSIDTLDHIRALDRLQEVPHEGPMCDLLWSDPDDRGGWGISPRGAGYTFGQDISETFNHANGLTLVSRAHQLVMEGYNWCHDRNVVTIFSAPNYCYRCGNQAAIMELDDTLKYSFLQFDPAPRRGEPHVTRRTPDYFL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction for the drug 'C[C@]12CCCC=C1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@]4(C#C)O)C.C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C#C)O)CCC4=C3C=CC(=C4)O', '[H][C@](N)(CC1=CC=C(O)C=C1)C=O''s interaction potential with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@]12CCCC=C1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@]4(C#C)O)C.C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C#C)O)CCC4=C3C=CC(=C4)O\", \"[H][C@](N)(CC1=CC=C(O)C=C1)C=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGDAPSPEEKLHLITRNLQEVLGEEKLKEILKERELKIYWGTATTGKPHVAYFVPMSKIADFLKAGCEVTILFADLHAYLDNMKAPWELLELRVSYYENVIKAMLESIGVPLEKLKFIKGTDYQLSKEYTLDVYRLSSVVTQHDSKKAGAEVVKQVEHPLLSGLLYPGLQALDEEYLKVDAQFGGIDQRKIFTFAEKYLPALGYSKRVHLMNPMVPGLTGSKMSSSEEESKIDLLDRKEDVKKKLKKAFCEPGNVENNGVLSFIKHVLFPLKSEFVILRDEKWGGNKTYTAYVDLEKDFAAEVVHPGDLKNSVEVALNKLLDPIREKFNTPALKKLASAAYPDPSKQKPMAKGPAKNSEPEEVIPSRLDIRVGKIITVEKHPDADSLYVEKIDVGEAEPRTVVSGLVQFVPKEELQDRLVVVLCNLKPQKMRGVESQGMLLCASIEGINRQVEPLDPPAGSAPGEHVFVKGYEKGQPDEELKPKKKVFEKLQADFKISEECIAQWKQTNFMTKLGSISCKSLKGGNIS\", \"result\": [{\"smile\": \"C[C@]12CCCC=C1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@]4(C#C)O)C.C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C#C)O)CCC4=C3C=CC(=C4)O\", \"value\": 0.0}, {\"smile\": \"[H][C@](N)(CC1=CC=C(O)C=C1)C=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug '[H][C@](N)(CC1=CC=C(O)C=C1)C=O' can interact with the protein, while the first one 'C[C@]12CCCC=C1CC[C@@H]3[C@@H]2CC[C@]4([C@H]3CC[C@]4(C#C)O)C.C[C@]12CC[C@H]3[C@H]([C@@H]1CC[C@]2(C#C)O)CCC4=C3C=CC(=C4)O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[H][C@@]12CC3=CC=C(OC4=C5C(CC[N+](C)(C)[C@]5([H])CC5=CC(OC6=C(OC)C=C(CCN1C)C2=C6)=C(O)C=C5)=CC(OC)=C4O)C=C3', 'CC(=O)[O-].CC(=O)[O-].[Ni+2]' interact with the target protein as expected? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]12CC3=CC=C(OC4=C5C(CC[N+](C)(C)[C@]5([H])CC5=CC(OC6=C(OC)C=C(CCN1C)C2=C6)=C(O)C=C5)=CC(OC)=C4O)C=C3\", \"CC(=O)[O-].CC(=O)[O-].[Ni+2]\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP\", \"result\": [{\"smile\": \"[H][C@@]12CC3=CC=C(OC4=C5C(CC[N+](C)(C)[C@]5([H])CC5=CC(OC6=C(OC)C=C(CCN1C)C2=C6)=C(O)C=C5)=CC(OC)=C4O)C=C3\", \"value\": 0.0}, {\"smile\": \"CC(=O)[O-].CC(=O)[O-].[Ni+2]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither '[H][C@@]12CC3=CC=C(OC4=C5C(CC[N+](C)(C)[C@]5([H])CC5=CC(OC6=C(OC)C=C(CCN1C)C2=C6)=C(O)C=C5)=CC(OC)=C4O)C=C3' nor 'CC(=O)[O-].CC(=O)[O-].[Ni+2]' can interact with the protein 'MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the drug 'C1[C@@H]([C@H](O[C@H]1N2C=C(C(=O)NC2=O)F)CO)O', 'C1CC(OC1)N2C=C(C(=O)NC2=O)F''s potential to interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1[C@@H]([C@H](O[C@H]1N2C=C(C(=O)NC2=O)F)CO)O\", \"C1CC(OC1)N2C=C(C(=O)NC2=O)F\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSFNPDYESVAKAFIQHYYSKFDVGDGMSRAQGLSDLYDPENSYMTFEGQQAKGRDGILQKFTTLGFTKIQRAITVIDSQPLYDGSIQVMVLGQLKTDEDPINPFSQVFILRPNNQGSYFIGNEIFRLDLHNN\", \"result\": [{\"smile\": \"C1[C@@H]([C@H](O[C@H]1N2C=C(C(=O)NC2=O)F)CO)O\", \"value\": 0.0}, {\"smile\": \"C1CC(OC1)N2C=C(C(=O)NC2=O)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C1[C@@H]([C@H](O[C@H]1N2C=C(C(=O)NC2=O)F)CO)O' nor 'C1CC(OC1)N2C=C(C(=O)NC2=O)F' can interact with the protein 'MSFNPDYESVAKAFIQHYYSKFDVGDGMSRAQGLSDLYDPENSYMTFEGQQAKGRDGILQKFTTLGFTKIQRAITVIDSQPLYDGSIQVMVLGQLKTDEDPINPFSQVFILRPNNQGSYFIGNEIFRLDLHNN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess the likelihood of interaction between the drug 'CCN(CC)C(=O)C1CN2CCC3=CC(OC)=C(OC)C=C3C2CC1OC(C)=O', 'CC(C)C(=O)[C@@]12C(O)=C(CC=C(C)C)C(=O)[C@@](CC=C(C)C)(C[C@H](CC=C(C)C)[C@@]1(C)CCC=C(C)C)C2=O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)C(=O)C1CN2CCC3=CC(OC)=C(OC)C=C3C2CC1OC(C)=O\", \"CC(C)C(=O)[C@@]12C(O)=C(CC=C(C)C)C(=O)[C@@](CC=C(C)C)(C[C@H](CC=C(C)C)[C@@]1(C)CCC=C(C)C)C2=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MARQKKMGQSVLRAVFFLVLGLLGHSHGGFPNTISIGGLFMRNTVQEHSAFRFAVQLYNTNQNTTEKPFHLNYHVDHLDSSNSFSVTNAFCSQFSRGVYAIFGFYDQMSMNTLTSFCGALHTSFVTPSFPTDADVQFVIQMRPALKGAILSLLGHYKWEKFVYLYDTERGFSILQAIMEAAVQNNWQVTARSVGNIKDVQEFRRIIEEMDRRQEKRYLIDCEVERINTILEQVVILGKHSRGYHYMLANLGFTDILLERVMHGGANITGFQIVNNENPMVQQFIQRWVRLDEREFPEAKNAPLKYTSALTHDAILVIAEAFRYLRRQRVDVSRRGSAGDCLANPAVPWSQGIDIERALKMVQVQGMTGNIQFDTYGRRTNYTIDVYEMKVSGSRKAGYWNEYERFVPFSDQQISNDSASSENRTIVVTTILESPYVMYKKNHEQLEGNERYEGYCVDLAYEIAKHVRIKYKLSIVGDGKYGARDPETKIWNGMVGELVYGRADIAVAPLTITLVREEVIDFSKPFMSLGISIMIKKPQKSKPGVFSFLDPLAYEIWMCIVFAYIGVSVVLFLVSRFSPYEWHLEDNNEEPRDPQSPPDPPNEFGIFNSLWFSLGAFMQQGCDISPRSLSGRIVGGVWWFFTLIIISSYTANLAAFLTVERMVSPIESAEDLAKQTEIAYGTLDSGSTKEFFRRSKIAVYEKMWSYMKSAEPSVFTKTTADGVARVRKSKGKFAFLLESTMNEYIEQRKPCDTMKVGGNLDSKGYGVATPKGSALRNAVNLAVLKLNEQGLLDKLKNKWWYDKGECGSGGGDSKDKTSALSLSNVAGVFYILVGGLGLAMMVALIEFCYKSRAESKRMKLTKNTQNFKPAPATNTQNYATYREGYNVYGTESVKI\", \"result\": [{\"smile\": \"CCN(CC)C(=O)C1CN2CCC3=CC(OC)=C(OC)C=C3C2CC1OC(C)=O\", \"value\": 0.0}, {\"smile\": \"CC(C)C(=O)[C@@]12C(O)=C(CC=C(C)C)C(=O)[C@@](CC=C(C)C)(C[C@H](CC=C(C)C)[C@@]1(C)CCC=C(C)C)C2=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCN(CC)C(=O)C1CN2CCC3=CC(OC)=C(OC)C=C3C2CC1OC(C)=O' nor 'CC(C)C(=O)[C@@]12C(O)=C(CC=C(C)C)C(=O)[C@@](CC=C(C)C)(C[C@H](CC=C(C)C)[C@@]1(C)CCC=C(C)C)C2=O' can interact with the protein 'MARQKKMGQSVLRAVFFLVLGLLGHSHGGFPNTISIGGLFMRNTVQEHSAFRFAVQLYNTNQNTTEKPFHLNYHVDHLDSSNSFSVTNAFCSQFSRGVYAIFGFYDQMSMNTLTSFCGALHTSFVTPSFPTDADVQFVIQMRPALKGAILSLLGHYKWEKFVYLYDTERGFSILQAIMEAAVQNNWQVTARSVGNIKDVQEFRRIIEEMDRRQEKRYLIDCEVERINTILEQVVILGKHSRGYHYMLANLGFTDILLERVMHGGANITGFQIVNNENPMVQQFIQRWVRLDEREFPEAKNAPLKYTSALTHDAILVIAEAFRYLRRQRVDVSRRGSAGDCLANPAVPWSQGIDIERALKMVQVQGMTGNIQFDTYGRRTNYTIDVYEMKVSGSRKAGYWNEYERFVPFSDQQISNDSASSENRTIVVTTILESPYVMYKKNHEQLEGNERYEGYCVDLAYEIAKHVRIKYKLSIVGDGKYGARDPETKIWNGMVGELVYGRADIAVAPLTITLVREEVIDFSKPFMSLGISIMIKKPQKSKPGVFSFLDPLAYEIWMCIVFAYIGVSVVLFLVSRFSPYEWHLEDNNEEPRDPQSPPDPPNEFGIFNSLWFSLGAFMQQGCDISPRSLSGRIVGGVWWFFTLIIISSYTANLAAFLTVERMVSPIESAEDLAKQTEIAYGTLDSGSTKEFFRRSKIAVYEKMWSYMKSAEPSVFTKTTADGVARVRKSKGKFAFLLESTMNEYIEQRKPCDTMKVGGNLDSKGYGVATPKGSALRNAVNLAVLKLNEQGLLDKLKNKWWYDKGECGSGGGDSKDKTSALSLSNVAGVFYILVGGLGLAMMVALIEFCYKSRAESKRMKLTKNTQNFKPAPATNTQNYATYREGYNVYGTESVKI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction between the drug 'CCCCNc1ncc(C(=O)Nc2ccc(S(=O)(=O)N3CCOCC3)cc2)c(N[C@H]2CCNC2)n1', 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCNc1ncc(C(=O)Nc2ccc(S(=O)(=O)N3CCOCC3)cc2)c(N[C@H]2CCNC2)n1\", \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\", \"result\": [{\"smile\": \"CCCCNc1ncc(C(=O)Nc2ccc(S(=O)(=O)N3CCOCC3)cc2)c(N[C@H]2CCNC2)n1\", \"value\": 0.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' can interact with the protein, while the first one 'CCCCNc1ncc(C(=O)Nc2ccc(S(=O)(=O)N3CCOCC3)cc2)c(N[C@H]2CCNC2)n1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'C[C@@]1(c2cc(NC(=O)c3ccc(Cl)cn3)ccc2F)CC2(CCC(F)(F)CC2)OC(N)=N1', 'NC1=NC2(CCO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc1ncc(C3=CCOCC3)cc12' will behave towards the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@]1(c2cc(NC(=O)c3ccc(Cl)cn3)ccc2F)CC2(CCC(F)(F)CC2)OC(N)=N1\", \"NC1=NC2(CCO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc1ncc(C3=CCOCC3)cc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\", \"result\": [{\"smile\": \"C[C@@]1(c2cc(NC(=O)c3ccc(Cl)cn3)ccc2F)CC2(CCC(F)(F)CC2)OC(N)=N1\", \"value\": 0.0}, {\"smile\": \"NC1=NC2(CCO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc1ncc(C3=CCOCC3)cc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C[C@@]1(c2cc(NC(=O)c3ccc(Cl)cn3)ccc2F)CC2(CCC(F)(F)CC2)OC(N)=N1' nor 'NC1=NC2(CCO1)c1cc(NC(=O)c3ccc(Cl)cn3)ccc1Oc1ncc(C3=CCOCC3)cc12' can interact with the protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the chances of interaction between the drug 'CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl', 'CC(C)(C)C1=CN=C(O1)CSC2=CN=C(S2)NC(=O)C3CCNCC3' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl\", \"CC(C)(C)C1=CN=C(O1)CSC2=CN=C(S2)NC(=O)C3CCNCC3\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\", \"result\": [{\"smile\": \"CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl\", \"value\": 0.0}, {\"smile\": \"CC(C)(C)C1=CN=C(O1)CSC2=CN=C(S2)NC(=O)C3CCNCC3\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CC(C)(C)C1=CN=C(O1)CSC2=CN=C(S2)NC(=O)C3CCNCC3' can interact with the protein, while the first one 'CS(=O)(=O)CCNCC1=CC=C(O1)C2=CC3=C(C=C2)N=CN=C3NC4=CC(=C(C=C4)OCC5=CC(=CC=C5)F)Cl' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'NC(=O)C1=CC=C[N+](=C1)[C@@H]1O[C@@H](COP(O)(O)=O)[C@H](O)[C@H]1O', 'NC1=NC2=C(N=CN2[C@@H]2O[C@H](COP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]2O)C(=O)N1' potentially engage with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)C1=CC=C[N+](=C1)[C@@H]1O[C@@H](COP(O)(O)=O)[C@H](O)[C@H]1O\", \"NC1=NC2=C(N=CN2[C@@H]2O[C@H](COP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]2O)C(=O)N1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATSKLPVVPGEEENTILMAKERLEALRTAFESGDLPQAASHLQELLASTESIRLEVGVTGESGAGKSSLINALRGLEAEDPGAALTGVMETTMQPSPYPHPQFPDVTLWDLPGAGSPGCPADKYLKQVDFSRYDFFLLVSPRRCGAVETRLAAEILCQGKKFYFVRTKVDEDLAATRTQRPSGFREAAVLQEIRDHCAERLREAGVADPRIFLVSNLSPARYDFPTLVSTWEHDLPSHRRHAGLLSLPDISLEALQKKKAMLQEQVLKTALVLGVIQALPVPGLAAAYDDALLIHSLRGYHRSFGLDDDSLAKLAEQVGKQAGDLRSVIRSPLANEVSPETVLRLYSQSSDGAMRVARAFERGIPVFGTLVAGGISFGAVYTMLQGCLNEMAEDAQRVRIKALEDDEPQPEVSLEVASDNGVEKGGSGEGGGEEAPLSTCRKLGLLLKYILDSWKKHDSEEK\", \"result\": [{\"smile\": \"NC(=O)C1=CC=C[N+](=C1)[C@@H]1O[C@@H](COP(O)(O)=O)[C@H](O)[C@H]1O\", \"value\": 0.0}, {\"smile\": \"NC1=NC2=C(N=CN2[C@@H]2O[C@H](COP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]2O)C(=O)N1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'NC1=NC2=C(N=CN2[C@@H]2O[C@H](COP(O)(=O)OP(O)(O)=O)[C@@H](O)[C@H]2O)C(=O)N1' can interact with the protein, while the first one 'NC(=O)C1=CC=C[N+](=C1)[C@@H]1O[C@@H](COP(O)(O)=O)[C@H](O)[C@H]1O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the potential interaction of the drug 'CC1(C)C(N)=N[C@](C)(c2cc(NC(=O)c3ncc(Cl)cc3F)ccc2F)CS1(=O)=O', 'CCNc1cc(C(=O)N[C@@H](Cc2ccccc2)[C@H](O)CNCc2cnn(CC)c2)c(F)c(N2CCCCS2(=O)=O)c1' with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)C(N)=N[C@](C)(c2cc(NC(=O)c3ncc(Cl)cc3F)ccc2F)CS1(=O)=O\", \"CCNc1cc(C(=O)N[C@@H](Cc2ccccc2)[C@H](O)CNCc2cnn(CC)c2)c(F)c(N2CCCCS2(=O)=O)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\", \"result\": [{\"smile\": \"CC1(C)C(N)=N[C@](C)(c2cc(NC(=O)c3ncc(Cl)cc3F)ccc2F)CS1(=O)=O\", \"value\": 1.0}, {\"smile\": \"CCNc1cc(C(=O)N[C@@H](Cc2ccccc2)[C@H](O)CNCc2cnn(CC)c2)c(F)c(N2CCCCS2(=O)=O)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC1(C)C(N)=N[C@](C)(c2cc(NC(=O)c3ncc(Cl)cc3F)ccc2F)CS1(=O)=O' and 'CCNc1cc(C(=O)N[C@@H](Cc2ccccc2)[C@H](O)CNCc2cnn(CC)c2)c(F)c(N2CCCCS2(=O)=O)c1' can interact with the protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a forecast for the interaction likelihood between the drug 'C1=C[C@@H]([C@H]([C@@H]1N2C=NC3=C2N=CN=C3N)O)O', '[H][C@@](O)(CN1C=NC2=C(N)N=CN=C12)[C@@]([H])(O)C(O)=O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1=C[C@@H]([C@H]([C@@H]1N2C=NC3=C2N=CN=C3N)O)O\", \"[H][C@@](O)(CN1C=NC2=C(N)N=CN=C12)[C@@]([H])(O)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSDKLPYKVADIGLAAWGRKALDIAENEMPGLMRMRERYSASKPLKGARIAGCLHMTVETAVLIETLVTLGAEVQWSSCNIFSTQDHAAAAIAKAGIPVYAWKGETDEEYLWCIEQTLYFKDGPLNMILDDGGDLTNLIHTKYPQLLPGIRGISEETTTGVHNLYKMMANGILKVPAINVNDSVTKSKFDNLYGCRESLIDGIKRATDVMIAGKVAVVAGYGDVGKGCAQALRGFGARVIITEIDPINALQAAMEGYEVTTMDEACQEGNIFVTTTGCIDIILGRHFEQMKDDAIVCNIGHFDVEIDVKWLNENAVEKVNIKPQVDRYRLKNGRRIILLAEGRLVNLGCAMGHPSFVMSNSFTNQVMAQIELWTHPDKYPVGVHFLPKKLDEAVAEAHLGKLNVKLTKLTEKQAQYLGMSCDGPFKPDHYRY\", \"result\": [{\"smile\": \"C1=C[C@@H]([C@H]([C@@H]1N2C=NC3=C2N=CN=C3N)O)O\", \"value\": 1.0}, {\"smile\": \"[H][C@@](O)(CN1C=NC2=C(N)N=CN=C12)[C@@]([H])(O)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'C1=C[C@@H]([C@H]([C@@H]1N2C=NC3=C2N=CN=C3N)O)O' and '[H][C@@](O)(CN1C=NC2=C(N)N=CN=C12)[C@@]([H])(O)C(O)=O' can interact with the protein 'MSDKLPYKVADIGLAAWGRKALDIAENEMPGLMRMRERYSASKPLKGARIAGCLHMTVETAVLIETLVTLGAEVQWSSCNIFSTQDHAAAAIAKAGIPVYAWKGETDEEYLWCIEQTLYFKDGPLNMILDDGGDLTNLIHTKYPQLLPGIRGISEETTTGVHNLYKMMANGILKVPAINVNDSVTKSKFDNLYGCRESLIDGIKRATDVMIAGKVAVVAGYGDVGKGCAQALRGFGARVIITEIDPINALQAAMEGYEVTTMDEACQEGNIFVTTTGCIDIILGRHFEQMKDDAIVCNIGHFDVEIDVKWLNENAVEKVNIKPQVDRYRLKNGRRIILLAEGRLVNLGCAMGHPSFVMSNSFTNQVMAQIELWTHPDKYPVGVHFLPKKLDEAVAEAHLGKLNVKLTKLTEKQAQYLGMSCDGPFKPDHYRY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think the drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O', 'CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O''s interaction potential with the target protein will be? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\", \"CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRKGVLKDPEIADLFYKDDPEELFIGLHEIGHGSFGAVYFATNAHTSEVVAIKKMSYSGKQTHEKWQDILKEVKFLRQLKHPNTIEYKGCYLKEHTAWLVMEYCLGSASDLLEVHKKPLQEVEIAAITHGALHGLAYLHSHALIHRDIKAGNILLTEPGQVKLADFGSASMASPANSFVGTPYWMAPEVILAMDEGQYDGKVDIWSLGITCIELAERKPPLFNMNAMSALYHIAQNDSPTLQSNEWTDSFRRFVDYCLQKIPQERPTSAELLRHDFVRRDRPLRVLIDLIQRTKDAVRELDNLQYRKMKKILFQETRNGPLNESQEDEEDSEHGTSLNREMDSLGSNHSIPSMSVSTGSQSSSVNSMQEVMDESSSELVMMHDDESTINSSSSVVHKKDHVFIRDEAGHGDPRPEPRPTQSVQSQALHYRNRERFATIKSASLVTRQIHEHEQENELREQMSGYKRMRRQHQKQLIALENKLKAEMDEHRLKLQKEVETHANNSSIELEKLAKKQVAIIEKEAKVAAADEKKFQQQILAQQKKDLTTFLESQKKQYKICKEKIKEEMNEDHSTPKKEKQERISKHKENLQHTQAEEEAHLLTQQRLYYDKNCRFFKRKIMIKRHEVEQQNIREELNKKRTQKEMEHAMLIRHDESTRELEYRQLHTLQKLRMDLIRLQHQTELENQLEYNKRRERELHRKHVMELRQQPKNLKAMEMQIKKQFQDTCKVQTKQYKALKNHQLEVTPKNEHKTILKTLKDEQTRKLAILAEQYEQSINEMMASQALRLDEAQEAECQALRLQLQQEMELLNAYQSKIKMQTEAQHERELQKLEQRVSLRRAHLEQKIEEELAALQKERSERIKNLLERQEREIETFDMESLRMGFGNLVTLDFPKEDYR\", \"result\": [{\"smile\": \"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\", \"value\": 0.0}, {\"smile\": \"CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O' nor 'CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O' can interact with the protein 'MRKGVLKDPEIADLFYKDDPEELFIGLHEIGHGSFGAVYFATNAHTSEVVAIKKMSYSGKQTHEKWQDILKEVKFLRQLKHPNTIEYKGCYLKEHTAWLVMEYCLGSASDLLEVHKKPLQEVEIAAITHGALHGLAYLHSHALIHRDIKAGNILLTEPGQVKLADFGSASMASPANSFVGTPYWMAPEVILAMDEGQYDGKVDIWSLGITCIELAERKPPLFNMNAMSALYHIAQNDSPTLQSNEWTDSFRRFVDYCLQKIPQERPTSAELLRHDFVRRDRPLRVLIDLIQRTKDAVRELDNLQYRKMKKILFQETRNGPLNESQEDEEDSEHGTSLNREMDSLGSNHSIPSMSVSTGSQSSSVNSMQEVMDESSSELVMMHDDESTINSSSSVVHKKDHVFIRDEAGHGDPRPEPRPTQSVQSQALHYRNRERFATIKSASLVTRQIHEHEQENELREQMSGYKRMRRQHQKQLIALENKLKAEMDEHRLKLQKEVETHANNSSIELEKLAKKQVAIIEKEAKVAAADEKKFQQQILAQQKKDLTTFLESQKKQYKICKEKIKEEMNEDHSTPKKEKQERISKHKENLQHTQAEEEAHLLTQQRLYYDKNCRFFKRKIMIKRHEVEQQNIREELNKKRTQKEMEHAMLIRHDESTRELEYRQLHTLQKLRMDLIRLQHQTELENQLEYNKRRERELHRKHVMELRQQPKNLKAMEMQIKKQFQDTCKVQTKQYKALKNHQLEVTPKNEHKTILKTLKDEQTRKLAILAEQYEQSINEMMASQALRLDEAQEAECQALRLQLQQEMELLNAYQSKIKMQTEAQHERELQKLEQRVSLRRAHLEQKIEEELAALQKERSERIKNLLERQEREIETFDMESLRMGFGNLVTLDFPKEDYR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that the drug '[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)C1=C(OCC)C=CC2=CC=CC=C12)C(O)=O', 'CCCCC\\C=C/C\\C=C/C\\C=C/CCCCCCC(O)=O' will interact with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)C1=C(OCC)C=CC2=CC=CC=C12)C(O)=O\", \"CCCCC\\C=C/C\\C=C/C\\C=C/CCCCCCC(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAVKKIAIFGATGQTGLTTLAQAVQAGYEVTVLVRDSSRLPSEGPRPAHVVVGDVLQAADVDKTVAGQDAVIVLLGTRNDLSPTTVMSEGARNIVAAMKAHGVDKVVACTSAFLLWDPTKVPPRLQAVTDDHIRMHKVLRESGLKYVAVMPPHIGDQPLTGAYTVTLDGRGPSRVISKHDLGHFMLRCLTTDEYDGHSTYPSHQYQ\", \"result\": [{\"smile\": \"[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)C1=C(OCC)C=CC2=CC=CC=C12)C(O)=O\", \"value\": 0.0}, {\"smile\": \"CCCCC\\C=C/C\\C=C/C\\C=C/CCCCCCC(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither '[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)C1=C(OCC)C=CC2=CC=CC=C12)C(O)=O' nor 'CCCCC\\C=C/C\\C=C/C\\C=C/CCCCCCC(O)=O' can interact with the protein 'MAVKKIAIFGATGQTGLTTLAQAVQAGYEVTVLVRDSSRLPSEGPRPAHVVVGDVLQAADVDKTVAGQDAVIVLLGTRNDLSPTTVMSEGARNIVAAMKAHGVDKVVACTSAFLLWDPTKVPPRLQAVTDDHIRMHKVLRESGLKYVAVMPPHIGDQPLTGAYTVTLDGRGPSRVISKHDLGHFMLRCLTTDEYDGHSTYPSHQYQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[C@@H]1([C@@H]([C@H](C([C@@H]1O)N)O)O)O', 'CNC(=O)C1=NC=CC(OC2=CC3=C(C=C2)N=C(NC2=CC(=C(Cl)C=C2)C(F)(F)F)N3)=C1' interact with the target protein according to your prediction? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[C@@H]1([C@@H]([C@H](C([C@@H]1O)N)O)O)O\", \"CNC(=O)C1=NC=CC(OC2=CC3=C(C=C2)N=C(NC2=CC(=C(Cl)C=C2)C(F)(F)F)N3)=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEEKLKKTKIIFVVGGPGSGKGTQCEKIVQKYGYTHLSTGDLLRSEVSSGSARGKKLSEIMEKGQLVPLETVLDMLRDAMVAKVNTSKGFLIDGYPREVQQGEEFERRIGQPTLLLYVDAGPETMTQRLLKRGETSGRVDDNEETIKKRLETYYKATEPVIAFYEKRGIVRKVNAEGSVDSVFSQVCTHLDALK\", \"result\": [{\"smile\": \"[C@@H]1([C@@H]([C@H](C([C@@H]1O)N)O)O)O\", \"value\": 0.0}, {\"smile\": \"CNC(=O)C1=NC=CC(OC2=CC3=C(C=C2)N=C(NC2=CC(=C(Cl)C=C2)C(F)(F)F)N3)=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither '[C@@H]1([C@@H]([C@H](C([C@@H]1O)N)O)O)O' nor 'CNC(=O)C1=NC=CC(OC2=CC3=C(C=C2)N=C(NC2=CC(=C(Cl)C=C2)C(F)(F)F)N3)=C1' can interact with the protein 'MEEKLKKTKIIFVVGGPGSGKGTQCEKIVQKYGYTHLSTGDLLRSEVSSGSARGKKLSEIMEKGQLVPLETVLDMLRDAMVAKVNTSKGFLIDGYPREVQQGEEFERRIGQPTLLLYVDAGPETMTQRLLKRGETSGRVDDNEETIKKRLETYYKATEPVIAFYEKRGIVRKVNAEGSVDSVFSQVCTHLDALK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O', 'COC1=CC(=O)C2=C(C(COC3=CC=C(C=C3)[N+]([O-])=O)=C(C)N2C)C1=O' will engage with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O\", \"COC1=CC(=O)C2=C(C(COC3=CC=C(C=C3)[N+]([O-])=O)=C(C)N2C)C1=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAARVGAFLKNAWDKEPVLVVSFVVGGLAVILPPLSPYFKYSVMINKATPYNYPVPVRDDGNMPDVPSHPQDPQGPSLEWLKKL\", \"result\": [{\"smile\": \"CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O\", \"value\": 0.0}, {\"smile\": \"COC1=CC(=O)C2=C(C(COC3=CC=C(C=C3)[N+]([O-])=O)=C(C)N2C)C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O' nor 'COC1=CC(=O)C2=C(C(COC3=CC=C(C=C3)[N+]([O-])=O)=C(C)N2C)C1=O' can interact with the protein 'MAARVGAFLKNAWDKEPVLVVSFVVGGLAVILPPLSPYFKYSVMINKATPYNYPVPVRDDGNMPDVPSHPQDPQGPSLEWLKKL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug 'NC(=N)SCCC1=CC=C(CCSC(N)=N)C=C1' will interact with the target protein 'MAVAQQLRAESDFEQLPDDVAISANIADIEEKRGFTSHFVFVIEVKTKGGSKYLIYRRYRQFHALQSKLEERFGPDSKSSALACTLPTLPAKVYVGVKQEIAEMRIPALNAYMKSLLSLPVWVLMDEDVRIFFYQSPYDSEQVPQALRRLRPRTRKVKSVSPQGNSVDRMAAPRAEALFDFTGNSKLELNFKAGDVIFLLSRINKDWLEGTVRGATGIFPLSFVKILKDFPEEDDPTNWLRCYYYEDTISTIKDIAVEEDLSSTPLLKDLLELTRREFQREDIALNYRDAEGDLVRLLSDEDVALMVRQARGLPSQKRLFPWKLHITQKDNYRVYNTMP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=N)SCCC1=CC=C(CCSC(N)=N)C=C1\"], \"target_seq\": \"MAVAQQLRAESDFEQLPDDVAISANIADIEEKRGFTSHFVFVIEVKTKGGSKYLIYRRYRQFHALQSKLEERFGPDSKSSALACTLPTLPAKVYVGVKQEIAEMRIPALNAYMKSLLSLPVWVLMDEDVRIFFYQSPYDSEQVPQALRRLRPRTRKVKSVSPQGNSVDRMAAPRAEALFDFTGNSKLELNFKAGDVIFLLSRINKDWLEGTVRGATGIFPLSFVKILKDFPEEDDPTNWLRCYYYEDTISTIKDIAVEEDLSSTPLLKDLLELTRREFQREDIALNYRDAEGDLVRLLSDEDVALMVRQARGLPSQKRLFPWKLHITQKDNYRVYNTMP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAVAQQLRAESDFEQLPDDVAISANIADIEEKRGFTSHFVFVIEVKTKGGSKYLIYRRYRQFHALQSKLEERFGPDSKSSALACTLPTLPAKVYVGVKQEIAEMRIPALNAYMKSLLSLPVWVLMDEDVRIFFYQSPYDSEQVPQALRRLRPRTRKVKSVSPQGNSVDRMAAPRAEALFDFTGNSKLELNFKAGDVIFLLSRINKDWLEGTVRGATGIFPLSFVKILKDFPEEDDPTNWLRCYYYEDTISTIKDIAVEEDLSSTPLLKDLLELTRREFQREDIALNYRDAEGDLVRLLSDEDVALMVRQARGLPSQKRLFPWKLHITQKDNYRVYNTMP\", \"result\": [{\"smile\": \"NC(=N)SCCC1=CC=C(CCSC(N)=N)C=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=N)SCCC1=CC=C(CCSC(N)=N)C=C1' can not interact with the protein MAVAQQLRAESDFEQLPDDVAISANIADIEEKRGFTSHFVFVIEVKTKGGSKYLIYRRYRQFHALQSKLEERFGPDSKSSALACTLPTLPAKVYVGVKQEIAEMRIPALNAYMKSLLSLPVWVLMDEDVRIFFYQSPYDSEQVPQALRRLRPRTRKVKSVSPQGNSVDRMAAPRAEALFDFTGNSKLELNFKAGDVIFLLSRINKDWLEGTVRGATGIFPLSFVKILKDFPEEDDPTNWLRCYYYEDTISTIKDIAVEEDLSSTPLLKDLLELTRREFQREDIALNYRDAEGDLVRLLSDEDVALMVRQARGLPSQKRLFPWKLHITQKDNYRVYNTMP."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there likely be an interaction between the drug '[H]C(Cl)=CC(O)(CC)C#C', 'CC(=O)NCCCS(O)(=O)=O' and the target protein 'MDNGMFSGFIMIKNLLLFCISMNLSSHFGFSQMPTSSVKDETNDNITIFTRILDGLLDGYDNRLRPGLGERITQVRTDIYVTSFGPVSDTEMEYTIDVFFRQSWKDERLRFKGPMQRLPLNNLLASKIWTPDTFFHNGKKSIAHNMTTPNKLLRLEDDGTLLYTMRLTISAECPMQLEDFPMDAHACPLKFGSYAYPNSEVVYVWTNGSTKSVVVAEDGSRLNQYHLMGQTVGTENISTSTGEYTIMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKKALEAAKIKKKREVILNKSTNAFTTGKMSHPPNIPKEQTPAGTSNTTSVSVKPSEEKTSESKKTYNSISKIDKMSRIVFPVLFGTFNLVYWATYLNREPVIKGAASPK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H]C(Cl)=CC(O)(CC)C#C\", \"CC(=O)NCCCS(O)(=O)=O\"], \"target_seq\": \"MDNGMFSGFIMIKNLLLFCISMNLSSHFGFSQMPTSSVKDETNDNITIFTRILDGLLDGYDNRLRPGLGERITQVRTDIYVTSFGPVSDTEMEYTIDVFFRQSWKDERLRFKGPMQRLPLNNLLASKIWTPDTFFHNGKKSIAHNMTTPNKLLRLEDDGTLLYTMRLTISAECPMQLEDFPMDAHACPLKFGSYAYPNSEVVYVWTNGSTKSVVVAEDGSRLNQYHLMGQTVGTENISTSTGEYTIMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKKALEAAKIKKKREVILNKSTNAFTTGKMSHPPNIPKEQTPAGTSNTTSVSVKPSEEKTSESKKTYNSISKIDKMSRIVFPVLFGTFNLVYWATYLNREPVIKGAASPK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDNGMFSGFIMIKNLLLFCISMNLSSHFGFSQMPTSSVKDETNDNITIFTRILDGLLDGYDNRLRPGLGERITQVRTDIYVTSFGPVSDTEMEYTIDVFFRQSWKDERLRFKGPMQRLPLNNLLASKIWTPDTFFHNGKKSIAHNMTTPNKLLRLEDDGTLLYTMRLTISAECPMQLEDFPMDAHACPLKFGSYAYPNSEVVYVWTNGSTKSVVVAEDGSRLNQYHLMGQTVGTENISTSTGEYTIMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKKALEAAKIKKKREVILNKSTNAFTTGKMSHPPNIPKEQTPAGTSNTTSVSVKPSEEKTSESKKTYNSISKIDKMSRIVFPVLFGTFNLVYWATYLNREPVIKGAASPK\", \"result\": [{\"smile\": \"[H]C(Cl)=CC(O)(CC)C#C\", \"value\": 1.0}, {\"smile\": \"CC(=O)NCCCS(O)(=O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs '[H]C(Cl)=CC(O)(CC)C#C' and 'CC(=O)NCCCS(O)(=O)=O' can interact with the protein 'MDNGMFSGFIMIKNLLLFCISMNLSSHFGFSQMPTSSVKDETNDNITIFTRILDGLLDGYDNRLRPGLGERITQVRTDIYVTSFGPVSDTEMEYTIDVFFRQSWKDERLRFKGPMQRLPLNNLLASKIWTPDTFFHNGKKSIAHNMTTPNKLLRLEDDGTLLYTMRLTISAECPMQLEDFPMDAHACPLKFGSYAYPNSEVVYVWTNGSTKSVVVAEDGSRLNQYHLMGQTVGTENISTSTGEYTIMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKKALEAAKIKKKREVILNKSTNAFTTGKMSHPPNIPKEQTPAGTSNTTSVSVKPSEEKTSESKKTYNSISKIDKMSRIVFPVLFGTFNLVYWATYLNREPVIKGAASPK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess if the drug 'CCN1C2=C(C(=NC=C2OCC3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N', 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' will engage with the target protein 'MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCN1C2=C(C(=NC=C2OCC3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\", \"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\"], \"target_seq\": \"MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH\", \"result\": [{\"smile\": \"CCN1C2=C(C(=NC=C2OCC3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\", \"value\": 1.0}, {\"smile\": \"CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CCN1C2=C(C(=NC=C2OCC3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N' can interact with the protein, while the second one 'CS(=O)(=O)N1CCN(CC1)CC2=CC3=C(S2)C(=NC(=N3)C4=C5C=NNC5=CC=C4)N6CCOCC6' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'NCC(O)=O', 'C/C=C(/C(=O)O)\\N' will interact with the target protein 'MESVVRRCPFLSRVPQAFLQKAGKSLLFYAQNCPKMMEVGAKPAPRALSTAAVHYQQIKETPPASEKDKTAKAKVQQTPDGSQQSPDGTQLPSGHPLPATSQGTASKCPFLAAQMNQRGSSVFCKASLELQEDVQEMNAVRKEVAETSAGPSVVSVKTDGGDPSGLLKNFQDIMQKQRPERVSHLLQDNLPKSVSTFQYDRFFEKKIDEKKNDHTYRVFKTVNRRAHIFPMADDYSDSLITKKQVSVWCSNDYLGMSRHPRVCGAVMDTLKQHGAGAGGTRNISGTSKFHVDLERELADLHGKDAALLFSSCFVANDSTLFTLAKMMPGCEIYSDSGNHASMIQGIRNSRVPKYIFRHNDVSHLRELLQRSDPSVPKIVAFETVHSMDGAVCPLEELCDVAHEFGAITFVDEVHAVGLYGARGGGIGDRDGVMPKMDIISGTLGKAFGCVGGYIASTSSLIDTVRSYAAGFIFTTSLPPMLLAGALESVRILKSAEGRVLRRQHQRNVKLMRQMLMDAGLPVVHCPSHIIPVRVADAAKNTEVCDELMSRHNIYVQAINYPTVPRGEELLRIAPTPHHTPQMMNYFLENLLVTWKQVGLELKPHSSAECNFCRRPLHFEVMSEREKSYFSGLSKLVSAQA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NCC(O)=O\", \"C/C=C(/C(=O)O)\\N\"], \"target_seq\": \"MESVVRRCPFLSRVPQAFLQKAGKSLLFYAQNCPKMMEVGAKPAPRALSTAAVHYQQIKETPPASEKDKTAKAKVQQTPDGSQQSPDGTQLPSGHPLPATSQGTASKCPFLAAQMNQRGSSVFCKASLELQEDVQEMNAVRKEVAETSAGPSVVSVKTDGGDPSGLLKNFQDIMQKQRPERVSHLLQDNLPKSVSTFQYDRFFEKKIDEKKNDHTYRVFKTVNRRAHIFPMADDYSDSLITKKQVSVWCSNDYLGMSRHPRVCGAVMDTLKQHGAGAGGTRNISGTSKFHVDLERELADLHGKDAALLFSSCFVANDSTLFTLAKMMPGCEIYSDSGNHASMIQGIRNSRVPKYIFRHNDVSHLRELLQRSDPSVPKIVAFETVHSMDGAVCPLEELCDVAHEFGAITFVDEVHAVGLYGARGGGIGDRDGVMPKMDIISGTLGKAFGCVGGYIASTSSLIDTVRSYAAGFIFTTSLPPMLLAGALESVRILKSAEGRVLRRQHQRNVKLMRQMLMDAGLPVVHCPSHIIPVRVADAAKNTEVCDELMSRHNIYVQAINYPTVPRGEELLRIAPTPHHTPQMMNYFLENLLVTWKQVGLELKPHSSAECNFCRRPLHFEVMSEREKSYFSGLSKLVSAQA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESVVRRCPFLSRVPQAFLQKAGKSLLFYAQNCPKMMEVGAKPAPRALSTAAVHYQQIKETPPASEKDKTAKAKVQQTPDGSQQSPDGTQLPSGHPLPATSQGTASKCPFLAAQMNQRGSSVFCKASLELQEDVQEMNAVRKEVAETSAGPSVVSVKTDGGDPSGLLKNFQDIMQKQRPERVSHLLQDNLPKSVSTFQYDRFFEKKIDEKKNDHTYRVFKTVNRRAHIFPMADDYSDSLITKKQVSVWCSNDYLGMSRHPRVCGAVMDTLKQHGAGAGGTRNISGTSKFHVDLERELADLHGKDAALLFSSCFVANDSTLFTLAKMMPGCEIYSDSGNHASMIQGIRNSRVPKYIFRHNDVSHLRELLQRSDPSVPKIVAFETVHSMDGAVCPLEELCDVAHEFGAITFVDEVHAVGLYGARGGGIGDRDGVMPKMDIISGTLGKAFGCVGGYIASTSSLIDTVRSYAAGFIFTTSLPPMLLAGALESVRILKSAEGRVLRRQHQRNVKLMRQMLMDAGLPVVHCPSHIIPVRVADAAKNTEVCDELMSRHNIYVQAINYPTVPRGEELLRIAPTPHHTPQMMNYFLENLLVTWKQVGLELKPHSSAECNFCRRPLHFEVMSEREKSYFSGLSKLVSAQA\", \"result\": [{\"smile\": \"NCC(O)=O\", \"value\": 1.0}, {\"smile\": \"C/C=C(/C(=O)O)\\N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'NCC(O)=O' can interact with the protein, while the second one 'C/C=C(/C(=O)O)\\N' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug 'CN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1', 'N#C[C@H](Cc1ccc(-c2ccc(C(N)=O)cc2)cc1F)NC(=O)[C@H]1N[C@H]2C[C@@H]1[C@@H]1C[C@H]21' and the target protein 'MWGLKVLLLPVVSFALYPEEILDTHWELWKKTHRKQYNNKVDEISRRLIWEKNLKYISIHNLEASLGVHTYELAMNHLGDMTSEEVVQKMTGLKVPLSHSRSNDTLYIPEWEGRAPDSVDYRKKGYVTPVKNQGQCGSCWAFSSVGALEGQLKKKTGKLLNLSPQNLVDCVSENDGCGGGYMTNAFQYVQKNRGIDSEDAYPYVGQEESCMYNPTGKAAKCRGYREIPEGNEKALKRAVARVGPVSVAIDASLTSFQFYSKGVYYDESCNSDNLNHAVLAVGYGIQKGNKHWIIKNSWGENWGNKGYILMARNKNNACGIANLASFPKM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\", \"N#C[C@H](Cc1ccc(-c2ccc(C(N)=O)cc2)cc1F)NC(=O)[C@H]1N[C@H]2C[C@@H]1[C@@H]1C[C@H]21\"], \"target_seq\": \"MWGLKVLLLPVVSFALYPEEILDTHWELWKKTHRKQYNNKVDEISRRLIWEKNLKYISIHNLEASLGVHTYELAMNHLGDMTSEEVVQKMTGLKVPLSHSRSNDTLYIPEWEGRAPDSVDYRKKGYVTPVKNQGQCGSCWAFSSVGALEGQLKKKTGKLLNLSPQNLVDCVSENDGCGGGYMTNAFQYVQKNRGIDSEDAYPYVGQEESCMYNPTGKAAKCRGYREIPEGNEKALKRAVARVGPVSVAIDASLTSFQFYSKGVYYDESCNSDNLNHAVLAVGYGIQKGNKHWIIKNSWGENWGNKGYILMARNKNNACGIANLASFPKM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWGLKVLLLPVVSFALYPEEILDTHWELWKKTHRKQYNNKVDEISRRLIWEKNLKYISIHNLEASLGVHTYELAMNHLGDMTSEEVVQKMTGLKVPLSHSRSNDTLYIPEWEGRAPDSVDYRKKGYVTPVKNQGQCGSCWAFSSVGALEGQLKKKTGKLLNLSPQNLVDCVSENDGCGGGYMTNAFQYVQKNRGIDSEDAYPYVGQEESCMYNPTGKAAKCRGYREIPEGNEKALKRAVARVGPVSVAIDASLTSFQFYSKGVYYDESCNSDNLNHAVLAVGYGIQKGNKHWIIKNSWGENWGNKGYILMARNKNNACGIANLASFPKM\", \"result\": [{\"smile\": \"CN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\", \"value\": 0.0}, {\"smile\": \"N#C[C@H](Cc1ccc(-c2ccc(C(N)=O)cc2)cc1F)NC(=O)[C@H]1N[C@H]2C[C@@H]1[C@@H]1C[C@H]21\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1' nor 'N#C[C@H](Cc1ccc(-c2ccc(C(N)=O)cc2)cc1F)NC(=O)[C@H]1N[C@H]2C[C@@H]1[C@@H]1C[C@H]21' can interact with the protein 'MWGLKVLLLPVVSFALYPEEILDTHWELWKKTHRKQYNNKVDEISRRLIWEKNLKYISIHNLEASLGVHTYELAMNHLGDMTSEEVVQKMTGLKVPLSHSRSNDTLYIPEWEGRAPDSVDYRKKGYVTPVKNQGQCGSCWAFSSVGALEGQLKKKTGKLLNLSPQNLVDCVSENDGCGGGYMTNAFQYVQKNRGIDSEDAYPYVGQEESCMYNPTGKAAKCRGYREIPEGNEKALKRAVARVGPVSVAIDASLTSFQFYSKGVYYDESCNSDNLNHAVLAVGYGIQKGNKHWIIKNSWGENWGNKGYILMARNKNNACGIANLASFPKM'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'O=C(Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1)Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1', 'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1' probably interact with the target protein 'MAADLGPWNDTINGTWDGDELGYRCRFNEDFKYVLLPVSYGVVCVPGLCLNAVALYIFLCRLKTWNASTTYMFHLAVSDALYAASLPLLVYYYARGDHWPFSTVLCKLVRFLFYTNLYCSILFLTCISVHRCLGVLRPLRSLRWGRARYARRVAGAVWVLVLACQAPVLYFVTTSARGGRVTCHDTSAPELFSRFVAYSSVMLGLLFAVPFAVILVCYVLMARRLLKPAYGTSGGLPRAKRKSVRTIAVVLAVFALCFLPFHVTRTLYYSFRSLDLSCHTLNAINMAYKVTRPLASANSCLDPVLYFLAGQRLVRFARDAKPPTGPSPATPARRRLGLRRSDRTDMQRIEDVLGSSEDSRRTESTPAGSENTKDIRL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1)Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1\", \"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\"], \"target_seq\": \"MAADLGPWNDTINGTWDGDELGYRCRFNEDFKYVLLPVSYGVVCVPGLCLNAVALYIFLCRLKTWNASTTYMFHLAVSDALYAASLPLLVYYYARGDHWPFSTVLCKLVRFLFYTNLYCSILFLTCISVHRCLGVLRPLRSLRWGRARYARRVAGAVWVLVLACQAPVLYFVTTSARGGRVTCHDTSAPELFSRFVAYSSVMLGLLFAVPFAVILVCYVLMARRLLKPAYGTSGGLPRAKRKSVRTIAVVLAVFALCFLPFHVTRTLYYSFRSLDLSCHTLNAINMAYKVTRPLASANSCLDPVLYFLAGQRLVRFARDAKPPTGPSPATPARRRLGLRRSDRTDMQRIEDVLGSSEDSRRTESTPAGSENTKDIRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAADLGPWNDTINGTWDGDELGYRCRFNEDFKYVLLPVSYGVVCVPGLCLNAVALYIFLCRLKTWNASTTYMFHLAVSDALYAASLPLLVYYYARGDHWPFSTVLCKLVRFLFYTNLYCSILFLTCISVHRCLGVLRPLRSLRWGRARYARRVAGAVWVLVLACQAPVLYFVTTSARGGRVTCHDTSAPELFSRFVAYSSVMLGLLFAVPFAVILVCYVLMARRLLKPAYGTSGGLPRAKRKSVRTIAVVLAVFALCFLPFHVTRTLYYSFRSLDLSCHTLNAINMAYKVTRPLASANSCLDPVLYFLAGQRLVRFARDAKPPTGPSPATPARRRLGLRRSDRTDMQRIEDVLGSSEDSRRTESTPAGSENTKDIRL\", \"result\": [{\"smile\": \"O=C(Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1)Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1\", \"value\": 0.0}, {\"smile\": \"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'O=C(Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1)Nc1cccc(C(=O)Nc2cc(C(=O)Nc3ccc(S(=O)(=O)[O-])c4cc(S(=O)(=O)[O-])cc(S(=O)(=O)[O-])c34)ccc2F)c1' nor 'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1' can interact with the protein 'MAADLGPWNDTINGTWDGDELGYRCRFNEDFKYVLLPVSYGVVCVPGLCLNAVALYIFLCRLKTWNASTTYMFHLAVSDALYAASLPLLVYYYARGDHWPFSTVLCKLVRFLFYTNLYCSILFLTCISVHRCLGVLRPLRSLRWGRARYARRVAGAVWVLVLACQAPVLYFVTTSARGGRVTCHDTSAPELFSRFVAYSSVMLGLLFAVPFAVILVCYVLMARRLLKPAYGTSGGLPRAKRKSVRTIAVVLAVFALCFLPFHVTRTLYYSFRSLDLSCHTLNAINMAYKVTRPLASANSCLDPVLYFLAGQRLVRFARDAKPPTGPSPATPARRRLGLRRSDRTDMQRIEDVLGSSEDSRRTESTPAGSENTKDIRL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'COCC(=O)O[C@]1(CCN(C)CCCc2nc3ccccc3[nH]2)CCc2cc(F)ccc2[C@@H]1C(C)C', 'O=C1CCc2ccc(OCCCCN3CCN(c4cccc(Cl)c4Cl)CC3)cc2N1''s interaction potential with the target protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COCC(=O)O[C@]1(CCN(C)CCCc2nc3ccccc3[nH]2)CCc2cc(F)ccc2[C@@H]1C(C)C\", \"O=C1CCc2ccc(OCCCCN3CCN(c4cccc(Cl)c4Cl)CC3)cc2N1\"], \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\", \"result\": [{\"smile\": \"COCC(=O)O[C@]1(CCN(C)CCCc2nc3ccccc3[nH]2)CCc2cc(F)ccc2[C@@H]1C(C)C\", \"value\": 0.0}, {\"smile\": \"O=C1CCc2ccc(OCCCCN3CCN(c4cccc(Cl)c4Cl)CC3)cc2N1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'COCC(=O)O[C@]1(CCN(C)CCCc2nc3ccccc3[nH]2)CCc2cc(F)ccc2[C@@H]1C(C)C' nor 'O=C1CCc2ccc(OCCCCN3CCN(c4cccc(Cl)c4Cl)CC3)cc2N1' can interact with the protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the chances of the drug 'CN(C)[C@@H](CCOC1=CC=CC2=CC=CC=C12)C1=CC=CC=C1', 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' interacting with the target protein 'MVSAKKVPAIALSAGVSFALLRFLCLAVCLNESPGQNQKEEKLCTENFTRILDSLLDGYDNRLRPGFGGPVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWIDKRLKYDGPIEILRLNNMMVTKVWTPDTFFRNGKKSVSHNMTAPNKLFRIMRNGTILYTMRLTISAECPMRLVDFPMDGHACPLKFGSYAYPKSEMIYTWTKGPEKSVEVPKESSSLVQYDLIGQTVSSETIKSITGEYIVMTVYFHLRRKMGYFMIQTYIPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNIQMEKAKRKTSKPPQEVPAAPVQREKHPEAPLQNTNANLNMRKRTNALVHSESDVGNRTEVGNHSSKSSTVVQESSKGTPRSYLASSPNPFSRANAAETISAARALPSASPTSIRTGYMPRKASVGSASTRHVFGSRLQRIKTTVNTIGATGKLSATPPPSAPPPSGSGTSKIDKYARILFPVTFGAFNMVYWVVYLSKDTMEKSESLM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)[C@@H](CCOC1=CC=CC2=CC=CC=C12)C1=CC=CC=C1\", \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\"], \"target_seq\": \"MVSAKKVPAIALSAGVSFALLRFLCLAVCLNESPGQNQKEEKLCTENFTRILDSLLDGYDNRLRPGFGGPVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWIDKRLKYDGPIEILRLNNMMVTKVWTPDTFFRNGKKSVSHNMTAPNKLFRIMRNGTILYTMRLTISAECPMRLVDFPMDGHACPLKFGSYAYPKSEMIYTWTKGPEKSVEVPKESSSLVQYDLIGQTVSSETIKSITGEYIVMTVYFHLRRKMGYFMIQTYIPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNIQMEKAKRKTSKPPQEVPAAPVQREKHPEAPLQNTNANLNMRKRTNALVHSESDVGNRTEVGNHSSKSSTVVQESSKGTPRSYLASSPNPFSRANAAETISAARALPSASPTSIRTGYMPRKASVGSASTRHVFGSRLQRIKTTVNTIGATGKLSATPPPSAPPPSGSGTSKIDKYARILFPVTFGAFNMVYWVVYLSKDTMEKSESLM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVSAKKVPAIALSAGVSFALLRFLCLAVCLNESPGQNQKEEKLCTENFTRILDSLLDGYDNRLRPGFGGPVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWIDKRLKYDGPIEILRLNNMMVTKVWTPDTFFRNGKKSVSHNMTAPNKLFRIMRNGTILYTMRLTISAECPMRLVDFPMDGHACPLKFGSYAYPKSEMIYTWTKGPEKSVEVPKESSSLVQYDLIGQTVSSETIKSITGEYIVMTVYFHLRRKMGYFMIQTYIPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNIQMEKAKRKTSKPPQEVPAAPVQREKHPEAPLQNTNANLNMRKRTNALVHSESDVGNRTEVGNHSSKSSTVVQESSKGTPRSYLASSPNPFSRANAAETISAARALPSASPTSIRTGYMPRKASVGSASTRHVFGSRLQRIKTTVNTIGATGKLSATPPPSAPPPSGSGTSKIDKYARILFPVTFGAFNMVYWVVYLSKDTMEKSESLM\", \"result\": [{\"smile\": \"CN(C)[C@@H](CCOC1=CC=CC2=CC=CC=C12)C1=CC=CC=C1\", \"value\": 0.0}, {\"smile\": \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' can interact with the protein, while the first one 'CN(C)[C@@H](CCOC1=CC=CC2=CC=CC=C12)C1=CC=CC=C1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you expect any interaction between the drug 'C#CC(CCC(=O)O)N', 'C1=CC(=C[N+](=C1)C2C(C(C(O2)COP(=O)(O)OP(=O)(O)OCC3C(C(C(O3)N4C=NC5=C4N=CN=C5N)OP(=O)(O)O)O)O)O)C(=O)N' and the target protein 'MIRTDNLSDLASARDYWRTDKYISFVITFQFYLLLDTMSDSFNWPAGNNEARERLGEKFESLHLDSRISTSHAKPLSNADPVVVPIYHSSTYRFKTVDQFNEDNHGANFVYRRCGNPTTENVEVVINEIEGGAGSLLYNSGLAAISAVFLEFLSSGAHMIVMNPIYSGTSSFINETLARFGVEITSVDVEKEKDFAGAVEKAIRPNTKMIYFESIANPTMAVPDILGTIEVAKKYKILTCLDATFSSPYNIQPLKLGADISLHSCSKYIGGHTDVIAGVVTVSSYDNWKKLKLQQLTTGSSLSPYDAALLTRGLKTLGLRVDRISENAQKTAEFLESHPKVERVFYPGLPSHPQHQYAKQVMKQFAGMIAFDVGTAENAIKLVESLKLIIHAVSLGGTESLIEHPLSMSHGKHLLRYLDGPTVAPGLLRFSVGIENVEDIIGDLNDALEKL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C#CC(CCC(=O)O)N\", \"C1=CC(=C[N+](=C1)C2C(C(C(O2)COP(=O)(O)OP(=O)(O)OCC3C(C(C(O3)N4C=NC5=C4N=CN=C5N)OP(=O)(O)O)O)O)O)C(=O)N\"], \"target_seq\": \"MIRTDNLSDLASARDYWRTDKYISFVITFQFYLLLDTMSDSFNWPAGNNEARERLGEKFESLHLDSRISTSHAKPLSNADPVVVPIYHSSTYRFKTVDQFNEDNHGANFVYRRCGNPTTENVEVVINEIEGGAGSLLYNSGLAAISAVFLEFLSSGAHMIVMNPIYSGTSSFINETLARFGVEITSVDVEKEKDFAGAVEKAIRPNTKMIYFESIANPTMAVPDILGTIEVAKKYKILTCLDATFSSPYNIQPLKLGADISLHSCSKYIGGHTDVIAGVVTVSSYDNWKKLKLQQLTTGSSLSPYDAALLTRGLKTLGLRVDRISENAQKTAEFLESHPKVERVFYPGLPSHPQHQYAKQVMKQFAGMIAFDVGTAENAIKLVESLKLIIHAVSLGGTESLIEHPLSMSHGKHLLRYLDGPTVAPGLLRFSVGIENVEDIIGDLNDALEKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MIRTDNLSDLASARDYWRTDKYISFVITFQFYLLLDTMSDSFNWPAGNNEARERLGEKFESLHLDSRISTSHAKPLSNADPVVVPIYHSSTYRFKTVDQFNEDNHGANFVYRRCGNPTTENVEVVINEIEGGAGSLLYNSGLAAISAVFLEFLSSGAHMIVMNPIYSGTSSFINETLARFGVEITSVDVEKEKDFAGAVEKAIRPNTKMIYFESIANPTMAVPDILGTIEVAKKYKILTCLDATFSSPYNIQPLKLGADISLHSCSKYIGGHTDVIAGVVTVSSYDNWKKLKLQQLTTGSSLSPYDAALLTRGLKTLGLRVDRISENAQKTAEFLESHPKVERVFYPGLPSHPQHQYAKQVMKQFAGMIAFDVGTAENAIKLVESLKLIIHAVSLGGTESLIEHPLSMSHGKHLLRYLDGPTVAPGLLRFSVGIENVEDIIGDLNDALEKL\", \"result\": [{\"smile\": \"C#CC(CCC(=O)O)N\", \"value\": 0.0}, {\"smile\": \"C1=CC(=C[N+](=C1)C2C(C(C(O2)COP(=O)(O)OP(=O)(O)OCC3C(C(C(O3)N4C=NC5=C4N=CN=C5N)OP(=O)(O)O)O)O)O)C(=O)N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C#CC(CCC(=O)O)N' nor 'C1=CC(=C[N+](=C1)C2C(C(C(O2)COP(=O)(O)OP(=O)(O)OCC3C(C(C(O3)N4C=NC5=C4N=CN=C5N)OP(=O)(O)O)O)O)O)C(=O)N' can interact with the protein 'MIRTDNLSDLASARDYWRTDKYISFVITFQFYLLLDTMSDSFNWPAGNNEARERLGEKFESLHLDSRISTSHAKPLSNADPVVVPIYHSSTYRFKTVDQFNEDNHGANFVYRRCGNPTTENVEVVINEIEGGAGSLLYNSGLAAISAVFLEFLSSGAHMIVMNPIYSGTSSFINETLARFGVEITSVDVEKEKDFAGAVEKAIRPNTKMIYFESIANPTMAVPDILGTIEVAKKYKILTCLDATFSSPYNIQPLKLGADISLHSCSKYIGGHTDVIAGVVTVSSYDNWKKLKLQQLTTGSSLSPYDAALLTRGLKTLGLRVDRISENAQKTAEFLESHPKVERVFYPGLPSHPQHQYAKQVMKQFAGMIAFDVGTAENAIKLVESLKLIIHAVSLGGTESLIEHPLSMSHGKHLLRYLDGPTVAPGLLRFSVGIENVEDIIGDLNDALEKL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted interaction outcome for the drug 'OC1(CN(C1)C(=O)C1=C(NC2=C(F)C=C(I)C=C2)C(F)=C(F)C=C1)[C@@H]1CCCCN1', '[2H]C(=O)[2H]' and the target protein 'MEEECRVLSIQSHVIRGYVGNRAATFPLQVLGFEIDAVNSVQFSNHTGYAHWKGQVLNSDELQELYEGLRLNNMNKYDYVLTGYTRDKSFLAMVVDIVQELKQQNPRLVYVCDPVLGDKWDGEGSMYVPEDLLPVYKEKVVPLADIITPNQFEAELLSGRKIHSQEEALRVMDMLHSMGPDTVVITSSDLPSPQGSNYLIVLGSQRRRNPAGSVVMERIRMDIRKVDAVFVGTGDLFAAMLLAWTHKHPNNLKVACEKTVSTLHHVLQRTIQCAKAQAGEGVRPSPMQLELRMVQSKRDIEDPEIVVQATVL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC1(CN(C1)C(=O)C1=C(NC2=C(F)C=C(I)C=C2)C(F)=C(F)C=C1)[C@@H]1CCCCN1\", \"[2H]C(=O)[2H]\"], \"target_seq\": \"MEEECRVLSIQSHVIRGYVGNRAATFPLQVLGFEIDAVNSVQFSNHTGYAHWKGQVLNSDELQELYEGLRLNNMNKYDYVLTGYTRDKSFLAMVVDIVQELKQQNPRLVYVCDPVLGDKWDGEGSMYVPEDLLPVYKEKVVPLADIITPNQFEAELLSGRKIHSQEEALRVMDMLHSMGPDTVVITSSDLPSPQGSNYLIVLGSQRRRNPAGSVVMERIRMDIRKVDAVFVGTGDLFAAMLLAWTHKHPNNLKVACEKTVSTLHHVLQRTIQCAKAQAGEGVRPSPMQLELRMVQSKRDIEDPEIVVQATVL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEEECRVLSIQSHVIRGYVGNRAATFPLQVLGFEIDAVNSVQFSNHTGYAHWKGQVLNSDELQELYEGLRLNNMNKYDYVLTGYTRDKSFLAMVVDIVQELKQQNPRLVYVCDPVLGDKWDGEGSMYVPEDLLPVYKEKVVPLADIITPNQFEAELLSGRKIHSQEEALRVMDMLHSMGPDTVVITSSDLPSPQGSNYLIVLGSQRRRNPAGSVVMERIRMDIRKVDAVFVGTGDLFAAMLLAWTHKHPNNLKVACEKTVSTLHHVLQRTIQCAKAQAGEGVRPSPMQLELRMVQSKRDIEDPEIVVQATVL\", \"result\": [{\"smile\": \"OC1(CN(C1)C(=O)C1=C(NC2=C(F)C=C(I)C=C2)C(F)=C(F)C=C1)[C@@H]1CCCCN1\", \"value\": 0.0}, {\"smile\": \"[2H]C(=O)[2H]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'OC1(CN(C1)C(=O)C1=C(NC2=C(F)C=C(I)C=C2)C(F)=C(F)C=C1)[C@@H]1CCCCN1' nor '[2H]C(=O)[2H]' can interact with the protein 'MEEECRVLSIQSHVIRGYVGNRAATFPLQVLGFEIDAVNSVQFSNHTGYAHWKGQVLNSDELQELYEGLRLNNMNKYDYVLTGYTRDKSFLAMVVDIVQELKQQNPRLVYVCDPVLGDKWDGEGSMYVPEDLLPVYKEKVVPLADIITPNQFEAELLSGRKIHSQEEALRVMDMLHSMGPDTVVITSSDLPSPQGSNYLIVLGSQRRRNPAGSVVMERIRMDIRKVDAVFVGTGDLFAAMLLAWTHKHPNNLKVACEKTVSTLHHVLQRTIQCAKAQAGEGVRPSPMQLELRMVQSKRDIEDPEIVVQATVL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you view the possibility of the drug 'CC(C)CCn1cnc(C(Cc2ccc(N)nc2)C(=O)O)c1', '[H][C@](N)(CC(=O)N1CC2=CC=CC=C2C[C@@]1([H])C(N)=O)CC1=CC=CC=C1F' interacting with the target protein 'MKLCSLAVLVPIVLFCEQHVFAFQSGQVLAALPRTSRQVQVLQNLTTTYEIVLWQPVTADLIVKKKQVHFFVNASDVDNVKAHLNVSGIPCSVLLADVEDLIQQQISNDTVSPRASASYYEQYHSLNEIYSWIEFITERHPDMLTKIHIGSSFEKYPLYVLKVSGKEQAAKNAIWIDCGIHAREWISPAFCLWFIGHITQFYGIIGQYTNLLRLVDFYVMPVVNVDGYDYSWKKNRMWRKNRSFYANNHCIGTDLNRNFASKHWCEEGASSSSCSETYCGLYPESEPEVKAVASFLRRNINQIKAYISMHSYSQHIVFPYSYTRSKSKDHEELSLVASEAVRAIEKISKNTRYTHGHGSETLYLAPGGGDDWIYDLGIKYSFTIELRDTGTYGFLLPERYIKPTCREAFAAVSKIAWHVIRNV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)CCn1cnc(C(Cc2ccc(N)nc2)C(=O)O)c1\", \"[H][C@](N)(CC(=O)N1CC2=CC=CC=C2C[C@@]1([H])C(N)=O)CC1=CC=CC=C1F\"], \"target_seq\": \"MKLCSLAVLVPIVLFCEQHVFAFQSGQVLAALPRTSRQVQVLQNLTTTYEIVLWQPVTADLIVKKKQVHFFVNASDVDNVKAHLNVSGIPCSVLLADVEDLIQQQISNDTVSPRASASYYEQYHSLNEIYSWIEFITERHPDMLTKIHIGSSFEKYPLYVLKVSGKEQAAKNAIWIDCGIHAREWISPAFCLWFIGHITQFYGIIGQYTNLLRLVDFYVMPVVNVDGYDYSWKKNRMWRKNRSFYANNHCIGTDLNRNFASKHWCEEGASSSSCSETYCGLYPESEPEVKAVASFLRRNINQIKAYISMHSYSQHIVFPYSYTRSKSKDHEELSLVASEAVRAIEKISKNTRYTHGHGSETLYLAPGGGDDWIYDLGIKYSFTIELRDTGTYGFLLPERYIKPTCREAFAAVSKIAWHVIRNV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKLCSLAVLVPIVLFCEQHVFAFQSGQVLAALPRTSRQVQVLQNLTTTYEIVLWQPVTADLIVKKKQVHFFVNASDVDNVKAHLNVSGIPCSVLLADVEDLIQQQISNDTVSPRASASYYEQYHSLNEIYSWIEFITERHPDMLTKIHIGSSFEKYPLYVLKVSGKEQAAKNAIWIDCGIHAREWISPAFCLWFIGHITQFYGIIGQYTNLLRLVDFYVMPVVNVDGYDYSWKKNRMWRKNRSFYANNHCIGTDLNRNFASKHWCEEGASSSSCSETYCGLYPESEPEVKAVASFLRRNINQIKAYISMHSYSQHIVFPYSYTRSKSKDHEELSLVASEAVRAIEKISKNTRYTHGHGSETLYLAPGGGDDWIYDLGIKYSFTIELRDTGTYGFLLPERYIKPTCREAFAAVSKIAWHVIRNV\", \"result\": [{\"smile\": \"CC(C)CCn1cnc(C(Cc2ccc(N)nc2)C(=O)O)c1\", \"value\": 1.0}, {\"smile\": \"[H][C@](N)(CC(=O)N1CC2=CC=CC=C2C[C@@]1([H])C(N)=O)CC1=CC=CC=C1F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CC(C)CCn1cnc(C(Cc2ccc(N)nc2)C(=O)O)c1' can interact with the protein, while the second one '[H][C@](N)(CC(=O)N1CC2=CC=CC=C2C[C@@]1([H])C(N)=O)CC1=CC=CC=C1F' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your prediction about the interaction of the drug 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2', 'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1' with the target protein 'MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\", \"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\"], \"target_seq\": \"MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV\", \"result\": [{\"smile\": \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\", \"value\": 1.0}, {\"smile\": \"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2' can interact with the protein, while the second one 'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5', 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' have a chance to interact with the target protein 'MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5\", \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD\", \"result\": [{\"smile\": \"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5\", \"value\": 0.0}, {\"smile\": \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' can interact with the protein, while the first one 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast for whether the drug 'Cc1[nH]c2ccccc2c1CCN1CCCC1c1ccc(/C=C/C(=O)NO)cc1', 'NC(=O)c1cccc(O[C@H]2C[C@@H]3CC[C@H](C2)N3Cc2cccnc2)c1' will interact with the target protein 'MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c2ccccc2c1CCN1CCCC1c1ccc(/C=C/C(=O)NO)cc1\", \"NC(=O)c1cccc(O[C@H]2C[C@@H]3CC[C@H](C2)N3Cc2cccnc2)c1\"], \"target_seq\": \"MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPVRRGHVAPQNTFLDTIIRKFEGQSRKFIIANARVENCAVIYCNDGFCELCGYSRAEVMQRPCTCDFLHGPRTQRRAAAQIAQALLGAEERKVEIAFYRKDGSCFLCLVDVVPVKNEDGAVIMFILNFEVVMEKDMVGSPAHDTNHRGPPTSWLAPGRAKTFRLKLPALLALTARESSVRSGGAGGAGAPGAVVVDVDLTPAAPSSESLALDEVTAMDNHVAGLGPAEERRALVGPGSPPRSAPGQLPSPRAHSLNPDASGSSCSLARTRSRESCASVRRASSADDIEAMRAGVLPPPPRHASTGAMHPLRSGLLNSTSDSDLVRYRTISKIPQITLNFVDLKGDPFLASPTSDREIIAPKIKERTHNVTEKVTQVLSLGADVLPEYKLQAPRIHRWTILHYSPFKAVWDWLILLLVIYTAVFTPYSAAFLLKETEEGPPATECGYACQPLAVVDLIVDIMFIVDILINFRTTYVNANEEVVSHPGRIAVHYFKGWFLIDMVAAIPFDLLIFGSGSEELIGLLKTARLLRLVRVARKLDRYSEYGAAVLFLLMCTFALIAHWLACIWYAIGNMEQPHMDSRIGWLHNLGDQIGKPYNSSGLGGPSIKDKYVTALYFTFSSLTSVGFGNVSPNTNSEKIFSICVMLIGSLMYASIFGNVSAIIQRLYSGTARYHTQMLRVREFIRFHQIPNPLRQRLEEYFQHAWSYTNGIDMNAVLKGFPECLQADICLHLNRSLLQHCKPFRGATKGCLRALAMKFKTTHAPPGDTLVHAGDLLTALYFISRGSIEILRGDVVVAILGKNDIFGEPLNLYARPGKSNGDVRALTYCDLHKIHRDDLLEVLDMYPEFSDHFWSSLEITFNLRDTNMIPGSPGSTELEGGFSRQRKRKLSFRRRTDKDTEQPGEVSALGPGRAGAGPSSRGRPGGPWGESPSSGPSSPESSEDEGPGRSSSPLRLVPFSSPRPPGEPPGGEPLMEDCEKSSDTCNPLSGAFSGVSNIFSFWGDSRGRQYQELPRCPAPTPSLLNIPLSSPGRRPRGDVESRLDALQRQLNRLETRLSADMATVLQLLQRQMTLVPPAYSAVTTPGPGPTSTSPLLPVSPLPTLTLDSLSQVSQFMACEELPPGAPELPQEGPTRRLSLPGQLGALTSQPLHRHGSDPGS\", \"result\": [{\"smile\": \"Cc1[nH]c2ccccc2c1CCN1CCCC1c1ccc(/C=C/C(=O)NO)cc1\", \"value\": 0.0}, {\"smile\": \"NC(=O)c1cccc(O[C@H]2C[C@@H]3CC[C@H](C2)N3Cc2cccnc2)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'NC(=O)c1cccc(O[C@H]2C[C@@H]3CC[C@H](C2)N3Cc2cccnc2)c1' can interact with the protein, while the first one 'Cc1[nH]c2ccccc2c1CCN1CCCC1c1ccc(/C=C/C(=O)NO)cc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the drug 'CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O', 'Cc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1-c1ccc2nc(NCCN3CCOCC3)ncc2c1''s potential to interact with the target protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O\", \"Cc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1-c1ccc2nc(NCCN3CCOCC3)ncc2c1\"], \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\", \"result\": [{\"smile\": \"CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O\", \"value\": 0.0}, {\"smile\": \"Cc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1-c1ccc2nc(NCCN3CCOCC3)ncc2c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN1CCC(C(C1)O)C2=C(C=C(C3=C2OC(=CC3=O)C4=CC=CC=C4Cl)O)O' nor 'Cc1ccc(C(=O)Nc2cccc(C(F)(F)F)c2)cc1-c1ccc2nc(NCCN3CCOCC3)ncc2c1' can interact with the protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it for the drug '[H][C@@](N)(COC1=CN=CC(=C1)C1=NCC2=NN=CC2=C1)CC1=C2C=CCC=C2N=C1', 'Cc1ncc(C#N)c(Nc2ccc3[nH]ccc3c2C)c1/C=C/c1cccc(S(=O)(=O)N2CCN(C)CC2)c1' and target protein 'MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK' to have an interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@](N)(COC1=CN=CC(=C1)C1=NCC2=NN=CC2=C1)CC1=C2C=CCC=C2N=C1\", \"Cc1ncc(C#N)c(Nc2ccc3[nH]ccc3c2C)c1/C=C/c1cccc(S(=O)(=O)N2CCN(C)CC2)c1\"], \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\", \"result\": [{\"smile\": \"[H][C@@](N)(COC1=CN=CC(=C1)C1=NCC2=NN=CC2=C1)CC1=C2C=CCC=C2N=C1\", \"value\": 0.0}, {\"smile\": \"Cc1ncc(C#N)c(Nc2ccc3[nH]ccc3c2C)c1/C=C/c1cccc(S(=O)(=O)N2CCN(C)CC2)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither '[H][C@@](N)(COC1=CN=CC(=C1)C1=NCC2=NN=CC2=C1)CC1=C2C=CCC=C2N=C1' nor 'Cc1ncc(C#N)c(Nc2ccc3[nH]ccc3c2C)c1/C=C/c1cccc(S(=O)(=O)N2CCN(C)CC2)c1' can interact with the protein 'MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline your prediction on the drug 'CN1C=C(C=N1)C2=NN3C(=NN=C3SC4=CC5=C(C=C4)N=CC=C5)C=C2', 'CC1=CN(C=N1)C1=CC(NC(=O)C2=CC=C(C)C(NC3=NC=CC(=N3)C3=CN=CC=C3)=C2)=CC(=C1)C(F)(F)F''s interaction with the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C=C(C=N1)C2=NN3C(=NN=C3SC4=CC5=C(C=C4)N=CC=C5)C=C2\", \"CC1=CN(C=N1)C1=CC(NC(=O)C2=CC=C(C)C(NC3=NC=CC(=N3)C3=CN=CC=C3)=C2)=CC(=C1)C(F)(F)F\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"CN1C=C(C=N1)C2=NN3C(=NN=C3SC4=CC5=C(C=C4)N=CC=C5)C=C2\", \"value\": 0.0}, {\"smile\": \"CC1=CN(C=N1)C1=CC(NC(=O)C2=CC=C(C)C(NC3=NC=CC(=N3)C3=CN=CC=C3)=C2)=CC(=C1)C(F)(F)F\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CC1=CN(C=N1)C1=CC(NC(=O)C2=CC=C(C)C(NC3=NC=CC(=N3)C3=CN=CC=C3)=C2)=CC(=C1)C(F)(F)F' can interact with the protein, while the first one 'CN1C=C(C=N1)C2=NN3C(=NN=C3SC4=CC5=C(C=C4)N=CC=C5)C=C2' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on whether the drug '[H][C@]1(CO)O[C@@]2(N=C(O)N(C)C2=O)[C@]([H])(O)[C@@]([H])(O)[C@]1([H])O', 'C([C@@H](C(=O)O)N)SO' will interact with the target protein 'MVHQVLYRALVSTKWLAESIRTGKLGPGLRVLDASWYSPGTREARKEYLERHVPGASFFDIEECRDTASPYEMMLPSEAGFAEYVGRLGISNHTHVVVYDGEHLGSFYAPRVWWMFRVFGHRTVSVLNGGFRNWLKEGHPVTSEPSRPEPAVFKATLDRSLLKTYEQVLENLESKRFQLVDSRSQGRFLGTEPEPDAVGLDSGHIRGAVNMPFMDFLTEDGFEKGPEELRALFQTKKVDLSQPLIATCRKGVTACHVALAAYLCGKPDVAVYDGSWSEWFRRAPPESRVSQGKSEKA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]1(CO)O[C@@]2(N=C(O)N(C)C2=O)[C@]([H])(O)[C@@]([H])(O)[C@]1([H])O\", \"C([C@@H](C(=O)O)N)SO\"], \"target_seq\": \"MVHQVLYRALVSTKWLAESIRTGKLGPGLRVLDASWYSPGTREARKEYLERHVPGASFFDIEECRDTASPYEMMLPSEAGFAEYVGRLGISNHTHVVVYDGEHLGSFYAPRVWWMFRVFGHRTVSVLNGGFRNWLKEGHPVTSEPSRPEPAVFKATLDRSLLKTYEQVLENLESKRFQLVDSRSQGRFLGTEPEPDAVGLDSGHIRGAVNMPFMDFLTEDGFEKGPEELRALFQTKKVDLSQPLIATCRKGVTACHVALAAYLCGKPDVAVYDGSWSEWFRRAPPESRVSQGKSEKA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVHQVLYRALVSTKWLAESIRTGKLGPGLRVLDASWYSPGTREARKEYLERHVPGASFFDIEECRDTASPYEMMLPSEAGFAEYVGRLGISNHTHVVVYDGEHLGSFYAPRVWWMFRVFGHRTVSVLNGGFRNWLKEGHPVTSEPSRPEPAVFKATLDRSLLKTYEQVLENLESKRFQLVDSRSQGRFLGTEPEPDAVGLDSGHIRGAVNMPFMDFLTEDGFEKGPEELRALFQTKKVDLSQPLIATCRKGVTACHVALAAYLCGKPDVAVYDGSWSEWFRRAPPESRVSQGKSEKA\", \"result\": [{\"smile\": \"[H][C@]1(CO)O[C@@]2(N=C(O)N(C)C2=O)[C@]([H])(O)[C@@]([H])(O)[C@]1([H])O\", \"value\": 0.0}, {\"smile\": \"C([C@@H](C(=O)O)N)SO\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'C([C@@H](C(=O)O)N)SO' can interact with the protein, while the first one '[H][C@]1(CO)O[C@@]2(N=C(O)N(C)C2=O)[C@]([H])(O)[C@@]([H])(O)[C@]1([H])O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the probability of interaction between the drug 'Cc1cc2ncc3c(n2n1)CN([C@@H]1CC[C@H](c2cc(F)ccc2F)[C@@H](N)C1)C3', '[H][C@@](N)(C(=O)N1CCC(F)(F)C1)[C@@]([H])(C(=O)N(C)C)[C@@]1([H])CC[C@@]([H])(CC1)C1=CN2N=CN=C2C=C1' and the target protein 'MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLTDYLKNTYRLKLYSLRWISDHEYLYKQENNILVFNAEYGNSSVFLENSTFDEFGHSINDYSISPDGQFILLEYNYVKQWRHSYTASYDIYDLNKRQLITEERIPNNTQWVTWSPVGHKLAYVWNNDIYVKIEPNLPSYRITWTGKEDIIYNGITDWVYEEEVFSAYSALWWSPNGTFLAYAQFNDTEVPLIEYSFYSDESLQYPKTVRVPYPKAGAVNPTVKFFVVNTDSLSSVTNATSIQITAPASMLIGDHYLCDVTWATQERISLQWLRRIQNYSVMDICDYDESSGRWNCLVARQHIEMSTTGWVGRFRPSEPHFTLDGNSFYKIISNEEGYRHICYFQIDKKDCTFITKGTWEVIGIEALTSDYLYYISNEYKGMPGGRNLYKIQLSDYTKVTCLSCELNPERCQYYSVSFSKEAKYYQLRCSGPGLPLYTLHSSVNDKGLRVLEDNSALDKMLQNVQMPSKKLDFIILNETKFWYQMILPPHFDKSKKYPLLLDVYAGPCSQKADTVFRLNWATYLASTENIIVASFDGRGSGYQGDKIMHAINRRLGTFEVEDQIEAARQFSKMGFVDNKRIAIWGWSYGGYVTSMVLGSGSGVFKCGIAVAPVSRWEYYDSVYTERYMGLPTPEDNLDHYRNSTVMSRAENFKQVEYLLIHGTADDNVHFQQSAQISKALVDVGVDFQAMWYTDEDHGIASSTAHQHIYTHMSHFIKQCFSLP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2ncc3c(n2n1)CN([C@@H]1CC[C@H](c2cc(F)ccc2F)[C@@H](N)C1)C3\", \"[H][C@@](N)(C(=O)N1CCC(F)(F)C1)[C@@]([H])(C(=O)N(C)C)[C@@]1([H])CC[C@@]([H])(CC1)C1=CN2N=CN=C2C=C1\"], \"target_seq\": \"MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLTDYLKNTYRLKLYSLRWISDHEYLYKQENNILVFNAEYGNSSVFLENSTFDEFGHSINDYSISPDGQFILLEYNYVKQWRHSYTASYDIYDLNKRQLITEERIPNNTQWVTWSPVGHKLAYVWNNDIYVKIEPNLPSYRITWTGKEDIIYNGITDWVYEEEVFSAYSALWWSPNGTFLAYAQFNDTEVPLIEYSFYSDESLQYPKTVRVPYPKAGAVNPTVKFFVVNTDSLSSVTNATSIQITAPASMLIGDHYLCDVTWATQERISLQWLRRIQNYSVMDICDYDESSGRWNCLVARQHIEMSTTGWVGRFRPSEPHFTLDGNSFYKIISNEEGYRHICYFQIDKKDCTFITKGTWEVIGIEALTSDYLYYISNEYKGMPGGRNLYKIQLSDYTKVTCLSCELNPERCQYYSVSFSKEAKYYQLRCSGPGLPLYTLHSSVNDKGLRVLEDNSALDKMLQNVQMPSKKLDFIILNETKFWYQMILPPHFDKSKKYPLLLDVYAGPCSQKADTVFRLNWATYLASTENIIVASFDGRGSGYQGDKIMHAINRRLGTFEVEDQIEAARQFSKMGFVDNKRIAIWGWSYGGYVTSMVLGSGSGVFKCGIAVAPVSRWEYYDSVYTERYMGLPTPEDNLDHYRNSTVMSRAENFKQVEYLLIHGTADDNVHFQQSAQISKALVDVGVDFQAMWYTDEDHGIASSTAHQHIYTHMSHFIKQCFSLP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLTDYLKNTYRLKLYSLRWISDHEYLYKQENNILVFNAEYGNSSVFLENSTFDEFGHSINDYSISPDGQFILLEYNYVKQWRHSYTASYDIYDLNKRQLITEERIPNNTQWVTWSPVGHKLAYVWNNDIYVKIEPNLPSYRITWTGKEDIIYNGITDWVYEEEVFSAYSALWWSPNGTFLAYAQFNDTEVPLIEYSFYSDESLQYPKTVRVPYPKAGAVNPTVKFFVVNTDSLSSVTNATSIQITAPASMLIGDHYLCDVTWATQERISLQWLRRIQNYSVMDICDYDESSGRWNCLVARQHIEMSTTGWVGRFRPSEPHFTLDGNSFYKIISNEEGYRHICYFQIDKKDCTFITKGTWEVIGIEALTSDYLYYISNEYKGMPGGRNLYKIQLSDYTKVTCLSCELNPERCQYYSVSFSKEAKYYQLRCSGPGLPLYTLHSSVNDKGLRVLEDNSALDKMLQNVQMPSKKLDFIILNETKFWYQMILPPHFDKSKKYPLLLDVYAGPCSQKADTVFRLNWATYLASTENIIVASFDGRGSGYQGDKIMHAINRRLGTFEVEDQIEAARQFSKMGFVDNKRIAIWGWSYGGYVTSMVLGSGSGVFKCGIAVAPVSRWEYYDSVYTERYMGLPTPEDNLDHYRNSTVMSRAENFKQVEYLLIHGTADDNVHFQQSAQISKALVDVGVDFQAMWYTDEDHGIASSTAHQHIYTHMSHFIKQCFSLP\", \"result\": [{\"smile\": \"Cc1cc2ncc3c(n2n1)CN([C@@H]1CC[C@H](c2cc(F)ccc2F)[C@@H](N)C1)C3\", \"value\": 1.0}, {\"smile\": \"[H][C@@](N)(C(=O)N1CCC(F)(F)C1)[C@@]([H])(C(=O)N(C)C)[C@@]1([H])CC[C@@]([H])(CC1)C1=CN2N=CN=C2C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'Cc1cc2ncc3c(n2n1)CN([C@@H]1CC[C@H](c2cc(F)ccc2F)[C@@H](N)C1)C3' and '[H][C@@](N)(C(=O)N1CCC(F)(F)C1)[C@@]([H])(C(=O)N(C)C)[C@@]1([H])CC[C@@]([H])(CC1)C1=CN2N=CN=C2C=C1' can interact with the protein 'MKTPWKVLLGLLGAAALVTIITVPVVLLNKGTDDATADSRKTYTLTDYLKNTYRLKLYSLRWISDHEYLYKQENNILVFNAEYGNSSVFLENSTFDEFGHSINDYSISPDGQFILLEYNYVKQWRHSYTASYDIYDLNKRQLITEERIPNNTQWVTWSPVGHKLAYVWNNDIYVKIEPNLPSYRITWTGKEDIIYNGITDWVYEEEVFSAYSALWWSPNGTFLAYAQFNDTEVPLIEYSFYSDESLQYPKTVRVPYPKAGAVNPTVKFFVVNTDSLSSVTNATSIQITAPASMLIGDHYLCDVTWATQERISLQWLRRIQNYSVMDICDYDESSGRWNCLVARQHIEMSTTGWVGRFRPSEPHFTLDGNSFYKIISNEEGYRHICYFQIDKKDCTFITKGTWEVIGIEALTSDYLYYISNEYKGMPGGRNLYKIQLSDYTKVTCLSCELNPERCQYYSVSFSKEAKYYQLRCSGPGLPLYTLHSSVNDKGLRVLEDNSALDKMLQNVQMPSKKLDFIILNETKFWYQMILPPHFDKSKKYPLLLDVYAGPCSQKADTVFRLNWATYLASTENIIVASFDGRGSGYQGDKIMHAINRRLGTFEVEDQIEAARQFSKMGFVDNKRIAIWGWSYGGYVTSMVLGSGSGVFKCGIAVAPVSRWEYYDSVYTERYMGLPTPEDNLDHYRNSTVMSRAENFKQVEYLLIHGTADDNVHFQQSAQISKALVDVGVDFQAMWYTDEDHGIASSTAHQHIYTHMSHFIKQCFSLP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood of the drug 'NC[C@H](O)C1=CC(O)=C(O)C=C1', 'CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12' engaging with the target protein 'MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC[C@H](O)C1=CC(O)=C(O)C=C1\", \"CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\"], \"target_seq\": \"MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV\", \"result\": [{\"smile\": \"NC[C@H](O)C1=CC(O)=C(O)C=C1\", \"value\": 1.0}, {\"smile\": \"CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'NC[C@H](O)C1=CC(O)=C(O)C=C1' and 'CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12' can interact with the protein 'MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you rate the chances of interaction between the drug 'O=C1Nc2ccc(S(=O)(=O)N3CCC[C@H]3COc3ccccc3)cc2C1=O', 'O=C1C(=O)N(Cc2ccc(OCCF)cc2)c2ccc(S(=O)(=O)N3CCC[C@H]3COc3cccnc3)cc21' and the target protein 'MDFSRNLYDIGEQLDSEDLASLKFLSLDYIPQRKQEPIKDALMLFQRLQEKRMLEESNLSFLKELLFRINRLDLLITYLNTRKEEMERELQTPGRAQISAYRVMLYQISEEVSRSELRSFKFLLQEEISKCKLDDDMNLLDIFIEMEKRVILGEGKLDILKRVCAQINKSLLKIINDYEEFSKERSSSLEGSPDEFSNGEELCGVMTISDSPREQDSESQTLDKVYQMKSKPRGYCLIINNHNFAKAREKVPKLHSIRDRNGTHLDAGALTTTFEELHFEIKPHDDCTVEQIYEILKIYQLMDHSNMDCFICCILSHGDKGIIYGTDGQEAPIYELTSQFTGLKCPSLAGKPKVFFIQACQGDNYQKGIPVETDSEEQPYLEMDLSSPQTRYIPDEADFLLGMATVNNCVSYRNPAEGTWYIQSLCQSLRERCPRGDDILTILTEVNYEVSNKDDKKNMGKQMPQPTFTLRKKLVFPSD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1Nc2ccc(S(=O)(=O)N3CCC[C@H]3COc3ccccc3)cc2C1=O\", \"O=C1C(=O)N(Cc2ccc(OCCF)cc2)c2ccc(S(=O)(=O)N3CCC[C@H]3COc3cccnc3)cc21\"], \"target_seq\": \"MDFSRNLYDIGEQLDSEDLASLKFLSLDYIPQRKQEPIKDALMLFQRLQEKRMLEESNLSFLKELLFRINRLDLLITYLNTRKEEMERELQTPGRAQISAYRVMLYQISEEVSRSELRSFKFLLQEEISKCKLDDDMNLLDIFIEMEKRVILGEGKLDILKRVCAQINKSLLKIINDYEEFSKERSSSLEGSPDEFSNGEELCGVMTISDSPREQDSESQTLDKVYQMKSKPRGYCLIINNHNFAKAREKVPKLHSIRDRNGTHLDAGALTTTFEELHFEIKPHDDCTVEQIYEILKIYQLMDHSNMDCFICCILSHGDKGIIYGTDGQEAPIYELTSQFTGLKCPSLAGKPKVFFIQACQGDNYQKGIPVETDSEEQPYLEMDLSSPQTRYIPDEADFLLGMATVNNCVSYRNPAEGTWYIQSLCQSLRERCPRGDDILTILTEVNYEVSNKDDKKNMGKQMPQPTFTLRKKLVFPSD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDFSRNLYDIGEQLDSEDLASLKFLSLDYIPQRKQEPIKDALMLFQRLQEKRMLEESNLSFLKELLFRINRLDLLITYLNTRKEEMERELQTPGRAQISAYRVMLYQISEEVSRSELRSFKFLLQEEISKCKLDDDMNLLDIFIEMEKRVILGEGKLDILKRVCAQINKSLLKIINDYEEFSKERSSSLEGSPDEFSNGEELCGVMTISDSPREQDSESQTLDKVYQMKSKPRGYCLIINNHNFAKAREKVPKLHSIRDRNGTHLDAGALTTTFEELHFEIKPHDDCTVEQIYEILKIYQLMDHSNMDCFICCILSHGDKGIIYGTDGQEAPIYELTSQFTGLKCPSLAGKPKVFFIQACQGDNYQKGIPVETDSEEQPYLEMDLSSPQTRYIPDEADFLLGMATVNNCVSYRNPAEGTWYIQSLCQSLRERCPRGDDILTILTEVNYEVSNKDDKKNMGKQMPQPTFTLRKKLVFPSD\", \"result\": [{\"smile\": \"O=C1Nc2ccc(S(=O)(=O)N3CCC[C@H]3COc3ccccc3)cc2C1=O\", \"value\": 0.0}, {\"smile\": \"O=C1C(=O)N(Cc2ccc(OCCF)cc2)c2ccc(S(=O)(=O)N3CCC[C@H]3COc3cccnc3)cc21\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'O=C1Nc2ccc(S(=O)(=O)N3CCC[C@H]3COc3ccccc3)cc2C1=O' nor 'O=C1C(=O)N(Cc2ccc(OCCF)cc2)c2ccc(S(=O)(=O)N3CCC[C@H]3COc3cccnc3)cc21' can interact with the protein 'MDFSRNLYDIGEQLDSEDLASLKFLSLDYIPQRKQEPIKDALMLFQRLQEKRMLEESNLSFLKELLFRINRLDLLITYLNTRKEEMERELQTPGRAQISAYRVMLYQISEEVSRSELRSFKFLLQEEISKCKLDDDMNLLDIFIEMEKRVILGEGKLDILKRVCAQINKSLLKIINDYEEFSKERSSSLEGSPDEFSNGEELCGVMTISDSPREQDSESQTLDKVYQMKSKPRGYCLIINNHNFAKAREKVPKLHSIRDRNGTHLDAGALTTTFEELHFEIKPHDDCTVEQIYEILKIYQLMDHSNMDCFICCILSHGDKGIIYGTDGQEAPIYELTSQFTGLKCPSLAGKPKVFFIQACQGDNYQKGIPVETDSEEQPYLEMDLSSPQTRYIPDEADFLLGMATVNNCVSYRNPAEGTWYIQSLCQSLRERCPRGDDILTILTEVNYEVSNKDDKKNMGKQMPQPTFTLRKKLVFPSD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an expected interaction between the drug 'CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)c2ccc(C(=O)OC)cc2)c2ccccc2N(Cc2noc3ccccc23)C1=O', 'CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)CC2CCOCC2)c2ccccc2N(Cc2c(OC)ccc3cc(Br)ccc23)C1=O' and the target protein 'MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)c2ccc(C(=O)OC)cc2)c2ccccc2N(Cc2noc3ccccc23)C1=O\", \"CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)CC2CCOCC2)c2ccccc2N(Cc2c(OC)ccc3cc(Br)ccc23)C1=O\"], \"target_seq\": \"MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT\", \"result\": [{\"smile\": \"CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)c2ccc(C(=O)OC)cc2)c2ccccc2N(Cc2noc3ccccc23)C1=O\", \"value\": 0.0}, {\"smile\": \"CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)CC2CCOCC2)c2ccccc2N(Cc2c(OC)ccc3cc(Br)ccc23)C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)c2ccc(C(=O)OC)cc2)c2ccccc2N(Cc2noc3ccccc23)C1=O' nor 'CN[C@@H](C)C(=O)N[C@H]1CN(C(=O)CC2CCOCC2)c2ccccc2N(Cc2c(OC)ccc3cc(Br)ccc23)C1=O' can interact with the protein 'MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate whether the drug 'CNC(=O)c1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)cc1', 'N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1OC1CCCOC1' will likely interact with the target protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)cc1\", \"N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1OC1CCCOC1\"], \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\", \"result\": [{\"smile\": \"CNC(=O)c1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)cc1\", \"value\": 1.0}, {\"smile\": \"N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1OC1CCCOC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CNC(=O)c1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)cc1' and 'N#Cc1cc(-c2ncnc(Nc3ccc(N4CCN(C5COC5)CC4)cc3)n2)ccc1OC1CCCOC1' can interact with the protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]', 'C([C@@H]([C@@H]([C@@H](C=O)O)O)O)O' and the target protein 'MAAVSMSVVLRQTLWRRRAVAVAALSVSRVPTRSLRTSTWRLAQDQTQDTQLITVDEKLDITTLTGVPEEHIKTRKVRIFVPARNNMQSGVNNTKKWKMEFDTRERWENPLMGWASTADPLSNMVLTFSTKEDAVSFAEKNGWSYDIEERKVPKPKSKSYGANFSWNKRTRVSTK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]\", \"C([C@@H]([C@@H]([C@@H](C=O)O)O)O)O\"], \"target_seq\": \"MAAVSMSVVLRQTLWRRRAVAVAALSVSRVPTRSLRTSTWRLAQDQTQDTQLITVDEKLDITTLTGVPEEHIKTRKVRIFVPARNNMQSGVNNTKKWKMEFDTRERWENPLMGWASTADPLSNMVLTFSTKEDAVSFAEKNGWSYDIEERKVPKPKSKSYGANFSWNKRTRVSTK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAVSMSVVLRQTLWRRRAVAVAALSVSRVPTRSLRTSTWRLAQDQTQDTQLITVDEKLDITTLTGVPEEHIKTRKVRIFVPARNNMQSGVNNTKKWKMEFDTRERWENPLMGWASTADPLSNMVLTFSTKEDAVSFAEKNGWSYDIEERKVPKPKSKSYGANFSWNKRTRVSTK\", \"result\": [{\"smile\": \"C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]\", \"value\": 0.0}, {\"smile\": \"C([C@@H]([C@@H]([C@@H](C=O)O)O)O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]' nor 'C([C@@H]([C@@H]([C@@H](C=O)O)O)O)O' can interact with the protein 'MAAVSMSVVLRQTLWRRRAVAVAALSVSRVPTRSLRTSTWRLAQDQTQDTQLITVDEKLDITTLTGVPEEHIKTRKVRIFVPARNNMQSGVNNTKKWKMEFDTRERWENPLMGWASTADPLSNMVLTFSTKEDAVSFAEKNGWSYDIEERKVPKPKSKSYGANFSWNKRTRVSTK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'Cn1cncc1-c1cccc(C2(NC(=O)C[C@H](N)Cc3ccccc3F)CCS(=O)(=O)CC2)c1', 'CN(C)C(=O)[C@@H](c1ccc(-c2ccc(F)cc2F)cc1)[C@H]([NH3+])C(=O)N1CC[C@H](F)C1''s interaction outcome with the target protein 'MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cncc1-c1cccc(C2(NC(=O)C[C@H](N)Cc3ccccc3F)CCS(=O)(=O)CC2)c1\", \"CN(C)C(=O)[C@@H](c1ccc(-c2ccc(F)cc2F)cc1)[C@H]([NH3+])C(=O)N1CC[C@H](F)C1\"], \"target_seq\": \"MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL\", \"result\": [{\"smile\": \"Cn1cncc1-c1cccc(C2(NC(=O)C[C@H](N)Cc3ccccc3F)CCS(=O)(=O)CC2)c1\", \"value\": 0.0}, {\"smile\": \"CN(C)C(=O)[C@@H](c1ccc(-c2ccc(F)cc2F)cc1)[C@H]([NH3+])C(=O)N1CC[C@H](F)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cn1cncc1-c1cccc(C2(NC(=O)C[C@H](N)Cc3ccccc3F)CCS(=O)(=O)CC2)c1' nor 'CN(C)C(=O)[C@@H](c1ccc(-c2ccc(F)cc2F)cc1)[C@H]([NH3+])C(=O)N1CC[C@H](F)C1' can interact with the protein 'MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'C=CCN(CC(=O)O)C(=O)/C(C)=C/c1ccc(C(=O)Oc2ccc(C(=N)N)cc2)cc1', 'N=C(N)c1ccc2nc(Cc3nc4ccc(C(=N)N)cc4[nH]3)[nH]c2c1' interact with the target protein 'MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE' according to your assessment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C=CCN(CC(=O)O)C(=O)/C(C)=C/c1ccc(C(=O)Oc2ccc(C(=N)N)cc2)cc1\", \"N=C(N)c1ccc2nc(Cc3nc4ccc(C(=N)N)cc4[nH]3)[nH]c2c1\"], \"target_seq\": \"MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE\", \"result\": [{\"smile\": \"C=CCN(CC(=O)O)C(=O)/C(C)=C/c1ccc(C(=O)Oc2ccc(C(=N)N)cc2)cc1\", \"value\": 0.0}, {\"smile\": \"N=C(N)c1ccc2nc(Cc3nc4ccc(C(=N)N)cc4[nH]3)[nH]c2c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C=CCN(CC(=O)O)C(=O)/C(C)=C/c1ccc(C(=O)Oc2ccc(C(=N)N)cc2)cc1' nor 'N=C(N)c1ccc2nc(Cc3nc4ccc(C(=N)N)cc4[nH]3)[nH]c2c1' can interact with the protein 'MAHVRGLQLPGCLALAALCSLVHSQHVFLAPQQARSLLQRVRRANTFLEEVRKGNLERECVEETCSYEEAFEALESSTATDVFWAKYTACETARTPRDKLAACLEGNCAEGLGTNYRGHVNITRSGIECQLWRSRYPHKPEINSTTHPGADLQENFCRNPDSSTTGPWCYTTDPTVRRQECSIPVCGQDQVTVAMTPRSEGSSVNLSPPLEQCVPDRGQQYQGRLAVTTHGLPCLAWASAQAKALSKHQDFNSAVQLVENFCRNPDGDEEGVWCYVAGKPGDFGYCDLNYCEEAVEEETGDGLDEDSDRAIEGRTATSEYQTFFNPRTFGSGEADCGLRPLFEKKSLEDKTERELLESYIDGRIVEGSDAEIGMSPWQVMLFRKSPQELLCGASLISDRWVLTAAHCLLYPPWDKNFTENDLLVRIGKHSRTRYERNIEKISMLEKIYIHPRYNWRENLDRDIALMKLKKPVAFSDYIHPVCLPDRETAASLLQAGYKGRVTGWGNLKETWTANVGKGQPSVLQVVNLPIVERPVCKDSTRIRITDNMFCAGYKPDEGKRGDACEGDSGGPFVMKSPFNNRWYQMGIVSWGEGCDRDGKYGFYTHVFRLKKWIQKVIDQFGE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected likelihood of the drug 'O=c1cc(CN2CCOc3c(Cl)cc(-n4ccc5cc(F)ccc54)cc3C2)cc[nH]1', 'CCS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)ccc2OCC(=O)O)c(C)c1' engaging with the target protein 'MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1cc(CN2CCOc3c(Cl)cc(-n4ccc5cc(F)ccc54)cc3C2)cc[nH]1\", \"CCS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)ccc2OCC(=O)O)c(C)c1\"], \"target_seq\": \"MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS\", \"result\": [{\"smile\": \"O=c1cc(CN2CCOc3c(Cl)cc(-n4ccc5cc(F)ccc54)cc3C2)cc[nH]1\", \"value\": 0.0}, {\"smile\": \"CCS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)ccc2OCC(=O)O)c(C)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CCS(=O)(=O)c1ccc(-c2cc(C(F)(F)F)ccc2OCC(=O)O)c(C)c1' can interact with the protein, while the first one 'O=c1cc(CN2CCOc3c(Cl)cc(-n4ccc5cc(F)ccc54)cc3C2)cc[nH]1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the drug 'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC', 'CCC1C(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' will engage with the target protein 'MPVLSARRRELADHAGSGRRSGPSPTARSGPHLSALRAQPARAAHLSGRGTYVRRDTAGGGPGQARPLGPPGTSLLGRGARRSGEGWCPGAFESGARAARPPSRVEPRLATAASREGAGLPRAEVAAGSGRGARSGEWGLAAAGAWETMHHCKRYRSPEPDPYLSYRWKRRRSYSREHEGRLRYPSRREPPPRRSRSRSHDRLPYQRRYRERRDSDTYRCEERSPSFGEDYYGPSRSRHRRRSRERGPYRTRKHAHHCHKRRTRSCSSASSRSQQSSKRSSRSVEDDKEGHLVCRIGDWLQERYEIVGNLGEGTFGKVVECLDHARGKSQVALKIIRNVGKYREAARLEINVLKKIKEKDKENKFLCVLMSDWFNFHGHMCIAFELLGKNTFEFLKENNFQPYPLPHVRHMAYQLCHALRFLHENQLTHTDLKPENILFVNSEFETLYNEHKSCEEKSVKNTSIRVADFGSATFDHEHHTTIVATRHYRPPEVILELGWAQPCDVWSIGCILFEYYRGFTLFQTHENREHLVMMEKILGPIPSHMIHRTRKQKYFYKGGLVWDENSSDGRYVKENCKPLKSYMLQDSLEHVQLFDLMRRMLEFDPAQRITLAEALLHPFFAGLTPEERSFHTSRNPSR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC\", \"CCC1C(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\"], \"target_seq\": \"MPVLSARRRELADHAGSGRRSGPSPTARSGPHLSALRAQPARAAHLSGRGTYVRRDTAGGGPGQARPLGPPGTSLLGRGARRSGEGWCPGAFESGARAARPPSRVEPRLATAASREGAGLPRAEVAAGSGRGARSGEWGLAAAGAWETMHHCKRYRSPEPDPYLSYRWKRRRSYSREHEGRLRYPSRREPPPRRSRSRSHDRLPYQRRYRERRDSDTYRCEERSPSFGEDYYGPSRSRHRRRSRERGPYRTRKHAHHCHKRRTRSCSSASSRSQQSSKRSSRSVEDDKEGHLVCRIGDWLQERYEIVGNLGEGTFGKVVECLDHARGKSQVALKIIRNVGKYREAARLEINVLKKIKEKDKENKFLCVLMSDWFNFHGHMCIAFELLGKNTFEFLKENNFQPYPLPHVRHMAYQLCHALRFLHENQLTHTDLKPENILFVNSEFETLYNEHKSCEEKSVKNTSIRVADFGSATFDHEHHTTIVATRHYRPPEVILELGWAQPCDVWSIGCILFEYYRGFTLFQTHENREHLVMMEKILGPIPSHMIHRTRKQKYFYKGGLVWDENSSDGRYVKENCKPLKSYMLQDSLEHVQLFDLMRRMLEFDPAQRITLAEALLHPFFAGLTPEERSFHTSRNPSR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPVLSARRRELADHAGSGRRSGPSPTARSGPHLSALRAQPARAAHLSGRGTYVRRDTAGGGPGQARPLGPPGTSLLGRGARRSGEGWCPGAFESGARAARPPSRVEPRLATAASREGAGLPRAEVAAGSGRGARSGEWGLAAAGAWETMHHCKRYRSPEPDPYLSYRWKRRRSYSREHEGRLRYPSRREPPPRRSRSRSHDRLPYQRRYRERRDSDTYRCEERSPSFGEDYYGPSRSRHRRRSRERGPYRTRKHAHHCHKRRTRSCSSASSRSQQSSKRSSRSVEDDKEGHLVCRIGDWLQERYEIVGNLGEGTFGKVVECLDHARGKSQVALKIIRNVGKYREAARLEINVLKKIKEKDKENKFLCVLMSDWFNFHGHMCIAFELLGKNTFEFLKENNFQPYPLPHVRHMAYQLCHALRFLHENQLTHTDLKPENILFVNSEFETLYNEHKSCEEKSVKNTSIRVADFGSATFDHEHHTTIVATRHYRPPEVILELGWAQPCDVWSIGCILFEYYRGFTLFQTHENREHLVMMEKILGPIPSHMIHRTRKQKYFYKGGLVWDENSSDGRYVKENCKPLKSYMLQDSLEHVQLFDLMRRMLEFDPAQRITLAEALLHPFFAGLTPEERSFHTSRNPSR\", \"result\": [{\"smile\": \"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC\", \"value\": 0.0}, {\"smile\": \"CCC1C(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)NC)OC' nor 'CCC1C(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' can interact with the protein 'MPVLSARRRELADHAGSGRRSGPSPTARSGPHLSALRAQPARAAHLSGRGTYVRRDTAGGGPGQARPLGPPGTSLLGRGARRSGEGWCPGAFESGARAARPPSRVEPRLATAASREGAGLPRAEVAAGSGRGARSGEWGLAAAGAWETMHHCKRYRSPEPDPYLSYRWKRRRSYSREHEGRLRYPSRREPPPRRSRSRSHDRLPYQRRYRERRDSDTYRCEERSPSFGEDYYGPSRSRHRRRSRERGPYRTRKHAHHCHKRRTRSCSSASSRSQQSSKRSSRSVEDDKEGHLVCRIGDWLQERYEIVGNLGEGTFGKVVECLDHARGKSQVALKIIRNVGKYREAARLEINVLKKIKEKDKENKFLCVLMSDWFNFHGHMCIAFELLGKNTFEFLKENNFQPYPLPHVRHMAYQLCHALRFLHENQLTHTDLKPENILFVNSEFETLYNEHKSCEEKSVKNTSIRVADFGSATFDHEHHTTIVATRHYRPPEVILELGWAQPCDVWSIGCILFEYYRGFTLFQTHENREHLVMMEKILGPIPSHMIHRTRKQKYFYKGGLVWDENSSDGRYVKENCKPLKSYMLQDSLEHVQLFDLMRRMLEFDPAQRITLAEALLHPFFAGLTPEERSFHTSRNPSR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the interaction potential between the drug 'CC1=CN([C@@H]2O[C@H](CO[P@@](O)(=O)OP(O)(O)=O)C=C2)C(=O)NC1=O', '[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])N' and the target protein 'MANCERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVGLKFMQASEDLLKEHYVDLKDRPFFAGLVKYMHSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVESAEKEIGLWFHPEELVDYTSCAQNWIYE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CN([C@@H]2O[C@H](CO[P@@](O)(=O)OP(O)(O)=O)C=C2)C(=O)NC1=O\", \"[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])N\"], \"target_seq\": \"MANCERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVGLKFMQASEDLLKEHYVDLKDRPFFAGLVKYMHSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVESAEKEIGLWFHPEELVDYTSCAQNWIYE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MANCERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVGLKFMQASEDLLKEHYVDLKDRPFFAGLVKYMHSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVESAEKEIGLWFHPEELVDYTSCAQNWIYE\", \"result\": [{\"smile\": \"CC1=CN([C@@H]2O[C@H](CO[P@@](O)(=O)OP(O)(O)=O)C=C2)C(=O)NC1=O\", \"value\": 1.0}, {\"smile\": \"[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC1=CN([C@@H]2O[C@H](CO[P@@](O)(=O)OP(O)(O)=O)C=C2)C(=O)NC1=O' and '[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])N' can interact with the protein 'MANCERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVGLKFMQASEDLLKEHYVDLKDRPFFAGLVKYMHSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVESAEKEIGLWFHPEELVDYTSCAQNWIYE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction probability for the drug 'Oc1c(Br)cc(C=C2c3cc(I)ccc3NC2O)cc1Br', 'C(C(CO)O)O' and the target protein 'MAFWGWRAAAALRLWGRVVERVEAGGGVGPFQACGCRLVLGGRDDVSAGLRGSHGARGEPLDPARPLQRPPRPEVPRAFRRQPRAAAPSFFFSSIKGGRRSISFSVGASSVVGSGGSSDKGKLSLQDVAELIRARACQRVVVMVGAGISTPSGIPDFRSPGSGLYSNLQQYDLPYPEAIFELPFFFHNPKPFFTLAKELYPGNYKPNVTHYFLRLLHDKGLLLRLYTQNIDGLERVSGIPASKLVEAHGTFASATCTVCQRPFPGEDIRADVMADRVPRCPVCTGVVKPDIVFFGEPLPQRFLLHVVDFPMADLLLILGTSLEVEPFASLTEAVRSSVPRLLINRDLVGPLAWHPRSRDVAQLGDVVHGVESLVELLGWTEEMRDLVQRETGKLDGPDK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1c(Br)cc(C=C2c3cc(I)ccc3NC2O)cc1Br\", \"C(C(CO)O)O\"], \"target_seq\": \"MAFWGWRAAAALRLWGRVVERVEAGGGVGPFQACGCRLVLGGRDDVSAGLRGSHGARGEPLDPARPLQRPPRPEVPRAFRRQPRAAAPSFFFSSIKGGRRSISFSVGASSVVGSGGSSDKGKLSLQDVAELIRARACQRVVVMVGAGISTPSGIPDFRSPGSGLYSNLQQYDLPYPEAIFELPFFFHNPKPFFTLAKELYPGNYKPNVTHYFLRLLHDKGLLLRLYTQNIDGLERVSGIPASKLVEAHGTFASATCTVCQRPFPGEDIRADVMADRVPRCPVCTGVVKPDIVFFGEPLPQRFLLHVVDFPMADLLLILGTSLEVEPFASLTEAVRSSVPRLLINRDLVGPLAWHPRSRDVAQLGDVVHGVESLVELLGWTEEMRDLVQRETGKLDGPDK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAFWGWRAAAALRLWGRVVERVEAGGGVGPFQACGCRLVLGGRDDVSAGLRGSHGARGEPLDPARPLQRPPRPEVPRAFRRQPRAAAPSFFFSSIKGGRRSISFSVGASSVVGSGGSSDKGKLSLQDVAELIRARACQRVVVMVGAGISTPSGIPDFRSPGSGLYSNLQQYDLPYPEAIFELPFFFHNPKPFFTLAKELYPGNYKPNVTHYFLRLLHDKGLLLRLYTQNIDGLERVSGIPASKLVEAHGTFASATCTVCQRPFPGEDIRADVMADRVPRCPVCTGVVKPDIVFFGEPLPQRFLLHVVDFPMADLLLILGTSLEVEPFASLTEAVRSSVPRLLINRDLVGPLAWHPRSRDVAQLGDVVHGVESLVELLGWTEEMRDLVQRETGKLDGPDK\", \"result\": [{\"smile\": \"Oc1c(Br)cc(C=C2c3cc(I)ccc3NC2O)cc1Br\", \"value\": 0.0}, {\"smile\": \"C(C(CO)O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'C(C(CO)O)O' can interact with the protein, while the first one 'Oc1c(Br)cc(C=C2c3cc(I)ccc3NC2O)cc1Br' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1ccc(Cl)c(-c2ncon2)c1', 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' is expected to interact with the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1ccc(Cl)c(-c2ncon2)c1\", \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\"], \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1ccc(Cl)c(-c2ncon2)c1\", \"value\": 0.0}, {\"smile\": \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' can interact with the protein, while the first one 'O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1ccc(Cl)c(-c2ncon2)c1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely do you think the drug 'C[C@H](O)C(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCCOCC5)cc4)n3)cc2C#N)[C@H](F)C1', 'Cn1cc(C(=O)N2C[C@@H](F)[C@@H](Oc3ccc(-c4ncnc(Nc5ccc(N6CCN(C7COC7)CC6)cc5)n4)cc3C#N)C(C)(C)C2)nn1' will engage with the target protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@H](O)C(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCCOCC5)cc4)n3)cc2C#N)[C@H](F)C1\", \"Cn1cc(C(=O)N2C[C@@H](F)[C@@H](Oc3ccc(-c4ncnc(Nc5ccc(N6CCN(C7COC7)CC6)cc5)n4)cc3C#N)C(C)(C)C2)nn1\"], \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\", \"result\": [{\"smile\": \"C[C@H](O)C(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCCOCC5)cc4)n3)cc2C#N)[C@H](F)C1\", \"value\": 1.0}, {\"smile\": \"Cn1cc(C(=O)N2C[C@@H](F)[C@@H](Oc3ccc(-c4ncnc(Nc5ccc(N6CCN(C7COC7)CC6)cc5)n4)cc3C#N)C(C)(C)C2)nn1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'C[C@H](O)C(=O)N1CC[C@H](Oc2ccc(-c3ncnc(Nc4ccc(N5CCCOCC5)cc4)n3)cc2C#N)[C@H](F)C1' and 'Cn1cc(C(=O)N2C[C@@H](F)[C@@H](Oc3ccc(-c4ncnc(Nc5ccc(N6CCN(C7COC7)CC6)cc5)n4)cc3C#N)C(C)(C)C2)nn1' can interact with the protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'Nc1nc2c(-c3cc(Br)c(Br)[nH]3)nccc2[nH]1', 'O=C1Cc2c([nH]c3ccc(Br)cc23)-c2ccccc2N1' likely form an interaction with the target protein 'GEEVAVKLESQKARHPQLLYESKLYKILQGGVGIPHIRWYGQEKDYNVLVMDLLGPSLEDLFNFCSRRFTMKTVLMLADQMISRIEYVHTKNFIHRDIKPDNFLMGIGRHCNKLFLIDFGLAKKY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc2c(-c3cc(Br)c(Br)[nH]3)nccc2[nH]1\", \"O=C1Cc2c([nH]c3ccc(Br)cc23)-c2ccccc2N1\"], \"target_seq\": \"GEEVAVKLESQKARHPQLLYESKLYKILQGGVGIPHIRWYGQEKDYNVLVMDLLGPSLEDLFNFCSRRFTMKTVLMLADQMISRIEYVHTKNFIHRDIKPDNFLMGIGRHCNKLFLIDFGLAKKY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"GEEVAVKLESQKARHPQLLYESKLYKILQGGVGIPHIRWYGQEKDYNVLVMDLLGPSLEDLFNFCSRRFTMKTVLMLADQMISRIEYVHTKNFIHRDIKPDNFLMGIGRHCNKLFLIDFGLAKKY\", \"result\": [{\"smile\": \"Nc1nc2c(-c3cc(Br)c(Br)[nH]3)nccc2[nH]1\", \"value\": 0.0}, {\"smile\": \"O=C1Cc2c([nH]c3ccc(Br)cc23)-c2ccccc2N1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Nc1nc2c(-c3cc(Br)c(Br)[nH]3)nccc2[nH]1' nor 'O=C1Cc2c([nH]c3ccc(Br)cc23)-c2ccccc2N1' can interact with the protein 'GEEVAVKLESQKARHPQLLYESKLYKILQGGVGIPHIRWYGQEKDYNVLVMDLLGPSLEDLFNFCSRRFTMKTVLMLADQMISRIEYVHTKNFIHRDIKPDNFLMGIGRHCNKLFLIDFGLAKKY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected interaction between the drug 'Cn1c(C(=O)Nc2ccc3c(c2)OCO3)cc2c(=O)n(C)c3ccccc3c21', 'CC/C(=C(/c1ccc(/C=C/C(=O)O)cc1)c1ccc2[nH]ncc2c1)c1ccc(F)cc1Cl' and the target protein 'MDIKNSPSSLNSPSSYNCSQSILPLEHGSIYIPSSYVDSHHEYPAMTFYSPAVMNYSIPSNVTNLEGGPGRQTTSPNVLWPTPGHLSPLVVHRQLSHLYAEPQKSPWCEARSLEHTLPVNRETLKRKVSGNRCASPVTGPGSKRDAHFCAVCSDYASGYHYGVWSCEGCKAFFKRSIQGHNDYICPATNQCTIDKNRRKSCQACRLRKCYEVGMVKCGSRRERCGYRLVRRQRSADEQLHCAGKAKRSGGHAPRVRELLLDALSPEQLVLTLLEAEPPHVLISRPSAPFTEASMMMSLTKLADKELVHMISWAKKIPGFVELSLFDQVRLLESCWMEVLMMGLMWRSIDHPGKLIFAPDLVLDRDEGKCVEGILEIFDMLLATTSRFRELKLQHKEYLCVKAMILLNSSMYPLVTATQDADSSRKLAHLLNAVTDALVWVIAKSGISSQQQSMRLANLLMLLSHVRHASNKGMEHLLNMKCKNVVPVYDLLLEMLNAHVLRGCKSSITGSECSPAEDSKSKEGSQNPQSQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1c(C(=O)Nc2ccc3c(c2)OCO3)cc2c(=O)n(C)c3ccccc3c21\", \"CC/C(=C(/c1ccc(/C=C/C(=O)O)cc1)c1ccc2[nH]ncc2c1)c1ccc(F)cc1Cl\"], \"target_seq\": \"MDIKNSPSSLNSPSSYNCSQSILPLEHGSIYIPSSYVDSHHEYPAMTFYSPAVMNYSIPSNVTNLEGGPGRQTTSPNVLWPTPGHLSPLVVHRQLSHLYAEPQKSPWCEARSLEHTLPVNRETLKRKVSGNRCASPVTGPGSKRDAHFCAVCSDYASGYHYGVWSCEGCKAFFKRSIQGHNDYICPATNQCTIDKNRRKSCQACRLRKCYEVGMVKCGSRRERCGYRLVRRQRSADEQLHCAGKAKRSGGHAPRVRELLLDALSPEQLVLTLLEAEPPHVLISRPSAPFTEASMMMSLTKLADKELVHMISWAKKIPGFVELSLFDQVRLLESCWMEVLMMGLMWRSIDHPGKLIFAPDLVLDRDEGKCVEGILEIFDMLLATTSRFRELKLQHKEYLCVKAMILLNSSMYPLVTATQDADSSRKLAHLLNAVTDALVWVIAKSGISSQQQSMRLANLLMLLSHVRHASNKGMEHLLNMKCKNVVPVYDLLLEMLNAHVLRGCKSSITGSECSPAEDSKSKEGSQNPQSQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDIKNSPSSLNSPSSYNCSQSILPLEHGSIYIPSSYVDSHHEYPAMTFYSPAVMNYSIPSNVTNLEGGPGRQTTSPNVLWPTPGHLSPLVVHRQLSHLYAEPQKSPWCEARSLEHTLPVNRETLKRKVSGNRCASPVTGPGSKRDAHFCAVCSDYASGYHYGVWSCEGCKAFFKRSIQGHNDYICPATNQCTIDKNRRKSCQACRLRKCYEVGMVKCGSRRERCGYRLVRRQRSADEQLHCAGKAKRSGGHAPRVRELLLDALSPEQLVLTLLEAEPPHVLISRPSAPFTEASMMMSLTKLADKELVHMISWAKKIPGFVELSLFDQVRLLESCWMEVLMMGLMWRSIDHPGKLIFAPDLVLDRDEGKCVEGILEIFDMLLATTSRFRELKLQHKEYLCVKAMILLNSSMYPLVTATQDADSSRKLAHLLNAVTDALVWVIAKSGISSQQQSMRLANLLMLLSHVRHASNKGMEHLLNMKCKNVVPVYDLLLEMLNAHVLRGCKSSITGSECSPAEDSKSKEGSQNPQSQ\", \"result\": [{\"smile\": \"Cn1c(C(=O)Nc2ccc3c(c2)OCO3)cc2c(=O)n(C)c3ccccc3c21\", \"value\": 1.0}, {\"smile\": \"CC/C(=C(/c1ccc(/C=C/C(=O)O)cc1)c1ccc2[nH]ncc2c1)c1ccc(F)cc1Cl\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'Cn1c(C(=O)Nc2ccc3c(c2)OCO3)cc2c(=O)n(C)c3ccccc3c21' and 'CC/C(=C(/c1ccc(/C=C/C(=O)O)cc1)c1ccc2[nH]ncc2c1)c1ccc(F)cc1Cl' can interact with the protein 'MDIKNSPSSLNSPSSYNCSQSILPLEHGSIYIPSSYVDSHHEYPAMTFYSPAVMNYSIPSNVTNLEGGPGRQTTSPNVLWPTPGHLSPLVVHRQLSHLYAEPQKSPWCEARSLEHTLPVNRETLKRKVSGNRCASPVTGPGSKRDAHFCAVCSDYASGYHYGVWSCEGCKAFFKRSIQGHNDYICPATNQCTIDKNRRKSCQACRLRKCYEVGMVKCGSRRERCGYRLVRRQRSADEQLHCAGKAKRSGGHAPRVRELLLDALSPEQLVLTLLEAEPPHVLISRPSAPFTEASMMMSLTKLADKELVHMISWAKKIPGFVELSLFDQVRLLESCWMEVLMMGLMWRSIDHPGKLIFAPDLVLDRDEGKCVEGILEIFDMLLATTSRFRELKLQHKEYLCVKAMILLNSSMYPLVTATQDADSSRKLAHLLNAVTDALVWVIAKSGISSQQQSMRLANLLMLLSHVRHASNKGMEHLLNMKCKNVVPVYDLLLEMLNAHVLRGCKSSITGSECSPAEDSKSKEGSQNPQSQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug 'C1=NC(=O)C2=C(N1)N(C=N2)C3C(C(C(O3)COP(=O)(O)O)O)O', 'C(=O)(C(=O)[O-])[O-].[Na+].[Na+]' and the target protein 'MSEFDELKYLTGFGNEHATSDPRVPDALPVGQNSPQKCSHGLYAEQLSGTAFTAPRSQNQRSWLYRIRPSVIHRPFEAMKENDQHWTNNFSSIPPNPNQYRWNPFPLPTKEGVTFVDNLYTVCGGGDVISRTGLAIHQFSCNASMEHTAMYNSDGDFLIVPQQGALEITTEFGRLLVNPQEIAVIPQGIRFSVAVRGPSRGYILEVYGTHFQLPDLGPIGANGLANPRDFEAPVAWFEDLDVEFTIINKYQGSWFQAKQGHSPFDVVGWHGNYVPYKYDLKKFMVINTVSFDHCDPSIFTVLTAPSVKHGTAIADFVIFPPRWGCADNTFRPPYYHRNCMSEYMGLITGCYEAKEGGFKPGGGSLHSMMTPHGPDFNCFEMASNADLKPQRVAEGTMSFMFESSLNMAITNWAVYQNVDKDYYKDWQPLKKHFTMPK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1=NC(=O)C2=C(N1)N(C=N2)C3C(C(C(O3)COP(=O)(O)O)O)O\", \"C(=O)(C(=O)[O-])[O-].[Na+].[Na+]\"], \"target_seq\": \"MSEFDELKYLTGFGNEHATSDPRVPDALPVGQNSPQKCSHGLYAEQLSGTAFTAPRSQNQRSWLYRIRPSVIHRPFEAMKENDQHWTNNFSSIPPNPNQYRWNPFPLPTKEGVTFVDNLYTVCGGGDVISRTGLAIHQFSCNASMEHTAMYNSDGDFLIVPQQGALEITTEFGRLLVNPQEIAVIPQGIRFSVAVRGPSRGYILEVYGTHFQLPDLGPIGANGLANPRDFEAPVAWFEDLDVEFTIINKYQGSWFQAKQGHSPFDVVGWHGNYVPYKYDLKKFMVINTVSFDHCDPSIFTVLTAPSVKHGTAIADFVIFPPRWGCADNTFRPPYYHRNCMSEYMGLITGCYEAKEGGFKPGGGSLHSMMTPHGPDFNCFEMASNADLKPQRVAEGTMSFMFESSLNMAITNWAVYQNVDKDYYKDWQPLKKHFTMPK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSEFDELKYLTGFGNEHATSDPRVPDALPVGQNSPQKCSHGLYAEQLSGTAFTAPRSQNQRSWLYRIRPSVIHRPFEAMKENDQHWTNNFSSIPPNPNQYRWNPFPLPTKEGVTFVDNLYTVCGGGDVISRTGLAIHQFSCNASMEHTAMYNSDGDFLIVPQQGALEITTEFGRLLVNPQEIAVIPQGIRFSVAVRGPSRGYILEVYGTHFQLPDLGPIGANGLANPRDFEAPVAWFEDLDVEFTIINKYQGSWFQAKQGHSPFDVVGWHGNYVPYKYDLKKFMVINTVSFDHCDPSIFTVLTAPSVKHGTAIADFVIFPPRWGCADNTFRPPYYHRNCMSEYMGLITGCYEAKEGGFKPGGGSLHSMMTPHGPDFNCFEMASNADLKPQRVAEGTMSFMFESSLNMAITNWAVYQNVDKDYYKDWQPLKKHFTMPK\", \"result\": [{\"smile\": \"C1=NC(=O)C2=C(N1)N(C=N2)C3C(C(C(O3)COP(=O)(O)O)O)O\", \"value\": 0.0}, {\"smile\": \"C(=O)(C(=O)[O-])[O-].[Na+].[Na+]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C1=NC(=O)C2=C(N1)N(C=N2)C3C(C(C(O3)COP(=O)(O)O)O)O' nor 'C(=O)(C(=O)[O-])[O-].[Na+].[Na+]' can interact with the protein 'MSEFDELKYLTGFGNEHATSDPRVPDALPVGQNSPQKCSHGLYAEQLSGTAFTAPRSQNQRSWLYRIRPSVIHRPFEAMKENDQHWTNNFSSIPPNPNQYRWNPFPLPTKEGVTFVDNLYTVCGGGDVISRTGLAIHQFSCNASMEHTAMYNSDGDFLIVPQQGALEITTEFGRLLVNPQEIAVIPQGIRFSVAVRGPSRGYILEVYGTHFQLPDLGPIGANGLANPRDFEAPVAWFEDLDVEFTIINKYQGSWFQAKQGHSPFDVVGWHGNYVPYKYDLKKFMVINTVSFDHCDPSIFTVLTAPSVKHGTAIADFVIFPPRWGCADNTFRPPYYHRNCMSEYMGLITGCYEAKEGGFKPGGGSLHSMMTPHGPDFNCFEMASNADLKPQRVAEGTMSFMFESSLNMAITNWAVYQNVDKDYYKDWQPLKKHFTMPK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be any interaction between the drug 'Cc1cc(Cl)ccc1C1c2c(nn(-c3ccc(C#N)cc3)c2C(C)C)C(=O)N1c1cccc(Cl)c1F', 'CCCN(C(C)=O)C1CCC(N2C(=O)Cc3cc(OC)c(OC(C)C)cc3C2c2ccc(Cl)cc2)CC1' and the target protein 'MTSFSTSAQCSTSDSACRISPGQINQVRPKLPLLKILHAAGAQGEMFTVKEVMHYLGQYIMVKQLYDQQEQHMVYCGGDLLGELLGRQSFSVKDPSPLYDMLRKNLVTLATATTDAAQTLALAQDHSMDIPSQDQLKQSAEESSTSRKRTTEDDIPTLPTSEHKCIHSREDEDLIENLAQDETSRLDLGFEEWDVAGLPWWFLGNLRSNYTPRSNGSTDLQTNQDVGTAIVSDTTDDLWFLNESVSEQLGVGIKVEAADTEQTSEEVGKVSDKKVIEVGKNDDLEDSKSLSDDTDVEVTSEDEWQCTECKKFNSPSKRYCFRCWALRKDWYSDCSKLTHSLSTSDITAIPEKENEGNDVPDCRRTISAPVVRPKDAYIKKENSKLFDPCNSVEFLDLAHSSESQETISSMGEQLDNLSEQRTDTENMEDCQNLLKPCSLCEKRPRDGNIIHGRTGHLVTCFHCARRLKKAGASCPICKKEIQLVIKVFIA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(Cl)ccc1C1c2c(nn(-c3ccc(C#N)cc3)c2C(C)C)C(=O)N1c1cccc(Cl)c1F\", \"CCCN(C(C)=O)C1CCC(N2C(=O)Cc3cc(OC)c(OC(C)C)cc3C2c2ccc(Cl)cc2)CC1\"], \"target_seq\": \"MTSFSTSAQCSTSDSACRISPGQINQVRPKLPLLKILHAAGAQGEMFTVKEVMHYLGQYIMVKQLYDQQEQHMVYCGGDLLGELLGRQSFSVKDPSPLYDMLRKNLVTLATATTDAAQTLALAQDHSMDIPSQDQLKQSAEESSTSRKRTTEDDIPTLPTSEHKCIHSREDEDLIENLAQDETSRLDLGFEEWDVAGLPWWFLGNLRSNYTPRSNGSTDLQTNQDVGTAIVSDTTDDLWFLNESVSEQLGVGIKVEAADTEQTSEEVGKVSDKKVIEVGKNDDLEDSKSLSDDTDVEVTSEDEWQCTECKKFNSPSKRYCFRCWALRKDWYSDCSKLTHSLSTSDITAIPEKENEGNDVPDCRRTISAPVVRPKDAYIKKENSKLFDPCNSVEFLDLAHSSESQETISSMGEQLDNLSEQRTDTENMEDCQNLLKPCSLCEKRPRDGNIIHGRTGHLVTCFHCARRLKKAGASCPICKKEIQLVIKVFIA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTSFSTSAQCSTSDSACRISPGQINQVRPKLPLLKILHAAGAQGEMFTVKEVMHYLGQYIMVKQLYDQQEQHMVYCGGDLLGELLGRQSFSVKDPSPLYDMLRKNLVTLATATTDAAQTLALAQDHSMDIPSQDQLKQSAEESSTSRKRTTEDDIPTLPTSEHKCIHSREDEDLIENLAQDETSRLDLGFEEWDVAGLPWWFLGNLRSNYTPRSNGSTDLQTNQDVGTAIVSDTTDDLWFLNESVSEQLGVGIKVEAADTEQTSEEVGKVSDKKVIEVGKNDDLEDSKSLSDDTDVEVTSEDEWQCTECKKFNSPSKRYCFRCWALRKDWYSDCSKLTHSLSTSDITAIPEKENEGNDVPDCRRTISAPVVRPKDAYIKKENSKLFDPCNSVEFLDLAHSSESQETISSMGEQLDNLSEQRTDTENMEDCQNLLKPCSLCEKRPRDGNIIHGRTGHLVTCFHCARRLKKAGASCPICKKEIQLVIKVFIA\", \"result\": [{\"smile\": \"Cc1cc(Cl)ccc1C1c2c(nn(-c3ccc(C#N)cc3)c2C(C)C)C(=O)N1c1cccc(Cl)c1F\", \"value\": 0.0}, {\"smile\": \"CCCN(C(C)=O)C1CCC(N2C(=O)Cc3cc(OC)c(OC(C)C)cc3C2c2ccc(Cl)cc2)CC1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cc1cc(Cl)ccc1C1c2c(nn(-c3ccc(C#N)cc3)c2C(C)C)C(=O)N1c1cccc(Cl)c1F' nor 'CCCN(C(C)=O)C1CCC(N2C(=O)Cc3cc(OC)c(OC(C)C)cc3C2c2ccc(Cl)cc2)CC1' can interact with the protein 'MTSFSTSAQCSTSDSACRISPGQINQVRPKLPLLKILHAAGAQGEMFTVKEVMHYLGQYIMVKQLYDQQEQHMVYCGGDLLGELLGRQSFSVKDPSPLYDMLRKNLVTLATATTDAAQTLALAQDHSMDIPSQDQLKQSAEESSTSRKRTTEDDIPTLPTSEHKCIHSREDEDLIENLAQDETSRLDLGFEEWDVAGLPWWFLGNLRSNYTPRSNGSTDLQTNQDVGTAIVSDTTDDLWFLNESVSEQLGVGIKVEAADTEQTSEEVGKVSDKKVIEVGKNDDLEDSKSLSDDTDVEVTSEDEWQCTECKKFNSPSKRYCFRCWALRKDWYSDCSKLTHSLSTSDITAIPEKENEGNDVPDCRRTISAPVVRPKDAYIKKENSKLFDPCNSVEFLDLAHSSESQETISSMGEQLDNLSEQRTDTENMEDCQNLLKPCSLCEKRPRDGNIIHGRTGHLVTCFHCARRLKKAGASCPICKKEIQLVIKVFIA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you view the chances of the drug '[H][C@]1(C)[C@]([H])(C(C)C)C2=C(C=C(C)C(=C2)C(=O)CCC(O)=O)C1(C)C', '[H][C@]12C(=O)N(C(=O)[C@@]1([H])[C@@]1(CC[C@@]2([H])CC1)NC(=O)OCC(O)=O)C1=CC=C(NC(C)=O)C=C1' interacting with the target protein 'RTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]1(C)[C@]([H])(C(C)C)C2=C(C=C(C)C(=C2)C(=O)CCC(O)=O)C1(C)C\", \"[H][C@]12C(=O)N(C(=O)[C@@]1([H])[C@@]1(CC[C@@]2([H])CC1)NC(=O)OCC(O)=O)C1=CC=C(NC(C)=O)C=C1\"], \"target_seq\": \"RTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"RTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC\", \"result\": [{\"smile\": \"[H][C@]1(C)[C@]([H])(C(C)C)C2=C(C=C(C)C(=C2)C(=O)CCC(O)=O)C1(C)C\", \"value\": 1.0}, {\"smile\": \"[H][C@]12C(=O)N(C(=O)[C@@]1([H])[C@@]1(CC[C@@]2([H])CC1)NC(=O)OCC(O)=O)C1=CC=C(NC(C)=O)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs '[H][C@]1(C)[C@]([H])(C(C)C)C2=C(C=C(C)C(=C2)C(=O)CCC(O)=O)C1(C)C' and '[H][C@]12C(=O)N(C(=O)[C@@]1([H])[C@@]1(CC[C@@]2([H])CC1)NC(=O)OCC(O)=O)C1=CC=C(NC(C)=O)C=C1' can interact with the protein 'RTVAAPSVFIFPPSDEQLKSGTASVVCLLNNFYPREAKVQWKVDNALQSGNSQESVTEQDSKDSTYSLSSTLTLSKADYEKHKVYACEVTHQGLSSPVTKSFNRGEC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction for the drug 'CCOCCn1cnc(-c2ccc(F)cc2)c1-c1ccc2ncc(C#N)n2c1', 'Fc1ccc(-c2ncn(CC(F)F)c2-c2ccc3nccn3c2)cc1Cl''s interaction potential with the target protein 'MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCOCCn1cnc(-c2ccc(F)cc2)c1-c1ccc2ncc(C#N)n2c1\", \"Fc1ccc(-c2ncn(CC(F)F)c2-c2ccc3nccn3c2)cc1Cl\"], \"target_seq\": \"MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK\", \"result\": [{\"smile\": \"CCOCCn1cnc(-c2ccc(F)cc2)c1-c1ccc2ncc(C#N)n2c1\", \"value\": 0.0}, {\"smile\": \"Fc1ccc(-c2ncn(CC(F)F)c2-c2ccc3nccn3c2)cc1Cl\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCOCCn1cnc(-c2ccc(F)cc2)c1-c1ccc2ncc(C#N)n2c1' nor 'Fc1ccc(-c2ncn(CC(F)F)c2-c2ccc3nccn3c2)cc1Cl' can interact with the protein 'MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'O=C1c2cccc3c2[C@H](CCC3)CN1[C@@H]1CN2CCC1CC2', '[H][C@]1(COS(=O)(=O)NCC)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])O' interact with the target protein 'MLLWVQQALLALLLPTLLAQGEARRSRNTTRPALLRLSDYLLTNYRKGVRPVRDWRKPTTVSIDVIVYAILNVDEKNQVLTTYIWYRQYWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYIRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDRSVFMNQGEWELLGVLPYFREFSMESSNYYAEMKFYVVIRRRPLFYVVSLLLPSIFLMVMDIVGFYLPPNSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLIGVYFVVCMALLVISLAETIFIVRLVHKQDLQQPVPAWLRHLVLERIAWLLCLREQSTSQRPPATSQATKTDDCSAMGNHCSHMGGPQDFEKSPRDRCSPPPPPREASLAVCGLLQELSSIRQFLEKRDEIREVARDWLRVGSVLDKLLFHIYLLAVLAYSITLVMLWSIWQYA' as expected? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1c2cccc3c2[C@H](CCC3)CN1[C@@H]1CN2CCC1CC2\", \"[H][C@]1(COS(=O)(=O)NCC)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])O\"], \"target_seq\": \"MLLWVQQALLALLLPTLLAQGEARRSRNTTRPALLRLSDYLLTNYRKGVRPVRDWRKPTTVSIDVIVYAILNVDEKNQVLTTYIWYRQYWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYIRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDRSVFMNQGEWELLGVLPYFREFSMESSNYYAEMKFYVVIRRRPLFYVVSLLLPSIFLMVMDIVGFYLPPNSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLIGVYFVVCMALLVISLAETIFIVRLVHKQDLQQPVPAWLRHLVLERIAWLLCLREQSTSQRPPATSQATKTDDCSAMGNHCSHMGGPQDFEKSPRDRCSPPPPPREASLAVCGLLQELSSIRQFLEKRDEIREVARDWLRVGSVLDKLLFHIYLLAVLAYSITLVMLWSIWQYA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLLWVQQALLALLLPTLLAQGEARRSRNTTRPALLRLSDYLLTNYRKGVRPVRDWRKPTTVSIDVIVYAILNVDEKNQVLTTYIWYRQYWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYIRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDRSVFMNQGEWELLGVLPYFREFSMESSNYYAEMKFYVVIRRRPLFYVVSLLLPSIFLMVMDIVGFYLPPNSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLIGVYFVVCMALLVISLAETIFIVRLVHKQDLQQPVPAWLRHLVLERIAWLLCLREQSTSQRPPATSQATKTDDCSAMGNHCSHMGGPQDFEKSPRDRCSPPPPPREASLAVCGLLQELSSIRQFLEKRDEIREVARDWLRVGSVLDKLLFHIYLLAVLAYSITLVMLWSIWQYA\", \"result\": [{\"smile\": \"O=C1c2cccc3c2[C@H](CCC3)CN1[C@@H]1CN2CCC1CC2\", \"value\": 1.0}, {\"smile\": \"[H][C@]1(COS(=O)(=O)NCC)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'O=C1c2cccc3c2[C@H](CCC3)CN1[C@@H]1CN2CCC1CC2' can interact with the protein, while the second one '[H][C@]1(COS(=O)(=O)NCC)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(O)[C@]1([H])O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the drug 'COc1ccc(C(=O)NCc2cccc(C(=O)Nc3ccc4c(c3)CNCC4)c2)cc1OC', 'CC(=O)Nc1cn2cc(-c3cnc(N)c(C(F)(F)F)c3)ccc2n1''s potential to interact with the target protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(C(=O)NCc2cccc(C(=O)Nc3ccc4c(c3)CNCC4)c2)cc1OC\", \"CC(=O)Nc1cn2cc(-c3cnc(N)c(C(F)(F)F)c3)ccc2n1\"], \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"COc1ccc(C(=O)NCc2cccc(C(=O)Nc3ccc4c(c3)CNCC4)c2)cc1OC\", \"value\": 0.0}, {\"smile\": \"CC(=O)Nc1cn2cc(-c3cnc(N)c(C(F)(F)F)c3)ccc2n1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'COc1ccc(C(=O)NCc2cccc(C(=O)Nc3ccc4c(c3)CNCC4)c2)cc1OC' nor 'CC(=O)Nc1cn2cc(-c3cnc(N)c(C(F)(F)F)c3)ccc2n1' can interact with the protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess the likelihood of interaction between the drug 'COc1ccc2nccc(C(=O)NCC(=O)N3CCC[C@H]3C#N)c2c1', 'N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12' and the target protein 'MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2nccc(C(=O)NCC(=O)N3CCC[C@H]3C#N)c2c1\", \"N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12\"], \"target_seq\": \"MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD\", \"result\": [{\"smile\": \"COc1ccc2nccc(C(=O)NCC(=O)N3CCC[C@H]3C#N)c2c1\", \"value\": 1.0}, {\"smile\": \"N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'COc1ccc2nccc(C(=O)NCC(=O)N3CCC[C@H]3C#N)c2c1' and 'N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12' can interact with the protein 'MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction between the drug '[H][C@@]1(C)O[C@@]([H])(O[C@@]2([H])[C@]([H])(O[C@@]3([H])[C@]([H])(O)[C@@]([H])(CO)O[C@@]([H])(O)[C@]3([H])N=C(C)O)O[C@]([H])(CO)[C@]([H])(O)[C@]2([H])O)[C@@]([H])(O)[C@]([H])(O)[C@]1([H])O', 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]' and the target protein 'MAAAAVARLWWRGILGASALTRGTGRPSVLLLPVRRESAGADTRPTVRPRNDVAHKQLSAFGEYVAEILPKYVQQVQVSCFNELEVCIHPDGVIPVLTFLRDHTNAQFKSLVDLTAVDVPTRQNRFEIVYNLLSLRFNSRIRVKTYTDELTPIESAVSVFKAANWYEREIWDMFGVFFANHPDLRRILTDYGFEGHPFRKDFPLSGYVELRYDDEVKRVVAEPVELAQEFRKFDLNSPWEAFPVYRQPPESLKLEAGDKKPDAK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]1(C)O[C@@]([H])(O[C@@]2([H])[C@]([H])(O[C@@]3([H])[C@]([H])(O)[C@@]([H])(CO)O[C@@]([H])(O)[C@]3([H])N=C(C)O)O[C@]([H])(CO)[C@]([H])(O)[C@]2([H])O)[C@@]([H])(O)[C@]([H])(O)[C@]1([H])O\", \"C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]\"], \"target_seq\": \"MAAAAVARLWWRGILGASALTRGTGRPSVLLLPVRRESAGADTRPTVRPRNDVAHKQLSAFGEYVAEILPKYVQQVQVSCFNELEVCIHPDGVIPVLTFLRDHTNAQFKSLVDLTAVDVPTRQNRFEIVYNLLSLRFNSRIRVKTYTDELTPIESAVSVFKAANWYEREIWDMFGVFFANHPDLRRILTDYGFEGHPFRKDFPLSGYVELRYDDEVKRVVAEPVELAQEFRKFDLNSPWEAFPVYRQPPESLKLEAGDKKPDAK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAAAVARLWWRGILGASALTRGTGRPSVLLLPVRRESAGADTRPTVRPRNDVAHKQLSAFGEYVAEILPKYVQQVQVSCFNELEVCIHPDGVIPVLTFLRDHTNAQFKSLVDLTAVDVPTRQNRFEIVYNLLSLRFNSRIRVKTYTDELTPIESAVSVFKAANWYEREIWDMFGVFFANHPDLRRILTDYGFEGHPFRKDFPLSGYVELRYDDEVKRVVAEPVELAQEFRKFDLNSPWEAFPVYRQPPESLKLEAGDKKPDAK\", \"result\": [{\"smile\": \"[H][C@@]1(C)O[C@@]([H])(O[C@@]2([H])[C@]([H])(O[C@@]3([H])[C@]([H])(O)[C@@]([H])(CO)O[C@@]([H])(O)[C@]3([H])N=C(C)O)O[C@]([H])(CO)[C@]([H])(O)[C@]2([H])O)[C@@]([H])(O)[C@]([H])(O)[C@]1([H])O\", \"value\": 0.0}, {\"smile\": \"C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither '[H][C@@]1(C)O[C@@]([H])(O[C@@]2([H])[C@]([H])(O[C@@]3([H])[C@]([H])(O)[C@@]([H])(CO)O[C@@]([H])(O)[C@]3([H])N=C(C)O)O[C@]([H])(CO)[C@]([H])(O)[C@]2([H])O)[C@@]([H])(O)[C@]([H])(O)[C@]1([H])O' nor 'C(C(=O)[O-])C(CC(=O)[O-])(C(=O)[O-])O.[67Ga+3]' can interact with the protein 'MAAAAVARLWWRGILGASALTRGTGRPSVLLLPVRRESAGADTRPTVRPRNDVAHKQLSAFGEYVAEILPKYVQQVQVSCFNELEVCIHPDGVIPVLTFLRDHTNAQFKSLVDLTAVDVPTRQNRFEIVYNLLSLRFNSRIRVKTYTDELTPIESAVSVFKAANWYEREIWDMFGVFFANHPDLRRILTDYGFEGHPFRKDFPLSGYVELRYDDEVKRVVAEPVELAQEFRKFDLNSPWEAFPVYRQPPESLKLEAGDKKPDAK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'COC(F)(F)C(Cl)Cl', 'CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1' will behave towards the target protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARLRFSIN'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC(F)(F)C(Cl)Cl\", \"CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1\"], \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARLRFSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARLRFSIN\", \"result\": [{\"smile\": \"COC(F)(F)C(Cl)Cl\", \"value\": 1.0}, {\"smile\": \"CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'COC(F)(F)C(Cl)Cl' and 'CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1' can interact with the protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARLRFSIN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the chances of interaction between the drug 'C[C@]1(c2cc(/C=C(\\F)c3cnc(C#N)cn3)ccc2F)N=C(N)S[C@@]2(C(=O)N3CCCCC3)C[C@H]21', 'CN(C)C(=O)[C@]12C[C@H]1[C@@](C)(c1cc(/C=C(\\F)c3ccc(C#N)cc3)ccc1F)N=C(N)S2' and the target protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@]1(c2cc(/C=C(\\F)c3cnc(C#N)cn3)ccc2F)N=C(N)S[C@@]2(C(=O)N3CCCCC3)C[C@H]21\", \"CN(C)C(=O)[C@]12C[C@H]1[C@@](C)(c1cc(/C=C(\\F)c3ccc(C#N)cc3)ccc1F)N=C(N)S2\"], \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\", \"result\": [{\"smile\": \"C[C@]1(c2cc(/C=C(\\F)c3cnc(C#N)cn3)ccc2F)N=C(N)S[C@@]2(C(=O)N3CCCCC3)C[C@H]21\", \"value\": 0.0}, {\"smile\": \"CN(C)C(=O)[C@]12C[C@H]1[C@@](C)(c1cc(/C=C(\\F)c3ccc(C#N)cc3)ccc1F)N=C(N)S2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C[C@]1(c2cc(/C=C(\\F)c3cnc(C#N)cn3)ccc2F)N=C(N)S[C@@]2(C(=O)N3CCCCC3)C[C@H]21' nor 'CN(C)C(=O)[C@]12C[C@H]1[C@@](C)(c1cc(/C=C(\\F)c3ccc(C#N)cc3)ccc1F)N=C(N)S2' can interact with the protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CNc1ncc2cc(-c3cc(C(=O)Nc4cc(C(F)(F)F)ccc4OC4CCN(C)CC4)ccc3C)ccc2n1', 'O=C(O)c1nc2ccccc2c2[nH]c3c(Br)cccc3c12' potentially engage with the target protein 'MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CNc1ncc2cc(-c3cc(C(=O)Nc4cc(C(F)(F)F)ccc4OC4CCN(C)CC4)ccc3C)ccc2n1\", \"O=C(O)c1nc2ccccc2c2[nH]c3c(Br)cccc3c12\"], \"target_seq\": \"MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP\", \"result\": [{\"smile\": \"CNc1ncc2cc(-c3cc(C(=O)Nc4cc(C(F)(F)F)ccc4OC4CCN(C)CC4)ccc3C)ccc2n1\", \"value\": 0.0}, {\"smile\": \"O=C(O)c1nc2ccccc2c2[nH]c3c(Br)cccc3c12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CNc1ncc2cc(-c3cc(C(=O)Nc4cc(C(F)(F)F)ccc4OC4CCN(C)CC4)ccc3C)ccc2n1' nor 'O=C(O)c1nc2ccccc2c2[nH]c3c(Br)cccc3c12' can interact with the protein 'MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the potential interaction of the drug 'C#CCN(Cc1ccc2nc(C)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1', 'C#CCN(Cc1ccc2nc(N)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1' with the target protein 'MTAFLWAQDRDGLIGKDGHLPWHLPDDLHYFRAQTVGKIMVVGRRTYESFPKRPLPERTNVVLTHQEDYQAQGAVVVHDVAAVFAYAKQHPDQELVIAGGAQIFTAFKDDVDTLLVTRLAGSFEGDTKMIPLNWDDFTKVSSRTVEDTNPALTHTYEVWQKKA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C#CCN(Cc1ccc2nc(C)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1\", \"C#CCN(Cc1ccc2nc(N)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1\"], \"target_seq\": \"MTAFLWAQDRDGLIGKDGHLPWHLPDDLHYFRAQTVGKIMVVGRRTYESFPKRPLPERTNVVLTHQEDYQAQGAVVVHDVAAVFAYAKQHPDQELVIAGGAQIFTAFKDDVDTLLVTRLAGSFEGDTKMIPLNWDDFTKVSSRTVEDTNPALTHTYEVWQKKA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTAFLWAQDRDGLIGKDGHLPWHLPDDLHYFRAQTVGKIMVVGRRTYESFPKRPLPERTNVVLTHQEDYQAQGAVVVHDVAAVFAYAKQHPDQELVIAGGAQIFTAFKDDVDTLLVTRLAGSFEGDTKMIPLNWDDFTKVSSRTVEDTNPALTHTYEVWQKKA\", \"result\": [{\"smile\": \"C#CCN(Cc1ccc2nc(C)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1\", \"value\": 0.0}, {\"smile\": \"C#CCN(Cc1ccc2nc(N)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'C#CCN(Cc1ccc2nc(N)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1' can interact with the protein, while the first one 'C#CCN(Cc1ccc2nc(C)[nH]c(=O)c2c1)c1ccc(C(=O)NC(CCC(=O)O)C(=O)O)cc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a forecast for the interaction likelihood between the drug 'O=C(O)CC1CCC2(CCN(c3ccc(-c4nc5cc(C(F)(F)F)ccc5[nH]4)cn3)CC2)O1', 'C[C@@H](O[C@H]1CN2C(=O)[C@H]3C[C@H](N)C[C@H]3[C@H]2[C@@H]1c1ccc(F)cc1)c1cc(C(F)(F)F)cc(C(F)(F)F)c1' and the target protein 'MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)CC1CCC2(CCN(c3ccc(-c4nc5cc(C(F)(F)F)ccc5[nH]4)cn3)CC2)O1\", \"C[C@@H](O[C@H]1CN2C(=O)[C@H]3C[C@H](N)C[C@H]3[C@H]2[C@@H]1c1ccc(F)cc1)c1cc(C(F)(F)F)cc(C(F)(F)F)c1\"], \"target_seq\": \"MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS\", \"result\": [{\"smile\": \"O=C(O)CC1CCC2(CCN(c3ccc(-c4nc5cc(C(F)(F)F)ccc5[nH]4)cn3)CC2)O1\", \"value\": 0.0}, {\"smile\": \"C[C@@H](O[C@H]1CN2C(=O)[C@H]3C[C@H](N)C[C@H]3[C@H]2[C@@H]1c1ccc(F)cc1)c1cc(C(F)(F)F)cc(C(F)(F)F)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'O=C(O)CC1CCC2(CCN(c3ccc(-c4nc5cc(C(F)(F)F)ccc5[nH]4)cn3)CC2)O1' nor 'C[C@@H](O[C@H]1CN2C(=O)[C@H]3C[C@H](N)C[C@H]3[C@H]2[C@@H]1c1ccc(F)cc1)c1cc(C(F)(F)F)cc(C(F)(F)F)c1' can interact with the protein 'MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think the drug 'CCCS(=O)(=O)N1CCC(CNC(=O)c2ccccc2C)(c2ccccn2)CC1', 'CCCS(=O)(=O)N1CCC(CCOC)(CNC(=O)c2ccc(Cl)cc2Cl)CC1''s interaction potential with the target protein 'MATKEKLQCLKDFHKDILKPSPGKSPGTRPEDEAEGKPPQREKWSSKIDFVLSVAGGFVGLGNVWRFPYLCYKNGGGAFLIPYFIFLFGSGLPVFFLEIIIGQYTSEGGITCWEKICPLFSGIGYASVVIVSLLNVYYIVILAWATYYLFQSFQKELPWAHCNHSWNTPHCMEDTMRKNKSVWITISSTNFTSPVIEFWERNVLSLSPGIDHPGSLKWDLALCLLLVWLVCFFCIWKGVRSTGKVVYFTATFPFAMLLVLLVRGLTLPGAGAGIKFYLYPDITRLEDPQVWIDAGTQIFFSYAICLGAMTSLGSYNKYKYNSYRDCMLLGCLNSGTSFVSGFAIFSILGFMAQEQGVDIADVAESGPGLAFIAYPKAVTMMPLPTFWSILFFIMLLLLGLDSQFVEVEGQITSLVDLYPSFLRKGYRREIFIAFVCSISYLLGLTMVTEGGMYVFQLFDYYAASGVCLLWVAFFECFVIAWIYGGDNLYDGIEDMIGYRPGPWMKYSWAVITPVLCVGCFIFSLVKYVPLTYNKTYVYPNWAIGLGWSLALSSMLCVPLVIVIRLCQTEGPFLVRVKYLLTPREPNRWAVEREGATPYNSRTVMNGALVKPTHIIVETMM' will be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCS(=O)(=O)N1CCC(CNC(=O)c2ccccc2C)(c2ccccn2)CC1\", \"CCCS(=O)(=O)N1CCC(CCOC)(CNC(=O)c2ccc(Cl)cc2Cl)CC1\"], \"target_seq\": \"MATKEKLQCLKDFHKDILKPSPGKSPGTRPEDEAEGKPPQREKWSSKIDFVLSVAGGFVGLGNVWRFPYLCYKNGGGAFLIPYFIFLFGSGLPVFFLEIIIGQYTSEGGITCWEKICPLFSGIGYASVVIVSLLNVYYIVILAWATYYLFQSFQKELPWAHCNHSWNTPHCMEDTMRKNKSVWITISSTNFTSPVIEFWERNVLSLSPGIDHPGSLKWDLALCLLLVWLVCFFCIWKGVRSTGKVVYFTATFPFAMLLVLLVRGLTLPGAGAGIKFYLYPDITRLEDPQVWIDAGTQIFFSYAICLGAMTSLGSYNKYKYNSYRDCMLLGCLNSGTSFVSGFAIFSILGFMAQEQGVDIADVAESGPGLAFIAYPKAVTMMPLPTFWSILFFIMLLLLGLDSQFVEVEGQITSLVDLYPSFLRKGYRREIFIAFVCSISYLLGLTMVTEGGMYVFQLFDYYAASGVCLLWVAFFECFVIAWIYGGDNLYDGIEDMIGYRPGPWMKYSWAVITPVLCVGCFIFSLVKYVPLTYNKTYVYPNWAIGLGWSLALSSMLCVPLVIVIRLCQTEGPFLVRVKYLLTPREPNRWAVEREGATPYNSRTVMNGALVKPTHIIVETMM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATKEKLQCLKDFHKDILKPSPGKSPGTRPEDEAEGKPPQREKWSSKIDFVLSVAGGFVGLGNVWRFPYLCYKNGGGAFLIPYFIFLFGSGLPVFFLEIIIGQYTSEGGITCWEKICPLFSGIGYASVVIVSLLNVYYIVILAWATYYLFQSFQKELPWAHCNHSWNTPHCMEDTMRKNKSVWITISSTNFTSPVIEFWERNVLSLSPGIDHPGSLKWDLALCLLLVWLVCFFCIWKGVRSTGKVVYFTATFPFAMLLVLLVRGLTLPGAGAGIKFYLYPDITRLEDPQVWIDAGTQIFFSYAICLGAMTSLGSYNKYKYNSYRDCMLLGCLNSGTSFVSGFAIFSILGFMAQEQGVDIADVAESGPGLAFIAYPKAVTMMPLPTFWSILFFIMLLLLGLDSQFVEVEGQITSLVDLYPSFLRKGYRREIFIAFVCSISYLLGLTMVTEGGMYVFQLFDYYAASGVCLLWVAFFECFVIAWIYGGDNLYDGIEDMIGYRPGPWMKYSWAVITPVLCVGCFIFSLVKYVPLTYNKTYVYPNWAIGLGWSLALSSMLCVPLVIVIRLCQTEGPFLVRVKYLLTPREPNRWAVEREGATPYNSRTVMNGALVKPTHIIVETMM\", \"result\": [{\"smile\": \"CCCS(=O)(=O)N1CCC(CNC(=O)c2ccccc2C)(c2ccccn2)CC1\", \"value\": 0.0}, {\"smile\": \"CCCS(=O)(=O)N1CCC(CCOC)(CNC(=O)c2ccc(Cl)cc2Cl)CC1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCCS(=O)(=O)N1CCC(CNC(=O)c2ccccc2C)(c2ccccn2)CC1' nor 'CCCS(=O)(=O)N1CCC(CCOC)(CNC(=O)c2ccc(Cl)cc2Cl)CC1' can interact with the protein 'MATKEKLQCLKDFHKDILKPSPGKSPGTRPEDEAEGKPPQREKWSSKIDFVLSVAGGFVGLGNVWRFPYLCYKNGGGAFLIPYFIFLFGSGLPVFFLEIIIGQYTSEGGITCWEKICPLFSGIGYASVVIVSLLNVYYIVILAWATYYLFQSFQKELPWAHCNHSWNTPHCMEDTMRKNKSVWITISSTNFTSPVIEFWERNVLSLSPGIDHPGSLKWDLALCLLLVWLVCFFCIWKGVRSTGKVVYFTATFPFAMLLVLLVRGLTLPGAGAGIKFYLYPDITRLEDPQVWIDAGTQIFFSYAICLGAMTSLGSYNKYKYNSYRDCMLLGCLNSGTSFVSGFAIFSILGFMAQEQGVDIADVAESGPGLAFIAYPKAVTMMPLPTFWSILFFIMLLLLGLDSQFVEVEGQITSLVDLYPSFLRKGYRREIFIAFVCSISYLLGLTMVTEGGMYVFQLFDYYAASGVCLLWVAFFECFVIAWIYGGDNLYDGIEDMIGYRPGPWMKYSWAVITPVLCVGCFIFSLVKYVPLTYNKTYVYPNWAIGLGWSLALSSMLCVPLVIVIRLCQTEGPFLVRVKYLLTPREPNRWAVEREGATPYNSRTVMNGALVKPTHIIVETMM'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that the drug 'O=c1[nH]c(-c2ccc(C(F)(F)F)cc2)nc2c1CSCC2', 'O=C(c1ccc(F)cc1)C1CCN(CC(=O)N(Cc2nc3c(c(=O)[nH]2)COCC3)CC(F)F)CC1' will interact with the target protein 'MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1[nH]c(-c2ccc(C(F)(F)F)cc2)nc2c1CSCC2\", \"O=C(c1ccc(F)cc1)C1CCN(CC(=O)N(Cc2nc3c(c(=O)[nH]2)COCC3)CC(F)F)CC1\"], \"target_seq\": \"MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG\", \"result\": [{\"smile\": \"O=c1[nH]c(-c2ccc(C(F)(F)F)cc2)nc2c1CSCC2\", \"value\": 1.0}, {\"smile\": \"O=C(c1ccc(F)cc1)C1CCN(CC(=O)N(Cc2nc3c(c(=O)[nH]2)COCC3)CC(F)F)CC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'O=c1[nH]c(-c2ccc(C(F)(F)F)cc2)nc2c1CSCC2' and 'O=C(c1ccc(F)cc1)C1CCN(CC(=O)N(Cc2nc3c(c(=O)[nH]2)COCC3)CC(F)F)CC1' can interact with the protein 'MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccccc4Cl)n3)c3ccccc23)cc1', 'CC(C)c1cc(NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5[nH]ncc5c4)n3)c3ccccc23)n(-c2cccc(OCCN3CCOCC3)c2)n1' interact with the target protein 'MSQERPTFYRQELNKTIWEVPERYQNLSPVGSGAYGSVCAAFDTKTGLRVAVKKLSRPFQSIIHAKRTYRELRLLKHMKHENVIGLLDVFTPARSLEEFNDVYLVTHLMGADLNNIVKCQKLTDDHVQFLIYQILRGLKYIHSADIIHRDLKPSNLAVNEDCELKILDFGLARHTDDEMTGYVATRWYRAPEIMLNWMHYNQTVDIWSVGCIMAELLTGRTLFPGTDHIDQLKLILRLVGTPGAELLKKISSESARNYIQSLTQMPKMNFANVFIGANPLAVDLLEKMLVLDSDKRITAAQALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES' according to your prediction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccccc4Cl)n3)c3ccccc23)cc1\", \"CC(C)c1cc(NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5[nH]ncc5c4)n3)c3ccccc23)n(-c2cccc(OCCN3CCOCC3)c2)n1\"], \"target_seq\": \"MSQERPTFYRQELNKTIWEVPERYQNLSPVGSGAYGSVCAAFDTKTGLRVAVKKLSRPFQSIIHAKRTYRELRLLKHMKHENVIGLLDVFTPARSLEEFNDVYLVTHLMGADLNNIVKCQKLTDDHVQFLIYQILRGLKYIHSADIIHRDLKPSNLAVNEDCELKILDFGLARHTDDEMTGYVATRWYRAPEIMLNWMHYNQTVDIWSVGCIMAELLTGRTLFPGTDHIDQLKLILRLVGTPGAELLKKISSESARNYIQSLTQMPKMNFANVFIGANPLAVDLLEKMLVLDSDKRITAAQALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSQERPTFYRQELNKTIWEVPERYQNLSPVGSGAYGSVCAAFDTKTGLRVAVKKLSRPFQSIIHAKRTYRELRLLKHMKHENVIGLLDVFTPARSLEEFNDVYLVTHLMGADLNNIVKCQKLTDDHVQFLIYQILRGLKYIHSADIIHRDLKPSNLAVNEDCELKILDFGLARHTDDEMTGYVATRWYRAPEIMLNWMHYNQTVDIWSVGCIMAELLTGRTLFPGTDHIDQLKLILRLVGTPGAELLKKISSESARNYIQSLTQMPKMNFANVFIGANPLAVDLLEKMLVLDSDKRITAAQALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES\", \"result\": [{\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccccc4Cl)n3)c3ccccc23)cc1\", \"value\": 1.0}, {\"smile\": \"CC(C)c1cc(NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5[nH]ncc5c4)n3)c3ccccc23)n(-c2cccc(OCCN3CCOCC3)c2)n1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(Oc3ccnc(Nc4ccccc4Cl)n3)c3ccccc23)cc1' and 'CC(C)c1cc(NC(=O)Nc2ccc(Oc3ccnc(Nc4ccc5[nH]ncc5c4)n3)c3ccccc23)n(-c2cccc(OCCN3CCOCC3)c2)n1' can interact with the protein 'MSQERPTFYRQELNKTIWEVPERYQNLSPVGSGAYGSVCAAFDTKTGLRVAVKKLSRPFQSIIHAKRTYRELRLLKHMKHENVIGLLDVFTPARSLEEFNDVYLVTHLMGADLNNIVKCQKLTDDHVQFLIYQILRGLKYIHSADIIHRDLKPSNLAVNEDCELKILDFGLARHTDDEMTGYVATRWYRAPEIMLNWMHYNQTVDIWSVGCIMAELLTGRTLFPGTDHIDQLKLILRLVGTPGAELLKKISSESARNYIQSLTQMPKMNFANVFIGANPLAVDLLEKMLVLDSDKRITAAQALAHAYFAQYHDPDDEPVADPYDQSFESRDLLIDEWKSLTYDEVISFVPPPLDQEEMES'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'OC(=O)CCC(O)=O', 'CC(C)(C(Cl)(Cl)Cl)O' will engage with the target protein 'MAASMFYGRLVAVATLRNHRPRTAQRAAAQVLGSSGLFNNHGLQVQQQQQRNLSLHEYMSMELLQEAGVSVPKGYVAKSPDEAYAIAKKLGSKDVVIKAQVLAGGRGKGTFESGLKGGVKIVFSPEEAKAVSSQMIGKKLFTKQTGEKGRICNQVLVCERKYPRREYYFAITMERSFQGPVLIGSSHGGVNIEDVAAESPEAIIKEPIDIEEGIKKEQALQLAQKMGFPPNIVESAAENMVKLYSLFLKYDATMIEINPMVEDSDGAVLCMDAKINFDSNSAYRQKKIFDLQDWTQEDERDKDAAKANLNYIGLDGNIGCLVNGAGLAMATMDIIKLHGGTPANFLDVGGGATVHQVTEAFKLITSDKKVLAILVNIFGGIMRCDVIAQGIVMAVKDLEIKIPVVVRLQGTRVDDAKALIADSGLKILACDDLDEAARMVVKLSEIVTLAKQAHVDVKFQLPI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC(=O)CCC(O)=O\", \"CC(C)(C(Cl)(Cl)Cl)O\"], \"target_seq\": \"MAASMFYGRLVAVATLRNHRPRTAQRAAAQVLGSSGLFNNHGLQVQQQQQRNLSLHEYMSMELLQEAGVSVPKGYVAKSPDEAYAIAKKLGSKDVVIKAQVLAGGRGKGTFESGLKGGVKIVFSPEEAKAVSSQMIGKKLFTKQTGEKGRICNQVLVCERKYPRREYYFAITMERSFQGPVLIGSSHGGVNIEDVAAESPEAIIKEPIDIEEGIKKEQALQLAQKMGFPPNIVESAAENMVKLYSLFLKYDATMIEINPMVEDSDGAVLCMDAKINFDSNSAYRQKKIFDLQDWTQEDERDKDAAKANLNYIGLDGNIGCLVNGAGLAMATMDIIKLHGGTPANFLDVGGGATVHQVTEAFKLITSDKKVLAILVNIFGGIMRCDVIAQGIVMAVKDLEIKIPVVVRLQGTRVDDAKALIADSGLKILACDDLDEAARMVVKLSEIVTLAKQAHVDVKFQLPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAASMFYGRLVAVATLRNHRPRTAQRAAAQVLGSSGLFNNHGLQVQQQQQRNLSLHEYMSMELLQEAGVSVPKGYVAKSPDEAYAIAKKLGSKDVVIKAQVLAGGRGKGTFESGLKGGVKIVFSPEEAKAVSSQMIGKKLFTKQTGEKGRICNQVLVCERKYPRREYYFAITMERSFQGPVLIGSSHGGVNIEDVAAESPEAIIKEPIDIEEGIKKEQALQLAQKMGFPPNIVESAAENMVKLYSLFLKYDATMIEINPMVEDSDGAVLCMDAKINFDSNSAYRQKKIFDLQDWTQEDERDKDAAKANLNYIGLDGNIGCLVNGAGLAMATMDIIKLHGGTPANFLDVGGGATVHQVTEAFKLITSDKKVLAILVNIFGGIMRCDVIAQGIVMAVKDLEIKIPVVVRLQGTRVDDAKALIADSGLKILACDDLDEAARMVVKLSEIVTLAKQAHVDVKFQLPI\", \"result\": [{\"smile\": \"OC(=O)CCC(O)=O\", \"value\": 1.0}, {\"smile\": \"CC(C)(C(Cl)(Cl)Cl)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'OC(=O)CCC(O)=O' can interact with the protein, while the second one 'CC(C)(C(Cl)(Cl)Cl)O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine if the drug 'CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1' will interact with the target protein 'MKKWSSTDLGAAADPLQKDTCPDPLDGDPNSRPPPAKPQLSTAKSRTRLFGKGDSEEAFPVDCPHEEGELDSCPTITVSPVITIQRPGDGPTGARLLSQDSVAASTEKTLRLYDRRSIFEAVAQNNCQDLESLLLFLQKSKKHLTDNEFKDPETGKTCLLKAMLNLHDGQNTTIPLLLEIARQTDSLKELVNASYTDSYYKGQTALHIAIERRNMALVTLLVENGADVQAAAHGDFFKKTKGRPGFYFGELPLSLAACTNQLGIVKFLLQNSWQTADISARDSVGNTVLHALVEVADNTADNTKFVTSMYNEILMLGAKLHPTLKLEELTNKKGMTPLALAAGTGKIGVLAYILQREIQEPECRHLSRKFTEWAYGPVHSSLYDLSCIDTCEKNSVLEVIAYSSSETPNRHDMLLVEPLNRLLQDKWDRFVKRIFYFNFLVYCLYMIIFTMAAYYRPVDGLPPFKMEKTGDYFRVTGEILSVLGGVYFFFRGIQYFLQRRPSMKTLFVDSYSEMLFFLQSLFMLATVVLYFSHLKEYVASMVFSLALGWTNMLYYTRGFQQMGIYAVMIEKMILRDLCRFMFVYIVFLFGFSTAVVTLIEDGKNDSLPSESTSHRWRGPACRPPDSSYNSLYSTCLELFKFTIGMGDLEFTENYDFKAVFIILLLAYVILTYILLLNMLIALMGETVNKIAQESKNIWKLQRAITILDTEKSFLKCMRKAFRSGKLLQVGYTPDGKDDYRWCFRVDEVNWTTWNTNVGIINEDPGNCEGVKRTLSFSLRSSRVSGRHWKNFALVPLLREASARDRQSAQPEEVYLRQFSGSLKPEDAEVFKSPAASGEK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1\"], \"target_seq\": \"MKKWSSTDLGAAADPLQKDTCPDPLDGDPNSRPPPAKPQLSTAKSRTRLFGKGDSEEAFPVDCPHEEGELDSCPTITVSPVITIQRPGDGPTGARLLSQDSVAASTEKTLRLYDRRSIFEAVAQNNCQDLESLLLFLQKSKKHLTDNEFKDPETGKTCLLKAMLNLHDGQNTTIPLLLEIARQTDSLKELVNASYTDSYYKGQTALHIAIERRNMALVTLLVENGADVQAAAHGDFFKKTKGRPGFYFGELPLSLAACTNQLGIVKFLLQNSWQTADISARDSVGNTVLHALVEVADNTADNTKFVTSMYNEILMLGAKLHPTLKLEELTNKKGMTPLALAAGTGKIGVLAYILQREIQEPECRHLSRKFTEWAYGPVHSSLYDLSCIDTCEKNSVLEVIAYSSSETPNRHDMLLVEPLNRLLQDKWDRFVKRIFYFNFLVYCLYMIIFTMAAYYRPVDGLPPFKMEKTGDYFRVTGEILSVLGGVYFFFRGIQYFLQRRPSMKTLFVDSYSEMLFFLQSLFMLATVVLYFSHLKEYVASMVFSLALGWTNMLYYTRGFQQMGIYAVMIEKMILRDLCRFMFVYIVFLFGFSTAVVTLIEDGKNDSLPSESTSHRWRGPACRPPDSSYNSLYSTCLELFKFTIGMGDLEFTENYDFKAVFIILLLAYVILTYILLLNMLIALMGETVNKIAQESKNIWKLQRAITILDTEKSFLKCMRKAFRSGKLLQVGYTPDGKDDYRWCFRVDEVNWTTWNTNVGIINEDPGNCEGVKRTLSFSLRSSRVSGRHWKNFALVPLLREASARDRQSAQPEEVYLRQFSGSLKPEDAEVFKSPAASGEK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKKWSSTDLGAAADPLQKDTCPDPLDGDPNSRPPPAKPQLSTAKSRTRLFGKGDSEEAFPVDCPHEEGELDSCPTITVSPVITIQRPGDGPTGARLLSQDSVAASTEKTLRLYDRRSIFEAVAQNNCQDLESLLLFLQKSKKHLTDNEFKDPETGKTCLLKAMLNLHDGQNTTIPLLLEIARQTDSLKELVNASYTDSYYKGQTALHIAIERRNMALVTLLVENGADVQAAAHGDFFKKTKGRPGFYFGELPLSLAACTNQLGIVKFLLQNSWQTADISARDSVGNTVLHALVEVADNTADNTKFVTSMYNEILMLGAKLHPTLKLEELTNKKGMTPLALAAGTGKIGVLAYILQREIQEPECRHLSRKFTEWAYGPVHSSLYDLSCIDTCEKNSVLEVIAYSSSETPNRHDMLLVEPLNRLLQDKWDRFVKRIFYFNFLVYCLYMIIFTMAAYYRPVDGLPPFKMEKTGDYFRVTGEILSVLGGVYFFFRGIQYFLQRRPSMKTLFVDSYSEMLFFLQSLFMLATVVLYFSHLKEYVASMVFSLALGWTNMLYYTRGFQQMGIYAVMIEKMILRDLCRFMFVYIVFLFGFSTAVVTLIEDGKNDSLPSESTSHRWRGPACRPPDSSYNSLYSTCLELFKFTIGMGDLEFTENYDFKAVFIILLLAYVILTYILLLNMLIALMGETVNKIAQESKNIWKLQRAITILDTEKSFLKCMRKAFRSGKLLQVGYTPDGKDDYRWCFRVDEVNWTTWNTNVGIINEDPGNCEGVKRTLSFSLRSSRVSGRHWKNFALVPLLREASARDRQSAQPEEVYLRQFSGSLKPEDAEVFKSPAASGEK\", \"result\": [{\"smile\": \"CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1' can not interact with the protein 'MKKWSSTDLGAAADPLQKDTCPDPLDGDPNSRPPPAKPQLSTAKSRTRLFGKGDSEEAFPVDCPHEEGELDSCPTITVSPVITIQRPGDGPTGARLLSQDSVAASTEKTLRLYDRRSIFEAVAQNNCQDLESLLLFLQKSKKHLTDNEFKDPETGKTCLLKAMLNLHDGQNTTIPLLLEIARQTDSLKELVNASYTDSYYKGQTALHIAIERRNMALVTLLVENGADVQAAAHGDFFKKTKGRPGFYFGELPLSLAACTNQLGIVKFLLQNSWQTADISARDSVGNTVLHALVEVADNTADNTKFVTSMYNEILMLGAKLHPTLKLEELTNKKGMTPLALAAGTGKIGVLAYILQREIQEPECRHLSRKFTEWAYGPVHSSLYDLSCIDTCEKNSVLEVIAYSSSETPNRHDMLLVEPLNRLLQDKWDRFVKRIFYFNFLVYCLYMIIFTMAAYYRPVDGLPPFKMEKTGDYFRVTGEILSVLGGVYFFFRGIQYFLQRRPSMKTLFVDSYSEMLFFLQSLFMLATVVLYFSHLKEYVASMVFSLALGWTNMLYYTRGFQQMGIYAVMIEKMILRDLCRFMFVYIVFLFGFSTAVVTLIEDGKNDSLPSESTSHRWRGPACRPPDSSYNSLYSTCLELFKFTIGMGDLEFTENYDFKAVFIILLLAYVILTYILLLNMLIALMGETVNKIAQESKNIWKLQRAITILDTEKSFLKCMRKAFRSGKLLQVGYTPDGKDDYRWCFRVDEVNWTTWNTNVGIINEDPGNCEGVKRTLSFSLRSSRVSGRHWKNFALVPLLREASARDRQSAQPEEVYLRQFSGSLKPEDAEVFKSPAASGEK'."
            },
            {
                "from": "human",
                "value": "Will the drug above possibly engage with the target protein 'MVLSEVWTTLSGVSGVCLACSLLSAAVVLRWTGRQKARGAATRARQKQRASLETMDKAVQRFRLQNPDLDSEALLTLPLLQLVQKLQSGELSPEAVFFTYLGKAWEVNKGTNCVTSYLTDCETQLSQAPRQGLLYGVPVSLKECFSYKGHDSTLGLSLNEGMPSESDCVVVQVLKLQGAVPFVHTNVPQSMLSFDCSNPLFGQTMNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSAFCGICGLKPTGNRLSKSGLKGCVYGQTAVQLSLGPMARDVESLALCLKALLCEHLFTLDPTVPPLPFREEVYRSSRPLRVGYYETDNYTMPSPAMRRALIETKQRLEAAGHTLIPFLPNNIPYALEVLSAGGLFSDGGRSFLQNFKGDFVDPCLGDLILILRLPSWFKRLLSLLLKPLFPRLAAFLNSMRPRSAEKLWKLQHEIEMYRQSVIAQWKAMNLDVLLTPMLGPALDLNTPGRATGAISYTVLYNCLDFPAGVVPVTTVTAEDDAQMELYKGYFGDIWDIILKKAMKNSVGLPVAVQCVALPWQEELCLRFMREVEQLMTPQKQPS'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVLSEVWTTLSGVSGVCLACSLLSAAVVLRWTGRQKARGAATRARQKQRASLETMDKAVQRFRLQNPDLDSEALLTLPLLQLVQKLQSGELSPEAVFFTYLGKAWEVNKGTNCVTSYLTDCETQLSQAPRQGLLYGVPVSLKECFSYKGHDSTLGLSLNEGMPSESDCVVVQVLKLQGAVPFVHTNVPQSMLSFDCSNPLFGQTMNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSAFCGICGLKPTGNRLSKSGLKGCVYGQTAVQLSLGPMARDVESLALCLKALLCEHLFTLDPTVPPLPFREEVYRSSRPLRVGYYETDNYTMPSPAMRRALIETKQRLEAAGHTLIPFLPNNIPYALEVLSAGGLFSDGGRSFLQNFKGDFVDPCLGDLILILRLPSWFKRLLSLLLKPLFPRLAAFLNSMRPRSAEKLWKLQHEIEMYRQSVIAQWKAMNLDVLLTPMLGPALDLNTPGRATGAISYTVLYNCLDFPAGVVPVTTVTAEDDAQMELYKGYFGDIWDIILKKAMKNSVGLPVAVQCVALPWQEELCLRFMREVEQLMTPQKQPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVLSEVWTTLSGVSGVCLACSLLSAAVVLRWTGRQKARGAATRARQKQRASLETMDKAVQRFRLQNPDLDSEALLTLPLLQLVQKLQSGELSPEAVFFTYLGKAWEVNKGTNCVTSYLTDCETQLSQAPRQGLLYGVPVSLKECFSYKGHDSTLGLSLNEGMPSESDCVVVQVLKLQGAVPFVHTNVPQSMLSFDCSNPLFGQTMNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSAFCGICGLKPTGNRLSKSGLKGCVYGQTAVQLSLGPMARDVESLALCLKALLCEHLFTLDPTVPPLPFREEVYRSSRPLRVGYYETDNYTMPSPAMRRALIETKQRLEAAGHTLIPFLPNNIPYALEVLSAGGLFSDGGRSFLQNFKGDFVDPCLGDLILILRLPSWFKRLLSLLLKPLFPRLAAFLNSMRPRSAEKLWKLQHEIEMYRQSVIAQWKAMNLDVLLTPMLGPALDLNTPGRATGAISYTVLYNCLDFPAGVVPVTTVTAEDDAQMELYKGYFGDIWDIILKKAMKNSVGLPVAVQCVALPWQEELCLRFMREVEQLMTPQKQPS\", \"result\": [{\"smile\": \"CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C(=O)n1nnc(Cc2ccc(-c3ccccc3)cc2)n1' can interact with the protein 'MVLSEVWTTLSGVSGVCLACSLLSAAVVLRWTGRQKARGAATRARQKQRASLETMDKAVQRFRLQNPDLDSEALLTLPLLQLVQKLQSGELSPEAVFFTYLGKAWEVNKGTNCVTSYLTDCETQLSQAPRQGLLYGVPVSLKECFSYKGHDSTLGLSLNEGMPSESDCVVVQVLKLQGAVPFVHTNVPQSMLSFDCSNPLFGQTMNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSAFCGICGLKPTGNRLSKSGLKGCVYGQTAVQLSLGPMARDVESLALCLKALLCEHLFTLDPTVPPLPFREEVYRSSRPLRVGYYETDNYTMPSPAMRRALIETKQRLEAAGHTLIPFLPNNIPYALEVLSAGGLFSDGGRSFLQNFKGDFVDPCLGDLILILRLPSWFKRLLSLLLKPLFPRLAAFLNSMRPRSAEKLWKLQHEIEMYRQSVIAQWKAMNLDVLLTPMLGPALDLNTPGRATGAISYTVLYNCLDFPAGVVPVTTVTAEDDAQMELYKGYFGDIWDIILKKAMKNSVGLPVAVQCVALPWQEELCLRFMREVEQLMTPQKQPS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the chances of interaction between the drug 'N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1' and the target protein 'MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTRGHAAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDREVTRCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLGITAACKLRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1\"], \"target_seq\": \"MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTRGHAAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDREVTRCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLGITAACKLRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTRGHAAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDREVTRCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLGITAACKLRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL\", \"result\": [{\"smile\": \"N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1' can interact with the protein 'MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTRGHAAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDREVTRCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLGITAACKLRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL'."
            },
            {
                "from": "human",
                "value": "What's your assessment of the drug above's potential to interact with the target protein 'MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTREHSAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDQEVIKCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLRIPAAYKHRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTREHSAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDQEVIKCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLRIPAAYKHRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTREHSAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDQEVIKCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLRIPAAYKHRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL\", \"result\": [{\"smile\": \"N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#Cc1ccc2cc3c(=O)[nH]c(=O)nc-3n(-c3cccc(-n4cnnn4)c3)c2c1' can not interact with the protein 'MASGSSSDAAEPAGPAGRAASAPEAAQAEEDRVKRRRLQCLGFALVGGCDPTMVPSVLRENDWQTQKALSAYFELPENDQGWPRQPPTSFKSEAYVDLTNEDANDTTILEASPSGTPLEDSSTISFITWNIDGLDGCNLPERARGVCSCLALYSPDVVFLQEVIPPYCAYLKKRAASYTIITGNEEGYFTAILLKKGRVKFKSQEIIPFPNTKMMRNLLCVNVSLGGNEFCLMTSHLESTREHSAERIRQLKTVLGKMQEAPDSTTVIFAGDTNLRDQEVIKCGGLPDNVFDAWEFLGKPKHCQYTWDTKANNNLRIPAAYKHRFDRIFFRAEEGHLIPQSLDLVGLEKLDCGRFPSDHWGLLCTLNVVL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be any interaction between the drug 'C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N' and the target protein 'MTVEKEAPDAHFTVDKQNISLWPREPPPKSGPSLVPGKTPTVRAALICLTLVLVASVLLQAVLYPRFMGTISDVKTNVQLLKGRVDNISTLDSEIKKNSDGMEAAGVQIQMVNESLGYVRSQFLKLKTSVEKANAQIQILTRSWEEVSTLNAQIPELKSDLEKASALNTKIRALQGSLENMSKLLKRQNDILQVVSQGWKYFKGNFYYFSLIPKTWYSAEQFCVSRNSHLTSVTSESEQEFLYKTAGGLIYWIGLTKAGMEGDWSWVDDTPFNKVQSVRFWIPGEPNNAGNNEHCGNIKAPSLQAWNDAPCDKTFLFICKRPYVPSEP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N\"], \"target_seq\": \"MTVEKEAPDAHFTVDKQNISLWPREPPPKSGPSLVPGKTPTVRAALICLTLVLVASVLLQAVLYPRFMGTISDVKTNVQLLKGRVDNISTLDSEIKKNSDGMEAAGVQIQMVNESLGYVRSQFLKLKTSVEKANAQIQILTRSWEEVSTLNAQIPELKSDLEKASALNTKIRALQGSLENMSKLLKRQNDILQVVSQGWKYFKGNFYYFSLIPKTWYSAEQFCVSRNSHLTSVTSESEQEFLYKTAGGLIYWIGLTKAGMEGDWSWVDDTPFNKVQSVRFWIPGEPNNAGNNEHCGNIKAPSLQAWNDAPCDKTFLFICKRPYVPSEP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTVEKEAPDAHFTVDKQNISLWPREPPPKSGPSLVPGKTPTVRAALICLTLVLVASVLLQAVLYPRFMGTISDVKTNVQLLKGRVDNISTLDSEIKKNSDGMEAAGVQIQMVNESLGYVRSQFLKLKTSVEKANAQIQILTRSWEEVSTLNAQIPELKSDLEKASALNTKIRALQGSLENMSKLLKRQNDILQVVSQGWKYFKGNFYYFSLIPKTWYSAEQFCVSRNSHLTSVTSESEQEFLYKTAGGLIYWIGLTKAGMEGDWSWVDDTPFNKVQSVRFWIPGEPNNAGNNEHCGNIKAPSLQAWNDAPCDKTFLFICKRPYVPSEP\", \"result\": [{\"smile\": \"C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N' can interact with the protein 'MTVEKEAPDAHFTVDKQNISLWPREPPPKSGPSLVPGKTPTVRAALICLTLVLVASVLLQAVLYPRFMGTISDVKTNVQLLKGRVDNISTLDSEIKKNSDGMEAAGVQIQMVNESLGYVRSQFLKLKTSVEKANAQIQILTRSWEEVSTLNAQIPELKSDLEKASALNTKIRALQGSLENMSKLLKRQNDILQVVSQGWKYFKGNFYYFSLIPKTWYSAEQFCVSRNSHLTSVTSESEQEFLYKTAGGLIYWIGLTKAGMEGDWSWVDDTPFNKVQSVRFWIPGEPNNAGNNEHCGNIKAPSLQAWNDAPCDKTFLFICKRPYVPSEP'."
            },
            {
                "from": "human",
                "value": "What is the expected interaction between the drug above and the target protein 'MAAGAAEAAVAAVEEVGSAGQFEELLRLKAKSLLVVHFWAPWAPQCAQMNEVMAELAKELPQVSFVKLEAEGVPEVSEKYEISSVPTFLFFKNSQKIDRLDGAHAPELTKKVQRHASSGSFLPSANEHLKEDLNLRLKKLTHAAPCMLFMKGTPQEPRCGFSKQMVEILHKHNIQFSSFDIFSDEEVRQGLKAYSSWPTYPQLYVSGELIGGLDIIKELEASEELDTICPKAPKLEERLKVLTNKASVMLFMKGNKQEAKCGFSKQILEILNSTGVEYETFDILEDEEVRQGLKAYSNWPTYPQLYVKGELVGGLDIVKELKENGELLPILRGEN'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAAGAAEAAVAAVEEVGSAGQFEELLRLKAKSLLVVHFWAPWAPQCAQMNEVMAELAKELPQVSFVKLEAEGVPEVSEKYEISSVPTFLFFKNSQKIDRLDGAHAPELTKKVQRHASSGSFLPSANEHLKEDLNLRLKKLTHAAPCMLFMKGTPQEPRCGFSKQMVEILHKHNIQFSSFDIFSDEEVRQGLKAYSSWPTYPQLYVSGELIGGLDIIKELEASEELDTICPKAPKLEERLKVLTNKASVMLFMKGNKQEAKCGFSKQILEILNSTGVEYETFDILEDEEVRQGLKAYSNWPTYPQLYVKGELVGGLDIVKELKENGELLPILRGEN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAGAAEAAVAAVEEVGSAGQFEELLRLKAKSLLVVHFWAPWAPQCAQMNEVMAELAKELPQVSFVKLEAEGVPEVSEKYEISSVPTFLFFKNSQKIDRLDGAHAPELTKKVQRHASSGSFLPSANEHLKEDLNLRLKKLTHAAPCMLFMKGTPQEPRCGFSKQMVEILHKHNIQFSSFDIFSDEEVRQGLKAYSSWPTYPQLYVSGELIGGLDIIKELEASEELDTICPKAPKLEERLKVLTNKASVMLFMKGNKQEAKCGFSKQILEILNSTGVEYETFDILEDEEVRQGLKAYSNWPTYPQLYVKGELVGGLDIVKELKENGELLPILRGEN\", \"result\": [{\"smile\": \"C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(CC(=O)N[C@@H](CS)C(=O)NCC(=O)O)[C@@H](C(=O)O)N' can interact with the protein 'MAAGAAEAAVAAVEEVGSAGQFEELLRLKAKSLLVVHFWAPWAPQCAQMNEVMAELAKELPQVSFVKLEAEGVPEVSEKYEISSVPTFLFFKNSQKIDRLDGAHAPELTKKVQRHASSGSFLPSANEHLKEDLNLRLKKLTHAAPCMLFMKGTPQEPRCGFSKQMVEILHKHNIQFSSFDIFSDEEVRQGLKAYSSWPTYPQLYVSGELIGGLDIIKELEASEELDTICPKAPKLEERLKVLTNKASVMLFMKGNKQEAKCGFSKQILEILNSTGVEYETFDILEDEEVRQGLKAYSNWPTYPQLYVKGELVGGLDIVKELKENGELLPILRGEN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your take on the drug 'COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O''s potential interaction with the target protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O\"], \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\", \"result\": [{\"smile\": \"COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O' can not interact with the protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'."
            },
            {
                "from": "human",
                "value": "What's your evaluation of the drug above's interaction potential with the target protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\", \"result\": [{\"smile\": \"COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccccc1N1C(=O)N(Cc2ccc(C(=O)NO)cc2)C(C)(C)C1=O' can interact with the protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug '[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O' will behave towards the target protein 'MVQAWYMDDAPGDPRQPHRPDPGRPVGLEQLRRLGVLYWKLDADKYENDPELEKIRRERNYSWMDIITICKDKLPNYEEKIKMFYEEHLHLDDEIRYILDGSGYFDVRDKEDQWIRIFMEKGDMVTLPAGIYHRFTVDEKNYTKAMRLFVGEPVWTAYNRPADHFEARGQYVKFLAQTA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O\"], \"target_seq\": \"MVQAWYMDDAPGDPRQPHRPDPGRPVGLEQLRRLGVLYWKLDADKYENDPELEKIRRERNYSWMDIITICKDKLPNYEEKIKMFYEEHLHLDDEIRYILDGSGYFDVRDKEDQWIRIFMEKGDMVTLPAGIYHRFTVDEKNYTKAMRLFVGEPVWTAYNRPADHFEARGQYVKFLAQTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVQAWYMDDAPGDPRQPHRPDPGRPVGLEQLRRLGVLYWKLDADKYENDPELEKIRRERNYSWMDIITICKDKLPNYEEKIKMFYEEHLHLDDEIRYILDGSGYFDVRDKEDQWIRIFMEKGDMVTLPAGIYHRFTVDEKNYTKAMRLFVGEPVWTAYNRPADHFEARGQYVKFLAQTA\", \"result\": [{\"smile\": \"[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O' can not interact with the protein 'MVQAWYMDDAPGDPRQPHRPDPGRPVGLEQLRRLGVLYWKLDADKYENDPELEKIRRERNYSWMDIITICKDKLPNYEEKIKMFYEEHLHLDDEIRYILDGSGYFDVRDKEDQWIRIFMEKGDMVTLPAGIYHRFTVDEKNYTKAMRLFVGEPVWTAYNRPADHFEARGQYVKFLAQTA'."
            },
            {
                "from": "human",
                "value": "Assess the chances of the drug above interacting with the target protein 'MEGNKLEEQDSSPPQSTPGLMKGNKREEQGLGPEPAAPQQPTAEEEALIEFHRSYRELFEFFCNNTTIHGAIRLVCSQHNRMKTAFWAVLWLCTFGMMYWQFGLLFGEYFSYPVSLNINLNSDKLVFPAVTICTLNPYRYPEIKEELEELDRITEQTLFDLYKYSSFTTLVAGSRSRRDLRGTLPHPLQRLRVPPPPHGARRARSVASSLRDNNPQVDWKDWKIGFQLCNQNKSDCFYQTYSSGVDAVREWYRFHYINILSRLPETLPSLEEDTLGNFIFACRFNQVSCNQANYSHFHHPMYGNCYTFNDKNNSNLWMSSMPGINNGLSLMLRAEQNDFIPLLSTVTGARVMVHGQDEPAFMDDGGFNLRPGVETSISMRKETLDRLGGDYGDCTKNGSDVPVENLYPSKYTQQVCIHSCFQESMIKECGCAYIFYPRPQNVEYCDYRKHSSWGYCYYKLQVDFSSDHLGCFTKCRKPCSVTSYQLSAGYSRWPSVTSQEWVFQMLSRQNNYTVNNKRNGVAKVNIFFKELNYKTNSESPSVTMVTLLSNLGSQWSLWFGSSVLSVVEMAELVFDLLVIMFLMLLRRFRSRYWSPGRGGRGAQEVASTLASSPPSHFCPHPMSLSLSQPGPAPSPALTAPPPAYATLGPRPSPGGSAGASSSTCPLGGP'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEGNKLEEQDSSPPQSTPGLMKGNKREEQGLGPEPAAPQQPTAEEEALIEFHRSYRELFEFFCNNTTIHGAIRLVCSQHNRMKTAFWAVLWLCTFGMMYWQFGLLFGEYFSYPVSLNINLNSDKLVFPAVTICTLNPYRYPEIKEELEELDRITEQTLFDLYKYSSFTTLVAGSRSRRDLRGTLPHPLQRLRVPPPPHGARRARSVASSLRDNNPQVDWKDWKIGFQLCNQNKSDCFYQTYSSGVDAVREWYRFHYINILSRLPETLPSLEEDTLGNFIFACRFNQVSCNQANYSHFHHPMYGNCYTFNDKNNSNLWMSSMPGINNGLSLMLRAEQNDFIPLLSTVTGARVMVHGQDEPAFMDDGGFNLRPGVETSISMRKETLDRLGGDYGDCTKNGSDVPVENLYPSKYTQQVCIHSCFQESMIKECGCAYIFYPRPQNVEYCDYRKHSSWGYCYYKLQVDFSSDHLGCFTKCRKPCSVTSYQLSAGYSRWPSVTSQEWVFQMLSRQNNYTVNNKRNGVAKVNIFFKELNYKTNSESPSVTMVTLLSNLGSQWSLWFGSSVLSVVEMAELVFDLLVIMFLMLLRRFRSRYWSPGRGGRGAQEVASTLASSPPSHFCPHPMSLSLSQPGPAPSPALTAPPPAYATLGPRPSPGGSAGASSSTCPLGGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGNKLEEQDSSPPQSTPGLMKGNKREEQGLGPEPAAPQQPTAEEEALIEFHRSYRELFEFFCNNTTIHGAIRLVCSQHNRMKTAFWAVLWLCTFGMMYWQFGLLFGEYFSYPVSLNINLNSDKLVFPAVTICTLNPYRYPEIKEELEELDRITEQTLFDLYKYSSFTTLVAGSRSRRDLRGTLPHPLQRLRVPPPPHGARRARSVASSLRDNNPQVDWKDWKIGFQLCNQNKSDCFYQTYSSGVDAVREWYRFHYINILSRLPETLPSLEEDTLGNFIFACRFNQVSCNQANYSHFHHPMYGNCYTFNDKNNSNLWMSSMPGINNGLSLMLRAEQNDFIPLLSTVTGARVMVHGQDEPAFMDDGGFNLRPGVETSISMRKETLDRLGGDYGDCTKNGSDVPVENLYPSKYTQQVCIHSCFQESMIKECGCAYIFYPRPQNVEYCDYRKHSSWGYCYYKLQVDFSSDHLGCFTKCRKPCSVTSYQLSAGYSRWPSVTSQEWVFQMLSRQNNYTVNNKRNGVAKVNIFFKELNYKTNSESPSVTMVTLLSNLGSQWSLWFGSSVLSVVEMAELVFDLLVIMFLMLLRRFRSRYWSPGRGGRGAQEVASTLASSPPSHFCPHPMSLSLSQPGPAPSPALTAPPPAYATLGPRPSPGGSAGASSSTCPLGGP\", \"result\": [{\"smile\": \"[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@](N)(COP(O)(=O)OC(C)C)C(O)=O' can not interact with the protein 'MEGNKLEEQDSSPPQSTPGLMKGNKREEQGLGPEPAAPQQPTAEEEALIEFHRSYRELFEFFCNNTTIHGAIRLVCSQHNRMKTAFWAVLWLCTFGMMYWQFGLLFGEYFSYPVSLNINLNSDKLVFPAVTICTLNPYRYPEIKEELEELDRITEQTLFDLYKYSSFTTLVAGSRSRRDLRGTLPHPLQRLRVPPPPHGARRARSVASSLRDNNPQVDWKDWKIGFQLCNQNKSDCFYQTYSSGVDAVREWYRFHYINILSRLPETLPSLEEDTLGNFIFACRFNQVSCNQANYSHFHHPMYGNCYTFNDKNNSNLWMSSMPGINNGLSLMLRAEQNDFIPLLSTVTGARVMVHGQDEPAFMDDGGFNLRPGVETSISMRKETLDRLGGDYGDCTKNGSDVPVENLYPSKYTQQVCIHSCFQESMIKECGCAYIFYPRPQNVEYCDYRKHSSWGYCYYKLQVDFSSDHLGCFTKCRKPCSVTSYQLSAGYSRWPSVTSQEWVFQMLSRQNNYTVNNKRNGVAKVNIFFKELNYKTNSESPSVTMVTLLSNLGSQWSLWFGSSVLSVVEMAELVFDLLVIMFLMLLRRFRSRYWSPGRGGRGAQEVASTLASSPPSHFCPHPMSLSLSQPGPAPSPALTAPPPAYATLGPRPSPGGSAGASSSTCPLGGP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC' and the target protein 'MTAVYMNGGGLVNPHYARWDRRDSVESGCQTESSKEGEEGQPRQLTPFEKLTQDMSQDEKVVREITLGKRIGFYRIRGEIGSGNFSQVKLGIHSLTKEKVAIKILDKTKLDQKTQRLLSREISSMEKLHHPNIIRLYEVVETLSKLHLVMEYAGGGELFGKISTEGKLSEPESKLIFSQIVSAVKHMHENQIIHRDLKAENVFYTSNTCVKVGDFGFSTVSKKGEMLNTFCGSPPYAAPELFRDEHYIGIYVDIWALGVLLYFMVTGTMPFRAETVAKLKKSILEGTYSVPPHVSEPCHRLIRGVLQQIPTERYGIDCIMNDEWMQGVPYPTPLEPFQLDPKHLSETSTLKEEENEVKSTLEHLGITEEHIRNNQGRDARSSITGVYRIILHRVQRKKALESVPVMMLPDPKERDLKKGSRVYRGIRHTSKFCSIL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC\"], \"target_seq\": \"MTAVYMNGGGLVNPHYARWDRRDSVESGCQTESSKEGEEGQPRQLTPFEKLTQDMSQDEKVVREITLGKRIGFYRIRGEIGSGNFSQVKLGIHSLTKEKVAIKILDKTKLDQKTQRLLSREISSMEKLHHPNIIRLYEVVETLSKLHLVMEYAGGGELFGKISTEGKLSEPESKLIFSQIVSAVKHMHENQIIHRDLKAENVFYTSNTCVKVGDFGFSTVSKKGEMLNTFCGSPPYAAPELFRDEHYIGIYVDIWALGVLLYFMVTGTMPFRAETVAKLKKSILEGTYSVPPHVSEPCHRLIRGVLQQIPTERYGIDCIMNDEWMQGVPYPTPLEPFQLDPKHLSETSTLKEEENEVKSTLEHLGITEEHIRNNQGRDARSSITGVYRIILHRVQRKKALESVPVMMLPDPKERDLKKGSRVYRGIRHTSKFCSIL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTAVYMNGGGLVNPHYARWDRRDSVESGCQTESSKEGEEGQPRQLTPFEKLTQDMSQDEKVVREITLGKRIGFYRIRGEIGSGNFSQVKLGIHSLTKEKVAIKILDKTKLDQKTQRLLSREISSMEKLHHPNIIRLYEVVETLSKLHLVMEYAGGGELFGKISTEGKLSEPESKLIFSQIVSAVKHMHENQIIHRDLKAENVFYTSNTCVKVGDFGFSTVSKKGEMLNTFCGSPPYAAPELFRDEHYIGIYVDIWALGVLLYFMVTGTMPFRAETVAKLKKSILEGTYSVPPHVSEPCHRLIRGVLQQIPTERYGIDCIMNDEWMQGVPYPTPLEPFQLDPKHLSETSTLKEEENEVKSTLEHLGITEEHIRNNQGRDARSSITGVYRIILHRVQRKKALESVPVMMLPDPKERDLKKGSRVYRGIRHTSKFCSIL\", \"result\": [{\"smile\": \"CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC' can not interact with the protein 'MTAVYMNGGGLVNPHYARWDRRDSVESGCQTESSKEGEEGQPRQLTPFEKLTQDMSQDEKVVREITLGKRIGFYRIRGEIGSGNFSQVKLGIHSLTKEKVAIKILDKTKLDQKTQRLLSREISSMEKLHHPNIIRLYEVVETLSKLHLVMEYAGGGELFGKISTEGKLSEPESKLIFSQIVSAVKHMHENQIIHRDLKAENVFYTSNTCVKVGDFGFSTVSKKGEMLNTFCGSPPYAAPELFRDEHYIGIYVDIWALGVLLYFMVTGTMPFRAETVAKLKKSILEGTYSVPPHVSEPCHRLIRGVLQQIPTERYGIDCIMNDEWMQGVPYPTPLEPFQLDPKHLSETSTLKEEENEVKSTLEHLGITEEHIRNNQGRDARSSITGVYRIILHRVQRKKALESVPVMMLPDPKERDLKKGSRVYRGIRHTSKFCSIL'."
            },
            {
                "from": "human",
                "value": "Will the drug above interact with the target protein 'MFRQEILNEVLFIVPNRYVDLLPSQFGNAMEVIAFDQISERRVVIKKVVLPENFDNWQHWRRAQRELFCTLHIQEENFVKMYSIYTWVETVEEMREFYTVREYMDWNLRNFILSTPEKLDHKVIKSIFFDVCLAVQYMHSIRVGHRDLKPENVLINYEAIAKISGFAHANREDPFVNTPYIVQRFYRAPEILCETMDNNKPSVDIWSLGCILAELLTGKILFTGQTQIDQFFQIVRFLGNPDLSFYMQMPDSARTFFLGLPMNQYQKPTNIHEHFPNSLFLDTMISEPIDCDLARDLLFRMLVINPDDRIDIQKILVHPYLEEVWSNIVIDNKIEEKYPPIALRRFFEFQAFSPPRQMKDEIFSTLTEFGQQYNIFNNSRN' according to your prediction? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MFRQEILNEVLFIVPNRYVDLLPSQFGNAMEVIAFDQISERRVVIKKVVLPENFDNWQHWRRAQRELFCTLHIQEENFVKMYSIYTWVETVEEMREFYTVREYMDWNLRNFILSTPEKLDHKVIKSIFFDVCLAVQYMHSIRVGHRDLKPENVLINYEAIAKISGFAHANREDPFVNTPYIVQRFYRAPEILCETMDNNKPSVDIWSLGCILAELLTGKILFTGQTQIDQFFQIVRFLGNPDLSFYMQMPDSARTFFLGLPMNQYQKPTNIHEHFPNSLFLDTMISEPIDCDLARDLLFRMLVINPDDRIDIQKILVHPYLEEVWSNIVIDNKIEEKYPPIALRRFFEFQAFSPPRQMKDEIFSTLTEFGQQYNIFNNSRN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFRQEILNEVLFIVPNRYVDLLPSQFGNAMEVIAFDQISERRVVIKKVVLPENFDNWQHWRRAQRELFCTLHIQEENFVKMYSIYTWVETVEEMREFYTVREYMDWNLRNFILSTPEKLDHKVIKSIFFDVCLAVQYMHSIRVGHRDLKPENVLINYEAIAKISGFAHANREDPFVNTPYIVQRFYRAPEILCETMDNNKPSVDIWSLGCILAELLTGKILFTGQTQIDQFFQIVRFLGNPDLSFYMQMPDSARTFFLGLPMNQYQKPTNIHEHFPNSLFLDTMISEPIDCDLARDLLFRMLVINPDDRIDIQKILVHPYLEEVWSNIVIDNKIEEKYPPIALRRFFEFQAFSPPRQMKDEIFSTLTEFGQQYNIFNNSRN\", \"result\": [{\"smile\": \"CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC' can interact with the protein 'MFRQEILNEVLFIVPNRYVDLLPSQFGNAMEVIAFDQISERRVVIKKVVLPENFDNWQHWRRAQRELFCTLHIQEENFVKMYSIYTWVETVEEMREFYTVREYMDWNLRNFILSTPEKLDHKVIKSIFFDVCLAVQYMHSIRVGHRDLKPENVLINYEAIAKISGFAHANREDPFVNTPYIVQRFYRAPEILCETMDNNKPSVDIWSLGCILAELLTGKILFTGQTQIDQFFQIVRFLGNPDLSFYMQMPDSARTFFLGLPMNQYQKPTNIHEHFPNSLFLDTMISEPIDCDLARDLLFRMLVINPDDRIDIQKILVHPYLEEVWSNIVIDNKIEEKYPPIALRRFFEFQAFSPPRQMKDEIFSTLTEFGQQYNIFNNSRN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12' likely form an interaction with the target protein 'MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12\"], \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12' can interact with the protein 'MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV'."
            },
            {
                "from": "human",
                "value": "Will there be an expected interaction between the drug above and the target protein 'MSTKKSPEELKRIFEKYAAKEGDPDQLSKDELKLLIQAEFPSLLKGPNTLDDLFQELDKNGDGEVSFEEFQVLVKKISQ'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSTKKSPEELKRIFEKYAAKEGDPDQLSKDELKLLIQAEFPSLLKGPNTLDDLFQELDKNGDGEVSFEEFQVLVKKISQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTKKSPEELKRIFEKYAAKEGDPDQLSKDELKLLIQAEFPSLLKGPNTLDDLFQELDKNGDGEVSFEEFQVLVKKISQ\", \"result\": [{\"smile\": \"NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=O)N1C2=CC=CC=C2C=CC2=CC=CC=C12' can not interact with the protein 'MSTKKSPEELKRIFEKYAAKEGDPDQLSKDELKLLIQAEFPSLLKGPNTLDDLFQELDKNGDGEVSFEEFQVLVKKISQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug 'CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O' and the target protein 'MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O\"], \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\", \"result\": [{\"smile\": \"CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O' can not interact with the protein 'MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP'."
            },
            {
                "from": "human",
                "value": "Can you determine if the drug above will interact with the target protein 'MNNFILLEEQLIKKSQQKRRTSPSNFKVRFFVLTKASLAYFEDRHGKKRTLKGSIELSRIKCVEIVKSDISIPCHYKYPFQVVHDNYLLYVFAPDRESRQRWVLALKEETRNNNSLVPKYHPNFWMDGKWRCCSQLEKLATGCAQYDPTKNASKKPLPPTPEDNRRPLWEPEETVVIALYDYQTNDPQELALRRNEEYCLLDSSEIHWWRVQDRNGHEGYVPSSYLVEKSPNNLETYEWYNKSISRDKAEKLLLDTGKEGAFMVRDSRTAGTYTVSVFTKAVVSENNPCIKHYHIKETNDNPKRYYVAEKYVFDSIPLLINYHQHNGGGLVTRLRYPVCFGRQKAPVTAGLRYGKWVIDPSELTFVQEIGSGQFGLVHLGYWLNKDKVAIKTIREGAMSEEDFIEEAEVMMKLSHPKLVQLYGVCLEQAPICLVFEFMEHGCLSDYLRTQRGLFAAETLLGMCLDVCEGMAYLEEACVIHRDLAARNCLVGENQVIKVSDFGMTRFVLDDQYTSSTGTKFPVKWASPEVFSFSRYSSKSDVWSFGVLMWEVFSEGKIPYENRSNSEVVEDISTGFRLYKPRLASTHVYQIMNHCWKERPEDRPAFSRLLRQLAEIAESGL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MNNFILLEEQLIKKSQQKRRTSPSNFKVRFFVLTKASLAYFEDRHGKKRTLKGSIELSRIKCVEIVKSDISIPCHYKYPFQVVHDNYLLYVFAPDRESRQRWVLALKEETRNNNSLVPKYHPNFWMDGKWRCCSQLEKLATGCAQYDPTKNASKKPLPPTPEDNRRPLWEPEETVVIALYDYQTNDPQELALRRNEEYCLLDSSEIHWWRVQDRNGHEGYVPSSYLVEKSPNNLETYEWYNKSISRDKAEKLLLDTGKEGAFMVRDSRTAGTYTVSVFTKAVVSENNPCIKHYHIKETNDNPKRYYVAEKYVFDSIPLLINYHQHNGGGLVTRLRYPVCFGRQKAPVTAGLRYGKWVIDPSELTFVQEIGSGQFGLVHLGYWLNKDKVAIKTIREGAMSEEDFIEEAEVMMKLSHPKLVQLYGVCLEQAPICLVFEFMEHGCLSDYLRTQRGLFAAETLLGMCLDVCEGMAYLEEACVIHRDLAARNCLVGENQVIKVSDFGMTRFVLDDQYTSSTGTKFPVKWASPEVFSFSRYSSKSDVWSFGVLMWEVFSEGKIPYENRSNSEVVEDISTGFRLYKPRLASTHVYQIMNHCWKERPEDRPAFSRLLRQLAEIAESGL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNNFILLEEQLIKKSQQKRRTSPSNFKVRFFVLTKASLAYFEDRHGKKRTLKGSIELSRIKCVEIVKSDISIPCHYKYPFQVVHDNYLLYVFAPDRESRQRWVLALKEETRNNNSLVPKYHPNFWMDGKWRCCSQLEKLATGCAQYDPTKNASKKPLPPTPEDNRRPLWEPEETVVIALYDYQTNDPQELALRRNEEYCLLDSSEIHWWRVQDRNGHEGYVPSSYLVEKSPNNLETYEWYNKSISRDKAEKLLLDTGKEGAFMVRDSRTAGTYTVSVFTKAVVSENNPCIKHYHIKETNDNPKRYYVAEKYVFDSIPLLINYHQHNGGGLVTRLRYPVCFGRQKAPVTAGLRYGKWVIDPSELTFVQEIGSGQFGLVHLGYWLNKDKVAIKTIREGAMSEEDFIEEAEVMMKLSHPKLVQLYGVCLEQAPICLVFEFMEHGCLSDYLRTQRGLFAAETLLGMCLDVCEGMAYLEEACVIHRDLAARNCLVGENQVIKVSDFGMTRFVLDDQYTSSTGTKFPVKWASPEVFSFSRYSSKSDVWSFGVLMWEVFSEGKIPYENRSNSEVVEDISTGFRLYKPRLASTHVYQIMNHCWKERPEDRPAFSRLLRQLAEIAESGL\", \"result\": [{\"smile\": \"CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCN(C(=O)C1CCCCC1)c1ccc2c(c1)nc(NC(=O)c1ccc(C#N)cc1)n2CCC(N)=O' can interact with the protein 'MNNFILLEEQLIKKSQQKRRTSPSNFKVRFFVLTKASLAYFEDRHGKKRTLKGSIELSRIKCVEIVKSDISIPCHYKYPFQVVHDNYLLYVFAPDRESRQRWVLALKEETRNNNSLVPKYHPNFWMDGKWRCCSQLEKLATGCAQYDPTKNASKKPLPPTPEDNRRPLWEPEETVVIALYDYQTNDPQELALRRNEEYCLLDSSEIHWWRVQDRNGHEGYVPSSYLVEKSPNNLETYEWYNKSISRDKAEKLLLDTGKEGAFMVRDSRTAGTYTVSVFTKAVVSENNPCIKHYHIKETNDNPKRYYVAEKYVFDSIPLLINYHQHNGGGLVTRLRYPVCFGRQKAPVTAGLRYGKWVIDPSELTFVQEIGSGQFGLVHLGYWLNKDKVAIKTIREGAMSEEDFIEEAEVMMKLSHPKLVQLYGVCLEQAPICLVFEFMEHGCLSDYLRTQRGLFAAETLLGMCLDVCEGMAYLEEACVIHRDLAARNCLVGENQVIKVSDFGMTRFVLDDQYTSSTGTKFPVKWASPEVFSFSRYSSKSDVWSFGVLMWEVFSEGKIPYENRSNSEVVEDISTGFRLYKPRLASTHVYQIMNHCWKERPEDRPAFSRLLRQLAEIAESGL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1' is expected to interact with the target protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1\"], \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\", \"result\": [{\"smile\": \"NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1' can interact with the protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'."
            },
            {
                "from": "human",
                "value": "Can you assess if the drug above will engage with the target protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\", \"result\": [{\"smile\": \"NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=N[C@@](c2cc(NC(=O)c3ccc(OCc4ncco4)cn3)ccc2F)(C(F)F)[C@H]2C[C@H]2O1' can not interact with the protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug 'COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1' interacting with the target protein 'MTTNAGPLHPYWPQHLRLDNFVPNDRPTWHILAGLFSVTGVLVVTTWLLSGRAAVVPLGTWRRLSLCWFAVCGFIHLVIEGWFVLYYEDLLGDQAFLSQLWKEYAKGDSRYILGDNFTVCMETITACLWGPLSLWVVIAFLRQHPLRFILQLVVSVGQIYGDVLYFLTEHRDGFQHGELGHPLYFWFYFVFMNALWLVLPGVLVLDAVKHLTHAQSTLDAKATKAKSKKN'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1\"], \"target_seq\": \"MTTNAGPLHPYWPQHLRLDNFVPNDRPTWHILAGLFSVTGVLVVTTWLLSGRAAVVPLGTWRRLSLCWFAVCGFIHLVIEGWFVLYYEDLLGDQAFLSQLWKEYAKGDSRYILGDNFTVCMETITACLWGPLSLWVVIAFLRQHPLRFILQLVVSVGQIYGDVLYFLTEHRDGFQHGELGHPLYFWFYFVFMNALWLVLPGVLVLDAVKHLTHAQSTLDAKATKAKSKKN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTTNAGPLHPYWPQHLRLDNFVPNDRPTWHILAGLFSVTGVLVVTTWLLSGRAAVVPLGTWRRLSLCWFAVCGFIHLVIEGWFVLYYEDLLGDQAFLSQLWKEYAKGDSRYILGDNFTVCMETITACLWGPLSLWVVIAFLRQHPLRFILQLVVSVGQIYGDVLYFLTEHRDGFQHGELGHPLYFWFYFVFMNALWLVLPGVLVLDAVKHLTHAQSTLDAKATKAKSKKN\", \"result\": [{\"smile\": \"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1' can not interact with the protein 'MTTNAGPLHPYWPQHLRLDNFVPNDRPTWHILAGLFSVTGVLVVTTWLLSGRAAVVPLGTWRRLSLCWFAVCGFIHLVIEGWFVLYYEDLLGDQAFLSQLWKEYAKGDSRYILGDNFTVCMETITACLWGPLSLWVVIAFLRQHPLRFILQLVVSVGQIYGDVLYFLTEHRDGFQHGELGHPLYFWFYFVFMNALWLVLPGVLVLDAVKHLTHAQSTLDAKATKAKSKKN'."
            },
            {
                "from": "human",
                "value": "Can you infer whether the drug above will engage with the target protein 'MGSGPLSLPLALSPPRLLLLLLLSLLPVARASEAEHRLFERLFEDYNEIIRPVANVSDPVIIHFEVSMSQLVKVDEVNQIMETNLWLKQIWNDYKLKWNPSDYGGAEFMRVPAQKIWKPDIVLYNNAVGDFQVDDKTKALLKYTGEVTWIPPAIFKSSCKIDVTYFPFDYQNCTMKFGSWSYDKAKIDLVLIGSSMNLKDYWESGEWAIIKAPGYKHDIKYNCCEEIYPDITYSLYIRRLPLFYTINLIIPCLLISFLTVLVFYLPSDCGEKVTLCISVLLSLTVFLLVITETIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHYRTPTTHTMPSWVKTVFLNLLPRVMFMTRPTSNEGNAQKPRPLYGAELSNLNCFSRAESKGCKEGYPCQDGMCGYCHHRRIKISNFSANLTRSSSSESVDAVLSLSALSPEIKEAIQSVKYIAENMKAQNEAKEIQDDWKYVAMVIDRIFLWVFTLVCILGTAGLFLQPLMAREDA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGSGPLSLPLALSPPRLLLLLLLSLLPVARASEAEHRLFERLFEDYNEIIRPVANVSDPVIIHFEVSMSQLVKVDEVNQIMETNLWLKQIWNDYKLKWNPSDYGGAEFMRVPAQKIWKPDIVLYNNAVGDFQVDDKTKALLKYTGEVTWIPPAIFKSSCKIDVTYFPFDYQNCTMKFGSWSYDKAKIDLVLIGSSMNLKDYWESGEWAIIKAPGYKHDIKYNCCEEIYPDITYSLYIRRLPLFYTINLIIPCLLISFLTVLVFYLPSDCGEKVTLCISVLLSLTVFLLVITETIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHYRTPTTHTMPSWVKTVFLNLLPRVMFMTRPTSNEGNAQKPRPLYGAELSNLNCFSRAESKGCKEGYPCQDGMCGYCHHRRIKISNFSANLTRSSSSESVDAVLSLSALSPEIKEAIQSVKYIAENMKAQNEAKEIQDDWKYVAMVIDRIFLWVFTLVCILGTAGLFLQPLMAREDA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSGPLSLPLALSPPRLLLLLLLSLLPVARASEAEHRLFERLFEDYNEIIRPVANVSDPVIIHFEVSMSQLVKVDEVNQIMETNLWLKQIWNDYKLKWNPSDYGGAEFMRVPAQKIWKPDIVLYNNAVGDFQVDDKTKALLKYTGEVTWIPPAIFKSSCKIDVTYFPFDYQNCTMKFGSWSYDKAKIDLVLIGSSMNLKDYWESGEWAIIKAPGYKHDIKYNCCEEIYPDITYSLYIRRLPLFYTINLIIPCLLISFLTVLVFYLPSDCGEKVTLCISVLLSLTVFLLVITETIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHYRTPTTHTMPSWVKTVFLNLLPRVMFMTRPTSNEGNAQKPRPLYGAELSNLNCFSRAESKGCKEGYPCQDGMCGYCHHRRIKISNFSANLTRSSSSESVDAVLSLSALSPEIKEAIQSVKYIAENMKAQNEAKEIQDDWKYVAMVIDRIFLWVFTLVCILGTAGLFLQPLMAREDA\", \"result\": [{\"smile\": \"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1' can not interact with the protein 'MGSGPLSLPLALSPPRLLLLLLLSLLPVARASEAEHRLFERLFEDYNEIIRPVANVSDPVIIHFEVSMSQLVKVDEVNQIMETNLWLKQIWNDYKLKWNPSDYGGAEFMRVPAQKIWKPDIVLYNNAVGDFQVDDKTKALLKYTGEVTWIPPAIFKSSCKIDVTYFPFDYQNCTMKFGSWSYDKAKIDLVLIGSSMNLKDYWESGEWAIIKAPGYKHDIKYNCCEEIYPDITYSLYIRRLPLFYTINLIIPCLLISFLTVLVFYLPSDCGEKVTLCISVLLSLTVFLLVITETIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHYRTPTTHTMPSWVKTVFLNLLPRVMFMTRPTSNEGNAQKPRPLYGAELSNLNCFSRAESKGCKEGYPCQDGMCGYCHHRRIKISNFSANLTRSSSSESVDAVLSLSALSPEIKEAIQSVKYIAENMKAQNEAKEIQDDWKYVAMVIDRIFLWVFTLVCILGTAGLFLQPLMAREDA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a forecast for the interaction likelihood between the drug 'CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21' and the target protein 'MGNLKSVAQEPGPPCGLGLGLGLGLCGKQGPATPAPEPSRAPASLLPPAPEHSPPSSPLTQPPEGPKFPRVKNWEVGSITYDTLSAQAQQDGPCTPRRCLGSLVFPRKLQGRPSPGPPAPEQLLSQARDFINQYYSSIKRSGSQAHEQRLQEVEAEVAATGTYQLRESELVFGAKQAWRNAPRCVGRIQWGKLQVFDARDCRSAQEMFTYICNHIKYATNRGNLRSAITVFPQRCPGRGDFRIWNSQLVRYAGYRQQDGSVRGDPANVEITELCIQHGWTPGNGRFDVLPLLLQAPDEPPELFLLPPELVLEVPLEHPTLEWFAALGLRWYALPAVSNMLLEIGGLEFPAAPFSGWYMSTEIGTRNLCDPHRYNILEDVAVCMDLDTRTTSSLWKDKAAVEINVAVLHSYQLAKVTIVDHHAATASFMKHLENEQKARGGCPADWAWIVPPISGSLTPVFHQEMVNYFLSPAFRYQPDPWKGSAAKGTGITRKKTFKEVANAVKISASLMGTVMAKRVKATILYGSETGRAQSYAQQLGRLFRKAFDPRVLCMDEYDVVSLEHETLVLVVTSTFGNGDPPENGESFAAALMEMSGPYNSSPRPEQHKSYKIRFNSISCSDPLVSSWRRKRKESSNTDSAGALGTLRFCVFGLGSRAYPHFCAFARAVDTRLEELGGERLLQLGQGDELCGQEEAFRGWAQAAFQAACETFCVGEDAKAAARDIFSPKRSWKRQRYRLSAQAEGLQLLPGLIHVHRRKMFQATIRSVENLQSSKSTRATILVRLDTGGQEGLQYQPGDHIGVCPPNRPGLVEALLSRVEDPPAPTEPVAVEQLEKGSPGGPPPGWVRDPRLPPCTLRQALTFFLDITSPPSPQLLRLLSTLAEEPREQQELEALSQDPRRYEEWKWFRCPTLLEVLEQFPSVALPAPLLLTQLPLLQPRYYSVSSAPSTHPGEIHLTVAVLAYRTQDGLGPLHYGVCSTWLSQLKPGDPVPCFIRGAPSFRLPPDPSLPCILVGPGTGIAPFRGFWQERLHDIESKGLQPTPMTLVFGCRCSQLDHLYRDEVQNAQQRGVFGRVLTAFSREPDNPKTYVQDILRTELAAEVHRVLCLERGHMFVCGDVTMATNVLQTVQRILATEGDMELDEAGDVIGVLRDQQRYHEDIFGLTLRTQEVTSRIRTQSFSLQERQLRGAVPWAFDPPGSDTNSP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21\"], \"target_seq\": \"MGNLKSVAQEPGPPCGLGLGLGLGLCGKQGPATPAPEPSRAPASLLPPAPEHSPPSSPLTQPPEGPKFPRVKNWEVGSITYDTLSAQAQQDGPCTPRRCLGSLVFPRKLQGRPSPGPPAPEQLLSQARDFINQYYSSIKRSGSQAHEQRLQEVEAEVAATGTYQLRESELVFGAKQAWRNAPRCVGRIQWGKLQVFDARDCRSAQEMFTYICNHIKYATNRGNLRSAITVFPQRCPGRGDFRIWNSQLVRYAGYRQQDGSVRGDPANVEITELCIQHGWTPGNGRFDVLPLLLQAPDEPPELFLLPPELVLEVPLEHPTLEWFAALGLRWYALPAVSNMLLEIGGLEFPAAPFSGWYMSTEIGTRNLCDPHRYNILEDVAVCMDLDTRTTSSLWKDKAAVEINVAVLHSYQLAKVTIVDHHAATASFMKHLENEQKARGGCPADWAWIVPPISGSLTPVFHQEMVNYFLSPAFRYQPDPWKGSAAKGTGITRKKTFKEVANAVKISASLMGTVMAKRVKATILYGSETGRAQSYAQQLGRLFRKAFDPRVLCMDEYDVVSLEHETLVLVVTSTFGNGDPPENGESFAAALMEMSGPYNSSPRPEQHKSYKIRFNSISCSDPLVSSWRRKRKESSNTDSAGALGTLRFCVFGLGSRAYPHFCAFARAVDTRLEELGGERLLQLGQGDELCGQEEAFRGWAQAAFQAACETFCVGEDAKAAARDIFSPKRSWKRQRYRLSAQAEGLQLLPGLIHVHRRKMFQATIRSVENLQSSKSTRATILVRLDTGGQEGLQYQPGDHIGVCPPNRPGLVEALLSRVEDPPAPTEPVAVEQLEKGSPGGPPPGWVRDPRLPPCTLRQALTFFLDITSPPSPQLLRLLSTLAEEPREQQELEALSQDPRRYEEWKWFRCPTLLEVLEQFPSVALPAPLLLTQLPLLQPRYYSVSSAPSTHPGEIHLTVAVLAYRTQDGLGPLHYGVCSTWLSQLKPGDPVPCFIRGAPSFRLPPDPSLPCILVGPGTGIAPFRGFWQERLHDIESKGLQPTPMTLVFGCRCSQLDHLYRDEVQNAQQRGVFGRVLTAFSREPDNPKTYVQDILRTELAAEVHRVLCLERGHMFVCGDVTMATNVLQTVQRILATEGDMELDEAGDVIGVLRDQQRYHEDIFGLTLRTQEVTSRIRTQSFSLQERQLRGAVPWAFDPPGSDTNSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGNLKSVAQEPGPPCGLGLGLGLGLCGKQGPATPAPEPSRAPASLLPPAPEHSPPSSPLTQPPEGPKFPRVKNWEVGSITYDTLSAQAQQDGPCTPRRCLGSLVFPRKLQGRPSPGPPAPEQLLSQARDFINQYYSSIKRSGSQAHEQRLQEVEAEVAATGTYQLRESELVFGAKQAWRNAPRCVGRIQWGKLQVFDARDCRSAQEMFTYICNHIKYATNRGNLRSAITVFPQRCPGRGDFRIWNSQLVRYAGYRQQDGSVRGDPANVEITELCIQHGWTPGNGRFDVLPLLLQAPDEPPELFLLPPELVLEVPLEHPTLEWFAALGLRWYALPAVSNMLLEIGGLEFPAAPFSGWYMSTEIGTRNLCDPHRYNILEDVAVCMDLDTRTTSSLWKDKAAVEINVAVLHSYQLAKVTIVDHHAATASFMKHLENEQKARGGCPADWAWIVPPISGSLTPVFHQEMVNYFLSPAFRYQPDPWKGSAAKGTGITRKKTFKEVANAVKISASLMGTVMAKRVKATILYGSETGRAQSYAQQLGRLFRKAFDPRVLCMDEYDVVSLEHETLVLVVTSTFGNGDPPENGESFAAALMEMSGPYNSSPRPEQHKSYKIRFNSISCSDPLVSSWRRKRKESSNTDSAGALGTLRFCVFGLGSRAYPHFCAFARAVDTRLEELGGERLLQLGQGDELCGQEEAFRGWAQAAFQAACETFCVGEDAKAAARDIFSPKRSWKRQRYRLSAQAEGLQLLPGLIHVHRRKMFQATIRSVENLQSSKSTRATILVRLDTGGQEGLQYQPGDHIGVCPPNRPGLVEALLSRVEDPPAPTEPVAVEQLEKGSPGGPPPGWVRDPRLPPCTLRQALTFFLDITSPPSPQLLRLLSTLAEEPREQQELEALSQDPRRYEEWKWFRCPTLLEVLEQFPSVALPAPLLLTQLPLLQPRYYSVSSAPSTHPGEIHLTVAVLAYRTQDGLGPLHYGVCSTWLSQLKPGDPVPCFIRGAPSFRLPPDPSLPCILVGPGTGIAPFRGFWQERLHDIESKGLQPTPMTLVFGCRCSQLDHLYRDEVQNAQQRGVFGRVLTAFSREPDNPKTYVQDILRTELAAEVHRVLCLERGHMFVCGDVTMATNVLQTVQRILATEGDMELDEAGDVIGVLRDQQRYHEDIFGLTLRTQEVTSRIRTQSFSLQERQLRGAVPWAFDPPGSDTNSP\", \"result\": [{\"smile\": \"CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21' can not interact with the protein 'MGNLKSVAQEPGPPCGLGLGLGLGLCGKQGPATPAPEPSRAPASLLPPAPEHSPPSSPLTQPPEGPKFPRVKNWEVGSITYDTLSAQAQQDGPCTPRRCLGSLVFPRKLQGRPSPGPPAPEQLLSQARDFINQYYSSIKRSGSQAHEQRLQEVEAEVAATGTYQLRESELVFGAKQAWRNAPRCVGRIQWGKLQVFDARDCRSAQEMFTYICNHIKYATNRGNLRSAITVFPQRCPGRGDFRIWNSQLVRYAGYRQQDGSVRGDPANVEITELCIQHGWTPGNGRFDVLPLLLQAPDEPPELFLLPPELVLEVPLEHPTLEWFAALGLRWYALPAVSNMLLEIGGLEFPAAPFSGWYMSTEIGTRNLCDPHRYNILEDVAVCMDLDTRTTSSLWKDKAAVEINVAVLHSYQLAKVTIVDHHAATASFMKHLENEQKARGGCPADWAWIVPPISGSLTPVFHQEMVNYFLSPAFRYQPDPWKGSAAKGTGITRKKTFKEVANAVKISASLMGTVMAKRVKATILYGSETGRAQSYAQQLGRLFRKAFDPRVLCMDEYDVVSLEHETLVLVVTSTFGNGDPPENGESFAAALMEMSGPYNSSPRPEQHKSYKIRFNSISCSDPLVSSWRRKRKESSNTDSAGALGTLRFCVFGLGSRAYPHFCAFARAVDTRLEELGGERLLQLGQGDELCGQEEAFRGWAQAAFQAACETFCVGEDAKAAARDIFSPKRSWKRQRYRLSAQAEGLQLLPGLIHVHRRKMFQATIRSVENLQSSKSTRATILVRLDTGGQEGLQYQPGDHIGVCPPNRPGLVEALLSRVEDPPAPTEPVAVEQLEKGSPGGPPPGWVRDPRLPPCTLRQALTFFLDITSPPSPQLLRLLSTLAEEPREQQELEALSQDPRRYEEWKWFRCPTLLEVLEQFPSVALPAPLLLTQLPLLQPRYYSVSSAPSTHPGEIHLTVAVLAYRTQDGLGPLHYGVCSTWLSQLKPGDPVPCFIRGAPSFRLPPDPSLPCILVGPGTGIAPFRGFWQERLHDIESKGLQPTPMTLVFGCRCSQLDHLYRDEVQNAQQRGVFGRVLTAFSREPDNPKTYVQDILRTELAAEVHRVLCLERGHMFVCGDVTMATNVLQTVQRILATEGDMELDEAGDVIGVLRDQQRYHEDIFGLTLRTQEVTSRIRTQSFSLQERQLRGAVPWAFDPPGSDTNSP'."
            },
            {
                "from": "human",
                "value": "Do you think the drug above will have any interaction with the target protein 'MACPWKFLFKTKFHQYAMNGEKDINNNVEKAPCATSSPVTQDDLQYHNLSKQQNESPQPLVETGKKSPESLVKLDATPLSSPRHVRIKNWGSGMTFQDTLHHKAKGILTCRSKSCLGSIMTPKSLTRGPRDKPTPPDELLPQAIEFVNQYYGSFKEAKIEEHLARVEAVTKEIETTGTYQLTGDELIFATKQAWRNAPRCIGRIQWSNLQVFDARSCSTAREMFEHICRHVRYSTNNGNIRSAITVFPQRSDGKHDFRVWNAQLIRYAGYQMPDGSIRGDPANVEFTQLCIDLGWKPKYGRFDVVPLVLQANGRDPELFEIPPDLVLEVAMEHPKYEWFRELELKWYALPAVANMLLEVGGLEFPGCPFNGWYMGTEIGVRDFCDVQRYNILEEVGRRMGLETHKLASLWKDQAVVEINIAVLHSFQKQNVTIMDHHSAAESFMKYMQNEYRSRGGCPADWIWLVPPMSGSITPVFHQEMLNYVLSPFYYYQVEAWKTHVWQDEKRRPKRREIPLKVLVKAVLFACMLMRKTMASRVRVTILFATETGKSEALAWDLGALFSCAFNPKVVCMDKYRLSCLEEERLLLVVTSTFGNGDCPGNGEKLKKSLFMLKELNNKFRYAVFGLGSSMYPRFCAFAHDIDQKLSHLGASQLTPMGEGDELSGQEDAFRSWAVQTFKAACETFDVRGKQHIQIPKLYTSNVTWDPHHYRLVQDSQPLDLSKALSSMHAKNVFTMRLKSRQNLQSPTSSRATILVELSCEDGQGLNYLPGEHLGVCPGNQPALVQGILERVVDGPTPHQTVRLEALDESGSYWVSDKRLPPCSLSQALTYFLDITTPPTQLLLQKLAQVATEEPERQRLEALCQPSEYSKWKFTNSPTFLEVLEEFPSLRVSAGFLLSQLPILKPRFYSISSSRDHTPTEIHLTVAVVTYHTRDGQGPLHHGVCSTWLNSLKPQDPVPCFVRNASGFHLPEDPSHPCILIGPGTGIAPFRSFWQQRLHDSQHKGVRGGRMTLVFGCRRPDEDHIYQEEMLEMAQKGVLHAVHTAYSRLPGKPKVYVQDILRQQLASEVLRVLHKEPGHLYVCGDVRMARDVAHTLKQLVAAKLKLNEEQVEDYFFQLKSQKRYHEDIFGAVFPYEAKKDRVAVQPSSLEMSAL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MACPWKFLFKTKFHQYAMNGEKDINNNVEKAPCATSSPVTQDDLQYHNLSKQQNESPQPLVETGKKSPESLVKLDATPLSSPRHVRIKNWGSGMTFQDTLHHKAKGILTCRSKSCLGSIMTPKSLTRGPRDKPTPPDELLPQAIEFVNQYYGSFKEAKIEEHLARVEAVTKEIETTGTYQLTGDELIFATKQAWRNAPRCIGRIQWSNLQVFDARSCSTAREMFEHICRHVRYSTNNGNIRSAITVFPQRSDGKHDFRVWNAQLIRYAGYQMPDGSIRGDPANVEFTQLCIDLGWKPKYGRFDVVPLVLQANGRDPELFEIPPDLVLEVAMEHPKYEWFRELELKWYALPAVANMLLEVGGLEFPGCPFNGWYMGTEIGVRDFCDVQRYNILEEVGRRMGLETHKLASLWKDQAVVEINIAVLHSFQKQNVTIMDHHSAAESFMKYMQNEYRSRGGCPADWIWLVPPMSGSITPVFHQEMLNYVLSPFYYYQVEAWKTHVWQDEKRRPKRREIPLKVLVKAVLFACMLMRKTMASRVRVTILFATETGKSEALAWDLGALFSCAFNPKVVCMDKYRLSCLEEERLLLVVTSTFGNGDCPGNGEKLKKSLFMLKELNNKFRYAVFGLGSSMYPRFCAFAHDIDQKLSHLGASQLTPMGEGDELSGQEDAFRSWAVQTFKAACETFDVRGKQHIQIPKLYTSNVTWDPHHYRLVQDSQPLDLSKALSSMHAKNVFTMRLKSRQNLQSPTSSRATILVELSCEDGQGLNYLPGEHLGVCPGNQPALVQGILERVVDGPTPHQTVRLEALDESGSYWVSDKRLPPCSLSQALTYFLDITTPPTQLLLQKLAQVATEEPERQRLEALCQPSEYSKWKFTNSPTFLEVLEEFPSLRVSAGFLLSQLPILKPRFYSISSSRDHTPTEIHLTVAVVTYHTRDGQGPLHHGVCSTWLNSLKPQDPVPCFVRNASGFHLPEDPSHPCILIGPGTGIAPFRSFWQQRLHDSQHKGVRGGRMTLVFGCRRPDEDHIYQEEMLEMAQKGVLHAVHTAYSRLPGKPKVYVQDILRQQLASEVLRVLHKEPGHLYVCGDVRMARDVAHTLKQLVAAKLKLNEEQVEDYFFQLKSQKRYHEDIFGAVFPYEAKKDRVAVQPSSLEMSAL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MACPWKFLFKTKFHQYAMNGEKDINNNVEKAPCATSSPVTQDDLQYHNLSKQQNESPQPLVETGKKSPESLVKLDATPLSSPRHVRIKNWGSGMTFQDTLHHKAKGILTCRSKSCLGSIMTPKSLTRGPRDKPTPPDELLPQAIEFVNQYYGSFKEAKIEEHLARVEAVTKEIETTGTYQLTGDELIFATKQAWRNAPRCIGRIQWSNLQVFDARSCSTAREMFEHICRHVRYSTNNGNIRSAITVFPQRSDGKHDFRVWNAQLIRYAGYQMPDGSIRGDPANVEFTQLCIDLGWKPKYGRFDVVPLVLQANGRDPELFEIPPDLVLEVAMEHPKYEWFRELELKWYALPAVANMLLEVGGLEFPGCPFNGWYMGTEIGVRDFCDVQRYNILEEVGRRMGLETHKLASLWKDQAVVEINIAVLHSFQKQNVTIMDHHSAAESFMKYMQNEYRSRGGCPADWIWLVPPMSGSITPVFHQEMLNYVLSPFYYYQVEAWKTHVWQDEKRRPKRREIPLKVLVKAVLFACMLMRKTMASRVRVTILFATETGKSEALAWDLGALFSCAFNPKVVCMDKYRLSCLEEERLLLVVTSTFGNGDCPGNGEKLKKSLFMLKELNNKFRYAVFGLGSSMYPRFCAFAHDIDQKLSHLGASQLTPMGEGDELSGQEDAFRSWAVQTFKAACETFDVRGKQHIQIPKLYTSNVTWDPHHYRLVQDSQPLDLSKALSSMHAKNVFTMRLKSRQNLQSPTSSRATILVELSCEDGQGLNYLPGEHLGVCPGNQPALVQGILERVVDGPTPHQTVRLEALDESGSYWVSDKRLPPCSLSQALTYFLDITTPPTQLLLQKLAQVATEEPERQRLEALCQPSEYSKWKFTNSPTFLEVLEEFPSLRVSAGFLLSQLPILKPRFYSISSSRDHTPTEIHLTVAVVTYHTRDGQGPLHHGVCSTWLNSLKPQDPVPCFVRNASGFHLPEDPSHPCILIGPGTGIAPFRSFWQQRLHDSQHKGVRGGRMTLVFGCRRPDEDHIYQEEMLEMAQKGVLHAVHTAYSRLPGKPKVYVQDILRQQLASEVLRVLHKEPGHLYVCGDVRMARDVAHTLKQLVAAKLKLNEEQVEDYFFQLKSQKRYHEDIFGAVFPYEAKKDRVAVQPSSLEMSAL\", \"result\": [{\"smile\": \"CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)CCCN1CCSc2cc(N=C(N)c3cccs3)ccc21' can not interact with the protein 'MACPWKFLFKTKFHQYAMNGEKDINNNVEKAPCATSSPVTQDDLQYHNLSKQQNESPQPLVETGKKSPESLVKLDATPLSSPRHVRIKNWGSGMTFQDTLHHKAKGILTCRSKSCLGSIMTPKSLTRGPRDKPTPPDELLPQAIEFVNQYYGSFKEAKIEEHLARVEAVTKEIETTGTYQLTGDELIFATKQAWRNAPRCIGRIQWSNLQVFDARSCSTAREMFEHICRHVRYSTNNGNIRSAITVFPQRSDGKHDFRVWNAQLIRYAGYQMPDGSIRGDPANVEFTQLCIDLGWKPKYGRFDVVPLVLQANGRDPELFEIPPDLVLEVAMEHPKYEWFRELELKWYALPAVANMLLEVGGLEFPGCPFNGWYMGTEIGVRDFCDVQRYNILEEVGRRMGLETHKLASLWKDQAVVEINIAVLHSFQKQNVTIMDHHSAAESFMKYMQNEYRSRGGCPADWIWLVPPMSGSITPVFHQEMLNYVLSPFYYYQVEAWKTHVWQDEKRRPKRREIPLKVLVKAVLFACMLMRKTMASRVRVTILFATETGKSEALAWDLGALFSCAFNPKVVCMDKYRLSCLEEERLLLVVTSTFGNGDCPGNGEKLKKSLFMLKELNNKFRYAVFGLGSSMYPRFCAFAHDIDQKLSHLGASQLTPMGEGDELSGQEDAFRSWAVQTFKAACETFDVRGKQHIQIPKLYTSNVTWDPHHYRLVQDSQPLDLSKALSSMHAKNVFTMRLKSRQNLQSPTSSRATILVELSCEDGQGLNYLPGEHLGVCPGNQPALVQGILERVVDGPTPHQTVRLEALDESGSYWVSDKRLPPCSLSQALTYFLDITTPPTQLLLQKLAQVATEEPERQRLEALCQPSEYSKWKFTNSPTFLEVLEEFPSLRVSAGFLLSQLPILKPRFYSISSSRDHTPTEIHLTVAVVTYHTRDGQGPLHHGVCSTWLNSLKPQDPVPCFVRNASGFHLPEDPSHPCILIGPGTGIAPFRSFWQQRLHDSQHKGVRGGRMTLVFGCRRPDEDHIYQEEMLEMAQKGVLHAVHTAYSRLPGKPKVYVQDILRQQLASEVLRVLHKEPGHLYVCGDVRMARDVAHTLKQLVAAKLKLNEEQVEDYFFQLKSQKRYHEDIFGAVFPYEAKKDRVAVQPSSLEMSAL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C''s interaction potential with the target protein 'MESMAVATDGGERPGVPAGSGLSASQRRAELRRRKLLMNSEQRINRIMGFHRPGSGAEEESQTKSKQQDSDKLNSLSVPSVSKRVVLGDSVSTGTTDQQGGVAEVKGTQLGDKLDSFIKPPECSSDVNLELRQRNRGDLTADSVQRGSRHGLEQYLSRFEEAMKLRKQLISEKPSQEDGNTTEEFDSFRIFRLVGCALLALGVRAFVCKYLSIFAPFLTLQLAYMGLYKYFPKSEKKIKTTVLTAALLLSGIPAEVINRSMDTYSKMGEVFTDLCVYFFTFIFCHELLDYWGSEVP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C\"], \"target_seq\": \"MESMAVATDGGERPGVPAGSGLSASQRRAELRRRKLLMNSEQRINRIMGFHRPGSGAEEESQTKSKQQDSDKLNSLSVPSVSKRVVLGDSVSTGTTDQQGGVAEVKGTQLGDKLDSFIKPPECSSDVNLELRQRNRGDLTADSVQRGSRHGLEQYLSRFEEAMKLRKQLISEKPSQEDGNTTEEFDSFRIFRLVGCALLALGVRAFVCKYLSIFAPFLTLQLAYMGLYKYFPKSEKKIKTTVLTAALLLSGIPAEVINRSMDTYSKMGEVFTDLCVYFFTFIFCHELLDYWGSEVP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESMAVATDGGERPGVPAGSGLSASQRRAELRRRKLLMNSEQRINRIMGFHRPGSGAEEESQTKSKQQDSDKLNSLSVPSVSKRVVLGDSVSTGTTDQQGGVAEVKGTQLGDKLDSFIKPPECSSDVNLELRQRNRGDLTADSVQRGSRHGLEQYLSRFEEAMKLRKQLISEKPSQEDGNTTEEFDSFRIFRLVGCALLALGVRAFVCKYLSIFAPFLTLQLAYMGLYKYFPKSEKKIKTTVLTAALLLSGIPAEVINRSMDTYSKMGEVFTDLCVYFFTFIFCHELLDYWGSEVP\", \"result\": [{\"smile\": \"CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C' can interact with the protein 'MESMAVATDGGERPGVPAGSGLSASQRRAELRRRKLLMNSEQRINRIMGFHRPGSGAEEESQTKSKQQDSDKLNSLSVPSVSKRVVLGDSVSTGTTDQQGGVAEVKGTQLGDKLDSFIKPPECSSDVNLELRQRNRGDLTADSVQRGSRHGLEQYLSRFEEAMKLRKQLISEKPSQEDGNTTEEFDSFRIFRLVGCALLALGVRAFVCKYLSIFAPFLTLQLAYMGLYKYFPKSEKKIKTTVLTAALLLSGIPAEVINRSMDTYSKMGEVFTDLCVYFFTFIFCHELLDYWGSEVP'."
            },
            {
                "from": "human",
                "value": "Can you give a forecast for whether the drug above will interact with the target protein 'MACLMAAFSVGTAMNASSYSAEMTEPKSVCVSVDEVVSSNMEATETDLLNGHLKKVDNNLTEAQRFSSLPRRAAVNIEFRDLSYSVPEGPWWRKKGYKTLLKGISGKFNSGELVAIMGPSGAGKSTLMNILAGYRETGMKGAVLINGLPRDLRCFRKVSCYIMQDDMLLPHLTVQEAMMVSAHLKLQEKDEGRREMVKEILTALGLLSCANTRTGSLSGGQRKRLAIALELVNNPPVMFFDEPTSGLDSASCFQVVSLMKGLAQGGRSIICTIHQPSAKLFELFDQLYVLSQGQCVYRGKVCNLVPYLRDLGLNCPTYHNPADFVMEVASGEYGDQNSRLVRAVREGMCDSDHKRDLGGDAEVNPFLWHRPSEEVKQTKRLKGLRKDSSSMEGCHSFSASCLTQFCILFKRTFLSIMRDSVLTHLRITSHIGIGLLIGLLYLGIGNEAKKVLSNSGFLFFSMLFLMFAALMPTVLTFPLEMGVFLREHLNYWYSLKAYYLAKTMADVPFQIMFPVAYCSIVYWMTSQPSDAVRFVLFAALGTMTSLVAQSLGLLIGAASTSLQVATFVGPVTAIPVLLFSGFFVSFDTIPTYLQWMSYISYVRYGFEGVILSIYGLDREDLHCDIDETCHFQKSEAILRELDVENAKLYLDFIVLGIFFISLRLIAYFVLRYKIRAER'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MACLMAAFSVGTAMNASSYSAEMTEPKSVCVSVDEVVSSNMEATETDLLNGHLKKVDNNLTEAQRFSSLPRRAAVNIEFRDLSYSVPEGPWWRKKGYKTLLKGISGKFNSGELVAIMGPSGAGKSTLMNILAGYRETGMKGAVLINGLPRDLRCFRKVSCYIMQDDMLLPHLTVQEAMMVSAHLKLQEKDEGRREMVKEILTALGLLSCANTRTGSLSGGQRKRLAIALELVNNPPVMFFDEPTSGLDSASCFQVVSLMKGLAQGGRSIICTIHQPSAKLFELFDQLYVLSQGQCVYRGKVCNLVPYLRDLGLNCPTYHNPADFVMEVASGEYGDQNSRLVRAVREGMCDSDHKRDLGGDAEVNPFLWHRPSEEVKQTKRLKGLRKDSSSMEGCHSFSASCLTQFCILFKRTFLSIMRDSVLTHLRITSHIGIGLLIGLLYLGIGNEAKKVLSNSGFLFFSMLFLMFAALMPTVLTFPLEMGVFLREHLNYWYSLKAYYLAKTMADVPFQIMFPVAYCSIVYWMTSQPSDAVRFVLFAALGTMTSLVAQSLGLLIGAASTSLQVATFVGPVTAIPVLLFSGFFVSFDTIPTYLQWMSYISYVRYGFEGVILSIYGLDREDLHCDIDETCHFQKSEAILRELDVENAKLYLDFIVLGIFFISLRLIAYFVLRYKIRAER\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MACLMAAFSVGTAMNASSYSAEMTEPKSVCVSVDEVVSSNMEATETDLLNGHLKKVDNNLTEAQRFSSLPRRAAVNIEFRDLSYSVPEGPWWRKKGYKTLLKGISGKFNSGELVAIMGPSGAGKSTLMNILAGYRETGMKGAVLINGLPRDLRCFRKVSCYIMQDDMLLPHLTVQEAMMVSAHLKLQEKDEGRREMVKEILTALGLLSCANTRTGSLSGGQRKRLAIALELVNNPPVMFFDEPTSGLDSASCFQVVSLMKGLAQGGRSIICTIHQPSAKLFELFDQLYVLSQGQCVYRGKVCNLVPYLRDLGLNCPTYHNPADFVMEVASGEYGDQNSRLVRAVREGMCDSDHKRDLGGDAEVNPFLWHRPSEEVKQTKRLKGLRKDSSSMEGCHSFSASCLTQFCILFKRTFLSIMRDSVLTHLRITSHIGIGLLIGLLYLGIGNEAKKVLSNSGFLFFSMLFLMFAALMPTVLTFPLEMGVFLREHLNYWYSLKAYYLAKTMADVPFQIMFPVAYCSIVYWMTSQPSDAVRFVLFAALGTMTSLVAQSLGLLIGAASTSLQVATFVGPVTAIPVLLFSGFFVSFDTIPTYLQWMSYISYVRYGFEGVILSIYGLDREDLHCDIDETCHFQKSEAILRELDVENAKLYLDFIVLGIFFISLRLIAYFVLRYKIRAER\", \"result\": [{\"smile\": \"CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC[C@@H]1NC(=O)[C@H]([C@H](O)[C@H](C)C\\C=C\\C)N(C)C(=O)[C@H](C(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](C)NC(=O)[C@H](C)NC(=O)[C@H](CC(C)C)N(C)C(=O)[C@@H](NC(=O)[C@H](CC(C)C)N(C)C(=O)CN(C)C1=O)C(C)C' can not interact with the protein 'MACLMAAFSVGTAMNASSYSAEMTEPKSVCVSVDEVVSSNMEATETDLLNGHLKKVDNNLTEAQRFSSLPRRAAVNIEFRDLSYSVPEGPWWRKKGYKTLLKGISGKFNSGELVAIMGPSGAGKSTLMNILAGYRETGMKGAVLINGLPRDLRCFRKVSCYIMQDDMLLPHLTVQEAMMVSAHLKLQEKDEGRREMVKEILTALGLLSCANTRTGSLSGGQRKRLAIALELVNNPPVMFFDEPTSGLDSASCFQVVSLMKGLAQGGRSIICTIHQPSAKLFELFDQLYVLSQGQCVYRGKVCNLVPYLRDLGLNCPTYHNPADFVMEVASGEYGDQNSRLVRAVREGMCDSDHKRDLGGDAEVNPFLWHRPSEEVKQTKRLKGLRKDSSSMEGCHSFSASCLTQFCILFKRTFLSIMRDSVLTHLRITSHIGIGLLIGLLYLGIGNEAKKVLSNSGFLFFSMLFLMFAALMPTVLTFPLEMGVFLREHLNYWYSLKAYYLAKTMADVPFQIMFPVAYCSIVYWMTSQPSDAVRFVLFAALGTMTSLVAQSLGLLIGAASTSLQVATFVGPVTAIPVLLFSGFFVSFDTIPTYLQWMSYISYVRYGFEGVILSIYGLDREDLHCDIDETCHFQKSEAILRELDVENAKLYLDFIVLGIFFISLRLIAYFVLRYKIRAER'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12' and the target protein 'MVMGIFANCIFCLKVKYLPQQQKKKLQTDIKENGGKFSFSLNPQCTHIILDNADVLSQYQLNSIQKNHVHIANPDFIWKSIREKRLLDVKNYDPYKPLDITPPPDQKASSSEVKTEGLCPDSATEEEDTVELTEFGMQNVEIPHLPQDFEVAKYNTLEKVGMEGGQEAVVVELQCSRDSRDCPFLISSHFLLDDGMETRRQFAIKKTSEDASEYFENYIEELKKQGFLLREHFTPEATQLASEQLQALLLEEVMNSSTLSQEVSDLVEMIWAEALGHLEHMLLKPVNRISLNDVSKAEGILLLVKAALKNGETAEQLQKMMTEFYRLIPHKGTMPKEVNLGLLAKKADLCQLIRDMVNVCETNLSKPNPPSLAKYRALRCKIEHVEQNTEEFLRVRKEVLQNHHSKSPVDVLQIFRVGRVNETTEFLSKLGNVRPLLHGSPVQNIVGILCRGLLLPKVVEDRGVQRTDVGNLGSGIYFSDSLSTSIKYSHPGETDGTRLLLICDVALGKCMDLHEKDFSLTEAPPGYDSVHGVSQTASVTTDFEDDEFVVYKTNQVKMKYIIKFSMPGDQIKDFHPSDHTELEEYRPEFSNFSKVEDYQLPDAKTSSSTKAGLQDASGNLVPLEDVHIKGRIIDTVAQVIVFQTYTNKSHVPIEAKYIFPLDDKAAVCGFEAFINGKHIVGEIKEKEEAQQEYLEAVTQGHGAYLMSQDAPDVFTVSVGNLPPKAKVLIKITYITELSILGTVGVFFMPATVAPWQQDKALNENLQDTVEKICIKEIGTKQSFSLTMSIEMPYVIEFIFSDTHELKQKRTDCKAVISTMEGSSLDSSGFSLHIGLSAAYLPRMWVEKHPEKESEACMLVFQPDLDVDLPDLASESEVIICLDCSSSMEGVTFLQAKQIALHALSLVGEKQKVNIIQFGTGYKELFSYPKHITSNTMAAEFIMSATPTMGNTDFWKTLRYLSLLYPARGSRNILLVSDGHLQDESLTLQLVKRSRPHTRLFACGIGSTANRHVLRILSQCGAGVFEYFNAKSKHSWRKQIEDQMTRLCSPSCHSVSVKWQQLNPDVPEALQAPAQVPSLFLNDRLLVYGFIPHCTQATLCALIQEKEFRTMVSTTELQKTTGTMIHKLAARALIRDYEDGILHENETSHEMKKQTLKSLIIKLSKENSLITQFTSFVAVEKRDENESPFPDIPKVSELIAKEDVDFLPYMSWQGEPQEAVRNQSLLASSEWPELRLSKRKHRKIPFSKRKMELSQPEVSEDFEEDGLGVLPAFTSNLERGGVEKLLDLSWTESCKPTATEPLFKKVSPWETSTSSFFPILAPAVGSYLPPTARAHSPASLSFASYRQVASFGSAAPPRQFDASQFSQGPVPGTCADWIPQSASCPTGPPQNPPSSPYCGIVFSGSSLSSAQSAPLQHPGGFTTRPSAGTFPELDSPQLHFSLPTDPDPIRGFGSYHPSASSPFHFQPSAASLTANLRLPMASALPEALCSQSRTTPVDLCLLEESVGSLEGSRCPVFAFQSSDTESDELSEVLQDSCFLQIKCDTKDDSILCFLEVKEEDEIVCIQHWQDAVPWTELLSLQTEDGFWKLTPELGLILNLNTNGLHSFLKQKGIQSLGVKGRECLLDLIATMLVLQFIRTRLEKEGIVFKSLMKMDDASISRNIPWAFEAIKQASEWVRRTEGQYPSICPRLELGNDWDSATKQLLGLQPISTVSPLHRVLHYSQG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12\"], \"target_seq\": \"MVMGIFANCIFCLKVKYLPQQQKKKLQTDIKENGGKFSFSLNPQCTHIILDNADVLSQYQLNSIQKNHVHIANPDFIWKSIREKRLLDVKNYDPYKPLDITPPPDQKASSSEVKTEGLCPDSATEEEDTVELTEFGMQNVEIPHLPQDFEVAKYNTLEKVGMEGGQEAVVVELQCSRDSRDCPFLISSHFLLDDGMETRRQFAIKKTSEDASEYFENYIEELKKQGFLLREHFTPEATQLASEQLQALLLEEVMNSSTLSQEVSDLVEMIWAEALGHLEHMLLKPVNRISLNDVSKAEGILLLVKAALKNGETAEQLQKMMTEFYRLIPHKGTMPKEVNLGLLAKKADLCQLIRDMVNVCETNLSKPNPPSLAKYRALRCKIEHVEQNTEEFLRVRKEVLQNHHSKSPVDVLQIFRVGRVNETTEFLSKLGNVRPLLHGSPVQNIVGILCRGLLLPKVVEDRGVQRTDVGNLGSGIYFSDSLSTSIKYSHPGETDGTRLLLICDVALGKCMDLHEKDFSLTEAPPGYDSVHGVSQTASVTTDFEDDEFVVYKTNQVKMKYIIKFSMPGDQIKDFHPSDHTELEEYRPEFSNFSKVEDYQLPDAKTSSSTKAGLQDASGNLVPLEDVHIKGRIIDTVAQVIVFQTYTNKSHVPIEAKYIFPLDDKAAVCGFEAFINGKHIVGEIKEKEEAQQEYLEAVTQGHGAYLMSQDAPDVFTVSVGNLPPKAKVLIKITYITELSILGTVGVFFMPATVAPWQQDKALNENLQDTVEKICIKEIGTKQSFSLTMSIEMPYVIEFIFSDTHELKQKRTDCKAVISTMEGSSLDSSGFSLHIGLSAAYLPRMWVEKHPEKESEACMLVFQPDLDVDLPDLASESEVIICLDCSSSMEGVTFLQAKQIALHALSLVGEKQKVNIIQFGTGYKELFSYPKHITSNTMAAEFIMSATPTMGNTDFWKTLRYLSLLYPARGSRNILLVSDGHLQDESLTLQLVKRSRPHTRLFACGIGSTANRHVLRILSQCGAGVFEYFNAKSKHSWRKQIEDQMTRLCSPSCHSVSVKWQQLNPDVPEALQAPAQVPSLFLNDRLLVYGFIPHCTQATLCALIQEKEFRTMVSTTELQKTTGTMIHKLAARALIRDYEDGILHENETSHEMKKQTLKSLIIKLSKENSLITQFTSFVAVEKRDENESPFPDIPKVSELIAKEDVDFLPYMSWQGEPQEAVRNQSLLASSEWPELRLSKRKHRKIPFSKRKMELSQPEVSEDFEEDGLGVLPAFTSNLERGGVEKLLDLSWTESCKPTATEPLFKKVSPWETSTSSFFPILAPAVGSYLPPTARAHSPASLSFASYRQVASFGSAAPPRQFDASQFSQGPVPGTCADWIPQSASCPTGPPQNPPSSPYCGIVFSGSSLSSAQSAPLQHPGGFTTRPSAGTFPELDSPQLHFSLPTDPDPIRGFGSYHPSASSPFHFQPSAASLTANLRLPMASALPEALCSQSRTTPVDLCLLEESVGSLEGSRCPVFAFQSSDTESDELSEVLQDSCFLQIKCDTKDDSILCFLEVKEEDEIVCIQHWQDAVPWTELLSLQTEDGFWKLTPELGLILNLNTNGLHSFLKQKGIQSLGVKGRECLLDLIATMLVLQFIRTRLEKEGIVFKSLMKMDDASISRNIPWAFEAIKQASEWVRRTEGQYPSICPRLELGNDWDSATKQLLGLQPISTVSPLHRVLHYSQG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVMGIFANCIFCLKVKYLPQQQKKKLQTDIKENGGKFSFSLNPQCTHIILDNADVLSQYQLNSIQKNHVHIANPDFIWKSIREKRLLDVKNYDPYKPLDITPPPDQKASSSEVKTEGLCPDSATEEEDTVELTEFGMQNVEIPHLPQDFEVAKYNTLEKVGMEGGQEAVVVELQCSRDSRDCPFLISSHFLLDDGMETRRQFAIKKTSEDASEYFENYIEELKKQGFLLREHFTPEATQLASEQLQALLLEEVMNSSTLSQEVSDLVEMIWAEALGHLEHMLLKPVNRISLNDVSKAEGILLLVKAALKNGETAEQLQKMMTEFYRLIPHKGTMPKEVNLGLLAKKADLCQLIRDMVNVCETNLSKPNPPSLAKYRALRCKIEHVEQNTEEFLRVRKEVLQNHHSKSPVDVLQIFRVGRVNETTEFLSKLGNVRPLLHGSPVQNIVGILCRGLLLPKVVEDRGVQRTDVGNLGSGIYFSDSLSTSIKYSHPGETDGTRLLLICDVALGKCMDLHEKDFSLTEAPPGYDSVHGVSQTASVTTDFEDDEFVVYKTNQVKMKYIIKFSMPGDQIKDFHPSDHTELEEYRPEFSNFSKVEDYQLPDAKTSSSTKAGLQDASGNLVPLEDVHIKGRIIDTVAQVIVFQTYTNKSHVPIEAKYIFPLDDKAAVCGFEAFINGKHIVGEIKEKEEAQQEYLEAVTQGHGAYLMSQDAPDVFTVSVGNLPPKAKVLIKITYITELSILGTVGVFFMPATVAPWQQDKALNENLQDTVEKICIKEIGTKQSFSLTMSIEMPYVIEFIFSDTHELKQKRTDCKAVISTMEGSSLDSSGFSLHIGLSAAYLPRMWVEKHPEKESEACMLVFQPDLDVDLPDLASESEVIICLDCSSSMEGVTFLQAKQIALHALSLVGEKQKVNIIQFGTGYKELFSYPKHITSNTMAAEFIMSATPTMGNTDFWKTLRYLSLLYPARGSRNILLVSDGHLQDESLTLQLVKRSRPHTRLFACGIGSTANRHVLRILSQCGAGVFEYFNAKSKHSWRKQIEDQMTRLCSPSCHSVSVKWQQLNPDVPEALQAPAQVPSLFLNDRLLVYGFIPHCTQATLCALIQEKEFRTMVSTTELQKTTGTMIHKLAARALIRDYEDGILHENETSHEMKKQTLKSLIIKLSKENSLITQFTSFVAVEKRDENESPFPDIPKVSELIAKEDVDFLPYMSWQGEPQEAVRNQSLLASSEWPELRLSKRKHRKIPFSKRKMELSQPEVSEDFEEDGLGVLPAFTSNLERGGVEKLLDLSWTESCKPTATEPLFKKVSPWETSTSSFFPILAPAVGSYLPPTARAHSPASLSFASYRQVASFGSAAPPRQFDASQFSQGPVPGTCADWIPQSASCPTGPPQNPPSSPYCGIVFSGSSLSSAQSAPLQHPGGFTTRPSAGTFPELDSPQLHFSLPTDPDPIRGFGSYHPSASSPFHFQPSAASLTANLRLPMASALPEALCSQSRTTPVDLCLLEESVGSLEGSRCPVFAFQSSDTESDELSEVLQDSCFLQIKCDTKDDSILCFLEVKEEDEIVCIQHWQDAVPWTELLSLQTEDGFWKLTPELGLILNLNTNGLHSFLKQKGIQSLGVKGRECLLDLIATMLVLQFIRTRLEKEGIVFKSLMKMDDASISRNIPWAFEAIKQASEWVRRTEGQYPSICPRLELGNDWDSATKQLLGLQPISTVSPLHRVLHYSQG\", \"result\": [{\"smile\": \"Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12' can not interact with the protein 'MVMGIFANCIFCLKVKYLPQQQKKKLQTDIKENGGKFSFSLNPQCTHIILDNADVLSQYQLNSIQKNHVHIANPDFIWKSIREKRLLDVKNYDPYKPLDITPPPDQKASSSEVKTEGLCPDSATEEEDTVELTEFGMQNVEIPHLPQDFEVAKYNTLEKVGMEGGQEAVVVELQCSRDSRDCPFLISSHFLLDDGMETRRQFAIKKTSEDASEYFENYIEELKKQGFLLREHFTPEATQLASEQLQALLLEEVMNSSTLSQEVSDLVEMIWAEALGHLEHMLLKPVNRISLNDVSKAEGILLLVKAALKNGETAEQLQKMMTEFYRLIPHKGTMPKEVNLGLLAKKADLCQLIRDMVNVCETNLSKPNPPSLAKYRALRCKIEHVEQNTEEFLRVRKEVLQNHHSKSPVDVLQIFRVGRVNETTEFLSKLGNVRPLLHGSPVQNIVGILCRGLLLPKVVEDRGVQRTDVGNLGSGIYFSDSLSTSIKYSHPGETDGTRLLLICDVALGKCMDLHEKDFSLTEAPPGYDSVHGVSQTASVTTDFEDDEFVVYKTNQVKMKYIIKFSMPGDQIKDFHPSDHTELEEYRPEFSNFSKVEDYQLPDAKTSSSTKAGLQDASGNLVPLEDVHIKGRIIDTVAQVIVFQTYTNKSHVPIEAKYIFPLDDKAAVCGFEAFINGKHIVGEIKEKEEAQQEYLEAVTQGHGAYLMSQDAPDVFTVSVGNLPPKAKVLIKITYITELSILGTVGVFFMPATVAPWQQDKALNENLQDTVEKICIKEIGTKQSFSLTMSIEMPYVIEFIFSDTHELKQKRTDCKAVISTMEGSSLDSSGFSLHIGLSAAYLPRMWVEKHPEKESEACMLVFQPDLDVDLPDLASESEVIICLDCSSSMEGVTFLQAKQIALHALSLVGEKQKVNIIQFGTGYKELFSYPKHITSNTMAAEFIMSATPTMGNTDFWKTLRYLSLLYPARGSRNILLVSDGHLQDESLTLQLVKRSRPHTRLFACGIGSTANRHVLRILSQCGAGVFEYFNAKSKHSWRKQIEDQMTRLCSPSCHSVSVKWQQLNPDVPEALQAPAQVPSLFLNDRLLVYGFIPHCTQATLCALIQEKEFRTMVSTTELQKTTGTMIHKLAARALIRDYEDGILHENETSHEMKKQTLKSLIIKLSKENSLITQFTSFVAVEKRDENESPFPDIPKVSELIAKEDVDFLPYMSWQGEPQEAVRNQSLLASSEWPELRLSKRKHRKIPFSKRKMELSQPEVSEDFEEDGLGVLPAFTSNLERGGVEKLLDLSWTESCKPTATEPLFKKVSPWETSTSSFFPILAPAVGSYLPPTARAHSPASLSFASYRQVASFGSAAPPRQFDASQFSQGPVPGTCADWIPQSASCPTGPPQNPPSSPYCGIVFSGSSLSSAQSAPLQHPGGFTTRPSAGTFPELDSPQLHFSLPTDPDPIRGFGSYHPSASSPFHFQPSAASLTANLRLPMASALPEALCSQSRTTPVDLCLLEESVGSLEGSRCPVFAFQSSDTESDELSEVLQDSCFLQIKCDTKDDSILCFLEVKEEDEIVCIQHWQDAVPWTELLSLQTEDGFWKLTPELGLILNLNTNGLHSFLKQKGIQSLGVKGRECLLDLIATMLVLQFIRTRLEKEGIVFKSLMKMDDASISRNIPWAFEAIKQASEWVRRTEGQYPSICPRLELGNDWDSATKQLLGLQPISTVSPLHRVLHYSQG'."
            },
            {
                "from": "human",
                "value": "What's the expected likelihood of the drug above engaging with the target protein 'MVAMAEAEAGVAVEVRGLPPAVPDELLTLYFENRRRSGGGPVLSWQRLGCGGVLTFREPADAERVLAQADHELHGAQLSLRPAPPRAPARLLLQGLPPGTTPQRLEQHVQALLRASGLPVQPCCALASPRPDRALVQLPKPLSEADVRVLEEQAQNLGLEGTLVSLARVPQARAVRVVGDGASVDLLLLELYLENERRSGGGPLEDLQRLPGPLGTVASFQQWQVAERVLQQEHRLQGSELSLVPHYDILEPEELAENTSGGDHPSTQGPRATKHALLRTGGLVTALQGAGTVTMGSGEEPGQSGASLRTGPMVQGRGIMTTGSGQEPGQSGTSLRTGPMGSLGQAEQVSSMPMGSLEHEGLVSLRPVGLQEQEGPMSLGPVGSAGPVETSKGLLGQEGLVEIAMDSPEQEGLVGPMEITMGSLEKAGPVSPGCVKLAGQEGLVEMVLLMEPGAMRFLQLYHEDLLAGLGDVALLPLEGPDMTGFRLCGAQASCQAAEEFLRSLLGSISCHVLCLEHPGSARFLLGPEGQHLLQGLEAQFQCVFGTERLATATLDTGLEEVDPTEALPVLPGNAHTLWTPDSTGGDQEDVSLEEVRELLATLEGLDLDGEDWLPRELEEEGPQEQPEEEVTPGHEEEEPVAPSTVAPRWLEEEAALQLALHRSLEPQGQVAEQEEAAALRQALTLSLLEQPPLEAEEPPDGGTDGKAQLVVHSAFEQDVEELDRALRAALEVHVQEETVGPWRRTLPAELRARLERCHGVSVALRGDCTILRGFGAHPARAARHLVALLAGPWDQSLAFPLAASGPTLAGQTLKGPWNNLERLAENTGEFQEVVRAFYDTLDAARSSIRVVRVERVSHPLLQQQYELYRERLLQRCERRPVEQVLYHGTTAPAVPDICAHGFNRSFCGRNATVYGKGVYFARRASLSVQDRYSPPNADGHKAVFVARVLTGDYGQGRRGLRAPPLRGPGHVLLRYDSAVDCICQPSIFVIFHDTQALPTHLITCEHVPRASPDDPSGLPGRSPDT'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVAMAEAEAGVAVEVRGLPPAVPDELLTLYFENRRRSGGGPVLSWQRLGCGGVLTFREPADAERVLAQADHELHGAQLSLRPAPPRAPARLLLQGLPPGTTPQRLEQHVQALLRASGLPVQPCCALASPRPDRALVQLPKPLSEADVRVLEEQAQNLGLEGTLVSLARVPQARAVRVVGDGASVDLLLLELYLENERRSGGGPLEDLQRLPGPLGTVASFQQWQVAERVLQQEHRLQGSELSLVPHYDILEPEELAENTSGGDHPSTQGPRATKHALLRTGGLVTALQGAGTVTMGSGEEPGQSGASLRTGPMVQGRGIMTTGSGQEPGQSGTSLRTGPMGSLGQAEQVSSMPMGSLEHEGLVSLRPVGLQEQEGPMSLGPVGSAGPVETSKGLLGQEGLVEIAMDSPEQEGLVGPMEITMGSLEKAGPVSPGCVKLAGQEGLVEMVLLMEPGAMRFLQLYHEDLLAGLGDVALLPLEGPDMTGFRLCGAQASCQAAEEFLRSLLGSISCHVLCLEHPGSARFLLGPEGQHLLQGLEAQFQCVFGTERLATATLDTGLEEVDPTEALPVLPGNAHTLWTPDSTGGDQEDVSLEEVRELLATLEGLDLDGEDWLPRELEEEGPQEQPEEEVTPGHEEEEPVAPSTVAPRWLEEEAALQLALHRSLEPQGQVAEQEEAAALRQALTLSLLEQPPLEAEEPPDGGTDGKAQLVVHSAFEQDVEELDRALRAALEVHVQEETVGPWRRTLPAELRARLERCHGVSVALRGDCTILRGFGAHPARAARHLVALLAGPWDQSLAFPLAASGPTLAGQTLKGPWNNLERLAENTGEFQEVVRAFYDTLDAARSSIRVVRVERVSHPLLQQQYELYRERLLQRCERRPVEQVLYHGTTAPAVPDICAHGFNRSFCGRNATVYGKGVYFARRASLSVQDRYSPPNADGHKAVFVARVLTGDYGQGRRGLRAPPLRGPGHVLLRYDSAVDCICQPSIFVIFHDTQALPTHLITCEHVPRASPDDPSGLPGRSPDT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVAMAEAEAGVAVEVRGLPPAVPDELLTLYFENRRRSGGGPVLSWQRLGCGGVLTFREPADAERVLAQADHELHGAQLSLRPAPPRAPARLLLQGLPPGTTPQRLEQHVQALLRASGLPVQPCCALASPRPDRALVQLPKPLSEADVRVLEEQAQNLGLEGTLVSLARVPQARAVRVVGDGASVDLLLLELYLENERRSGGGPLEDLQRLPGPLGTVASFQQWQVAERVLQQEHRLQGSELSLVPHYDILEPEELAENTSGGDHPSTQGPRATKHALLRTGGLVTALQGAGTVTMGSGEEPGQSGASLRTGPMVQGRGIMTTGSGQEPGQSGTSLRTGPMGSLGQAEQVSSMPMGSLEHEGLVSLRPVGLQEQEGPMSLGPVGSAGPVETSKGLLGQEGLVEIAMDSPEQEGLVGPMEITMGSLEKAGPVSPGCVKLAGQEGLVEMVLLMEPGAMRFLQLYHEDLLAGLGDVALLPLEGPDMTGFRLCGAQASCQAAEEFLRSLLGSISCHVLCLEHPGSARFLLGPEGQHLLQGLEAQFQCVFGTERLATATLDTGLEEVDPTEALPVLPGNAHTLWTPDSTGGDQEDVSLEEVRELLATLEGLDLDGEDWLPRELEEEGPQEQPEEEVTPGHEEEEPVAPSTVAPRWLEEEAALQLALHRSLEPQGQVAEQEEAAALRQALTLSLLEQPPLEAEEPPDGGTDGKAQLVVHSAFEQDVEELDRALRAALEVHVQEETVGPWRRTLPAELRARLERCHGVSVALRGDCTILRGFGAHPARAARHLVALLAGPWDQSLAFPLAASGPTLAGQTLKGPWNNLERLAENTGEFQEVVRAFYDTLDAARSSIRVVRVERVSHPLLQQQYELYRERLLQRCERRPVEQVLYHGTTAPAVPDICAHGFNRSFCGRNATVYGKGVYFARRASLSVQDRYSPPNADGHKAVFVARVLTGDYGQGRRGLRAPPLRGPGHVLLRYDSAVDCICQPSIFVIFHDTQALPTHLITCEHVPRASPDDPSGLPGRSPDT\", \"result\": [{\"smile\": \"Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cccc2c(=O)[nH]c(CCC(=O)Nc3ccc(C(=O)Nc4cccc5cccnc45)cc3)nc12' can not interact with the protein 'MVAMAEAEAGVAVEVRGLPPAVPDELLTLYFENRRRSGGGPVLSWQRLGCGGVLTFREPADAERVLAQADHELHGAQLSLRPAPPRAPARLLLQGLPPGTTPQRLEQHVQALLRASGLPVQPCCALASPRPDRALVQLPKPLSEADVRVLEEQAQNLGLEGTLVSLARVPQARAVRVVGDGASVDLLLLELYLENERRSGGGPLEDLQRLPGPLGTVASFQQWQVAERVLQQEHRLQGSELSLVPHYDILEPEELAENTSGGDHPSTQGPRATKHALLRTGGLVTALQGAGTVTMGSGEEPGQSGASLRTGPMVQGRGIMTTGSGQEPGQSGTSLRTGPMGSLGQAEQVSSMPMGSLEHEGLVSLRPVGLQEQEGPMSLGPVGSAGPVETSKGLLGQEGLVEIAMDSPEQEGLVGPMEITMGSLEKAGPVSPGCVKLAGQEGLVEMVLLMEPGAMRFLQLYHEDLLAGLGDVALLPLEGPDMTGFRLCGAQASCQAAEEFLRSLLGSISCHVLCLEHPGSARFLLGPEGQHLLQGLEAQFQCVFGTERLATATLDTGLEEVDPTEALPVLPGNAHTLWTPDSTGGDQEDVSLEEVRELLATLEGLDLDGEDWLPRELEEEGPQEQPEEEVTPGHEEEEPVAPSTVAPRWLEEEAALQLALHRSLEPQGQVAEQEEAAALRQALTLSLLEQPPLEAEEPPDGGTDGKAQLVVHSAFEQDVEELDRALRAALEVHVQEETVGPWRRTLPAELRARLERCHGVSVALRGDCTILRGFGAHPARAARHLVALLAGPWDQSLAFPLAASGPTLAGQTLKGPWNNLERLAENTGEFQEVVRAFYDTLDAARSSIRVVRVERVSHPLLQQQYELYRERLLQRCERRPVEQVLYHGTTAPAVPDICAHGFNRSFCGRNATVYGKGVYFARRASLSVQDRYSPPNADGHKAVFVARVLTGDYGQGRRGLRAPPLRGPGHVLLRYDSAVDCICQPSIFVIFHDTQALPTHLITCEHVPRASPDDPSGLPGRSPDT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess if the drug 'CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O' will engage with the target protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O\"], \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\", \"result\": [{\"smile\": \"CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O' can not interact with the protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW'."
            },
            {
                "from": "human",
                "value": "Will the drug above interact with the target protein 'MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG' as expected? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG\", \"result\": [{\"smile\": \"CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1CCCc2c1nc(N1CCN(Cc3ccccc3C(F)(F)F)CC1)[nH]c2=O' can interact with the protein 'MSGRRCAGGGAACASAAAEAVEPAARELFEACRNGDVERVKRLVTPEKVNSRDTAGRKSTPLHFAAGFGRKDVVEYLLQNGANVQARDDGGLIPLHNACSFGHAEVVNLLLRHGADPNARDNWNYTPLHEAAIKGKIDVCIVLLQHGAEPTIRNTDGRTALDLADPSAKAVLTGEYKKDELLESARSGNEEKMMALLTPLNVNCHASDGRKSTPLHLAAGYNRVKIVQLLLQHGADVHAKDKGDLVPLHNACSYGHYEVTELLVKHGACVNAMDLWQFTPLHEAASKNRVEVCSLLLSYGADPTLLNCHNKSAIDLAPTPQLKERLAYEFKGHSLLQAAREADVTRIKKHLSLEMVNFKHPQTHETALHCAAASPYPKRKQICELLLRKGANINEKTKEFLTPLHVASEKAHNDVVEVVVKHEAKVNALDNLGQTSLHRAAYCGHLQTCRLLLSYGCDPNIISLQGFTALQMGNENVQQLLQEGISLGNSEADRQLLEAAKAGDVETVKKLCTVQSVNCRDIEGRQSTPLHFAAGYNRVSVVEYLLQHGADVHAKDKGGLVPLHNACSYGHYEVAELLVKHGAVVNVADLWKFTPLHEAAAKGKYEICKLLLQHGADPTKKNRDGNTPLDLVKDGDTDIQDLLRGDAALLDAAKKGCLARVKKLSSPDNVNCRDTQGRHSTPLHLAAGYNNLEVAEYLLQHGADVNAQDKGGLIPLHNAASYGHVDVAALLIKYNACVNATDKWAFTPLHEAAQKGRTQLCALLLAHGADPTLKNQEGQTPLDLVSADDVSALLTAAMPPSALPSCYKPQVLNGVRSPGATADALSSGPSSPSSLSAASSLDNLSGSFSELSSVVSSSGTEGASSLEKKEVPGVDFSITQFVRNLGLEHLMDIFEREQITLDVLVEMGHKELKEIGINAYGHRHKLIKGVERLISGQQGLNPYLTLNTSGSGTILIDLSPDDKEFQSVEEEMQSTVREHRDGGHAGGIFNRYNILKIQKVCNKKLWERYTHRRKEVSEENHNHANERMLFHGSPFVNAIIHKGFDERHAYIGGMFGAGIYFAENSSKSNQYVYGIGGGTGCPVHKDRSCYICHRQLLFCRVTLGKSFLQFSAMKMAHSPPGHHSVTGRPSVNGLALAEYVIYRGEQAYPEYLITYQIMRPEGMVDG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the drug 'NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1''s potential to interact with the target protein 'MWLPLLLGALLWAVLWLLRDRQSLPASNAFVFITGCDSGFGRLLALQLDQRGFRVLASCLTPSGAEDLQRVASSRLHTTLLDITDPQSVQQAAKWVEMHVKEAGLFGLVNNAGVAGIIGPTPWLTRDDFQRVLNVNTMGPIGVTLALLPLLQQARGRVINITSVLGRLAANGGGYCVSKFGLEAFSDSLRRDVAHFGIRVSIVEPGFFRTPVTNLESLEKTLQACWARLPPATQAHYGGAFLTKYLKMQQRIMNLICDPDLTKVSRCLEHALTARHPRTRYSPGWDAKLLWLPASYLPASLVDAVLTWVLPKPAQAVY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1\"], \"target_seq\": \"MWLPLLLGALLWAVLWLLRDRQSLPASNAFVFITGCDSGFGRLLALQLDQRGFRVLASCLTPSGAEDLQRVASSRLHTTLLDITDPQSVQQAAKWVEMHVKEAGLFGLVNNAGVAGIIGPTPWLTRDDFQRVLNVNTMGPIGVTLALLPLLQQARGRVINITSVLGRLAANGGGYCVSKFGLEAFSDSLRRDVAHFGIRVSIVEPGFFRTPVTNLESLEKTLQACWARLPPATQAHYGGAFLTKYLKMQQRIMNLICDPDLTKVSRCLEHALTARHPRTRYSPGWDAKLLWLPASYLPASLVDAVLTWVLPKPAQAVY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWLPLLLGALLWAVLWLLRDRQSLPASNAFVFITGCDSGFGRLLALQLDQRGFRVLASCLTPSGAEDLQRVASSRLHTTLLDITDPQSVQQAAKWVEMHVKEAGLFGLVNNAGVAGIIGPTPWLTRDDFQRVLNVNTMGPIGVTLALLPLLQQARGRVINITSVLGRLAANGGGYCVSKFGLEAFSDSLRRDVAHFGIRVSIVEPGFFRTPVTNLESLEKTLQACWARLPPATQAHYGGAFLTKYLKMQQRIMNLICDPDLTKVSRCLEHALTARHPRTRYSPGWDAKLLWLPASYLPASLVDAVLTWVLPKPAQAVY\", \"result\": [{\"smile\": \"NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1' can not interact with the protein 'MWLPLLLGALLWAVLWLLRDRQSLPASNAFVFITGCDSGFGRLLALQLDQRGFRVLASCLTPSGAEDLQRVASSRLHTTLLDITDPQSVQQAAKWVEMHVKEAGLFGLVNNAGVAGIIGPTPWLTRDDFQRVLNVNTMGPIGVTLALLPLLQQARGRVINITSVLGRLAANGGGYCVSKFGLEAFSDSLRRDVAHFGIRVSIVEPGFFRTPVTNLESLEKTLQACWARLPPATQAHYGGAFLTKYLKMQQRIMNLICDPDLTKVSRCLEHALTARHPRTRYSPGWDAKLLWLPASYLPASLVDAVLTWVLPKPAQAVY'."
            },
            {
                "from": "human",
                "value": "How do you view the chances of the drug above interacting with the target protein 'MRALLARLLLCVLVVSDSKGSNELHQVPSNCDCLNGGTCVSNKYFSNIHWCNCPKKFGGQHCEIDKSKTCYEGNGHFYRGKASTDTMGRPCLPWNSATVLQQTYHAHRSDALQLGLGKHNYCRNPDNRRRPWCYVQVGLKPLVQECMVHDCADGKKPSSPPEELKFQCGQKTLRPRFKIIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVENLILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKEENGLAL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRALLARLLLCVLVVSDSKGSNELHQVPSNCDCLNGGTCVSNKYFSNIHWCNCPKKFGGQHCEIDKSKTCYEGNGHFYRGKASTDTMGRPCLPWNSATVLQQTYHAHRSDALQLGLGKHNYCRNPDNRRRPWCYVQVGLKPLVQECMVHDCADGKKPSSPPEELKFQCGQKTLRPRFKIIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVENLILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKEENGLAL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRALLARLLLCVLVVSDSKGSNELHQVPSNCDCLNGGTCVSNKYFSNIHWCNCPKKFGGQHCEIDKSKTCYEGNGHFYRGKASTDTMGRPCLPWNSATVLQQTYHAHRSDALQLGLGKHNYCRNPDNRRRPWCYVQVGLKPLVQECMVHDCADGKKPSSPPEELKFQCGQKTLRPRFKIIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVENLILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKEENGLAL\", \"result\": [{\"smile\": \"NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NCC1=CC=C(NC(=O)C2=CC=C3C=C(C=CC3=C2)C(N)=N)C=C1' can interact with the protein 'MRALLARLLLCVLVVSDSKGSNELHQVPSNCDCLNGGTCVSNKYFSNIHWCNCPKKFGGQHCEIDKSKTCYEGNGHFYRGKASTDTMGRPCLPWNSATVLQQTYHAHRSDALQLGLGKHNYCRNPDNRRRPWCYVQVGLKPLVQECMVHDCADGKKPSSPPEELKFQCGQKTLRPRFKIIGGEFTTIENQPWFAAIYRRHRGGSVTYVCGGSLISPCWVISATHCFIDYPKKEDYIVYLGRSRLNSNTQGEMKFEVENLILHKDYSADTLAHHNDIALLKIRSKEGRCAQPSRTIQTICLPSMYNDPQFGTSCEITGFGKENSTDYLYPEQLKMTVVKLISHRECQQPHYYGSEVTTKMLCAADPQWKTDSCQGDSGGPLVCSLQGRMTLTGIVSWGRGCALKDKPGVYTRVSHFLPWIRSHTKEENGLAL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C' possibly engage with the target protein 'MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDFPNCFFVLGRAWSEHRRHRNLNIRSE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C\"], \"target_seq\": \"MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDFPNCFFVLGRAWSEHRRHRNLNIRSE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDFPNCFFVLGRAWSEHRRHRNLNIRSE\", \"result\": [{\"smile\": \"CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C' can interact with the protein 'MRHHHHHHRDHFALDRPSETHADYLLRTGQVVDISDTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGDQVQCFACGGKLKNWEPGDFPNCFFVLGRAWSEHRRHRNLNIRSE'."
            },
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug above and the target protein 'MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT\", \"result\": [{\"smile\": \"CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN[C@@H](C)C(=O)N[C@@H]1C(=O)N(Cc2nn(-c3ccccc3C#N)c3ccccc23)c2ccccc2N(C(=O)CS(C)(=O)=O)[C@H]1C' can not interact with the protein 'MRHHHHHHRSDAVSSDRNFPNSTNLPRNPSMADYEARIFTFGTWIYSVNKEQLARAGFYALGEGDKVKCFHCGGGLTDWKPSEDPWEQHAKWYPGCKYLLEQKGQEYINNIHLTHSLEECLVRTT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it for the drug 'O[C@H](CC(O)=O)C(O)=O' and target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' to have an interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O[C@H](CC(O)=O)C(O)=O\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"O[C@H](CC(O)=O)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O[C@H](CC(O)=O)C(O)=O' can not interact with the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'."
            },
            {
                "from": "human",
                "value": "Can you evaluate if the drug above is expected to interact with the target protein 'MFSLKTLPFLLLLHVQISKAFPVSSKEKNTKTVQDYLEKFYQLPSNQYQSTRKNGTNVIVEKLKEMQRFFGLNVTGKPNEETLDMMKKPRCGVPDSGGFMLTPGNPKWERTNLTYRIRNYTPQLSEAEVERAIKDAFELWSVASPLIFTRISQGEADINIAFYQRDHGDNSPFDGPNGILAHAFQPGQGIGGDAHFDAEETWTNTSANYNLFLVAAHEFGHSLGLAHSSDPGALMYPNYAFRETSNYSLPQDDIDGIQAIYGLSSNPIQPTGPSTPKPCDPSLTFDAITTLRGEILFFKDRYFWRRHPQLQRVEMNFISLFWPSLPTGIQAAYEDFDRDLIFLFKGNQYWALSGYDILQGYPKDISNYGFPSSVQAIDAAVFYRSKTYFFVNDQFWRYDNQRQFMEPGYPKSISGAFPGIESKVDAVFQQEHFFHVFSGPRYYAFDLIAQRVTRVARGNKWLNCRYG'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MFSLKTLPFLLLLHVQISKAFPVSSKEKNTKTVQDYLEKFYQLPSNQYQSTRKNGTNVIVEKLKEMQRFFGLNVTGKPNEETLDMMKKPRCGVPDSGGFMLTPGNPKWERTNLTYRIRNYTPQLSEAEVERAIKDAFELWSVASPLIFTRISQGEADINIAFYQRDHGDNSPFDGPNGILAHAFQPGQGIGGDAHFDAEETWTNTSANYNLFLVAAHEFGHSLGLAHSSDPGALMYPNYAFRETSNYSLPQDDIDGIQAIYGLSSNPIQPTGPSTPKPCDPSLTFDAITTLRGEILFFKDRYFWRRHPQLQRVEMNFISLFWPSLPTGIQAAYEDFDRDLIFLFKGNQYWALSGYDILQGYPKDISNYGFPSSVQAIDAAVFYRSKTYFFVNDQFWRYDNQRQFMEPGYPKSISGAFPGIESKVDAVFQQEHFFHVFSGPRYYAFDLIAQRVTRVARGNKWLNCRYG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFSLKTLPFLLLLHVQISKAFPVSSKEKNTKTVQDYLEKFYQLPSNQYQSTRKNGTNVIVEKLKEMQRFFGLNVTGKPNEETLDMMKKPRCGVPDSGGFMLTPGNPKWERTNLTYRIRNYTPQLSEAEVERAIKDAFELWSVASPLIFTRISQGEADINIAFYQRDHGDNSPFDGPNGILAHAFQPGQGIGGDAHFDAEETWTNTSANYNLFLVAAHEFGHSLGLAHSSDPGALMYPNYAFRETSNYSLPQDDIDGIQAIYGLSSNPIQPTGPSTPKPCDPSLTFDAITTLRGEILFFKDRYFWRRHPQLQRVEMNFISLFWPSLPTGIQAAYEDFDRDLIFLFKGNQYWALSGYDILQGYPKDISNYGFPSSVQAIDAAVFYRSKTYFFVNDQFWRYDNQRQFMEPGYPKSISGAFPGIESKVDAVFQQEHFFHVFSGPRYYAFDLIAQRVTRVARGNKWLNCRYG\", \"result\": [{\"smile\": \"O[C@H](CC(O)=O)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O[C@H](CC(O)=O)C(O)=O' can not interact with the protein 'MFSLKTLPFLLLLHVQISKAFPVSSKEKNTKTVQDYLEKFYQLPSNQYQSTRKNGTNVIVEKLKEMQRFFGLNVTGKPNEETLDMMKKPRCGVPDSGGFMLTPGNPKWERTNLTYRIRNYTPQLSEAEVERAIKDAFELWSVASPLIFTRISQGEADINIAFYQRDHGDNSPFDGPNGILAHAFQPGQGIGGDAHFDAEETWTNTSANYNLFLVAAHEFGHSLGLAHSSDPGALMYPNYAFRETSNYSLPQDDIDGIQAIYGLSSNPIQPTGPSTPKPCDPSLTFDAITTLRGEILFFKDRYFWRRHPQLQRVEMNFISLFWPSLPTGIQAAYEDFDRDLIFLFKGNQYWALSGYDILQGYPKDISNYGFPSSVQAIDAAVFYRSKTYFFVNDQFWRYDNQRQFMEPGYPKSISGAFPGIESKVDAVFQQEHFFHVFSGPRYYAFDLIAQRVTRVARGNKWLNCRYG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1' is likely to interact with the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1\"], \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1' can not interact with the protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'."
            },
            {
                "from": "human",
                "value": "Is there a possibility of the drug above interacting with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@H]1CN(c2ccc(C(=O)Nc3ccccc3N)cn2)CCN1C(=O)OCc1ccccc1' can not interact with the protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C' interact with the target protein 'MHVIKRDGRQERVMFDKITSRIQKLCYGLNMDFVDPAQITMKVIQGLYSGVTTVELDTLAAETAATLTTKHPDYAILAARIAVSNLHKETKKVFSDVMEDLYNYINPHNGKHSPMVAKSTLDIVLANKDRLNSAIIYDRDFSYNYFGFKTLERSYLLKINGKVAERPQHMLMRVSVGIHKEDIDAAIETYNLLSERWFTHASPTLFNAGTNRPQLSSCFLLSMKDDSIEGIYDTLKQCALISKSAGGIGVAVSCIRATGSYIAGTNGNSNGLVPMLRVYNNTARYVDQGGNKRPGAFAIYLEPWHLDIFEFLDLKKNTGKEEQRARDLFFALWIPDLFMKRVETNQDWSLMCPNECPGLDEVWGEEFEKLYASYEKQGRVRKVVKAQQLWYAIIESQTETGTPYMLYKDSCNRKSNQQNLGTIKCSNLCTEIVEYTSKDEVAVCNLASLALNMYVTSEHTYDFKKLAEVTKVVVRNLNKIIDINYYPVPEACLSNKRHRPIGIGVQGLADAFILMRYPFESAEAQLLNKQIFETIYYGALEASCDLAKEQGPYETYEGSPVSKGILQYDMWNVTPTDLWDWKVLKEKIAKYGIRNSLLIAPMPTASTAQILGNNESIEPYTSNIYTRRVLSGEFQIVNPHLLKDLTERGLWHEEMKNQIIACNGSIQSIPEIPDDLKQLYKTVWEISQKTVLKMAAERGAFIDQSQSLNIHIAEPNYGKLTSMHFYGWKQGLKTGMYYLRTRPAANPIQFTLNKEKLKDKEKVSKEEEEKERNTAAMVCSLENRDECLMCGS' according to your assessment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C\"], \"target_seq\": \"MHVIKRDGRQERVMFDKITSRIQKLCYGLNMDFVDPAQITMKVIQGLYSGVTTVELDTLAAETAATLTTKHPDYAILAARIAVSNLHKETKKVFSDVMEDLYNYINPHNGKHSPMVAKSTLDIVLANKDRLNSAIIYDRDFSYNYFGFKTLERSYLLKINGKVAERPQHMLMRVSVGIHKEDIDAAIETYNLLSERWFTHASPTLFNAGTNRPQLSSCFLLSMKDDSIEGIYDTLKQCALISKSAGGIGVAVSCIRATGSYIAGTNGNSNGLVPMLRVYNNTARYVDQGGNKRPGAFAIYLEPWHLDIFEFLDLKKNTGKEEQRARDLFFALWIPDLFMKRVETNQDWSLMCPNECPGLDEVWGEEFEKLYASYEKQGRVRKVVKAQQLWYAIIESQTETGTPYMLYKDSCNRKSNQQNLGTIKCSNLCTEIVEYTSKDEVAVCNLASLALNMYVTSEHTYDFKKLAEVTKVVVRNLNKIIDINYYPVPEACLSNKRHRPIGIGVQGLADAFILMRYPFESAEAQLLNKQIFETIYYGALEASCDLAKEQGPYETYEGSPVSKGILQYDMWNVTPTDLWDWKVLKEKIAKYGIRNSLLIAPMPTASTAQILGNNESIEPYTSNIYTRRVLSGEFQIVNPHLLKDLTERGLWHEEMKNQIIACNGSIQSIPEIPDDLKQLYKTVWEISQKTVLKMAAERGAFIDQSQSLNIHIAEPNYGKLTSMHFYGWKQGLKTGMYYLRTRPAANPIQFTLNKEKLKDKEKVSKEEEEKERNTAAMVCSLENRDECLMCGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MHVIKRDGRQERVMFDKITSRIQKLCYGLNMDFVDPAQITMKVIQGLYSGVTTVELDTLAAETAATLTTKHPDYAILAARIAVSNLHKETKKVFSDVMEDLYNYINPHNGKHSPMVAKSTLDIVLANKDRLNSAIIYDRDFSYNYFGFKTLERSYLLKINGKVAERPQHMLMRVSVGIHKEDIDAAIETYNLLSERWFTHASPTLFNAGTNRPQLSSCFLLSMKDDSIEGIYDTLKQCALISKSAGGIGVAVSCIRATGSYIAGTNGNSNGLVPMLRVYNNTARYVDQGGNKRPGAFAIYLEPWHLDIFEFLDLKKNTGKEEQRARDLFFALWIPDLFMKRVETNQDWSLMCPNECPGLDEVWGEEFEKLYASYEKQGRVRKVVKAQQLWYAIIESQTETGTPYMLYKDSCNRKSNQQNLGTIKCSNLCTEIVEYTSKDEVAVCNLASLALNMYVTSEHTYDFKKLAEVTKVVVRNLNKIIDINYYPVPEACLSNKRHRPIGIGVQGLADAFILMRYPFESAEAQLLNKQIFETIYYGALEASCDLAKEQGPYETYEGSPVSKGILQYDMWNVTPTDLWDWKVLKEKIAKYGIRNSLLIAPMPTASTAQILGNNESIEPYTSNIYTRRVLSGEFQIVNPHLLKDLTERGLWHEEMKNQIIACNGSIQSIPEIPDDLKQLYKTVWEISQKTVLKMAAERGAFIDQSQSLNIHIAEPNYGKLTSMHFYGWKQGLKTGMYYLRTRPAANPIQFTLNKEKLKDKEKVSKEEEEKERNTAAMVCSLENRDECLMCGS\", \"result\": [{\"smile\": \"CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C' can not interact with the protein 'MHVIKRDGRQERVMFDKITSRIQKLCYGLNMDFVDPAQITMKVIQGLYSGVTTVELDTLAAETAATLTTKHPDYAILAARIAVSNLHKETKKVFSDVMEDLYNYINPHNGKHSPMVAKSTLDIVLANKDRLNSAIIYDRDFSYNYFGFKTLERSYLLKINGKVAERPQHMLMRVSVGIHKEDIDAAIETYNLLSERWFTHASPTLFNAGTNRPQLSSCFLLSMKDDSIEGIYDTLKQCALISKSAGGIGVAVSCIRATGSYIAGTNGNSNGLVPMLRVYNNTARYVDQGGNKRPGAFAIYLEPWHLDIFEFLDLKKNTGKEEQRARDLFFALWIPDLFMKRVETNQDWSLMCPNECPGLDEVWGEEFEKLYASYEKQGRVRKVVKAQQLWYAIIESQTETGTPYMLYKDSCNRKSNQQNLGTIKCSNLCTEIVEYTSKDEVAVCNLASLALNMYVTSEHTYDFKKLAEVTKVVVRNLNKIIDINYYPVPEACLSNKRHRPIGIGVQGLADAFILMRYPFESAEAQLLNKQIFETIYYGALEASCDLAKEQGPYETYEGSPVSKGILQYDMWNVTPTDLWDWKVLKEKIAKYGIRNSLLIAPMPTASTAQILGNNESIEPYTSNIYTRRVLSGEFQIVNPHLLKDLTERGLWHEEMKNQIIACNGSIQSIPEIPDDLKQLYKTVWEISQKTVLKMAAERGAFIDQSQSLNIHIAEPNYGKLTSMHFYGWKQGLKTGMYYLRTRPAANPIQFTLNKEKLKDKEKVSKEEEEKERNTAAMVCSLENRDECLMCGS'."
            },
            {
                "from": "human",
                "value": "Will the drug above probably interact with the target protein 'MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP\", \"result\": [{\"smile\": \"CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC1=CC=CC2=C1OC(=C2)C(O)CNC(C)(C)C' can not interact with the protein 'MLNLLLLALPVLASRAYAAPAPGQALQRVGIVGGQEAPRSKWPWQVSLRVHGPYWMHFCGGSLIHPQWVLTAAHCVGPDVKDLAALRVQLREQHLYYQDQLLPVSRIIVHPQFYTAQIGADIALLELEEPVNVSSHVHTVTLPPASETFPPGMPCWVTGWGDVDNDERLPPPFPLKQVKVPIMENHICDAKYHLGAYTGDDVRIVRDDMLCAGNTRRDSCQGDSGGPLVCKVNGTWLQAGVVSWGEGCAQPNRPGIYTRVTYYLDWIHHYVPKKP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a possibility of the drug '[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O' interacting with the target protein 'MEVSPLQPVNENMQVNKIKKNEDAKKRLSVERIYQKKTQLEHILLRPDTYIGSVELVTQQMWVYDEDVGINYREVTFVPGLYKIFDEILVNAADNKQRDPKMSCIRVTIDPENNLISIWNNGKGIPVVEHKVEKMYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFSTKFTVETASREYKKMFKQTWMDNMGRAGEMELKPFNGEDYTCITFQPDLSKFKMQSLDKDIVALMVRRAYDIAGSTKDVKVFLNGNKLPVKGFRSYVDMYLKDKLDETGNSLKVIHEQVNHRWEVCLTMSEKGFQQISFVNSIATSKGGRHVDYVADQIVTKLVDVVKKKNKGGVAVKAHQVKNHMWIFVNALIENPTFDSQTKENMTLQPKSFGSTCQLSEKFIKAAIGCGIVESILNWVKFKAQVQLNKKCSAVKHNRIKGIPKLDDANDAGGRNSTECTLILTEGDSAKTLAVSGLGVVGRDKYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKNYEDEDSLKTLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLRHRFLEEFITPIVKVSKNKQEMAFYSLPEFEEWKSSTPNHKKWKVKYYKGLGTSTSKEAKEYFADMKRHRIQFKYSGPEDDAAISLAFSKKQIDDRKEWLTNFMEDRRQRKLLGLPEDYLYGQTTTYLTYNDFINKELILFSNSDNERSIPSMVDGLKPGQRKVLFTCFKRNDKREVKVAQLAGSVAEMSSYHHGEMSLMMTIINLAQNFVGSNNLNLLQPIGQFGTRLHGGKDSASPRYIFTMLSSLARLLFPPKDDHTLKFLYDDNQRVEPEWYIPIIPMVLINGAEGIGTGWSCKIPNFDVREIVNNIRRLMDGEEPLPMLPSYKNFKGTIEELAPNQYVISGEVAILNSTTIEISELPVRTWTQTYKEQVLEPMLNGTEKTPPLITDYREYHTDTTVKFVVKMTEEKLAEAERVGLHKVFKLQTSLTCNSMVLFDHVGCLKKYDTVLDILRDFFELRLKYYGLRKEWLLGMLGAESAKLNNQARFILEKIDGKIIIENKPKKELIKVLIQRGYDSDPVKAWKEAQQKVPDEEENEESDNEKETEKSDSVTDSGPTFNYLLDMPLWYLTKEKKDELCRLRNEKEQELDTLKRKSPSDLWKEDLATFIEELEAVEAKEKQDEQVGLPGKGGKAKGKKTQMAEVLPSPRGQRVIPRITIEMKAEAEKKNKKKIKNENTEGSPQEDGVELEGLKQRLEKKQKREPGTKTKKQTTLAFKPIKKGKKRNPWSDSESDRSSDESNFDVPPRETEPRRAATKTKFTMDLDSDEDFSDFDEKTDDEDFVPSDASPPKTKTSPKLSNKELKPQKSVVSDLEADDVKGSVPLSSSPPATHFPDETEITNPVPKKNVTVKKTAAKSQSSTSTTGAKKRAAPKGTKRDPALNSGVSQKPDPAKTKNRRKRKPSTSDDSDSNFEKIVSKAVTSKKSKGESDDFHMDFDSAVAPRAKSVRAKKPIKYLEESDEDDLF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O\"], \"target_seq\": \"MEVSPLQPVNENMQVNKIKKNEDAKKRLSVERIYQKKTQLEHILLRPDTYIGSVELVTQQMWVYDEDVGINYREVTFVPGLYKIFDEILVNAADNKQRDPKMSCIRVTIDPENNLISIWNNGKGIPVVEHKVEKMYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFSTKFTVETASREYKKMFKQTWMDNMGRAGEMELKPFNGEDYTCITFQPDLSKFKMQSLDKDIVALMVRRAYDIAGSTKDVKVFLNGNKLPVKGFRSYVDMYLKDKLDETGNSLKVIHEQVNHRWEVCLTMSEKGFQQISFVNSIATSKGGRHVDYVADQIVTKLVDVVKKKNKGGVAVKAHQVKNHMWIFVNALIENPTFDSQTKENMTLQPKSFGSTCQLSEKFIKAAIGCGIVESILNWVKFKAQVQLNKKCSAVKHNRIKGIPKLDDANDAGGRNSTECTLILTEGDSAKTLAVSGLGVVGRDKYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKNYEDEDSLKTLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLRHRFLEEFITPIVKVSKNKQEMAFYSLPEFEEWKSSTPNHKKWKVKYYKGLGTSTSKEAKEYFADMKRHRIQFKYSGPEDDAAISLAFSKKQIDDRKEWLTNFMEDRRQRKLLGLPEDYLYGQTTTYLTYNDFINKELILFSNSDNERSIPSMVDGLKPGQRKVLFTCFKRNDKREVKVAQLAGSVAEMSSYHHGEMSLMMTIINLAQNFVGSNNLNLLQPIGQFGTRLHGGKDSASPRYIFTMLSSLARLLFPPKDDHTLKFLYDDNQRVEPEWYIPIIPMVLINGAEGIGTGWSCKIPNFDVREIVNNIRRLMDGEEPLPMLPSYKNFKGTIEELAPNQYVISGEVAILNSTTIEISELPVRTWTQTYKEQVLEPMLNGTEKTPPLITDYREYHTDTTVKFVVKMTEEKLAEAERVGLHKVFKLQTSLTCNSMVLFDHVGCLKKYDTVLDILRDFFELRLKYYGLRKEWLLGMLGAESAKLNNQARFILEKIDGKIIIENKPKKELIKVLIQRGYDSDPVKAWKEAQQKVPDEEENEESDNEKETEKSDSVTDSGPTFNYLLDMPLWYLTKEKKDELCRLRNEKEQELDTLKRKSPSDLWKEDLATFIEELEAVEAKEKQDEQVGLPGKGGKAKGKKTQMAEVLPSPRGQRVIPRITIEMKAEAEKKNKKKIKNENTEGSPQEDGVELEGLKQRLEKKQKREPGTKTKKQTTLAFKPIKKGKKRNPWSDSESDRSSDESNFDVPPRETEPRRAATKTKFTMDLDSDEDFSDFDEKTDDEDFVPSDASPPKTKTSPKLSNKELKPQKSVVSDLEADDVKGSVPLSSSPPATHFPDETEITNPVPKKNVTVKKTAAKSQSSTSTTGAKKRAAPKGTKRDPALNSGVSQKPDPAKTKNRRKRKPSTSDDSDSNFEKIVSKAVTSKKSKGESDDFHMDFDSAVAPRAKSVRAKKPIKYLEESDEDDLF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEVSPLQPVNENMQVNKIKKNEDAKKRLSVERIYQKKTQLEHILLRPDTYIGSVELVTQQMWVYDEDVGINYREVTFVPGLYKIFDEILVNAADNKQRDPKMSCIRVTIDPENNLISIWNNGKGIPVVEHKVEKMYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFSTKFTVETASREYKKMFKQTWMDNMGRAGEMELKPFNGEDYTCITFQPDLSKFKMQSLDKDIVALMVRRAYDIAGSTKDVKVFLNGNKLPVKGFRSYVDMYLKDKLDETGNSLKVIHEQVNHRWEVCLTMSEKGFQQISFVNSIATSKGGRHVDYVADQIVTKLVDVVKKKNKGGVAVKAHQVKNHMWIFVNALIENPTFDSQTKENMTLQPKSFGSTCQLSEKFIKAAIGCGIVESILNWVKFKAQVQLNKKCSAVKHNRIKGIPKLDDANDAGGRNSTECTLILTEGDSAKTLAVSGLGVVGRDKYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKNYEDEDSLKTLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLRHRFLEEFITPIVKVSKNKQEMAFYSLPEFEEWKSSTPNHKKWKVKYYKGLGTSTSKEAKEYFADMKRHRIQFKYSGPEDDAAISLAFSKKQIDDRKEWLTNFMEDRRQRKLLGLPEDYLYGQTTTYLTYNDFINKELILFSNSDNERSIPSMVDGLKPGQRKVLFTCFKRNDKREVKVAQLAGSVAEMSSYHHGEMSLMMTIINLAQNFVGSNNLNLLQPIGQFGTRLHGGKDSASPRYIFTMLSSLARLLFPPKDDHTLKFLYDDNQRVEPEWYIPIIPMVLINGAEGIGTGWSCKIPNFDVREIVNNIRRLMDGEEPLPMLPSYKNFKGTIEELAPNQYVISGEVAILNSTTIEISELPVRTWTQTYKEQVLEPMLNGTEKTPPLITDYREYHTDTTVKFVVKMTEEKLAEAERVGLHKVFKLQTSLTCNSMVLFDHVGCLKKYDTVLDILRDFFELRLKYYGLRKEWLLGMLGAESAKLNNQARFILEKIDGKIIIENKPKKELIKVLIQRGYDSDPVKAWKEAQQKVPDEEENEESDNEKETEKSDSVTDSGPTFNYLLDMPLWYLTKEKKDELCRLRNEKEQELDTLKRKSPSDLWKEDLATFIEELEAVEAKEKQDEQVGLPGKGGKAKGKKTQMAEVLPSPRGQRVIPRITIEMKAEAEKKNKKKIKNENTEGSPQEDGVELEGLKQRLEKKQKREPGTKTKKQTTLAFKPIKKGKKRNPWSDSESDRSSDESNFDVPPRETEPRRAATKTKFTMDLDSDEDFSDFDEKTDDEDFVPSDASPPKTKTSPKLSNKELKPQKSVVSDLEADDVKGSVPLSSSPPATHFPDETEITNPVPKKNVTVKKTAAKSQSSTSTTGAKKRAAPKGTKRDPALNSGVSQKPDPAKTKNRRKRKPSTSDDSDSNFEKIVSKAVTSKKSKGESDDFHMDFDSAVAPRAKSVRAKKPIKYLEESDEDDLF\", \"result\": [{\"smile\": \"[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O' can interact with the protein 'MEVSPLQPVNENMQVNKIKKNEDAKKRLSVERIYQKKTQLEHILLRPDTYIGSVELVTQQMWVYDEDVGINYREVTFVPGLYKIFDEILVNAADNKQRDPKMSCIRVTIDPENNLISIWNNGKGIPVVEHKVEKMYVPALIFGQLLTSSNYDDDEKKVTGGRNGYGAKLCNIFSTKFTVETASREYKKMFKQTWMDNMGRAGEMELKPFNGEDYTCITFQPDLSKFKMQSLDKDIVALMVRRAYDIAGSTKDVKVFLNGNKLPVKGFRSYVDMYLKDKLDETGNSLKVIHEQVNHRWEVCLTMSEKGFQQISFVNSIATSKGGRHVDYVADQIVTKLVDVVKKKNKGGVAVKAHQVKNHMWIFVNALIENPTFDSQTKENMTLQPKSFGSTCQLSEKFIKAAIGCGIVESILNWVKFKAQVQLNKKCSAVKHNRIKGIPKLDDANDAGGRNSTECTLILTEGDSAKTLAVSGLGVVGRDKYGVFPLRGKILNVREASHKQIMENAEINNIIKIVGLQYKKNYEDEDSLKTLRYGKIMIMTDQDQDGSHIKGLLINFIHHNWPSLLRHRFLEEFITPIVKVSKNKQEMAFYSLPEFEEWKSSTPNHKKWKVKYYKGLGTSTSKEAKEYFADMKRHRIQFKYSGPEDDAAISLAFSKKQIDDRKEWLTNFMEDRRQRKLLGLPEDYLYGQTTTYLTYNDFINKELILFSNSDNERSIPSMVDGLKPGQRKVLFTCFKRNDKREVKVAQLAGSVAEMSSYHHGEMSLMMTIINLAQNFVGSNNLNLLQPIGQFGTRLHGGKDSASPRYIFTMLSSLARLLFPPKDDHTLKFLYDDNQRVEPEWYIPIIPMVLINGAEGIGTGWSCKIPNFDVREIVNNIRRLMDGEEPLPMLPSYKNFKGTIEELAPNQYVISGEVAILNSTTIEISELPVRTWTQTYKEQVLEPMLNGTEKTPPLITDYREYHTDTTVKFVVKMTEEKLAEAERVGLHKVFKLQTSLTCNSMVLFDHVGCLKKYDTVLDILRDFFELRLKYYGLRKEWLLGMLGAESAKLNNQARFILEKIDGKIIIENKPKKELIKVLIQRGYDSDPVKAWKEAQQKVPDEEENEESDNEKETEKSDSVTDSGPTFNYLLDMPLWYLTKEKKDELCRLRNEKEQELDTLKRKSPSDLWKEDLATFIEELEAVEAKEKQDEQVGLPGKGGKAKGKKTQMAEVLPSPRGQRVIPRITIEMKAEAEKKNKKKIKNENTEGSPQEDGVELEGLKQRLEKKQKREPGTKTKKQTTLAFKPIKKGKKRNPWSDSESDRSSDESNFDVPPRETEPRRAATKTKFTMDLDSDEDFSDFDEKTDDEDFVPSDASPPKTKTSPKLSNKELKPQKSVVSDLEADDVKGSVPLSSSPPATHFPDETEITNPVPKKNVTVKKTAAKSQSSTSTTGAKKRAAPKGTKRDPALNSGVSQKPDPAKTKNRRKRKPSTSDDSDSNFEKIVSKAVTSKKSKGESDDFHMDFDSAVAPRAKSVRAKKPIKYLEESDEDDLF'."
            },
            {
                "from": "human",
                "value": "Do you expect the drug above to have an interaction with the target protein 'MGAGGPRRGEGPPDGGWGWVVLGACFVVTGFAYGFPKAVSVFFRALMRDFDAGYSDTAWVSSIMLAMLYGTGPVSSILVTRFGCRPVMLAGGLLASAGMILASFATRLLELYLTAGVLTGLGLALNFQPSLIMLGLYFERRRPLANGLAAAGSPVFLSALSPLGQQLLERFGWRGGFLLLGGLLLHCCACGAVMRPPPGPGPRPRRDSAGDRAGDAPGEAEADGAGLQLREASPRVRPRRRLLDLAVCTDRAFAVYAVTKFLMALGLFVPAILLVNYAKDAGVPDTDAAFLLSIVGFVDIVARPACGALAGLARLRPHVPYLFSLALLANGLTDLSSARARSYGALVAFCVAFGLSYGMVGALQFEVLMAAVGAPRFPSALGLVLLVEAAAVLIGPPSAGRLVDVLKNYEIIFYLAGSEVALAGVFMAVATNCCLRCAKAAPSGPGTEGGASDTEDAEAEGDSEPLPVVAEEPGNLEALEVLSARGEPTEPEIEARPRLAAESV'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGAGGPRRGEGPPDGGWGWVVLGACFVVTGFAYGFPKAVSVFFRALMRDFDAGYSDTAWVSSIMLAMLYGTGPVSSILVTRFGCRPVMLAGGLLASAGMILASFATRLLELYLTAGVLTGLGLALNFQPSLIMLGLYFERRRPLANGLAAAGSPVFLSALSPLGQQLLERFGWRGGFLLLGGLLLHCCACGAVMRPPPGPGPRPRRDSAGDRAGDAPGEAEADGAGLQLREASPRVRPRRRLLDLAVCTDRAFAVYAVTKFLMALGLFVPAILLVNYAKDAGVPDTDAAFLLSIVGFVDIVARPACGALAGLARLRPHVPYLFSLALLANGLTDLSSARARSYGALVAFCVAFGLSYGMVGALQFEVLMAAVGAPRFPSALGLVLLVEAAAVLIGPPSAGRLVDVLKNYEIIFYLAGSEVALAGVFMAVATNCCLRCAKAAPSGPGTEGGASDTEDAEAEGDSEPLPVVAEEPGNLEALEVLSARGEPTEPEIEARPRLAAESV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGAGGPRRGEGPPDGGWGWVVLGACFVVTGFAYGFPKAVSVFFRALMRDFDAGYSDTAWVSSIMLAMLYGTGPVSSILVTRFGCRPVMLAGGLLASAGMILASFATRLLELYLTAGVLTGLGLALNFQPSLIMLGLYFERRRPLANGLAAAGSPVFLSALSPLGQQLLERFGWRGGFLLLGGLLLHCCACGAVMRPPPGPGPRPRRDSAGDRAGDAPGEAEADGAGLQLREASPRVRPRRRLLDLAVCTDRAFAVYAVTKFLMALGLFVPAILLVNYAKDAGVPDTDAAFLLSIVGFVDIVARPACGALAGLARLRPHVPYLFSLALLANGLTDLSSARARSYGALVAFCVAFGLSYGMVGALQFEVLMAAVGAPRFPSALGLVLLVEAAAVLIGPPSAGRLVDVLKNYEIIFYLAGSEVALAGVFMAVATNCCLRCAKAAPSGPGTEGGASDTEDAEAEGDSEPLPVVAEEPGNLEALEVLSARGEPTEPEIEARPRLAAESV\", \"result\": [{\"smile\": \"[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]12CN(C[C@]1([H])OCCN2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1C#N)C(O)=O' can not interact with the protein 'MGAGGPRRGEGPPDGGWGWVVLGACFVVTGFAYGFPKAVSVFFRALMRDFDAGYSDTAWVSSIMLAMLYGTGPVSSILVTRFGCRPVMLAGGLLASAGMILASFATRLLELYLTAGVLTGLGLALNFQPSLIMLGLYFERRRPLANGLAAAGSPVFLSALSPLGQQLLERFGWRGGFLLLGGLLLHCCACGAVMRPPPGPGPRPRRDSAGDRAGDAPGEAEADGAGLQLREASPRVRPRRRLLDLAVCTDRAFAVYAVTKFLMALGLFVPAILLVNYAKDAGVPDTDAAFLLSIVGFVDIVARPACGALAGLARLRPHVPYLFSLALLANGLTDLSSARARSYGALVAFCVAFGLSYGMVGALQFEVLMAAVGAPRFPSALGLVLLVEAAAVLIGPPSAGRLVDVLKNYEIIFYLAGSEVALAGVFMAVATNCCLRCAKAAPSGPGTEGGASDTEDAEAEGDSEPLPVVAEEPGNLEALEVLSARGEPTEPEIEARPRLAAESV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug 'N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12' interacting with the target protein 'MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12\"], \"target_seq\": \"MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD\", \"result\": [{\"smile\": \"N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12' can interact with the protein 'MKTWLKTVFGVTTLAALALVVICIVLRPSRVYKPEGNTKRALTLKDILNGTFSYKTYFPNWISEQEYLHQSEDDNIVFYNIETRESYIILSNSTMKSVNATDYGLSPDRQFVYLESDYSKLWRYSYTATYYIYDLQNGEFVRGYELPRPIQYLCWSPVGSKLAYVYQNNIYLKQRPGDPPFQITYTGRENRIFNGIPDWVYEEEMLATKYALWWSPDGKFLAYVEFNDSDIPIIAYSYYGDGQYPRTINIPYPKAGAKNPVVRVFIVDTTYPHHVGPMEVPVPEMIASSDYYFSWLTWVSSERVCLQWLKRVQNVSVLSICDFREDWHAWECPKNQEHVEESRTGWAGGFFVSTPAFSQDATSYYKIFSDKDGYKHIHYIKDTVENAIQITSGKWEAIYIFRVTQDSLFYSSNEFEGYPGRRNIYRISIGNSPPSKKCVTCHLRKERCQYYTASFSYKAKYYALVCYGPGLPISTLHDGRTDQEIQVLEENKELENSLRNIQLPKVEIKKLKDGGLTFWYKMILPPQFDRSKKYPLLIQVYGGPCSQSVKSVFAVNWITYLASKEGIVIALVDGRGTAFQGDKFLHAVYRKLGVYEVEDQLTAVRKFIEMGFIDEERIAIWGWSYGGYVSSLALASGTGLFKCGIAVAPVSSWEYYASIYSERFMGLPTKDDNLEHYKNSTVMARAEYFRNVDYLLIHGTADDNVHFQNSAQIAKALVNAQVDFQAMWYSDQNHGISSGRSQNHLYTHMTHFLKQCFSLSD'."
            },
            {
                "from": "human",
                "value": "What do you think about the drug above's potential to interact with the target protein 'MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL\", \"result\": [{\"smile\": \"N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#C[C@@H]1CC(F)(F)CN1C(=O)CNC(=O)c1ccnc2ccccc12' can not interact with the protein 'MGSAPWAPVLLLALGLRGLQAGARRAPDPGFQERFFQQRLDHFNFERFGNKTFPQRFLVSDRFWVRGEGPIFFYTGNEGDVWAFANNSAFVAELAAERGALLVFAEHRYYGKSLPFGAQSTQRGHTELLTVEQALADFAELLRALRRDLGAQDAPAIAFGGSYGGMLSAYLRMKYPHLVAGALAASAPVLAVAGLGDSNQFFRDVTADFEGQSPKCTQGVREAFRQIKDLFLQGAYDTVRWEFGTCQPLSDEKDLTQLFMFARNAFTVLAMMDYPYPTDFLGPLPANPVKVGCDRLLSEAQRITGLRALAGLVYNASGSEHCYDIYRLYHSCADPTGCGTGPDARAWDYQACTEINLTFASNNVTDMFPDLPFTDELRQRYCLDTWGVWPRPDWLLTSFWGGDLRAASNIIFSNGNLDPWAGGGIRRNLSASVIAVTIQGGAHHLDLRASHPEDPASVVEARKLEATIIGEWVKAARREQQPALRGGPRLSL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted interaction outcome for the drug 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12' and the target protein 'MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12\"], \"target_seq\": \"MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT\", \"result\": [{\"smile\": \"CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12' can not interact with the protein 'MEAEGSSAPARAGSGEGSDSAGGATLKAPKHLWRHEQHHQYPLRQPQFRLLHPHHHLPPPPPPSPQPQPQCPLQPPPPPPLPPPPPPPGAARGRYASSGATGRVRHRGYSDTERYLYCRAMDRTSYAVETGHRPGLKKSRMSWPSSFQGLRRFDVDNGTSAGRSPLDPMTSPGSGLILQANFVHSQRRESFLYRSDSDYDLSPKSMSRNSSIASDIHGDDLIVTPFAQVLASLRTVRNNFAALTNLQDRAPSKRSPMCNQPSINKATITEEAYQKLASETLEELDWCLDQLETLQTRHSVSEMASNKFKRMLNRELTHLSEMSRSGNQVSEFISNTFLDKQHEVEIPSPTQKEKEKKKRPMSQISGVKKLMHSSSLTNSSIPRFGVKTEQEDVLAKELEDVNKWGLHVFRIAELSGNRPLTVIMHTIFQERDLLKTFKIPVDTLITYLMTLEDHYHADVAYHNNIHAADVVQSTHVLLSTPALEAVFTDLEILAAIFASAIHDVDHPGVSNQFLINTNSELALMYNDSSVLENHHLAVGFKLLQEENCDIFQNLTKKQRQSLRKMVIDIVLATDMSKHMNLLADLKTMVETKKVTSSGVLLLDNYSDRIQVLQNMVHCADLSNPTKPLQLYRQWTDRIMEEFFRQGDRERERGMEISPMCDKHNASVEKSQVGFIDYIVHPLWETWADLVHPDAQDILDTLEDNREWYQSTIPQSPSPAPDDPEEGRQGQTEKFQFELTLEEDGESDTEKDSGSQVEEDTSCSDSKTLCTQDSESTEIPLDEQVEEEAVGEEEESQPEACVIDDRSPDT'."
            },
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug above and the target protein 'MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA\", \"result\": [{\"smile\": \"CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12' can not interact with the protein 'MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O' interact with the target protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL' according to your assessment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O\"], \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\", \"result\": [{\"smile\": \"CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O' can interact with the protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL'."
            },
            {
                "from": "human",
                "value": "Will there be an interaction expected between the drug above and the target protein 'MAPDPVAAETAAQGPTPRYFTWDEVAQRSGCEERWLVIDRKVYNISEFTRRHPGGSRVISHYAGQDATDPFVAFHINKGLVKKYMNSLLIGELSPEQPSFEPTKNKELTDEFRELRATVERMGLMKANHVFFLLYLLHILLLDGAAWLTLWVFGTSFLPFLLCAVLLSAVQAQAGWLQHDFGHLSVFSTSKWNHLLHHFVIGHLKGAPASWWNHMHFQHHAKPNCFRKDPDINMHPFFFALGKILSVELGKQKKKYMPYNHQHKYFFLIGPPALLPLYFQWYIFYFVIQRKKWVDLAWMITFYVRFFLTYVPLLGLKAFLGLFFIVRFLESNWFVWVTQMNHIPMHIDHDRNMDWVSTQLQATCNVHKSAFNDWFSGHLNFQIEHHLFPTMPRHNYHKVAPLVQSLCAKHGIEYQSKPLLSAFADIIHSLKESGQLWLDAYLHQ'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPDPVAAETAAQGPTPRYFTWDEVAQRSGCEERWLVIDRKVYNISEFTRRHPGGSRVISHYAGQDATDPFVAFHINKGLVKKYMNSLLIGELSPEQPSFEPTKNKELTDEFRELRATVERMGLMKANHVFFLLYLLHILLLDGAAWLTLWVFGTSFLPFLLCAVLLSAVQAQAGWLQHDFGHLSVFSTSKWNHLLHHFVIGHLKGAPASWWNHMHFQHHAKPNCFRKDPDINMHPFFFALGKILSVELGKQKKKYMPYNHQHKYFFLIGPPALLPLYFQWYIFYFVIQRKKWVDLAWMITFYVRFFLTYVPLLGLKAFLGLFFIVRFLESNWFVWVTQMNHIPMHIDHDRNMDWVSTQLQATCNVHKSAFNDWFSGHLNFQIEHHLFPTMPRHNYHKVAPLVQSLCAKHGIEYQSKPLLSAFADIIHSLKESGQLWLDAYLHQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPDPVAAETAAQGPTPRYFTWDEVAQRSGCEERWLVIDRKVYNISEFTRRHPGGSRVISHYAGQDATDPFVAFHINKGLVKKYMNSLLIGELSPEQPSFEPTKNKELTDEFRELRATVERMGLMKANHVFFLLYLLHILLLDGAAWLTLWVFGTSFLPFLLCAVLLSAVQAQAGWLQHDFGHLSVFSTSKWNHLLHHFVIGHLKGAPASWWNHMHFQHHAKPNCFRKDPDINMHPFFFALGKILSVELGKQKKKYMPYNHQHKYFFLIGPPALLPLYFQWYIFYFVIQRKKWVDLAWMITFYVRFFLTYVPLLGLKAFLGLFFIVRFLESNWFVWVTQMNHIPMHIDHDRNMDWVSTQLQATCNVHKSAFNDWFSGHLNFQIEHHLFPTMPRHNYHKVAPLVQSLCAKHGIEYQSKPLLSAFADIIHSLKESGQLWLDAYLHQ\", \"result\": [{\"smile\": \"CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC\\C=C/C\\C=C/C\\C=C/C\\C=C/C\\C=C/CCCC(O)=O' can interact with the protein 'MAPDPVAAETAAQGPTPRYFTWDEVAQRSGCEERWLVIDRKVYNISEFTRRHPGGSRVISHYAGQDATDPFVAFHINKGLVKKYMNSLLIGELSPEQPSFEPTKNKELTDEFRELRATVERMGLMKANHVFFLLYLLHILLLDGAAWLTLWVFGTSFLPFLLCAVLLSAVQAQAGWLQHDFGHLSVFSTSKWNHLLHHFVIGHLKGAPASWWNHMHFQHHAKPNCFRKDPDINMHPFFFALGKILSVELGKQKKKYMPYNHQHKYFFLIGPPALLPLYFQWYIFYFVIQRKKWVDLAWMITFYVRFFLTYVPLLGLKAFLGLFFIVRFLESNWFVWVTQMNHIPMHIDHDRNMDWVSTQLQATCNVHKSAFNDWFSGHLNFQIEHHLFPTMPRHNYHKVAPLVQSLCAKHGIEYQSKPLLSAFADIIHSLKESGQLWLDAYLHQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the chances of the drug 'CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O' interacting with the target protein 'MSQTGSHPGRGLAGRWLWGAQPCLLLPIVPLSWLVWLLLLLLASLLPSARLASPLPREEEIVFPEKLNGSVLPGSGAPARLLCRLQAFGETLLLELEQDSGVQVEGLTVQYLGQAPELLGGAEPGTYLTGTINGDPESVASLHWDGGALLGVLQYRGAELHLQPLEGGTPNSAGGPGAHILRRKSPASGQGPMCNVKAPLGSPSPRPRRAKRFASLSRFVETLVVADDKMAAFHGAGLKRYLLTVMAAAAKAFKHPSIRNPVSLVVTRLVILGSGEEGPQVGPSAAQTLRSFCAWQRGLNTPEDSDPDHFDTAILFTRQDLCGVSTCDTLGMADVGTVCDPARSCAIVEDDGLQSAFTAAHELGHVFNMLHDNSKPCISLNGPLSTSRHVMAPVMAHVDPEEPWSPCSARFITDFLDNGYGHCLLDKPEAPLHLPVTFPGKDYDADRQCQLTFGPDSRHCPQLPPPCAALWCSGHLNGHAMCQTKHSPWADGTPCGPAQACMGGRCLHMDQLQDFNIPQAGGWGPWGPWGDCSRTCGGGVQFSSRDCTRPVPRNGGKYCEGRRTRFRSCNTEDCPTGSALTFREEQCAAYNHRTDLFKSFPGPMDWVPRYTGVAPQDQCKLTCQAQALGYYYVLEPRVVDGTPCSPDSSSVCVQGRCIHAGCDRIIGSKKKFDKCMVCGGDGSGCSKQSGSFRKFRYGYNNVVTIPAGATHILVRQQGNPGHRSIYLALKLPDGSYALNGEYTLMPSPTDVVLPGAVSLRYSGATAASETLSGHGPLAQPLTLQVLVAGNPQDTRLRYSFFVPRPTPSTPRPTPQDWLHRRAQILEILRRRPWAGRK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O\"], \"target_seq\": \"MSQTGSHPGRGLAGRWLWGAQPCLLLPIVPLSWLVWLLLLLLASLLPSARLASPLPREEEIVFPEKLNGSVLPGSGAPARLLCRLQAFGETLLLELEQDSGVQVEGLTVQYLGQAPELLGGAEPGTYLTGTINGDPESVASLHWDGGALLGVLQYRGAELHLQPLEGGTPNSAGGPGAHILRRKSPASGQGPMCNVKAPLGSPSPRPRRAKRFASLSRFVETLVVADDKMAAFHGAGLKRYLLTVMAAAAKAFKHPSIRNPVSLVVTRLVILGSGEEGPQVGPSAAQTLRSFCAWQRGLNTPEDSDPDHFDTAILFTRQDLCGVSTCDTLGMADVGTVCDPARSCAIVEDDGLQSAFTAAHELGHVFNMLHDNSKPCISLNGPLSTSRHVMAPVMAHVDPEEPWSPCSARFITDFLDNGYGHCLLDKPEAPLHLPVTFPGKDYDADRQCQLTFGPDSRHCPQLPPPCAALWCSGHLNGHAMCQTKHSPWADGTPCGPAQACMGGRCLHMDQLQDFNIPQAGGWGPWGPWGDCSRTCGGGVQFSSRDCTRPVPRNGGKYCEGRRTRFRSCNTEDCPTGSALTFREEQCAAYNHRTDLFKSFPGPMDWVPRYTGVAPQDQCKLTCQAQALGYYYVLEPRVVDGTPCSPDSSSVCVQGRCIHAGCDRIIGSKKKFDKCMVCGGDGSGCSKQSGSFRKFRYGYNNVVTIPAGATHILVRQQGNPGHRSIYLALKLPDGSYALNGEYTLMPSPTDVVLPGAVSLRYSGATAASETLSGHGPLAQPLTLQVLVAGNPQDTRLRYSFFVPRPTPSTPRPTPQDWLHRRAQILEILRRRPWAGRK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSQTGSHPGRGLAGRWLWGAQPCLLLPIVPLSWLVWLLLLLLASLLPSARLASPLPREEEIVFPEKLNGSVLPGSGAPARLLCRLQAFGETLLLELEQDSGVQVEGLTVQYLGQAPELLGGAEPGTYLTGTINGDPESVASLHWDGGALLGVLQYRGAELHLQPLEGGTPNSAGGPGAHILRRKSPASGQGPMCNVKAPLGSPSPRPRRAKRFASLSRFVETLVVADDKMAAFHGAGLKRYLLTVMAAAAKAFKHPSIRNPVSLVVTRLVILGSGEEGPQVGPSAAQTLRSFCAWQRGLNTPEDSDPDHFDTAILFTRQDLCGVSTCDTLGMADVGTVCDPARSCAIVEDDGLQSAFTAAHELGHVFNMLHDNSKPCISLNGPLSTSRHVMAPVMAHVDPEEPWSPCSARFITDFLDNGYGHCLLDKPEAPLHLPVTFPGKDYDADRQCQLTFGPDSRHCPQLPPPCAALWCSGHLNGHAMCQTKHSPWADGTPCGPAQACMGGRCLHMDQLQDFNIPQAGGWGPWGPWGDCSRTCGGGVQFSSRDCTRPVPRNGGKYCEGRRTRFRSCNTEDCPTGSALTFREEQCAAYNHRTDLFKSFPGPMDWVPRYTGVAPQDQCKLTCQAQALGYYYVLEPRVVDGTPCSPDSSSVCVQGRCIHAGCDRIIGSKKKFDKCMVCGGDGSGCSKQSGSFRKFRYGYNNVVTIPAGATHILVRQQGNPGHRSIYLALKLPDGSYALNGEYTLMPSPTDVVLPGAVSLRYSGATAASETLSGHGPLAQPLTLQVLVAGNPQDTRLRYSFFVPRPTPSTPRPTPQDWLHRRAQILEILRRRPWAGRK\", \"result\": [{\"smile\": \"CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O' can interact with the protein 'MSQTGSHPGRGLAGRWLWGAQPCLLLPIVPLSWLVWLLLLLLASLLPSARLASPLPREEEIVFPEKLNGSVLPGSGAPARLLCRLQAFGETLLLELEQDSGVQVEGLTVQYLGQAPELLGGAEPGTYLTGTINGDPESVASLHWDGGALLGVLQYRGAELHLQPLEGGTPNSAGGPGAHILRRKSPASGQGPMCNVKAPLGSPSPRPRRAKRFASLSRFVETLVVADDKMAAFHGAGLKRYLLTVMAAAAKAFKHPSIRNPVSLVVTRLVILGSGEEGPQVGPSAAQTLRSFCAWQRGLNTPEDSDPDHFDTAILFTRQDLCGVSTCDTLGMADVGTVCDPARSCAIVEDDGLQSAFTAAHELGHVFNMLHDNSKPCISLNGPLSTSRHVMAPVMAHVDPEEPWSPCSARFITDFLDNGYGHCLLDKPEAPLHLPVTFPGKDYDADRQCQLTFGPDSRHCPQLPPPCAALWCSGHLNGHAMCQTKHSPWADGTPCGPAQACMGGRCLHMDQLQDFNIPQAGGWGPWGPWGDCSRTCGGGVQFSSRDCTRPVPRNGGKYCEGRRTRFRSCNTEDCPTGSALTFREEQCAAYNHRTDLFKSFPGPMDWVPRYTGVAPQDQCKLTCQAQALGYYYVLEPRVVDGTPCSPDSSSVCVQGRCIHAGCDRIIGSKKKFDKCMVCGGDGSGCSKQSGSFRKFRYGYNNVVTIPAGATHILVRQQGNPGHRSIYLALKLPDGSYALNGEYTLMPSPTDVVLPGAVSLRYSGATAASETLSGHGPLAQPLTLQVLVAGNPQDTRLRYSFFVPRPTPSTPRPTPQDWLHRRAQILEILRRRPWAGRK'."
            },
            {
                "from": "human",
                "value": "Will the drug above possibly engage with the target protein 'MHSFPPLLLLLFWGVVSHSFPATLETQEQDVDLVQKYLEKYYNLKNDGRQVEKRRNSGPVVEKLKQMQEFFGLKVTGKPDAETLKVMKQPRCGVPDVAQFVLTEGNPRWEQTHLTYRIENYTPDLPRADVDHAIEKAFQLWSNVTPLTFTKVSEGQADIMISFVRGDHRDNSPFDGPGGNLAHAFQPGPGIGGDAHFDEDERWTNNFREYNLHRVAAHELGHSLGLSHSTDIGALMYPSYTFSGDVQLAQDDIDGIQAIYGRSQNPVQPIGPQTPKACDSKLTFDAITTIRGEVMFFKDRFYMRTNPFYPEVELNFISVFWPQLPNGLEAAYEFADRDEVRFFKGNKYWAVQGQNVLHGYPKDIYSSFGFPRTVKHIDAALSEENTGKTYFFVANKYWRYDEYKRSMDPGYPKMIAHDFPGIGHKVDAVFMKDGFFYFFHGTRQYKFDPKTKRILTLQKANSWFNCRKN'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MHSFPPLLLLLFWGVVSHSFPATLETQEQDVDLVQKYLEKYYNLKNDGRQVEKRRNSGPVVEKLKQMQEFFGLKVTGKPDAETLKVMKQPRCGVPDVAQFVLTEGNPRWEQTHLTYRIENYTPDLPRADVDHAIEKAFQLWSNVTPLTFTKVSEGQADIMISFVRGDHRDNSPFDGPGGNLAHAFQPGPGIGGDAHFDEDERWTNNFREYNLHRVAAHELGHSLGLSHSTDIGALMYPSYTFSGDVQLAQDDIDGIQAIYGRSQNPVQPIGPQTPKACDSKLTFDAITTIRGEVMFFKDRFYMRTNPFYPEVELNFISVFWPQLPNGLEAAYEFADRDEVRFFKGNKYWAVQGQNVLHGYPKDIYSSFGFPRTVKHIDAALSEENTGKTYFFVANKYWRYDEYKRSMDPGYPKMIAHDFPGIGHKVDAVFMKDGFFYFFHGTRQYKFDPKTKRILTLQKANSWFNCRKN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MHSFPPLLLLLFWGVVSHSFPATLETQEQDVDLVQKYLEKYYNLKNDGRQVEKRRNSGPVVEKLKQMQEFFGLKVTGKPDAETLKVMKQPRCGVPDVAQFVLTEGNPRWEQTHLTYRIENYTPDLPRADVDHAIEKAFQLWSNVTPLTFTKVSEGQADIMISFVRGDHRDNSPFDGPGGNLAHAFQPGPGIGGDAHFDEDERWTNNFREYNLHRVAAHELGHSLGLSHSTDIGALMYPSYTFSGDVQLAQDDIDGIQAIYGRSQNPVQPIGPQTPKACDSKLTFDAITTIRGEVMFFKDRFYMRTNPFYPEVELNFISVFWPQLPNGLEAAYEFADRDEVRFFKGNKYWAVQGQNVLHGYPKDIYSSFGFPRTVKHIDAALSEENTGKTYFFVANKYWRYDEYKRSMDPGYPKMIAHDFPGIGHKVDAVFMKDGFFYFFHGTRQYKFDPKTKRILTLQKANSWFNCRKN\", \"result\": [{\"smile\": \"CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)[C@@H](NS(=O)(=O)c1ccc(-c2ccc(COc3ccc(C(=O)C4CCCCC4)cc3)cc2)cc1)C(=O)O' can not interact with the protein 'MHSFPPLLLLLFWGVVSHSFPATLETQEQDVDLVQKYLEKYYNLKNDGRQVEKRRNSGPVVEKLKQMQEFFGLKVTGKPDAETLKVMKQPRCGVPDVAQFVLTEGNPRWEQTHLTYRIENYTPDLPRADVDHAIEKAFQLWSNVTPLTFTKVSEGQADIMISFVRGDHRDNSPFDGPGGNLAHAFQPGPGIGGDAHFDEDERWTNNFREYNLHRVAAHELGHSLGLSHSTDIGALMYPSYTFSGDVQLAQDDIDGIQAIYGRSQNPVQPIGPQTPKACDSKLTFDAITTIRGEVMFFKDRFYMRTNPFYPEVELNFISVFWPQLPNGLEAAYEFADRDEVRFFKGNKYWAVQGQNVLHGYPKDIYSSFGFPRTVKHIDAALSEENTGKTYFFVANKYWRYDEYKRSMDPGYPKMIAHDFPGIGHKVDAVFMKDGFFYFFHGTRQYKFDPKTKRILTLQKANSWFNCRKN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1' and the target protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1\"], \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\", \"result\": [{\"smile\": \"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1' can interact with the protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'."
            },
            {
                "from": "human",
                "value": "Do you foresee any interaction between the drug above and the target protein 'MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGDDDHPDSIEYGLGYDCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERILYVDLDLHHGDGVEDAFSFTSKVMTVSLHKFSPGFFPGTGDVSDVGLGKGRYYSVNVPIQDGIQDEKYYQICESVLKEVYQAFNPKAVVLQLGADTIAGDPMCSFNMTPVGIGKCLKYILQWQLATLILGGGGYNLANTARCWTYLTGVILGKTLSSEIPDHEFFTAYGPDYVLEITPSCRPDRNEPHRIQQILNYIKGNLKHVV'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGDDDHPDSIEYGLGYDCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERILYVDLDLHHGDGVEDAFSFTSKVMTVSLHKFSPGFFPGTGDVSDVGLGKGRYYSVNVPIQDGIQDEKYYQICESVLKEVYQAFNPKAVVLQLGADTIAGDPMCSFNMTPVGIGKCLKYILQWQLATLILGGGGYNLANTARCWTYLTGVILGKTLSSEIPDHEFFTAYGPDYVLEITPSCRPDRNEPHRIQQILNYIKGNLKHVV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGDDDHPDSIEYGLGYDCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERILYVDLDLHHGDGVEDAFSFTSKVMTVSLHKFSPGFFPGTGDVSDVGLGKGRYYSVNVPIQDGIQDEKYYQICESVLKEVYQAFNPKAVVLQLGADTIAGDPMCSFNMTPVGIGKCLKYILQWQLATLILGGGGYNLANTARCWTYLTGVILGKTLSSEIPDHEFFTAYGPDYVLEITPSCRPDRNEPHRIQQILNYIKGNLKHVV\", \"result\": [{\"smile\": \"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc2nccnc2c1' can not interact with the protein 'MEEPEEPADSGQSLVPVYIYSPEYVSMCDSLAKIPKRASMVHSLIEAYALHKQMRIVKPKVASMEEMATFHTDAYLQHLQKVSQEGDDDHPDSIEYGLGYDCPATEGIFDYAAAIGGATITAAQCLIDGMCKVAINWSGGWHHAKKDEASGFCYLNDAVLGILRLRRKFERILYVDLDLHHGDGVEDAFSFTSKVMTVSLHKFSPGFFPGTGDVSDVGLGKGRYYSVNVPIQDGIQDEKYYQICESVLKEVYQAFNPKAVVLQLGADTIAGDPMCSFNMTPVGIGKCLKYILQWQLATLILGGGGYNLANTARCWTYLTGVILGKTLSSEIPDHEFFTAYGPDYVLEITPSCRPDRNEPHRIQQILNYIKGNLKHVV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'N[C@@H](CSO)C(O)=O' interact with the target protein 'MESPTKEIEEFESNSLKYLQPEQIEKIWLRLRGLRKYKKTSQRLRSLVKQLERGEASVVDLKKNLEYAATVLESVYIDETRRLLDTEDELSDIQSDAVPSEVRDWLASTFTRQMGMMLRRSDEKPRFKSIVHAVQAGIFVERMYRRTSNMVGLSYPPAVIEALKDVDKWSFDVFSLNEASGDHALKFIFYELLTRYDLISRFKIPISALVSFVEALEVGYSKHKNPYHNLMHAADVTQTVHYLLYKTGVANWLTELEIFAIIFSAAIHDYEHTGTTNNFHIQTRSDPAILYNDRSVLENHHLSAAYRLLQDDEEMNILINLSKDDWREFRTLVIEMVMATDMSCHFQQIKAMKTALQQPEAIEKPKALSLMLHTADISHPAKAWDLHHRWTMSLLEEFFRQGDREAELGLPFSPLCDRKSTMVAQSQVGFIDFIVEPTFTVLTDMTEKIVSPLIDETSQTGGTGQRRSSLNSISSSDAKRSGVKTSGSEGSAPINNSVISVDYKSFKATWTEVVHINRERWRAKVPKEEKAKKEAEEKARLAAEEQQKEMEAKSQAEEGASGKAEKKTSGETKNQVNGTRANKSDNPRGKNSKAEKSSGEQQQNGDFKDGKNKTDKKDHSNIGNDSKKTDGTKQRSHGSPAPSTSSTCRLTLPVIKPPLRHFKRPAYASSSYAPSVSKKTDEHPARYKMLDQRIKMKKIQNISHNWNRK', based on your evaluation? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CSO)C(O)=O\"], \"target_seq\": \"MESPTKEIEEFESNSLKYLQPEQIEKIWLRLRGLRKYKKTSQRLRSLVKQLERGEASVVDLKKNLEYAATVLESVYIDETRRLLDTEDELSDIQSDAVPSEVRDWLASTFTRQMGMMLRRSDEKPRFKSIVHAVQAGIFVERMYRRTSNMVGLSYPPAVIEALKDVDKWSFDVFSLNEASGDHALKFIFYELLTRYDLISRFKIPISALVSFVEALEVGYSKHKNPYHNLMHAADVTQTVHYLLYKTGVANWLTELEIFAIIFSAAIHDYEHTGTTNNFHIQTRSDPAILYNDRSVLENHHLSAAYRLLQDDEEMNILINLSKDDWREFRTLVIEMVMATDMSCHFQQIKAMKTALQQPEAIEKPKALSLMLHTADISHPAKAWDLHHRWTMSLLEEFFRQGDREAELGLPFSPLCDRKSTMVAQSQVGFIDFIVEPTFTVLTDMTEKIVSPLIDETSQTGGTGQRRSSLNSISSSDAKRSGVKTSGSEGSAPINNSVISVDYKSFKATWTEVVHINRERWRAKVPKEEKAKKEAEEKARLAAEEQQKEMEAKSQAEEGASGKAEKKTSGETKNQVNGTRANKSDNPRGKNSKAEKSSGEQQQNGDFKDGKNKTDKKDHSNIGNDSKKTDGTKQRSHGSPAPSTSSTCRLTLPVIKPPLRHFKRPAYASSSYAPSVSKKTDEHPARYKMLDQRIKMKKIQNISHNWNRK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESPTKEIEEFESNSLKYLQPEQIEKIWLRLRGLRKYKKTSQRLRSLVKQLERGEASVVDLKKNLEYAATVLESVYIDETRRLLDTEDELSDIQSDAVPSEVRDWLASTFTRQMGMMLRRSDEKPRFKSIVHAVQAGIFVERMYRRTSNMVGLSYPPAVIEALKDVDKWSFDVFSLNEASGDHALKFIFYELLTRYDLISRFKIPISALVSFVEALEVGYSKHKNPYHNLMHAADVTQTVHYLLYKTGVANWLTELEIFAIIFSAAIHDYEHTGTTNNFHIQTRSDPAILYNDRSVLENHHLSAAYRLLQDDEEMNILINLSKDDWREFRTLVIEMVMATDMSCHFQQIKAMKTALQQPEAIEKPKALSLMLHTADISHPAKAWDLHHRWTMSLLEEFFRQGDREAELGLPFSPLCDRKSTMVAQSQVGFIDFIVEPTFTVLTDMTEKIVSPLIDETSQTGGTGQRRSSLNSISSSDAKRSGVKTSGSEGSAPINNSVISVDYKSFKATWTEVVHINRERWRAKVPKEEKAKKEAEEKARLAAEEQQKEMEAKSQAEEGASGKAEKKTSGETKNQVNGTRANKSDNPRGKNSKAEKSSGEQQQNGDFKDGKNKTDKKDHSNIGNDSKKTDGTKQRSHGSPAPSTSSTCRLTLPVIKPPLRHFKRPAYASSSYAPSVSKKTDEHPARYKMLDQRIKMKKIQNISHNWNRK\", \"result\": [{\"smile\": \"N[C@@H](CSO)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CSO)C(O)=O' can not interact with the protein 'MESPTKEIEEFESNSLKYLQPEQIEKIWLRLRGLRKYKKTSQRLRSLVKQLERGEASVVDLKKNLEYAATVLESVYIDETRRLLDTEDELSDIQSDAVPSEVRDWLASTFTRQMGMMLRRSDEKPRFKSIVHAVQAGIFVERMYRRTSNMVGLSYPPAVIEALKDVDKWSFDVFSLNEASGDHALKFIFYELLTRYDLISRFKIPISALVSFVEALEVGYSKHKNPYHNLMHAADVTQTVHYLLYKTGVANWLTELEIFAIIFSAAIHDYEHTGTTNNFHIQTRSDPAILYNDRSVLENHHLSAAYRLLQDDEEMNILINLSKDDWREFRTLVIEMVMATDMSCHFQQIKAMKTALQQPEAIEKPKALSLMLHTADISHPAKAWDLHHRWTMSLLEEFFRQGDREAELGLPFSPLCDRKSTMVAQSQVGFIDFIVEPTFTVLTDMTEKIVSPLIDETSQTGGTGQRRSSLNSISSSDAKRSGVKTSGSEGSAPINNSVISVDYKSFKATWTEVVHINRERWRAKVPKEEKAKKEAEEKARLAAEEQQKEMEAKSQAEEGASGKAEKKTSGETKNQVNGTRANKSDNPRGKNSKAEKSSGEQQQNGDFKDGKNKTDKKDHSNIGNDSKKTDGTKQRSHGSPAPSTSSTCRLTLPVIKPPLRHFKRPAYASSSYAPSVSKKTDEHPARYKMLDQRIKMKKIQNISHNWNRK'."
            },
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug above and the target protein 'MTELKAKGPRAPHVAGGPPSPEVGSPLLCRPAAGPFPGSQTSDTLPEVSAIPISLDGLLFPRPCQGQDPSDEKTQDQQSLSDVEGAYSRAEATRGAGGSSSSPPEKDSGLLDSVLDTLLAPSGPGQSQPSPPACEVTSSWCLFGPELPEDPPAAPATQRVLSPLMSRSGCKVGDSSGTAAAHKVLPRGLSPARQLLLPASESPHWSGAPVKPSPQAAAVEVEEEDGSESEESAGPLLKGKPRALGGAAAGGGAAAVPPGAAAGGVALVPKEDSRFSAPRVALVEQDAPMAPGRSPLATTVMDFIHVPILPLNHALLAARTRQLLEDESYDGGAGAASAFAPPRSSPCASSTPVAVGDFPDCAYPPDAEPKDDAYPLYSDFQPPALKIKEEEEGAEASARSPRSYLVAGANPAAFPDFPLGPPPPLPPRATPSRPGEAAVTAAPASASVSSASSSGSTLECILYKAEGAPPQQGPFAPPPCKAPGASGCLLPRDGLPSTSASAAAAGAAPALYPALGLNGLPQLGYQAAVLKEGLPQVYPPYLNYLRPDSEASQSPQYSFESLPQKICLICGDEASGCHYGVLTCGSCKVFFKRAMEGQHNYLCAGRNDCIVDKIRRKNCPACRLRKCCQAGMVLGGRKFKKFNKVRVVRALDAVALPQPVGVPNESQALSQRFTFSPGQDIQLIPPLINLLMSIEPDVIYAGHDNTKPDTSSSLLTSLNQLGERQLLSVVKWSKSLPGFRNLHIDDQITLIQYSWMSLMVFGLGWRSYKHVSGQMLYFAPDLILNEQRMKESSFYSLCLTMWQIPQEFVKLQVSQEEFLCMKVLLLLNTIPLEGLRSQTQFEEMRSSYIRELIKAIGLRQKGVVSSSQRFYQLTKLLDNLHDLVKQLHLYCLNTFIQSRALSVEFPEMMSEVIAAQLPKILAGMVKPLLFHKK'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTELKAKGPRAPHVAGGPPSPEVGSPLLCRPAAGPFPGSQTSDTLPEVSAIPISLDGLLFPRPCQGQDPSDEKTQDQQSLSDVEGAYSRAEATRGAGGSSSSPPEKDSGLLDSVLDTLLAPSGPGQSQPSPPACEVTSSWCLFGPELPEDPPAAPATQRVLSPLMSRSGCKVGDSSGTAAAHKVLPRGLSPARQLLLPASESPHWSGAPVKPSPQAAAVEVEEEDGSESEESAGPLLKGKPRALGGAAAGGGAAAVPPGAAAGGVALVPKEDSRFSAPRVALVEQDAPMAPGRSPLATTVMDFIHVPILPLNHALLAARTRQLLEDESYDGGAGAASAFAPPRSSPCASSTPVAVGDFPDCAYPPDAEPKDDAYPLYSDFQPPALKIKEEEEGAEASARSPRSYLVAGANPAAFPDFPLGPPPPLPPRATPSRPGEAAVTAAPASASVSSASSSGSTLECILYKAEGAPPQQGPFAPPPCKAPGASGCLLPRDGLPSTSASAAAAGAAPALYPALGLNGLPQLGYQAAVLKEGLPQVYPPYLNYLRPDSEASQSPQYSFESLPQKICLICGDEASGCHYGVLTCGSCKVFFKRAMEGQHNYLCAGRNDCIVDKIRRKNCPACRLRKCCQAGMVLGGRKFKKFNKVRVVRALDAVALPQPVGVPNESQALSQRFTFSPGQDIQLIPPLINLLMSIEPDVIYAGHDNTKPDTSSSLLTSLNQLGERQLLSVVKWSKSLPGFRNLHIDDQITLIQYSWMSLMVFGLGWRSYKHVSGQMLYFAPDLILNEQRMKESSFYSLCLTMWQIPQEFVKLQVSQEEFLCMKVLLLLNTIPLEGLRSQTQFEEMRSSYIRELIKAIGLRQKGVVSSSQRFYQLTKLLDNLHDLVKQLHLYCLNTFIQSRALSVEFPEMMSEVIAAQLPKILAGMVKPLLFHKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTELKAKGPRAPHVAGGPPSPEVGSPLLCRPAAGPFPGSQTSDTLPEVSAIPISLDGLLFPRPCQGQDPSDEKTQDQQSLSDVEGAYSRAEATRGAGGSSSSPPEKDSGLLDSVLDTLLAPSGPGQSQPSPPACEVTSSWCLFGPELPEDPPAAPATQRVLSPLMSRSGCKVGDSSGTAAAHKVLPRGLSPARQLLLPASESPHWSGAPVKPSPQAAAVEVEEEDGSESEESAGPLLKGKPRALGGAAAGGGAAAVPPGAAAGGVALVPKEDSRFSAPRVALVEQDAPMAPGRSPLATTVMDFIHVPILPLNHALLAARTRQLLEDESYDGGAGAASAFAPPRSSPCASSTPVAVGDFPDCAYPPDAEPKDDAYPLYSDFQPPALKIKEEEEGAEASARSPRSYLVAGANPAAFPDFPLGPPPPLPPRATPSRPGEAAVTAAPASASVSSASSSGSTLECILYKAEGAPPQQGPFAPPPCKAPGASGCLLPRDGLPSTSASAAAAGAAPALYPALGLNGLPQLGYQAAVLKEGLPQVYPPYLNYLRPDSEASQSPQYSFESLPQKICLICGDEASGCHYGVLTCGSCKVFFKRAMEGQHNYLCAGRNDCIVDKIRRKNCPACRLRKCCQAGMVLGGRKFKKFNKVRVVRALDAVALPQPVGVPNESQALSQRFTFSPGQDIQLIPPLINLLMSIEPDVIYAGHDNTKPDTSSSLLTSLNQLGERQLLSVVKWSKSLPGFRNLHIDDQITLIQYSWMSLMVFGLGWRSYKHVSGQMLYFAPDLILNEQRMKESSFYSLCLTMWQIPQEFVKLQVSQEEFLCMKVLLLLNTIPLEGLRSQTQFEEMRSSYIRELIKAIGLRQKGVVSSSQRFYQLTKLLDNLHDLVKQLHLYCLNTFIQSRALSVEFPEMMSEVIAAQLPKILAGMVKPLLFHKK\", \"result\": [{\"smile\": \"N[C@@H](CSO)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CSO)C(O)=O' can not interact with the protein 'MTELKAKGPRAPHVAGGPPSPEVGSPLLCRPAAGPFPGSQTSDTLPEVSAIPISLDGLLFPRPCQGQDPSDEKTQDQQSLSDVEGAYSRAEATRGAGGSSSSPPEKDSGLLDSVLDTLLAPSGPGQSQPSPPACEVTSSWCLFGPELPEDPPAAPATQRVLSPLMSRSGCKVGDSSGTAAAHKVLPRGLSPARQLLLPASESPHWSGAPVKPSPQAAAVEVEEEDGSESEESAGPLLKGKPRALGGAAAGGGAAAVPPGAAAGGVALVPKEDSRFSAPRVALVEQDAPMAPGRSPLATTVMDFIHVPILPLNHALLAARTRQLLEDESYDGGAGAASAFAPPRSSPCASSTPVAVGDFPDCAYPPDAEPKDDAYPLYSDFQPPALKIKEEEEGAEASARSPRSYLVAGANPAAFPDFPLGPPPPLPPRATPSRPGEAAVTAAPASASVSSASSSGSTLECILYKAEGAPPQQGPFAPPPCKAPGASGCLLPRDGLPSTSASAAAAGAAPALYPALGLNGLPQLGYQAAVLKEGLPQVYPPYLNYLRPDSEASQSPQYSFESLPQKICLICGDEASGCHYGVLTCGSCKVFFKRAMEGQHNYLCAGRNDCIVDKIRRKNCPACRLRKCCQAGMVLGGRKFKKFNKVRVVRALDAVALPQPVGVPNESQALSQRFTFSPGQDIQLIPPLINLLMSIEPDVIYAGHDNTKPDTSSSLLTSLNQLGERQLLSVVKWSKSLPGFRNLHIDDQITLIQYSWMSLMVFGLGWRSYKHVSGQMLYFAPDLILNEQRMKESSFYSLCLTMWQIPQEFVKLQVSQEEFLCMKVLLLLNTIPLEGLRSQTQFEEMRSSYIRELIKAIGLRQKGVVSSSQRFYQLTKLLDNLHDLVKQLHLYCLNTFIQSRALSVEFPEMMSEVIAAQLPKILAGMVKPLLFHKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug 'C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1' and the target protein 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1\"], \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\", \"result\": [{\"smile\": \"C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1' can interact with the protein 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL'."
            },
            {
                "from": "human",
                "value": "Can you forecast if the drug above will interact with the target protein 'MSGGGPSGGGPGGSGRARTSSFAEPGGGGGGGGGGPGGSASGPGGTGGGKASVGAMGGGVGASSSGGGPGGSGGGGSGGPGAGTSFPPPGVKLGRDSGKVTTVVATLGQGPERSQEVAYTDIKVIGNGSFGVVYQARLAETRELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDELYLNLVLEYVPETVYRVARHFTKAKLTIPILYVKVYMYQLFRSLAYIHSQGVCHRDIKPQNLLVDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFKSRTPPEAIALCSSLLEYTPSSRLSPLEACAHSFFDELRCLGTQLPNNRPLPPLFNFSAGELSIQPSLNAILIPPHLRSPAGTTTLTPSSQALTETPTSSDWQSTDATPTLTNSS'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSGGGPSGGGPGGSGRARTSSFAEPGGGGGGGGGGPGGSASGPGGTGGGKASVGAMGGGVGASSSGGGPGGSGGGGSGGPGAGTSFPPPGVKLGRDSGKVTTVVATLGQGPERSQEVAYTDIKVIGNGSFGVVYQARLAETRELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDELYLNLVLEYVPETVYRVARHFTKAKLTIPILYVKVYMYQLFRSLAYIHSQGVCHRDIKPQNLLVDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFKSRTPPEAIALCSSLLEYTPSSRLSPLEACAHSFFDELRCLGTQLPNNRPLPPLFNFSAGELSIQPSLNAILIPPHLRSPAGTTTLTPSSQALTETPTSSDWQSTDATPTLTNSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSGGGPSGGGPGGSGRARTSSFAEPGGGGGGGGGGPGGSASGPGGTGGGKASVGAMGGGVGASSSGGGPGGSGGGGSGGPGAGTSFPPPGVKLGRDSGKVTTVVATLGQGPERSQEVAYTDIKVIGNGSFGVVYQARLAETRELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDELYLNLVLEYVPETVYRVARHFTKAKLTIPILYVKVYMYQLFRSLAYIHSQGVCHRDIKPQNLLVDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFKSRTPPEAIALCSSLLEYTPSSRLSPLEACAHSFFDELRCLGTQLPNNRPLPPLFNFSAGELSIQPSLNAILIPPHLRSPAGTTTLTPSSQALTETPTSSDWQSTDATPTLTNSS\", \"result\": [{\"smile\": \"C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C#Cc1cc(Nc2cc(Oc3ccc(NC(=O)Nc4cc(C(C)(C)C)cc(NS(C)(=O)=O)c4OC)c4ccccc34)ccn2)cc(C(=O)NCCOCCOCCOC)c1' can not interact with the protein 'MSGGGPSGGGPGGSGRARTSSFAEPGGGGGGGGGGPGGSASGPGGTGGGKASVGAMGGGVGASSSGGGPGGSGGGGSGGPGAGTSFPPPGVKLGRDSGKVTTVVATLGQGPERSQEVAYTDIKVIGNGSFGVVYQARLAETRELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDELYLNLVLEYVPETVYRVARHFTKAKLTIPILYVKVYMYQLFRSLAYIHSQGVCHRDIKPQNLLVDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFKSRTPPEAIALCSSLLEYTPSSRLSPLEACAHSFFDELRCLGTQLPNNRPLPPLFNFSAGELSIQPSLNAILIPPHLRSPAGTTTLTPSSQALTETPTSSDWQSTDATPTLTNSS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction on whether the drug 'COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2' will interact with the target protein 'MQSWGTIICIRILLRFLLLWVLIGNSHTEEDIIITTKNGKVRGMNLPVLGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSNIWNATKYANSCYQNTDQSFPGFLGSEMWNPNTELSEDCLYLNVWIPAPKPKNATVMIWIYGGGFQTGTSSLPVYDGKFLARVERVIVVSMNYRVGALGFLALSENPEAPGNMGLFDQQLALQWVQKNIAAFGGNPRSVTLFGESAGAASVSLHLLSPRSQPLFTRAILQSGSSNAPWAVTSLYEARNRTLTLAKRMGCSRDNETEMIKCLRDKDPQEILLNEVFVVPYDTLLSVNFGPTVDGDFLTDMPDTLLQLGQFKRTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPRVSEFGRESILFHYMDWLDDQRAENYREALDDVVGDYNIICPALEFTKKFSELGNDAFFYYFEHRSTKLPWPEWMGVMHGYEIEFVFGLPLERRVNYTKAEEILSRSIMKRWANFAKYGNPNGTQSNSTRWPVFKSTEQKYLTLNTESPKVYTKLRAQQCRFWTLFFPKVLELTGNIDEAEREWKAGFHRWNNYMMDWKNQFNDYTSKKESCSDF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2\"], \"target_seq\": \"MQSWGTIICIRILLRFLLLWVLIGNSHTEEDIIITTKNGKVRGMNLPVLGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSNIWNATKYANSCYQNTDQSFPGFLGSEMWNPNTELSEDCLYLNVWIPAPKPKNATVMIWIYGGGFQTGTSSLPVYDGKFLARVERVIVVSMNYRVGALGFLALSENPEAPGNMGLFDQQLALQWVQKNIAAFGGNPRSVTLFGESAGAASVSLHLLSPRSQPLFTRAILQSGSSNAPWAVTSLYEARNRTLTLAKRMGCSRDNETEMIKCLRDKDPQEILLNEVFVVPYDTLLSVNFGPTVDGDFLTDMPDTLLQLGQFKRTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPRVSEFGRESILFHYMDWLDDQRAENYREALDDVVGDYNIICPALEFTKKFSELGNDAFFYYFEHRSTKLPWPEWMGVMHGYEIEFVFGLPLERRVNYTKAEEILSRSIMKRWANFAKYGNPNGTQSNSTRWPVFKSTEQKYLTLNTESPKVYTKLRAQQCRFWTLFFPKVLELTGNIDEAEREWKAGFHRWNNYMMDWKNQFNDYTSKKESCSDF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSWGTIICIRILLRFLLLWVLIGNSHTEEDIIITTKNGKVRGMNLPVLGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSNIWNATKYANSCYQNTDQSFPGFLGSEMWNPNTELSEDCLYLNVWIPAPKPKNATVMIWIYGGGFQTGTSSLPVYDGKFLARVERVIVVSMNYRVGALGFLALSENPEAPGNMGLFDQQLALQWVQKNIAAFGGNPRSVTLFGESAGAASVSLHLLSPRSQPLFTRAILQSGSSNAPWAVTSLYEARNRTLTLAKRMGCSRDNETEMIKCLRDKDPQEILLNEVFVVPYDTLLSVNFGPTVDGDFLTDMPDTLLQLGQFKRTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPRVSEFGRESILFHYMDWLDDQRAENYREALDDVVGDYNIICPALEFTKKFSELGNDAFFYYFEHRSTKLPWPEWMGVMHGYEIEFVFGLPLERRVNYTKAEEILSRSIMKRWANFAKYGNPNGTQSNSTRWPVFKSTEQKYLTLNTESPKVYTKLRAQQCRFWTLFFPKVLELTGNIDEAEREWKAGFHRWNNYMMDWKNQFNDYTSKKESCSDF\", \"result\": [{\"smile\": \"COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2' can not interact with the protein 'MQSWGTIICIRILLRFLLLWVLIGNSHTEEDIIITTKNGKVRGMNLPVLGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSNIWNATKYANSCYQNTDQSFPGFLGSEMWNPNTELSEDCLYLNVWIPAPKPKNATVMIWIYGGGFQTGTSSLPVYDGKFLARVERVIVVSMNYRVGALGFLALSENPEAPGNMGLFDQQLALQWVQKNIAAFGGNPRSVTLFGESAGAASVSLHLLSPRSQPLFTRAILQSGSSNAPWAVTSLYEARNRTLTLAKRMGCSRDNETEMIKCLRDKDPQEILLNEVFVVPYDTLLSVNFGPTVDGDFLTDMPDTLLQLGQFKRTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPRVSEFGRESILFHYMDWLDDQRAENYREALDDVVGDYNIICPALEFTKKFSELGNDAFFYYFEHRSTKLPWPEWMGVMHGYEIEFVFGLPLERRVNYTKAEEILSRSIMKRWANFAKYGNPNGTQSNSTRWPVFKSTEQKYLTLNTESPKVYTKLRAQQCRFWTLFFPKVLELTGNIDEAEREWKAGFHRWNNYMMDWKNQFNDYTSKKESCSDF'."
            },
            {
                "from": "human",
                "value": "What's the chance that the drug above interacts with the target protein 'MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGGRLRGIRLKTPGGPVSAFLGIPFAEPPMGPRRFLPPEPKQPWSGVVDATTFQSVCYQYVDTLYPGFEGTEMWNPNRELSEDCLYLNVWTPYPRPTSPTPVLVWIYGGGFYSGASSLDVYDGRFLVQAERTVLVSMNYRVGAFGFLALPGSREAPGNVGLLDQRLALQWVQENVAAFGGDPTSVTLFGESAGAASVGMHLLSPPSRGLFHRAVLQSGAPNGPWATVGMGEARRRATQLAHLVGCPPGGTGGNDTELVACLRTRPAQVLVNHEWHVLPQESVFRFSFVPVVDGDFLSDTPEALINAGDFHGLQVLVGVVKDEGSYFLVYGAPGFSKDNESLISRAEFLAGVRVGVPQVSDLAAEAVVLHYTDWLHPEDPARLREALSDVVGDHNVVCPVAQLAGRLAAQGARVYAYVFEHRASTLSWPLWMGVPHGYEIEFIFGIPLDPSRNYTAEEKIFAQRLMRYWANFARTGDPNEPRDPKAPQWPPYTAGAQQYVSLDLRPLEVRRGLRAQACAFWNRFLPKLLSATDTLDEAERQWKAEFHRWSSYMVHWKNQFDHYSKQDRCSDL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGGRLRGIRLKTPGGPVSAFLGIPFAEPPMGPRRFLPPEPKQPWSGVVDATTFQSVCYQYVDTLYPGFEGTEMWNPNRELSEDCLYLNVWTPYPRPTSPTPVLVWIYGGGFYSGASSLDVYDGRFLVQAERTVLVSMNYRVGAFGFLALPGSREAPGNVGLLDQRLALQWVQENVAAFGGDPTSVTLFGESAGAASVGMHLLSPPSRGLFHRAVLQSGAPNGPWATVGMGEARRRATQLAHLVGCPPGGTGGNDTELVACLRTRPAQVLVNHEWHVLPQESVFRFSFVPVVDGDFLSDTPEALINAGDFHGLQVLVGVVKDEGSYFLVYGAPGFSKDNESLISRAEFLAGVRVGVPQVSDLAAEAVVLHYTDWLHPEDPARLREALSDVVGDHNVVCPVAQLAGRLAAQGARVYAYVFEHRASTLSWPLWMGVPHGYEIEFIFGIPLDPSRNYTAEEKIFAQRLMRYWANFARTGDPNEPRDPKAPQWPPYTAGAQQYVSLDLRPLEVRRGLRAQACAFWNRFLPKLLSATDTLDEAERQWKAEFHRWSSYMVHWKNQFDHYSKQDRCSDL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGGRLRGIRLKTPGGPVSAFLGIPFAEPPMGPRRFLPPEPKQPWSGVVDATTFQSVCYQYVDTLYPGFEGTEMWNPNRELSEDCLYLNVWTPYPRPTSPTPVLVWIYGGGFYSGASSLDVYDGRFLVQAERTVLVSMNYRVGAFGFLALPGSREAPGNVGLLDQRLALQWVQENVAAFGGDPTSVTLFGESAGAASVGMHLLSPPSRGLFHRAVLQSGAPNGPWATVGMGEARRRATQLAHLVGCPPGGTGGNDTELVACLRTRPAQVLVNHEWHVLPQESVFRFSFVPVVDGDFLSDTPEALINAGDFHGLQVLVGVVKDEGSYFLVYGAPGFSKDNESLISRAEFLAGVRVGVPQVSDLAAEAVVLHYTDWLHPEDPARLREALSDVVGDHNVVCPVAQLAGRLAAQGARVYAYVFEHRASTLSWPLWMGVPHGYEIEFIFGIPLDPSRNYTAEEKIFAQRLMRYWANFARTGDPNEPRDPKAPQWPPYTAGAQQYVSLDLRPLEVRRGLRAQACAFWNRFLPKLLSATDTLDEAERQWKAEFHRWSSYMVHWKNQFDHYSKQDRCSDL\", \"result\": [{\"smile\": \"COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc2cc3[n+](cc2c1OC)CCc1cc2c(cc1-3)OCO2' can interact with the protein 'MRPPQCLLHTPSLASPLLLLLLWLLGGGVGAEGREDAELLVTVRGGRLRGIRLKTPGGPVSAFLGIPFAEPPMGPRRFLPPEPKQPWSGVVDATTFQSVCYQYVDTLYPGFEGTEMWNPNRELSEDCLYLNVWTPYPRPTSPTPVLVWIYGGGFYSGASSLDVYDGRFLVQAERTVLVSMNYRVGAFGFLALPGSREAPGNVGLLDQRLALQWVQENVAAFGGDPTSVTLFGESAGAASVGMHLLSPPSRGLFHRAVLQSGAPNGPWATVGMGEARRRATQLAHLVGCPPGGTGGNDTELVACLRTRPAQVLVNHEWHVLPQESVFRFSFVPVVDGDFLSDTPEALINAGDFHGLQVLVGVVKDEGSYFLVYGAPGFSKDNESLISRAEFLAGVRVGVPQVSDLAAEAVVLHYTDWLHPEDPARLREALSDVVGDHNVVCPVAQLAGRLAAQGARVYAYVFEHRASTLSWPLWMGVPHGYEIEFIFGIPLDPSRNYTAEEKIFAQRLMRYWANFARTGDPNEPRDPKAPQWPPYTAGAQQYVSLDLRPLEVRRGLRAQACAFWNRFLPKLLSATDTLDEAERQWKAEFHRWSSYMVHWKNQFDHYSKQDRCSDL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome for the drug 'Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1' interacting with the target protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1\"], \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL\", \"result\": [{\"smile\": \"Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1' can not interact with the protein 'MQPSSLLPLALCLLAAPASALVRIPLHKFTSIRRTMSEVGGSVEDLIAKGPVSKYSQAVPAVTEGPIPEVLKNYMDAQYYGEIGIGTPPQCFTVVFDTGSSNLWVPSIHCKLLDIACWIHHKYNSDKSSTYVKNGTSFDIHYGSGSLSGYLSQDTVSVPCQSASSASALGGVKVERQVFGEATKQPGITFIAAKFDGILGMAYPRISVNNVLPVFDNLMQQKLVDQNIFSFYLSRDPDAQPGGELMLGGTDSKYYKGSLSYLNVTRKAYWQVHLDQVEVASGLTLCKEGCEAIVDTGTSLMVGPVDEVRELQKAIGAVPLIQGEYMIPCEKVSTLPAITLKLGGKGYKLSPEDYTLKVSQAGKTLCLSGFMGMDIPPPSGPLWILGDVFIGRYYTVFDRDNNRVGFAEAARL'."
            },
            {
                "from": "human",
                "value": "What's your forecast for the drug above's interaction with the target protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\", \"result\": [{\"smile\": \"Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cc(C(F)F)cnc1C(=O)Nc1ccc(F)c([C@@]2(C)N=C(N)C3(CCC3)S(=O)(=O)[C@@H]2F)c1' can interact with the protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug '[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1''s interaction with the target protein 'MFADRWLFSTNHKDIGTLYLLFGAWAGVLGTALSLLIRAELGQPGNLLGNDHIYNVIVTAHAFVMIFFMVMPIMIGGFGNWLVPLMIGAPDMAFPRMNNMSFWLLPPSLLLLLASAMVEAGAGTGWTVYPPLAGNYSHPGASVDLTIFSLHLAGVSSILGAINFITTIINMKPPAMTQYQTPLFVWSVLITAVLLLLSLPVLAAGITMLLTDRNLNTTFFDPAGGGDPILYQHLFWFFGHPEVYILILPGFGMISHIVTYYSGKKEPFGYMGMVWAMMSIGFLGFIVWAHHMFTVGMDVDTRAYFTSATMIIAIPTGVKVFSWLATLHGSNMKWSAAVLWALGFIFLFTVGGLTGIVLANSSLDIVLHDTYYVVAHFHYVLSMGAVFAIMGGFIHWFPLFSGYTLDQTYAKIHFTIMFIGVNLTFFPQHFLGLSGMPRRYSDYPDAYTTWNILSSVGSFISLTAVMLMIFMIWEAFASKRKVLMVEEPSMNLEWLYGCPPPYHTFEEPVYMKS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1\"], \"target_seq\": \"MFADRWLFSTNHKDIGTLYLLFGAWAGVLGTALSLLIRAELGQPGNLLGNDHIYNVIVTAHAFVMIFFMVMPIMIGGFGNWLVPLMIGAPDMAFPRMNNMSFWLLPPSLLLLLASAMVEAGAGTGWTVYPPLAGNYSHPGASVDLTIFSLHLAGVSSILGAINFITTIINMKPPAMTQYQTPLFVWSVLITAVLLLLSLPVLAAGITMLLTDRNLNTTFFDPAGGGDPILYQHLFWFFGHPEVYILILPGFGMISHIVTYYSGKKEPFGYMGMVWAMMSIGFLGFIVWAHHMFTVGMDVDTRAYFTSATMIIAIPTGVKVFSWLATLHGSNMKWSAAVLWALGFIFLFTVGGLTGIVLANSSLDIVLHDTYYVVAHFHYVLSMGAVFAIMGGFIHWFPLFSGYTLDQTYAKIHFTIMFIGVNLTFFPQHFLGLSGMPRRYSDYPDAYTTWNILSSVGSFISLTAVMLMIFMIWEAFASKRKVLMVEEPSMNLEWLYGCPPPYHTFEEPVYMKS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFADRWLFSTNHKDIGTLYLLFGAWAGVLGTALSLLIRAELGQPGNLLGNDHIYNVIVTAHAFVMIFFMVMPIMIGGFGNWLVPLMIGAPDMAFPRMNNMSFWLLPPSLLLLLASAMVEAGAGTGWTVYPPLAGNYSHPGASVDLTIFSLHLAGVSSILGAINFITTIINMKPPAMTQYQTPLFVWSVLITAVLLLLSLPVLAAGITMLLTDRNLNTTFFDPAGGGDPILYQHLFWFFGHPEVYILILPGFGMISHIVTYYSGKKEPFGYMGMVWAMMSIGFLGFIVWAHHMFTVGMDVDTRAYFTSATMIIAIPTGVKVFSWLATLHGSNMKWSAAVLWALGFIFLFTVGGLTGIVLANSSLDIVLHDTYYVVAHFHYVLSMGAVFAIMGGFIHWFPLFSGYTLDQTYAKIHFTIMFIGVNLTFFPQHFLGLSGMPRRYSDYPDAYTTWNILSSVGSFISLTAVMLMIFMIWEAFASKRKVLMVEEPSMNLEWLYGCPPPYHTFEEPVYMKS\", \"result\": [{\"smile\": \"[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1' can not interact with the protein 'MFADRWLFSTNHKDIGTLYLLFGAWAGVLGTALSLLIRAELGQPGNLLGNDHIYNVIVTAHAFVMIFFMVMPIMIGGFGNWLVPLMIGAPDMAFPRMNNMSFWLLPPSLLLLLASAMVEAGAGTGWTVYPPLAGNYSHPGASVDLTIFSLHLAGVSSILGAINFITTIINMKPPAMTQYQTPLFVWSVLITAVLLLLSLPVLAAGITMLLTDRNLNTTFFDPAGGGDPILYQHLFWFFGHPEVYILILPGFGMISHIVTYYSGKKEPFGYMGMVWAMMSIGFLGFIVWAHHMFTVGMDVDTRAYFTSATMIIAIPTGVKVFSWLATLHGSNMKWSAAVLWALGFIFLFTVGGLTGIVLANSSLDIVLHDTYYVVAHFHYVLSMGAVFAIMGGFIHWFPLFSGYTLDQTYAKIHFTIMFIGVNLTFFPQHFLGLSGMPRRYSDYPDAYTTWNILSSVGSFISLTAVMLMIFMIWEAFASKRKVLMVEEPSMNLEWLYGCPPPYHTFEEPVYMKS'."
            },
            {
                "from": "human",
                "value": "What's the expected outcome for the drug above interacting with the target protein 'MSASAQQLAEELQIFGLDCEEALIEKLVELCVQYGQNEEGMVGELIAFCTSTHKVGLTSEILNSFEHEFLSKRLSKARHSTCKDSGHAGARDIVSIQELIEVEEEEEILLNSYTTPSKGSQKRAISTPETPLTKRSVSTRSPHQLLSPSSFSPSATPSQKYNSRSNRGEVVTSFGLAQGVSWSGRGGAGNISLKVLGCPEALTGSYKSMFQKLPDIREVLTCKIEELGSELKEHYKIEAFTPLLAPAQEPVTLLGQIGCDSNGKLNNKSVILEGDREHSSGAQIPVDLSELKEYSLFPGQVVIMEGINTTGRKLVATKLYEGVPLPFYQPTEEDADFEQSMVLVACGPYTTSDSITYDPLLDLIAVINHDRPDVCILFGPFLDAKHEQVENCLLTSPFEDIFKQCLRTIIEGTRSSGSHLVFVPSLRDVHHEPVYPQPPFSYSDLSREDKKQVQFVSEPCSLSINGVIFGLTSTDLLFHLGAEEISSSSGTSDRFSRILKHILTQRSYYPLYPPQEDMAIDYESFYVYAQLPVTPDVLIIPSELRYFVKDVLGCVCVNPGRLTKGQVGGTFARLYLRRPAADGAERQSPCIAVQVVRI'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSASAQQLAEELQIFGLDCEEALIEKLVELCVQYGQNEEGMVGELIAFCTSTHKVGLTSEILNSFEHEFLSKRLSKARHSTCKDSGHAGARDIVSIQELIEVEEEEEILLNSYTTPSKGSQKRAISTPETPLTKRSVSTRSPHQLLSPSSFSPSATPSQKYNSRSNRGEVVTSFGLAQGVSWSGRGGAGNISLKVLGCPEALTGSYKSMFQKLPDIREVLTCKIEELGSELKEHYKIEAFTPLLAPAQEPVTLLGQIGCDSNGKLNNKSVILEGDREHSSGAQIPVDLSELKEYSLFPGQVVIMEGINTTGRKLVATKLYEGVPLPFYQPTEEDADFEQSMVLVACGPYTTSDSITYDPLLDLIAVINHDRPDVCILFGPFLDAKHEQVENCLLTSPFEDIFKQCLRTIIEGTRSSGSHLVFVPSLRDVHHEPVYPQPPFSYSDLSREDKKQVQFVSEPCSLSINGVIFGLTSTDLLFHLGAEEISSSSGTSDRFSRILKHILTQRSYYPLYPPQEDMAIDYESFYVYAQLPVTPDVLIIPSELRYFVKDVLGCVCVNPGRLTKGQVGGTFARLYLRRPAADGAERQSPCIAVQVVRI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSASAQQLAEELQIFGLDCEEALIEKLVELCVQYGQNEEGMVGELIAFCTSTHKVGLTSEILNSFEHEFLSKRLSKARHSTCKDSGHAGARDIVSIQELIEVEEEEEILLNSYTTPSKGSQKRAISTPETPLTKRSVSTRSPHQLLSPSSFSPSATPSQKYNSRSNRGEVVTSFGLAQGVSWSGRGGAGNISLKVLGCPEALTGSYKSMFQKLPDIREVLTCKIEELGSELKEHYKIEAFTPLLAPAQEPVTLLGQIGCDSNGKLNNKSVILEGDREHSSGAQIPVDLSELKEYSLFPGQVVIMEGINTTGRKLVATKLYEGVPLPFYQPTEEDADFEQSMVLVACGPYTTSDSITYDPLLDLIAVINHDRPDVCILFGPFLDAKHEQVENCLLTSPFEDIFKQCLRTIIEGTRSSGSHLVFVPSLRDVHHEPVYPQPPFSYSDLSREDKKQVQFVSEPCSLSINGVIFGLTSTDLLFHLGAEEISSSSGTSDRFSRILKHILTQRSYYPLYPPQEDMAIDYESFYVYAQLPVTPDVLIIPSELRYFVKDVLGCVCVNPGRLTKGQVGGTFARLYLRRPAADGAERQSPCIAVQVVRI\", \"result\": [{\"smile\": \"[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]1(CCCN1C(=O)C(=O)C(C)(C)CC)C(=O)OCCCC1=CN=CC=C1' can not interact with the protein 'MSASAQQLAEELQIFGLDCEEALIEKLVELCVQYGQNEEGMVGELIAFCTSTHKVGLTSEILNSFEHEFLSKRLSKARHSTCKDSGHAGARDIVSIQELIEVEEEEEILLNSYTTPSKGSQKRAISTPETPLTKRSVSTRSPHQLLSPSSFSPSATPSQKYNSRSNRGEVVTSFGLAQGVSWSGRGGAGNISLKVLGCPEALTGSYKSMFQKLPDIREVLTCKIEELGSELKEHYKIEAFTPLLAPAQEPVTLLGQIGCDSNGKLNNKSVILEGDREHSSGAQIPVDLSELKEYSLFPGQVVIMEGINTTGRKLVATKLYEGVPLPFYQPTEEDADFEQSMVLVACGPYTTSDSITYDPLLDLIAVINHDRPDVCILFGPFLDAKHEQVENCLLTSPFEDIFKQCLRTIIEGTRSSGSHLVFVPSLRDVHHEPVYPQPPFSYSDLSREDKKQVQFVSEPCSLSINGVIFGLTSTDLLFHLGAEEISSSSGTSDRFSRILKHILTQRSYYPLYPPQEDMAIDYESFYVYAQLPVTPDVLIIPSELRYFVKDVLGCVCVNPGRLTKGQVGGTFARLYLRRPAADGAERQSPCIAVQVVRI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the drug 'O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2' will interact with the target protein 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2\"], \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\", \"result\": [{\"smile\": \"O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2' can not interact with the protein 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR'."
            },
            {
                "from": "human",
                "value": "How likely do you think it is that the drug above will interact with the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C1Nc2ccc(Br)cc2/C1=C/c1cc2c([nH]1)CCCC2' can not interact with the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5' interact with the target protein 'MSTIQSETDCYDIIEVLGKGTFGEVAKGWRRSTGEMVAIKILKNDAYRNRIIKNELKLLHCMRGLDPEEAHVIRFLEFFHDALKFYLVFELLEQNLFEFQKENNFAPLPARHIRTVTLQVLTALARLKELAIIHADLKPENIMLVDQTRCPFRVKVIDFGSASIFSEVRYVKEPYIQSRFYRAPEILLGLPFCEKVDVWSLGCVMAELHLGWPLYPGNNEYDQVRYICETQGLPKPHLLHAACKAHHFFKRNPHPDAANPWQLKSSADYLAETKVRPLERRKYMLKSLDQIETVNGGSVASRLTFPDREALAEHADLKSMVELIKRMLTWESHERISPSAALRHPFVSMQQLRSAHETTHYYQLSLRSYRLSLQVEGKPPTPVVAAEDGTPYYCLAEEKEAAGMGSVAGSSPFFREEKAPGMQRAIDQLDDLSLQEAGHGLWGETCTNAVSDMMVPLKAAITGHHVPDSGPEPILAFYSSRLAGRHKARKPPAGSKSDSNFSNLIRLSQVSPEDDRPCRGSSWEEGEHLGASAEPLAILQRDEDGPNIDNMTMEAERPDPELFDPSSCPGEWLSEPDCTLESVRGPRAQGLPPRRSHQHGPPRGATSFLQHVTGHH' as expected? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\"], \"target_seq\": \"MSTIQSETDCYDIIEVLGKGTFGEVAKGWRRSTGEMVAIKILKNDAYRNRIIKNELKLLHCMRGLDPEEAHVIRFLEFFHDALKFYLVFELLEQNLFEFQKENNFAPLPARHIRTVTLQVLTALARLKELAIIHADLKPENIMLVDQTRCPFRVKVIDFGSASIFSEVRYVKEPYIQSRFYRAPEILLGLPFCEKVDVWSLGCVMAELHLGWPLYPGNNEYDQVRYICETQGLPKPHLLHAACKAHHFFKRNPHPDAANPWQLKSSADYLAETKVRPLERRKYMLKSLDQIETVNGGSVASRLTFPDREALAEHADLKSMVELIKRMLTWESHERISPSAALRHPFVSMQQLRSAHETTHYYQLSLRSYRLSLQVEGKPPTPVVAAEDGTPYYCLAEEKEAAGMGSVAGSSPFFREEKAPGMQRAIDQLDDLSLQEAGHGLWGETCTNAVSDMMVPLKAAITGHHVPDSGPEPILAFYSSRLAGRHKARKPPAGSKSDSNFSNLIRLSQVSPEDDRPCRGSSWEEGEHLGASAEPLAILQRDEDGPNIDNMTMEAERPDPELFDPSSCPGEWLSEPDCTLESVRGPRAQGLPPRRSHQHGPPRGATSFLQHVTGHH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTIQSETDCYDIIEVLGKGTFGEVAKGWRRSTGEMVAIKILKNDAYRNRIIKNELKLLHCMRGLDPEEAHVIRFLEFFHDALKFYLVFELLEQNLFEFQKENNFAPLPARHIRTVTLQVLTALARLKELAIIHADLKPENIMLVDQTRCPFRVKVIDFGSASIFSEVRYVKEPYIQSRFYRAPEILLGLPFCEKVDVWSLGCVMAELHLGWPLYPGNNEYDQVRYICETQGLPKPHLLHAACKAHHFFKRNPHPDAANPWQLKSSADYLAETKVRPLERRKYMLKSLDQIETVNGGSVASRLTFPDREALAEHADLKSMVELIKRMLTWESHERISPSAALRHPFVSMQQLRSAHETTHYYQLSLRSYRLSLQVEGKPPTPVVAAEDGTPYYCLAEEKEAAGMGSVAGSSPFFREEKAPGMQRAIDQLDDLSLQEAGHGLWGETCTNAVSDMMVPLKAAITGHHVPDSGPEPILAFYSSRLAGRHKARKPPAGSKSDSNFSNLIRLSQVSPEDDRPCRGSSWEEGEHLGASAEPLAILQRDEDGPNIDNMTMEAERPDPELFDPSSCPGEWLSEPDCTLESVRGPRAQGLPPRRSHQHGPPRGATSFLQHVTGHH\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5' can not interact with the protein 'MSTIQSETDCYDIIEVLGKGTFGEVAKGWRRSTGEMVAIKILKNDAYRNRIIKNELKLLHCMRGLDPEEAHVIRFLEFFHDALKFYLVFELLEQNLFEFQKENNFAPLPARHIRTVTLQVLTALARLKELAIIHADLKPENIMLVDQTRCPFRVKVIDFGSASIFSEVRYVKEPYIQSRFYRAPEILLGLPFCEKVDVWSLGCVMAELHLGWPLYPGNNEYDQVRYICETQGLPKPHLLHAACKAHHFFKRNPHPDAANPWQLKSSADYLAETKVRPLERRKYMLKSLDQIETVNGGSVASRLTFPDREALAEHADLKSMVELIKRMLTWESHERISPSAALRHPFVSMQQLRSAHETTHYYQLSLRSYRLSLQVEGKPPTPVVAAEDGTPYYCLAEEKEAAGMGSVAGSSPFFREEKAPGMQRAIDQLDDLSLQEAGHGLWGETCTNAVSDMMVPLKAAITGHHVPDSGPEPILAFYSSRLAGRHKARKPPAGSKSDSNFSNLIRLSQVSPEDDRPCRGSSWEEGEHLGASAEPLAILQRDEDGPNIDNMTMEAERPDPELFDPSSCPGEWLSEPDCTLESVRGPRAQGLPPRRSHQHGPPRGATSFLQHVTGHH'."
            },
            {
                "from": "human",
                "value": "How do you assess the possibility of the drug above interacting with the target protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5' can not interact with the protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1' is expected to interact with the target protein 'MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1\"], \"target_seq\": \"MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS\", \"result\": [{\"smile\": \"COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1' can interact with the protein 'MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS'."
            },
            {
                "from": "human",
                "value": "Can you analyze whether the drug above is likely to engage with the target protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\", \"result\": [{\"smile\": \"COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1cccc(CN(CCN(C)C)C(=O)Nc2ccc(-c3cn[nH]c3)cc2)c1' can not interact with the protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O' interact with the target protein 'MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF' according to your prediction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O\"], \"target_seq\": \"MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF\", \"result\": [{\"smile\": \"CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O' can interact with the protein 'MCDEDETTALVCDNGSGLVKAGFAGDDAPRAVFPSIVGRPRHQGVMVGMGQKDSYVGDEAQSKRGILTLKYPIEHGIITNWDDMEKIWHHTFYNELRVAPEEHPTLLTEAPLNPKANREKMTQIMFETFNVPAMYVAIQAVLSLYASGRTTGIVLDSGDGVTHNVPIYEGYALPHAIMRLDLAGRDLTDYLMKILTERGYSFVTTAEREIVRDIKEKLCYVALDFENEMATAASSSSLEKSYELPDGQVITIGNERFRCPETLFQPSFIGMESAGIHETTYNSIMKCDIDIRKDLYANNVMSGGTTMYPGIADRMQKEITALAPSTMKIKIIAPPERKYSVWIGGSILASLSTFQQMWITKQEYDEAGPSIVHRKCF'."
            },
            {
                "from": "human",
                "value": "How likely is it for the drug above and target protein 'MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS' to have an interaction? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS\", \"result\": [{\"smile\": \"CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C1=CCC2=C(C3=CC=C(C=C3OC2=C1)N(C)C)C1=CC=C(C=C1C(O)=O)N1C(=O)C=CC1=O' can not interact with the protein 'MEVRPKESWNHADFVHCEDTESVPGKPSVNADEEVGGPQICRVCGDKATGYHFNVMTCEGCKGFFRRAMKRNARLRCPFRKGACEITRKTRRQCQACRLRKCLESGMKKEMIMSDEAVEERRALIKRKKSERTGTQPLGVQGLTEEQRMMIRELMDAQMKTFDTTFSHFKNFRLPGVLSSGCELPESLQAPSREEAAKWSQVRKDLCSLKVSLQLRGEDGSVWNYKPPADSGGKEIFSLLPHMADMSTYMFKGIISFAKVISYFRDLPIEDQISLLKGAAFELCQLRFNTVFNAETGTWECGRLSYCLEDTAGGFQQLLLEPMLKFHYMLKKLQLHEEEYVLMQAISLFSPDRPGVLQHRVVDQLQEQFAITLKSYIECNRPQPAHRFLFLKIMAMLTELRSINAQHTQRLLRIQDIHPFATPLMQELFGITGS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the drug 'NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O''s potential to interact with the target protein 'MSTFFSDTAWICLAVPTVLCGTVFCKYKKSSGQLWSWMVCLAGLCAVCLLILSPFWGLILFSVSCFLMYTYLSGQELLPVDQKAVLVTGGDCGLGHALCKYLDELGFTVFAGVLNENGPGAEELRRTCSPRLSVLQMDITKPVQIKDAYSKVAAMLQDRGLWAVINNAGVLGFPTDGELLLMTDYKQCMAVNFFGTVEVTKTFLPLLRKSKGRLVNVSSMGGGAPMERLASYGSSKAAVTMFSSVMRLELSKWGIKVASIQPGGFLTNIAGTSDKWEKLEKDILDHLPAEVQEDYGQDYILAQRNFLLLINSLASKDFSPVLRDIQHAILAKSPFAYYTPGKGAYLWICLAHYLPIGIYDYFAKRHFGQDKPMPRALRMPNYKKKAT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O\"], \"target_seq\": \"MSTFFSDTAWICLAVPTVLCGTVFCKYKKSSGQLWSWMVCLAGLCAVCLLILSPFWGLILFSVSCFLMYTYLSGQELLPVDQKAVLVTGGDCGLGHALCKYLDELGFTVFAGVLNENGPGAEELRRTCSPRLSVLQMDITKPVQIKDAYSKVAAMLQDRGLWAVINNAGVLGFPTDGELLLMTDYKQCMAVNFFGTVEVTKTFLPLLRKSKGRLVNVSSMGGGAPMERLASYGSSKAAVTMFSSVMRLELSKWGIKVASIQPGGFLTNIAGTSDKWEKLEKDILDHLPAEVQEDYGQDYILAQRNFLLLINSLASKDFSPVLRDIQHAILAKSPFAYYTPGKGAYLWICLAHYLPIGIYDYFAKRHFGQDKPMPRALRMPNYKKKAT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTFFSDTAWICLAVPTVLCGTVFCKYKKSSGQLWSWMVCLAGLCAVCLLILSPFWGLILFSVSCFLMYTYLSGQELLPVDQKAVLVTGGDCGLGHALCKYLDELGFTVFAGVLNENGPGAEELRRTCSPRLSVLQMDITKPVQIKDAYSKVAAMLQDRGLWAVINNAGVLGFPTDGELLLMTDYKQCMAVNFFGTVEVTKTFLPLLRKSKGRLVNVSSMGGGAPMERLASYGSSKAAVTMFSSVMRLELSKWGIKVASIQPGGFLTNIAGTSDKWEKLEKDILDHLPAEVQEDYGQDYILAQRNFLLLINSLASKDFSPVLRDIQHAILAKSPFAYYTPGKGAYLWICLAHYLPIGIYDYFAKRHFGQDKPMPRALRMPNYKKKAT\", \"result\": [{\"smile\": \"NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O' can interact with the protein 'MSTFFSDTAWICLAVPTVLCGTVFCKYKKSSGQLWSWMVCLAGLCAVCLLILSPFWGLILFSVSCFLMYTYLSGQELLPVDQKAVLVTGGDCGLGHALCKYLDELGFTVFAGVLNENGPGAEELRRTCSPRLSVLQMDITKPVQIKDAYSKVAAMLQDRGLWAVINNAGVLGFPTDGELLLMTDYKQCMAVNFFGTVEVTKTFLPLLRKSKGRLVNVSSMGGGAPMERLASYGSSKAAVTMFSSVMRLELSKWGIKVASIQPGGFLTNIAGTSDKWEKLEKDILDHLPAEVQEDYGQDYILAQRNFLLLINSLASKDFSPVLRDIQHAILAKSPFAYYTPGKGAYLWICLAHYLPIGIYDYFAKRHFGQDKPMPRALRMPNYKKKAT'."
            },
            {
                "from": "human",
                "value": "How likely is it that the drug above will interact with the target protein 'MAAALLARASGPARRALCPRAWRQLHTIYQSVELPETHQMLLQTCRDFAEKELFPIAAQVDKEHLFPAAQVKKMGGLGLLAMDVPEELGGAGLDYLAYAIAMEEISRGCASTGVIMSVNNSLYLGPILKFGSKEQKQAWVTPFTSGDKIGCFALSEPGNGSDAGAASTTARAEGDSWVLNGTKAWITNAWEASAAVVFASTDRALQNKGISAFLVPMPTPGLTLGKKEDKLGIRGSSTANLIFEDCRIPKDSILGEPGMGFKIAMQTLDMGRIGIASQALGIAQTALDCAVNYAENRMAFGAPLTKLQVIQFKLADMALALESARLLTWRAAMLKDNKKPFIKEAAMAKLAASEAATAISHQAIQILGGMGYVTEMPAERHYRDARITEIYEGTSEIQRLVIAGHLLRSYRS'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAAALLARASGPARRALCPRAWRQLHTIYQSVELPETHQMLLQTCRDFAEKELFPIAAQVDKEHLFPAAQVKKMGGLGLLAMDVPEELGGAGLDYLAYAIAMEEISRGCASTGVIMSVNNSLYLGPILKFGSKEQKQAWVTPFTSGDKIGCFALSEPGNGSDAGAASTTARAEGDSWVLNGTKAWITNAWEASAAVVFASTDRALQNKGISAFLVPMPTPGLTLGKKEDKLGIRGSSTANLIFEDCRIPKDSILGEPGMGFKIAMQTLDMGRIGIASQALGIAQTALDCAVNYAENRMAFGAPLTKLQVIQFKLADMALALESARLLTWRAAMLKDNKKPFIKEAAMAKLAASEAATAISHQAIQILGGMGYVTEMPAERHYRDARITEIYEGTSEIQRLVIAGHLLRSYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAALLARASGPARRALCPRAWRQLHTIYQSVELPETHQMLLQTCRDFAEKELFPIAAQVDKEHLFPAAQVKKMGGLGLLAMDVPEELGGAGLDYLAYAIAMEEISRGCASTGVIMSVNNSLYLGPILKFGSKEQKQAWVTPFTSGDKIGCFALSEPGNGSDAGAASTTARAEGDSWVLNGTKAWITNAWEASAAVVFASTDRALQNKGISAFLVPMPTPGLTLGKKEDKLGIRGSSTANLIFEDCRIPKDSILGEPGMGFKIAMQTLDMGRIGIASQALGIAQTALDCAVNYAENRMAFGAPLTKLQVIQFKLADMALALESARLLTWRAAMLKDNKKPFIKEAAMAKLAASEAATAISHQAIQILGGMGYVTEMPAERHYRDARITEIYEGTSEIQRLVIAGHLLRSYRS\", \"result\": [{\"smile\": \"NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=O)C1=CN(C=CC1)[C@@H]1O[C@H](CO[P@](O)(=O)O[P@](O)(=O)OC[C@H]2O[C@H]([C@H](O)[C@@H]2O)N2C=NC3=C(N)N=CN=C23)[C@@H](O)[C@H]1O' can interact with the protein 'MAAALLARASGPARRALCPRAWRQLHTIYQSVELPETHQMLLQTCRDFAEKELFPIAAQVDKEHLFPAAQVKKMGGLGLLAMDVPEELGGAGLDYLAYAIAMEEISRGCASTGVIMSVNNSLYLGPILKFGSKEQKQAWVTPFTSGDKIGCFALSEPGNGSDAGAASTTARAEGDSWVLNGTKAWITNAWEASAAVVFASTDRALQNKGISAFLVPMPTPGLTLGKKEDKLGIRGSSTANLIFEDCRIPKDSILGEPGMGFKIAMQTLDMGRIGIASQALGIAQTALDCAVNYAENRMAFGAPLTKLQVIQFKLADMALALESARLLTWRAAMLKDNKKPFIKEAAMAKLAASEAATAISHQAIQILGGMGYVTEMPAERHYRDARITEIYEGTSEIQRLVIAGHLLRSYRS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think the drug 'ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1''s interaction potential with the target protein 'MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL' will be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1\"], \"target_seq\": \"MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL\", \"result\": [{\"smile\": \"ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1' can interact with the protein 'MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL'."
            },
            {
                "from": "human",
                "value": "What is your prediction regarding the drug above's interaction with the target protein 'MASSLPWLCIILWLENALGKLEVEGNFYSENVSRILDNLLEGYDNRLRPGFGGAVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWTDERLKFGGPTEILSLNNLMVSKIWTPDTFFRNGKKSIAHNMTTPNKLFRIMQNGTILYTMRLTINADCPMRLVNFPMDGHACPLKFGSYAYPKSEIIYTWKKGPLYSVEVPEESSSLLQYDLIGQTVSSETIKSNTGEYVIMTVYFHLQRKMGYFMIQIYTPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNLQTQKAKRKAQFAAPPTVTISKATEPLEAEIVLHPDSKYHLKKRITSLSLPIVSSSEANKVLTRAPILQSTPVTPPPLSPAFGGTSKIDQYSRILFPVAFAGFNLVYWVVYLSKDTMEVSSSVE'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASSLPWLCIILWLENALGKLEVEGNFYSENVSRILDNLLEGYDNRLRPGFGGAVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWTDERLKFGGPTEILSLNNLMVSKIWTPDTFFRNGKKSIAHNMTTPNKLFRIMQNGTILYTMRLTINADCPMRLVNFPMDGHACPLKFGSYAYPKSEIIYTWKKGPLYSVEVPEESSSLLQYDLIGQTVSSETIKSNTGEYVIMTVYFHLQRKMGYFMIQIYTPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNLQTQKAKRKAQFAAPPTVTISKATEPLEAEIVLHPDSKYHLKKRITSLSLPIVSSSEANKVLTRAPILQSTPVTPPPLSPAFGGTSKIDQYSRILFPVAFAGFNLVYWVVYLSKDTMEVSSSVE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASSLPWLCIILWLENALGKLEVEGNFYSENVSRILDNLLEGYDNRLRPGFGGAVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWTDERLKFGGPTEILSLNNLMVSKIWTPDTFFRNGKKSIAHNMTTPNKLFRIMQNGTILYTMRLTINADCPMRLVNFPMDGHACPLKFGSYAYPKSEIIYTWKKGPLYSVEVPEESSSLLQYDLIGQTVSSETIKSNTGEYVIMTVYFHLQRKMGYFMIQIYTPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNLQTQKAKRKAQFAAPPTVTISKATEPLEAEIVLHPDSKYHLKKRITSLSLPIVSSSEANKVLTRAPILQSTPVTPPPLSPAFGGTSKIDQYSRILFPVAFAGFNLVYWVVYLSKDTMEVSSSVE\", \"result\": [{\"smile\": \"ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'ClC1=CC2=C(C=C1)N1C=NN=C1CN=C2C1=CC=CC=C1' can interact with the protein 'MASSLPWLCIILWLENALGKLEVEGNFYSENVSRILDNLLEGYDNRLRPGFGGAVTEVKTDIYVTSFGPVSDVEMEYTMDVFFRQTWTDERLKFGGPTEILSLNNLMVSKIWTPDTFFRNGKKSIAHNMTTPNKLFRIMQNGTILYTMRLTINADCPMRLVNFPMDGHACPLKFGSYAYPKSEIIYTWKKGPLYSVEVPEESSSLLQYDLIGQTVSSETIKSNTGEYVIMTVYFHLQRKMGYFMIQIYTPCIMTVILSQVSFWINKESVPARTVFGITTVLTMTTLSISARHSLPKVSYATAMDWFIAVCFAFVFSALIEFAAVNYFTNLQTQKAKRKAQFAAPPTVTISKATEPLEAEIVLHPDSKYHLKKRITSLSLPIVSSSEANKVLTRAPILQSTPVTPPPLSPAFGGTSKIDQYSRILFPVAFAGFNLVYWVVYLSKDTMEVSSSVE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' possibly engage with the target protein 'MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\"], \"target_seq\": \"MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL\", \"result\": [{\"smile\": \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' can interact with the protein 'MSSPNIWSTGSSVYSTPVFSQKMTVWILLLLSLYPGFTSQKSDDDYEDYASNKTWVLTPKVPEGDVTVILNNLLEGYDNKLRPDIGVKPTLIHTDMYVNSIGPVNAINMEYTIDIFFAQTWYDRRLKFNSTIKVLRLNSNMVGKIWIPDTFFRNSKKADAHWITTPNRMLRIWNDGRVLYTLRLTIDAECQLQLHNFPMDEHSCPLEFSSYGYPREEIVYQWKRSSVEVGDTRSWRLYQFSFVGLRNTTEVVKTTSGDYVVMSVYFDLSRRMGYFTIQTYIPCTLIVVLSWVSFWINKDAVPARTSLGITTVLTMTTLSTIARKSLPKVSYVTAMDLFVSVCFIFVFSALVEYGTLHYFVSNRKPSKDKDKKKKNPAPTIDIRPRSATIQMNNATHLQERDEEYGYECLDGKDCASFFCCFEDCRTGAWRHGRIHIRIAKMDSYARIFFPTAFCLFNLVYWVSYLYL'."
            },
            {
                "from": "human",
                "value": "What's your evaluation of the drug above's interaction potential with the target protein 'MLSKVLPVLLGILLILQSRVEGPQTESKNEASSRDVVYGPQPQPLENQLLSEETKSTETETGSRVGKLPEASRILNTILSNYDHKLRPGIGEKPTVVTVEISVNSLGPLSILDMEYTIDIIFSQTWYDERLCYNDTFESLVLNGNVVSQLWIPDTFFRNSKRTHEHEITMPNQMVRIYKDGKVLYTIRMTIDAGCSLHMLRFPMDSHSCPLSFSSFSYPENEMIYKWENFKLEINEKNSWKLFQFDFTGVSNKTEIITTPVGDFMVMTIFFNVSRRFGYVAFQNYVPSSVTTMLSWVSFWIKTESAPARTSLGITSVLTMTTLGTFSRKNFPRVSYITALDFYIAICFVFCFCALLEFAVLNFLIYNQTKAHASPKLRHPRINSRAHARTRARSRACARQHQEAFVCQIVTTEGSDGEERPSCSAQQPPSPGSPEGPRSLCSKLACCEWCKRFKKYFCMVPDCEGSTWQQGRLCIHVYRLDNYSRVVFPVTFFFFNVLYWLVCLNL'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLSKVLPVLLGILLILQSRVEGPQTESKNEASSRDVVYGPQPQPLENQLLSEETKSTETETGSRVGKLPEASRILNTILSNYDHKLRPGIGEKPTVVTVEISVNSLGPLSILDMEYTIDIIFSQTWYDERLCYNDTFESLVLNGNVVSQLWIPDTFFRNSKRTHEHEITMPNQMVRIYKDGKVLYTIRMTIDAGCSLHMLRFPMDSHSCPLSFSSFSYPENEMIYKWENFKLEINEKNSWKLFQFDFTGVSNKTEIITTPVGDFMVMTIFFNVSRRFGYVAFQNYVPSSVTTMLSWVSFWIKTESAPARTSLGITSVLTMTTLGTFSRKNFPRVSYITALDFYIAICFVFCFCALLEFAVLNFLIYNQTKAHASPKLRHPRINSRAHARTRARSRACARQHQEAFVCQIVTTEGSDGEERPSCSAQQPPSPGSPEGPRSLCSKLACCEWCKRFKKYFCMVPDCEGSTWQQGRLCIHVYRLDNYSRVVFPVTFFFFNVLYWLVCLNL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLSKVLPVLLGILLILQSRVEGPQTESKNEASSRDVVYGPQPQPLENQLLSEETKSTETETGSRVGKLPEASRILNTILSNYDHKLRPGIGEKPTVVTVEISVNSLGPLSILDMEYTIDIIFSQTWYDERLCYNDTFESLVLNGNVVSQLWIPDTFFRNSKRTHEHEITMPNQMVRIYKDGKVLYTIRMTIDAGCSLHMLRFPMDSHSCPLSFSSFSYPENEMIYKWENFKLEINEKNSWKLFQFDFTGVSNKTEIITTPVGDFMVMTIFFNVSRRFGYVAFQNYVPSSVTTMLSWVSFWIKTESAPARTSLGITSVLTMTTLGTFSRKNFPRVSYITALDFYIAICFVFCFCALLEFAVLNFLIYNQTKAHASPKLRHPRINSRAHARTRARSRACARQHQEAFVCQIVTTEGSDGEERPSCSAQQPPSPGSPEGPRSLCSKLACCEWCKRFKKYFCMVPDCEGSTWQQGRLCIHVYRLDNYSRVVFPVTFFFFNVLYWLVCLNL\", \"result\": [{\"smile\": \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' can interact with the protein 'MLSKVLPVLLGILLILQSRVEGPQTESKNEASSRDVVYGPQPQPLENQLLSEETKSTETETGSRVGKLPEASRILNTILSNYDHKLRPGIGEKPTVVTVEISVNSLGPLSILDMEYTIDIIFSQTWYDERLCYNDTFESLVLNGNVVSQLWIPDTFFRNSKRTHEHEITMPNQMVRIYKDGKVLYTIRMTIDAGCSLHMLRFPMDSHSCPLSFSSFSYPENEMIYKWENFKLEINEKNSWKLFQFDFTGVSNKTEIITTPVGDFMVMTIFFNVSRRFGYVAFQNYVPSSVTTMLSWVSFWIKTESAPARTSLGITSVLTMTTLGTFSRKNFPRVSYITALDFYIAICFVFCFCALLEFAVLNFLIYNQTKAHASPKLRHPRINSRAHARTRARSRACARQHQEAFVCQIVTTEGSDGEERPSCSAQQPPSPGSPEGPRSLCSKLACCEWCKRFKKYFCMVPDCEGSTWQQGRLCIHVYRLDNYSRVVFPVTFFFFNVLYWLVCLNL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1' interact with the target protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH' according to your prediction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1\"], \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\", \"result\": [{\"smile\": \"O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1' can interact with the protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'."
            },
            {
                "from": "human",
                "value": "What's your forecast regarding the drug above's potential to interact with the target protein 'MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA\", \"result\": [{\"smile\": \"O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(NO)c1ccc(CN2c3ccccc3S(=O)(=O)C[C@H]3CCCC[C@H]32)cc1' can not interact with the protein 'MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you assess the possibility of the drug 'CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12' interacting with the target protein 'MGSTATETEELENTTFKYLIGEQTEKMWQRLKGILRCLVKQLEKGDVNVIDLKKNIEYAASVLEAVYIDETRRLLDTDDELSDIQSDSVPSEVRDWLASTFTRKMGMMKKKSEEKPRFRSIVHVVQAGIFVERMYRKSYHMVGLAYPEAVIVTLKDVDKWSFDVFALNEASGEHSLKFMIYELFTRYDLINRFKIPVSCLIAFAEALEVGYSKYKNPYHNLIHAADVTQTVHYIMLHTGIMHWLTELEILAMVFAAAIHDYEHTGTTNNFHIQTRSDVAILYNDRSVLENHHVSAAYRLMQEEEMNVLINLSKDDWRDLRNLVIEMVLSTDMSGHFQQIKNIRNSLQQPEGLDKAKTMSLILHAADISHPAKSWKLHHRWTMALMEEFFLQGDKEAELGLPFSPLCDRKSTMVAQSQIGFIDFIVEPTFSLLTDSTEKIIIPLIEEDSKTKTPSYGASRRSNMKGTTNDGTYSPDYSLASVDLKSFKNSLVDIIQQNKERWKELAAQGEPDPHKNSDLVNAEEKHAETHS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12\"], \"target_seq\": \"MGSTATETEELENTTFKYLIGEQTEKMWQRLKGILRCLVKQLEKGDVNVIDLKKNIEYAASVLEAVYIDETRRLLDTDDELSDIQSDSVPSEVRDWLASTFTRKMGMMKKKSEEKPRFRSIVHVVQAGIFVERMYRKSYHMVGLAYPEAVIVTLKDVDKWSFDVFALNEASGEHSLKFMIYELFTRYDLINRFKIPVSCLIAFAEALEVGYSKYKNPYHNLIHAADVTQTVHYIMLHTGIMHWLTELEILAMVFAAAIHDYEHTGTTNNFHIQTRSDVAILYNDRSVLENHHVSAAYRLMQEEEMNVLINLSKDDWRDLRNLVIEMVLSTDMSGHFQQIKNIRNSLQQPEGLDKAKTMSLILHAADISHPAKSWKLHHRWTMALMEEFFLQGDKEAELGLPFSPLCDRKSTMVAQSQIGFIDFIVEPTFSLLTDSTEKIIIPLIEEDSKTKTPSYGASRRSNMKGTTNDGTYSPDYSLASVDLKSFKNSLVDIIQQNKERWKELAAQGEPDPHKNSDLVNAEEKHAETHS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSTATETEELENTTFKYLIGEQTEKMWQRLKGILRCLVKQLEKGDVNVIDLKKNIEYAASVLEAVYIDETRRLLDTDDELSDIQSDSVPSEVRDWLASTFTRKMGMMKKKSEEKPRFRSIVHVVQAGIFVERMYRKSYHMVGLAYPEAVIVTLKDVDKWSFDVFALNEASGEHSLKFMIYELFTRYDLINRFKIPVSCLIAFAEALEVGYSKYKNPYHNLIHAADVTQTVHYIMLHTGIMHWLTELEILAMVFAAAIHDYEHTGTTNNFHIQTRSDVAILYNDRSVLENHHVSAAYRLMQEEEMNVLINLSKDDWRDLRNLVIEMVLSTDMSGHFQQIKNIRNSLQQPEGLDKAKTMSLILHAADISHPAKSWKLHHRWTMALMEEFFLQGDKEAELGLPFSPLCDRKSTMVAQSQIGFIDFIVEPTFSLLTDSTEKIIIPLIEEDSKTKTPSYGASRRSNMKGTTNDGTYSPDYSLASVDLKSFKNSLVDIIQQNKERWKELAAQGEPDPHKNSDLVNAEEKHAETHS\", \"result\": [{\"smile\": \"CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12' can interact with the protein 'MGSTATETEELENTTFKYLIGEQTEKMWQRLKGILRCLVKQLEKGDVNVIDLKKNIEYAASVLEAVYIDETRRLLDTDDELSDIQSDSVPSEVRDWLASTFTRKMGMMKKKSEEKPRFRSIVHVVQAGIFVERMYRKSYHMVGLAYPEAVIVTLKDVDKWSFDVFALNEASGEHSLKFMIYELFTRYDLINRFKIPVSCLIAFAEALEVGYSKYKNPYHNLIHAADVTQTVHYIMLHTGIMHWLTELEILAMVFAAAIHDYEHTGTTNNFHIQTRSDVAILYNDRSVLENHHVSAAYRLMQEEEMNVLINLSKDDWRDLRNLVIEMVLSTDMSGHFQQIKNIRNSLQQPEGLDKAKTMSLILHAADISHPAKSWKLHHRWTMALMEEFFLQGDKEAELGLPFSPLCDRKSTMVAQSQIGFIDFIVEPTFSLLTDSTEKIIIPLIEEDSKTKTPSYGASRRSNMKGTTNDGTYSPDYSLASVDLKSFKNSLVDIIQQNKERWKELAAQGEPDPHKNSDLVNAEEKHAETHS'."
            },
            {
                "from": "human",
                "value": "How do you see the drug above interacting with the target protein 'MRRDERDAKAMRSPPPPDGAASPPESVRNGYVKGCVSPLRQDPPRGFFFHLCRFCNVELLLPPPASPQQPRRGSPFSRARLLLGALAAFVLALLLGSGPESWAAGAARLRTLLSVCSQSLSPLFSIACAFFFLTCFLTRTKRGAGPGRSGGGSWWLLALPACCYLGDFLVGQWESWSRGDGDARAPVPHTPPAVAGRWFLVLSCVGLLTLAQPGRLRHSIVVLLFSSFVWWVSFTSLGALPPALRPLLSCLVGGVGCLLALGLDHFFQIREAPQQPQLSSTAEEKVPVIRPRRRSSCVSFGETSGGYYGSCKMFRRPSLPCISREQMILWDWDLKQWYKPHYQISGGGSGVDLSVLNEARNMVSDLLVDPTLPPQVIASLRSISSLMGAFSGSCRPKMNPLTPFPGFYPCSEIEDPAEKGDRKLHKGLNSRNSLPTPHLRRSSGTSGLPPIDQTSPRWERNNGKRPHQEFGILSQGCYLNGPFSSNLLTVPKQRSSSVSLTHHVGLRRAGGLPSMNSPSHGPVSAGSPANRSPIEFPDTAEFLTKPPVILHKSLSSTPNSPDFYQQLRNSDSNLCNRSCGHQILKYDSASEPESGTDCHREKKGGEDNTVFSKASFNLTEAQQEVETENRDGRKLLWEGENHLKAAAQNEEQPNIEQEASQDLILIEDYDSLIENMSNWNFQIFELVEKMGEKSGRILSQVVYTLFQDTGLLEIFKIPTLQFMNYFHALENGYRDIPYHNRIHATDVLHAVWYLTTRPIPGLQEIHSDHGAGNETACSSAINPERIAYISSRSCSIPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWTLYLSRPEYNFLLNLDNVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPTKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWIESEEDDDTESCYEEEDAEELDTEDEEIEDNLNSKPQRRKGRRQIFCQLMHHLTENHKIWKEIIEEEEKCKADGNKLQMENSSLPQGDEIQVIEEADEEEERQLE'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRRDERDAKAMRSPPPPDGAASPPESVRNGYVKGCVSPLRQDPPRGFFFHLCRFCNVELLLPPPASPQQPRRGSPFSRARLLLGALAAFVLALLLGSGPESWAAGAARLRTLLSVCSQSLSPLFSIACAFFFLTCFLTRTKRGAGPGRSGGGSWWLLALPACCYLGDFLVGQWESWSRGDGDARAPVPHTPPAVAGRWFLVLSCVGLLTLAQPGRLRHSIVVLLFSSFVWWVSFTSLGALPPALRPLLSCLVGGVGCLLALGLDHFFQIREAPQQPQLSSTAEEKVPVIRPRRRSSCVSFGETSGGYYGSCKMFRRPSLPCISREQMILWDWDLKQWYKPHYQISGGGSGVDLSVLNEARNMVSDLLVDPTLPPQVIASLRSISSLMGAFSGSCRPKMNPLTPFPGFYPCSEIEDPAEKGDRKLHKGLNSRNSLPTPHLRRSSGTSGLPPIDQTSPRWERNNGKRPHQEFGILSQGCYLNGPFSSNLLTVPKQRSSSVSLTHHVGLRRAGGLPSMNSPSHGPVSAGSPANRSPIEFPDTAEFLTKPPVILHKSLSSTPNSPDFYQQLRNSDSNLCNRSCGHQILKYDSASEPESGTDCHREKKGGEDNTVFSKASFNLTEAQQEVETENRDGRKLLWEGENHLKAAAQNEEQPNIEQEASQDLILIEDYDSLIENMSNWNFQIFELVEKMGEKSGRILSQVVYTLFQDTGLLEIFKIPTLQFMNYFHALENGYRDIPYHNRIHATDVLHAVWYLTTRPIPGLQEIHSDHGAGNETACSSAINPERIAYISSRSCSIPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWTLYLSRPEYNFLLNLDNVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPTKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWIESEEDDDTESCYEEEDAEELDTEDEEIEDNLNSKPQRRKGRRQIFCQLMHHLTENHKIWKEIIEEEEKCKADGNKLQMENSSLPQGDEIQVIEEADEEEERQLE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRRDERDAKAMRSPPPPDGAASPPESVRNGYVKGCVSPLRQDPPRGFFFHLCRFCNVELLLPPPASPQQPRRGSPFSRARLLLGALAAFVLALLLGSGPESWAAGAARLRTLLSVCSQSLSPLFSIACAFFFLTCFLTRTKRGAGPGRSGGGSWWLLALPACCYLGDFLVGQWESWSRGDGDARAPVPHTPPAVAGRWFLVLSCVGLLTLAQPGRLRHSIVVLLFSSFVWWVSFTSLGALPPALRPLLSCLVGGVGCLLALGLDHFFQIREAPQQPQLSSTAEEKVPVIRPRRRSSCVSFGETSGGYYGSCKMFRRPSLPCISREQMILWDWDLKQWYKPHYQISGGGSGVDLSVLNEARNMVSDLLVDPTLPPQVIASLRSISSLMGAFSGSCRPKMNPLTPFPGFYPCSEIEDPAEKGDRKLHKGLNSRNSLPTPHLRRSSGTSGLPPIDQTSPRWERNNGKRPHQEFGILSQGCYLNGPFSSNLLTVPKQRSSSVSLTHHVGLRRAGGLPSMNSPSHGPVSAGSPANRSPIEFPDTAEFLTKPPVILHKSLSSTPNSPDFYQQLRNSDSNLCNRSCGHQILKYDSASEPESGTDCHREKKGGEDNTVFSKASFNLTEAQQEVETENRDGRKLLWEGENHLKAAAQNEEQPNIEQEASQDLILIEDYDSLIENMSNWNFQIFELVEKMGEKSGRILSQVVYTLFQDTGLLEIFKIPTLQFMNYFHALENGYRDIPYHNRIHATDVLHAVWYLTTRPIPGLQEIHSDHGAGNETACSSAINPERIAYISSRSCSIPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWTLYLSRPEYNFLLNLDNVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPTKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWIESEEDDDTESCYEEEDAEELDTEDEEIEDNLNSKPQRRKGRRQIFCQLMHHLTENHKIWKEIIEEEEKCKADGNKLQMENSSLPQGDEIQVIEEADEEEERQLE\", \"result\": [{\"smile\": \"CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)CCc1nc2c([nH]1)C(=O)N(C)C1=N[C@@H]3CCC[C@@H]3N12' can not interact with the protein 'MRRDERDAKAMRSPPPPDGAASPPESVRNGYVKGCVSPLRQDPPRGFFFHLCRFCNVELLLPPPASPQQPRRGSPFSRARLLLGALAAFVLALLLGSGPESWAAGAARLRTLLSVCSQSLSPLFSIACAFFFLTCFLTRTKRGAGPGRSGGGSWWLLALPACCYLGDFLVGQWESWSRGDGDARAPVPHTPPAVAGRWFLVLSCVGLLTLAQPGRLRHSIVVLLFSSFVWWVSFTSLGALPPALRPLLSCLVGGVGCLLALGLDHFFQIREAPQQPQLSSTAEEKVPVIRPRRRSSCVSFGETSGGYYGSCKMFRRPSLPCISREQMILWDWDLKQWYKPHYQISGGGSGVDLSVLNEARNMVSDLLVDPTLPPQVIASLRSISSLMGAFSGSCRPKMNPLTPFPGFYPCSEIEDPAEKGDRKLHKGLNSRNSLPTPHLRRSSGTSGLPPIDQTSPRWERNNGKRPHQEFGILSQGCYLNGPFSSNLLTVPKQRSSSVSLTHHVGLRRAGGLPSMNSPSHGPVSAGSPANRSPIEFPDTAEFLTKPPVILHKSLSSTPNSPDFYQQLRNSDSNLCNRSCGHQILKYDSASEPESGTDCHREKKGGEDNTVFSKASFNLTEAQQEVETENRDGRKLLWEGENHLKAAAQNEEQPNIEQEASQDLILIEDYDSLIENMSNWNFQIFELVEKMGEKSGRILSQVVYTLFQDTGLLEIFKIPTLQFMNYFHALENGYRDIPYHNRIHATDVLHAVWYLTTRPIPGLQEIHSDHGAGNETACSSAINPERIAYISSRSCSIPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWTLYLSRPEYNFLLNLDNVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPTKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWIESEEDDDTESCYEEEDAEELDTEDEEIEDNLNSKPQRRKGRRQIFCQLMHHLTENHKIWKEIIEEEEKCKADGNKLQMENSSLPQGDEIQVIEEADEEEERQLE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be any interaction between the drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1' and the target protein 'MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMIVFKPYQASQHDMCRFHSEDYIDFLQRVSPTNMQGFTKSLNAFNVGDDCPVFPGLFEFCSRYTGASLQGATQLNNKICDIAINWAGGLHHAKKFEASGFCYVNDIVIGILELLKYHPRVLYIDIDIHHGDGVQEAFYLTDRVMTVSFHKYGNYFFPGTGDMYEVGAESGRYYCLNVPLRDGIDDQSYKHLFQPVINQVVDFYQPTCIVLQCGADSLGCDRLGCFNLSIRGHGECVEYVKSFNIPLLVLGGGGYTVRNVARCWTYETSLLVEEAISEELPYSEYFEYFAPDFTLHPDVSTRIENQNSRQYLDQIRQTIFENLKMLNHAPSVQIHDVPADLLTYDRTDEADAEERGPEENYSRPEAPNEFYDGDHDNDKESDVEI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1\"], \"target_seq\": \"MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMIVFKPYQASQHDMCRFHSEDYIDFLQRVSPTNMQGFTKSLNAFNVGDDCPVFPGLFEFCSRYTGASLQGATQLNNKICDIAINWAGGLHHAKKFEASGFCYVNDIVIGILELLKYHPRVLYIDIDIHHGDGVQEAFYLTDRVMTVSFHKYGNYFFPGTGDMYEVGAESGRYYCLNVPLRDGIDDQSYKHLFQPVINQVVDFYQPTCIVLQCGADSLGCDRLGCFNLSIRGHGECVEYVKSFNIPLLVLGGGGYTVRNVARCWTYETSLLVEEAISEELPYSEYFEYFAPDFTLHPDVSTRIENQNSRQYLDQIRQTIFENLKMLNHAPSVQIHDVPADLLTYDRTDEADAEERGPEENYSRPEAPNEFYDGDHDNDKESDVEI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMIVFKPYQASQHDMCRFHSEDYIDFLQRVSPTNMQGFTKSLNAFNVGDDCPVFPGLFEFCSRYTGASLQGATQLNNKICDIAINWAGGLHHAKKFEASGFCYVNDIVIGILELLKYHPRVLYIDIDIHHGDGVQEAFYLTDRVMTVSFHKYGNYFFPGTGDMYEVGAESGRYYCLNVPLRDGIDDQSYKHLFQPVINQVVDFYQPTCIVLQCGADSLGCDRLGCFNLSIRGHGECVEYVKSFNIPLLVLGGGGYTVRNVARCWTYETSLLVEEAISEELPYSEYFEYFAPDFTLHPDVSTRIENQNSRQYLDQIRQTIFENLKMLNHAPSVQIHDVPADLLTYDRTDEADAEERGPEENYSRPEAPNEFYDGDHDNDKESDVEI\", \"result\": [{\"smile\": \"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1' can not interact with the protein 'MAKTVAYFYDPDVGNFHYGAGHPMKPHRLALTHSLVLHYGLYKKMIVFKPYQASQHDMCRFHSEDYIDFLQRVSPTNMQGFTKSLNAFNVGDDCPVFPGLFEFCSRYTGASLQGATQLNNKICDIAINWAGGLHHAKKFEASGFCYVNDIVIGILELLKYHPRVLYIDIDIHHGDGVQEAFYLTDRVMTVSFHKYGNYFFPGTGDMYEVGAESGRYYCLNVPLRDGIDDQSYKHLFQPVINQVVDFYQPTCIVLQCGADSLGCDRLGCFNLSIRGHGECVEYVKSFNIPLLVLGGGGYTVRNVARCWTYETSLLVEEAISEELPYSEYFEYFAPDFTLHPDVSTRIENQNSRQYLDQIRQTIFENLKMLNHAPSVQIHDVPADLLTYDRTDEADAEERGPEENYSRPEAPNEFYDGDHDNDKESDVEI'."
            },
            {
                "from": "human",
                "value": "Can you evaluate if the drug above is expected to interact with the target protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA\", \"result\": [{\"smile\": \"Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Nc1ccc(-c2cccs2)cc1NC(=O)c1ccc(N2CCC3(CCNC3)CC2)nc1' can interact with the protein 'MAQTQGTRRKVCYYYDGDVGNYYYGQGHPMKPHRIRMTHNLLLNYGLYRKMEIYRPHKANAEEMTKYHSDDYIKFLRSIRPDNMSEYSKQMQRFNVGEDCPVFDGLFEFCQLSTGGSVASAVKLNKQQTDIAVNWAGGLHHAKKSEASGFCYVNDIVLAILELLKYHQRVLYIDIDIHHGDGVEEAFYTTDRVMTVSFHKYGEYFPGTGDLRDIGAGKGKYYAVNYPLRDGIDDESYEAIFKPVMSKVMEMFQPSAVVLQCGSDSLSGDRLGCFNLTIKGHAKCVEFVKSFNLPMLMLGGGGYTIRNVARCWTYETAVALDTEIPNELPYNDYFEYFGPDFKLHISPSNMTNQNTNEYLEKIKQRLFENLRMLPHAPGVQMQAIPEDAIPEESGDEDEDDPDKRISICSSDKRIACEEEFSDSEEEGEGGRKNSSNFKKAKRVKTEDEKEKDPEEKKEVTEEEKTKEEKPEAKGVKEEVKLA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction probability for the drug 'CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4' and the target protein 'MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHHVKEGFEKADPSQFELLKVLGQGSYGKVFLVRKVKGSDAGQLYAMKVLKKATLKVRDRVRSKMERDILAEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKITDFGLSKEAIDHDKRAYSFCGTIEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGSLPFQGKDRKETMALILKAKLGMPQFLSGEAQSLLRALFKRNPCNRLGAGIDGVEEIKRHPFFVTIDWNTLYRKEIKPPFKPAVGRPEDTFHFDPEFTARTPTDSPGVPPSANAHHLFRGFSFVASSLIQEPSQQDLHKVPVHPIVQQLHGNNIHFTDGYEIKEDIGVGSYSVCKRCVHKATDTEYAVKIIDKSKRDPSEEIEILLRYGQHPNIITLKDVYDDGKFVYLVMELMRGGELLDRILRQRYFSEREASDVLCTITKTMDYLHSQGVVHRDLKPSNILYRDESGSPESIRVCDFGFAKQLRAGNGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGILLYTMLAGFTPFANGPDDTPEEILARIGSGKYALSGGNWDSISDAAKDVVSKMLHVDPHQRLTAMQVLKHPWVVNREYLSPNQLSRQDVHLVKGAMAATYFALNRTPQAPRLEPVLSSNLAQRRGMKRLTSTRL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4\"], \"target_seq\": \"MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHHVKEGFEKADPSQFELLKVLGQGSYGKVFLVRKVKGSDAGQLYAMKVLKKATLKVRDRVRSKMERDILAEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKITDFGLSKEAIDHDKRAYSFCGTIEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGSLPFQGKDRKETMALILKAKLGMPQFLSGEAQSLLRALFKRNPCNRLGAGIDGVEEIKRHPFFVTIDWNTLYRKEIKPPFKPAVGRPEDTFHFDPEFTARTPTDSPGVPPSANAHHLFRGFSFVASSLIQEPSQQDLHKVPVHPIVQQLHGNNIHFTDGYEIKEDIGVGSYSVCKRCVHKATDTEYAVKIIDKSKRDPSEEIEILLRYGQHPNIITLKDVYDDGKFVYLVMELMRGGELLDRILRQRYFSEREASDVLCTITKTMDYLHSQGVVHRDLKPSNILYRDESGSPESIRVCDFGFAKQLRAGNGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGILLYTMLAGFTPFANGPDDTPEEILARIGSGKYALSGGNWDSISDAAKDVVSKMLHVDPHQRLTAMQVLKHPWVVNREYLSPNQLSRQDVHLVKGAMAATYFALNRTPQAPRLEPVLSSNLAQRRGMKRLTSTRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHHVKEGFEKADPSQFELLKVLGQGSYGKVFLVRKVKGSDAGQLYAMKVLKKATLKVRDRVRSKMERDILAEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKITDFGLSKEAIDHDKRAYSFCGTIEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGSLPFQGKDRKETMALILKAKLGMPQFLSGEAQSLLRALFKRNPCNRLGAGIDGVEEIKRHPFFVTIDWNTLYRKEIKPPFKPAVGRPEDTFHFDPEFTARTPTDSPGVPPSANAHHLFRGFSFVASSLIQEPSQQDLHKVPVHPIVQQLHGNNIHFTDGYEIKEDIGVGSYSVCKRCVHKATDTEYAVKIIDKSKRDPSEEIEILLRYGQHPNIITLKDVYDDGKFVYLVMELMRGGELLDRILRQRYFSEREASDVLCTITKTMDYLHSQGVVHRDLKPSNILYRDESGSPESIRVCDFGFAKQLRAGNGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGILLYTMLAGFTPFANGPDDTPEEILARIGSGKYALSGGNWDSISDAAKDVVSKMLHVDPHQRLTAMQVLKHPWVVNREYLSPNQLSRQDVHLVKGAMAATYFALNRTPQAPRLEPVLSSNLAQRRGMKRLTSTRL\", \"result\": [{\"smile\": \"CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4' can not interact with the protein 'MDLSMKKFAVRRFFSVYLRRKSRSKSSSLSRLEEEGVVKEIDISHHVKEGFEKADPSQFELLKVLGQGSYGKVFLVRKVKGSDAGQLYAMKVLKKATLKVRDRVRSKMERDILAEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKITDFGLSKEAIDHDKRAYSFCGTIEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGSLPFQGKDRKETMALILKAKLGMPQFLSGEAQSLLRALFKRNPCNRLGAGIDGVEEIKRHPFFVTIDWNTLYRKEIKPPFKPAVGRPEDTFHFDPEFTARTPTDSPGVPPSANAHHLFRGFSFVASSLIQEPSQQDLHKVPVHPIVQQLHGNNIHFTDGYEIKEDIGVGSYSVCKRCVHKATDTEYAVKIIDKSKRDPSEEIEILLRYGQHPNIITLKDVYDDGKFVYLVMELMRGGELLDRILRQRYFSEREASDVLCTITKTMDYLHSQGVVHRDLKPSNILYRDESGSPESIRVCDFGFAKQLRAGNGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGILLYTMLAGFTPFANGPDDTPEEILARIGSGKYALSGGNWDSISDAAKDVVSKMLHVDPHQRLTAMQVLKHPWVVNREYLSPNQLSRQDVHLVKGAMAATYFALNRTPQAPRLEPVLSSNLAQRRGMKRLTSTRL'."
            },
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug above and the target protein 'MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS\", \"result\": [{\"smile\": \"CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4' can not interact with the protein 'MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the prediction for the drug 'Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1''s interaction with the target protein 'MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1\"], \"target_seq\": \"MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP\", \"result\": [{\"smile\": \"Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1' can interact with the protein 'MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP'."
            },
            {
                "from": "human",
                "value": "Do you expect any interaction between the drug above and the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1ccc(-n2nccn2)c(C(=O)N2CCOC[C@H]2Cc2cccc(-n3ccnn3)c2)c1' can not interact with the protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on whether the drug 'COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C' will interact with the target protein 'MATANGAVENGQPDRKPPALPRPIRNLEVKFTKIFINNEWHESKSGKKFATCNPSTREQICEVEEGDKPDVDKAVEAAQVAFQRGSPWRRLDALSRGRLLHQLADLVERDRATLAALETMDTGKPFLHAFFIDLEGCIRTLRYFAGWADKIQGKTIPTDDNVVCFTRHEPIGVCGAITPWNFPLLMLVWKLAPALCCGNTMVLKPAEQTPLTALYLGSLIKEAGFPPGVVNIVPGFGPTVGAAISSHPQINKIAFTGSTEVGKLVKEAASRSNLKRVTLELGGKNPCIVCADADLDLAVECAHQGVFFNQGQCCTAASRVFVEEQVYSEFVRRSVEYAKKRPVGDPFDVKTEQGPQIDQKQFDKILELIESGKKEGAKLECGGSAMEDKGLFIKPTVFSEVTDNMRIAKEEIFGPVQPILKFKSIEEVIKRANSTDYGLTAAVFTKNLDKALKLASALESGTVWINCYNALYAQAPFGGFKMSGNGRELGEYALAEYTEVKTVTIKLGDKNP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C\"], \"target_seq\": \"MATANGAVENGQPDRKPPALPRPIRNLEVKFTKIFINNEWHESKSGKKFATCNPSTREQICEVEEGDKPDVDKAVEAAQVAFQRGSPWRRLDALSRGRLLHQLADLVERDRATLAALETMDTGKPFLHAFFIDLEGCIRTLRYFAGWADKIQGKTIPTDDNVVCFTRHEPIGVCGAITPWNFPLLMLVWKLAPALCCGNTMVLKPAEQTPLTALYLGSLIKEAGFPPGVVNIVPGFGPTVGAAISSHPQINKIAFTGSTEVGKLVKEAASRSNLKRVTLELGGKNPCIVCADADLDLAVECAHQGVFFNQGQCCTAASRVFVEEQVYSEFVRRSVEYAKKRPVGDPFDVKTEQGPQIDQKQFDKILELIESGKKEGAKLECGGSAMEDKGLFIKPTVFSEVTDNMRIAKEEIFGPVQPILKFKSIEEVIKRANSTDYGLTAAVFTKNLDKALKLASALESGTVWINCYNALYAQAPFGGFKMSGNGRELGEYALAEYTEVKTVTIKLGDKNP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATANGAVENGQPDRKPPALPRPIRNLEVKFTKIFINNEWHESKSGKKFATCNPSTREQICEVEEGDKPDVDKAVEAAQVAFQRGSPWRRLDALSRGRLLHQLADLVERDRATLAALETMDTGKPFLHAFFIDLEGCIRTLRYFAGWADKIQGKTIPTDDNVVCFTRHEPIGVCGAITPWNFPLLMLVWKLAPALCCGNTMVLKPAEQTPLTALYLGSLIKEAGFPPGVVNIVPGFGPTVGAAISSHPQINKIAFTGSTEVGKLVKEAASRSNLKRVTLELGGKNPCIVCADADLDLAVECAHQGVFFNQGQCCTAASRVFVEEQVYSEFVRRSVEYAKKRPVGDPFDVKTEQGPQIDQKQFDKILELIESGKKEGAKLECGGSAMEDKGLFIKPTVFSEVTDNMRIAKEEIFGPVQPILKFKSIEEVIKRANSTDYGLTAAVFTKNLDKALKLASALESGTVWINCYNALYAQAPFGGFKMSGNGRELGEYALAEYTEVKTVTIKLGDKNP\", \"result\": [{\"smile\": \"COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C' can not interact with the protein 'MATANGAVENGQPDRKPPALPRPIRNLEVKFTKIFINNEWHESKSGKKFATCNPSTREQICEVEEGDKPDVDKAVEAAQVAFQRGSPWRRLDALSRGRLLHQLADLVERDRATLAALETMDTGKPFLHAFFIDLEGCIRTLRYFAGWADKIQGKTIPTDDNVVCFTRHEPIGVCGAITPWNFPLLMLVWKLAPALCCGNTMVLKPAEQTPLTALYLGSLIKEAGFPPGVVNIVPGFGPTVGAAISSHPQINKIAFTGSTEVGKLVKEAASRSNLKRVTLELGGKNPCIVCADADLDLAVECAHQGVFFNQGQCCTAASRVFVEEQVYSEFVRRSVEYAKKRPVGDPFDVKTEQGPQIDQKQFDKILELIESGKKEGAKLECGGSAMEDKGLFIKPTVFSEVTDNMRIAKEEIFGPVQPILKFKSIEEVIKRANSTDYGLTAAVFTKNLDKALKLASALESGTVWINCYNALYAQAPFGGFKMSGNGRELGEYALAEYTEVKTVTIKLGDKNP'."
            },
            {
                "from": "human",
                "value": "Can you outline the chances of interaction between the drug above and the target protein 'MSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDVIATDKEDVAFKDLDVAILVGSMPRREGMERKDLLKANVKIFKSQGAALDKYAKKSVKVIVVGNPANTNCLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTANDVKNVIIWGNHSSTQYPDVNHAKVKLQGKEVGVYEALKDDSWLKGEFVTTVQQRGAAVIKARKLSSAMSAAKAICDHVRDIWFGTPEGEFVSMGVISDGNSYGVPDDLLYSFPVVIKNKTWKFVEGLPINDFSREKMDLTAKELTEEKESAFEFLSSA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDVIATDKEDVAFKDLDVAILVGSMPRREGMERKDLLKANVKIFKSQGAALDKYAKKSVKVIVVGNPANTNCLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTANDVKNVIIWGNHSSTQYPDVNHAKVKLQGKEVGVYEALKDDSWLKGEFVTTVQQRGAAVIKARKLSSAMSAAKAICDHVRDIWFGTPEGEFVSMGVISDGNSYGVPDDLLYSFPVVIKNKTWKFVEGLPINDFSREKMDLTAKELTEEKESAFEFLSSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDVIATDKEDVAFKDLDVAILVGSMPRREGMERKDLLKANVKIFKSQGAALDKYAKKSVKVIVVGNPANTNCLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTANDVKNVIIWGNHSSTQYPDVNHAKVKLQGKEVGVYEALKDDSWLKGEFVTTVQQRGAAVIKARKLSSAMSAAKAICDHVRDIWFGTPEGEFVSMGVISDGNSYGVPDDLLYSFPVVIKNKTWKFVEGLPINDFSREKMDLTAKELTEEKESAFEFLSSA\", \"result\": [{\"smile\": \"COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=CC2=NNN=C2C=C1C(=O)NCC1CCCN1CC=C' can not interact with the protein 'MSEPIRVLVTGAAGQIAYSLLYSIGNGSVFGKDQPIILVLLDITPMMGVLDGVLMELQDCALPLLKDVIATDKEDVAFKDLDVAILVGSMPRREGMERKDLLKANVKIFKSQGAALDKYAKKSVKVIVVGNPANTNCLTASKSAPSIPKENFSCLTRLDHNRAKAQIALKLGVTANDVKNVIIWGNHSSTQYPDVNHAKVKLQGKEVGVYEALKDDSWLKGEFVTTVQQRGAAVIKARKLSSAMSAAKAICDHVRDIWFGTPEGEFVSMGVISDGNSYGVPDDLLYSFPVVIKNKTWKFVEGLPINDFSREKMDLTAKELTEEKESAFEFLSSA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be any interaction between the drug 'CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1' and the target protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1\"], \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\", \"result\": [{\"smile\": \"CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1' can not interact with the protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA'."
            },
            {
                "from": "human",
                "value": "How do you predict the drug above will behave towards the target protein 'MRLARLLRGAALAGPGPGLRAAGFSRSFSSDSGSSPASERGVPGQVDFYARFSPSPLSMKQFLDFGSVNACEKTSFMFLRQELPVRLANIMKEISLLPDNLLRTPSVQLVQSWYIQSLQELLDFKDKSAEDAKAIYDFTDTVIRIRNRHNDVIPTMAQGVIEYKESFGVDPVTSQNVQYFLDRFYMSRISIRMLLNQHSLLFGGKGKGSPSHRKHIGSINPNCNVLEVIKDGYENARRLCDLYYINSPELELEELNAKSPGQPIQVVYVPSHLYHMVFELFKNAMRATMEHHANRGVYPPIQVHVTLGNEDLTVKMSDRGGGVPLRKIDRLFNYMYSTAPRPRVETSRAVPLAGFGYGLPISRLYAQYFQGDLKLYSLEGYGTDAVIYIKALSTDSIERLPVYNKAAWKHYNTNHEADDWCVPSREPKDMTTFRSA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRLARLLRGAALAGPGPGLRAAGFSRSFSSDSGSSPASERGVPGQVDFYARFSPSPLSMKQFLDFGSVNACEKTSFMFLRQELPVRLANIMKEISLLPDNLLRTPSVQLVQSWYIQSLQELLDFKDKSAEDAKAIYDFTDTVIRIRNRHNDVIPTMAQGVIEYKESFGVDPVTSQNVQYFLDRFYMSRISIRMLLNQHSLLFGGKGKGSPSHRKHIGSINPNCNVLEVIKDGYENARRLCDLYYINSPELELEELNAKSPGQPIQVVYVPSHLYHMVFELFKNAMRATMEHHANRGVYPPIQVHVTLGNEDLTVKMSDRGGGVPLRKIDRLFNYMYSTAPRPRVETSRAVPLAGFGYGLPISRLYAQYFQGDLKLYSLEGYGTDAVIYIKALSTDSIERLPVYNKAAWKHYNTNHEADDWCVPSREPKDMTTFRSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRLARLLRGAALAGPGPGLRAAGFSRSFSSDSGSSPASERGVPGQVDFYARFSPSPLSMKQFLDFGSVNACEKTSFMFLRQELPVRLANIMKEISLLPDNLLRTPSVQLVQSWYIQSLQELLDFKDKSAEDAKAIYDFTDTVIRIRNRHNDVIPTMAQGVIEYKESFGVDPVTSQNVQYFLDRFYMSRISIRMLLNQHSLLFGGKGKGSPSHRKHIGSINPNCNVLEVIKDGYENARRLCDLYYINSPELELEELNAKSPGQPIQVVYVPSHLYHMVFELFKNAMRATMEHHANRGVYPPIQVHVTLGNEDLTVKMSDRGGGVPLRKIDRLFNYMYSTAPRPRVETSRAVPLAGFGYGLPISRLYAQYFQGDLKLYSLEGYGTDAVIYIKALSTDSIERLPVYNKAAWKHYNTNHEADDWCVPSREPKDMTTFRSA\", \"result\": [{\"smile\": \"CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C(=O)N1CCn2cc(C3=C(c4cnc5ccccn45)C(=O)NC3=O)c3cccc(c32)C1' can not interact with the protein 'MRLARLLRGAALAGPGPGLRAAGFSRSFSSDSGSSPASERGVPGQVDFYARFSPSPLSMKQFLDFGSVNACEKTSFMFLRQELPVRLANIMKEISLLPDNLLRTPSVQLVQSWYIQSLQELLDFKDKSAEDAKAIYDFTDTVIRIRNRHNDVIPTMAQGVIEYKESFGVDPVTSQNVQYFLDRFYMSRISIRMLLNQHSLLFGGKGKGSPSHRKHIGSINPNCNVLEVIKDGYENARRLCDLYYINSPELELEELNAKSPGQPIQVVYVPSHLYHMVFELFKNAMRATMEHHANRGVYPPIQVHVTLGNEDLTVKMSDRGGGVPLRKIDRLFNYMYSTAPRPRVETSRAVPLAGFGYGLPISRLYAQYFQGDLKLYSLEGYGTDAVIYIKALSTDSIERLPVYNKAAWKHYNTNHEADDWCVPSREPKDMTTFRSA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if there will be an interaction between the drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O' and the target protein 'MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\"], \"target_seq\": \"MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP\", \"result\": [{\"smile\": \"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O' can not interact with the protein 'MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP'."
            },
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug above and the target protein 'MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA\", \"result\": [{\"smile\": \"C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C1COCCN1C2=NC(=NC3=C2OC4=C3C=CC=N4)C5=CC(=CC=C5)O' can not interact with the protein 'MESGGGNAPAGALGAASESPQCPPPPGVEGAAGPAEPDGAAEGAAGGSGEGESGGGPRRALRAVYVRSESSQGGAAGGPEAGARQCLLRACEAEGAHLTSVPFGELDFGETAVLDAFYDADVAVVDMSDVSRQPSLFYHLGVRESFDMANNVILYHDTDADTALSLKDMVTQKNTASSGNYYFIPYIVTPCADYFCCESDAQRRASEYMQPNWDNILGPLCMPLVDRFISLLKDIHVTSCVYYKETLLNDIRKAREKYQGEELAKELARIKLRMDNTEVLTSDIIINLLLSYRDIQDYDAMVKLVETLEMLPTCDLADQHNIKFHYAFALNRRNSTGDREKALQIMLQVLQSCDHPGPDMFCLCGRIYKDIFLDSDCKDDTSRDSAIEWYRKGFELQSSLYSGINLAVLLIVAGQQFETSLELRKIGVRLNSLLGRKGSLEKMNNYWDVGQFFSVSMLAHDVGKAVQAAERLFKLKPPVWYLRSLVQNLLLIRRFKKTIIEHSPRQERLNFWLDIIFEATNEVTNGLRFPVLVIEPTKVYQPSYVSINNEAEERTVSLWHVSPTEMKQMHEWNFTASSIKGISLSKFDERCCFLYVHDNSDDFQIYFSTEEQCSRFFSLVKEMITNTAGSTVELEGETDGDTLEYEYDHDANGERVVLGKGTYGIVYAGRDLSNQVRIAIKEIPERDSRYSQPLHEEIALHKYLKHRNIVQYLGSVSENGYIKIFMEQVPGGSLSALLRSKWGPMKEPTIKFYTKQILEGLKYLHENQIVHRDIKGDNVLVNTYSGVVKISDFGTSKRLAGVNPCTETFTGTLQYMAPEIIDQGPRGYGAPADIWSLGCTIIEMATSKPPFHELGEPQAAMFKVGMFKIHPEIPEALSAEARAFILSCFEPDPHKRATTAELLREGFLRQVNKGKKNRIAFKPSEGPRGVVLALPTQGEPMATSSSEHGSVSPDSDAQPDALFERTRAPRHHLGHLLSVPDESSALEDRGLASSPEDRDQGLFLLRKDSERRAILYKILWEEQNQVASNLQECVAQSSEELHLSVGHIKQIIGILRDFIRSPEHRVMATTISKLKVDLDFDSSSISQIHLVLFGFQDAVNKILRNHLIRPHWMFAMDNIIRRAVQAAVTILIPELRAHFEPTCETEGVDKDMDEAEEGYPPATGPGQEAQPHQQHLSLQLGELRQETNRLLEHLVEKEREYQNLLRQTLEQKTQELYHLQLKLKSNCITENPAGPYGQRTDKELIDWLRLQGADAKTIEKIVEEGYTLSDILNEITKEDLRYLRLRGGLLCRLWSAVSQYRRAQEASETKDKA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the drug 'O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1''s potential to interact with the target protein 'MHPGVLAAFLFLSWTHCRALPLPSGGDEDDLSEEDLQFAERYLRSYYHPTNLAGILKENAASSMTERLREMQSFFGLEVTGKLDDNTLDVMKKPRCGVPDVGEYNVFPRTLKWSKMNLTYRIVNYTPDMTHSEVEKAFKKAFKVWSDVTPLNFTRLHDGIADIMISFGIKEHGDFYPFDGPSGLLAHAFPPGPNYGGDAHFDDDETWTSSSKGYNLFLVAAHEFGHSLGLDHSKDPGALMFPIYTYTGKSHFMLPDDDVQGIQSLYGPGDEDPNPKHPKTPDKCDPSLSLDAITSLRGETMIFKDRFFWRLHPQQVDAELFLTKSFWPELPNRIDAAYEHPSHDLIFIFRGRKFWALNGYDILEGYPKKISELGLPKEVKKISAAVHFEDTGKTLLFSGNQVWRYDDTNHIMDKDYPRLIEEDFPGIGDKVDAVYEKNGYIYFFNGPIQFEYSIWSNRIVRVMPANSILWC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1\"], \"target_seq\": \"MHPGVLAAFLFLSWTHCRALPLPSGGDEDDLSEEDLQFAERYLRSYYHPTNLAGILKENAASSMTERLREMQSFFGLEVTGKLDDNTLDVMKKPRCGVPDVGEYNVFPRTLKWSKMNLTYRIVNYTPDMTHSEVEKAFKKAFKVWSDVTPLNFTRLHDGIADIMISFGIKEHGDFYPFDGPSGLLAHAFPPGPNYGGDAHFDDDETWTSSSKGYNLFLVAAHEFGHSLGLDHSKDPGALMFPIYTYTGKSHFMLPDDDVQGIQSLYGPGDEDPNPKHPKTPDKCDPSLSLDAITSLRGETMIFKDRFFWRLHPQQVDAELFLTKSFWPELPNRIDAAYEHPSHDLIFIFRGRKFWALNGYDILEGYPKKISELGLPKEVKKISAAVHFEDTGKTLLFSGNQVWRYDDTNHIMDKDYPRLIEEDFPGIGDKVDAVYEKNGYIYFFNGPIQFEYSIWSNRIVRVMPANSILWC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MHPGVLAAFLFLSWTHCRALPLPSGGDEDDLSEEDLQFAERYLRSYYHPTNLAGILKENAASSMTERLREMQSFFGLEVTGKLDDNTLDVMKKPRCGVPDVGEYNVFPRTLKWSKMNLTYRIVNYTPDMTHSEVEKAFKKAFKVWSDVTPLNFTRLHDGIADIMISFGIKEHGDFYPFDGPSGLLAHAFPPGPNYGGDAHFDDDETWTSSSKGYNLFLVAAHEFGHSLGLDHSKDPGALMFPIYTYTGKSHFMLPDDDVQGIQSLYGPGDEDPNPKHPKTPDKCDPSLSLDAITSLRGETMIFKDRFFWRLHPQQVDAELFLTKSFWPELPNRIDAAYEHPSHDLIFIFRGRKFWALNGYDILEGYPKKISELGLPKEVKKISAAVHFEDTGKTLLFSGNQVWRYDDTNHIMDKDYPRLIEEDFPGIGDKVDAVYEKNGYIYFFNGPIQFEYSIWSNRIVRVMPANSILWC\", \"result\": [{\"smile\": \"O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1' can not interact with the protein 'MHPGVLAAFLFLSWTHCRALPLPSGGDEDDLSEEDLQFAERYLRSYYHPTNLAGILKENAASSMTERLREMQSFFGLEVTGKLDDNTLDVMKKPRCGVPDVGEYNVFPRTLKWSKMNLTYRIVNYTPDMTHSEVEKAFKKAFKVWSDVTPLNFTRLHDGIADIMISFGIKEHGDFYPFDGPSGLLAHAFPPGPNYGGDAHFDDDETWTSSSKGYNLFLVAAHEFGHSLGLDHSKDPGALMFPIYTYTGKSHFMLPDDDVQGIQSLYGPGDEDPNPKHPKTPDKCDPSLSLDAITSLRGETMIFKDRFFWRLHPQQVDAELFLTKSFWPELPNRIDAAYEHPSHDLIFIFRGRKFWALNGYDILEGYPKKISELGLPKEVKKISAAVHFEDTGKTLLFSGNQVWRYDDTNHIMDKDYPRLIEEDFPGIGDKVDAVYEKNGYIYFFNGPIQFEYSIWSNRIVRVMPANSILWC'."
            },
            {
                "from": "human",
                "value": "Will the drug above interact with the target protein 'MRLTVLCAVCLLPGSLALPLPQEAGGMSELQWEQAQDYLKRFYLYDSETKNANSLEAKLKEMQKFFGLPITGMLNSRVIEIMQKPRCGVPDVAEYSLFPNSPKWTSKVVTYRIVSYTRDLPHITVDRLVSKALNMWGKEIPLHFRKVVWGTADIMIGFARGAHGDSYPFDGPGNTLAHAFAPGTGLGGDAHFDEDERWTDGSSLGINFLYAATHELGHSLGMGHSSDPNAVMYPTYGNGDPQNFKLSQDDIKGIQKLYGKRSNSRKK', based on your evaluation? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRLTVLCAVCLLPGSLALPLPQEAGGMSELQWEQAQDYLKRFYLYDSETKNANSLEAKLKEMQKFFGLPITGMLNSRVIEIMQKPRCGVPDVAEYSLFPNSPKWTSKVVTYRIVSYTRDLPHITVDRLVSKALNMWGKEIPLHFRKVVWGTADIMIGFARGAHGDSYPFDGPGNTLAHAFAPGTGLGGDAHFDEDERWTDGSSLGINFLYAATHELGHSLGMGHSSDPNAVMYPTYGNGDPQNFKLSQDDIKGIQKLYGKRSNSRKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRLTVLCAVCLLPGSLALPLPQEAGGMSELQWEQAQDYLKRFYLYDSETKNANSLEAKLKEMQKFFGLPITGMLNSRVIEIMQKPRCGVPDVAEYSLFPNSPKWTSKVVTYRIVSYTRDLPHITVDRLVSKALNMWGKEIPLHFRKVVWGTADIMIGFARGAHGDSYPFDGPGNTLAHAFAPGTGLGGDAHFDEDERWTDGSSLGINFLYAATHELGHSLGMGHSSDPNAVMYPTYGNGDPQNFKLSQDDIKGIQKLYGKRSNSRKK\", \"result\": [{\"smile\": \"O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C1NC(=O)[C@](CNC(=O)[C@@H](Cc2ccc(C(F)(F)F)cc2)C2CC2)(C2CC2)N1' can not interact with the protein 'MRLTVLCAVCLLPGSLALPLPQEAGGMSELQWEQAQDYLKRFYLYDSETKNANSLEAKLKEMQKFFGLPITGMLNSRVIEIMQKPRCGVPDVAEYSLFPNSPKWTSKVVTYRIVSYTRDLPHITVDRLVSKALNMWGKEIPLHFRKVVWGTADIMIGFARGAHGDSYPFDGPGNTLAHAFAPGTGLGGDAHFDEDERWTDGSSLGINFLYAATHELGHSLGMGHSSDPNAVMYPTYGNGDPQNFKLSQDDIKGIQKLYGKRSNSRKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be an interaction of the drug 'NNC(N)=N' with the target protein 'MTPWLGLIVLLGSWSLGDWGAEACTCSPSHPQDAFCNSDIVIRAKVVGKKLVKEGPFGTLVYTIKQMKMYRGFTKMPHVQYIHTEASESLCGLKLEVNKYQYLLTGRVYDGKMYTGLCNFVERWDQLTLSQRKGLNYRYHLGCNCKIKSCYYLPCFVTSKNECLWTDMLSNFGYPGYQSKHYACIRQKGGYCSWYRGWAPPDKSIINATDP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NNC(N)=N\"], \"target_seq\": \"MTPWLGLIVLLGSWSLGDWGAEACTCSPSHPQDAFCNSDIVIRAKVVGKKLVKEGPFGTLVYTIKQMKMYRGFTKMPHVQYIHTEASESLCGLKLEVNKYQYLLTGRVYDGKMYTGLCNFVERWDQLTLSQRKGLNYRYHLGCNCKIKSCYYLPCFVTSKNECLWTDMLSNFGYPGYQSKHYACIRQKGGYCSWYRGWAPPDKSIINATDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTPWLGLIVLLGSWSLGDWGAEACTCSPSHPQDAFCNSDIVIRAKVVGKKLVKEGPFGTLVYTIKQMKMYRGFTKMPHVQYIHTEASESLCGLKLEVNKYQYLLTGRVYDGKMYTGLCNFVERWDQLTLSQRKGLNYRYHLGCNCKIKSCYYLPCFVTSKNECLWTDMLSNFGYPGYQSKHYACIRQKGGYCSWYRGWAPPDKSIINATDP\", \"result\": [{\"smile\": \"NNC(N)=N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NNC(N)=N' can interact with the protein 'MTPWLGLIVLLGSWSLGDWGAEACTCSPSHPQDAFCNSDIVIRAKVVGKKLVKEGPFGTLVYTIKQMKMYRGFTKMPHVQYIHTEASESLCGLKLEVNKYQYLLTGRVYDGKMYTGLCNFVERWDQLTLSQRKGLNYRYHLGCNCKIKSCYYLPCFVTSKNECLWTDMLSNFGYPGYQSKHYACIRQKGGYCSWYRGWAPPDKSIINATDP'."
            },
            {
                "from": "human",
                "value": "What's the expected outcome for the drug above interacting with the target protein 'MASRVLSAYVSRLPAAFAPLPRVRMLAVARPLSTALCSAGTQTRLGTLQPALVLAQVPGRVTQLCRQYSDMPPLTLEGIQDRVLYVLKLYDKIDPEKLSVNSHFMKDLGLDSLDQVEIIMAMEDEFGFEIPDIDAEKLMCPQEIVDYIADKKDVYE'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASRVLSAYVSRLPAAFAPLPRVRMLAVARPLSTALCSAGTQTRLGTLQPALVLAQVPGRVTQLCRQYSDMPPLTLEGIQDRVLYVLKLYDKIDPEKLSVNSHFMKDLGLDSLDQVEIIMAMEDEFGFEIPDIDAEKLMCPQEIVDYIADKKDVYE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASRVLSAYVSRLPAAFAPLPRVRMLAVARPLSTALCSAGTQTRLGTLQPALVLAQVPGRVTQLCRQYSDMPPLTLEGIQDRVLYVLKLYDKIDPEKLSVNSHFMKDLGLDSLDQVEIIMAMEDEFGFEIPDIDAEKLMCPQEIVDYIADKKDVYE\", \"result\": [{\"smile\": \"NNC(N)=N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NNC(N)=N' can not interact with the protein 'MASRVLSAYVSRLPAAFAPLPRVRMLAVARPLSTALCSAGTQTRLGTLQPALVLAQVPGRVTQLCRQYSDMPPLTLEGIQDRVLYVLKLYDKIDPEKLSVNSHFMKDLGLDSLDQVEIIMAMEDEFGFEIPDIDAEKLMCPQEIVDYIADKKDVYE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the drug 'CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1' can interact with the target protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1\"], \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\", \"result\": [{\"smile\": \"CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1' can not interact with the protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'."
            },
            {
                "from": "human",
                "value": "What do you think about the drug above's potential to interact with the target protein 'MEVKLEEHFNKTFVTENNTAASQNTASPAWEDYRGTENNTSAARNTAFPVWEDYRGSVDDLQYFLIGLYTFVSLLGFMGNLLILMAVMKKRNQKTTVNFLIGNLAFSDILVVLFCSPFTLTSVLLDQWMFGKAMCHIMPFLQCVSVLVSTLILISIAIVRYHMIKHPISNNLTANHGYFLIATVWTLGFAICSPLPVFHSLVELKETFGSALLSSKYLCVESWPSDSYRIAFTISLLLVQYILPLVCLTVSHTSVCRSISCGLSHKENRLEENEMINLTLHPSKKSRDQAKPPSTQKWSYSFIRKHRRRYSKKTACVLPAPAGPSQEKHLTVPENPGSVRSQLSPSSKVIPGVPICFEVKPEESSDAQEMRVKRSLTRIKKRSRSVFYRLTILILVFAVSWMPLHVFHVVTDFNDNLISNRHFKLVYCICHLLGMMSCCLNPILYGFLNNGIKADLRALIHCLHMS'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEVKLEEHFNKTFVTENNTAASQNTASPAWEDYRGTENNTSAARNTAFPVWEDYRGSVDDLQYFLIGLYTFVSLLGFMGNLLILMAVMKKRNQKTTVNFLIGNLAFSDILVVLFCSPFTLTSVLLDQWMFGKAMCHIMPFLQCVSVLVSTLILISIAIVRYHMIKHPISNNLTANHGYFLIATVWTLGFAICSPLPVFHSLVELKETFGSALLSSKYLCVESWPSDSYRIAFTISLLLVQYILPLVCLTVSHTSVCRSISCGLSHKENRLEENEMINLTLHPSKKSRDQAKPPSTQKWSYSFIRKHRRRYSKKTACVLPAPAGPSQEKHLTVPENPGSVRSQLSPSSKVIPGVPICFEVKPEESSDAQEMRVKRSLTRIKKRSRSVFYRLTILILVFAVSWMPLHVFHVVTDFNDNLISNRHFKLVYCICHLLGMMSCCLNPILYGFLNNGIKADLRALIHCLHMS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEVKLEEHFNKTFVTENNTAASQNTASPAWEDYRGTENNTSAARNTAFPVWEDYRGSVDDLQYFLIGLYTFVSLLGFMGNLLILMAVMKKRNQKTTVNFLIGNLAFSDILVVLFCSPFTLTSVLLDQWMFGKAMCHIMPFLQCVSVLVSTLILISIAIVRYHMIKHPISNNLTANHGYFLIATVWTLGFAICSPLPVFHSLVELKETFGSALLSSKYLCVESWPSDSYRIAFTISLLLVQYILPLVCLTVSHTSVCRSISCGLSHKENRLEENEMINLTLHPSKKSRDQAKPPSTQKWSYSFIRKHRRRYSKKTACVLPAPAGPSQEKHLTVPENPGSVRSQLSPSSKVIPGVPICFEVKPEESSDAQEMRVKRSLTRIKKRSRSVFYRLTILILVFAVSWMPLHVFHVVTDFNDNLISNRHFKLVYCICHLLGMMSCCLNPILYGFLNNGIKADLRALIHCLHMS\", \"result\": [{\"smile\": \"CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCS(=O)(=O)c1ccc2nc(-c3ccc(-c4ccccc4)cc3)[nH]c2c1' can interact with the protein 'MEVKLEEHFNKTFVTENNTAASQNTASPAWEDYRGTENNTSAARNTAFPVWEDYRGSVDDLQYFLIGLYTFVSLLGFMGNLLILMAVMKKRNQKTTVNFLIGNLAFSDILVVLFCSPFTLTSVLLDQWMFGKAMCHIMPFLQCVSVLVSTLILISIAIVRYHMIKHPISNNLTANHGYFLIATVWTLGFAICSPLPVFHSLVELKETFGSALLSSKYLCVESWPSDSYRIAFTISLLLVQYILPLVCLTVSHTSVCRSISCGLSHKENRLEENEMINLTLHPSKKSRDQAKPPSTQKWSYSFIRKHRRRYSKKTACVLPAPAGPSQEKHLTVPENPGSVRSQLSPSSKVIPGVPICFEVKPEESSDAQEMRVKRSLTRIKKRSRSVFYRLTILILVFAVSWMPLHVFHVVTDFNDNLISNRHFKLVYCICHLLGMMSCCLNPILYGFLNNGIKADLRALIHCLHMS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think the drug 'CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1''s interaction potential with the target protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH' will be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\"], \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\", \"result\": [{\"smile\": \"CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1' can not interact with the protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'."
            },
            {
                "from": "human",
                "value": "What's your assessment of the potential interaction of the drug above with the target protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\", \"result\": [{\"smile\": \"CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CS(=O)(=O)CCNCc1ccc(-c2ccc3ncnc(Nc4ccc(OCc5cccc(F)c5)c(Cl)c4)c3c2)o1' can not interact with the protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug 'c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1' and the target protein 'MEGISIYTSDNYTEEMGSGDYDSMKEPCFREENANFNKIFLPTIYSIIFLTGIVGNGLVILVMGYQKKLRSMTDKYRLHLSVADLLFVITLPFWAVDAVANWYFGNFLCKAVHVIYTVNLYSSVLILAFISLDRYLAIVHATNSQRPRKLLAEKVVYVGVWIPALLLTIPDFIFANVSEADDRYICDRFYPNDLWVVVFQFQHIMVGLILPGIVILSCYCIIISKLSHSKGHQKRKALKTTVILILAFFACWLPYYIGISIDSFILLEIIKQGCEFENTVHKWISITEALAFFHCCLNPILYAFLGAKFKTSAQHALTSVSRGSSLKILSKGKRGGHSSVSTESESSSFHSS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1\"], \"target_seq\": \"MEGISIYTSDNYTEEMGSGDYDSMKEPCFREENANFNKIFLPTIYSIIFLTGIVGNGLVILVMGYQKKLRSMTDKYRLHLSVADLLFVITLPFWAVDAVANWYFGNFLCKAVHVIYTVNLYSSVLILAFISLDRYLAIVHATNSQRPRKLLAEKVVYVGVWIPALLLTIPDFIFANVSEADDRYICDRFYPNDLWVVVFQFQHIMVGLILPGIVILSCYCIIISKLSHSKGHQKRKALKTTVILILAFFACWLPYYIGISIDSFILLEIIKQGCEFENTVHKWISITEALAFFHCCLNPILYAFLGAKFKTSAQHALTSVSRGSSLKILSKGKRGGHSSVSTESESSSFHSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGISIYTSDNYTEEMGSGDYDSMKEPCFREENANFNKIFLPTIYSIIFLTGIVGNGLVILVMGYQKKLRSMTDKYRLHLSVADLLFVITLPFWAVDAVANWYFGNFLCKAVHVIYTVNLYSSVLILAFISLDRYLAIVHATNSQRPRKLLAEKVVYVGVWIPALLLTIPDFIFANVSEADDRYICDRFYPNDLWVVVFQFQHIMVGLILPGIVILSCYCIIISKLSHSKGHQKRKALKTTVILILAFFACWLPYYIGISIDSFILLEIIKQGCEFENTVHKWISITEALAFFHCCLNPILYAFLGAKFKTSAQHALTSVSRGSSLKILSKGKRGGHSSVSTESESSSFHSS\", \"result\": [{\"smile\": \"c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1' can interact with the protein 'MEGISIYTSDNYTEEMGSGDYDSMKEPCFREENANFNKIFLPTIYSIIFLTGIVGNGLVILVMGYQKKLRSMTDKYRLHLSVADLLFVITLPFWAVDAVANWYFGNFLCKAVHVIYTVNLYSSVLILAFISLDRYLAIVHATNSQRPRKLLAEKVVYVGVWIPALLLTIPDFIFANVSEADDRYICDRFYPNDLWVVVFQFQHIMVGLILPGIVILSCYCIIISKLSHSKGHQKRKALKTTVILILAFFACWLPYYIGISIDSFILLEIIKQGCEFENTVHKWISITEALAFFHCCLNPILYAFLGAKFKTSAQHALTSVSRGSSLKILSKGKRGGHSSVSTESESSSFHSS'."
            },
            {
                "from": "human",
                "value": "What is your prediction regarding the drug above's interaction with the target protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'? MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF\", \"result\": [{\"smile\": \"c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'c1cc(CN2CCCNCCNCCCNCC2)ccc1CN1CCCNCCNCCCNCC1' can not interact with the protein 'MLEKFCNSTFWNSSFLDSPEADLPLCFEQTVLVWIPLGYLWLLAPWQLLHVYKSRTKRSSTTKLYLAKQVFVGFLLILAAIELALVLTEDSGQATVPAVRYTNPSLYLGTWLLVLLIQYSRQWCVQKNSWFLSLFWILSILCGTFQFQTLIRTLLQGDNSNLAYSCLFFISYGFQILILIFSAFSENNESSNNPSSIASFLSSITYSWYDSIILKGYKRPLTLEDVWEVDEEMKTKTLVSKFETHMKRELQKARRALQRRQEKSSQQNSGARLPGLNKNQSQSQDALVLEDVEKKKKKSGTKKDVPKSWLMKALFKTFYMVLLKSFLLKLVNDIFTFVSPQLLKLLISFASDRDTYLWIGYLCAILLFTAALIQSFCLQCYFQLCFKLGVKVRTAIMASVYKKALTLSNLARKEYTVGETVNLMSVDAQKLMDVTNFMHMLWSSVLQIVLSIFFLWRELGPSVLAGVGVMVLVIPINAILSTKSKTIQVKNMKNKDKRLKIMNEILSGIKILKYFAWEPSFRDQVQNLRKKELKNLLAFSQLQCVVIFVFQLTPVLVSVVTFSVYVLVDSNNILDAQKAFTSITLFNILRFPLSMLPMMISSMLQASVSTERLEKYLGGDDLDTSAIRHDCNFDKAMQFSEASFTWEHDSEATVRDVNLDIMAGQLVAVIGPVGSGKSSLISAMLGEMENVHGHITIKGTTAYVPQQSWIQNGTIKDNILFGTEFNEKRYQQVLEACALLPDLEMLPGGDLAEIGEKGINLSGGQKQRISLARATYQNLDIYLLDDPLSAVDAHVGKHIFNKVLGPNGLLKGKTRLLVTHSMHFLPQVDEIVVLGNGTIVEKGSYSALLAKKGEFAKNLKTFLRHTGPEEEATVHDGSEEEDDDYGLISSVEEIPEDAASITMRRENSFRRTLSRSSRSNGRHLKSLRNSLKTRNVNSLKEDEELVKGQKLIKKEFIETGKVKFSIYLEYLQAIGLFSIFFIILAFVMNSVAFIGSNLWLSAWTSDSKIFNSTDYPASQRDMRVGVYGALGLAQGIFVFIAHFWSAFGFVHASNILHKQLLNNILRAPMRFFDTTPTGRIVNRFAGDISTVDDTLPQSLRSWITCFLGIISTLVMICMATPVFTIIVIPLGIIYVSVQMFYVSTSRQLRRLDSVTRSPIYSHFSETVSGLPVIRAFEHQQRFLKHNEVRIDTNQKCVFSWITSNRWLAIRLELVGNLTVFFSALMMVIYRDTLSGDTVGFVLSNALNITQTLNWLVRMTSEIETNIVAVERITEYTKVENEAPWVTDKRPPPDWPSKGKIQFNNYQVRYRPELDLVLRGITCDIGSMEKIGVVGRTGAGKSSLTNCLFRILEAAGGQIIIDGVDIASIGLHDLREKLTIIPQDPILFSGSLRMNLDPFNNYSDEEIWKALELAHLKSFVASLQLGLSHEVTEAGGNLSIGQRQLLCLGRALLRKSKILVLDEATAAVDLETDNLIQTTIQNEFAHCTVITIAHRLHTIMDSDKVMVLDNGKIIECGSPEELLQIPGPFYFMAKEAGIENVNSTKF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '=(CC=(CC=NCCC1)C)1CCCA' possibly engage with the target protein 'MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"=(CC=(CC=NCCC1)C)1CCCA\"], \"target_seq\": \"MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile fitting for the drug is 'CNC(C)(C)CC1=CC=CC=C1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(C)(C)CC1=CC=CC=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE\", \"result\": [{\"smile\": \"CNC(C)(C)CC1=CC=CC=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CNC(C)(C)CC1=CC=CC=C1' can not interact with the protein 'MSTEGGGRRCQAQVSRRISFSASHRLYSKFLSDEENLKLFGKCNNPNGHGHNYKVVVTVHGEIDPATGMVMNLADLKKYMEEAIMQPLDHKNLDMDVPYFADVVSTTENVAVYIWDNLQKVLPVGVLYKVKVYETDNNIVVYKGE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CC1OC(=)CN=CCC(=C=1CCC3N=)NCC=CC=C42=)C2CCC=C3C4=N(A' likely form an interaction with the target protein 'MGAAAKLAFAVFLISCSSGAILGRSETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPEMEVTQPTSNPVTPKPPYYNILLYSLVPLMLIAGIVICAFWVYRHHKMAYPPVLVPTQDPGPPPPSPLLGLKPLQLLEVKARGRFGCVWKAQLLNEYVAVKIFPIQDKQSWQNEYEVYSLPGMKHENILQFIGAEKRGTSVDVDLWLITAFHEKGSLSDFLKANVVSWNELCHIAETMARGLAYLHEDIPGLKDGHKPAISHRDIKSKNVLLKNNLTACIADFGLALKFEAGKSAGDTHGQVGTRRYMAPEVLEGAINFQRDAFLRIDMYAMGLVLWELASRCTAADGPVDEYMLPFEEEIGQHPSLEDMQEVVVHKKKRPVLRDYWQKHAGMAMLCETIEECWDHDAEARLSAGCVGERITQMQRLTNIITTEDIVTVVTMVTNVDFPPKESSL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1OC(=)CN=CCC(=C=1CCC3N=)NCC=CC=C42=)C2CCC=C3C4=N(A\"], \"target_seq\": \"MGAAAKLAFAVFLISCSSGAILGRSETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPEMEVTQPTSNPVTPKPPYYNILLYSLVPLMLIAGIVICAFWVYRHHKMAYPPVLVPTQDPGPPPPSPLLGLKPLQLLEVKARGRFGCVWKAQLLNEYVAVKIFPIQDKQSWQNEYEVYSLPGMKHENILQFIGAEKRGTSVDVDLWLITAFHEKGSLSDFLKANVVSWNELCHIAETMARGLAYLHEDIPGLKDGHKPAISHRDIKSKNVLLKNNLTACIADFGLALKFEAGKSAGDTHGQVGTRRYMAPEVLEGAINFQRDAFLRIDMYAMGLVLWELASRCTAADGPVDEYMLPFEEEIGQHPSLEDMQEVVVHKKKRPVLRDYWQKHAGMAMLCETIEECWDHDAEARLSAGCVGERITQMQRLTNIITTEDIVTVVTMVTNVDFPPKESSL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGAAAKLAFAVFLISCSSGAILGRSETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPEMEVTQPTSNPVTPKPPYYNILLYSLVPLMLIAGIVICAFWVYRHHKMAYPPVLVPTQDPGPPPPSPLLGLKPLQLLEVKARGRFGCVWKAQLLNEYVAVKIFPIQDKQSWQNEYEVYSLPGMKHENILQFIGAEKRGTSVDVDLWLITAFHEKGSLSDFLKANVVSWNELCHIAETMARGLAYLHEDIPGLKDGHKPAISHRDIKSKNVLLKNNLTACIADFGLALKFEAGKSAGDTHGQVGTRRYMAPEVLEGAINFQRDAFLRIDMYAMGLVLWELASRCTAADGPVDEYMLPFEEEIGQHPSLEDMQEVVVHKKKRPVLRDYWQKHAGMAMLCETIEECWDHDAEARLSAGCVGERITQMQRLTNIITTEDIVTVVTMVTNVDFPPKESSL\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N' represents the valid smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGAAAKLAFAVFLISCSSGAILGRSETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPEMEVTQPTSNPVTPKPPYYNILLYSLVPLMLIAGIVICAFWVYRHHKMAYPPVLVPTQDPGPPPPSPLLGLKPLQLLEVKARGRFGCVWKAQLLNEYVAVKIFPIQDKQSWQNEYEVYSLPGMKHENILQFIGAEKRGTSVDVDLWLITAFHEKGSLSDFLKANVVSWNELCHIAETMARGLAYLHEDIPGLKDGHKPAISHRDIKSKNVLLKNNLTACIADFGLALKFEAGKSAGDTHGQVGTRRYMAPEVLEGAINFQRDAFLRIDMYAMGLVLWELASRCTAADGPVDEYMLPFEEEIGQHPSLEDMQEVVVHKKKRPVLRDYWQKHAGMAMLCETIEECWDHDAEARLSAGCVGERITQMQRLTNIITTEDIVTVVTMVTNVDFPPKESSL\", \"result\": [{\"smile\": \"CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C2C=C(C=CC2=NN1)C3=CC(=CN=C3)OCC(CC4=CC=CC=C4)N' can not interact with the protein 'MGAAAKLAFAVFLISCSSGAILGRSETQECLFFNANWEKDRTNQTGVEPCYGDKDKRRHCFATWKNISGSIEIVKQGCWLDDINCYDRTDCVEKKDSPEVYFCCCEGNMCNEKFSYFPEMEVTQPTSNPVTPKPPYYNILLYSLVPLMLIAGIVICAFWVYRHHKMAYPPVLVPTQDPGPPPPSPLLGLKPLQLLEVKARGRFGCVWKAQLLNEYVAVKIFPIQDKQSWQNEYEVYSLPGMKHENILQFIGAEKRGTSVDVDLWLITAFHEKGSLSDFLKANVVSWNELCHIAETMARGLAYLHEDIPGLKDGHKPAISHRDIKSKNVLLKNNLTACIADFGLALKFEAGKSAGDTHGQVGTRRYMAPEVLEGAINFQRDAFLRIDMYAMGLVLWELASRCTAADGPVDEYMLPFEEEIGQHPSLEDMQEVVVHKKKRPVLRDYWQKHAGMAMLCETIEECWDHDAEARLSAGCVGERITQMQRLTNIITTEDIVTVVTMVTNVDFPPKESSL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if there will be an interaction between the drug '3CCc11ccC]cc32c3ccc#)c(nCcH11n(C)ccN[@c(NcccnCN)Ccc23A' and the target protein 'MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"3CCc11ccC]cc32c3ccc#)c(nCcH11n(C)ccN[@c(NcccnCN)Ccc23A\"], \"target_seq\": \"MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'C[C@H]1CN(c2nnc(Cc3ccccc3)c3ccccc23)CCN1c1ccc(C#N)cn1' serves as the correct smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1CN(c2nnc(Cc3ccccc3)c3ccccc23)CCN1c1ccc(C#N)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH\", \"result\": [{\"smile\": \"C[C@H]1CN(c2nnc(Cc3ccccc3)c3ccccc23)CCN1c1ccc(C#N)cn1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@H]1CN(c2nnc(Cc3ccccc3)c3ccccc23)CCN1c1ccc(C#N)cn1' can not interact with the protein 'MPSTDLLMLKAFEPYLEILEVYSTKAKNYVNGHCTKYEPWQLIAWSVVWTLLIVWGYEFVFQPESLWSRFKKKCFKLTRKMPIIGRKIQDKLNKTKDDISKNMSFLKVDKEYVKALPSQGLSSSAVLEKLKEYSSMDAFWQEGRASGTVYSGEEKLTELLVKAYGDFAWSNPLHPDIFPGLRKIEAEIVRIACSLFNGGPDSCGCVTSGGTESILMACKAYRDLAFEKGIKTPEIVAPQSAHAAFNKAASYFGMKIVRVPLTKMMEVDVRAMRRAISRNTAMLVCSTPQFPHGVIDPVPEVAKLAVKYKIPLHVDACLGGFLIVFMEKAGYPLEHPFDFRVKGVTSISADTHKYGYAPKGSSLVLYSDKKYRNYQFFVDTDWQGGIYASPTIAGSRPGGISAACWAALMHFGENGYVEATKQIIKTARFLKSELENIKGIFVFGNPQLSVIALGSRDFDIYRLSNLMTAKGWNLNQLQFPPSIHFCITLLHARKRVAIQFLKDIRESVTQIMKNPKAKTTGMGAIYGMAQTTVDRNMVAELSSVFLDSLYSTDTVTQGSQMNGSPKPH'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'C31C==(C==))=N=1N)1CC=C=OCCCO23NCN=CC1(C2(CCCCNA' is expected to interact with the target protein 'MWLLLTMASLISVLGTTHGLFGKLHPGSPEVTMNISQMITYWGYPNEEYEVVTEDGYILEVNRIPYGKKNSGNTGQRPVVFLQHGLLASATNWISNLPNNSLAFILADAGYDVWLGNSRGNTWARRNLYYSPDSVEFWAFSFDEMAKYDLPATIDFIVKKTGQKQLHYVGHSQGTTIGFIAFSTNPSLAKRIKTFYALAPVATVKYTKSLINKLRFVPQSLFKFIFGDKIFYPHNFFDQFLATEVCSREMLNLLCSNALFIICGFDSKNFNTSRLDVYLSHNPAGTSVQNMFHWTQAVKSGKFQAYDWGSPVQNRMHYDQSQPPYYNVTAMNVPIAVWNGGKDLLADPQDVGLLLPKLPNLIYHKEIPFYNHLDFIWAMDAPQEVYNDIVSMISEDKK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C31C==(C==))=N=1N)1CC=C=OCCCO23NCN=CC1(C2(CCCCNA\"], \"target_seq\": \"MWLLLTMASLISVLGTTHGLFGKLHPGSPEVTMNISQMITYWGYPNEEYEVVTEDGYILEVNRIPYGKKNSGNTGQRPVVFLQHGLLASATNWISNLPNNSLAFILADAGYDVWLGNSRGNTWARRNLYYSPDSVEFWAFSFDEMAKYDLPATIDFIVKKTGQKQLHYVGHSQGTTIGFIAFSTNPSLAKRIKTFYALAPVATVKYTKSLINKLRFVPQSLFKFIFGDKIFYPHNFFDQFLATEVCSREMLNLLCSNALFIICGFDSKNFNTSRLDVYLSHNPAGTSVQNMFHWTQAVKSGKFQAYDWGSPVQNRMHYDQSQPPYYNVTAMNVPIAVWNGGKDLLADPQDVGLLLPKLPNLIYHKEIPFYNHLDFIWAMDAPQEVYNDIVSMISEDKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWLLLTMASLISVLGTTHGLFGKLHPGSPEVTMNISQMITYWGYPNEEYEVVTEDGYILEVNRIPYGKKNSGNTGQRPVVFLQHGLLASATNWISNLPNNSLAFILADAGYDVWLGNSRGNTWARRNLYYSPDSVEFWAFSFDEMAKYDLPATIDFIVKKTGQKQLHYVGHSQGTTIGFIAFSTNPSLAKRIKTFYALAPVATVKYTKSLINKLRFVPQSLFKFIFGDKIFYPHNFFDQFLATEVCSREMLNLLCSNALFIICGFDSKNFNTSRLDVYLSHNPAGTSVQNMFHWTQAVKSGKFQAYDWGSPVQNRMHYDQSQPPYYNVTAMNVPIAVWNGGKDLLADPQDVGLLLPKLPNLIYHKEIPFYNHLDFIWAMDAPQEVYNDIVSMISEDKK\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The correct smile for the drug is 'CN1N=C(C(O)=O)C2=C1C1=NC(NC3=CC=CC=C3)=NC=C1CC2'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1N=C(C(O)=O)C2=C1C1=NC(NC3=CC=CC=C3)=NC=C1CC2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWLLLTMASLISVLGTTHGLFGKLHPGSPEVTMNISQMITYWGYPNEEYEVVTEDGYILEVNRIPYGKKNSGNTGQRPVVFLQHGLLASATNWISNLPNNSLAFILADAGYDVWLGNSRGNTWARRNLYYSPDSVEFWAFSFDEMAKYDLPATIDFIVKKTGQKQLHYVGHSQGTTIGFIAFSTNPSLAKRIKTFYALAPVATVKYTKSLINKLRFVPQSLFKFIFGDKIFYPHNFFDQFLATEVCSREMLNLLCSNALFIICGFDSKNFNTSRLDVYLSHNPAGTSVQNMFHWTQAVKSGKFQAYDWGSPVQNRMHYDQSQPPYYNVTAMNVPIAVWNGGKDLLADPQDVGLLLPKLPNLIYHKEIPFYNHLDFIWAMDAPQEVYNDIVSMISEDKK\", \"result\": [{\"smile\": \"CN1N=C(C(O)=O)C2=C1C1=NC(NC3=CC=CC=C3)=NC=C1CC2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1N=C(C(O)=O)C2=C1C1=NC(NC3=CC=CC=C3)=NC=C1CC2' can not interact with the protein 'MWLLLTMASLISVLGTTHGLFGKLHPGSPEVTMNISQMITYWGYPNEEYEVVTEDGYILEVNRIPYGKKNSGNTGQRPVVFLQHGLLASATNWISNLPNNSLAFILADAGYDVWLGNSRGNTWARRNLYYSPDSVEFWAFSFDEMAKYDLPATIDFIVKKTGQKQLHYVGHSQGTTIGFIAFSTNPSLAKRIKTFYALAPVATVKYTKSLINKLRFVPQSLFKFIFGDKIFYPHNFFDQFLATEVCSREMLNLLCSNALFIICGFDSKNFNTSRLDVYLSHNPAGTSVQNMFHWTQAVKSGKFQAYDWGSPVQNRMHYDQSQPPYYNVTAMNVPIAVWNGGKDLLADPQDVGLLLPKLPNLIYHKEIPFYNHLDFIWAMDAPQEVYNDIVSMISEDKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of interaction between the drug 'CCC3N6C6=C)CCC=CC54NCO9C=CC3C=(CN(CO5C28=C)=1=C)8CCC(C4CC9O21=)=CCO=C)=NC=7=7(CCCC)C=((A' and the target protein 'MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC3N6C6=C)CCC=CC54NCO9C=CC3C=(CN(CO5C28=C)=1=C)8CCC(C4CC9O21=)=CCO=C)=NC=7=7(CCCC)C=((A\"], \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\", \"result\": [{\"smile\": \"CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC12C(C(CC(O1)N3C4=CC=CC=C4C5=C6C(=C7C8=CC=CC=C8N2C7=C53)CNC6=O)N(C)C(=O)C9=CC=CC=C9)OC' can not interact with the protein 'MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast for whether the drug 'OO)Cc1cCcC2CCCCcC(Ncc)CcCOCc2C(ccCOc1cA' will interact with the target protein 'MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OO)Cc1cCcC2CCCCcC(Ncc)CcCOCc2C(ccCOc1cA\"], \"target_seq\": \"MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The proper smile to use with the drug is 'OCc1cc(C(O)CNCCCCCCOCCCCc2ccccc2)ccc1O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OCc1cc(C(O)CNCCCCCCOCCCCc2ccccc2)ccc1O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA\", \"result\": [{\"smile\": \"OCc1cc(C(O)CNCCCCCCOCCCCc2ccccc2)ccc1O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'OCc1cc(C(O)CNCCCCCCOCCCCc2ccccc2)ccc1O' can not interact with the protein 'MDALCGSGELGSKFWDSNLSVHTENPDLTPCFQNSLLAWVPCIYLWVALPCYLLYLRHHCRGYIILSHLSKLKMVLGVLLWCVSWADLFYSFHGLVHGRAPAPVFFVTPLVVGVTMLLATLLIQYERLQGVQSSGVLIIFWFLCVVCAIVPFRSKILLAKAEGEISDPFRFTTFYIHFALVLSALILACFREKPPFFSAKNVDPNPYPETSAGFLSRLFFWWFTKMAIYGYRHPLEEKDLWSLKEEDRSQMVVQQLLEAWRKQEKQTARHKASAAPGKNASGEDEVLLGARPRPRKPSFLKALLATFGSSFLISACFKLIQDLLSFINPQLLSILIRFISNPMAPSWWGFLVAGLMFLCSMMQSLILQHYYHYIFVTGVKFRTGIMGVIYRKALVITNSVKRASTVGEIVNLMSVDAQRFMDLAPFLNLLWSAPLQIILAIYFLWQNLGPSVLAGVAFMVLLIPLNGAVAVKMRAFQVKQMKLKDSRIKLMSEILNGIKVLKLYAWEPSFLKQVEGIRQGELQLLRTAAYLHTTTTFTWMCSPFLVTLITLWVYVYVDPNNVLDAEKAFVSVSLFNILRLPLNMLPQLISNLTQASVSLKRIQQFLSQEELDPQSVERKTISPGYAITIHSGTFTWAQDLPPTLHSLDIQVPKGALVAVVGPVGCGKSSLVSALLGEMEKLEGKVHMKGSVAYVPQQAWIQNCTLQENVLFGKALNPKRYQQTLEACALLADLEMLPGGDQTEIGEKGINLSGGQRQRVSLARAVYSDADIFLLDDPLSAVDSHVAKHIFDHVIGPEGVLAGKTRVLVTHGISFLPQTDFIIVLADGQVSEMGPYPALLQRNGSFANFLCNYAPDEDQGHLEDSWTALEGAEDKEALLIEDTLSNHTDLTDNDPVTYVVQKQFMRQLSALSSDGEGQGRPVPRRHLGPSEKVQVTEAKADGALTQEEKAAIGTVELSVFWDYAKAVGLCTTLAICLLYVGQSAAAIGANVWLSAWTNDAMADSRQNNTSLRLGVYAALGILQGFLVMLAAMAMAAGGIQAARVLHQALLHNKIRSPQSFFDTTPSGRILNCFSKDIYVVDEVLAPVILMLLNSFFNAISTLVVIMASTPLFTVVILPLAVLYTLVQRFYAATSRQLKRLESVSRSPIYSHFSETVTGASVIRAYNRSRDFEIISDTKVDANQRSCYPYIISNRWLSIGVEFVGNCVVLFAALFAVIGRSSLNPGLVGLSVSYSLQVTFALNWMIRMMSDLESNIVAVERVKEYSKTETEAPWVVEGSRPPEGWPPRGEVEFRNYSVRYRPGLDLVLRDLSLHVHGGEKVGIVGRTGAGKSSMTLCLFRILEAAKGEIRIDGLNVADIGLHDLRSQLTIIPQDPILFSGTLRMNLDPFGSYSEEDIWWALELSHLHTFVSSQPAGLDFQCSEGGENLSVGQRQLVCLARALLRKSRILVLDEATAAIDLETDNLIQATIRTQFDTCTVLTIAHRLNTIMDYTRVLVLDKGVVAEFDSPANLIAARGIFYGMARDAGLA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'C)((CCN====NCHCHC11CN)(CNC((C12C2)O=C+]C12C)CC(CC()==CCNCC[)=1=2[1CC2C])N=CC=A' possibly engage with the target protein 'MQEKDASSQGFLPHFQHFATQAIHVGQDPEQWTSRAVVPPISLSTTFKQGAPGQHSGFEYSRSGNPTRNCLEKAVAALDGAKYCLAFASGLAATVTITHLLKAGDQIICMDDVYGGTNRYFRQVASEFGLKISFVDCSKIKLLEAAITPETKLVWIETPTNPTQKVIDIEGCAHIVHKHGDIILVVDNTFMSPYFQRPLALGADISMYSATKYMNGHSDVVMGLVSVNCESLHNRLRFLQNSLGAVPSPIDCYLCNRGLKTLHVRMEKHFKNGMAVAQFLESNPWVEKVIYPGLPSHPQHELVKRQCTGCTGMVTFYIKGTLQHAEIFLKNLKLFTLAESLGGFESLAELPAIMTHASVLKNDRDVLGISDTLIRLSVGLEDEEDLLEDLDQALKAAHPPSGSHS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C)((CCN====NCHCHC11CN)(CNC((C12C2)O=C+]C12C)CC(CC()==CCNCC[)=1=2[1CC2C])N=CC=A\"], \"target_seq\": \"MQEKDASSQGFLPHFQHFATQAIHVGQDPEQWTSRAVVPPISLSTTFKQGAPGQHSGFEYSRSGNPTRNCLEKAVAALDGAKYCLAFASGLAATVTITHLLKAGDQIICMDDVYGGTNRYFRQVASEFGLKISFVDCSKIKLLEAAITPETKLVWIETPTNPTQKVIDIEGCAHIVHKHGDIILVVDNTFMSPYFQRPLALGADISMYSATKYMNGHSDVVMGLVSVNCESLHNRLRFLQNSLGAVPSPIDCYLCNRGLKTLHVRMEKHFKNGMAVAQFLESNPWVEKVIYPGLPSHPQHELVKRQCTGCTGMVTFYIKGTLQHAEIFLKNLKLFTLAESLGGFESLAELPAIMTHASVLKNDRDVLGISDTLIRLSVGLEDEEDLLEDLDQALKAAHPPSGSHS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQEKDASSQGFLPHFQHFATQAIHVGQDPEQWTSRAVVPPISLSTTFKQGAPGQHSGFEYSRSGNPTRNCLEKAVAALDGAKYCLAFASGLAATVTITHLLKAGDQIICMDDVYGGTNRYFRQVASEFGLKISFVDCSKIKLLEAAITPETKLVWIETPTNPTQKVIDIEGCAHIVHKHGDIILVVDNTFMSPYFQRPLALGADISMYSATKYMNGHSDVVMGLVSVNCESLHNRLRFLQNSLGAVPSPIDCYLCNRGLKTLHVRMEKHFKNGMAVAQFLESNPWVEKVIYPGLPSHPQHELVKRQCTGCTGMVTFYIKGTLQHAEIFLKNLKLFTLAESLGGFESLAELPAIMTHASVLKNDRDVLGISDTLIRLSVGLEDEEDLLEDLDQALKAAHPPSGSHS\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'[H]N=C(C)N1CCC(CC1)OC1=CC2=C(C=C1)N=C(C)N2CC1=CC2=C(C=C1)C=CC(=C2)C(N)=[NH2+]' is the valid smile corresponding to the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H]N=C(C)N1CCC(CC1)OC1=CC2=C(C=C1)N=C(C)N2CC1=CC2=C(C=C1)C=CC(=C2)C(N)=[NH2+]\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQEKDASSQGFLPHFQHFATQAIHVGQDPEQWTSRAVVPPISLSTTFKQGAPGQHSGFEYSRSGNPTRNCLEKAVAALDGAKYCLAFASGLAATVTITHLLKAGDQIICMDDVYGGTNRYFRQVASEFGLKISFVDCSKIKLLEAAITPETKLVWIETPTNPTQKVIDIEGCAHIVHKHGDIILVVDNTFMSPYFQRPLALGADISMYSATKYMNGHSDVVMGLVSVNCESLHNRLRFLQNSLGAVPSPIDCYLCNRGLKTLHVRMEKHFKNGMAVAQFLESNPWVEKVIYPGLPSHPQHELVKRQCTGCTGMVTFYIKGTLQHAEIFLKNLKLFTLAESLGGFESLAELPAIMTHASVLKNDRDVLGISDTLIRLSVGLEDEEDLLEDLDQALKAAHPPSGSHS\", \"result\": [{\"smile\": \"[H]N=C(C)N1CCC(CC1)OC1=CC2=C(C=C1)N=C(C)N2CC1=CC2=C(C=C1)C=CC(=C2)C(N)=[NH2+]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H]N=C(C)N1CCC(CC1)OC1=CC2=C(C=C1)N=C(C)N2CC1=CC2=C(C=C1)C=CC(=C2)C(N)=[NH2+]' can not interact with the protein 'MQEKDASSQGFLPHFQHFATQAIHVGQDPEQWTSRAVVPPISLSTTFKQGAPGQHSGFEYSRSGNPTRNCLEKAVAALDGAKYCLAFASGLAATVTITHLLKAGDQIICMDDVYGGTNRYFRQVASEFGLKISFVDCSKIKLLEAAITPETKLVWIETPTNPTQKVIDIEGCAHIVHKHGDIILVVDNTFMSPYFQRPLALGADISMYSATKYMNGHSDVVMGLVSVNCESLHNRLRFLQNSLGAVPSPIDCYLCNRGLKTLHVRMEKHFKNGMAVAQFLESNPWVEKVIYPGLPSHPQHELVKRQCTGCTGMVTFYIKGTLQHAEIFLKNLKLFTLAESLGGFESLAELPAIMTHASVLKNDRDVLGISDTLIRLSVGLEDEEDLLEDLDQALKAAHPPSGSHS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug '3)cc4cnnc(c)Cc)n-ncc3OO1C)(4c(2NCccCC(C2cCNn#1A' will interact with the target protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"3)cc4cnnc(c)Cc)n-ncc3OO1C)(4c(2NCccCC(C2cCNn#1A\"], \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The proper smile for the drug is 'Cn1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)n1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": [{\"smile\": \"Cn1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)n1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cn1ccc(Nc2ncnc(-c3ccc(OC4CCOCC4)c(C#N)c3)n2)n1' can not interact with the protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the likelihood of interaction between the drug 'C(OCCCCOlCC=C#()@()=F)(C)=C=[1N(OC((](1C)CFC()1CNC1C)FFCC))===A' and the target protein 'MSPVLHFYVRPSGHEGAAPGHTRRKLQGKLPELQGVETELCYNVNWTAEALPSAEETKKLMWLFGCPLLLDDVARESWLLPGSNDLLLEVGPRLNFSTPTSTNIVSVCRATGLGPVDRVETTRRYRLSFAHPPSAEVEAIALATLHDRMTEQHFPHPIQSFSPESMPEPLNGPINILGEGRLALEKANQELGLALDSWDLDFYTKRFQELQRNPSTVEAFDLAQSNSEHSRHWFFKGQLHVDGQKLVHSLFESIMSTQESSNPNNVLKFCDNSSAIQGKEVRFLRPEDPTRPSRFQQQQGLRHVVFTAETHNFPTGVCPFSGATTGTGGRIRDVQCTGRGAHVVAGTAGYCFGNLHIPGYNLPWEDPSFQYPGNFARPLEVAIEASNGASDYGNKFGEPVLAGFARSLGLQLPDGQRREWIKPIMFSGGIGSMEADHISKEAPEPGMEVVKVGGPVYRIGVGGGAASSVQVQGDNTSDLDFGAVQRGDPEMEQKMNRVIRACVEAPKGNPICSLHDQGAGGNGNVLKELSDPAGAIIYTSRFQLGDPTLNALEIWGAEYQESNALLLRSPNRDFLTHVSARERCPACFVGTITGDRRIVLVDDRECPVRRNGQGDAPPTPLPTPVDLELEWVLGKMPRKEFFLQRKPPMLQPLALPPGLSVHQALERVLRLPAVASKRYLTNKVDRSVGGLVAQQQCVGPLQTPLADVAVVALSHEELIGAATALGEQPVKSLLDPKVAARLAVAEALTNLVFALVTDLRDVKCSGNWMWAAKLPGEGAALADACEAMVAVMAALGVAVDGGKDSLSMAARVGTETVRAPGSLVISAYAVCPDITATVTPDLKHPEGRGHLLYVALSPGQHRLGGTALAQCFSQLGEHPPDLDLPENLVRAFSITQGLLKDRLLCSGHDVSDGGLVTCLLEMAFAGNCGLQVDVPVPRVDVLSVLFAEEPGLVLEVQEPDLAQVLKRYRDAGLHCLELGHTGEAGPHAMVRVSVNGAVVLEEPVGELRALWEETSFQLDRLQAEPRCVAEEERGLRERMGPSYCLPPTFPKASVPREPGGPSPRVAILREEGSNGDREMADAFHLAGFEVWDVTMQDLCSGAIGLDTFRGVAFVGGFSYADVLGSAKGWAAAVTFHPRAGAELRRFRKRPDTFSLGVCNGCQLLALLGWVGGDPNEDAAEMGPDSQPARPGLLLRHNLSGRYESRWASVRVGPGPALMLRGMEGAVLPVWSAHGEGYVAFSSPELQAQIEARGLAPLHWADDDGNPTEQYPLNPNGSPGGVAGICSCDGRHLAVMPHPERAVRPWQWAWRPPPFDTLTTSPWLQLFINARNWTLEGSC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(OCCCCOlCC=C#()@()=F)(C)=C=[1N(OC((](1C)CFC()1CNC1C)FFCC))===A\"], \"target_seq\": \"MSPVLHFYVRPSGHEGAAPGHTRRKLQGKLPELQGVETELCYNVNWTAEALPSAEETKKLMWLFGCPLLLDDVARESWLLPGSNDLLLEVGPRLNFSTPTSTNIVSVCRATGLGPVDRVETTRRYRLSFAHPPSAEVEAIALATLHDRMTEQHFPHPIQSFSPESMPEPLNGPINILGEGRLALEKANQELGLALDSWDLDFYTKRFQELQRNPSTVEAFDLAQSNSEHSRHWFFKGQLHVDGQKLVHSLFESIMSTQESSNPNNVLKFCDNSSAIQGKEVRFLRPEDPTRPSRFQQQQGLRHVVFTAETHNFPTGVCPFSGATTGTGGRIRDVQCTGRGAHVVAGTAGYCFGNLHIPGYNLPWEDPSFQYPGNFARPLEVAIEASNGASDYGNKFGEPVLAGFARSLGLQLPDGQRREWIKPIMFSGGIGSMEADHISKEAPEPGMEVVKVGGPVYRIGVGGGAASSVQVQGDNTSDLDFGAVQRGDPEMEQKMNRVIRACVEAPKGNPICSLHDQGAGGNGNVLKELSDPAGAIIYTSRFQLGDPTLNALEIWGAEYQESNALLLRSPNRDFLTHVSARERCPACFVGTITGDRRIVLVDDRECPVRRNGQGDAPPTPLPTPVDLELEWVLGKMPRKEFFLQRKPPMLQPLALPPGLSVHQALERVLRLPAVASKRYLTNKVDRSVGGLVAQQQCVGPLQTPLADVAVVALSHEELIGAATALGEQPVKSLLDPKVAARLAVAEALTNLVFALVTDLRDVKCSGNWMWAAKLPGEGAALADACEAMVAVMAALGVAVDGGKDSLSMAARVGTETVRAPGSLVISAYAVCPDITATVTPDLKHPEGRGHLLYVALSPGQHRLGGTALAQCFSQLGEHPPDLDLPENLVRAFSITQGLLKDRLLCSGHDVSDGGLVTCLLEMAFAGNCGLQVDVPVPRVDVLSVLFAEEPGLVLEVQEPDLAQVLKRYRDAGLHCLELGHTGEAGPHAMVRVSVNGAVVLEEPVGELRALWEETSFQLDRLQAEPRCVAEEERGLRERMGPSYCLPPTFPKASVPREPGGPSPRVAILREEGSNGDREMADAFHLAGFEVWDVTMQDLCSGAIGLDTFRGVAFVGGFSYADVLGSAKGWAAAVTFHPRAGAELRRFRKRPDTFSLGVCNGCQLLALLGWVGGDPNEDAAEMGPDSQPARPGLLLRHNLSGRYESRWASVRVGPGPALMLRGMEGAVLPVWSAHGEGYVAFSSPELQAQIEARGLAPLHWADDDGNPTEQYPLNPNGSPGGVAGICSCDGRHLAVMPHPERAVRPWQWAWRPPPFDTLTTSPWLQLFINARNWTLEGSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPVLHFYVRPSGHEGAAPGHTRRKLQGKLPELQGVETELCYNVNWTAEALPSAEETKKLMWLFGCPLLLDDVARESWLLPGSNDLLLEVGPRLNFSTPTSTNIVSVCRATGLGPVDRVETTRRYRLSFAHPPSAEVEAIALATLHDRMTEQHFPHPIQSFSPESMPEPLNGPINILGEGRLALEKANQELGLALDSWDLDFYTKRFQELQRNPSTVEAFDLAQSNSEHSRHWFFKGQLHVDGQKLVHSLFESIMSTQESSNPNNVLKFCDNSSAIQGKEVRFLRPEDPTRPSRFQQQQGLRHVVFTAETHNFPTGVCPFSGATTGTGGRIRDVQCTGRGAHVVAGTAGYCFGNLHIPGYNLPWEDPSFQYPGNFARPLEVAIEASNGASDYGNKFGEPVLAGFARSLGLQLPDGQRREWIKPIMFSGGIGSMEADHISKEAPEPGMEVVKVGGPVYRIGVGGGAASSVQVQGDNTSDLDFGAVQRGDPEMEQKMNRVIRACVEAPKGNPICSLHDQGAGGNGNVLKELSDPAGAIIYTSRFQLGDPTLNALEIWGAEYQESNALLLRSPNRDFLTHVSARERCPACFVGTITGDRRIVLVDDRECPVRRNGQGDAPPTPLPTPVDLELEWVLGKMPRKEFFLQRKPPMLQPLALPPGLSVHQALERVLRLPAVASKRYLTNKVDRSVGGLVAQQQCVGPLQTPLADVAVVALSHEELIGAATALGEQPVKSLLDPKVAARLAVAEALTNLVFALVTDLRDVKCSGNWMWAAKLPGEGAALADACEAMVAVMAALGVAVDGGKDSLSMAARVGTETVRAPGSLVISAYAVCPDITATVTPDLKHPEGRGHLLYVALSPGQHRLGGTALAQCFSQLGEHPPDLDLPENLVRAFSITQGLLKDRLLCSGHDVSDGGLVTCLLEMAFAGNCGLQVDVPVPRVDVLSVLFAEEPGLVLEVQEPDLAQVLKRYRDAGLHCLELGHTGEAGPHAMVRVSVNGAVVLEEPVGELRALWEETSFQLDRLQAEPRCVAEEERGLRERMGPSYCLPPTFPKASVPREPGGPSPRVAILREEGSNGDREMADAFHLAGFEVWDVTMQDLCSGAIGLDTFRGVAFVGGFSYADVLGSAKGWAAAVTFHPRAGAELRRFRKRPDTFSLGVCNGCQLLALLGWVGGDPNEDAAEMGPDSQPARPGLLLRHNLSGRYESRWASVRVGPGPALMLRGMEGAVLPVWSAHGEGYVAFSSPELQAQIEARGLAPLHWADDDGNPTEQYPLNPNGSPGGVAGICSCDGRHLAVMPHPERAVRPWQWAWRPPPFDTLTTSPWLQLFINARNWTLEGSC\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'C[C@](O)(COC1=CC=C(Cl)C(F)=C1)C(=O)NC1=CC(=C(C=C1)C#N)C(F)(F)F'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@](O)(COC1=CC=C(Cl)C(F)=C1)C(=O)NC1=CC(=C(C=C1)C#N)C(F)(F)F\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPVLHFYVRPSGHEGAAPGHTRRKLQGKLPELQGVETELCYNVNWTAEALPSAEETKKLMWLFGCPLLLDDVARESWLLPGSNDLLLEVGPRLNFSTPTSTNIVSVCRATGLGPVDRVETTRRYRLSFAHPPSAEVEAIALATLHDRMTEQHFPHPIQSFSPESMPEPLNGPINILGEGRLALEKANQELGLALDSWDLDFYTKRFQELQRNPSTVEAFDLAQSNSEHSRHWFFKGQLHVDGQKLVHSLFESIMSTQESSNPNNVLKFCDNSSAIQGKEVRFLRPEDPTRPSRFQQQQGLRHVVFTAETHNFPTGVCPFSGATTGTGGRIRDVQCTGRGAHVVAGTAGYCFGNLHIPGYNLPWEDPSFQYPGNFARPLEVAIEASNGASDYGNKFGEPVLAGFARSLGLQLPDGQRREWIKPIMFSGGIGSMEADHISKEAPEPGMEVVKVGGPVYRIGVGGGAASSVQVQGDNTSDLDFGAVQRGDPEMEQKMNRVIRACVEAPKGNPICSLHDQGAGGNGNVLKELSDPAGAIIYTSRFQLGDPTLNALEIWGAEYQESNALLLRSPNRDFLTHVSARERCPACFVGTITGDRRIVLVDDRECPVRRNGQGDAPPTPLPTPVDLELEWVLGKMPRKEFFLQRKPPMLQPLALPPGLSVHQALERVLRLPAVASKRYLTNKVDRSVGGLVAQQQCVGPLQTPLADVAVVALSHEELIGAATALGEQPVKSLLDPKVAARLAVAEALTNLVFALVTDLRDVKCSGNWMWAAKLPGEGAALADACEAMVAVMAALGVAVDGGKDSLSMAARVGTETVRAPGSLVISAYAVCPDITATVTPDLKHPEGRGHLLYVALSPGQHRLGGTALAQCFSQLGEHPPDLDLPENLVRAFSITQGLLKDRLLCSGHDVSDGGLVTCLLEMAFAGNCGLQVDVPVPRVDVLSVLFAEEPGLVLEVQEPDLAQVLKRYRDAGLHCLELGHTGEAGPHAMVRVSVNGAVVLEEPVGELRALWEETSFQLDRLQAEPRCVAEEERGLRERMGPSYCLPPTFPKASVPREPGGPSPRVAILREEGSNGDREMADAFHLAGFEVWDVTMQDLCSGAIGLDTFRGVAFVGGFSYADVLGSAKGWAAAVTFHPRAGAELRRFRKRPDTFSLGVCNGCQLLALLGWVGGDPNEDAAEMGPDSQPARPGLLLRHNLSGRYESRWASVRVGPGPALMLRGMEGAVLPVWSAHGEGYVAFSSPELQAQIEARGLAPLHWADDDGNPTEQYPLNPNGSPGGVAGICSCDGRHLAVMPHPERAVRPWQWAWRPPPFDTLTTSPWLQLFINARNWTLEGSC\", \"result\": [{\"smile\": \"C[C@](O)(COC1=CC=C(Cl)C(F)=C1)C(=O)NC1=CC(=C(C=C1)C#N)C(F)(F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@](O)(COC1=CC=C(Cl)C(F)=C1)C(=O)NC1=CC(=C(C=C1)C#N)C(F)(F)F' can not interact with the protein 'MSPVLHFYVRPSGHEGAAPGHTRRKLQGKLPELQGVETELCYNVNWTAEALPSAEETKKLMWLFGCPLLLDDVARESWLLPGSNDLLLEVGPRLNFSTPTSTNIVSVCRATGLGPVDRVETTRRYRLSFAHPPSAEVEAIALATLHDRMTEQHFPHPIQSFSPESMPEPLNGPINILGEGRLALEKANQELGLALDSWDLDFYTKRFQELQRNPSTVEAFDLAQSNSEHSRHWFFKGQLHVDGQKLVHSLFESIMSTQESSNPNNVLKFCDNSSAIQGKEVRFLRPEDPTRPSRFQQQQGLRHVVFTAETHNFPTGVCPFSGATTGTGGRIRDVQCTGRGAHVVAGTAGYCFGNLHIPGYNLPWEDPSFQYPGNFARPLEVAIEASNGASDYGNKFGEPVLAGFARSLGLQLPDGQRREWIKPIMFSGGIGSMEADHISKEAPEPGMEVVKVGGPVYRIGVGGGAASSVQVQGDNTSDLDFGAVQRGDPEMEQKMNRVIRACVEAPKGNPICSLHDQGAGGNGNVLKELSDPAGAIIYTSRFQLGDPTLNALEIWGAEYQESNALLLRSPNRDFLTHVSARERCPACFVGTITGDRRIVLVDDRECPVRRNGQGDAPPTPLPTPVDLELEWVLGKMPRKEFFLQRKPPMLQPLALPPGLSVHQALERVLRLPAVASKRYLTNKVDRSVGGLVAQQQCVGPLQTPLADVAVVALSHEELIGAATALGEQPVKSLLDPKVAARLAVAEALTNLVFALVTDLRDVKCSGNWMWAAKLPGEGAALADACEAMVAVMAALGVAVDGGKDSLSMAARVGTETVRAPGSLVISAYAVCPDITATVTPDLKHPEGRGHLLYVALSPGQHRLGGTALAQCFSQLGEHPPDLDLPENLVRAFSITQGLLKDRLLCSGHDVSDGGLVTCLLEMAFAGNCGLQVDVPVPRVDVLSVLFAEEPGLVLEVQEPDLAQVLKRYRDAGLHCLELGHTGEAGPHAMVRVSVNGAVVLEEPVGELRALWEETSFQLDRLQAEPRCVAEEERGLRERMGPSYCLPPTFPKASVPREPGGPSPRVAILREEGSNGDREMADAFHLAGFEVWDVTMQDLCSGAIGLDTFRGVAFVGGFSYADVLGSAKGWAAAVTFHPRAGAELRRFRKRPDTFSLGVCNGCQLLALLGWVGGDPNEDAAEMGPDSQPARPGLLLRHNLSGRYESRWASVRVGPGPALMLRGMEGAVLPVWSAHGEGYVAFSSPELQAQIEARGLAPLHWADDDGNPTEQYPLNPNGSPGGVAGICSCDGRHLAVMPHPERAVRPWQWAWRPPPFDTLTTSPWLQLFINARNWTLEGSC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood that the drug 'CCOC)C))CN)=(=(4)CCONC((NC=C4CC11=O==C=32C=CNCF2CC3(CA' will engage with the target protein 'MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCOC)C))CN)=(=(4)CCONC((NC=C4CC11=O==C=32C=CNCF2CC3(CA\"], \"target_seq\": \"MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The proper smile to use with the drug is 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NN4C3=C(C(=C4)OCC(C)O)C'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NN4C3=C(C(=C4)OCC(C)O)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP\", \"result\": [{\"smile\": \"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NN4C3=C(C(=C4)OCC(C)O)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NN4C3=C(C(=C4)OCC(C)O)C' can not interact with the protein 'MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug '(C(1NSCC))O1==OCCOCCOA' will engage with the target protein 'MITARNRFTQGCSCMKYYNNNGDKVFQKSKRNRGMKASGVVLVAVSTVALTIILSNAIHQSYKSNSKPLPKLSIATNIKQLINVDNIRSNLHSLTKKPHVAGTENNLRVAEMIRDQMITQGLENVHFNEYNVMLSYPNWTTPNIIEILKENRDLVFSTTGRSVSVIKEEQNDPLAEIQWLAYSAAGTVEGDIVYVNNANPSDIEYLESLGIDLKDKIFLARYSSNYRGNIAQMAVKKGAKACLVYSDPKQVASLGTGPNETYGNTDKMPSNTVQMGSVYIGLGDPRTPAFPSIGDLFKEKTEQDLLDEKKIPTIPMLPITYATAQILFENMKGDAVNADFQGKLNVTYRYGPGLINNQKLRVTVHAENEERKIQNIMGYIKGSQEPDKFVLVSNHYDAWTYGAVDPNSGTSTLLEVSRALKQYQNQTGWIPARSILFAHWDAEEYGLIGSTEFAEEYRLQLMRRAVAVINMDLIGGNQTLLGLSNPTVANVLRSAAANVEQPNPTEMEQGRKTLYDSWKYYAPSKNNRSTHPYQRIPAGGSDHLPFFDYLGIPIVFFITSSLDAPPTYPLYHTIYETPYLIENIMDPGYKVHKAIAGMFIECILKFTESKILPYDLNELMDDSIFEYLPKLEDRLNKTLMIGTKTDYLLDAQKQFKLLQKTVLELSEIVQRRNVSKLEELPFGSRVDINNRLIEFEKCFINPHGAIGNPQARHVLFHPSPDNWYDGDAISQVHDLISKISNSTDSKELGKLSRQLAKEIALVNVAYICAKHSLGEFFTM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"(C(1NSCC))O1==OCCOCCOA\"], \"target_seq\": \"MITARNRFTQGCSCMKYYNNNGDKVFQKSKRNRGMKASGVVLVAVSTVALTIILSNAIHQSYKSNSKPLPKLSIATNIKQLINVDNIRSNLHSLTKKPHVAGTENNLRVAEMIRDQMITQGLENVHFNEYNVMLSYPNWTTPNIIEILKENRDLVFSTTGRSVSVIKEEQNDPLAEIQWLAYSAAGTVEGDIVYVNNANPSDIEYLESLGIDLKDKIFLARYSSNYRGNIAQMAVKKGAKACLVYSDPKQVASLGTGPNETYGNTDKMPSNTVQMGSVYIGLGDPRTPAFPSIGDLFKEKTEQDLLDEKKIPTIPMLPITYATAQILFENMKGDAVNADFQGKLNVTYRYGPGLINNQKLRVTVHAENEERKIQNIMGYIKGSQEPDKFVLVSNHYDAWTYGAVDPNSGTSTLLEVSRALKQYQNQTGWIPARSILFAHWDAEEYGLIGSTEFAEEYRLQLMRRAVAVINMDLIGGNQTLLGLSNPTVANVLRSAAANVEQPNPTEMEQGRKTLYDSWKYYAPSKNNRSTHPYQRIPAGGSDHLPFFDYLGIPIVFFITSSLDAPPTYPLYHTIYETPYLIENIMDPGYKVHKAIAGMFIECILKFTESKILPYDLNELMDDSIFEYLPKLEDRLNKTLMIGTKTDYLLDAQKQFKLLQKTVLELSEIVQRRNVSKLEELPFGSRVDINNRLIEFEKCFINPHGAIGNPQARHVLFHPSPDNWYDGDAISQVHDLISKISNSTDSKELGKLSRQLAKEIALVNVAYICAKHSLGEFFTM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MITARNRFTQGCSCMKYYNNNGDKVFQKSKRNRGMKASGVVLVAVSTVALTIILSNAIHQSYKSNSKPLPKLSIATNIKQLINVDNIRSNLHSLTKKPHVAGTENNLRVAEMIRDQMITQGLENVHFNEYNVMLSYPNWTTPNIIEILKENRDLVFSTTGRSVSVIKEEQNDPLAEIQWLAYSAAGTVEGDIVYVNNANPSDIEYLESLGIDLKDKIFLARYSSNYRGNIAQMAVKKGAKACLVYSDPKQVASLGTGPNETYGNTDKMPSNTVQMGSVYIGLGDPRTPAFPSIGDLFKEKTEQDLLDEKKIPTIPMLPITYATAQILFENMKGDAVNADFQGKLNVTYRYGPGLINNQKLRVTVHAENEERKIQNIMGYIKGSQEPDKFVLVSNHYDAWTYGAVDPNSGTSTLLEVSRALKQYQNQTGWIPARSILFAHWDAEEYGLIGSTEFAEEYRLQLMRRAVAVINMDLIGGNQTLLGLSNPTVANVLRSAAANVEQPNPTEMEQGRKTLYDSWKYYAPSKNNRSTHPYQRIPAGGSDHLPFFDYLGIPIVFFITSSLDAPPTYPLYHTIYETPYLIENIMDPGYKVHKAIAGMFIECILKFTESKILPYDLNELMDDSIFEYLPKLEDRLNKTLMIGTKTDYLLDAQKQFKLLQKTVLELSEIVQRRNVSKLEELPFGSRVDINNRLIEFEKCFINPHGAIGNPQARHVLFHPSPDNWYDGDAISQVHDLISKISNSTDSKELGKLSRQLAKEIALVNVAYICAKHSLGEFFTM\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'C1COCCN1CCCS(=O)(=O)O' is the smile most proper for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1COCCN1CCCS(=O)(=O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MITARNRFTQGCSCMKYYNNNGDKVFQKSKRNRGMKASGVVLVAVSTVALTIILSNAIHQSYKSNSKPLPKLSIATNIKQLINVDNIRSNLHSLTKKPHVAGTENNLRVAEMIRDQMITQGLENVHFNEYNVMLSYPNWTTPNIIEILKENRDLVFSTTGRSVSVIKEEQNDPLAEIQWLAYSAAGTVEGDIVYVNNANPSDIEYLESLGIDLKDKIFLARYSSNYRGNIAQMAVKKGAKACLVYSDPKQVASLGTGPNETYGNTDKMPSNTVQMGSVYIGLGDPRTPAFPSIGDLFKEKTEQDLLDEKKIPTIPMLPITYATAQILFENMKGDAVNADFQGKLNVTYRYGPGLINNQKLRVTVHAENEERKIQNIMGYIKGSQEPDKFVLVSNHYDAWTYGAVDPNSGTSTLLEVSRALKQYQNQTGWIPARSILFAHWDAEEYGLIGSTEFAEEYRLQLMRRAVAVINMDLIGGNQTLLGLSNPTVANVLRSAAANVEQPNPTEMEQGRKTLYDSWKYYAPSKNNRSTHPYQRIPAGGSDHLPFFDYLGIPIVFFITSSLDAPPTYPLYHTIYETPYLIENIMDPGYKVHKAIAGMFIECILKFTESKILPYDLNELMDDSIFEYLPKLEDRLNKTLMIGTKTDYLLDAQKQFKLLQKTVLELSEIVQRRNVSKLEELPFGSRVDINNRLIEFEKCFINPHGAIGNPQARHVLFHPSPDNWYDGDAISQVHDLISKISNSTDSKELGKLSRQLAKEIALVNVAYICAKHSLGEFFTM\", \"result\": [{\"smile\": \"C1COCCN1CCCS(=O)(=O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C1COCCN1CCCS(=O)(=O)O' can interact with the protein 'MITARNRFTQGCSCMKYYNNNGDKVFQKSKRNRGMKASGVVLVAVSTVALTIILSNAIHQSYKSNSKPLPKLSIATNIKQLINVDNIRSNLHSLTKKPHVAGTENNLRVAEMIRDQMITQGLENVHFNEYNVMLSYPNWTTPNIIEILKENRDLVFSTTGRSVSVIKEEQNDPLAEIQWLAYSAAGTVEGDIVYVNNANPSDIEYLESLGIDLKDKIFLARYSSNYRGNIAQMAVKKGAKACLVYSDPKQVASLGTGPNETYGNTDKMPSNTVQMGSVYIGLGDPRTPAFPSIGDLFKEKTEQDLLDEKKIPTIPMLPITYATAQILFENMKGDAVNADFQGKLNVTYRYGPGLINNQKLRVTVHAENEERKIQNIMGYIKGSQEPDKFVLVSNHYDAWTYGAVDPNSGTSTLLEVSRALKQYQNQTGWIPARSILFAHWDAEEYGLIGSTEFAEEYRLQLMRRAVAVINMDLIGGNQTLLGLSNPTVANVLRSAAANVEQPNPTEMEQGRKTLYDSWKYYAPSKNNRSTHPYQRIPAGGSDHLPFFDYLGIPIVFFITSSLDAPPTYPLYHTIYETPYLIENIMDPGYKVHKAIAGMFIECILKFTESKILPYDLNELMDDSIFEYLPKLEDRLNKTLMIGTKTDYLLDAQKQFKLLQKTVLELSEIVQRRNVSKLEELPFGSRVDINNRLIEFEKCFINPHGAIGNPQARHVLFHPSPDNWYDGDAISQVHDLISKISNSTDSKELGKLSRQLAKEIALVNVAYICAKHSLGEFFTM'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the likelihood of interaction between the drug '))C(CC==1C)@OC((CCOC[H]1NCC=CA' and the target protein 'MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"))C(CC==1C)@OC((CCOC[H]1NCC=CA\"], \"target_seq\": \"MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'C[C@H](CC1=C(C=CC(=C1)OC)OC)N'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@H](CC1=C(C=CC(=C1)OC)OC)N\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ\", \"result\": [{\"smile\": \"C[C@H](CC1=C(C=CC(=C1)OC)OC)N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@H](CC1=C(C=CC(=C1)OC)OC)N' can not interact with the protein 'MGTSLSPNDPWPLNPLSIQQTTLLLLLSVLATVHVGQRLLRQRRRQLRSAPPGPFAWPLIGNAAAVGQAAHLSFARLARRYGDVFQIRLGSCPIVVLNGERAIHQALVQQGSAFADRPAFASFRVVSGGRSMAFGHYSEHWKVQRRAAHSMMRNFFTRQPRSRQVLEGHVLSEARELVALLVRGSADGAFLDPRPLTVVAVANVMSAVCFGCRYSHDDPEFRELLSHNEEFGRTVGAGSLVDVMPWLQYFPNPVRTVFREFEQLNRNFSNFILDKFLRHCESLRPGAAPRDMMDAFILSAEKKAAGDSHGGGARLDLENVPATITDIFGASQDTLSTALQWLLLLFTRYPDVQTRVQAELDQVVGRDRLPCMGDQPNLPYVLAFLYEAMRFSSFVPVTIPHATTANTSVLGYHIPKDTVVFVNQWSVNHDPLKWPNPENFDPARFLDKDGLINKDLTSRVMIFSVGKRRCIGEELSKMQLFLFISILAHQCDFRANPNEPAKMNFSYGLTIKPKSFKVNVTLRESMELLDSAVQNLQAKETCQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '=(C1CC1NC)NC=O1CC=1==CCCA' interact with the target protein 'MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ' according to your assessment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"=(C1CC1NC)NC=O1CC=1==CCCA\"], \"target_seq\": \"MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'NCC1=CC=C(O1)C1=CC=CN=C1' is the proper smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NCC1=CC=C(O1)C1=CC=CN=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ\", \"result\": [{\"smile\": \"NCC1=CC=C(O1)C1=CC=CN=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NCC1=CC=C(O1)C1=CC=CN=C1' can not interact with the protein 'MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'C=2CCC=C=CN33=====4CC=)4(C(CC5CCN(CC)CC(C1COCCCCCCCN))O)C1CON5C(OCN(2NC)A' is likely to interact with the target protein 'MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C=2CCC=C=CN33=====4CC=)4(C(CC5CCN(CC)CC(C1COCCCCCCCN))O)C1CON5C(OCN(2NC)A\"], \"target_seq\": \"MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC(C)OC1=CC=C(C=C1)NC(=O)N2CCN(CC2)C3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCCC5' works as the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)OC1=CC=C(C=C1)NC(=O)N2CCN(CC2)C3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCCC5\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN\", \"result\": [{\"smile\": \"CC(C)OC1=CC=C(C=C1)NC(=O)N2CCN(CC2)C3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCCC5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)OC1=CC=C(C=C1)NC(=O)N2CCN(CC2)C3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCCC5' can not interact with the protein 'MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'N(CC==SCN5NO)N=CN2C4CNNCC)O=CC)CC=lC1CC1)C==C45CCC(OCC((C(CC(C()C(C32C)C)C=3)===A' will behave towards the target protein 'MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N(CC==SCN5NO)N=CN2C4CNNCC)O=CC)CC=lC1CC1)C==C45CCC(OCC((C(CC(C()C(C32C)C)C=3)===A\"], \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\", \"result\": [{\"smile\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=NC(=NC=C2Cl)NC3=C(C=C(C=C3)N4CCC(CC4)N5CCN(CC5)C)OC' can interact with the protein 'MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a forecast for the interaction likelihood between the drug ')CONc)c1F)(nF(c[c)CsF2nFccCc2ccCcF()(Fccc)2)c@2C(F)2(c)C]C)(c1(lc(()cc2(FcA' and the target protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\")CONc)c1F)(nF(c[c)CsF2nFccCc2ccCcF()(Fccc)2)c@2C(F)2(c)C]C)(c1(lc(()cc2(FcA\"], \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile to associate with the drug is 'Cc1sc(N[C@](Cc2ccccc2)(c2cc(F)cc(OC(F)(F)C(F)F)c2)c2ccc(Cl)cn2)nc1C(F)(F)F'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1sc(N[C@](Cc2ccccc2)(c2cc(F)cc(OC(F)(F)C(F)F)c2)c2ccc(Cl)cn2)nc1C(F)(F)F\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"Cc1sc(N[C@](Cc2ccccc2)(c2cc(F)cc(OC(F)(F)C(F)F)c2)c2ccc(Cl)cn2)nc1C(F)(F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1sc(N[C@](Cc2ccccc2)(c2cc(F)cc(OC(F)(F)C(F)F)c2)c2ccc(Cl)cn2)nc1C(F)(F)F' can not interact with the protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug ')])OCCCC[N(O=CC[C[1IO=]=O(=+]-(CC)(C=C=-)NO)(COCC1A' will engage with the target protein 'MAWTPLFLFLLTCCPGSNSQTVVTQEPSLTVSPGGTVTLTCASSTGAVTSGYYPNWFQQKPGQAPRALIYSTSNKHSWTPARFSGSLLGGKAALTLSGVQPEDEAEYYCLLYYGGAQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\")])OCCCC[N(O=CC[C[1IO=]=O(=+]-(CC)(C=C=-)NO)(COCC1A\"], \"target_seq\": \"MAWTPLFLFLLTCCPGSNSQTVVTQEPSLTVSPGGTVTLTCASSTGAVTSGYYPNWFQQKPGQAPRALIYSTSNKHSWTPARFSGSLLGGKAALTLSGVQPEDEAEYYCLLYYGGAQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAWTPLFLFLLTCCPGSNSQTVVTQEPSLTVSPGGTVTLTCASSTGAVTSGYYPNWFQQKPGQAPRALIYSTSNKHSWTPARFSGSLLGGKAALTLSGVQPEDEAEYYCLLYYGGAQ\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O' is the smile most proper for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAWTPLFLFLLTCCPGSNSQTVVTQEPSLTVSPGGTVTLTCASSTGAVTSGYYPNWFQQKPGQAPRALIYSTSNKHSWTPARFSGSLLGGKAALTLSGVQPEDEAEYYCLLYYGGAQ\", \"result\": [{\"smile\": \"OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'OC1=C(C=C(CC(=O)NCCCCCC([O-])=O)C=C1I)[N+]([O-])=O' can interact with the protein 'MAWTPLFLFLLTCCPGSNSQTVVTQEPSLTVSPGGTVTLTCASSTGAVTSGYYPNWFQQKPGQAPRALIYSTSNKHSWTPARFSGSLLGGKAALTLSGVQPEDEAEYYCLLYYGGAQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you expect the drug '1=)(C1C(2)CCCOC==C=)OCCC2=CCC(C=CN=A' to have an interaction with the target protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"1=)(C1C(2)CCCOC==C=)OCCC2=CCC(C=CN=A\"], \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile proper for the drug is 'CC1=C(C)C(NC2=CC=CC=C2C(O)=O)=CC=C1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C)C(NC2=CC=CC=C2C(O)=O)=CC=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\", \"result\": [{\"smile\": \"CC1=C(C)C(NC2=CC=CC=C2C(O)=O)=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C)C(NC2=CC=CC=C2C(O)=O)=CC=C1' can interact with the protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction for the drug 'O)])(CO@[)OCC(@=OCH(C=NA''s interaction potential with the target protein 'MPGKRGLGWWWARLPLCLLLSLYGPWMPSSLGKPKGHPHMNSIRIDGDITLGGLFPVHGRGSEGKPCGELKKEKGIHRLEAMLFALDRINNDPDLLPNITLGARILDTCSRDTHALEQSLTFVQALIEKDGTEVRCGSGGPPIITKPERVVGVIGASGSSVSIMVANILRLFKIPQISYASTAPDLSDNSRYDFFSRVVPSDTYQAQAMVDIVRALKWNYVSTVASEGSYGESGVEAFIQKSREDGGVCIAQSVKIPREPKAGEFDKIIRRLLETSNARAVIIFANEDDIRRVLEAARRANQTGHFFWMGSDSWGSKIAPVLHLEEVAEGAVTILPKRMSVRGFDRYFSSRTLDNNRRNIWFAEFWEDNFHCKLSRHALKKGSHVKKCTNRERIGQDSAYEQEGKVQFVIDAVYAMGHALHAMHRDLCPGRVGLCPRMDPVDGTQLLKYIRNVNFSGIAGNPVTFNENGDAPGRYDIYQYQLRNDSAEYKVIGSWTDHLHLRIERMHWPGSGQQLPRSICSLPCQPGERKKTVKGMPCCWHCEPCTGYQYQVDRYTCKTCPYDMRPTENRTGCRPIPIIKLEWGSPWAVLPLFLAVVGIAATLFVVITFVRYNDTPIVKASGRELSYVLLAGIFLCYATTFLMIAEPDLGTCSLRRIFLGLGMSISYAALLTKTNRIYRIFEQGKRSVSAPRFISPASQLAITFSLISLQLLGICVWFVVDPSHSVVDFQDQRTLDPRFARGVLKCDISDLSLICLLGYSMLLMVTCTVYAIKTRGVPETFNEAKPIGFTMYTTCIVWLAFIPIFFGTSQSADKLYIQTTTLTVSVSLSASVSLGMLYMPKVYIILFHPEQNVPKRKRSLKAVVTAATMSNKFTQKGNFRPNGEAKSELCENLEAPALATKQTYVTYTNHAI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O)])(CO@[)OCC(@=OCH(C=NA\"], \"target_seq\": \"MPGKRGLGWWWARLPLCLLLSLYGPWMPSSLGKPKGHPHMNSIRIDGDITLGGLFPVHGRGSEGKPCGELKKEKGIHRLEAMLFALDRINNDPDLLPNITLGARILDTCSRDTHALEQSLTFVQALIEKDGTEVRCGSGGPPIITKPERVVGVIGASGSSVSIMVANILRLFKIPQISYASTAPDLSDNSRYDFFSRVVPSDTYQAQAMVDIVRALKWNYVSTVASEGSYGESGVEAFIQKSREDGGVCIAQSVKIPREPKAGEFDKIIRRLLETSNARAVIIFANEDDIRRVLEAARRANQTGHFFWMGSDSWGSKIAPVLHLEEVAEGAVTILPKRMSVRGFDRYFSSRTLDNNRRNIWFAEFWEDNFHCKLSRHALKKGSHVKKCTNRERIGQDSAYEQEGKVQFVIDAVYAMGHALHAMHRDLCPGRVGLCPRMDPVDGTQLLKYIRNVNFSGIAGNPVTFNENGDAPGRYDIYQYQLRNDSAEYKVIGSWTDHLHLRIERMHWPGSGQQLPRSICSLPCQPGERKKTVKGMPCCWHCEPCTGYQYQVDRYTCKTCPYDMRPTENRTGCRPIPIIKLEWGSPWAVLPLFLAVVGIAATLFVVITFVRYNDTPIVKASGRELSYVLLAGIFLCYATTFLMIAEPDLGTCSLRRIFLGLGMSISYAALLTKTNRIYRIFEQGKRSVSAPRFISPASQLAITFSLISLQLLGICVWFVVDPSHSVVDFQDQRTLDPRFARGVLKCDISDLSLICLLGYSMLLMVTCTVYAIKTRGVPETFNEAKPIGFTMYTTCIVWLAFIPIFFGTSQSADKLYIQTTTLTVSVSLSASVSLGMLYMPKVYIILFHPEQNVPKRKRSLKAVVTAATMSNKFTQKGNFRPNGEAKSELCENLEAPALATKQTYVTYTNHAI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPGKRGLGWWWARLPLCLLLSLYGPWMPSSLGKPKGHPHMNSIRIDGDITLGGLFPVHGRGSEGKPCGELKKEKGIHRLEAMLFALDRINNDPDLLPNITLGARILDTCSRDTHALEQSLTFVQALIEKDGTEVRCGSGGPPIITKPERVVGVIGASGSSVSIMVANILRLFKIPQISYASTAPDLSDNSRYDFFSRVVPSDTYQAQAMVDIVRALKWNYVSTVASEGSYGESGVEAFIQKSREDGGVCIAQSVKIPREPKAGEFDKIIRRLLETSNARAVIIFANEDDIRRVLEAARRANQTGHFFWMGSDSWGSKIAPVLHLEEVAEGAVTILPKRMSVRGFDRYFSSRTLDNNRRNIWFAEFWEDNFHCKLSRHALKKGSHVKKCTNRERIGQDSAYEQEGKVQFVIDAVYAMGHALHAMHRDLCPGRVGLCPRMDPVDGTQLLKYIRNVNFSGIAGNPVTFNENGDAPGRYDIYQYQLRNDSAEYKVIGSWTDHLHLRIERMHWPGSGQQLPRSICSLPCQPGERKKTVKGMPCCWHCEPCTGYQYQVDRYTCKTCPYDMRPTENRTGCRPIPIIKLEWGSPWAVLPLFLAVVGIAATLFVVITFVRYNDTPIVKASGRELSYVLLAGIFLCYATTFLMIAEPDLGTCSLRRIFLGLGMSISYAALLTKTNRIYRIFEQGKRSVSAPRFISPASQLAITFSLISLQLLGICVWFVVDPSHSVVDFQDQRTLDPRFARGVLKCDISDLSLICLLGYSMLLMVTCTVYAIKTRGVPETFNEAKPIGFTMYTTCIVWLAFIPIFFGTSQSADKLYIQTTTLTVSVSLSASVSLGMLYMPKVYIILFHPEQNVPKRKRSLKAVVTAATMSNKFTQKGNFRPNGEAKSELCENLEAPALATKQTYVTYTNHAI\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile you should use for the drug is 'N[C@@H](CCC(O)=O)C(O)=O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CCC(O)=O)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPGKRGLGWWWARLPLCLLLSLYGPWMPSSLGKPKGHPHMNSIRIDGDITLGGLFPVHGRGSEGKPCGELKKEKGIHRLEAMLFALDRINNDPDLLPNITLGARILDTCSRDTHALEQSLTFVQALIEKDGTEVRCGSGGPPIITKPERVVGVIGASGSSVSIMVANILRLFKIPQISYASTAPDLSDNSRYDFFSRVVPSDTYQAQAMVDIVRALKWNYVSTVASEGSYGESGVEAFIQKSREDGGVCIAQSVKIPREPKAGEFDKIIRRLLETSNARAVIIFANEDDIRRVLEAARRANQTGHFFWMGSDSWGSKIAPVLHLEEVAEGAVTILPKRMSVRGFDRYFSSRTLDNNRRNIWFAEFWEDNFHCKLSRHALKKGSHVKKCTNRERIGQDSAYEQEGKVQFVIDAVYAMGHALHAMHRDLCPGRVGLCPRMDPVDGTQLLKYIRNVNFSGIAGNPVTFNENGDAPGRYDIYQYQLRNDSAEYKVIGSWTDHLHLRIERMHWPGSGQQLPRSICSLPCQPGERKKTVKGMPCCWHCEPCTGYQYQVDRYTCKTCPYDMRPTENRTGCRPIPIIKLEWGSPWAVLPLFLAVVGIAATLFVVITFVRYNDTPIVKASGRELSYVLLAGIFLCYATTFLMIAEPDLGTCSLRRIFLGLGMSISYAALLTKTNRIYRIFEQGKRSVSAPRFISPASQLAITFSLISLQLLGICVWFVVDPSHSVVDFQDQRTLDPRFARGVLKCDISDLSLICLLGYSMLLMVTCTVYAIKTRGVPETFNEAKPIGFTMYTTCIVWLAFIPIFFGTSQSADKLYIQTTTLTVSVSLSASVSLGMLYMPKVYIILFHPEQNVPKRKRSLKAVVTAATMSNKFTQKGNFRPNGEAKSELCENLEAPALATKQTYVTYTNHAI\", \"result\": [{\"smile\": \"N[C@@H](CCC(O)=O)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CCC(O)=O)C(O)=O' can interact with the protein 'MPGKRGLGWWWARLPLCLLLSLYGPWMPSSLGKPKGHPHMNSIRIDGDITLGGLFPVHGRGSEGKPCGELKKEKGIHRLEAMLFALDRINNDPDLLPNITLGARILDTCSRDTHALEQSLTFVQALIEKDGTEVRCGSGGPPIITKPERVVGVIGASGSSVSIMVANILRLFKIPQISYASTAPDLSDNSRYDFFSRVVPSDTYQAQAMVDIVRALKWNYVSTVASEGSYGESGVEAFIQKSREDGGVCIAQSVKIPREPKAGEFDKIIRRLLETSNARAVIIFANEDDIRRVLEAARRANQTGHFFWMGSDSWGSKIAPVLHLEEVAEGAVTILPKRMSVRGFDRYFSSRTLDNNRRNIWFAEFWEDNFHCKLSRHALKKGSHVKKCTNRERIGQDSAYEQEGKVQFVIDAVYAMGHALHAMHRDLCPGRVGLCPRMDPVDGTQLLKYIRNVNFSGIAGNPVTFNENGDAPGRYDIYQYQLRNDSAEYKVIGSWTDHLHLRIERMHWPGSGQQLPRSICSLPCQPGERKKTVKGMPCCWHCEPCTGYQYQVDRYTCKTCPYDMRPTENRTGCRPIPIIKLEWGSPWAVLPLFLAVVGIAATLFVVITFVRYNDTPIVKASGRELSYVLLAGIFLCYATTFLMIAEPDLGTCSLRRIFLGLGMSISYAALLTKTNRIYRIFEQGKRSVSAPRFISPASQLAITFSLISLQLLGICVWFVVDPSHSVVDFQDQRTLDPRFARGVLKCDISDLSLICLLGYSMLLMVTCTVYAIKTRGVPETFNEAKPIGFTMYTTCIVWLAFIPIFFGTSQSADKLYIQTTTLTVSVSLSASVSLGMLYMPKVYIILFHPEQNVPKRKRSLKAVVTAATMSNKFTQKGNFRPNGEAKSELCENLEAPALATKQTYVTYTNHAI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the probability of interaction between the drug 'C=)1=)CN=3NCCCN4=4C=CC=2=CCN(CC)3COO21CCCC(CC(==A' and the target protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C=)1=)CN=3NCCCN4=4C=CC=2=CCN(CC)3COO21CCCC(CC(==A\"], \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile to associate with the drug is 'C1CC(=NO)C2=C1C=C(C=C2)C3=CN(N=C3C4=CC=NC=C4)CCO'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1CC(=NO)C2=C1C=C(C=C2)C3=CN(N=C3C4=CC=NC=C4)CCO\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"C1CC(=NO)C2=C1C=C(C=C2)C3=CN(N=C3C4=CC=NC=C4)CCO\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C1CC(=NO)C2=C1C=C(C=C2)C3=CN(N=C3C4=CC=NC=C4)CCO' can not interact with the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the drug 'C)((Oc))CC@)C1[(333HC=CC)C]CH]Cc[))c@2@C+((NNCC][FC=(]cc(HHC2cOCO[N1A' with the target protein 'MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C)((Oc))CC@)C1[(333HC=CC)C]CH]Cc[))c@2@C+((NNCC][FC=(]cc(HHC2cOCO[N1A\"], \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile that matches the drug is 'COc1ccc(C(=O)NC2CCC([C@H](C)[C@H]([NH3+])C(=O)N3CC[C@H](F)C3)CC2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(C(=O)NC2CCC([C@H](C)[C@H]([NH3+])C(=O)N3CC[C@H](F)C3)CC2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\", \"result\": [{\"smile\": \"COc1ccc(C(=O)NC2CCC([C@H](C)[C@H]([NH3+])C(=O)N3CC[C@H](F)C3)CC2)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc(C(=O)NC2CCC([C@H](C)[C@H]([NH3+])C(=O)N3CC[C@H](F)C3)CC2)cc1' can not interact with the protein 'MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the drug 'CC=C1C)OCN1C()==N1=C(CC1CCOC=A''s potential to interact with the target protein 'MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC=C1C)OCN1C()==N1=C(CC1CCOC=A\"], \"target_seq\": \"MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile you should use for the drug is 'CCC1(C(=O)NCNC1=O)C1=CC=CC=C1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1(C(=O)NCNC1=O)C1=CC=CC=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI\", \"result\": [{\"smile\": \"CCC1(C(=O)NCNC1=O)C1=CC=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC1(C(=O)NCNC1=O)C1=CC=CC=C1' can interact with the protein 'MELGGPGAPRLLPPLLLLLGTGLLRASSHVETRAHAEERLLKKLFSGYNKWSRPVANISDVVLVRFGLSIAQLIDVDEKNQMMTTNVWVKQEWHDYKLRWDPADYENVTSIRIPSELIWRPDIVLYNNADGDFAVTHLTKAHLFHDGRVQWTPPAIYKSSCSIDVTFFPFDQQNCTMKFGSWTYDKAKIDLVNMHSRVDQLDFWESGEWVIVDAVGTYNTRKYECCAEIYPDITYAFVIRRLPLFYTINLIIPCLLISCLTVLVFYLPSECGEKITLCISVLLSLTVFLLLITEIIPSTSLVIPLIGEYLLFTMIFVTLSIVITVFVLNVHHRSPRTHTMPTWVRRVFLDIVPRLLLMKRPSVVKDNCRRLIESMHKMASAPRFWPEPEGEPPATSGTQSLHPPSPSFCVPLDVPAEPGPSCKSPSDQLPPQQPLEAEKASPHPSPGPCRPPHGTQAPGLAKARSLSVQHMSSPGEAVEGGVRCRSRSIQYCVPRDDAAPEADGQAAGALASRNTHSAELPPPDQPSPCKCTCKKEPSSVSPSATVKTRSTKAPPPHLPLSPALTRAVEGVQYIADHLKAEDTDFSVKEDWKYVAMVIDRIFLWMFIIVCLLGTVGLFLPPWLAGMI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'c2cccOcc(c)nc-c3ccccCc(nc2C2(c)=3c)3cc3c21ncOcc1nA''s interaction outcome with the target protein 'MRRDERDAKAMRSLQPPDGAGSPPESLRNGYVKSCVSPLRQDPPRGFFFHLCRFCNVELRPPPASPQQPRRCSPFCRARLSLGALAAFVLALLLGAEPESWAAGAAWLRTLLSVCSHSLSPLFSIACAFFFLTCFLTRTKRGPGPGRSCGSWWLLALPACCYLGDFLVWQWWSWPWGDGDAGSAAPHTPPEAAAGRLLLVLSCVGLLLTLAHPLRLRHCVLVLLLASFVWWVSFTSLGSLPSALRPLLSGLVGGAGCLLALGLDHFFQIREAPLHPRLSSAAEEKVPVIRPRRRSSCVSLGETAASYYGSCKIFRRPSLPCISREQMILWDWDLKQWYKPHYQNSGGGNGVDLSVLNEARNMVSDLLTDPSLPPQVISSLRSISSLMGAFSGSCRPKINPLTPFPGFYPCSEIEDPAEKGDRKLNKGLNRNSLPTPQLRRSSGTSGLLPVEQSSRWDRNNGKRPHQEFGISSQGCYLNGPFNSNLLTIPKQRSSSVSLTHHVGLRRAGVLSSLSPVNSSNHGPVSTGSLTNRSPIEFPDTADFLNKPSVILQRSLGNAPNTPDFYQQLRNSDSNLCNSCGHQMLKYVSTSESDGTDCCSGKSGEEENIFSKESFKLMETQQEEETEKKDSRKLFQEGDKWLTEEAQSEQQTNIEQEVSLDLILVEEYDSLIEKMSNWNFPIFELVEKMGEKSGRILSQVMYTLFQDTGLLEIFKIPTQQFMNYFRALENGYRDIPYHNRIHATDVLHAVWYLTTRPVPGLQQIHNGCGTGNETDSDGRINHGRIAYISSKSCSNPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWNLYLSRPEYNFLLHLDHVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPAKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWLEAEEDNDTESGDDEDGEELDTEDEEMENNLNPKPPRRKSRRRIFCQLMHHLTENHKIWKEIVEEEEKCKADGNKLQVENSSLPQADEIQVIEEADEEE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"c2cccOcc(c)nc-c3ccccCc(nc2C2(c)=3c)3cc3c21ncOcc1nA\"], \"target_seq\": \"MRRDERDAKAMRSLQPPDGAGSPPESLRNGYVKSCVSPLRQDPPRGFFFHLCRFCNVELRPPPASPQQPRRCSPFCRARLSLGALAAFVLALLLGAEPESWAAGAAWLRTLLSVCSHSLSPLFSIACAFFFLTCFLTRTKRGPGPGRSCGSWWLLALPACCYLGDFLVWQWWSWPWGDGDAGSAAPHTPPEAAAGRLLLVLSCVGLLLTLAHPLRLRHCVLVLLLASFVWWVSFTSLGSLPSALRPLLSGLVGGAGCLLALGLDHFFQIREAPLHPRLSSAAEEKVPVIRPRRRSSCVSLGETAASYYGSCKIFRRPSLPCISREQMILWDWDLKQWYKPHYQNSGGGNGVDLSVLNEARNMVSDLLTDPSLPPQVISSLRSISSLMGAFSGSCRPKINPLTPFPGFYPCSEIEDPAEKGDRKLNKGLNRNSLPTPQLRRSSGTSGLLPVEQSSRWDRNNGKRPHQEFGISSQGCYLNGPFNSNLLTIPKQRSSSVSLTHHVGLRRAGVLSSLSPVNSSNHGPVSTGSLTNRSPIEFPDTADFLNKPSVILQRSLGNAPNTPDFYQQLRNSDSNLCNSCGHQMLKYVSTSESDGTDCCSGKSGEEENIFSKESFKLMETQQEEETEKKDSRKLFQEGDKWLTEEAQSEQQTNIEQEVSLDLILVEEYDSLIEKMSNWNFPIFELVEKMGEKSGRILSQVMYTLFQDTGLLEIFKIPTQQFMNYFRALENGYRDIPYHNRIHATDVLHAVWYLTTRPVPGLQQIHNGCGTGNETDSDGRINHGRIAYISSKSCSNPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWNLYLSRPEYNFLLHLDHVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPAKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWLEAEEDNDTESGDDEDGEELDTEDEEMENNLNPKPPRRKSRRRIFCQLMHHLTENHKIWKEIVEEEEKCKADGNKLQVENSSLPQADEIQVIEEADEEE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRRDERDAKAMRSLQPPDGAGSPPESLRNGYVKSCVSPLRQDPPRGFFFHLCRFCNVELRPPPASPQQPRRCSPFCRARLSLGALAAFVLALLLGAEPESWAAGAAWLRTLLSVCSHSLSPLFSIACAFFFLTCFLTRTKRGPGPGRSCGSWWLLALPACCYLGDFLVWQWWSWPWGDGDAGSAAPHTPPEAAAGRLLLVLSCVGLLLTLAHPLRLRHCVLVLLLASFVWWVSFTSLGSLPSALRPLLSGLVGGAGCLLALGLDHFFQIREAPLHPRLSSAAEEKVPVIRPRRRSSCVSLGETAASYYGSCKIFRRPSLPCISREQMILWDWDLKQWYKPHYQNSGGGNGVDLSVLNEARNMVSDLLTDPSLPPQVISSLRSISSLMGAFSGSCRPKINPLTPFPGFYPCSEIEDPAEKGDRKLNKGLNRNSLPTPQLRRSSGTSGLLPVEQSSRWDRNNGKRPHQEFGISSQGCYLNGPFNSNLLTIPKQRSSSVSLTHHVGLRRAGVLSSLSPVNSSNHGPVSTGSLTNRSPIEFPDTADFLNKPSVILQRSLGNAPNTPDFYQQLRNSDSNLCNSCGHQMLKYVSTSESDGTDCCSGKSGEEENIFSKESFKLMETQQEEETEKKDSRKLFQEGDKWLTEEAQSEQQTNIEQEVSLDLILVEEYDSLIEKMSNWNFPIFELVEKMGEKSGRILSQVMYTLFQDTGLLEIFKIPTQQFMNYFRALENGYRDIPYHNRIHATDVLHAVWYLTTRPVPGLQQIHNGCGTGNETDSDGRINHGRIAYISSKSCSNPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWNLYLSRPEYNFLLHLDHVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPAKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWLEAEEDNDTESGDDEDGEELDTEDEEMENNLNPKPPRRKSRRRIFCQLMHHLTENHKIWKEIVEEEEKCKADGNKLQVENSSLPQADEIQVIEEADEEE\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile to associate with the drug is 'Cn1c(C(=O)c2ccc(Oc3ncccc3-c3cccnc3)cc2)nc2ccccc21'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1c(C(=O)c2ccc(Oc3ncccc3-c3cccnc3)cc2)nc2ccccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRRDERDAKAMRSLQPPDGAGSPPESLRNGYVKSCVSPLRQDPPRGFFFHLCRFCNVELRPPPASPQQPRRCSPFCRARLSLGALAAFVLALLLGAEPESWAAGAAWLRTLLSVCSHSLSPLFSIACAFFFLTCFLTRTKRGPGPGRSCGSWWLLALPACCYLGDFLVWQWWSWPWGDGDAGSAAPHTPPEAAAGRLLLVLSCVGLLLTLAHPLRLRHCVLVLLLASFVWWVSFTSLGSLPSALRPLLSGLVGGAGCLLALGLDHFFQIREAPLHPRLSSAAEEKVPVIRPRRRSSCVSLGETAASYYGSCKIFRRPSLPCISREQMILWDWDLKQWYKPHYQNSGGGNGVDLSVLNEARNMVSDLLTDPSLPPQVISSLRSISSLMGAFSGSCRPKINPLTPFPGFYPCSEIEDPAEKGDRKLNKGLNRNSLPTPQLRRSSGTSGLLPVEQSSRWDRNNGKRPHQEFGISSQGCYLNGPFNSNLLTIPKQRSSSVSLTHHVGLRRAGVLSSLSPVNSSNHGPVSTGSLTNRSPIEFPDTADFLNKPSVILQRSLGNAPNTPDFYQQLRNSDSNLCNSCGHQMLKYVSTSESDGTDCCSGKSGEEENIFSKESFKLMETQQEEETEKKDSRKLFQEGDKWLTEEAQSEQQTNIEQEVSLDLILVEEYDSLIEKMSNWNFPIFELVEKMGEKSGRILSQVMYTLFQDTGLLEIFKIPTQQFMNYFRALENGYRDIPYHNRIHATDVLHAVWYLTTRPVPGLQQIHNGCGTGNETDSDGRINHGRIAYISSKSCSNPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWNLYLSRPEYNFLLHLDHVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPAKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWLEAEEDNDTESGDDEDGEELDTEDEEMENNLNPKPPRRKSRRRIFCQLMHHLTENHKIWKEIVEEEEKCKADGNKLQVENSSLPQADEIQVIEEADEEE\", \"result\": [{\"smile\": \"Cn1c(C(=O)c2ccc(Oc3ncccc3-c3cccnc3)cc2)nc2ccccc21\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cn1c(C(=O)c2ccc(Oc3ncccc3-c3cccnc3)cc2)nc2ccccc21' can not interact with the protein 'MRRDERDAKAMRSLQPPDGAGSPPESLRNGYVKSCVSPLRQDPPRGFFFHLCRFCNVELRPPPASPQQPRRCSPFCRARLSLGALAAFVLALLLGAEPESWAAGAAWLRTLLSVCSHSLSPLFSIACAFFFLTCFLTRTKRGPGPGRSCGSWWLLALPACCYLGDFLVWQWWSWPWGDGDAGSAAPHTPPEAAAGRLLLVLSCVGLLLTLAHPLRLRHCVLVLLLASFVWWVSFTSLGSLPSALRPLLSGLVGGAGCLLALGLDHFFQIREAPLHPRLSSAAEEKVPVIRPRRRSSCVSLGETAASYYGSCKIFRRPSLPCISREQMILWDWDLKQWYKPHYQNSGGGNGVDLSVLNEARNMVSDLLTDPSLPPQVISSLRSISSLMGAFSGSCRPKINPLTPFPGFYPCSEIEDPAEKGDRKLNKGLNRNSLPTPQLRRSSGTSGLLPVEQSSRWDRNNGKRPHQEFGISSQGCYLNGPFNSNLLTIPKQRSSSVSLTHHVGLRRAGVLSSLSPVNSSNHGPVSTGSLTNRSPIEFPDTADFLNKPSVILQRSLGNAPNTPDFYQQLRNSDSNLCNSCGHQMLKYVSTSESDGTDCCSGKSGEEENIFSKESFKLMETQQEEETEKKDSRKLFQEGDKWLTEEAQSEQQTNIEQEVSLDLILVEEYDSLIEKMSNWNFPIFELVEKMGEKSGRILSQVMYTLFQDTGLLEIFKIPTQQFMNYFRALENGYRDIPYHNRIHATDVLHAVWYLTTRPVPGLQQIHNGCGTGNETDSDGRINHGRIAYISSKSCSNPDESYGCLSSNIPALELMALYVAAAMHDYDHPGRTNAFLVATNAPQAVLYNDRSVLENHHAASAWNLYLSRPEYNFLLHLDHVEFKRFRFLVIEAILATDLKKHFDFLAEFNAKANDVNSNGIEWSNENDRLLVCQVCIKLADINGPAKVRDLHLKWTEGIVNEFYEQGDEEANLGLPISPFMDRSSPQLAKLQESFITHIVGPLCNSYDAAGLLPGQWLEAEEDNDTESGDDEDGEELDTEDEEMENNLNPKPPRRKSRRRIFCQLMHHLTENHKIWKEIVEEEEKCKADGNKLQVENSSLPQADEIQVIEEADEEE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge whether the drug 'O1=C=CNCC2=2C=C)((1(NCCNCN\\\\=N))N=A' will interact with the target protein 'MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O1=C=CNCC2=2C=C)((1(NCCNCN\\\\=N))N=A\"], \"target_seq\": \"MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'NC1=C(\\N=N\\C2=CC=C(O)C=C2)C(N)=NN1' is the accurate smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=C(\\N=N\\C2=CC=C(O)C=C2)C(N)=NN1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED\", \"result\": [{\"smile\": \"NC1=C(\\N=N\\C2=CC=C(O)C=C2)C(N)=NN1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=C(\\N=N\\C2=CC=C(O)C=C2)C(N)=NN1' can not interact with the protein 'MFRCGGLAAGALKQKLVPLVRTVCVRSPRQRNRLPGNLFQRWHVPLELQMTRQMASSGASGGKIDNSVLVLIVGLSTVGAGAYAYKTMKEDEKRYNERISGLGLTPEQKQKKAALSASEGEEVPQDKAPSHVPFLLIGGGTAAFAAARSIRARDPGARVLIVSEDPELPYMRPPLSKELWFSDDPNVTKTLRFKQWNGKERSIYFQPPSFYVSAQDLPHIENGGVAVLTGKKVVQLDVRDNMVKLNDGSQITYEKCLIATGGTPRSLSAIDRAGAEVKSRTTLFRKIGDFRSLEKISREVKSITIIGGGFLGSELACALGRKARALGTEVIQLFPEKGNMGKILPEYLSNWTMEKVRREGVKVMPNAIVQSVGVSSGKLLIKLKDGRKVETDHIVAAVGLEPNVELAKTGGLEIDSDFGGFRVNAELQARSNIWVAGDAACFYDIKLGRRRVEHHDHAVVSGRLAGENMTGAAKPYWHQSMFWSDLGPDVGYEAIGLVDSSLPTVGVFAKATAQDNPKSATEQSGTGIRSESETESEASEITIPPSTPAVPQAPVQGEDYGKGVIFYLRDKVVVGIVLWNIFNRMPIARKIIKDGEQHEDLNEVAKLFNIHED'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug '2-Cc)cclc)1cc(cCOcccCc(1nnn2Cnnc2cCCc)c(n2C--]11c[A' and the target protein 'MILNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLVVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWIPHQIFTFLDVLIQLGIIRDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKRYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSTKKPAPCFEVE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"2-Cc)cclc)1cc(cCOcccCc(1nnn2Cnnc2cCCc)c(n2C--]11c[A\"], \"target_seq\": \"MILNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLVVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWIPHQIFTFLDVLIQLGIIRDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKRYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSTKKPAPCFEVE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MILNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLVVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWIPHQIFTFLDVLIQLGIIRDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKRYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSTKKPAPCFEVE\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CCCCc1nc(Cl)c(CO)n1Cc1ccc(-c2ccccc2-c2nn[n-]n2)cc1' is the true smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCc1nc(Cl)c(CO)n1Cc1ccc(-c2ccccc2-c2nn[n-]n2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MILNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLVVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWIPHQIFTFLDVLIQLGIIRDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKRYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSTKKPAPCFEVE\", \"result\": [{\"smile\": \"CCCCc1nc(Cl)c(CO)n1Cc1ccc(-c2ccccc2-c2nn[n-]n2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCCc1nc(Cl)c(CO)n1Cc1ccc(-c2ccccc2-c2nn[n-]n2)cc1' can interact with the protein 'MILNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLVVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWIPHQIFTFLDVLIQLGIIRDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKRYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSTKKPAPCFEVE'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'cC1)()((Occ(CCCC32NC2Cs1CCC=nn)c=cc)OCC)NCN((3)A' likely interact with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"cC1)()((Occ(CCCC32NC2Cs1CCC=nn)c=cc)OCC)NCN((3)A\"], \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The proper smile for the drug is 'CC(C)(C)C(=O)NCCC1CCN(c2ncnc3cc(C(N)=O)sc23)CC1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)C(=O)NCCC1CCN(c2ncnc3cc(C(N)=O)sc23)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CC(C)(C)C(=O)NCCC1CCN(c2ncnc3cc(C(N)=O)sc23)CC1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)(C)C(=O)NCCC1CCN(c2ncnc3cc(C(N)=O)sc23)CC1' can not interact with the protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the chances of the drug '@O(]O)COCO=)((OOHC)[@PA' interacting with the target protein 'MSPEMGELTQTRLQKIWIPHSSGSSRLQRRRGSSIPQFTNSPTMVIMVGLPARGKTYISTKLTRYLNWIGTPTKVFNLGQYRREAVSYKNYEFFLPDNMEALQIRKQCALAALKDVHNYLSHEEGHVAVFDATNTTRERRSLILQFAKEHGYKVFFIESICNDPGIIAENIRQVKLGSPDYIDCDREKVLEDFLKRIECYEVNYQPLDEELDSHLSYIKIFDVGTRYMVNRVQDHIQSRTVYYLMNIHVTPRSIYLCRHGESELNIRGRIGGDSGLSVRGKQYAYALANFIQSQGISSLKVWTSHMKRTIQTAEALGVPYEQWKALNEIDAGVCEEMTYEEIQEHYPEEFALRDQDKYRYRYPKGESYEDLVQRLEPVIMELERQENVLVICHQAVMRCLLAYFLDKSSDELPYLKCPLHTVLKLTPVAYGCKVESIYLNVEAVNTHREKPENVDITREPEEALDTVPAHY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"@O(]O)COCO=)((OOHC)[@PA\"], \"target_seq\": \"MSPEMGELTQTRLQKIWIPHSSGSSRLQRRRGSSIPQFTNSPTMVIMVGLPARGKTYISTKLTRYLNWIGTPTKVFNLGQYRREAVSYKNYEFFLPDNMEALQIRKQCALAALKDVHNYLSHEEGHVAVFDATNTTRERRSLILQFAKEHGYKVFFIESICNDPGIIAENIRQVKLGSPDYIDCDREKVLEDFLKRIECYEVNYQPLDEELDSHLSYIKIFDVGTRYMVNRVQDHIQSRTVYYLMNIHVTPRSIYLCRHGESELNIRGRIGGDSGLSVRGKQYAYALANFIQSQGISSLKVWTSHMKRTIQTAEALGVPYEQWKALNEIDAGVCEEMTYEEIQEHYPEEFALRDQDKYRYRYPKGESYEDLVQRLEPVIMELERQENVLVICHQAVMRCLLAYFLDKSSDELPYLKCPLHTVLKLTPVAYGCKVESIYLNVEAVNTHREKPENVDITREPEEALDTVPAHY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPEMGELTQTRLQKIWIPHSSGSSRLQRRRGSSIPQFTNSPTMVIMVGLPARGKTYISTKLTRYLNWIGTPTKVFNLGQYRREAVSYKNYEFFLPDNMEALQIRKQCALAALKDVHNYLSHEEGHVAVFDATNTTRERRSLILQFAKEHGYKVFFIESICNDPGIIAENIRQVKLGSPDYIDCDREKVLEDFLKRIECYEVNYQPLDEELDSHLSYIKIFDVGTRYMVNRVQDHIQSRTVYYLMNIHVTPRSIYLCRHGESELNIRGRIGGDSGLSVRGKQYAYALANFIQSQGISSLKVWTSHMKRTIQTAEALGVPYEQWKALNEIDAGVCEEMTYEEIQEHYPEEFALRDQDKYRYRYPKGESYEDLVQRLEPVIMELERQENVLVICHQAVMRCLLAYFLDKSSDELPYLKCPLHTVLKLTPVAYGCKVESIYLNVEAVNTHREKPENVDITREPEEALDTVPAHY\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile that’s right for this drug is 'OC[C@@H](O)COP(O)(O)=O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC[C@@H](O)COP(O)(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPEMGELTQTRLQKIWIPHSSGSSRLQRRRGSSIPQFTNSPTMVIMVGLPARGKTYISTKLTRYLNWIGTPTKVFNLGQYRREAVSYKNYEFFLPDNMEALQIRKQCALAALKDVHNYLSHEEGHVAVFDATNTTRERRSLILQFAKEHGYKVFFIESICNDPGIIAENIRQVKLGSPDYIDCDREKVLEDFLKRIECYEVNYQPLDEELDSHLSYIKIFDVGTRYMVNRVQDHIQSRTVYYLMNIHVTPRSIYLCRHGESELNIRGRIGGDSGLSVRGKQYAYALANFIQSQGISSLKVWTSHMKRTIQTAEALGVPYEQWKALNEIDAGVCEEMTYEEIQEHYPEEFALRDQDKYRYRYPKGESYEDLVQRLEPVIMELERQENVLVICHQAVMRCLLAYFLDKSSDELPYLKCPLHTVLKLTPVAYGCKVESIYLNVEAVNTHREKPENVDITREPEEALDTVPAHY\", \"result\": [{\"smile\": \"OC[C@@H](O)COP(O)(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'OC[C@@H](O)COP(O)(O)=O' can interact with the protein 'MSPEMGELTQTRLQKIWIPHSSGSSRLQRRRGSSIPQFTNSPTMVIMVGLPARGKTYISTKLTRYLNWIGTPTKVFNLGQYRREAVSYKNYEFFLPDNMEALQIRKQCALAALKDVHNYLSHEEGHVAVFDATNTTRERRSLILQFAKEHGYKVFFIESICNDPGIIAENIRQVKLGSPDYIDCDREKVLEDFLKRIECYEVNYQPLDEELDSHLSYIKIFDVGTRYMVNRVQDHIQSRTVYYLMNIHVTPRSIYLCRHGESELNIRGRIGGDSGLSVRGKQYAYALANFIQSQGISSLKVWTSHMKRTIQTAEALGVPYEQWKALNEIDAGVCEEMTYEEIQEHYPEEFALRDQDKYRYRYPKGESYEDLVQRLEPVIMELERQENVLVICHQAVMRCLLAYFLDKSSDELPYLKCPLHTVLKLTPVAYGCKVESIYLNVEAVNTHREKPENVDITREPEEALDTVPAHY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the drug '1cn3cO-1cCccC(oC3O2=2c)cc2ccc(12c)ccncccc1c)cC(OA' with the target protein 'MPEASSPRRTPQNVPYQDLPHLVNADGQYLFCRYWKPSGTPKALIFVSHGAGEHCGRYDELAHMLKGLDMLVFAHDHVGHGQSEGERMVVSDFQVFVRDVLQHVDTIQKDYPDVPIFLLGHSMGGAISILVAAERPTYFSGMVLISPLVLANPESASTLKVLAAKLLNFVLPNMTLGRIDSSVLSRNKSEVDLYNSDPLVCRAGLKVCFGIQLLNAVARVERAMPRLTLPFLLLQGSADRLCDSKGAYLLMESSRSQDKTLKMYEGAYHVLHRELPEVTNSVLHEVNSWVSHRIAAAGAGCPP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"1cn3cO-1cCccC(oC3O2=2c)cc2ccc(12c)ccncccc1c)cC(OA\"], \"target_seq\": \"MPEASSPRRTPQNVPYQDLPHLVNADGQYLFCRYWKPSGTPKALIFVSHGAGEHCGRYDELAHMLKGLDMLVFAHDHVGHGQSEGERMVVSDFQVFVRDVLQHVDTIQKDYPDVPIFLLGHSMGGAISILVAAERPTYFSGMVLISPLVLANPESASTLKVLAAKLLNFVLPNMTLGRIDSSVLSRNKSEVDLYNSDPLVCRAGLKVCFGIQLLNAVARVERAMPRLTLPFLLLQGSADRLCDSKGAYLLMESSRSQDKTLKMYEGAYHVLHRELPEVTNSVLHEVNSWVSHRIAAAGAGCPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPEASSPRRTPQNVPYQDLPHLVNADGQYLFCRYWKPSGTPKALIFVSHGAGEHCGRYDELAHMLKGLDMLVFAHDHVGHGQSEGERMVVSDFQVFVRDVLQHVDTIQKDYPDVPIFLLGHSMGGAISILVAAERPTYFSGMVLISPLVLANPESASTLKVLAAKLLNFVLPNMTLGRIDSSVLSRNKSEVDLYNSDPLVCRAGLKVCFGIQLLNAVARVERAMPRLTLPFLLLQGSADRLCDSKGAYLLMESSRSQDKTLKMYEGAYHVLHRELPEVTNSVLHEVNSWVSHRIAAAGAGCPP\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile that's valid for the drug is 'O=C(c1ncc(-c2ccccn2)o1)C1CCc2cc(Oc3ccccc3)ccc2O1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ncc(-c2ccccn2)o1)C1CCc2cc(Oc3ccccc3)ccc2O1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPEASSPRRTPQNVPYQDLPHLVNADGQYLFCRYWKPSGTPKALIFVSHGAGEHCGRYDELAHMLKGLDMLVFAHDHVGHGQSEGERMVVSDFQVFVRDVLQHVDTIQKDYPDVPIFLLGHSMGGAISILVAAERPTYFSGMVLISPLVLANPESASTLKVLAAKLLNFVLPNMTLGRIDSSVLSRNKSEVDLYNSDPLVCRAGLKVCFGIQLLNAVARVERAMPRLTLPFLLLQGSADRLCDSKGAYLLMESSRSQDKTLKMYEGAYHVLHRELPEVTNSVLHEVNSWVSHRIAAAGAGCPP\", \"result\": [{\"smile\": \"O=C(c1ncc(-c2ccccn2)o1)C1CCc2cc(Oc3ccccc3)ccc2O1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(c1ncc(-c2ccccn2)o1)C1CCc2cc(Oc3ccccc3)ccc2O1' can not interact with the protein 'MPEASSPRRTPQNVPYQDLPHLVNADGQYLFCRYWKPSGTPKALIFVSHGAGEHCGRYDELAHMLKGLDMLVFAHDHVGHGQSEGERMVVSDFQVFVRDVLQHVDTIQKDYPDVPIFLLGHSMGGAISILVAAERPTYFSGMVLISPLVLANPESASTLKVLAAKLLNFVLPNMTLGRIDSSVLSRNKSEVDLYNSDPLVCRAGLKVCFGIQLLNAVARVERAMPRLTLPFLLLQGSADRLCDSKGAYLLMESSRSQDKTLKMYEGAYHVLHRELPEVTNSVLHEVNSWVSHRIAAAGAGCPP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug '=[)HCO](CC@@1S=C==C)O1CN(CCCCA' and the target protein 'MLGQSIRRFTTSVVRRSHYEEGPGKNLPFSVENKWSLLAKMCLYFGSAFATPFLVVRHQLLKT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"=[)HCO](CC@@1S=C==C)O1CN(CCCCA\"], \"target_seq\": \"MLGQSIRRFTTSVVRRSHYEEGPGKNLPFSVENKWSLLAKMCLYFGSAFATPFLVVRHQLLKT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLGQSIRRFTTSVVRRSHYEEGPGKNLPFSVENKWSLLAKMCLYFGSAFATPFLVVRHQLLKT\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The right smile for the drug is 'N[C@@H](CSCC1=CC=CC=C1)C(O)=O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CSCC1=CC=CC=C1)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLGQSIRRFTTSVVRRSHYEEGPGKNLPFSVENKWSLLAKMCLYFGSAFATPFLVVRHQLLKT\", \"result\": [{\"smile\": \"N[C@@H](CSCC1=CC=CC=C1)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CSCC1=CC=CC=C1)C(O)=O' can not interact with the protein 'MLGQSIRRFTTSVVRRSHYEEGPGKNLPFSVENKWSLLAKMCLYFGSAFATPFLVVRHQLLKT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that the drug 'SO)(O)C(((C=()C=SO)OC)A' will interact with the target protein 'MVKLDFSSQDEENDEDLTKEFVRDEAPMEETTSEAVKQIATTTKETLKDVVVNKVIDVKDVVKEKVMQQTGMPEWAFVFLGFVFILLVLACAFCLIRKLFGKKRHGEKNKKGGLKGFFGKGQDVVDGKNIQGMAQDLEELGDAMEQNEKEQAEEKEEVKLGRIQYKLDYDFQQGQLTVTVIQAEDLPGMDMSGTSDPYVKLYLLPEKKKKVETKVHRKTLNPVFNETFIFKVAFNEITAKTLVFAIYDFDRFSKHDQIGQVLIPLGKIDLGAVIEEWKDIAPPPDDKEAEKSLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKLEMFGEDRKRLKKKKTNIKRKTLNPYFNESFVFDKLPLHKMKKVSLMITVMDYDKLGSNDAIGRCLLGCNGTGAELRHWMDMLASPRRPIAQWHTLGPVEEEGDKKDDKK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"SO)(O)C(((C=()C=SO)OC)A\"], \"target_seq\": \"MVKLDFSSQDEENDEDLTKEFVRDEAPMEETTSEAVKQIATTTKETLKDVVVNKVIDVKDVVKEKVMQQTGMPEWAFVFLGFVFILLVLACAFCLIRKLFGKKRHGEKNKKGGLKGFFGKGQDVVDGKNIQGMAQDLEELGDAMEQNEKEQAEEKEEVKLGRIQYKLDYDFQQGQLTVTVIQAEDLPGMDMSGTSDPYVKLYLLPEKKKKVETKVHRKTLNPVFNETFIFKVAFNEITAKTLVFAIYDFDRFSKHDQIGQVLIPLGKIDLGAVIEEWKDIAPPPDDKEAEKSLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKLEMFGEDRKRLKKKKTNIKRKTLNPYFNESFVFDKLPLHKMKKVSLMITVMDYDKLGSNDAIGRCLLGCNGTGAELRHWMDMLASPRRPIAQWHTLGPVEEEGDKKDDKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVKLDFSSQDEENDEDLTKEFVRDEAPMEETTSEAVKQIATTTKETLKDVVVNKVIDVKDVVKEKVMQQTGMPEWAFVFLGFVFILLVLACAFCLIRKLFGKKRHGEKNKKGGLKGFFGKGQDVVDGKNIQGMAQDLEELGDAMEQNEKEQAEEKEEVKLGRIQYKLDYDFQQGQLTVTVIQAEDLPGMDMSGTSDPYVKLYLLPEKKKKVETKVHRKTLNPVFNETFIFKVAFNEITAKTLVFAIYDFDRFSKHDQIGQVLIPLGKIDLGAVIEEWKDIAPPPDDKEAEKSLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKLEMFGEDRKRLKKKKTNIKRKTLNPYFNESFVFDKLPLHKMKKVSLMITVMDYDKLGSNDAIGRCLLGCNGTGAELRHWMDMLASPRRPIAQWHTLGPVEEEGDKKDDKK\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'C(C(C(=O)O)S)(C(=O)O)S' works as the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(C(=O)O)S)(C(=O)O)S\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVKLDFSSQDEENDEDLTKEFVRDEAPMEETTSEAVKQIATTTKETLKDVVVNKVIDVKDVVKEKVMQQTGMPEWAFVFLGFVFILLVLACAFCLIRKLFGKKRHGEKNKKGGLKGFFGKGQDVVDGKNIQGMAQDLEELGDAMEQNEKEQAEEKEEVKLGRIQYKLDYDFQQGQLTVTVIQAEDLPGMDMSGTSDPYVKLYLLPEKKKKVETKVHRKTLNPVFNETFIFKVAFNEITAKTLVFAIYDFDRFSKHDQIGQVLIPLGKIDLGAVIEEWKDIAPPPDDKEAEKSLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKLEMFGEDRKRLKKKKTNIKRKTLNPYFNESFVFDKLPLHKMKKVSLMITVMDYDKLGSNDAIGRCLLGCNGTGAELRHWMDMLASPRRPIAQWHTLGPVEEEGDKKDDKK\", \"result\": [{\"smile\": \"C(C(C(=O)O)S)(C(=O)O)S\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(C(C(=O)O)S)(C(=O)O)S' can not interact with the protein 'MVKLDFSSQDEENDEDLTKEFVRDEAPMEETTSEAVKQIATTTKETLKDVVVNKVIDVKDVVKEKVMQQTGMPEWAFVFLGFVFILLVLACAFCLIRKLFGKKRHGEKNKKGGLKGFFGKGQDVVDGKNIQGMAQDLEELGDAMEQNEKEQAEEKEEVKLGRIQYKLDYDFQQGQLTVTVIQAEDLPGMDMSGTSDPYVKLYLLPEKKKKVETKVHRKTLNPVFNETFIFKVAFNEITAKTLVFAIYDFDRFSKHDQIGQVLIPLGKIDLGAVIEEWKDIAPPPDDKEAEKSLGDICFSLRYVPTAGKLTVVILEAKNLKKMDVGGLSDPYVKLEMFGEDRKRLKKKKTNIKRKTLNPYFNESFVFDKLPLHKMKKVSLMITVMDYDKLGSNDAIGRCLLGCNGTGAELRHWMDMLASPRRPIAQWHTLGPVEEEGDKKDDKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug 'C[CHC=SN2C(NN=C@CC)2)]1(C1CCA''s interaction outcome with the target protein 'MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[CHC=SN2C(NN=C@CC)2)]1(C1CCA\"], \"target_seq\": \"MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The valid expression for the drug is 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV\", \"result\": [{\"smile\": \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2' can interact with the protein 'MVNLRNAVHSFLVHLIGLLVWQCDISVSPVAAIVTDIFNTSDGGRFKFPDGVQNWPALSIVIIIIMTIGGNILVIMAVSMEKKLHNATNYFLMSLAIADMLVGLLVMPLSLLAILYDYVWPLPRYLCPVWISLDVLFSTASIMHLCAISLDRYVAIRNPIEHSRFNSRTKAIMKIAIVWAISIGVSVPIPVIGLRDEEKVFVNNTTCVLNDPNFVLIGSFVAFFIPLTIMVITYCLTIYVLRRQALMLLHGHTEEPPGLSLDFLKCCKRNTAEEENSANPNQDQNARRRKKKERRPRGTMQAINNERKASKVLGIVFFVFLIMWCPFFITNILSVLCEKSCNQKLMEKLLNVFVWIGYVCSGINPLVYTLFNKIYRRAFSNYLRCNYKVEKKPPVRQIPRVAATALSGRELNVNIYRHTNEPVIEKASDNEPGIEMQVENLELPVNPSSVVSERISSV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the drug 'CO(O=[]=@)O@HCO()(CN)CA' will engage with the target protein 'MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CO(O=[]=@)O@HCO()(CN)CA\"], \"target_seq\": \"MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The valid-suited smile for the drug is 'N[C@@H](CC(O)=O)C(O)=O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CC(O)=O)C(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP\", \"result\": [{\"smile\": \"N[C@@H](CC(O)=O)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CC(O)=O)C(O)=O' can interact with the protein 'MAAAKVALTKRADPAELRTIFLKYASIEKNGEFFMSPNDFVTRYLNIFGESQPNPKTVELLSGVVDQTKDGLISFQEFVAFESVLCAPDALFMVAFQLFDKAGKGEVTFEDVKQVFGQTTIHQHIPFNWDSEFVQLHFGKERKRHLTYAEFTQFLLEIQLEHAKQAFVQRDNARTGRVTAIDFRDIMVTIRPHVLTPFVEECLVAAAGGTTSHQVSFSYFNGFNSLLNNMELIRKIYSTLAGTRKDVEVTKEEFVLAAQKFGQVTPMEVDILFQLADLYEPRGRMTLADIERIAPLEEGTLPFNLAEAQRQKASGDSARPVLLQVAESAYRFGLGSVAGAVGATAVYPIDLVKTRMQNQRSTGSFVGELMYKNSFDCFKKVLRYEGFFGLYRGLLPQLLGVAPEKAIKLTVNDFVRDKFMHKDGSVPLAAEILAGGCAGGSQVIFTNPLEIVKIRLQVAGEITTGPRVSALSVVRDLGFFGIYKGAKACFLRDIPFSAIYFPCYAHVKASFANEDGQVSPGSLLLAGAIAGMPAASLVTPADVIKTRLQVAARAGQTTYSGVIDCFRKILREEGPKALWKGAGARVFRSSPQFGVTLLTYELLQRWFYIDFGGVKPMGSEPVPKSRINLPAPNPDHVGGYKLAVATFAGIENKFGLYLPLFKPSVSTSKAIGGGP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '==CCC(N==CC51N()C3=ONCCCCC14C5=CNOC3(2CFC(O2CCC4C=C=C=CC))C)A' interact with the target protein 'MVSRDQAHLGPKYVGLWDFKSRTDEELSFRAGDVFHVARKEEQWWWATLLDEAGGAVAQGYVPHNYLAERETVESEPWFFGCISRSEAVRRLQAEGNATGAFLIRVSEKPSADYVLSVRDTQAVRHYKIWRRAGGRLHLNEAVSFLSLPELVNYHRAQSLSHGLRLAAPCRKHEPEPLPHWDDWERPREEFTLCRKLGSGYFGEVFEGLWKDRVQVAIKVISRDNLLHQQMLQSEIQAMKKLRHKHILALYAVVSVGDPVYIITELMAKGSLLELLRDSDEKVLPVSELLDIAWQVAEGMCYLESQNYIHRDLAARNILVGENTLCKVGDFGLARLIKEDVYLSHDHNIPYKWTAPEALSRGHYSTKSDVWSFGILLHEMFSRGQVPYPGMSNHEAFLRVDAGYRMPCPLECPPSVHKLMLTCWCRDPEQRPCFKALRERLSSFTSYENPT', based on your analysis? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"==CCC(N==CC51N()C3=ONCCCCC14C5=CNOC3(2CFC(O2CCC4C=C=C=CC))C)A\"], \"target_seq\": \"MVSRDQAHLGPKYVGLWDFKSRTDEELSFRAGDVFHVARKEEQWWWATLLDEAGGAVAQGYVPHNYLAERETVESEPWFFGCISRSEAVRRLQAEGNATGAFLIRVSEKPSADYVLSVRDTQAVRHYKIWRRAGGRLHLNEAVSFLSLPELVNYHRAQSLSHGLRLAAPCRKHEPEPLPHWDDWERPREEFTLCRKLGSGYFGEVFEGLWKDRVQVAIKVISRDNLLHQQMLQSEIQAMKKLRHKHILALYAVVSVGDPVYIITELMAKGSLLELLRDSDEKVLPVSELLDIAWQVAEGMCYLESQNYIHRDLAARNILVGENTLCKVGDFGLARLIKEDVYLSHDHNIPYKWTAPEALSRGHYSTKSDVWSFGILLHEMFSRGQVPYPGMSNHEAFLRVDAGYRMPCPLECPPSVHKLMLTCWCRDPEQRPCFKALRERLSSFTSYENPT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVSRDQAHLGPKYVGLWDFKSRTDEELSFRAGDVFHVARKEEQWWWATLLDEAGGAVAQGYVPHNYLAERETVESEPWFFGCISRSEAVRRLQAEGNATGAFLIRVSEKPSADYVLSVRDTQAVRHYKIWRRAGGRLHLNEAVSFLSLPELVNYHRAQSLSHGLRLAAPCRKHEPEPLPHWDDWERPREEFTLCRKLGSGYFGEVFEGLWKDRVQVAIKVISRDNLLHQQMLQSEIQAMKKLRHKHILALYAVVSVGDPVYIITELMAKGSLLELLRDSDEKVLPVSELLDIAWQVAEGMCYLESQNYIHRDLAARNILVGENTLCKVGDFGLARLIKEDVYLSHDHNIPYKWTAPEALSRGHYSTKSDVWSFGILLHEMFSRGQVPYPGMSNHEAFLRVDAGYRMPCPLECPPSVHKLMLTCWCRDPEQRPCFKALRERLSSFTSYENPT\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5' is the valid smile corresponding to the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVSRDQAHLGPKYVGLWDFKSRTDEELSFRAGDVFHVARKEEQWWWATLLDEAGGAVAQGYVPHNYLAERETVESEPWFFGCISRSEAVRRLQAEGNATGAFLIRVSEKPSADYVLSVRDTQAVRHYKIWRRAGGRLHLNEAVSFLSLPELVNYHRAQSLSHGLRLAAPCRKHEPEPLPHWDDWERPREEFTLCRKLGSGYFGEVFEGLWKDRVQVAIKVISRDNLLHQQMLQSEIQAMKKLRHKHILALYAVVSVGDPVYIITELMAKGSLLELLRDSDEKVLPVSELLDIAWQVAEGMCYLESQNYIHRDLAARNILVGENTLCKVGDFGLARLIKEDVYLSHDHNIPYKWTAPEALSRGHYSTKSDVWSFGILLHEMFSRGQVPYPGMSNHEAFLRVDAGYRMPCPLECPPSVHKLMLTCWCRDPEQRPCFKALRERLSSFTSYENPT\", \"result\": [{\"smile\": \"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5' can not interact with the protein 'MVSRDQAHLGPKYVGLWDFKSRTDEELSFRAGDVFHVARKEEQWWWATLLDEAGGAVAQGYVPHNYLAERETVESEPWFFGCISRSEAVRRLQAEGNATGAFLIRVSEKPSADYVLSVRDTQAVRHYKIWRRAGGRLHLNEAVSFLSLPELVNYHRAQSLSHGLRLAAPCRKHEPEPLPHWDDWERPREEFTLCRKLGSGYFGEVFEGLWKDRVQVAIKVISRDNLLHQQMLQSEIQAMKKLRHKHILALYAVVSVGDPVYIITELMAKGSLLELLRDSDEKVLPVSELLDIAWQVAEGMCYLESQNYIHRDLAARNILVGENTLCKVGDFGLARLIKEDVYLSHDHNIPYKWTAPEALSRGHYSTKSDVWSFGILLHEMFSRGQVPYPGMSNHEAFLRVDAGYRMPCPLECPPSVHKLMLTCWCRDPEQRPCFKALRERLSSFTSYENPT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you view the chances of the drug 'c)ccc2cc1@22@[C(1)c]2H(clcHCCCN(C]@clC)[cCCcA' interacting with the target protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"c)ccc2cc1@22@[C(1)c]2H(clcHCCCN(C]@clC)[cCCcA\"], \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'NCC[C@@H]1Cc2ccccc2[C@H](c2ccc(Cl)c(Cl)c2)C1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NCC[C@@H]1Cc2ccccc2[C@H](c2ccc(Cl)c(Cl)c2)C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"NCC[C@@H]1Cc2ccccc2[C@H](c2ccc(Cl)c(Cl)c2)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NCC[C@@H]1Cc2ccccc2[C@H](c2ccc(Cl)c(Cl)c2)C1' can not interact with the protein 'MDSLVVLVLCLSCLLLLSLWRQSSGRGKLPPGPTPLPVIGNILQIGIKDISKSLTNLSKVYGPVFTLYFGLKPIVVLHGYEAVKEALIDLGEEFSGRGIFPLAERANRGFGIVFSNGKKWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFHKRFDYKDQQFLNLMEKLNENIKILSSPWIQICNNFSPIIDYFPGTHNKLLKNVAFMKSYILEKVKEHQESMDMNNPQDFIDCFLMKMEKEKHNQPSEFTIESLENTAVDLFGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVIGRNRSPCMQDRSHMPYTDAVVHEVQRYIDLLPTSLPHAVTCDIKFRNYLIPKGTTILISLTSVLHDNKEFPNPEMFDPHHFLDEGGNFKKSKYFMPFSAGKRICVGEALAGMELFLFLTSILQNFNLKSLVDPKNLDTTPVVNGFASVPPFYQLCFIPV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you assess the possibility of the drug ')CO(C)NNHC=)=C@CC1@(OO]1O)P=C(NO)O((C[=A' interacting with the target protein 'MGRGARVPSEAPGAGVERRWLGAALVALCLLPALVLLARLGAPAVPAWSAAQGDVAALGLSAVPPTRVPGPLAPRRRRYTLTPARLRWDHFNLTYRILSFPRNLLSPRETRRALAAAFRMWSDVSPFSFREVAPEQPSDLRIGFYPINHTDCLVSALHHCFDGPTGELAHAFFPPHGGIHFDDSEYWVLGPTRYSWKKGVWLTDLVHVAAHEIGHALGLMHSQHGRALMHLNATLRGWKALSQDELWGLHRLYGCLDRLFVCASWARRGFCDARRRLMKRLCPSSCDFCYEFPFPTVATTPPPPRTKTRLVPEGRNVTFRCGQKILHKKGKVYWYKDQEPLEFSYPGYLALGEAHLSIIANAVNEGTYTCVVRRQQRVLTTYSWRVRVRG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\")CO(C)NNHC=)=C@CC1@(OO]1O)P=C(NO)O((C[=A\"], \"target_seq\": \"MGRGARVPSEAPGAGVERRWLGAALVALCLLPALVLLARLGAPAVPAWSAAQGDVAALGLSAVPPTRVPGPLAPRRRRYTLTPARLRWDHFNLTYRILSFPRNLLSPRETRRALAAAFRMWSDVSPFSFREVAPEQPSDLRIGFYPINHTDCLVSALHHCFDGPTGELAHAFFPPHGGIHFDDSEYWVLGPTRYSWKKGVWLTDLVHVAAHEIGHALGLMHSQHGRALMHLNATLRGWKALSQDELWGLHRLYGCLDRLFVCASWARRGFCDARRRLMKRLCPSSCDFCYEFPFPTVATTPPPPRTKTRLVPEGRNVTFRCGQKILHKKGKVYWYKDQEPLEFSYPGYLALGEAHLSIIANAVNEGTYTCVVRRQQRVLTTYSWRVRVRG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGRGARVPSEAPGAGVERRWLGAALVALCLLPALVLLARLGAPAVPAWSAAQGDVAALGLSAVPPTRVPGPLAPRRRRYTLTPARLRWDHFNLTYRILSFPRNLLSPRETRRALAAAFRMWSDVSPFSFREVAPEQPSDLRIGFYPINHTDCLVSALHHCFDGPTGELAHAFFPPHGGIHFDDSEYWVLGPTRYSWKKGVWLTDLVHVAAHEIGHALGLMHSQHGRALMHLNATLRGWKALSQDELWGLHRLYGCLDRLFVCASWARRGFCDARRRLMKRLCPSSCDFCYEFPFPTVATTPPPPRTKTRLVPEGRNVTFRCGQKILHKKGKVYWYKDQEPLEFSYPGYLALGEAHLSIIANAVNEGTYTCVVRRQQRVLTTYSWRVRVRG\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile that's valid for the drug is 'NC1=NC(=O)N(C[C@@H](CO)OCP(O)(O)=O)C=C1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NC(=O)N(C[C@@H](CO)OCP(O)(O)=O)C=C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGRGARVPSEAPGAGVERRWLGAALVALCLLPALVLLARLGAPAVPAWSAAQGDVAALGLSAVPPTRVPGPLAPRRRRYTLTPARLRWDHFNLTYRILSFPRNLLSPRETRRALAAAFRMWSDVSPFSFREVAPEQPSDLRIGFYPINHTDCLVSALHHCFDGPTGELAHAFFPPHGGIHFDDSEYWVLGPTRYSWKKGVWLTDLVHVAAHEIGHALGLMHSQHGRALMHLNATLRGWKALSQDELWGLHRLYGCLDRLFVCASWARRGFCDARRRLMKRLCPSSCDFCYEFPFPTVATTPPPPRTKTRLVPEGRNVTFRCGQKILHKKGKVYWYKDQEPLEFSYPGYLALGEAHLSIIANAVNEGTYTCVVRRQQRVLTTYSWRVRVRG\", \"result\": [{\"smile\": \"NC1=NC(=O)N(C[C@@H](CO)OCP(O)(O)=O)C=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=NC(=O)N(C[C@@H](CO)OCP(O)(O)=O)C=C1' can not interact with the protein 'MGRGARVPSEAPGAGVERRWLGAALVALCLLPALVLLARLGAPAVPAWSAAQGDVAALGLSAVPPTRVPGPLAPRRRRYTLTPARLRWDHFNLTYRILSFPRNLLSPRETRRALAAAFRMWSDVSPFSFREVAPEQPSDLRIGFYPINHTDCLVSALHHCFDGPTGELAHAFFPPHGGIHFDDSEYWVLGPTRYSWKKGVWLTDLVHVAAHEIGHALGLMHSQHGRALMHLNATLRGWKALSQDELWGLHRLYGCLDRLFVCASWARRGFCDARRRLMKRLCPSSCDFCYEFPFPTVATTPPPPRTKTRLVPEGRNVTFRCGQKILHKKGKVYWYKDQEPLEFSYPGYLALGEAHLSIIANAVNEGTYTCVVRRQQRVLTTYSWRVRVRG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'N(Cc(lC2()()O)lccC2c(=N)c)cOcc1)NNCcCCc)CccN(1c=)n((2Cc2C=NcCccA' will engage with the target protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N(Cc(lC2()()O)lccC2c(=N)c)cOcc1)NNCcCCc)CccN(1c=)n((2Cc2C=NcCccA\"], \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CN(CCN(C)C(=N)N)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(CCN(C)C(=N)N)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\", \"result\": [{\"smile\": \"CN(CCN(C)C(=N)N)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(CCN(C)C(=N)N)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1' can not interact with the protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess if the drug '(cccc)OOcC-=c1cc(CoC)(cc2Clccc)c(c1()33cON2)A' will engage with the target protein 'MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"(cccc)OOcC-=c1cc(CoC)(cc2Clccc)c(c1()33cON2)A\"], \"target_seq\": \"MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC\", \"result\": [{\"smile\": \"COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1cc(NC(=O)c2ccc(-c3ccc(Cl)cc3)o2)cc(OC)c1' can not interact with the protein 'MASLSQLSSHLNYTCGAENSTGASQARPHAYYALSYCALILAIVFGNGLVCMAVLKERALQTTTNYLVVSLAVADLLVATLVMPWVVYLEVTGGVWNFSRICCDVFVTLDVMMCTASILNLCAISIDRYTAVVMPVHYQHGTGQSSCRRVALMITAVWVLAFAVSCPLLFGFNTTGDPTVCSISNPDFVIYSSVVSFYLPFGVTVLVYARIYVVLKQRRRKRILTRQNSQCNSVRPGFPQQTLSPDPAHLELKRYYSICQDTALGGPGFQERGGELKREEKTRNSLSPTIAPKLSLEVRKLSNGRLSTSLKLGPLQPRGVPLREKKATQMVAIVLGAFIVCWLPFFLTHVLNTHCQTCHVSPELYSATTWLGYVNSALNPVIYTTFNIEFRKAFLKILSC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug '][OC(C(OCCO(c=1C)1=H)c)c=)O)(OcCC(C(COF=)@(C)cNCcNA' will interact with the target protein 'MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"][OC(C(OCCO(c=1C)1=H)c)c=)O)(OcCC(C(COF=)@(C)cNCcNA\"], \"target_seq\": \"MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The drug's correct smile is 'CC(C)[C@H](OC(=O)Nc1ccccc1)C(=O)NC(CC(=O)O)C(=O)CF'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)[C@H](OC(=O)Nc1ccccc1)C(=O)NC(CC(=O)O)C(=O)CF\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA\", \"result\": [{\"smile\": \"CC(C)[C@H](OC(=O)Nc1ccccc1)C(=O)NC(CC(=O)O)C(=O)CF\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)[C@H](OC(=O)Nc1ccccc1)C(=O)NC(CC(=O)O)C(=O)CF' can not interact with the protein 'MSEEIITPVYCTGVSAQVQKQRARELGLGRHENAIKYLGQDYEQLRVRCLQSGTLFRDEAFPPVPQSLGYKDLGPNSSKTYGIKWKRPTELLSNPQFIVDGATRTDICQGALGDCWLLAAIASLTLNDTLLHRVVPHGQSFQNGYAGIFHFQLWQFGEWVDVVVDDLLPIKDGKLVFVHSAEGNEFWSALLEKAYAKVNGSYEALSGGSTSEGFEDFTGGVTEWYELRKAPSDLYQIILKALERGSLLGCSIDISSVLDMEAITFKKLVKGHAYSVTGAKQVNYRGQVVSLIRMRNPWGEVEWTGAWSDSSSEWNNVDPYERDQLRVKMEDGEFWMSFRDFMREFTRLEICNLTPDALKSRTIRKWNTTLYEGTWRRGSTAGGCRNYPATFWVNPQFKIRLDETDDPDDYGDRESGCSFVLALMQKHRRRERRFGRDMETIGFAVYEVPPELVGQPAVHLKRDFFLANASRARSEQFINLREVSTRFRLPPGEYVVVPSTFEPNKEGDFVLRFFSEKSAGTVELDDQIQANLPDEQVLSEEEIDENFKALFRQLAGEDMEISVKELRTILNRIISKHKDLRTKGFSLESCRSMVNLMDRDGNGKLGLVEFNILWNRIRNYLSIFRKFDLDKSGSMSAYEMRMAIESAGFKLNKKLYELIITRYSEPDLAVDFDNFVCCLVRLETMFRFFKTLDTDLDGVVTFDLFKWLQLTMFA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the drug '=C[COCC2(=]C)CO[]N)[N=CNHC@)Oc])HCO)2cnC()C[NCNC]N@(c(O1()NOOO@CC[cnNccCN(OC@(c=HHC)c@C=COHc@c@1CO1=HcCNCc[cc)Cn)1()C(CC1=](C1C=c])cCCc((A' will behave towards the target protein 'MNSSCCLSSVSPMLPNLSEHPAAPPASNRSGSGFCEQVFIKPEVFLALGIVSLMENILVILAVVRNGNLHSPMYFFLCSLAAADMLVSLSNSLETIMIAVINSDSLTLEDQFIQHMDNIFDSMICISLVASICNLLAIAIDRYVTIFYALRYHSIMTVRKALTLIGVIWVCCGICGVMFIIYSESKMVIVCLITMFFAMVLLMGTLYIHMFLFARLHVQRIAVLPPAGVVAPQQHSCMKGAVTITILLGVFIFCWAPFFLHLVLIITCPTNPYCICYTAHFNTYLVLIMCNSVIDPLIYAFRSLELRNTFKEILCGCNSMNLG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"=C[COCC2(=]C)CO[]N)[N=CNHC@)Oc])HCO)2cnC()C[NCNC]N@(c(O1()NOOO@CC[cnNccCN(OC@(c=HHC)c@C=COHc@c@1CO1=HcCNCc[cc)Cn)1()C(CC1=](C1C=c])cCCc((A\"], \"target_seq\": \"MNSSCCLSSVSPMLPNLSEHPAAPPASNRSGSGFCEQVFIKPEVFLALGIVSLMENILVILAVVRNGNLHSPMYFFLCSLAAADMLVSLSNSLETIMIAVINSDSLTLEDQFIQHMDNIFDSMICISLVASICNLLAIAIDRYVTIFYALRYHSIMTVRKALTLIGVIWVCCGICGVMFIIYSESKMVIVCLITMFFAMVLLMGTLYIHMFLFARLHVQRIAVLPPAGVVAPQQHSCMKGAVTITILLGVFIFCWAPFFLHLVLIITCPTNPYCICYTAHFNTYLVLIMCNSVIDPLIYAFRSLELRNTFKEILCGCNSMNLG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNSSCCLSSVSPMLPNLSEHPAAPPASNRSGSGFCEQVFIKPEVFLALGIVSLMENILVILAVVRNGNLHSPMYFFLCSLAAADMLVSLSNSLETIMIAVINSDSLTLEDQFIQHMDNIFDSMICISLVASICNLLAIAIDRYVTIFYALRYHSIMTVRKALTLIGVIWVCCGICGVMFIIYSESKMVIVCLITMFFAMVLLMGTLYIHMFLFARLHVQRIAVLPPAGVVAPQQHSCMKGAVTITILLGVFIFCWAPFFLHLVLIITCPTNPYCICYTAHFNTYLVLIMCNSVIDPLIYAFRSLELRNTFKEILCGCNSMNLG\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC(=O)N[C@@H](Cc1cnc[nH]1)C(=O)N[C@H](Cc1ccccc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCCCOCCOCCOCCCNC(=O)COCC(N)=O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)N[C@@H](Cc1cnc[nH]1)C(=O)N[C@H](Cc1ccccc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCCCOCCOCCOCCCNC(=O)COCC(N)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNSSCCLSSVSPMLPNLSEHPAAPPASNRSGSGFCEQVFIKPEVFLALGIVSLMENILVILAVVRNGNLHSPMYFFLCSLAAADMLVSLSNSLETIMIAVINSDSLTLEDQFIQHMDNIFDSMICISLVASICNLLAIAIDRYVTIFYALRYHSIMTVRKALTLIGVIWVCCGICGVMFIIYSESKMVIVCLITMFFAMVLLMGTLYIHMFLFARLHVQRIAVLPPAGVVAPQQHSCMKGAVTITILLGVFIFCWAPFFLHLVLIITCPTNPYCICYTAHFNTYLVLIMCNSVIDPLIYAFRSLELRNTFKEILCGCNSMNLG\", \"result\": [{\"smile\": \"CC(=O)N[C@@H](Cc1cnc[nH]1)C(=O)N[C@H](Cc1ccccc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCCCOCCOCCOCCCNC(=O)COCC(N)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(=O)N[C@@H](Cc1cnc[nH]1)C(=O)N[C@H](Cc1ccccc1)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCCCOCCOCCOCCCNC(=O)COCC(N)=O' can not interact with the protein 'MNSSCCLSSVSPMLPNLSEHPAAPPASNRSGSGFCEQVFIKPEVFLALGIVSLMENILVILAVVRNGNLHSPMYFFLCSLAAADMLVSLSNSLETIMIAVINSDSLTLEDQFIQHMDNIFDSMICISLVASICNLLAIAIDRYVTIFYALRYHSIMTVRKALTLIGVIWVCCGICGVMFIIYSESKMVIVCLITMFFAMVLLMGTLYIHMFLFARLHVQRIAVLPPAGVVAPQQHSCMKGAVTITILLGVFIFCWAPFFLHLVLIITCPTNPYCICYTAHFNTYLVLIMCNSVIDPLIYAFRSLELRNTFKEILCGCNSMNLG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug '(CC1=CCCOOCN==1C(C)C=CO)=A' will interact with the target protein 'MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"(CC1=CCCOOCN==1C(C)C=CO)=A\"], \"target_seq\": \"MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'NC1=CC=CC=C1C(=O)CCC(O)=O' works as the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=CC=CC=C1C(=O)CCC(O)=O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK\", \"result\": [{\"smile\": \"NC1=CC=CC=C1C(=O)CCC(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=CC=CC=C1C(=O)CCC(O)=O' can not interact with the protein 'MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'NBCC1C2)C(CHO+N=[1N]COOA' interact with the target protein 'MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA' as expected? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NBCC1C2)C(CHO+N=[1N]COOA\"], \"target_seq\": \"MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'NC(=[NH2+])NCCCOB1OCCO1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=[NH2+])NCCCOB1OCCO1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA\", \"result\": [{\"smile\": \"NC(=[NH2+])NCCCOB1OCCO1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=[NH2+])NCCCOB1OCCO1' can not interact with the protein 'MGTALVYHEDMTATRLLWDDPECEIERPERLTAALDRLRQRGLEQRCLRLSAREASEEELGLVHSPEYVSLVRETQVLGKEELQALSGQFDAIYFHPSTFHCARLAAGAGLQLVDAVLTGAVQNGLALVRPPGHHGQRAAANGFCVFNNVAIAAAHAKQKHGLHRILVVDWDVHHGQGIQYLFEDDPSVLYFSWHRYEHGRFWPFLRESDADAVGRGQGLGFTVNLPWNQVGMGNADYVAAFLHLLLPLAFEFDPELVLVSAGFDSAIGDPEGQMQATPECFAHLTQLLQVLAGGRVCAVLEGGYHLESLAESVCMTVQTLLGDPAPPLSGPMAPCQSALESIQSARAAQAPHWKSLQQQDVTAVPMSPSSHSPEGRPPPLLPGGPVCKAAASAPSSLLDQPCLCPAPSVRTAVALTTPDITLVLPPDVIQQEASALREETEAWARPHESLAREEALTALGKLLYLLDGMLDGQVNSGIAATPASAAAATLDVAVRRGLSHGAQRLLCVALGQLDRPPDLAHDGRSLWLNIRGKEAAALSMFHVSTPLPVMTGGFLSCILGLVLPLAYGFQPDLVLVALGPGHGLQGPHAALLAAMLRGLAGGRVLALLEENSTPQLAGILARVLNGEAPPSLGPSSVASPEDVQALMYLRGQLEPQWKMLQCHPHLVA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that the drug 'cNCcCcN(C1]C3C=C)c())c=F3[H2C(\\=C)Ccc1/C)CcOOc2n((cC)N(A' will interact with the target protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"cNCcCcN(C1]C3C=C)c())c=F3[H2C(\\=C)Ccc1/C)CcOOc2n((cC)N(A\"], \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "The smile that corresponds to the drug is 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' can not interact with the protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug '13OHC[[CC()CCC@C4C]C2CC[C)4C=C@CC(C2COC@@C@((]#)]1@@C])CC3H()][@@C[A' and the target protein 'MNYSLHLAFVCLSLFTERMCIQGSQFNVEVGRSDKLSLPGFENLTAGYNKFLRPNFGGEPVQIALTLDIASISSISESNMDYTATIYLRQRWMDQRLVFEGNKSFTLDARLVEFLWVPDTYIVESKKSFLHEVTVGNRLIRLFSNGTVLYALRITTTVACNMDLSKYPMDTQTCKLQLESWGYDGNDVEFTWLRGNDSVRGLEHLRLAQYTIERYFTLVTRSQQETGNYTRLVLQFELRRNVLYFILETYVPSTFLVVLSWVSFWISLDSVPARTCIGVTTVLSMTTLMIGSRTSLPNTNCFIKAIDVYLGICFSFVFGALLEYAVAHYSSLQQMAAKDRGTTKEVEEVSITNIINSSISSFKRKISFASIEISSDNVDYSDLTMKTSDKFKFVFREKMGRIVDYFTIQNPSNVDHYSKLLFPLIFMLANVFYWAYYMYF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"13OHC[[CC()CCC@C4C]C2CC[C)4C=C@CC(C2COC@@C@((]#)]1@@C])CC3H()][@@C[A\"], \"target_seq\": \"MNYSLHLAFVCLSLFTERMCIQGSQFNVEVGRSDKLSLPGFENLTAGYNKFLRPNFGGEPVQIALTLDIASISSISESNMDYTATIYLRQRWMDQRLVFEGNKSFTLDARLVEFLWVPDTYIVESKKSFLHEVTVGNRLIRLFSNGTVLYALRITTTVACNMDLSKYPMDTQTCKLQLESWGYDGNDVEFTWLRGNDSVRGLEHLRLAQYTIERYFTLVTRSQQETGNYTRLVLQFELRRNVLYFILETYVPSTFLVVLSWVSFWISLDSVPARTCIGVTTVLSMTTLMIGSRTSLPNTNCFIKAIDVYLGICFSFVFGALLEYAVAHYSSLQQMAAKDRGTTKEVEEVSITNIINSSISSFKRKISFASIEISSDNVDYSDLTMKTSDKFKFVFREKMGRIVDYFTIQNPSNVDHYSKLLFPLIFMLANVFYWAYYMYF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNYSLHLAFVCLSLFTERMCIQGSQFNVEVGRSDKLSLPGFENLTAGYNKFLRPNFGGEPVQIALTLDIASISSISESNMDYTATIYLRQRWMDQRLVFEGNKSFTLDARLVEFLWVPDTYIVESKKSFLHEVTVGNRLIRLFSNGTVLYALRITTTVACNMDLSKYPMDTQTCKLQLESWGYDGNDVEFTWLRGNDSVRGLEHLRLAQYTIERYFTLVTRSQQETGNYTRLVLQFELRRNVLYFILETYVPSTFLVVLSWVSFWISLDSVPARTCIGVTTVLSMTTLMIGSRTSLPNTNCFIKAIDVYLGICFSFVFGALLEYAVAHYSSLQQMAAKDRGTTKEVEEVSITNIINSSISSFKRKISFASIEISSDNVDYSDLTMKTSDKFKFVFREKMGRIVDYFTIQNPSNVDHYSKLLFPLIFMLANVFYWAYYMYF\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC(=O)[C@H]1CCC2[C@@]1(CCC3C2CC[C@@H]4[C@@]3(CC[C@@](C4)(CC#C)O)C)C'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)[C@H]1CCC2[C@@]1(CCC3C2CC[C@@H]4[C@@]3(CC[C@@](C4)(CC#C)O)C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNYSLHLAFVCLSLFTERMCIQGSQFNVEVGRSDKLSLPGFENLTAGYNKFLRPNFGGEPVQIALTLDIASISSISESNMDYTATIYLRQRWMDQRLVFEGNKSFTLDARLVEFLWVPDTYIVESKKSFLHEVTVGNRLIRLFSNGTVLYALRITTTVACNMDLSKYPMDTQTCKLQLESWGYDGNDVEFTWLRGNDSVRGLEHLRLAQYTIERYFTLVTRSQQETGNYTRLVLQFELRRNVLYFILETYVPSTFLVVLSWVSFWISLDSVPARTCIGVTTVLSMTTLMIGSRTSLPNTNCFIKAIDVYLGICFSFVFGALLEYAVAHYSSLQQMAAKDRGTTKEVEEVSITNIINSSISSFKRKISFASIEISSDNVDYSDLTMKTSDKFKFVFREKMGRIVDYFTIQNPSNVDHYSKLLFPLIFMLANVFYWAYYMYF\", \"result\": [{\"smile\": \"CC(=O)[C@H]1CCC2[C@@]1(CCC3C2CC[C@@H]4[C@@]3(CC[C@@](C4)(CC#C)O)C)C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(=O)[C@H]1CCC2[C@@]1(CCC3C2CC[C@@H]4[C@@]3(CC[C@@](C4)(CC#C)O)C)C' can not interact with the protein 'MNYSLHLAFVCLSLFTERMCIQGSQFNVEVGRSDKLSLPGFENLTAGYNKFLRPNFGGEPVQIALTLDIASISSISESNMDYTATIYLRQRWMDQRLVFEGNKSFTLDARLVEFLWVPDTYIVESKKSFLHEVTVGNRLIRLFSNGTVLYALRITTTVACNMDLSKYPMDTQTCKLQLESWGYDGNDVEFTWLRGNDSVRGLEHLRLAQYTIERYFTLVTRSQQETGNYTRLVLQFELRRNVLYFILETYVPSTFLVVLSWVSFWISLDSVPARTCIGVTTVLSMTTLMIGSRTSLPNTNCFIKAIDVYLGICFSFVFGALLEYAVAHYSSLQQMAAKDRGTTKEVEEVSITNIINSSISSFKRKISFASIEISSDNVDYSDLTMKTSDKFKFVFREKMGRIVDYFTIQNPSNVDHYSKLLFPLIFMLANVFYWAYYMYF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug '])N(CO(C#1)CH[(3C@=C1)C1CHC2C32#@[CC]N@CCNCCNCC1A' and the target protein 'MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"])N(CO(C#1)CH[(3C@=C1)C1CHC2C32#@[CC]N@CCNCCNCC1A\"], \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'N#C[C@@H]1CC[C@H](C#N)N1C(=O)CNC12CC3CC(CC1C3)C2'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N#C[C@@H]1CC[C@H](C#N)N1C(=O)CNC12CC3CC(CC1C3)C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL\", \"result\": [{\"smile\": \"N#C[C@@H]1CC[C@H](C#N)N1C(=O)CNC12CC3CC(CC1C3)C2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#C[C@@H]1CC[C@H](C#N)N1C(=O)CNC12CC3CC(CC1C3)C2' can not interact with the protein 'MATTGTPTADRGDAAATDDPAARFQVQKHSWDGLRSIIHGSRKYSGLIVNKAPHDFQFVQKTDESGPHSHRLYYLGMPYGSRENSLLYSEIPKKVRKEALLLLSWKQMLDHFQATPHHGVYSREEELLRERKRLGVFGITSYDFHSESGLFLFQASNSLFHCRDGGKNGFMVSPMKPLEIKTQCSGPRMDPKICPADPAFFSFINNSDLWVANIETGEERRLTFCHQGLSNVLDDPKSAGVATFVIQEEFDRFTGYWWCPTASWEGSEGLKTLRILYEEVDESEVEVIHVPSPALEERKTDSYRYPRTGSKNPKIALKLAEFQTDSQGKIVSTQEKELVQPFSSLFPKVEYIARAGWTRDGKYAWAMFLDRPQQWLQLVLLPPALFIPSTENEEQRLASARAVPRNVQPYVVYEEVTNVWINVHDIFYPFPQSEGEDELCFLRANECKTGFCHLYKVTAVLKSQGYDWSEPFSPGEDEFKCPIKEEIALTSGEWEVLARHGSKIWVNEETKLVYFQGTKDTPLEHHLYVVSYEAAGEIVRLTTPGFSHSCSMSQNFDMFVSHYSSVSTPPCVHVYKLSGPDDDPLHKQPRFWASMMEAASCPPDYVPPEIFHFHTRSDVRLYGMIYKPHALQPGKKHPTVLFVYGGPQVQLVNNSFKGIKYLRLNTLASLGYAVVVIDGRGSCQRGLRFEGALKNQMGQVEIEDQVEGLQFVAEKYGFIDLSRVAIHGWSYGGFLSLMGLIHKPQVFKVAIAGAPVTVWMAYDTGYTERYMDVPENNQHGYEAGSVALHVEKLPNEPNRLLILHGFLDENVHFFHTNFLVSQLIRAGKPYQLQIYPNERHSIRCPESGEHYEVTLLHFLQEYL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated likelihood of interaction between the drug '@N)cC1=HcCCcNC(@cOcCc@(c=]Cc]C)cC(2c)OCCc=2[(c(cNc1HccN2)c)C2c(c)c[(Oc2c2cOc@)A' and the target protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"@N)cC1=HcCCcNC(@cOcCc@(c=]Cc]C)cC(2c)OCCc=2[(c(cNc1HccN2)c)C2c(c)c[(Oc2c2cOc@)A\"], \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CCOc1ccc(C[C@@H](NC(=O)c2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)NCc2ccc(CN)cc2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1ccc(C[C@@H](NC(=O)c2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)NCc2ccc(CN)cc2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\", \"result\": [{\"smile\": \"CCOc1ccc(C[C@@H](NC(=O)c2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)NCc2ccc(CN)cc2)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCOc1ccc(C[C@@H](NC(=O)c2ccccc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)NCc2ccc(CN)cc2)cc1' can not interact with the protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the chances of the drug ')(ccc)1cc=cNN)ccCcSc)C=(c(C3(2(12OO3OCCc=)OOA' interacting with the target protein 'MILLTFSTGRRLDFVHHSGVFFLQTLLWILCATVCGTEQYFNVEVWLQKYGYLPPTDPRMSVLRSAETMQSALAAMQQFYGINMTGKVDRNTIDWMKKPRCGVPDQTRGSSKFHIRRKRYALTGQKWQHKHITYSIKNVTPKVGDPETRKAIRRAFDVWQNVTPLTFEEVPYSELENGKRDVDITIIFASGFHGDSSPFDGEGGFLAHAYFPGPGIGGDTHFDSDEPWTLGNPNHDGNDLFLVAVHELGHALGLEHSNDPTAIMAPFYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSIPPADPRKNDRPKPPRPPTGRPSYPGAKPNICDGNFNTLAILRREMFVFKDQWFWRVRNNRVMDGYPMQITYFWRGLPPSIDAVYENSDGNFVFFKGNKYWVFKDTTLQPGYPHDLITLGSGIPPHGIDSAIWWEDVGKTYFFKGDRYWRYSEEMKTMDPGYPKPITVWKGIPESPQGAFVHKENGFTYFYKGKEYWKFNNQILKVEPGYPRSILKDFMGCDGPTDRVKEGHSPPDDVDIVIKLDNTASTVKAIAIVIPCILALCLLVLVYTVFQFKRKGTPRHILYCKRSMQEWV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\")(ccc)1cc=cNN)ccCcSc)C=(c(C3(2(12OO3OCCc=)OOA\"], \"target_seq\": \"MILLTFSTGRRLDFVHHSGVFFLQTLLWILCATVCGTEQYFNVEVWLQKYGYLPPTDPRMSVLRSAETMQSALAAMQQFYGINMTGKVDRNTIDWMKKPRCGVPDQTRGSSKFHIRRKRYALTGQKWQHKHITYSIKNVTPKVGDPETRKAIRRAFDVWQNVTPLTFEEVPYSELENGKRDVDITIIFASGFHGDSSPFDGEGGFLAHAYFPGPGIGGDTHFDSDEPWTLGNPNHDGNDLFLVAVHELGHALGLEHSNDPTAIMAPFYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSIPPADPRKNDRPKPPRPPTGRPSYPGAKPNICDGNFNTLAILRREMFVFKDQWFWRVRNNRVMDGYPMQITYFWRGLPPSIDAVYENSDGNFVFFKGNKYWVFKDTTLQPGYPHDLITLGSGIPPHGIDSAIWWEDVGKTYFFKGDRYWRYSEEMKTMDPGYPKPITVWKGIPESPQGAFVHKENGFTYFYKGKEYWKFNNQILKVEPGYPRSILKDFMGCDGPTDRVKEGHSPPDDVDIVIKLDNTASTVKAIAIVIPCILALCLLVLVYTVFQFKRKGTPRHILYCKRSMQEWV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MILLTFSTGRRLDFVHHSGVFFLQTLLWILCATVCGTEQYFNVEVWLQKYGYLPPTDPRMSVLRSAETMQSALAAMQQFYGINMTGKVDRNTIDWMKKPRCGVPDQTRGSSKFHIRRKRYALTGQKWQHKHITYSIKNVTPKVGDPETRKAIRRAFDVWQNVTPLTFEEVPYSELENGKRDVDITIIFASGFHGDSSPFDGEGGFLAHAYFPGPGIGGDTHFDSDEPWTLGNPNHDGNDLFLVAVHELGHALGLEHSNDPTAIMAPFYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSIPPADPRKNDRPKPPRPPTGRPSYPGAKPNICDGNFNTLAILRREMFVFKDQWFWRVRNNRVMDGYPMQITYFWRGLPPSIDAVYENSDGNFVFFKGNKYWVFKDTTLQPGYPHDLITLGSGIPPHGIDSAIWWEDVGKTYFFKGDRYWRYSEEMKTMDPGYPKPITVWKGIPESPQGAFVHKENGFTYFYKGKEYWKFNNQILKVEPGYPRSILKDFMGCDGPTDRVKEGHSPPDDVDIVIKLDNTASTVKAIAIVIPCILALCLLVLVYTVFQFKRKGTPRHILYCKRSMQEWV\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'Cc1ccc(S(=O)(=O)N2CCc3cc(O)ccc3C2C(=O)NO)cc1' matches the required smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(S(=O)(=O)N2CCc3cc(O)ccc3C2C(=O)NO)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MILLTFSTGRRLDFVHHSGVFFLQTLLWILCATVCGTEQYFNVEVWLQKYGYLPPTDPRMSVLRSAETMQSALAAMQQFYGINMTGKVDRNTIDWMKKPRCGVPDQTRGSSKFHIRRKRYALTGQKWQHKHITYSIKNVTPKVGDPETRKAIRRAFDVWQNVTPLTFEEVPYSELENGKRDVDITIIFASGFHGDSSPFDGEGGFLAHAYFPGPGIGGDTHFDSDEPWTLGNPNHDGNDLFLVAVHELGHALGLEHSNDPTAIMAPFYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSIPPADPRKNDRPKPPRPPTGRPSYPGAKPNICDGNFNTLAILRREMFVFKDQWFWRVRNNRVMDGYPMQITYFWRGLPPSIDAVYENSDGNFVFFKGNKYWVFKDTTLQPGYPHDLITLGSGIPPHGIDSAIWWEDVGKTYFFKGDRYWRYSEEMKTMDPGYPKPITVWKGIPESPQGAFVHKENGFTYFYKGKEYWKFNNQILKVEPGYPRSILKDFMGCDGPTDRVKEGHSPPDDVDIVIKLDNTASTVKAIAIVIPCILALCLLVLVYTVFQFKRKGTPRHILYCKRSMQEWV\", \"result\": [{\"smile\": \"Cc1ccc(S(=O)(=O)N2CCc3cc(O)ccc3C2C(=O)NO)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1ccc(S(=O)(=O)N2CCc3cc(O)ccc3C2C(=O)NO)cc1' can interact with the protein 'MILLTFSTGRRLDFVHHSGVFFLQTLLWILCATVCGTEQYFNVEVWLQKYGYLPPTDPRMSVLRSAETMQSALAAMQQFYGINMTGKVDRNTIDWMKKPRCGVPDQTRGSSKFHIRRKRYALTGQKWQHKHITYSIKNVTPKVGDPETRKAIRRAFDVWQNVTPLTFEEVPYSELENGKRDVDITIIFASGFHGDSSPFDGEGGFLAHAYFPGPGIGGDTHFDSDEPWTLGNPNHDGNDLFLVAVHELGHALGLEHSNDPTAIMAPFYQYMETDNFKLPNDDLQGIQKIYGPPDKIPPPTRPLPTVPPHRSIPPADPRKNDRPKPPRPPTGRPSYPGAKPNICDGNFNTLAILRREMFVFKDQWFWRVRNNRVMDGYPMQITYFWRGLPPSIDAVYENSDGNFVFFKGNKYWVFKDTTLQPGYPHDLITLGSGIPPHGIDSAIWWEDVGKTYFFKGDRYWRYSEEMKTMDPGYPKPITVWKGIPESPQGAFVHKENGFTYFYKGKEYWKFNNQILKVEPGYPRSILKDFMGCDGPTDRVKEGHSPPDDVDIVIKLDNTASTVKAIAIVIPCILALCLLVLVYTVFQFKRKGTPRHILYCKRSMQEWV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the drug '2C=C5=CC42CSC3N==3CCC5C)C(C((NCCC=(=(NC(CC4=CN=)CCCCO1C)N)N=C1)==CCC)=A' with the target protein 'MSVLGEYERHCDSINSDFGSESGGCGDSSPGPSASQGPRAGGGAAEQEELHYIPIRVLGRGAFGEATLYRRTEDDSLVVWKEVDLTRLSEKERRDALNEIVILALLQHDNIIAYYNHFMDNTTLLIELEYCNGGNLYDKILRQKDKLFEEEMVVWYLFQIVSAVSCIHKAGILHRDIKTLNIFLTKANLIKLGDYGLAKKLNSEYSMAETLVGTPYYMSPELCQGVKYNFKSDIWAVGCVIFELLTLKRTFDATNPLNLCVKIVQGIRAMEVDSSQYSLELIQMVHSCLDQDPEQRPTADELLDRPLLRKRRREMEEKVTLLNAPTKRPRSSTVTEAPIAVVTSRTSEVYVWGGGKSTPQKLDVIKSGCSARQVCAGNTHFAVVTVEKELYTWVNMQGGTKLHGQLGHGDKASYRQPKHVEKLQGKAIRQVSCGDDFTVCVTDEGQLYAFGSDYYGCMGVDKVAGPEVLEPMQLNFFLSNPVEQVSCGDNHVVVLTRNKEVYSWGCGEYGRLGLDSEEDYYTPQKVDVPKALIIVAVQCGCDGTFLLTQSGKVLACGLNEFNKLGLNQCMSGIINHEAYHEVPYTTSFTLAKQLSFYKIRTIAPGKTHTAAIDERGRLLTFGCNKCGQLGVGNYKKRLGINLLGGPLGGKQVIRVSCGDEFTIAATDDNHIFAWGNGGNGRLAMTPTERPHGSDICTSWPRPIFGSLHHVPDLSCRGWHTILIVEKVLNSKTIRSNSSGLSIGTVFQSSSPGGGGGGGGGEEEDSQQESETPDPSGGFRGTMEADRGMEGLISPTEAMGNSNGASSSCPGWLRKELENAEFIPMPDSPSPLSAAFSESEKDTLPYEELQGLKVASEAPLEHKPQVEASSPRLNPAVTCAGKGTPLTPPACACSSLQVEVERLQGLVLKCLAEQQKLQQENLQIFTQLQKLNKKLEGGQQVGMHSKGTQTAKEEMEMDPKPDLDSDSWCLLGTDSCRPSL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"2C=C5=CC42CSC3N==3CCC5C)C(C((NCCC=(=(NC(CC4=CN=)CCCCO1C)N)N=C1)==CCC)=A\"], \"target_seq\": \"MSVLGEYERHCDSINSDFGSESGGCGDSSPGPSASQGPRAGGGAAEQEELHYIPIRVLGRGAFGEATLYRRTEDDSLVVWKEVDLTRLSEKERRDALNEIVILALLQHDNIIAYYNHFMDNTTLLIELEYCNGGNLYDKILRQKDKLFEEEMVVWYLFQIVSAVSCIHKAGILHRDIKTLNIFLTKANLIKLGDYGLAKKLNSEYSMAETLVGTPYYMSPELCQGVKYNFKSDIWAVGCVIFELLTLKRTFDATNPLNLCVKIVQGIRAMEVDSSQYSLELIQMVHSCLDQDPEQRPTADELLDRPLLRKRRREMEEKVTLLNAPTKRPRSSTVTEAPIAVVTSRTSEVYVWGGGKSTPQKLDVIKSGCSARQVCAGNTHFAVVTVEKELYTWVNMQGGTKLHGQLGHGDKASYRQPKHVEKLQGKAIRQVSCGDDFTVCVTDEGQLYAFGSDYYGCMGVDKVAGPEVLEPMQLNFFLSNPVEQVSCGDNHVVVLTRNKEVYSWGCGEYGRLGLDSEEDYYTPQKVDVPKALIIVAVQCGCDGTFLLTQSGKVLACGLNEFNKLGLNQCMSGIINHEAYHEVPYTTSFTLAKQLSFYKIRTIAPGKTHTAAIDERGRLLTFGCNKCGQLGVGNYKKRLGINLLGGPLGGKQVIRVSCGDEFTIAATDDNHIFAWGNGGNGRLAMTPTERPHGSDICTSWPRPIFGSLHHVPDLSCRGWHTILIVEKVLNSKTIRSNSSGLSIGTVFQSSSPGGGGGGGGGEEEDSQQESETPDPSGGFRGTMEADRGMEGLISPTEAMGNSNGASSSCPGWLRKELENAEFIPMPDSPSPLSAAFSESEKDTLPYEELQGLKVASEAPLEHKPQVEASSPRLNPAVTCAGKGTPLTPPACACSSLQVEVERLQGLVLKCLAEQQKLQQENLQIFTQLQKLNKKLEGGQQVGMHSKGTQTAKEEMEMDPKPDLDSDSWCLLGTDSCRPSL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSVLGEYERHCDSINSDFGSESGGCGDSSPGPSASQGPRAGGGAAEQEELHYIPIRVLGRGAFGEATLYRRTEDDSLVVWKEVDLTRLSEKERRDALNEIVILALLQHDNIIAYYNHFMDNTTLLIELEYCNGGNLYDKILRQKDKLFEEEMVVWYLFQIVSAVSCIHKAGILHRDIKTLNIFLTKANLIKLGDYGLAKKLNSEYSMAETLVGTPYYMSPELCQGVKYNFKSDIWAVGCVIFELLTLKRTFDATNPLNLCVKIVQGIRAMEVDSSQYSLELIQMVHSCLDQDPEQRPTADELLDRPLLRKRRREMEEKVTLLNAPTKRPRSSTVTEAPIAVVTSRTSEVYVWGGGKSTPQKLDVIKSGCSARQVCAGNTHFAVVTVEKELYTWVNMQGGTKLHGQLGHGDKASYRQPKHVEKLQGKAIRQVSCGDDFTVCVTDEGQLYAFGSDYYGCMGVDKVAGPEVLEPMQLNFFLSNPVEQVSCGDNHVVVLTRNKEVYSWGCGEYGRLGLDSEEDYYTPQKVDVPKALIIVAVQCGCDGTFLLTQSGKVLACGLNEFNKLGLNQCMSGIINHEAYHEVPYTTSFTLAKQLSFYKIRTIAPGKTHTAAIDERGRLLTFGCNKCGQLGVGNYKKRLGINLLGGPLGGKQVIRVSCGDEFTIAATDDNHIFAWGNGGNGRLAMTPTERPHGSDICTSWPRPIFGSLHHVPDLSCRGWHTILIVEKVLNSKTIRSNSSGLSIGTVFQSSSPGGGGGGGGGEEEDSQQESETPDPSGGFRGTMEADRGMEGLISPTEAMGNSNGASSSCPGWLRKELENAEFIPMPDSPSPLSAAFSESEKDTLPYEELQGLKVASEAPLEHKPQVEASSPRLNPAVTCAGKGTPLTPPACACSSLQVEVERLQGLVLKCLAEQQKLQQENLQIFTQLQKLNKKLEGGQQVGMHSKGTQTAKEEMEMDPKPDLDSDSWCLLGTDSCRPSL\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSVLGEYERHCDSINSDFGSESGGCGDSSPGPSASQGPRAGGGAAEQEELHYIPIRVLGRGAFGEATLYRRTEDDSLVVWKEVDLTRLSEKERRDALNEIVILALLQHDNIIAYYNHFMDNTTLLIELEYCNGGNLYDKILRQKDKLFEEEMVVWYLFQIVSAVSCIHKAGILHRDIKTLNIFLTKANLIKLGDYGLAKKLNSEYSMAETLVGTPYYMSPELCQGVKYNFKSDIWAVGCVIFELLTLKRTFDATNPLNLCVKIVQGIRAMEVDSSQYSLELIQMVHSCLDQDPEQRPTADELLDRPLLRKRRREMEEKVTLLNAPTKRPRSSTVTEAPIAVVTSRTSEVYVWGGGKSTPQKLDVIKSGCSARQVCAGNTHFAVVTVEKELYTWVNMQGGTKLHGQLGHGDKASYRQPKHVEKLQGKAIRQVSCGDDFTVCVTDEGQLYAFGSDYYGCMGVDKVAGPEVLEPMQLNFFLSNPVEQVSCGDNHVVVLTRNKEVYSWGCGEYGRLGLDSEEDYYTPQKVDVPKALIIVAVQCGCDGTFLLTQSGKVLACGLNEFNKLGLNQCMSGIINHEAYHEVPYTTSFTLAKQLSFYKIRTIAPGKTHTAAIDERGRLLTFGCNKCGQLGVGNYKKRLGINLLGGPLGGKQVIRVSCGDEFTIAATDDNHIFAWGNGGNGRLAMTPTERPHGSDICTSWPRPIFGSLHHVPDLSCRGWHTILIVEKVLNSKTIRSNSSGLSIGTVFQSSSPGGGGGGGGGEEEDSQQESETPDPSGGFRGTMEADRGMEGLISPTEAMGNSNGASSSCPGWLRKELENAEFIPMPDSPSPLSAAFSESEKDTLPYEELQGLKVASEAPLEHKPQVEASSPRLNPAVTCAGKGTPLTPPACACSSLQVEVERLQGLVLKCLAEQQKLQQENLQIFTQLQKLNKKLEGGQQVGMHSKGTQTAKEEMEMDPKPDLDSDSWCLLGTDSCRPSL\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC(=CS4)C5=CN=CC=C5' can not interact with the protein 'MSVLGEYERHCDSINSDFGSESGGCGDSSPGPSASQGPRAGGGAAEQEELHYIPIRVLGRGAFGEATLYRRTEDDSLVVWKEVDLTRLSEKERRDALNEIVILALLQHDNIIAYYNHFMDNTTLLIELEYCNGGNLYDKILRQKDKLFEEEMVVWYLFQIVSAVSCIHKAGILHRDIKTLNIFLTKANLIKLGDYGLAKKLNSEYSMAETLVGTPYYMSPELCQGVKYNFKSDIWAVGCVIFELLTLKRTFDATNPLNLCVKIVQGIRAMEVDSSQYSLELIQMVHSCLDQDPEQRPTADELLDRPLLRKRRREMEEKVTLLNAPTKRPRSSTVTEAPIAVVTSRTSEVYVWGGGKSTPQKLDVIKSGCSARQVCAGNTHFAVVTVEKELYTWVNMQGGTKLHGQLGHGDKASYRQPKHVEKLQGKAIRQVSCGDDFTVCVTDEGQLYAFGSDYYGCMGVDKVAGPEVLEPMQLNFFLSNPVEQVSCGDNHVVVLTRNKEVYSWGCGEYGRLGLDSEEDYYTPQKVDVPKALIIVAVQCGCDGTFLLTQSGKVLACGLNEFNKLGLNQCMSGIINHEAYHEVPYTTSFTLAKQLSFYKIRTIAPGKTHTAAIDERGRLLTFGCNKCGQLGVGNYKKRLGINLLGGPLGGKQVIRVSCGDEFTIAATDDNHIFAWGNGGNGRLAMTPTERPHGSDICTSWPRPIFGSLHHVPDLSCRGWHTILIVEKVLNSKTIRSNSSGLSIGTVFQSSSPGGGGGGGGGEEEDSQQESETPDPSGGFRGTMEADRGMEGLISPTEAMGNSNGASSSCPGWLRKELENAEFIPMPDSPSPLSAAFSESEKDTLPYEELQGLKVASEAPLEHKPQVEASSPRLNPAVTCAGKGTPLTPPACACSSLQVEVERLQGLVLKCLAEQQKLQQENLQIFTQLQKLNKKLEGGQQVGMHSKGTQTAKEEMEMDPKPDLDSDSWCLLGTDSCRPSL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug ']C]=)@C=]2P@N1N]OC)3)@)P()[O])=1C(H)HOC(OOOOO[O[)[]N[(=)](()(=C]P@O@=2)C)CN[O(C([HO)(=[3CHON(((A' is expected to interact with the target protein 'MQSWSRVYCSLAKRGHFNRISHGLQGLSAVPLRTYADQPIDADVTVIGSGPGGYVAAIKAAQLGFKTVCIEKNETLGGTCLNVGCIPSKALLNNSHYYHMAHGKDFASRGIEMSEVRLNLDKMMEQKSTAVKALTGGIAHLFKQNKVVHVNGYGKITGKNQVTATKADGGTQVIDTKNILIATGSEVTPFPGITIDEDTIVSSTGALSLKKVPEKMVVIGAGVIGVELGSVWQRLGADVTAVEFLGHVGGVGIDMEISKNFQRILQKQGFKFKLNTKVTGATKKSDGKIDVSIEAASGGKAEVITCDVLLVCIGRRPFTKNLGLEELGIELDPRGRIPVNTRFQTKIPNIYAIGDVVAGPMLAHKAEDEGIICVEGMAGGAVHIDYNCVPSVIYTHPEVAWVGKSEEQLKEEGIEYKVGKFPFAANSRAKTNADTDGMVKILGQKSTDRVLGAHILGPGAGEMVNEAALALEYGASCEDIARVCHAHPTLSEAFREANLAASFGKSINF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"]C]=)@C=]2P@N1N]OC)3)@)P()[O])=1C(H)HOC(OOOOO[O[)[]N[(=)](()(=C]P@O@=2)C)CN[O(C([HO)(=[3CHON(((A\"], \"target_seq\": \"MQSWSRVYCSLAKRGHFNRISHGLQGLSAVPLRTYADQPIDADVTVIGSGPGGYVAAIKAAQLGFKTVCIEKNETLGGTCLNVGCIPSKALLNNSHYYHMAHGKDFASRGIEMSEVRLNLDKMMEQKSTAVKALTGGIAHLFKQNKVVHVNGYGKITGKNQVTATKADGGTQVIDTKNILIATGSEVTPFPGITIDEDTIVSSTGALSLKKVPEKMVVIGAGVIGVELGSVWQRLGADVTAVEFLGHVGGVGIDMEISKNFQRILQKQGFKFKLNTKVTGATKKSDGKIDVSIEAASGGKAEVITCDVLLVCIGRRPFTKNLGLEELGIELDPRGRIPVNTRFQTKIPNIYAIGDVVAGPMLAHKAEDEGIICVEGMAGGAVHIDYNCVPSVIYTHPEVAWVGKSEEQLKEEGIEYKVGKFPFAANSRAKTNADTDGMVKILGQKSTDRVLGAHILGPGAGEMVNEAALALEYGASCEDIARVCHAHPTLSEAFREANLAASFGKSINF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSWSRVYCSLAKRGHFNRISHGLQGLSAVPLRTYADQPIDADVTVIGSGPGGYVAAIKAAQLGFKTVCIEKNETLGGTCLNVGCIPSKALLNNSHYYHMAHGKDFASRGIEMSEVRLNLDKMMEQKSTAVKALTGGIAHLFKQNKVVHVNGYGKITGKNQVTATKADGGTQVIDTKNILIATGSEVTPFPGITIDEDTIVSSTGALSLKKVPEKMVVIGAGVIGVELGSVWQRLGADVTAVEFLGHVGGVGIDMEISKNFQRILQKQGFKFKLNTKVTGATKKSDGKIDVSIEAASGGKAEVITCDVLLVCIGRRPFTKNLGLEELGIELDPRGRIPVNTRFQTKIPNIYAIGDVVAGPMLAHKAEDEGIICVEGMAGGAVHIDYNCVPSVIYTHPEVAWVGKSEEQLKEEGIEYKVGKFPFAANSRAKTNADTDGMVKILGQKSTDRVLGAHILGPGAGEMVNEAALALEYGASCEDIARVCHAHPTLSEAFREANLAASFGKSINF\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(OP(O)(O)=O)[C@]1([H])O'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(OP(O)(O)=O)[C@]1([H])O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSWSRVYCSLAKRGHFNRISHGLQGLSAVPLRTYADQPIDADVTVIGSGPGGYVAAIKAAQLGFKTVCIEKNETLGGTCLNVGCIPSKALLNNSHYYHMAHGKDFASRGIEMSEVRLNLDKMMEQKSTAVKALTGGIAHLFKQNKVVHVNGYGKITGKNQVTATKADGGTQVIDTKNILIATGSEVTPFPGITIDEDTIVSSTGALSLKKVPEKMVVIGAGVIGVELGSVWQRLGADVTAVEFLGHVGGVGIDMEISKNFQRILQKQGFKFKLNTKVTGATKKSDGKIDVSIEAASGGKAEVITCDVLLVCIGRRPFTKNLGLEELGIELDPRGRIPVNTRFQTKIPNIYAIGDVVAGPMLAHKAEDEGIICVEGMAGGAVHIDYNCVPSVIYTHPEVAWVGKSEEQLKEEGIEYKVGKFPFAANSRAKTNADTDGMVKILGQKSTDRVLGAHILGPGAGEMVNEAALALEYGASCEDIARVCHAHPTLSEAFREANLAASFGKSINF\", \"result\": [{\"smile\": \"[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(OP(O)(O)=O)[C@]1([H])O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]1(COP(O)(=O)OP(O)(O)=O)O[C@@]([H])(N2C=NC3=C(N)N=CN=C23)[C@]([H])(OP(O)(O)=O)[C@]1([H])O' can not interact with the protein 'MQSWSRVYCSLAKRGHFNRISHGLQGLSAVPLRTYADQPIDADVTVIGSGPGGYVAAIKAAQLGFKTVCIEKNETLGGTCLNVGCIPSKALLNNSHYYHMAHGKDFASRGIEMSEVRLNLDKMMEQKSTAVKALTGGIAHLFKQNKVVHVNGYGKITGKNQVTATKADGGTQVIDTKNILIATGSEVTPFPGITIDEDTIVSSTGALSLKKVPEKMVVIGAGVIGVELGSVWQRLGADVTAVEFLGHVGGVGIDMEISKNFQRILQKQGFKFKLNTKVTGATKKSDGKIDVSIEAASGGKAEVITCDVLLVCIGRRPFTKNLGLEELGIELDPRGRIPVNTRFQTKIPNIYAIGDVVAGPMLAHKAEDEGIICVEGMAGGAVHIDYNCVPSVIYTHPEVAWVGKSEEQLKEEGIEYKVGKFPFAANSRAKTNADTDGMVKILGQKSTDRVLGAHILGPGAGEMVNEAALALEYGASCEDIARVCHAHPTLSEAFREANLAASFGKSINF'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '2c(((2ccc)O2cC(cC))n)F-(1(ccF11)ccOn12ccccc)CFc=ccccccA' probably interact with the target protein 'MANATLKPLCPVLKDMSLLGSHSNSSLRYMDHISVLLHGLAALLGLVENGLIVFVVGCRMRQTVVTTWALHLALSDLLASAALPFFTYFLAVGHSWELGTAFCKLHSSVFFLNMFASGFLLSAISLDRCVRVVHPVWAQNHRSVSVARRVCAVLWALALLNTVPYFVFRDTILRRDGRTMCYYNVLLLAPAGDHNATCGTRQMALALSKFLLAFALPLGIIAASHAVVSARLQRRPQGGVRPGRFVRLVAAVVAAFALCWGPYHAFSLIEARAHAVPSLRPLAWRALPFVSSLAFINSVVNPLLYVLTCPDVGRKLRRSLRAVLESVLVDDGELGSRYRRRGGSSSPAVASASSSLSLAPATHQACSLLRWLRGSRGTGSDDAPSSASGQG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"2c(((2ccc)O2cC(cC))n)F-(1(ccF11)ccOn12ccccc)CFc=ccccccA\"], \"target_seq\": \"MANATLKPLCPVLKDMSLLGSHSNSSLRYMDHISVLLHGLAALLGLVENGLIVFVVGCRMRQTVVTTWALHLALSDLLASAALPFFTYFLAVGHSWELGTAFCKLHSSVFFLNMFASGFLLSAISLDRCVRVVHPVWAQNHRSVSVARRVCAVLWALALLNTVPYFVFRDTILRRDGRTMCYYNVLLLAPAGDHNATCGTRQMALALSKFLLAFALPLGIIAASHAVVSARLQRRPQGGVRPGRFVRLVAAVVAAFALCWGPYHAFSLIEARAHAVPSLRPLAWRALPFVSSLAFINSVVNPLLYVLTCPDVGRKLRRSLRAVLESVLVDDGELGSRYRRRGGSSSPAVASASSSLSLAPATHQACSLLRWLRGSRGTGSDDAPSSASGQG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MANATLKPLCPVLKDMSLLGSHSNSSLRYMDHISVLLHGLAALLGLVENGLIVFVVGCRMRQTVVTTWALHLALSDLLASAALPFFTYFLAVGHSWELGTAFCKLHSSVFFLNMFASGFLLSAISLDRCVRVVHPVWAQNHRSVSVARRVCAVLWALALLNTVPYFVFRDTILRRDGRTMCYYNVLLLAPAGDHNATCGTRQMALALSKFLLAFALPLGIIAASHAVVSARLQRRPQGGVRPGRFVRLVAAVVAAFALCWGPYHAFSLIEARAHAVPSLRPLAWRALPFVSSLAFINSVVNPLLYVLTCPDVGRKLRRSLRAVLESVLVDDGELGSRYRRRGGSSSPAVASASSSLSLAPATHQACSLLRWLRGSRGTGSDDAPSSASGQG\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'O=C(O)Cc1cnc(C(c2ccc(F)cc2)c2ccc(F)cc2)nc1-c1cccc(F)c1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)Cc1cnc(C(c2ccc(F)cc2)c2ccc(F)cc2)nc1-c1cccc(F)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MANATLKPLCPVLKDMSLLGSHSNSSLRYMDHISVLLHGLAALLGLVENGLIVFVVGCRMRQTVVTTWALHLALSDLLASAALPFFTYFLAVGHSWELGTAFCKLHSSVFFLNMFASGFLLSAISLDRCVRVVHPVWAQNHRSVSVARRVCAVLWALALLNTVPYFVFRDTILRRDGRTMCYYNVLLLAPAGDHNATCGTRQMALALSKFLLAFALPLGIIAASHAVVSARLQRRPQGGVRPGRFVRLVAAVVAAFALCWGPYHAFSLIEARAHAVPSLRPLAWRALPFVSSLAFINSVVNPLLYVLTCPDVGRKLRRSLRAVLESVLVDDGELGSRYRRRGGSSSPAVASASSSLSLAPATHQACSLLRWLRGSRGTGSDDAPSSASGQG\", \"result\": [{\"smile\": \"O=C(O)Cc1cnc(C(c2ccc(F)cc2)c2ccc(F)cc2)nc1-c1cccc(F)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(O)Cc1cnc(C(c2ccc(F)cc2)c2ccc(F)cc2)nc1-c1cccc(F)c1' can interact with the protein 'MANATLKPLCPVLKDMSLLGSHSNSSLRYMDHISVLLHGLAALLGLVENGLIVFVVGCRMRQTVVTTWALHLALSDLLASAALPFFTYFLAVGHSWELGTAFCKLHSSVFFLNMFASGFLLSAISLDRCVRVVHPVWAQNHRSVSVARRVCAVLWALALLNTVPYFVFRDTILRRDGRTMCYYNVLLLAPAGDHNATCGTRQMALALSKFLLAFALPLGIIAASHAVVSARLQRRPQGGVRPGRFVRLVAAVVAAFALCWGPYHAFSLIEARAHAVPSLRPLAWRALPFVSSLAFINSVVNPLLYVLTCPDVGRKLRRSLRAVLESVLVDDGELGSRYRRRGGSSSPAVASASSSLSLAPATHQACSLLRWLRGSRGTGSDDAPSSASGQG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the drug 'CcC1(1cCOc(c1===O([((@1C))()O(@cCHO)]OC(cSNC)(]C)CC)cCc)[@OcNcCHC)cC=cA' can interact with the target protein 'MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CcC1(1cCOc(c1===O([((@1C))()O(@cCHO)]OC(cSNC)(]C)CC)cCc)[@OcNcCHC)cC=cA\"], \"target_seq\": \"MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYH\", \"result\": \"invalid smile\"} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug smile is probably wrong, please provide the correct smile."
            },
            {
                "from": "human",
                "value": "'CC(C)[C@H](NC(=O)C(C)(O)c1ccccc1)C(=O)N[C@@H](CC(=O)O)C(=O)CSCc1ccccc1'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)[C@H](NC(=O)C(C)(O)c1ccccc1)C(=O)N[C@@H](CC(=O)O)C(=O)CSCc1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYH\", \"result\": [{\"smile\": \"CC(C)[C@H](NC(=O)C(C)(O)c1ccccc1)C(=O)N[C@@H](CC(=O)O)C(=O)CSCc1ccccc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)[C@H](NC(=O)C(C)(O)c1ccccc1)C(=O)N[C@@H](CC(=O)O)C(=O)CSCc1ccccc1' can interact with the protein 'MENTENSVDSKSIKNLEPKIIHGSESMDSGISLDNSYKMDYPEMGLCIIINNKNFHKSTGMTSRSGTDVDAANLRETFRNLKYEVRNKNDLTREEIVELMRDVSKEDHSKRSSFVCVLLSHGEEGIIFGTNGPVDLKKITNFFRGDRCRSLTGKPKLFIIQACRGTELDCGIETDSGVDDDMACHKIPVEADFLYAYSTAPGYYSWRNSKDGSWFIQSLCAMLKQYADKLEFMHILTRVNRKVATEFESFSFDATFHAKKQIPCIVSMLTKELYFYH'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential interaction between the drug 'CCC1(CC)C(=O)NCC(C)C1=O' and the target protein 'IDLTRYQLITGIGRISMEQVFLKLSTMEIDEFRVLNDLAANWHCRRTYALDTSSGLVLVTNNVNPDTDGDMTFAKINAMLDELSVIFDAATFYHLLRGIVIVLLRNRVPEPFYGGKDFVYRGDGLRRGMDVQNSLPVLGTQSYKQRLKDVYYPVALSSFVFDRPIFPEVSLYPIFTRQLVVPNNIVIIYQDSVFGVFTTGCIELILNSRTPNESDTDMAREVRRTYLSARRNSITIGGASTRVRSKPLEKCGHNYPFEKAVWMVYAAGSKMVELYFGVLTSTYRGSLTRQFVWNWIHIEKSKVDAHFCDSFYLILKGNPYSTVSSGKFRLNLGADQESVKQGWCFLYIWYAATMIARIFGLHANIEYVMWTREDKIGFIFAHQGNKGRMKRLYSDFVPTDIPRVGPDVAMMARKNGRPTLNNIVLMSRSKWHTLGGLIKLLTLESFMTYHLALIPCDLKDYPNTVANDRKDLQAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1(CC)C(=O)NCC(C)C1=O\"], \"target_seq\": \"IDLTRYQLITGIGRISMEQVFLKLSTMEIDEFRVLNDLAANWHCRRTYALDTSSGLVLVTNNVNPDTDGDMTFAKINAMLDELSVIFDAATFYHLLRGIVIVLLRNRVPEPFYGGKDFVYRGDGLRRGMDVQNSLPVLGTQSYKQRLKDVYYPVALSSFVFDRPIFPEVSLYPIFTRQLVVPNNIVIIYQDSVFGVFTTGCIELILNSRTPNESDTDMAREVRRTYLSARRNSITIGGASTRVRSKPLEKCGHNYPFEKAVWMVYAAGSKMVELYFGVLTSTYRGSLTRQFVWNWIHIEKSKVDAHFCDSFYLILKGNPYSTVSSGKFRLNLGADQESVKQGWCFLYIWYAATMIARIFGLHANIEYVMWTREDKIGFIFAHQGNKGRMKRLYSDFVPTDIPRVGPDVAMMARKNGRPTLNNIVLMSRSKWHTLGGLIKLLTLESFMTYHLALIPCDLKDYPNTVANDRKDLQAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The valid sequence for the protein is 'MWGLAGGRLFGIFSAPVLVAVVCCAQSVNDPGNMSFVKETVDKLLKGYDIRLRPDFGGPPVCVGMNIDIASIDMVSEVNMDYTLTMYFQQYWRDKRLAYSGIPLNLTLDNRVADQLWVPDTYFLNDKKSFVHGVTVKNRMIRLHPDGTVLYGLRITTTAACMMDLRRYPLDEQNCTLEIESYGYTTDDIEFYWRGGDKAVTGVERIELPQFSIVEHRLVSRNVVFATGAYPRLSLSFRLKRNIGYFILQTYMPSILITILSWVSFWINYDASAARVALGITTVLTMTTINTHLRETLPKIPYVKAIDMYLMGCFVFVFLALLEYAFVNYIFFGRGPQRQKKLAEKTAKAKNDRSKSESNRVDAHGNILLTSLEVHNEMNEVSGGIGDTRNSAISFDNSGIQYRKQSMPREGHGRFLGDRSLPHKKTHLRRRSSQLKIKIPDLTDVNAIDRWSRIVFPFTFSLFNLVYWLYYVN'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MWGLAGGRLFGIFSAPVLVAVVCCAQSVNDPGNMSFVKETVDKLLKGYDIRLRPDFGGPPVCVGMNIDIASIDMVSEVNMDYTLTMYFQQYWRDKRLAYSGIPLNLTLDNRVADQLWVPDTYFLNDKKSFVHGVTVKNRMIRLHPDGTVLYGLRITTTAACMMDLRRYPLDEQNCTLEIESYGYTTDDIEFYWRGGDKAVTGVERIELPQFSIVEHRLVSRNVVFATGAYPRLSLSFRLKRNIGYFILQTYMPSILITILSWVSFWINYDASAARVALGITTVLTMTTINTHLRETLPKIPYVKAIDMYLMGCFVFVFLALLEYAFVNYIFFGRGPQRQKKLAEKTAKAKNDRSKSESNRVDAHGNILLTSLEVHNEMNEVSGGIGDTRNSAISFDNSGIQYRKQSMPREGHGRFLGDRSLPHKKTHLRRRSSQLKIKIPDLTDVNAIDRWSRIVFPFTFSLFNLVYWLYYVN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWGLAGGRLFGIFSAPVLVAVVCCAQSVNDPGNMSFVKETVDKLLKGYDIRLRPDFGGPPVCVGMNIDIASIDMVSEVNMDYTLTMYFQQYWRDKRLAYSGIPLNLTLDNRVADQLWVPDTYFLNDKKSFVHGVTVKNRMIRLHPDGTVLYGLRITTTAACMMDLRRYPLDEQNCTLEIESYGYTTDDIEFYWRGGDKAVTGVERIELPQFSIVEHRLVSRNVVFATGAYPRLSLSFRLKRNIGYFILQTYMPSILITILSWVSFWINYDASAARVALGITTVLTMTTINTHLRETLPKIPYVKAIDMYLMGCFVFVFLALLEYAFVNYIFFGRGPQRQKKLAEKTAKAKNDRSKSESNRVDAHGNILLTSLEVHNEMNEVSGGIGDTRNSAISFDNSGIQYRKQSMPREGHGRFLGDRSLPHKKTHLRRRSSQLKIKIPDLTDVNAIDRWSRIVFPFTFSLFNLVYWLYYVN\", \"result\": [{\"smile\": \"CCC1(CC)C(=O)NCC(C)C1=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC1(CC)C(=O)NCC(C)C1=O' can interact with the protein 'MWGLAGGRLFGIFSAPVLVAVVCCAQSVNDPGNMSFVKETVDKLLKGYDIRLRPDFGGPPVCVGMNIDIASIDMVSEVNMDYTLTMYFQQYWRDKRLAYSGIPLNLTLDNRVADQLWVPDTYFLNDKKSFVHGVTVKNRMIRLHPDGTVLYGLRITTTAACMMDLRRYPLDEQNCTLEIESYGYTTDDIEFYWRGGDKAVTGVERIELPQFSIVEHRLVSRNVVFATGAYPRLSLSFRLKRNIGYFILQTYMPSILITILSWVSFWINYDASAARVALGITTVLTMTTINTHLRETLPKIPYVKAIDMYLMGCFVFVFLALLEYAFVNYIFFGRGPQRQKKLAEKTAKAKNDRSKSESNRVDAHGNILLTSLEVHNEMNEVSGGIGDTRNSAISFDNSGIQYRKQSMPREGHGRFLGDRSLPHKKTHLRRRSSQLKIKIPDLTDVNAIDRWSRIVFPFTFSLFNLVYWLYYVN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on whether the drug 'Cc1nc(-c2ccccc2)c(-c2ccc(S(N)(=O)=O)cc2)s1' will interact with the target protein 'YLLNQSGQGKKKIEGIFNNAIHLVCKVNSIHDYKDQPAFIKTKYESLYYGTGSPVPESVQDTTIGQCFLDCLAVYPLVEKIRLDIIGSNHKTFAVSEGTNALLIGYLLTLMHAVESVFQYACHLFVQPQNWNGRDSYSTFYLPSLETFKQLTFNNLACIPENIFGVCLYVHVFLDVLSCETAAALSELAFIRLFSMTFRMVMINNLAIFWGPHLNYDQSFKPAELCPILVKARLACLLRKYECDEFKVPFLGMLLMQGPIVVVLHEAFGQALWFTDGFEDDQTKVQGPSLMKQTDEQQRGAQRYPAWCPTNSPKAANRNEYSATPGFGHIFTKIEEQYPSDNDLETNEPIQDVTSPHPQKPKQYKIRPFKTCRRTFVYQHARRGLAMTRSDIGIDGYVVYVIVLLYRFIREIPECPIQPDGHASDMPSNGSVSFEIRTLQPPKGFSEAVFYRVEMSHNRDYTLQRGLMAKEDRSLPWESKTGYEMIMWPTGPDIGQLLSFLEINELNEQTHKVLKVPKRNISTFTRGITVQEYNYHKHRAFHSKDGEFQPPLDGNFYVPKGFAGPSLLLKVLNSHEKFTNFFNRQPKHTPPTLAGRILSEMGDMAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc(-c2ccccc2)c(-c2ccc(S(N)(=O)=O)cc2)s1\"], \"target_seq\": \"YLLNQSGQGKKKIEGIFNNAIHLVCKVNSIHDYKDQPAFIKTKYESLYYGTGSPVPESVQDTTIGQCFLDCLAVYPLVEKIRLDIIGSNHKTFAVSEGTNALLIGYLLTLMHAVESVFQYACHLFVQPQNWNGRDSYSTFYLPSLETFKQLTFNNLACIPENIFGVCLYVHVFLDVLSCETAAALSELAFIRLFSMTFRMVMINNLAIFWGPHLNYDQSFKPAELCPILVKARLACLLRKYECDEFKVPFLGMLLMQGPIVVVLHEAFGQALWFTDGFEDDQTKVQGPSLMKQTDEQQRGAQRYPAWCPTNSPKAANRNEYSATPGFGHIFTKIEEQYPSDNDLETNEPIQDVTSPHPQKPKQYKIRPFKTCRRTFVYQHARRGLAMTRSDIGIDGYVVYVIVLLYRFIREIPECPIQPDGHASDMPSNGSVSFEIRTLQPPKGFSEAVFYRVEMSHNRDYTLQRGLMAKEDRSLPWESKTGYEMIMWPTGPDIGQLLSFLEINELNEQTHKVLKVPKRNISTFTRGITVQEYNYHKHRAFHSKDGEFQPPLDGNFYVPKGFAGPSLLLKVLNSHEKFTNFFNRQPKHTPPTLAGRILSEMGDMAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL' fits the protein's sequence requirement. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL\", \"result\": [{\"smile\": \"Cc1nc(-c2ccccc2)c(-c2ccc(S(N)(=O)=O)cc2)s1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1nc(-c2ccccc2)c(-c2ccc(S(N)(=O)=O)cc2)s1' can interact with the protein 'MLARALLLCAVLALSHTANPCCSHPCQNRGVCMSVGFDQYKCDCTRTGFYGENCSTPEFLTRIKLFLKPTPNTVHYILTHFKGFWNVVNNIPFLRNAIMSYVLTSRSHLIDSPPTYNADYGYKSWEAFSNLSYYTRALPPVPDDCPTPLGVKGKKQLPDSNEIVEKLLLRRKFIPDPQGSNMMFAFFAQHFTHQFFKTDHKRGPAFTNGLGHGVDLNHIYGETLARQRKLRLFKDGKMKYQIIDGEMYPPTVKDTQAEMIYPPQVPEHLRFAVGQEVFGLVPGLMMYATIWLREHNRVCDVLKQEHPEWGDEQLFQTSRLILIGETIKIVIEDYVQHLSGYHFKLKFDPELLFNKQFQYQNRIAAEFNTLYHWHPLLPDTFQIHDQKYNYQQFIYNNSILLEHGITQFVESFTRQIAGRVAGGRNVPPAVQKVSQASIDQSRQMKYQSFNEYRKRFMLKPYESFEELTGEKEMSAELEALYGDIDAVELYPALLVEKPRPDAIFGETMVEVGAPFSLKGLMGNVICSPAYWKPSTFGGEVGFQIINTASIQSLICNNVKGCPFTSFSVPDPELIKTVTINASSSRSGLDDINPTVLLKERSTEL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'COC1=NC(N)=NC2=C1N=CN2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O' potentially engage with the target protein 'ALDFVHMDCLELDMYLEFDTQNKLQENASFKLEHSLLAPPIQLFGGVKFLFGPSNGIKCKTKDIPLLLVSKERPEWYRLLRLNIDKYVMITEQNLSMDTKNECDPRVTHQYSIQLRNDSRETQVEKDNFRAPTFLQIALNHGGDGGRRVELFGITCPDADHQTLQVDMENKPKLAPLKYEELFKVAPESDSIVAVKIRKQMDEWDDCVPLKYDEAKGKEWAIVTQCINRPPQLEKVDHEKREISPRKDTRNDLERDGRNPMYQEKLQLIAVFQIVYFLRSTCDHKLFIECRSQLEKSKDCAEMAAAWNELNYVFEYMENQSEMAQPAIEGEDGTLRSKGVTDSTEAPPPCLLEFGKQQLVYTLYVQGDGIDIYLQLVSVSKDICTFLAEMVMSAHKGGKMIEISLILIFYEPVRHKIGQDNVKDPPNITRKKMSSKRFSFLYLLDSIGCACKDYVKMTGAALVNEADPSDFLVEDEAQYEIMICSMCSKLPTNSSPLPYFEKQSDKGMNVVLVENIAIMVKERESLVPECYDSIVYDIKGVNLYLRAISPVKEQLVGEHQQKVPEQPLEYITEATAREKYEVKVEHRAIHDLKPCMDCEDISIQQLSLSVRKAAKRSPGEVGVERLAHITDACFCKNPREIQIDPVGHLEYQIDVQALETFESELGMGLFRGVIDELEMDGLPGCFEDLNHACKPKLRVEPVLYGLIKKFAIATTRETAVFKEFQKLDDSVRSDLTVDKVISQKQGPKKIVFILKAEAEPVAVVRGIDSKQPFIDALMRSGPLGFYFNKHFSRNKLTASKFLVLVAFSGSTAMLFVDGRDQVEELFHFPCGKVGKDWYEVGTNTANQQMTKLAEMDAVLEYTINSEFFEVPCKPYSEDVGPKITVLVHVGASVKKIQEITVTWLVNPDAASLKEDNWKNLHGSQSGNRLYQSVIWYVLTKYKRPYCQQQEEQNKTEHQKELIVNYLTEEYPFRVEQSLDYVDSISIDKIFGGISVKGLSTWPEKANSNNMLGLRQSNDNMLLPSAGRAQKITKEQKAHPNVYWTLKDLKKEDDQAEEAAHGLAQPVIIKKIILAIFLVKDIGPLWADADKYIELYDSNLAGMVGFQALKATFDRPRKTETKDRDYKYFRYCVVVVSLDSPAGITIDVKLHYPVENCLPNELITMTNRQRGVCHDKNQNGASRKSSRINTAGSGNKTTFKGDFDKDKFQKKFLEGPFVKFKVKDDLASRLGWYQLYVAPKSKMVQDSPDPTTDVGGPKPDIEEPAQGEKSKDGNSIENEKKSEIRKPKPVDHEDELPGIELYFKKPAKEDVEQVLLNATCKASDDTNEGGSGKKTADETDLLGQTLLKIVVNAISTKKCMPEYGAKIYDKKSIFQKSTEGNLQLRDESLEKLLNPFMRLDFYYWTYDASKSTYGLIAADKLSGNERAAFLADFKVMCVIPSSLDDAETKLPESKEQLSVKPSRAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC1=NC(N)=NC2=C1N=CN2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O\"], \"target_seq\": \"ALDFVHMDCLELDMYLEFDTQNKLQENASFKLEHSLLAPPIQLFGGVKFLFGPSNGIKCKTKDIPLLLVSKERPEWYRLLRLNIDKYVMITEQNLSMDTKNECDPRVTHQYSIQLRNDSRETQVEKDNFRAPTFLQIALNHGGDGGRRVELFGITCPDADHQTLQVDMENKPKLAPLKYEELFKVAPESDSIVAVKIRKQMDEWDDCVPLKYDEAKGKEWAIVTQCINRPPQLEKVDHEKREISPRKDTRNDLERDGRNPMYQEKLQLIAVFQIVYFLRSTCDHKLFIECRSQLEKSKDCAEMAAAWNELNYVFEYMENQSEMAQPAIEGEDGTLRSKGVTDSTEAPPPCLLEFGKQQLVYTLYVQGDGIDIYLQLVSVSKDICTFLAEMVMSAHKGGKMIEISLILIFYEPVRHKIGQDNVKDPPNITRKKMSSKRFSFLYLLDSIGCACKDYVKMTGAALVNEADPSDFLVEDEAQYEIMICSMCSKLPTNSSPLPYFEKQSDKGMNVVLVENIAIMVKERESLVPECYDSIVYDIKGVNLYLRAISPVKEQLVGEHQQKVPEQPLEYITEATAREKYEVKVEHRAIHDLKPCMDCEDISIQQLSLSVRKAAKRSPGEVGVERLAHITDACFCKNPREIQIDPVGHLEYQIDVQALETFESELGMGLFRGVIDELEMDGLPGCFEDLNHACKPKLRVEPVLYGLIKKFAIATTRETAVFKEFQKLDDSVRSDLTVDKVISQKQGPKKIVFILKAEAEPVAVVRGIDSKQPFIDALMRSGPLGFYFNKHFSRNKLTASKFLVLVAFSGSTAMLFVDGRDQVEELFHFPCGKVGKDWYEVGTNTANQQMTKLAEMDAVLEYTINSEFFEVPCKPYSEDVGPKITVLVHVGASVKKIQEITVTWLVNPDAASLKEDNWKNLHGSQSGNRLYQSVIWYVLTKYKRPYCQQQEEQNKTEHQKELIVNYLTEEYPFRVEQSLDYVDSISIDKIFGGISVKGLSTWPEKANSNNMLGLRQSNDNMLLPSAGRAQKITKEQKAHPNVYWTLKDLKKEDDQAEEAAHGLAQPVIIKKIILAIFLVKDIGPLWADADKYIELYDSNLAGMVGFQALKATFDRPRKTETKDRDYKYFRYCVVVVSLDSPAGITIDVKLHYPVENCLPNELITMTNRQRGVCHDKNQNGASRKSSRINTAGSGNKTTFKGDFDKDKFQKKFLEGPFVKFKVKDDLASRLGWYQLYVAPKSKMVQDSPDPTTDVGGPKPDIEEPAQGEKSKDGNSIENEKKSEIRKPKPVDHEDELPGIELYFKKPAKEDVEQVLLNATCKASDDTNEGGSGKKTADETDLLGQTLLKIVVNAISTKKCMPEYGAKIYDKKSIFQKSTEGNLQLRDESLEKLLNPFMRLDFYYWTYDASKSTYGLIAADKLSGNERAAFLADFKVMCVIPSSLDDAETKLPESKEQLSVKPSRAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS\", \"result\": [{\"smile\": \"COC1=NC(N)=NC2=C1N=CN2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=NC(N)=NC2=C1N=CN2[C@@H]1O[C@H](CO)[C@@H](O)[C@@H]1O' can interact with the protein 'MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug 'CNC1=C(C=C(SC2=NN=CN2C)C=C1)C(=O)NC1=NC(C)=CS1' will interact with the target protein 'QAGTHDKGKYDDYCKESTYTKGYMLMRIHSYAVQRKLGPNLKITLNEHAFTYGPGEIVRTKKKRDHAIKEDTVCRITVESGWLSTMLEGDENIHQDRCVFPIWGESMLPSKDRSFKLTDLLYRIKFAENTYEFSFETKTDETFPDRIQSLTKQKAKGGEFTKNKMFPLIFSLLMIIGVLTEESKPKSPLRTGKLRLRIETGRQFRQEHKPNNEQLGGVSMNEETKQVYGMLYLYFSQDQGARLAFAKRFVMAHVADVTRTKYLDERLLGVKGAAGNHSNSIYRDIDKEYAKCMSRTGKREPGPRPVRLLFIYDLRLDSSTHAHCYKGFRIDFRPGIPVGLSLAQPQRRYGANLFALDGRAEPHELKLDAKFLTCPPERVLGGTPYPGLLNLEGFDPGPHCKNVAYSPTDGEHYDVAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CNC1=C(C=C(SC2=NN=CN2C)C=C1)C(=O)NC1=NC(C)=CS1\"], \"target_seq\": \"QAGTHDKGKYDDYCKESTYTKGYMLMRIHSYAVQRKLGPNLKITLNEHAFTYGPGEIVRTKKKRDHAIKEDTVCRITVESGWLSTMLEGDENIHQDRCVFPIWGESMLPSKDRSFKLTDLLYRIKFAENTYEFSFETKTDETFPDRIQSLTKQKAKGGEFTKNKMFPLIFSLLMIIGVLTEESKPKSPLRTGKLRLRIETGRQFRQEHKPNNEQLGGVSMNEETKQVYGMLYLYFSQDQGARLAFAKRFVMAHVADVTRTKYLDERLLGVKGAAGNHSNSIYRDIDKEYAKCMSRTGKREPGPRPVRLLFIYDLRLDSSTHAHCYKGFRIDFRPGIPVGLSLAQPQRRYGANLFALDGRAEPHELKLDAKFLTCPPERVLGGTPYPGLLNLEGFDPGPHCKNVAYSPTDGEHYDVAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK' is the sequence to use for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK\", \"result\": [{\"smile\": \"CNC1=C(C=C(SC2=NN=CN2C)C=C1)C(=O)NC1=NC(C)=CS1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CNC1=C(C=C(SC2=NN=CN2C)C=C1)C(=O)NC1=NC(C)=CS1' can not interact with the protein 'MDFDKKGGKGETEEGRRMSKAGGGRSSHGIRSSGTSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLSATEGVPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLITRMEYVHTKSLIYRDVKPENFLVGRPGTKRQHAIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRATPIEVLCENFPEEMATYLRYVRRLDFFEKPDYDYLRKLFTDLFDRSGFVFDYEYDWAGKPLPTPIGTVHTDLPSQPQLRDKTQPHSKNQALNSTNGELNADDPTAGHSNAPITAPAEVEVADETKCCCFFKRRKRKSLQRHK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'COc1cnc(C(=O)Nc2cc(C)c(F)c([C@]3(CF)C[C@@H](C(F)(F)F)OC(N)=N3)c2)c(C)c1' is likely to interact with the target protein 'SSELGESTITLSHSPGAVPGLQQSGVDQEMSDTAGADEPLRDQWLHRESIHTLDLIISWIKSGGGFDVLRRQYEEWTLIAVRRSPVPQYTHKLYRFIFSDLPTLFAAVAQKEAPQPVRDMTPEFQQRAFEHIGTEEILLLGPSTDLCPLLKVLDYVKQGATQVLVFLRDDGIFRDLGLIDTIQFLCMPITWQYAYLIADGAMVESPLKVITHFWVQLLDENYSYYSNPLSVWVAVFIEWEYMEMLAWRGNPKGTLGQEFTSPATKKLERESKGCLVEFSDGCDAIRAYEVTHSNGALIAHGDVIEGFHMTKYPRVTTFRFDACAGVVSSTRAGANNCNTFSAATPVQIKARDNIELRVAMIKYNDYISITYWRGIRSVQHVDSTWYNVPPGSVFIGLLLNRLAEGGFGKGVYGGPRVNVYLAFQLMDRMTNVQAVVEMFQIIDLPMNYLSGCSRGTQGGLMGFRCLSECPPMCSAEKAGDGLPCVNAPGSGKPYGLPLLVDAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cnc(C(=O)Nc2cc(C)c(F)c([C@]3(CF)C[C@@H](C(F)(F)F)OC(N)=N3)c2)c(C)c1\"], \"target_seq\": \"SSELGESTITLSHSPGAVPGLQQSGVDQEMSDTAGADEPLRDQWLHRESIHTLDLIISWIKSGGGFDVLRRQYEEWTLIAVRRSPVPQYTHKLYRFIFSDLPTLFAAVAQKEAPQPVRDMTPEFQQRAFEHIGTEEILLLGPSTDLCPLLKVLDYVKQGATQVLVFLRDDGIFRDLGLIDTIQFLCMPITWQYAYLIADGAMVESPLKVITHFWVQLLDENYSYYSNPLSVWVAVFIEWEYMEMLAWRGNPKGTLGQEFTSPATKKLERESKGCLVEFSDGCDAIRAYEVTHSNGALIAHGDVIEGFHMTKYPRVTTFRFDACAGVVSSTRAGANNCNTFSAATPVQIKARDNIELRVAMIKYNDYISITYWRGIRSVQHVDSTWYNVPPGSVFIGLLLNRLAEGGFGKGVYGGPRVNVYLAFQLMDRMTNVQAVVEMFQIIDLPMNYLSGCSRGTQGGLMGFRCLSECPPMCSAEKAGDGLPCVNAPGSGKPYGLPLLVDAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\", \"result\": [{\"smile\": \"COc1cnc(C(=O)Nc2cc(C)c(F)c([C@]3(CF)C[C@@H](C(F)(F)F)OC(N)=N3)c2)c(C)c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1cnc(C(=O)Nc2cc(C)c(F)c([C@]3(CF)C[C@@H](C(F)(F)F)OC(N)=N3)c2)c(C)c1' can interact with the protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[H][C@@]12CCC(=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC=C2C[C@@]([H])(O)CC[C@]12C' interact with the target protein 'AFWSSGRSQQAMPHGRVGISYTDAIPMLGTKEYQLVVSVPQGTLKMTTNRAPFVVRDLKSQNGCILLIKWWVCYLRSFVRHAWPVSLPIRQVSGEIEAPAVALVIRLRTWCLFAYPSAWDKPLPTKCTGLSGASGPPMRLTVTLCELHLEPDTHVEVLATRNVSRDFSHGRVVFLVYQVGVPGELSPSTREDLQPTNVHVSWETMAPFWLAFASGPGRCEEVPAVYSAGAVQDPTEKIGSFVTEFALQFVIGYCMHLWDLRVGSPYLGAAESVGACISDLSESIPLHRRTITFVEDHDDGAAGEHFNPHGTSWSSPIFLSAFPVSTSATPTLDSPRRIGVSPPLDAALNHFVHDVRPVMLSLAQLQSDEIAQKGIPKKAYTCEPIKTNVGTAAYSMTRQRGVAWVQRSLNSHPCAYRSAARMHQKPAVEGVIGLLATKYGPVEGSITYHFMVGQPDAWVGPVSLLHEHADDRPVFQSSQLIRVRACRPPASTWQNACVGHEAFAWSNMVALLWCVAPQAPLLLMQTYGYRDSSLFLSMPLAPDHPFLALMSPGCHRALEDGRIEVKGGRNLGRIMDWPYGVLKPLRAATCMLQELSVSRLPLESDSVHAPLSHPSLAFLGDLSYEGMRRGEHHSWLTRLRGGELVRPGAIRRLSEGSAYCCHVAGRAQDTGYLEGCVPKAASPAGIAMGTEGHRSDEGSVSRPNTPRQAWSPLFAPTLSGPKVLMFLLLESKENFAELRKAVPPDRWLHSQVGLQFLGLGGEMDPRRVWFSAPRAMGLLVFDAWTAIRGQSVGGDRSSFEPAVTAACAHILGAMRLSIAAAFVTGYINAHTDKSNVAPSGDFALGDLVLPGGGATLVRRGGGEGGQLGEDRSPYRVNGGVGLLGVDTSGHLVEPSQPWQDLVWLPTDNTAQALAMKFLAGMTGAQKINSSFSLFVGVPAQSQPSAYGLGLGVANQEAARWIQALGVTPGVGNARYRLPVVVSSGSILRANFRSPAFRFKSNRAPNCFPVYWTLTPHQPVLETFFGSCPRGPAKVTAFGAVATGFRLSTLDVINGFEDQLEYLLFSQDPRIRRKYVTVVIHRGSIKPYQGPWERQSRQRWKKSPASVLGKPVNVVKEYARLTSCRIRVLQFGPSLFPGCPFLQTRLTWNPGGKVDQAVTTPLCSPGFASCPESSFLCWNEAIVVALMSTPLLYAPLPTSPRPPIPGDAVALRPHFDESLVSFEHLAGENPESLMAAAAAAAAAAA' according to your assessment? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]12CCC(=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC=C2C[C@@]([H])(O)CC[C@]12C\"], \"target_seq\": \"AFWSSGRSQQAMPHGRVGISYTDAIPMLGTKEYQLVVSVPQGTLKMTTNRAPFVVRDLKSQNGCILLIKWWVCYLRSFVRHAWPVSLPIRQVSGEIEAPAVALVIRLRTWCLFAYPSAWDKPLPTKCTGLSGASGPPMRLTVTLCELHLEPDTHVEVLATRNVSRDFSHGRVVFLVYQVGVPGELSPSTREDLQPTNVHVSWETMAPFWLAFASGPGRCEEVPAVYSAGAVQDPTEKIGSFVTEFALQFVIGYCMHLWDLRVGSPYLGAAESVGACISDLSESIPLHRRTITFVEDHDDGAAGEHFNPHGTSWSSPIFLSAFPVSTSATPTLDSPRRIGVSPPLDAALNHFVHDVRPVMLSLAQLQSDEIAQKGIPKKAYTCEPIKTNVGTAAYSMTRQRGVAWVQRSLNSHPCAYRSAARMHQKPAVEGVIGLLATKYGPVEGSITYHFMVGQPDAWVGPVSLLHEHADDRPVFQSSQLIRVRACRPPASTWQNACVGHEAFAWSNMVALLWCVAPQAPLLLMQTYGYRDSSLFLSMPLAPDHPFLALMSPGCHRALEDGRIEVKGGRNLGRIMDWPYGVLKPLRAATCMLQELSVSRLPLESDSVHAPLSHPSLAFLGDLSYEGMRRGEHHSWLTRLRGGELVRPGAIRRLSEGSAYCCHVAGRAQDTGYLEGCVPKAASPAGIAMGTEGHRSDEGSVSRPNTPRQAWSPLFAPTLSGPKVLMFLLLESKENFAELRKAVPPDRWLHSQVGLQFLGLGGEMDPRRVWFSAPRAMGLLVFDAWTAIRGQSVGGDRSSFEPAVTAACAHILGAMRLSIAAAFVTGYINAHTDKSNVAPSGDFALGDLVLPGGGATLVRRGGGEGGQLGEDRSPYRVNGGVGLLGVDTSGHLVEPSQPWQDLVWLPTDNTAQALAMKFLAGMTGAQKINSSFSLFVGVPAQSQPSAYGLGLGVANQEAARWIQALGVTPGVGNARYRLPVVVSSGSILRANFRSPAFRFKSNRAPNCFPVYWTLTPHQPVLETFFGSCPRGPAKVTAFGAVATGFRLSTLDVINGFEDQLEYLLFSQDPRIRRKYVTVVIHRGSIKPYQGPWERQSRQRWKKSPASVLGKPVNVVKEYARLTSCRIRVLQFGPSLFPGCPFLQTRLTWNPGGKVDQAVTTPLCSPGFASCPESSFLCWNEAIVVALMSTPLLYAPLPTSPRPPIPGDAVALRPHFDESLVSFEHLAGENPESLMAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MGGALGPALLLTSLFGAWAGLGPGQGEQGMTVAVVFSSSGPPQAQFRARLTPQSFLDLPLEIQPLTVGVNTTNPSSLLTQICGLLGAAHVHGIVFEDNVDTEAVAQILDFISSQTHVPILSISGGSAVVLTPKEPGSAFLQLGVSLEQQLQVLFKVLEEYDWSAFAVITSLHPGHALFLEGVRAVADASHVSWRLLDVVTLELGPGGPRARTQRLLRQLDAPVFVAYCSREEAEVLFAEAAQAGLVGPGHVWLVPNLALGSTDAPPATFPVGLISVVTESWRLSLRQKVRDGVAILALGAHSYWRQHGTLPAPAGDCRVHPGPVSPAREAFYRHLLNVTWEGRDFSFSPGGYLVQPTMVVIALNRHRLWEMVGRWEHGVLYMKYPVWPRYSASLQPVVDSRHLTVATLEERPFVIVESPDPGTGGCVPNTVPCRRQSNHTFSSGDVAPYTKLCCKGFCIDILKKLARVVKFSYDLYLVTNGKHGKRVRGVWNGMIGEVYYKRADMAIGSLTINEERSEIVDFSVPFVETGISVMVARSNGTVSPSAFLEPYSPAVWVMMFVMCLTVVAITVFMFEYFSPVSYNQNLTRGKKSGGPAFTIGKSVWLLWALVFNNSVPIENPRGTTSKIMVLVWAFFAVIFLASYTANLAAFMIQEQYIDTVSGLSDKKFQRPQDQYPPFRFGTVPNGSTERNIRSNYRDMHTHMVKFNQRSVEDALTSLKMGKLDAFIYDAAVLNYMAGKDEGCKLVTIGSGKVFATTGYGIAMQKDSHWKRAIDLALLQFLGDGETQKLETVWLSGICQNEKNEVMSSKLDIDNMAGVFYMLLVAMGLALLVFAWEHLVYWKLRHSVPNSSQLDFLLAFSRGIYSCFSGVQSLASPPRQASPDLTASSAQASVLKMLQAARDMVTTAGVSSSLDRATRTIENWGGGRRAPPPSPCPTPRSGPSPCLPTPDPPPEPSPTGWGPPDGGRAALVRRAPQPPGRPPTPGPPLSDVSRVSRRPAWEARWPVRTGHCGRHLSASERPLSPARCHYSSFPRADRSGRPFLPLFPELEDLPLLGPEQLARREALLHAAWARGSRPRHASLPSSVAEAFARPSSLPAGCTGPACARPDGHSACRRLAQAQSMCLPIYREACQEGEQAGAPAWQHRQHVCLHAHAHLPFCWGAVCPHLPPCASHGSWLSGAWGPLGHRGRTLGLGTGYRDSGGLDEISRVARGTQGFPGPCTWRRISSLESEV'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGGALGPALLLTSLFGAWAGLGPGQGEQGMTVAVVFSSSGPPQAQFRARLTPQSFLDLPLEIQPLTVGVNTTNPSSLLTQICGLLGAAHVHGIVFEDNVDTEAVAQILDFISSQTHVPILSISGGSAVVLTPKEPGSAFLQLGVSLEQQLQVLFKVLEEYDWSAFAVITSLHPGHALFLEGVRAVADASHVSWRLLDVVTLELGPGGPRARTQRLLRQLDAPVFVAYCSREEAEVLFAEAAQAGLVGPGHVWLVPNLALGSTDAPPATFPVGLISVVTESWRLSLRQKVRDGVAILALGAHSYWRQHGTLPAPAGDCRVHPGPVSPAREAFYRHLLNVTWEGRDFSFSPGGYLVQPTMVVIALNRHRLWEMVGRWEHGVLYMKYPVWPRYSASLQPVVDSRHLTVATLEERPFVIVESPDPGTGGCVPNTVPCRRQSNHTFSSGDVAPYTKLCCKGFCIDILKKLARVVKFSYDLYLVTNGKHGKRVRGVWNGMIGEVYYKRADMAIGSLTINEERSEIVDFSVPFVETGISVMVARSNGTVSPSAFLEPYSPAVWVMMFVMCLTVVAITVFMFEYFSPVSYNQNLTRGKKSGGPAFTIGKSVWLLWALVFNNSVPIENPRGTTSKIMVLVWAFFAVIFLASYTANLAAFMIQEQYIDTVSGLSDKKFQRPQDQYPPFRFGTVPNGSTERNIRSNYRDMHTHMVKFNQRSVEDALTSLKMGKLDAFIYDAAVLNYMAGKDEGCKLVTIGSGKVFATTGYGIAMQKDSHWKRAIDLALLQFLGDGETQKLETVWLSGICQNEKNEVMSSKLDIDNMAGVFYMLLVAMGLALLVFAWEHLVYWKLRHSVPNSSQLDFLLAFSRGIYSCFSGVQSLASPPRQASPDLTASSAQASVLKMLQAARDMVTTAGVSSSLDRATRTIENWGGGRRAPPPSPCPTPRSGPSPCLPTPDPPPEPSPTGWGPPDGGRAALVRRAPQPPGRPPTPGPPLSDVSRVSRRPAWEARWPVRTGHCGRHLSASERPLSPARCHYSSFPRADRSGRPFLPLFPELEDLPLLGPEQLARREALLHAAWARGSRPRHASLPSSVAEAFARPSSLPAGCTGPACARPDGHSACRRLAQAQSMCLPIYREACQEGEQAGAPAWQHRQHVCLHAHAHLPFCWGAVCPHLPPCASHGSWLSGAWGPLGHRGRTLGLGTGYRDSGGLDEISRVARGTQGFPGPCTWRRISSLESEV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGGALGPALLLTSLFGAWAGLGPGQGEQGMTVAVVFSSSGPPQAQFRARLTPQSFLDLPLEIQPLTVGVNTTNPSSLLTQICGLLGAAHVHGIVFEDNVDTEAVAQILDFISSQTHVPILSISGGSAVVLTPKEPGSAFLQLGVSLEQQLQVLFKVLEEYDWSAFAVITSLHPGHALFLEGVRAVADASHVSWRLLDVVTLELGPGGPRARTQRLLRQLDAPVFVAYCSREEAEVLFAEAAQAGLVGPGHVWLVPNLALGSTDAPPATFPVGLISVVTESWRLSLRQKVRDGVAILALGAHSYWRQHGTLPAPAGDCRVHPGPVSPAREAFYRHLLNVTWEGRDFSFSPGGYLVQPTMVVIALNRHRLWEMVGRWEHGVLYMKYPVWPRYSASLQPVVDSRHLTVATLEERPFVIVESPDPGTGGCVPNTVPCRRQSNHTFSSGDVAPYTKLCCKGFCIDILKKLARVVKFSYDLYLVTNGKHGKRVRGVWNGMIGEVYYKRADMAIGSLTINEERSEIVDFSVPFVETGISVMVARSNGTVSPSAFLEPYSPAVWVMMFVMCLTVVAITVFMFEYFSPVSYNQNLTRGKKSGGPAFTIGKSVWLLWALVFNNSVPIENPRGTTSKIMVLVWAFFAVIFLASYTANLAAFMIQEQYIDTVSGLSDKKFQRPQDQYPPFRFGTVPNGSTERNIRSNYRDMHTHMVKFNQRSVEDALTSLKMGKLDAFIYDAAVLNYMAGKDEGCKLVTIGSGKVFATTGYGIAMQKDSHWKRAIDLALLQFLGDGETQKLETVWLSGICQNEKNEVMSSKLDIDNMAGVFYMLLVAMGLALLVFAWEHLVYWKLRHSVPNSSQLDFLLAFSRGIYSCFSGVQSLASPPRQASPDLTASSAQASVLKMLQAARDMVTTAGVSSSLDRATRTIENWGGGRRAPPPSPCPTPRSGPSPCLPTPDPPPEPSPTGWGPPDGGRAALVRRAPQPPGRPPTPGPPLSDVSRVSRRPAWEARWPVRTGHCGRHLSASERPLSPARCHYSSFPRADRSGRPFLPLFPELEDLPLLGPEQLARREALLHAAWARGSRPRHASLPSSVAEAFARPSSLPAGCTGPACARPDGHSACRRLAQAQSMCLPIYREACQEGEQAGAPAWQHRQHVCLHAHAHLPFCWGAVCPHLPPCASHGSWLSGAWGPLGHRGRTLGLGTGYRDSGGLDEISRVARGTQGFPGPCTWRRISSLESEV\", \"result\": [{\"smile\": \"[H][C@@]12CCC(=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC=C2C[C@@]([H])(O)CC[C@]12C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@]12CCC(=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC=C2C[C@@]([H])(O)CC[C@]12C' can interact with the protein 'MGGALGPALLLTSLFGAWAGLGPGQGEQGMTVAVVFSSSGPPQAQFRARLTPQSFLDLPLEIQPLTVGVNTTNPSSLLTQICGLLGAAHVHGIVFEDNVDTEAVAQILDFISSQTHVPILSISGGSAVVLTPKEPGSAFLQLGVSLEQQLQVLFKVLEEYDWSAFAVITSLHPGHALFLEGVRAVADASHVSWRLLDVVTLELGPGGPRARTQRLLRQLDAPVFVAYCSREEAEVLFAEAAQAGLVGPGHVWLVPNLALGSTDAPPATFPVGLISVVTESWRLSLRQKVRDGVAILALGAHSYWRQHGTLPAPAGDCRVHPGPVSPAREAFYRHLLNVTWEGRDFSFSPGGYLVQPTMVVIALNRHRLWEMVGRWEHGVLYMKYPVWPRYSASLQPVVDSRHLTVATLEERPFVIVESPDPGTGGCVPNTVPCRRQSNHTFSSGDVAPYTKLCCKGFCIDILKKLARVVKFSYDLYLVTNGKHGKRVRGVWNGMIGEVYYKRADMAIGSLTINEERSEIVDFSVPFVETGISVMVARSNGTVSPSAFLEPYSPAVWVMMFVMCLTVVAITVFMFEYFSPVSYNQNLTRGKKSGGPAFTIGKSVWLLWALVFNNSVPIENPRGTTSKIMVLVWAFFAVIFLASYTANLAAFMIQEQYIDTVSGLSDKKFQRPQDQYPPFRFGTVPNGSTERNIRSNYRDMHTHMVKFNQRSVEDALTSLKMGKLDAFIYDAAVLNYMAGKDEGCKLVTIGSGKVFATTGYGIAMQKDSHWKRAIDLALLQFLGDGETQKLETVWLSGICQNEKNEVMSSKLDIDNMAGVFYMLLVAMGLALLVFAWEHLVYWKLRHSVPNSSQLDFLLAFSRGIYSCFSGVQSLASPPRQASPDLTASSAQASVLKMLQAARDMVTTAGVSSSLDRATRTIENWGGGRRAPPPSPCPTPRSGPSPCLPTPDPPPEPSPTGWGPPDGGRAALVRRAPQPPGRPPTPGPPLSDVSRVSRRPAWEARWPVRTGHCGRHLSASERPLSPARCHYSSFPRADRSGRPFLPLFPELEDLPLLGPEQLARREALLHAAWARGSRPRHASLPSSVAEAFARPSSLPAGCTGPACARPDGHSACRRLAQAQSMCLPIYREACQEGEQAGAPAWQHRQHVCLHAHAHLPFCWGAVCPHLPPCASHGSWLSGAWGPLGHRGRTLGLGTGYRDSGGLDEISRVARGTQGFPGPCTWRRISSLESEV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the probability of interaction between the drug 'O=C(O)[C@@H]1CCCN(S(=O)(=O)c2ccc3ccccc3c2)C1' and the target protein 'VLHIAVVHCKNSIRIIFLSKSPAPAIQVDLQVGLTKLLFVYGGVKYVCLNQIPPNQRDAEYSYNKGDWYDKEENGKFVYYLCPENSVVAKAADFDENPYLNATSTLLIEEMGESPLTDYPKIAKISVKHVNVQRRVLNHPRCWDKWMGFAIVRLCRANKAKVRLTECAALDFKLFNDEGLQAPQKRRLIGLQQKSDARIGYATSCQGIDPKSFGLLLPHEVFLKAKNSHIKELDKRVPDLLEVELSLFLAYNFPRERPSNSLTVLGAQRSKCENIPVRVPKLDAYGSLGMLVDLEKPHEEMVEYVENTFEKAYSAREQDFDPHAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)[C@@H]1CCCN(S(=O)(=O)c2ccc3ccccc3c2)C1\"], \"target_seq\": \"VLHIAVVHCKNSIRIIFLSKSPAPAIQVDLQVGLTKLLFVYGGVKYVCLNQIPPNQRDAEYSYNKGDWYDKEENGKFVYYLCPENSVVAKAADFDENPYLNATSTLLIEEMGESPLTDYPKIAKISVKHVNVQRRVLNHPRCWDKWMGFAIVRLCRANKAKVRLTECAALDFKLFNDEGLQAPQKRRLIGLQQKSDARIGYATSCQGIDPKSFGLLLPHEVFLKAKNSHIKELDKRVPDLLEVELSLFLAYNFPRERPSNSLTVLGAQRSKCENIPVRVPKLDAYGSLGMLVDLEKPHEEMVEYVENTFEKAYSAREQDFDPHAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MDSKYQCVKLNDGHFMPVLGFGTYAPAEVPKSKALEATKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWCNSHRPELVRPALERSLKNLQLDYVDLYLIHFPVSVKPGEEVIPKDENGKILFDTVDLCATWEAVEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNQRKLLDFCKSKDIVLVAYSALGSHREEPWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTSEEMKAIDGLNRNVRYLTLDIFAGPPNYPFSDEY' works as the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDSKYQCVKLNDGHFMPVLGFGTYAPAEVPKSKALEATKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWCNSHRPELVRPALERSLKNLQLDYVDLYLIHFPVSVKPGEEVIPKDENGKILFDTVDLCATWEAVEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNQRKLLDFCKSKDIVLVAYSALGSHREEPWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTSEEMKAIDGLNRNVRYLTLDIFAGPPNYPFSDEY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDSKYQCVKLNDGHFMPVLGFGTYAPAEVPKSKALEATKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWCNSHRPELVRPALERSLKNLQLDYVDLYLIHFPVSVKPGEEVIPKDENGKILFDTVDLCATWEAVEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNQRKLLDFCKSKDIVLVAYSALGSHREEPWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTSEEMKAIDGLNRNVRYLTLDIFAGPPNYPFSDEY\", \"result\": [{\"smile\": \"O=C(O)[C@@H]1CCCN(S(=O)(=O)c2ccc3ccccc3c2)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(O)[C@@H]1CCCN(S(=O)(=O)c2ccc3ccccc3c2)C1' can not interact with the protein 'MDSKYQCVKLNDGHFMPVLGFGTYAPAEVPKSKALEATKLAIEAGFRHIDSAHLYNNEEQVGLAIRSKIADGSVKREDIFYTSKLWCNSHRPELVRPALERSLKNLQLDYVDLYLIHFPVSVKPGEEVIPKDENGKILFDTVDLCATWEAVEKCKDAGLAKSIGVSNFNRRQLEMILNKPGLKYKPVCNQVECHPYFNQRKLLDFCKSKDIVLVAYSALGSHREEPWVDPNSPVLLEDPVLCALAKKHKRTPALIALRYQLQRGVVVLAKSYNEQRIRQNVQVFEFQLTSEEMKAIDGLNRNVRYLTLDIFAGPPNYPFSDEY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the likelihood of interaction between the drug '[H][C@](O)(C\\C=C/CCCCC)\\C=C\\CC(=O)CCCCC(O)=O' and the target protein 'RESIEGALADLLCPGSHGLALYGTTLKQMMLSAGFKMNLRSDALPTAHLEVADFRILEKYGDNDLREIVYRIRDRFKWDSGKIVNEKQKREDPEGDKMATILPAQIFPLLSGEPIPGTLLKATSQGKAVLNGMNQTWYPTQMTTSCEMDNSITHGFKVISDFPLVMKARARALSFASSRSPIFFFVEHESIMTDAEDVSMGRTGYFFRVRFQGQRFEVGKATFQVPYLIVDDKIANVAVMVRHLPAWLVNIRWTIAALNMLIGSFRFALRVETDVDPLLDYNYGFAKQLKMDKLMLTLAIDAAIIGAAGYAGGPFTNHNYHTAYAVSSFCIEPERRQEQPISGLMSKLFEISGTFDSVTKSIIGGPYHSLLTGAPMGSAMRRQSKSILECAIAHPYLPEKSKTSVTKENNKCFALILEAVVTFTYTSLISPVVKWRGRFRSNIMMYLVWWVNKQETDVSPYYENDYFMNDVECVASTLQHWKEVSGKRRRIREFQNMDASIIGDSPQWNKMGRVITKKAELCFHLGWDAIILAIERSDDAINKPQRVYGTHAWLLPLDFTIDEVLGKESSQIYPVLDDSFYGGLKGLVILRLDVIRVNYDYCGISPILVMTETAKNWFVYYTAQNTPLQETELGAKACKFLGLSMTDGPNLKVKATLDGRWVYGAHSRRSGMEWRFGLIKNITHHKFVLGLWQPNESMWTLIISMLDHAIRNLTAMVGEGINALDIGCPNIVMVFQEGPKSATDTPEDSELSLAELFLKIILDVQSSRLANFRCVCIDSAPERITDDLGQTTGMHAFALADNQVCWHRILKRVHSCEPGGELQRLFFTDIFVAWYIKSSRVNSDLRLVPANFLMELIREQYDYGETPVNSPFSTTLLSDMEHNIYLLEMRKNRYPAPAAVHKQLIQWKVLRFCYATGISAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@](O)(C\\C=C/CCCCC)\\C=C\\CC(=O)CCCCC(O)=O\"], \"target_seq\": \"RESIEGALADLLCPGSHGLALYGTTLKQMMLSAGFKMNLRSDALPTAHLEVADFRILEKYGDNDLREIVYRIRDRFKWDSGKIVNEKQKREDPEGDKMATILPAQIFPLLSGEPIPGTLLKATSQGKAVLNGMNQTWYPTQMTTSCEMDNSITHGFKVISDFPLVMKARARALSFASSRSPIFFFVEHESIMTDAEDVSMGRTGYFFRVRFQGQRFEVGKATFQVPYLIVDDKIANVAVMVRHLPAWLVNIRWTIAALNMLIGSFRFALRVETDVDPLLDYNYGFAKQLKMDKLMLTLAIDAAIIGAAGYAGGPFTNHNYHTAYAVSSFCIEPERRQEQPISGLMSKLFEISGTFDSVTKSIIGGPYHSLLTGAPMGSAMRRQSKSILECAIAHPYLPEKSKTSVTKENNKCFALILEAVVTFTYTSLISPVVKWRGRFRSNIMMYLVWWVNKQETDVSPYYENDYFMNDVECVASTLQHWKEVSGKRRRIREFQNMDASIIGDSPQWNKMGRVITKKAELCFHLGWDAIILAIERSDDAINKPQRVYGTHAWLLPLDFTIDEVLGKESSQIYPVLDDSFYGGLKGLVILRLDVIRVNYDYCGISPILVMTETAKNWFVYYTAQNTPLQETELGAKACKFLGLSMTDGPNLKVKATLDGRWVYGAHSRRSGMEWRFGLIKNITHHKFVLGLWQPNESMWTLIISMLDHAIRNLTAMVGEGINALDIGCPNIVMVFQEGPKSATDTPEDSELSLAELFLKIILDVQSSRLANFRCVCIDSAPERITDDLGQTTGMHAFALADNQVCWHRILKRVHSCEPGGELQRLFFTDIFVAWYIKSSRVNSDLRLVPANFLMELIREQYDYGETPVNSPFSTTLLSDMEHNIYLLEMRKNRYPAPAAVHKQLIQWKVLRFCYATGISAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP' is the sequence that’s right for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP\", \"result\": [{\"smile\": \"[H][C@](O)(C\\C=C/CCCCC)\\C=C\\CC(=O)CCCCC(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@](O)(C\\C=C/CCCCC)\\C=C\\CC(=O)CCCCC(O)=O' can not interact with the protein 'MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected interaction between the drug 'CC(=O)NCCC1=CNC2=C1C=C(O)C=C2' and the target protein 'KVEQGSDVDKVEHERMVTILSDPAEDKQQGWDSLFCLPFETLTGSKRADMTKAVVVIEKVDEDVDYIQKHIMHEKVGRTTGSGIKNMLTICDKLNRLDGLAQVAAAKQEKPSGSPSYQHVEHAYFDDESSDKYMVADTVFFGEVELFLEPEPKIVKAGPAMGDWFEPKDEKFLGDTNETHCALIPFMECDIYVLIKVEPGEDQFPLYGFNQGIMPYYLFPRWMDRLKVEVQDNENLEQKEQDKKPGMEKNELYVYQIDNNMQNFITLALKSEESCNFCIDYVSFFKKNLQQLIQYLVKFYEEGGGVDEKKRLNMVWHEVHQNIQERPMVASVEEKLSLLHRACKLLQVLAYLRTEDQKKAPQLDDDPNGKPYCQEYTTNAQAWGVRARSVRDKYRAVGAGLDIIKRNRWHDDSEEASALEPKEYPSFLINNQRDHPLTIADIICENQGQSPFLILNGRSEQEFIREFGVLDGDFNACDGESVLFRGFILKRKMNSETSVEQDPGRDDGVHLADQWQSKKNTEVSIVQDPVKTKKSRATTKYETQAEEDFKEDQIGGYFLRDSSTAVCTILAFDPVTVASQSKVRKGYGKEPSMRLVFLNWQNLSPRVKLYVLKEKTMIVFLGNVLVVIKLYQKAWMVTLVDELKAGLKSLEVVYLEQAKIDYSEGFPGCNAPRTIVCPHIPPPLYPKPLTGHGSILTIFLLPTISKTSIVECLSYAGPNFPVSEFDYVQAVFCIKYILVFKNDKGPGDLSLPAANAALKKPTAFKGSYLLKKNLLKDLMDYQVIGDHYGFSGNYYKCKLNPSMLASARNEEPFRHPIIQAPHTPIKIWEAKEVCSQIGRQTDQRALFAEGFFVQNDLLIAVITATARKDLHKEYQRFDNLNGISEAKIIGKLCDNNQTPDLGEGDKLPLRSILLKKKPVEDLNMKLTSGYLGKMITAESLMSDREEYDTVEMKLIPSLVQKMISEDLVSRSKSREDADSECVILPKDPQGKRFTWAPTVSYYELDSNIKMLASQLWFGIENLSEEEGVKRIEKPDRKIHDAPQTKIKIDKAVFKISVYGMDSKNASDVGMIQSLNSILEVQINAKLNKISIFKRLLMTPNPYQKADLYMVLPCPKCLDRLSDGLKPRKPLGEVHTMEPNSLAEKETNTAGWIIYNKDKSPKGSLCADAHAFKAIVNIVFYLTLLIGVPSSDHVLSDLPFLDGYDGKTAECVFKRVLDSRTQALSHDPKVRGTTDDTEYFCGDTRHEEEIINFCVAKQLFKDLTTAEKAGEKTEDAGQLIARARLAGELDYKEVPDNQISTMVCLVPPQECSTELEADNMYPYMRTLGKTAVNLLSAKSLEQSFEPLELVDQSEVRVVVFVACKREQVRSDLIEKKKIAEDLEQMLYNDHKTISKYNEFSEKQQDQRVRPKVEVQKKGCKKSESISAEYKDADTGFAKVCEAKPKEESPITKFCDIKRVDKQGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)NCCC1=CNC2=C1C=C(O)C=C2\"], \"target_seq\": \"KVEQGSDVDKVEHERMVTILSDPAEDKQQGWDSLFCLPFETLTGSKRADMTKAVVVIEKVDEDVDYIQKHIMHEKVGRTTGSGIKNMLTICDKLNRLDGLAQVAAAKQEKPSGSPSYQHVEHAYFDDESSDKYMVADTVFFGEVELFLEPEPKIVKAGPAMGDWFEPKDEKFLGDTNETHCALIPFMECDIYVLIKVEPGEDQFPLYGFNQGIMPYYLFPRWMDRLKVEVQDNENLEQKEQDKKPGMEKNELYVYQIDNNMQNFITLALKSEESCNFCIDYVSFFKKNLQQLIQYLVKFYEEGGGVDEKKRLNMVWHEVHQNIQERPMVASVEEKLSLLHRACKLLQVLAYLRTEDQKKAPQLDDDPNGKPYCQEYTTNAQAWGVRARSVRDKYRAVGAGLDIIKRNRWHDDSEEASALEPKEYPSFLINNQRDHPLTIADIICENQGQSPFLILNGRSEQEFIREFGVLDGDFNACDGESVLFRGFILKRKMNSETSVEQDPGRDDGVHLADQWQSKKNTEVSIVQDPVKTKKSRATTKYETQAEEDFKEDQIGGYFLRDSSTAVCTILAFDPVTVASQSKVRKGYGKEPSMRLVFLNWQNLSPRVKLYVLKEKTMIVFLGNVLVVIKLYQKAWMVTLVDELKAGLKSLEVVYLEQAKIDYSEGFPGCNAPRTIVCPHIPPPLYPKPLTGHGSILTIFLLPTISKTSIVECLSYAGPNFPVSEFDYVQAVFCIKYILVFKNDKGPGDLSLPAANAALKKPTAFKGSYLLKKNLLKDLMDYQVIGDHYGFSGNYYKCKLNPSMLASARNEEPFRHPIIQAPHTPIKIWEAKEVCSQIGRQTDQRALFAEGFFVQNDLLIAVITATARKDLHKEYQRFDNLNGISEAKIIGKLCDNNQTPDLGEGDKLPLRSILLKKKPVEDLNMKLTSGYLGKMITAESLMSDREEYDTVEMKLIPSLVQKMISEDLVSRSKSREDADSECVILPKDPQGKRFTWAPTVSYYELDSNIKMLASQLWFGIENLSEEEGVKRIEKPDRKIHDAPQTKIKIDKAVFKISVYGMDSKNASDVGMIQSLNSILEVQINAKLNKISIFKRLLMTPNPYQKADLYMVLPCPKCLDRLSDGLKPRKPLGEVHTMEPNSLAEKETNTAGWIIYNKDKSPKGSLCADAHAFKAIVNIVFYLTLLIGVPSSDHVLSDLPFLDGYDGKTAECVFKRVLDSRTQALSHDPKVRGTTDDTEYFCGDTRHEEEIINFCVAKQLFKDLTTAEKAGEKTEDAGQLIARARLAGELDYKEVPDNQISTMVCLVPPQECSTELEADNMYPYMRTLGKTAVNLLSAKSLEQSFEPLELVDQSEVRVVVFVACKREQVRSDLIEKKKIAEDLEQMLYNDHKTISKYNEFSEKQQDQRVRPKVEVQKKGCKKSESISAEYKDADTGFAKVCEAKPKEESPITKFCDIKRVDKQGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence that’s right for this protein is 'MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS\", \"result\": [{\"smile\": \"CC(=O)NCCC1=CNC2=C1C=C(O)C=C2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(=O)NCCC1=CNC2=C1C=C(O)C=C2' can not interact with the protein 'MAPVHGDDSLSDSGSFVSSRARREKKSKKGRQEALERLKKAKAGEKYKYEVEDFTGVYEEVDEEQYSKLVQARQDDDWIVDDDGIGYVEDGREIFDDDLEDDALDADEKGKDGKARNKDKRNVKKLAVTKPNNIKSMFIACAGKKTADKAVDLSKDGLLGDILQDLNTETPQITPPPVMILKKKRSIGASPNPFSVHTATAVPSGKIASPVSRKEPPLTPVPLKRAEFAGDDVQVESTEEEQESGAMEFEDGDFDEPMEVEEVDLEPMAAKAWDKESEPAEEVKQEADSGKGTVSYLGSFLPDVSCWDIDQEGDSSFSVQEVQVDSSHLPLVKGADEEQVFHFYWLDAYEDQYNQPGVVFLFGKVWIESAETHVSCCVMVKNIERTLYFLPREMKIDLNTGKETGTPISMKDVYEEFDEKIATKYKIMKFKSKPVEKNYAFEIPDVPEKSEYLEVKYSAEMPQLPQDLKGETFSHVFGTNTSSLELFLMNRKIKGPCWLEVKSPQLLNQPVSWCKVEAMALKPDLVNVIKDVSPPPLVVMAFSMKTMQNAKNHQNEIIAMAALVHHSFALDKAAPKPPFQSHFCVVSKPKDCIFPYAFKEVIEKKNVKVEVAATERTLLGFFLAKVHKIDPDIIVGHNIYGFELEVLLQRINVCKAPHWSKIGRLKRSNMPKLGGRSGFGERNATCGRMICDVEISAKELIRCKSYHLSELVQQILKTERVVIPMENIQNMYSESSQLLYLLEHTWKDAKFILQIMCELNVLPLALQITNIAGNIMSRTLMGGRSERNEFLLLHAFYENNYIVPDKQIFRKPQQKLGDEDEEIDGDTNKYKKGRKKAAYAGGLVLDPKVGFYDKFILLLDFNSLYPSIIQEFNICFTTVQRVASEAQKVTEDGEQEQIPELPDPSLEMGILPREIRKLVERRKQVKQLMKQQDLNPDLILQYDIRQKALKLTANSMYGCLGFSYSRFYAKPLAALVTYKGREILMHTKEMVQKMNLEVIYGDTDSIMINTNSTNLEEVFKLGNKVKSEVNKLYKLLEIDIDGVFKSLLLLKKKKYAALVVEPTSDGNYVTKQELKGLDIVRRDWCDLAKDTGNFVIGQILSDQSRDTIVENIQKRLIEIGENVLNGSVPVSQFEINKALTKDPQDYPDKKSLPHVHVALWINSQGGRKVKAGDTVSYVICQDGSNLTASQRAYAPEQLQKQDNLTIDTQYYLAQQIHPVVARICEPIDGIDAVLIATWLGLDPTQFRVHHYHKDEENDALLGGPAQLTDEEKYRDCERFKCPCPTCGTENIYDNVFDGSGTDMEPSLYRCSNIDCKASPLTFTVQLSNKLIMDIRRFIKKYYDGWLICEEPTCRNRTRHLPLQFSRTGPLCPACMKATLQPEYSDKSLYTQLCFYRYIFDAECALEKLTTDHEKDKLKKQFFTPKVLQDYRKLKNTAEQFLSRSGYSEVNLSKLFAGCAVKS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected likelihood of the drug 'COc1ccc(NS(C)(=O)=O)cc1-c1ccc([C@H]2C[C@@H]2NC2CCC(N)CC2)cc1' engaging with the target protein 'LSPNVPAGFVGRVTPVAQVERKTGKNSKFPVATLQGLALGESNGFTEWAAPRKGAVREALGLGAVAAGLGAMQNGFLPLKEADGNCMPWTEPFTGFGPDMAVLTFHELLGFRKPFPPFRLTMPHAYFQQKEPVEAGPRVGNPRPVHATKEVLPGPEGRPGLKTRSGRGHTNLAAAEQSGARGLPTKTIADSKRAVWLPKAEIFLMEQQVHDLKQNAYGQVLSKEQRPATHIDFDPPTVKSDNKERILLPLIQNSQQELGCANQWADLVYVLDSSLRDAEPTKEYIPTSSTELHKNAKLFLLQKINLEEFESDRDRLFASRKHQPALKFPRYQQEANPLGVLSVGGSKVTPKPYTATSIDGLVIVEMKAPVEVEGEPGAQQVLYYPQEPLTCVLLEPEDPEEQAKQDTPGKTYTAAIVQGPLTRSTSSMADSTLGENKKQDLEESGLLFAILLGQLCGVQASHHVFGSIGPMYENRCSYLPPLTLSRSWAERADENPSKIPLMPPPADCYGLASGPNGCVLAAPKTPANSLGKVFKAVAFKGSGVIKAEMRDLERKGDQPWQVPSLKGNLLYAAKQPYEEQVINAVPPHAQNQPERVGKAAAAVEANRSGTEFYKAREAPPSITRESIFARNVAITAGILRAEDCGSIRLEVKENKVVSMEEELRSHVAALTAFQGTGSHKDARVEDLDGEGGWAEMLRGAVSLNIVAAIKDQSTLKQLHIKVKNGDQTYQLAGIYWILAAIASGRENQTLGVESARDEVYLQEDEMMTIIAVVPKDERNERSYPHVVSRVITISVFNNIMRSEKLDACLYQQIKVPKGLRTPEGLFTEGDSTAESAYSEVAAELAVTFSSWPAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(NS(C)(=O)=O)cc1-c1ccc([C@H]2C[C@@H]2NC2CCC(N)CC2)cc1\"], \"target_seq\": \"LSPNVPAGFVGRVTPVAQVERKTGKNSKFPVATLQGLALGESNGFTEWAAPRKGAVREALGLGAVAAGLGAMQNGFLPLKEADGNCMPWTEPFTGFGPDMAVLTFHELLGFRKPFPPFRLTMPHAYFQQKEPVEAGPRVGNPRPVHATKEVLPGPEGRPGLKTRSGRGHTNLAAAEQSGARGLPTKTIADSKRAVWLPKAEIFLMEQQVHDLKQNAYGQVLSKEQRPATHIDFDPPTVKSDNKERILLPLIQNSQQELGCANQWADLVYVLDSSLRDAEPTKEYIPTSSTELHKNAKLFLLQKINLEEFESDRDRLFASRKHQPALKFPRYQQEANPLGVLSVGGSKVTPKPYTATSIDGLVIVEMKAPVEVEGEPGAQQVLYYPQEPLTCVLLEPEDPEEQAKQDTPGKTYTAAIVQGPLTRSTSSMADSTLGENKKQDLEESGLLFAILLGQLCGVQASHHVFGSIGPMYENRCSYLPPLTLSRSWAERADENPSKIPLMPPPADCYGLASGPNGCVLAAPKTPANSLGKVFKAVAFKGSGVIKAEMRDLERKGDQPWQVPSLKGNLLYAAKQPYEEQVINAVPPHAQNQPERVGKAAAAVEANRSGTEFYKAREAPPSITRESIFARNVAITAGILRAEDCGSIRLEVKENKVVSMEEELRSHVAALTAFQGTGSHKDARVEDLDGEGGWAEMLRGAVSLNIVAAIKDQSTLKQLHIKVKNGDQTYQLAGIYWILAAIASGRENQTLGVESARDEVYLQEDEMMTIIAVVPKDERNERSYPHVVSRVITISVFNNIMRSEKLDACLYQQIKVPKGLRTPEGLFTEGDSTAESAYSEVAAELAVTFSSWPAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM\", \"result\": [{\"smile\": \"COc1ccc(NS(C)(=O)=O)cc1-c1ccc([C@H]2C[C@@H]2NC2CCC(N)CC2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc(NS(C)(=O)=O)cc1-c1ccc([C@H]2C[C@@H]2NC2CCC(N)CC2)cc1' can interact with the protein 'MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction on whether the drug 'CC(C)C(C(=O)O)NC' will interact with the target protein 'EMDTVHNLTIIIIVRQRKQQRSAIKLFAKGLIKSNAANDEGAHILCALFAFLAKEKIQELMYAPSTDSETKDFFSPEAADLHVLDPILSGTSTSYDETEELSSAAIEDEGNPKRFTFRENAWIVPFTVEIPLHYRNAENESGEDPGADAIPLHLTNDERGFFHLKIQAKRVVALGFKESTEKSSAAIEGDNFFREFGADNPRETNRVQKSKYIPFKELLMDAKTKIIEREADLVKSCLVETVIHIGMLFDLERQEKTFPEDESPLMGVAHSAIRAVVRLAELTLENDGSAALIRSLHSGQLLEKDQMDNRKNIHDFGGYFLGVDGIEEAAMTEPFAPVKRHKRSMPAWRFYCTASQLAEIFFCECIQRDQIKEFKRAFPVEGLTKNGVLGEALDFNTDMIQVDICNSERADTDFPVSDQVVLFKFPTRENVRGQYKTVMKFETGASIGRLSTLLQGHFKIGISFLSHFERVTVKAKGLADVRDPNIDPDKEFQIMYKAVVDPITFSVVDDDILRQVTGIGGTQNKDLLVGILRFVKTDSKLHYPESNDKCIISTRRCNGLSIFLANEKRYTVVEMRYTDGHKIGPATFTYFEDGERNVELETYLRQAKIFRVSANDDLDREFPVLESTLGKLKRIVVEFVNDDVSSCSAAHSRDGLLILIIDFQGNDSMKAVLQAPTTMRSVNATLRKFRNAYSYEMAFFNDTLDTTLLNLIPLSEQINTKNSSGLKLTFRKAVEFRVLKNGLGHLVLLTPAEEQGDHAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)C(C(=O)O)NC\"], \"target_seq\": \"EMDTVHNLTIIIIVRQRKQQRSAIKLFAKGLIKSNAANDEGAHILCALFAFLAKEKIQELMYAPSTDSETKDFFSPEAADLHVLDPILSGTSTSYDETEELSSAAIEDEGNPKRFTFRENAWIVPFTVEIPLHYRNAENESGEDPGADAIPLHLTNDERGFFHLKIQAKRVVALGFKESTEKSSAAIEGDNFFREFGADNPRETNRVQKSKYIPFKELLMDAKTKIIEREADLVKSCLVETVIHIGMLFDLERQEKTFPEDESPLMGVAHSAIRAVVRLAELTLENDGSAALIRSLHSGQLLEKDQMDNRKNIHDFGGYFLGVDGIEEAAMTEPFAPVKRHKRSMPAWRFYCTASQLAEIFFCECIQRDQIKEFKRAFPVEGLTKNGVLGEALDFNTDMIQVDICNSERADTDFPVSDQVVLFKFPTRENVRGQYKTVMKFETGASIGRLSTLLQGHFKIGISFLSHFERVTVKAKGLADVRDPNIDPDKEFQIMYKAVVDPITFSVVDDDILRQVTGIGGTQNKDLLVGILRFVKTDSKLHYPESNDKCIISTRRCNGLSIFLANEKRYTVVEMRYTDGHKIGPATFTYFEDGERNVELETYLRQAKIFRVSANDDLDREFPVLESTLGKLKRIVVEFVNDDVSSCSAAHSRDGLLILIIDFQGNDSMKAVLQAPTTMRSVNATLRKFRNAYSYEMAFFNDTLDTTLLNLIPLSEQINTKNSSGLKLTFRKAVEFRVLKNGLGHLVLLTPAEEQGDHAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The right sequence for the protein is 'MGLIQRLPQDVVNRMAAGEVLARPCNAIKELVENSLDAGATEIMVNMQNGGLKLLQVSDNGKGIEREDFALVCERFATSKLQKFEDLMHMKTYGFRGEALASLSHVAKVNIVSKRADAKCAYQANFLDGKMTADTKPAAGKNGTCITATDLFYNLPTRRNKMTTHGEEAKMVNDTLLRFAIHRPDVSFALRQNQAGDFRTKGDGNFRDVVCNLLGRDVADTILPLSLNSTRLKFTFTGHISKPIASATAAIAQNRKTSRSFFSVFINGRSVRCDILKHPIDEVLGARQLHAQFCALHLQIDETRIDVNVHPTKNSVIFLEKEEIIEEIRAYFEKVIGEIFGFEALDVEKPEEEQPDIENLVMIPMSQSLKSIEAIRKPDTKPEFKSSPSAWKSDKKRVDYMEVRTDAKERKIDEFVTRGGAVGPTTSNDDIFGGSGILKRARTEDSTGGEKEPEDLNTDFDDVSMVSLVSTADGRRLNESQDLGEDDDVDFEYGKTHREFHFESIEVLRKEIIANSSQSLREMFKTSTFVGSINVKQVLIQFGTSLYHLDFSTVLREFFYQISVFSFGNYGSYRLDEEPPAIIEILELLGELSTREPNYAAFEVFANVENRFAAEKLLAEHADLLHDYFAIKLDQLENGRLHITEIPSLVHYFVPQLEKLPFLIATLVLNVDYDDEQNTFRTICRAIGDLFTLDTNFITLDKKISAFSATPWKTLIKEVLMPLVKRKFIPPEHFKQAGVIRQLADSHDLYKVFERCGT'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLIQRLPQDVVNRMAAGEVLARPCNAIKELVENSLDAGATEIMVNMQNGGLKLLQVSDNGKGIEREDFALVCERFATSKLQKFEDLMHMKTYGFRGEALASLSHVAKVNIVSKRADAKCAYQANFLDGKMTADTKPAAGKNGTCITATDLFYNLPTRRNKMTTHGEEAKMVNDTLLRFAIHRPDVSFALRQNQAGDFRTKGDGNFRDVVCNLLGRDVADTILPLSLNSTRLKFTFTGHISKPIASATAAIAQNRKTSRSFFSVFINGRSVRCDILKHPIDEVLGARQLHAQFCALHLQIDETRIDVNVHPTKNSVIFLEKEEIIEEIRAYFEKVIGEIFGFEALDVEKPEEEQPDIENLVMIPMSQSLKSIEAIRKPDTKPEFKSSPSAWKSDKKRVDYMEVRTDAKERKIDEFVTRGGAVGPTTSNDDIFGGSGILKRARTEDSTGGEKEPEDLNTDFDDVSMVSLVSTADGRRLNESQDLGEDDDVDFEYGKTHREFHFESIEVLRKEIIANSSQSLREMFKTSTFVGSINVKQVLIQFGTSLYHLDFSTVLREFFYQISVFSFGNYGSYRLDEEPPAIIEILELLGELSTREPNYAAFEVFANVENRFAAEKLLAEHADLLHDYFAIKLDQLENGRLHITEIPSLVHYFVPQLEKLPFLIATLVLNVDYDDEQNTFRTICRAIGDLFTLDTNFITLDKKISAFSATPWKTLIKEVLMPLVKRKFIPPEHFKQAGVIRQLADSHDLYKVFERCGT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLIQRLPQDVVNRMAAGEVLARPCNAIKELVENSLDAGATEIMVNMQNGGLKLLQVSDNGKGIEREDFALVCERFATSKLQKFEDLMHMKTYGFRGEALASLSHVAKVNIVSKRADAKCAYQANFLDGKMTADTKPAAGKNGTCITATDLFYNLPTRRNKMTTHGEEAKMVNDTLLRFAIHRPDVSFALRQNQAGDFRTKGDGNFRDVVCNLLGRDVADTILPLSLNSTRLKFTFTGHISKPIASATAAIAQNRKTSRSFFSVFINGRSVRCDILKHPIDEVLGARQLHAQFCALHLQIDETRIDVNVHPTKNSVIFLEKEEIIEEIRAYFEKVIGEIFGFEALDVEKPEEEQPDIENLVMIPMSQSLKSIEAIRKPDTKPEFKSSPSAWKSDKKRVDYMEVRTDAKERKIDEFVTRGGAVGPTTSNDDIFGGSGILKRARTEDSTGGEKEPEDLNTDFDDVSMVSLVSTADGRRLNESQDLGEDDDVDFEYGKTHREFHFESIEVLRKEIIANSSQSLREMFKTSTFVGSINVKQVLIQFGTSLYHLDFSTVLREFFYQISVFSFGNYGSYRLDEEPPAIIEILELLGELSTREPNYAAFEVFANVENRFAAEKLLAEHADLLHDYFAIKLDQLENGRLHITEIPSLVHYFVPQLEKLPFLIATLVLNVDYDDEQNTFRTICRAIGDLFTLDTNFITLDKKISAFSATPWKTLIKEVLMPLVKRKFIPPEHFKQAGVIRQLADSHDLYKVFERCGT\", \"result\": [{\"smile\": \"CC(C)C(C(=O)O)NC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(C)C(C(=O)O)NC' can not interact with the protein 'MGLIQRLPQDVVNRMAAGEVLARPCNAIKELVENSLDAGATEIMVNMQNGGLKLLQVSDNGKGIEREDFALVCERFATSKLQKFEDLMHMKTYGFRGEALASLSHVAKVNIVSKRADAKCAYQANFLDGKMTADTKPAAGKNGTCITATDLFYNLPTRRNKMTTHGEEAKMVNDTLLRFAIHRPDVSFALRQNQAGDFRTKGDGNFRDVVCNLLGRDVADTILPLSLNSTRLKFTFTGHISKPIASATAAIAQNRKTSRSFFSVFINGRSVRCDILKHPIDEVLGARQLHAQFCALHLQIDETRIDVNVHPTKNSVIFLEKEEIIEEIRAYFEKVIGEIFGFEALDVEKPEEEQPDIENLVMIPMSQSLKSIEAIRKPDTKPEFKSSPSAWKSDKKRVDYMEVRTDAKERKIDEFVTRGGAVGPTTSNDDIFGGSGILKRARTEDSTGGEKEPEDLNTDFDDVSMVSLVSTADGRRLNESQDLGEDDDVDFEYGKTHREFHFESIEVLRKEIIANSSQSLREMFKTSTFVGSINVKQVLIQFGTSLYHLDFSTVLREFFYQISVFSFGNYGSYRLDEEPPAIIEILELLGELSTREPNYAAFEVFANVENRFAAEKLLAEHADLLHDYFAIKLDQLENGRLHITEIPSLVHYFVPQLEKLPFLIATLVLNVDYDDEQNTFRTICRAIGDLFTLDTNFITLDKKISAFSATPWKTLIKEVLMPLVKRKFIPPEHFKQAGVIRQLADSHDLYKVFERCGT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you assess if the drug 'O=C(CCCCC1CCSS1)NCCCNc1c2c(nc3cc(Cl)ccc13)CCCC2' will engage with the target protein 'CGVVTYVIAFFISLIALNLEWGLSYFNTQGKDEAVLIENTQASKFTTGNLMESLRSIRLPANNIWQCEDVWCQTKSEDFLKDYFIGSIQLSLNGFWFKAQKKLEGTVMSEKPGQTPNIKLAWYTGLSPVGWITKEFNVKHKQRDPIHSDYGENHTPRKIYLEEREKSVLAQRGFGSASVGTVLEVFLKALINVNVEWQGGMILFTPAMDTFTSEVNSATWDVHANFLAAEFQLIGGLFHETDLVFAENRYPLADKRTFSDWIADPNAGIWMFLKNFVFHLFGYAAVNLNNFEQYVLGSTTSWCRNEYSHLLVRNIFELFLIFNANGEPNASSGKPKAFTESMNYGRQRPVNGCDPPMPKWVKSMSQLIVWNLADDKPQREEYHQNTGPKKFTPLTKFGYKIIARLWNELYNAINLGTGFGTATNGRVGFMKTRPYKKNLVWLINYIMTLEFFAGGAVALERYDSWRPPGVSSCVMLDLFYYLGGPLPEMIWCDIKIPDHQIEEEFGSLKEVQGTPRTRHTITSENLWLWVPGKVKFGFGAEKADCTQPPFLSSERFLVTNCGPSSDDGLFQLNYIRNGSFCSKSPTRSTRMGNSAGKFDDKLAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(CCCCC1CCSS1)NCCCNc1c2c(nc3cc(Cl)ccc13)CCCC2\"], \"target_seq\": \"CGVVTYVIAFFISLIALNLEWGLSYFNTQGKDEAVLIENTQASKFTTGNLMESLRSIRLPANNIWQCEDVWCQTKSEDFLKDYFIGSIQLSLNGFWFKAQKKLEGTVMSEKPGQTPNIKLAWYTGLSPVGWITKEFNVKHKQRDPIHSDYGENHTPRKIYLEEREKSVLAQRGFGSASVGTVLEVFLKALINVNVEWQGGMILFTPAMDTFTSEVNSATWDVHANFLAAEFQLIGGLFHETDLVFAENRYPLADKRTFSDWIADPNAGIWMFLKNFVFHLFGYAAVNLNNFEQYVLGSTTSWCRNEYSHLLVRNIFELFLIFNANGEPNASSGKPKAFTESMNYGRQRPVNGCDPPMPKWVKSMSQLIVWNLADDKPQREEYHQNTGPKKFTPLTKFGYKIIARLWNELYNAINLGTGFGTATNGRVGFMKTRPYKKNLVWLINYIMTLEFFAGGAVALERYDSWRPPGVSSCVMLDLFYYLGGPLPEMIWCDIKIPDHQIEEEFGSLKEVQGTPRTRHTITSENLWLWVPGKVKFGFGAEKADCTQPPFLSSERFLVTNCGPSSDDGLFQLNYIRNGSFCSKSPTRSTRMGNSAGKFDDKLAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MHSKVTIICIRFLFWFLLLCMLIGKSHTEDDIIIATKNGKVRGMNLTVFGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSDIWNATKYANSCCQNIDQSFPGFHGSEMWNPNTDLSEDCLYLNVWIPAPKPKNATVLIWIYGGGFQTGTSSLHVYDGKFLARVERVIVVSMNYRVGALGFLALPGNPEAPGNMGLFDQQLALQWVQKNIAAFGGNPKSVTLFGESAGAASVSLHLLSPGSHSLFTRAILQSGSFNAPWAVTSLYEARNRTLNLAKLTGCSRENETEIIKCLRNKDPQEILLNEAFVVPYGTPLSVNFGPTVDGDFLTDMPDILLELGQFKKTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPGVSEFGKESILFHYTDWVDDQRPENYREALGDVVGDYNFICPALEFTKKFSEWGNNAFFYYFEHRSSKLPWPEWMGVMHGYEIEFVFGLPLERRDNYTKAEEILSRSIVKRWANFAKYGNPNETQNNSTSWPVFKSTEQKYLTLNTESTRIMTKLRAQQCRFWTSFFPKVLEMTGNIDEAEWEWKAGFHRWNNYMMDWKNQFNDYTSKKESCVGL'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MHSKVTIICIRFLFWFLLLCMLIGKSHTEDDIIIATKNGKVRGMNLTVFGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSDIWNATKYANSCCQNIDQSFPGFHGSEMWNPNTDLSEDCLYLNVWIPAPKPKNATVLIWIYGGGFQTGTSSLHVYDGKFLARVERVIVVSMNYRVGALGFLALPGNPEAPGNMGLFDQQLALQWVQKNIAAFGGNPKSVTLFGESAGAASVSLHLLSPGSHSLFTRAILQSGSFNAPWAVTSLYEARNRTLNLAKLTGCSRENETEIIKCLRNKDPQEILLNEAFVVPYGTPLSVNFGPTVDGDFLTDMPDILLELGQFKKTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPGVSEFGKESILFHYTDWVDDQRPENYREALGDVVGDYNFICPALEFTKKFSEWGNNAFFYYFEHRSSKLPWPEWMGVMHGYEIEFVFGLPLERRDNYTKAEEILSRSIVKRWANFAKYGNPNETQNNSTSWPVFKSTEQKYLTLNTESTRIMTKLRAQQCRFWTSFFPKVLEMTGNIDEAEWEWKAGFHRWNNYMMDWKNQFNDYTSKKESCVGL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MHSKVTIICIRFLFWFLLLCMLIGKSHTEDDIIIATKNGKVRGMNLTVFGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSDIWNATKYANSCCQNIDQSFPGFHGSEMWNPNTDLSEDCLYLNVWIPAPKPKNATVLIWIYGGGFQTGTSSLHVYDGKFLARVERVIVVSMNYRVGALGFLALPGNPEAPGNMGLFDQQLALQWVQKNIAAFGGNPKSVTLFGESAGAASVSLHLLSPGSHSLFTRAILQSGSFNAPWAVTSLYEARNRTLNLAKLTGCSRENETEIIKCLRNKDPQEILLNEAFVVPYGTPLSVNFGPTVDGDFLTDMPDILLELGQFKKTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPGVSEFGKESILFHYTDWVDDQRPENYREALGDVVGDYNFICPALEFTKKFSEWGNNAFFYYFEHRSSKLPWPEWMGVMHGYEIEFVFGLPLERRDNYTKAEEILSRSIVKRWANFAKYGNPNETQNNSTSWPVFKSTEQKYLTLNTESTRIMTKLRAQQCRFWTSFFPKVLEMTGNIDEAEWEWKAGFHRWNNYMMDWKNQFNDYTSKKESCVGL\", \"result\": [{\"smile\": \"O=C(CCCCC1CCSS1)NCCCNc1c2c(nc3cc(Cl)ccc13)CCCC2\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(CCCCC1CCSS1)NCCCNc1c2c(nc3cc(Cl)ccc13)CCCC2' can interact with the protein 'MHSKVTIICIRFLFWFLLLCMLIGKSHTEDDIIIATKNGKVRGMNLTVFGGTVTAFLGIPYAQPPLGRLRFKKPQSLTKWSDIWNATKYANSCCQNIDQSFPGFHGSEMWNPNTDLSEDCLYLNVWIPAPKPKNATVLIWIYGGGFQTGTSSLHVYDGKFLARVERVIVVSMNYRVGALGFLALPGNPEAPGNMGLFDQQLALQWVQKNIAAFGGNPKSVTLFGESAGAASVSLHLLSPGSHSLFTRAILQSGSFNAPWAVTSLYEARNRTLNLAKLTGCSRENETEIIKCLRNKDPQEILLNEAFVVPYGTPLSVNFGPTVDGDFLTDMPDILLELGQFKKTQILVGVNKDEGTAFLVYGAPGFSKDNNSIITRKEFQEGLKIFFPGVSEFGKESILFHYTDWVDDQRPENYREALGDVVGDYNFICPALEFTKKFSEWGNNAFFYYFEHRSSKLPWPEWMGVMHGYEIEFVFGLPLERRDNYTKAEEILSRSIVKRWANFAKYGNPNETQNNSTSWPVFKSTEQKYLTLNTESTRIMTKLRAQQCRFWTSFFPKVLEMTGNIDEAEWEWKAGFHRWNNYMMDWKNQFNDYTSKKESCVGL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'OCCn1cc(-c2cnc3nnn(Cc4ccc5ncccc5c4)c3n2)cn1' and the target protein 'YSCKHGISNKQTLNVMIRTSGLNRTLFRVLYGFKFYRTPKNPITYPCRAEVAIGLFTFFEVKLRGLFLDFNVCEAPILGELPGNQCTLMSLKEDQEEKKSTHIPELTKSILLQFFISIKPFEDVCPDPIGNFEVFPWNPEKEIRDSPYGILAAPEYKLGGMTVSSIETRVKCAIVNGLRRDQYVLALRFITEGLVCSFNQFGLLLVEIQRDIIPLNDPRGPNIEFGDEHVPELFPVLPRLDQVVITFRLLGEEVVELLFNVLFTDVSTKPSDFNWHNARKLSKSSKRILLIALCCFKFNFVMAERNHKRTLPSDDDVTEKQYKESPLFKLMNMKRMPPHLNSSFTPSAFNDIHPINHDLMIGVTKVVTIGFASDKLQDDSKAIEVEPPRTEDQNLLCESQRKMKQYEILMKVNEIISLAYQIGRHVTSGDCYNIREQLFFAGVRGNPTGGNLIIFVMLVKTCTLACMQLGLRAFTRDGHPMWLTLILDEGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OCCn1cc(-c2cnc3nnn(Cc4ccc5ncccc5c4)c3n2)cn1\"], \"target_seq\": \"YSCKHGISNKQTLNVMIRTSGLNRTLFRVLYGFKFYRTPKNPITYPCRAEVAIGLFTFFEVKLRGLFLDFNVCEAPILGELPGNQCTLMSLKEDQEEKKSTHIPELTKSILLQFFISIKPFEDVCPDPIGNFEVFPWNPEKEIRDSPYGILAAPEYKLGGMTVSSIETRVKCAIVNGLRRDQYVLALRFITEGLVCSFNQFGLLLVEIQRDIIPLNDPRGPNIEFGDEHVPELFPVLPRLDQVVITFRLLGEEVVELLFNVLFTDVSTKPSDFNWHNARKLSKSSKRILLIALCCFKFNFVMAERNHKRTLPSDDDVTEKQYKESPLFKLMNMKRMPPHLNSSFTPSAFNDIHPINHDLMIGVTKVVTIGFASDKLQDDSKAIEVEPPRTEDQNLLCESQRKMKQYEILMKVNEIISLAYQIGRHVTSGDCYNIREQLFFAGVRGNPTGGNLIIFVMLVKTCTLACMQLGLRAFTRDGHPMWLTLILDEGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"OCCn1cc(-c2cnc3nnn(Cc4ccc5ncccc5c4)c3n2)cn1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'OCCn1cc(-c2cnc3nnn(Cc4ccc5ncccc5c4)c3n2)cn1' can not interact with the protein 'MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug 'CCn1sc(=O)n(Cc2ccc(F)cc2)c1=O' will interact with the target protein 'WINEKQGKCNELILSFTLLEKRLQSAFFRLDSKCYGVLPRISFQGRSKSIGSLMYRAKASVKGNLSWMVEESADKITSGLEFCTCKPDLVKKKDSAKMSIPRSVLASFKLVCCSYAFEDDEAHHSAVERDKPSESKDSQCPSCLRIQQFKATFHESNLENVAKKENNMKHCPLKSNAIKCEAETEKDASQICKSAKYYKELNEELAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCn1sc(=O)n(Cc2ccc(F)cc2)c1=O\"], \"target_seq\": \"WINEKQGKCNELILSFTLLEKRLQSAFFRLDSKCYGVLPRISFQGRSKSIGSLMYRAKASVKGNLSWMVEESADKITSGLEFCTCKPDLVKKKDSAKMSIPRSVLASFKLVCCSYAFEDDEAHHSAVERDKPSESKDSQCPSCLRIQQFKATFHESNLENVAKKENNMKHCPLKSNAIKCEAETEKDASQICKSAKYYKELNEELAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence fitting the protein is 'MCKGLAGLPASCLRSAKDMKHRLGFLLQKSDSCEHNSSHNKKDKVVICQRVSQEEVKKWAESLENLISHECGLAAFKAFLKSEYSEENIDFWISCEEYKKIKSPSKLSPKAKKIYNEFISVQATKEVNLDSCTREETSRNMLEPTITCFDEAQKKIFNLMEKDSYRRFLKSRFYLDLVNPSSCGAEKQKGAKSSADCASLVPQCA'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MCKGLAGLPASCLRSAKDMKHRLGFLLQKSDSCEHNSSHNKKDKVVICQRVSQEEVKKWAESLENLISHECGLAAFKAFLKSEYSEENIDFWISCEEYKKIKSPSKLSPKAKKIYNEFISVQATKEVNLDSCTREETSRNMLEPTITCFDEAQKKIFNLMEKDSYRRFLKSRFYLDLVNPSSCGAEKQKGAKSSADCASLVPQCA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MCKGLAGLPASCLRSAKDMKHRLGFLLQKSDSCEHNSSHNKKDKVVICQRVSQEEVKKWAESLENLISHECGLAAFKAFLKSEYSEENIDFWISCEEYKKIKSPSKLSPKAKKIYNEFISVQATKEVNLDSCTREETSRNMLEPTITCFDEAQKKIFNLMEKDSYRRFLKSRFYLDLVNPSSCGAEKQKGAKSSADCASLVPQCA\", \"result\": [{\"smile\": \"CCn1sc(=O)n(Cc2ccc(F)cc2)c1=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCn1sc(=O)n(Cc2ccc(F)cc2)c1=O' can interact with the protein 'MCKGLAGLPASCLRSAKDMKHRLGFLLQKSDSCEHNSSHNKKDKVVICQRVSQEEVKKWAESLENLISHECGLAAFKAFLKSEYSEENIDFWISCEEYKKIKSPSKLSPKAKKIYNEFISVQATKEVNLDSCTREETSRNMLEPTITCFDEAQKKIFNLMEKDSYRRFLKSRFYLDLVNPSSCGAEKQKGAKSSADCASLVPQCA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the drug 'NC(=O)NCC1=CC(=CC=C1)C1=C(O)C(=CC=C1)C1=CC2=C(N1)C=CN=C2''s potential to interact with the target protein 'LEYDSIREGWKHTKLNTFLLESWCNDNDAYNRQLRAYQSFLWQGTLFNKITALLYRLYFFENQLEATLFRQLKMEKRHNFIIIGFKKSTQLKVCNRLLHKIAAYAGSQNCALKIRKICYVLSHCLRSEIRFIQNTMLQTTLLKSTMIPYQDQEDLSELVRGNELLVQELNELLYMHNSYLESFIVMGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)NCC1=CC(=CC=C1)C1=C(O)C(=CC=C1)C1=CC2=C(N1)C=CN=C2\"], \"target_seq\": \"LEYDSIREGWKHTKLNTFLLESWCNDNDAYNRQLRAYQSFLWQGTLFNKITALLYRLYFFENQLEATLFRQLKMEKRHNFIIIGFKKSTQLKVCNRLLHKIAAYAGSQNCALKIRKICYVLSHCLRSEIRFIQNTMLQTTLLKSTMIPYQDQEDLSELVRGNELLVQELNELLYMHNSYLESFIVMGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MTNKCLLQIALLLCFSTTALSMSYNLLGFLQRSSNFQCQKLLWQLNGRLEYCLKDRMNFDIPEEIKQLQQFQKEDAALTIYEMLQNIFAIFRQDSSSTGWNETIVENLLANVYHQINHLKTVLEEKLEKEDFTRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTGYLRN' serves as the correct sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTNKCLLQIALLLCFSTTALSMSYNLLGFLQRSSNFQCQKLLWQLNGRLEYCLKDRMNFDIPEEIKQLQQFQKEDAALTIYEMLQNIFAIFRQDSSSTGWNETIVENLLANVYHQINHLKTVLEEKLEKEDFTRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTGYLRN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTNKCLLQIALLLCFSTTALSMSYNLLGFLQRSSNFQCQKLLWQLNGRLEYCLKDRMNFDIPEEIKQLQQFQKEDAALTIYEMLQNIFAIFRQDSSSTGWNETIVENLLANVYHQINHLKTVLEEKLEKEDFTRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTGYLRN\", \"result\": [{\"smile\": \"NC(=O)NCC1=CC(=CC=C1)C1=C(O)C(=CC=C1)C1=CC2=C(N1)C=CN=C2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC(=O)NCC1=CC(=CC=C1)C1=C(O)C(=CC=C1)C1=CC2=C(N1)C=CN=C2' can not interact with the protein 'MTNKCLLQIALLLCFSTTALSMSYNLLGFLQRSSNFQCQKLLWQLNGRLEYCLKDRMNFDIPEEIKQLQQFQKEDAALTIYEMLQNIFAIFRQDSSSTGWNETIVENLLANVYHQINHLKTVLEEKLEKEDFTRGKLMSSLHLKRYYGRILHYLKAKEYSHCAWTIVRVEILRNFYFINRLTGYLRN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug 'C[C@]1(c2cc(/C=C(\\F)c3ccc(C#N)cn3)cnc2F)N=C(N)S[C@@]2(C(=O)NCC(F)F)C[C@H]21' and the target protein 'VQVTSRAGAEREACIILEAGMIGRSDHPFVRNTLTGPGIVGTGMRVFTLINRVLLPDLPGSIGETDIDDLSNAEVRPQCHAAVGPTDCLSVAKVLQCYAADKIHYATDIDKQARTWPEEKSGGHAISFVAFITMKGDPSQSLTLDYVLRTGFEKLTLNRRSGVYEPIICAFQTVNGAEFLSCEYQLLEGVQYQVSIYGSDLWEPGMWIDGSMGQVSMPSGGRRFVHSYEDLADFSSTVWVNTCAAQHETLDNGSGVSGGLREIVQGWARSTRIEIRMPAYRSYKHGYPYRDMQQDCLFSLTLVAQEWGPAHSLPLPRESGYLELPTFFFYPNLNEARLVVLPESAENINGIGMPLLVDLWADWVGNYVTTNGTVNVTKDGWCKQLKQQFIPMPDSKQGGYVFSKAKRPFIWEKMYPVFIGPEELFPIVASHCGTGQAVTHYLLTYLPVLYCLDRRMRAILRDEIQVLFSIDLDMFTIVFPSNKLAASLELAMLLDKGFGQAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@]1(c2cc(/C=C(\\F)c3ccc(C#N)cn3)cnc2F)N=C(N)S[C@@]2(C(=O)NCC(F)F)C[C@H]21\"], \"target_seq\": \"VQVTSRAGAEREACIILEAGMIGRSDHPFVRNTLTGPGIVGTGMRVFTLINRVLLPDLPGSIGETDIDDLSNAEVRPQCHAAVGPTDCLSVAKVLQCYAADKIHYATDIDKQARTWPEEKSGGHAISFVAFITMKGDPSQSLTLDYVLRTGFEKLTLNRRSGVYEPIICAFQTVNGAEFLSCEYQLLEGVQYQVSIYGSDLWEPGMWIDGSMGQVSMPSGGRRFVHSYEDLADFSSTVWVNTCAAQHETLDNGSGVSGGLREIVQGWARSTRIEIRMPAYRSYKHGYPYRDMQQDCLFSLTLVAQEWGPAHSLPLPRESGYLELPTFFFYPNLNEARLVVLPESAENINGIGMPLLVDLWADWVGNYVTTNGTVNVTKDGWCKQLKQQFIPMPDSKQGGYVFSKAKRPFIWEKMYPVFIGPEELFPIVASHCGTGQAVTHYLLTYLPVLYCLDRRMRAILRDEIQVLFSIDLDMFTIVFPSNKLAASLELAMLLDKGFGQAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The correct sequence associated with the protein is 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK\", \"result\": [{\"smile\": \"C[C@]1(c2cc(/C=C(\\F)c3ccc(C#N)cn3)cnc2F)N=C(N)S[C@@]2(C(=O)NCC(F)F)C[C@H]21\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C[C@]1(c2cc(/C=C(\\F)c3ccc(C#N)cn3)cnc2F)N=C(N)S[C@@]2(C(=O)NCC(F)F)C[C@H]21' can interact with the protein 'MAQALPWLLLWMGAGVLPAHGTQHGIRLPLRSGLGGAPLGLRLPRETDEEPEEPGRRGSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGAAPHPFLHRYYQRQLSSTYRDLRKGVYVPYTQGKWEGELGTDLVSIPHGPNVTVRANIAAITESDKFFINGSNWEGILGLAYAEIARPDDSLEPFFDSLVKQTHVPNLFSLQLCGAGFPLNQSEVLASVGGSMIIGGIDHSLYTGSLWYTPIRREWYYEVIIVRVEINGQDLKMDCKEYNYDKSIVDSGTTNLRLPKKVFEAAVKSIKAASSTEKFPDGFWLGEQLVCWQAGTTPWNIFPVISLYLMGEVTNQSFRITILPQQYLRPVEDVATSQDDCYKFAISQSSTGTVMGAVIMEGFYVVFDRARKRIGFAVSACHVHDEFRTAAVEGPFVTLDMEDCGYNIPQTDESTLMTIAYVMAAICALFMLPLCLMVCQWRCLRCLRQQHDDFADDISLLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on whether the drug '[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2' will interact with the target protein 'GDIMELSKRMSSYPIDVRTQSLKLVMLMEWIDYWYVGNKVQKSRKPMMLGEVENMSTSDAEGKYEDTQASSGHEQSDPITNPIYRQHGAWCSLGTELVQKLVRLPNEESHISWEDRESICPLYIGKKLEIARTKNRLQYYFEIPKGLQRHTLSVADLYKCRTLVTYTIEQVKGSAFSKSTLDRAHELPEDYRLNYVLVPLFNVFYPTHKLDALYDKSSVGSCGIYTTTANLTTLMFQFREQERGMIGRARSCPFYAHKELIKDTLMRIPLSDETLSECELQLIVKKAGSEVGTDINNEVLFLNLLSKVFDLTFSSAIAVMRNLGSRLSFSKKSGIMVLPESCNVDGVLDRQYSAFRVTGLPGKYSTDFRFRSSLKASNRAPIIKKVKVLRISNLLGDDPSDNEYKIGLKKSHQLKFTNITTNIKETSLQLQAVRACHPGKYVSAVWGIDIASDGACIMITKLGKIFIIVPASTEFKKLGNLLPNHSKRPWENDILPEYTHCMCLDLLTKKKYELLATTQVYYDQSSDLEEGASLNSALPSGRHKSYMAEIMKDRRNLAKLGIGNNPTRKCIDVKILTRIVRRVIRKAVIKHGIDQTDTESFPFLSDLTATPVLPLFLPGDGTWIHKCNSNSFGQIENCSGDFILEVFKEYKPATGNVVATNPFDPRRRSYFKVNEMRWDGTFINNHVANSIQESEPMKTNDKSKPFPSDRSGTTPSCYLRFGVDITGEPLLKDLSEILSSKAGPEEGFTTEPEVVAINLRHSTFSHSSTEMSNEHKEAHKTKKFDESDVTDDFNLGPEKAVATSEARTITLTKKSNNNSKYFSKIPACQVITRSLCKPHQCLTKVLFLKTSTRGYLASLDTQLDLMYVSYKRSFGCQLTEPTKMRTGVSAPATSQAYFASDILLLIGERGQISIPRLPHNELIEQFKSSCEQRWESQKFTRIEIQGIVKWHYAHGRAKLLLLTYTCVKQEPNPKWGADMHTEKCPVNQIKMDFLIRMGGSNTSFEEGGYSVYNPKASMRDRIIPEMKSIISMVETLSHREIRQSWKIVPHAGEHPKAVANDEDPDKMPSCVKCCYQYFCAILLTDSARPYARSGDTTYMNADAILHKTSLSKIYGETDSSYLRAREQVTLVAKTGKNSRNSWDEAIPLLPLFCTLVYITLSGIDPSKLNPVIDRSHCQSMGWSSKECNSLTLKYFNGTKAEDNEEWTVVRGTSFTIQECRPCSAASLNTGVEALFAGTVIYIGPVDESTTKYQVWDLGMGASSVNPENIATKSCFLMVTTDGLATSILVYLIRAEKRSESKFCTVKLRAHTKISYFSTDVNGLSIDIVFYIESVKISLAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2\"], \"target_seq\": \"GDIMELSKRMSSYPIDVRTQSLKLVMLMEWIDYWYVGNKVQKSRKPMMLGEVENMSTSDAEGKYEDTQASSGHEQSDPITNPIYRQHGAWCSLGTELVQKLVRLPNEESHISWEDRESICPLYIGKKLEIARTKNRLQYYFEIPKGLQRHTLSVADLYKCRTLVTYTIEQVKGSAFSKSTLDRAHELPEDYRLNYVLVPLFNVFYPTHKLDALYDKSSVGSCGIYTTTANLTTLMFQFREQERGMIGRARSCPFYAHKELIKDTLMRIPLSDETLSECELQLIVKKAGSEVGTDINNEVLFLNLLSKVFDLTFSSAIAVMRNLGSRLSFSKKSGIMVLPESCNVDGVLDRQYSAFRVTGLPGKYSTDFRFRSSLKASNRAPIIKKVKVLRISNLLGDDPSDNEYKIGLKKSHQLKFTNITTNIKETSLQLQAVRACHPGKYVSAVWGIDIASDGACIMITKLGKIFIIVPASTEFKKLGNLLPNHSKRPWENDILPEYTHCMCLDLLTKKKYELLATTQVYYDQSSDLEEGASLNSALPSGRHKSYMAEIMKDRRNLAKLGIGNNPTRKCIDVKILTRIVRRVIRKAVIKHGIDQTDTESFPFLSDLTATPVLPLFLPGDGTWIHKCNSNSFGQIENCSGDFILEVFKEYKPATGNVVATNPFDPRRRSYFKVNEMRWDGTFINNHVANSIQESEPMKTNDKSKPFPSDRSGTTPSCYLRFGVDITGEPLLKDLSEILSSKAGPEEGFTTEPEVVAINLRHSTFSHSSTEMSNEHKEAHKTKKFDESDVTDDFNLGPEKAVATSEARTITLTKKSNNNSKYFSKIPACQVITRSLCKPHQCLTKVLFLKTSTRGYLASLDTQLDLMYVSYKRSFGCQLTEPTKMRTGVSAPATSQAYFASDILLLIGERGQISIPRLPHNELIEQFKSSCEQRWESQKFTRIEIQGIVKWHYAHGRAKLLLLTYTCVKQEPNPKWGADMHTEKCPVNQIKMDFLIRMGGSNTSFEEGGYSVYNPKASMRDRIIPEMKSIISMVETLSHREIRQSWKIVPHAGEHPKAVANDEDPDKMPSCVKCCYQYFCAILLTDSARPYARSGDTTYMNADAILHKTSLSKIYGETDSSYLRAREQVTLVAKTGKNSRNSWDEAIPLLPLFCTLVYITLSGIDPSKLNPVIDRSHCQSMGWSSKECNSLTLKYFNGTKAEDNEEWTVVRGTSFTIQECRPCSAASLNTGVEALFAGTVIYIGPVDESTTKYQVWDLGMGASSVNPENIATKSCFLMVTTDGLATSILVYLIRAEKRSESKFCTVKLRAHTKISYFSTDVNGLSIDIVFYIESVKISLAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI' is the proper sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI\", \"result\": [{\"smile\": \"[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2' can not interact with the protein 'MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an expected interaction between the drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O' and the target protein 'AIAAFMARRKEFLTPKENEDVMMTPRYLASGKRNKVTLSMVAAERSVENEAFDIRTVDNEGAMINHSPCTIQPNKIDFAGYRGSAYARLEFSIVGEGHKLELMGWFLITGSLSLPHVGGQELSLRAGRVFLASRMVLTDYQDLKFYDSLFDPQYMKAVGDEPGTGETLFLLKFVYEDLKVHGLKPRHMTMNKLTTIWDSDVLRDLQKVRAQEESDYVEEVVRLFDDMIMVVYTIDKSTPATQPELPPWMWTVHGRDDAHQQYKFKKLKGGEEKAVTVDYTHRQRVFRPTCVCQLTPYIPIIARELLRPTSPVYGQKDLALGKDLEFYLVDLLAAYREDESEGLGQLVKKDLADALWQAAPEKLKKRDAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\"], \"target_seq\": \"AIAAFMARRKEFLTPKENEDVMMTPRYLASGKRNKVTLSMVAAERSVENEAFDIRTVDNEGAMINHSPCTIQPNKIDFAGYRGSAYARLEFSIVGEGHKLELMGWFLITGSLSLPHVGGQELSLRAGRVFLASRMVLTDYQDLKFYDSLFDPQYMKAVGDEPGTGETLFLLKFVYEDLKVHGLKPRHMTMNKLTTIWDSDVLRDLQKVRAQEESDYVEEVVRLFDDMIMVVYTIDKSTPATQPELPPWMWTVHGRDDAHQQYKFKKLKGGEEKAVTVDYTHRQRVFRPTCVCQLTPYIPIIARELLRPTSPVYGQKDLALGKDLEFYLVDLLAAYREDESEGLGQLVKKDLADALWQAAPEKLKKRDAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL\", \"result\": [{\"smile\": \"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O' can not interact with the protein 'MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'FC(F)(F)[C@]1(OC(=O)NC2=C1C=C(Cl)C=C2)C#CC1CC1' will engage with the target protein 'IFLDELIPKFLKVADVHPKGPSVPSIEARRKDLSDKFSKMEPWARTLMTGLYFDTITGAGFSGFEEEFQLNVEVQNGCDSGELFVVAVVTNKYVQMGTLYEQFMKLFIRDLENLENKPQRLISSVLYVDELLMMYLELLIQAILGNIMYQEASLVTPNLVPKLFFLHHPMKSVCFGAGPSFLFDEQSKGFVTQEKIVYFGCSSEIKLHLEQETFTRAKCRKKVPCKKIVGMDLSTDVMSLAVEGSDPVKATFDENDMTPSIPSMILNLFILPKKVLLILVANYSQHSFSQSVYLGKDQGVLSEECYRIRVPPVIPKVVKPWPWNTDELYFKFELIVLAKYKGVPLGMWFNKNITPSADVFSRHKYEVFRREKTRMNPTVPPGYLEFLGLVRGTDLTERNKLFALKTFDRAMGISYTLLTNPGKDDPPPIHSMVLTERVGKHQPLMKRLTPLATNIAPKLIRPIICAGMISPRAMILDDFDPRLTYRSKILNFFDLLDETISVQAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"FC(F)(F)[C@]1(OC(=O)NC2=C1C=C(Cl)C=C2)C#CC1CC1\"], \"target_seq\": \"IFLDELIPKFLKVADVHPKGPSVPSIEARRKDLSDKFSKMEPWARTLMTGLYFDTITGAGFSGFEEEFQLNVEVQNGCDSGELFVVAVVTNKYVQMGTLYEQFMKLFIRDLENLENKPQRLISSVLYVDELLMMYLELLIQAILGNIMYQEASLVTPNLVPKLFFLHHPMKSVCFGAGPSFLFDEQSKGFVTQEKIVYFGCSSEIKLHLEQETFTRAKCRKKVPCKKIVGMDLSTDVMSLAVEGSDPVKATFDENDMTPSIPSMILNLFILPKKVLLILVANYSQHSFSQSVYLGKDQGVLSEECYRIRVPPVIPKVVKPWPWNTDELYFKFELIVLAKYKGVPLGMWFNKNITPSADVFSRHKYEVFRREKTRMNPTVPPGYLEFLGLVRGTDLTERNKLFALKTFDRAMGISYTLLTNPGKDDPPPIHSMVLTERVGKHQPLMKRLTPLATNIAPKLIRPIICAGMISPRAMILDDFDPRLTYRSKILNFFDLLDETISVQAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA' is the right match for the protein’s sequence. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"FC(F)(F)[C@]1(OC(=O)NC2=C1C=C(Cl)C=C2)C#CC1CC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'FC(F)(F)[C@]1(OC(=O)NC2=C1C=C(Cl)C=C2)C#CC1CC1' can interact with the protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction between the drug 'COC(=O)[C@H]1[C@@H](OC(=O)c2ccccc2)C[C@@H]2CC[C@H]1N2C' and the target protein 'LQARELGWGCGGATISAIFLYVLLVHVPHRLECTITGQCGALWIFCPRNGPIFSSKVLGPAHWIVWALISELFTLQTAAILTVAETGRKCGVGMCLEGGTGSAIFRVDNGLVPLALVRCWHLPGLFMDFVELGCNVVLIDEMVGVDGGSAVMYTKRLKDAEVSFAAVVLGWGAFVVDTMWHYFPNNAFGSWVNPLRSFKRFPNYGDPQPDAGSRELCSEALPVSALASGVGIGKIKALTAIYFYFSAPSDLNYARVNPTIIKGYVRPRYMYPMVLVITAGNAGDWCCPALWETLSTLMGLLVNGGSDGVSLPVSLFYFWWLMGPFKIATRLTGVKALHWLLPACIGTITTYQRFPLLKPCASRGRSRGRFSLLEIPSGPQELPGIHFYATVGTISTVHKMRPSTSRVLETHPKVNLPLVCGPPSLASAFESYTCHVSYLLIGLSVNDALSASPSVWPLFAHSDGVKLLIASCAATAFFSWSPVLHFYIGDLMPFVGELRQPYVASTGNFQSWFGVSFWHRIVAAPVHEFEECVGTGGQASASGIVPPYAVVVFYVPQSALLDAKVEFGLFLYYTYIEQAVLTSLIMYETSNLWKSTRPPRPSRANFTIPFWKPQDKRSRFVATALSLGNPTVDARVPPSLPLVCLHKSSVITRGMTRIFFATVAAYGSDESLRSVVVAIVLLFVEYHAAWLAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)[C@H]1[C@@H](OC(=O)c2ccccc2)C[C@@H]2CC[C@H]1N2C\"], \"target_seq\": \"LQARELGWGCGGATISAIFLYVLLVHVPHRLECTITGQCGALWIFCPRNGPIFSSKVLGPAHWIVWALISELFTLQTAAILTVAETGRKCGVGMCLEGGTGSAIFRVDNGLVPLALVRCWHLPGLFMDFVELGCNVVLIDEMVGVDGGSAVMYTKRLKDAEVSFAAVVLGWGAFVVDTMWHYFPNNAFGSWVNPLRSFKRFPNYGDPQPDAGSRELCSEALPVSALASGVGIGKIKALTAIYFYFSAPSDLNYARVNPTIIKGYVRPRYMYPMVLVITAGNAGDWCCPALWETLSTLMGLLVNGGSDGVSLPVSLFYFWWLMGPFKIATRLTGVKALHWLLPACIGTITTYQRFPLLKPCASRGRSRGRFSLLEIPSGPQELPGIHFYATVGTISTVHKMRPSTSRVLETHPKVNLPLVCGPPSLASAFESYTCHVSYLLIGLSVNDALSASPSVWPLFAHSDGVKLLIASCAATAFFSWSPVLHFYIGDLMPFVGELRQPYVASTGNFQSWFGVSFWHRIVAAPVHEFEECVGTGGQASASGIVPPYAVVVFYVPQSALLDAKVEFGLFLYYTYIEQAVLTSLIMYETSNLWKSTRPPRPSRANFTIPFWKPQDKRSRFVATALSLGNPTVDARVPPSLPLVCLHKSSVITRGMTRIFFATVAAYGSDESLRSVVVAIVLLFVEYHAAWLAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MSEGRCSVAHMSSVVAPAKEANAMGPKAVELVLVKEQNGVQLTNSTLLNPPQSPTEAQDRETWSKKADFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLFFMVVAGVPLFYMELALGQFNREGAAGVWKICPILRGVGYTAILISLYIGFFYNVIIAWALHYLLSSFTTELPWTHCNHSWNSPRCSDARAPNASSGPNGTSRTTPAAEYFERGVLHLHESQGIDDLGPPRWQLTSCLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLFALLLRGITLPGAVDAIRAYLSVDFHRLCEASVWIDAAIQICFSLGVGLGVLIAFSSYNKFTNNCYRDAIITTSVNSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEALATLPLSSVWAVVFFVMLLTLGIDSAMGGMESVITGLADEFQLLHRHRELFTLLVVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLMEVIGVAWFYGVWQFSDDIKQMTGRRPSLYWRLCWKFVSPCFLLFVVVVSIATFRPPHYGAYVFPEWATALGWAIAASSMSVVPIYAAYKLCSLPGSSREKLAYAITPETEHGRVDSGGGAPVHAPPLARGVGRWRKRKSCWVPSRGPGRGGPPTPSPRLAGHTRAFPWTGAPPVPRELTPPSTCRCVPPLVCAHPAVESTGLCSVY' is the sequence meant for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSEGRCSVAHMSSVVAPAKEANAMGPKAVELVLVKEQNGVQLTNSTLLNPPQSPTEAQDRETWSKKADFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLFFMVVAGVPLFYMELALGQFNREGAAGVWKICPILRGVGYTAILISLYIGFFYNVIIAWALHYLLSSFTTELPWTHCNHSWNSPRCSDARAPNASSGPNGTSRTTPAAEYFERGVLHLHESQGIDDLGPPRWQLTSCLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLFALLLRGITLPGAVDAIRAYLSVDFHRLCEASVWIDAAIQICFSLGVGLGVLIAFSSYNKFTNNCYRDAIITTSVNSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEALATLPLSSVWAVVFFVMLLTLGIDSAMGGMESVITGLADEFQLLHRHRELFTLLVVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLMEVIGVAWFYGVWQFSDDIKQMTGRRPSLYWRLCWKFVSPCFLLFVVVVSIATFRPPHYGAYVFPEWATALGWAIAASSMSVVPIYAAYKLCSLPGSSREKLAYAITPETEHGRVDSGGGAPVHAPPLARGVGRWRKRKSCWVPSRGPGRGGPPTPSPRLAGHTRAFPWTGAPPVPRELTPPSTCRCVPPLVCAHPAVESTGLCSVY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSEGRCSVAHMSSVVAPAKEANAMGPKAVELVLVKEQNGVQLTNSTLLNPPQSPTEAQDRETWSKKADFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLFFMVVAGVPLFYMELALGQFNREGAAGVWKICPILRGVGYTAILISLYIGFFYNVIIAWALHYLLSSFTTELPWTHCNHSWNSPRCSDARAPNASSGPNGTSRTTPAAEYFERGVLHLHESQGIDDLGPPRWQLTSCLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLFALLLRGITLPGAVDAIRAYLSVDFHRLCEASVWIDAAIQICFSLGVGLGVLIAFSSYNKFTNNCYRDAIITTSVNSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEALATLPLSSVWAVVFFVMLLTLGIDSAMGGMESVITGLADEFQLLHRHRELFTLLVVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLMEVIGVAWFYGVWQFSDDIKQMTGRRPSLYWRLCWKFVSPCFLLFVVVVSIATFRPPHYGAYVFPEWATALGWAIAASSMSVVPIYAAYKLCSLPGSSREKLAYAITPETEHGRVDSGGGAPVHAPPLARGVGRWRKRKSCWVPSRGPGRGGPPTPSPRLAGHTRAFPWTGAPPVPRELTPPSTCRCVPPLVCAHPAVESTGLCSVY\", \"result\": [{\"smile\": \"COC(=O)[C@H]1[C@@H](OC(=O)c2ccccc2)C[C@@H]2CC[C@H]1N2C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC(=O)[C@H]1[C@@H](OC(=O)c2ccccc2)C[C@@H]2CC[C@H]1N2C' can interact with the protein 'MSEGRCSVAHMSSVVAPAKEANAMGPKAVELVLVKEQNGVQLTNSTLLNPPQSPTEAQDRETWSKKADFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLFFMVVAGVPLFYMELALGQFNREGAAGVWKICPILRGVGYTAILISLYIGFFYNVIIAWALHYLLSSFTTELPWTHCNHSWNSPRCSDARAPNASSGPNGTSRTTPAAEYFERGVLHLHESQGIDDLGPPRWQLTSCLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLFALLLRGITLPGAVDAIRAYLSVDFHRLCEASVWIDAAIQICFSLGVGLGVLIAFSSYNKFTNNCYRDAIITTSVNSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEALATLPLSSVWAVVFFVMLLTLGIDSAMGGMESVITGLADEFQLLHRHRELFTLLVVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLMEVIGVAWFYGVWQFSDDIKQMTGRRPSLYWRLCWKFVSPCFLLFVVVVSIATFRPPHYGAYVFPEWATALGWAIAASSMSVVPIYAAYKLCSLPGSSREKLAYAITPETEHGRVDSGGGAPVHAPPLARGVGRWRKRKSCWVPSRGPGRGGPPTPSPRLAGHTRAFPWTGAPPVPRELTPPSTCRCVPPLVCAHPAVESTGLCSVY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the drug 'Cc1cccc(C(=O)N2C[C@@H](C)OC[C@H]2Cc2cc(-c3ncccn3)ccc2F)c1-n1nccn1' can interact with the target protein 'FAPKDKFNHFYSLIQNAAWYPACGMSLIWADARIGRPSKPRQVTTARLTVLLFVGFHELALDALNYTSVCLVEVNMKSVGWLVAEWRPIVCVRIVPGVSYAPRCATGDCLIECDKLLRSSSWLIFETQSVVSIALCSVHILPKLGGVDLLLLMSHISVYRSAPKYNLKHSFERVSALKEAALGFPRVSVYVHSRILLAAGKSLRLVSIEPFVTLSSLWVPVDAWLIMPQARPVIMVEAVVGFAGPYVSLVYVLSKQGSQMRPLSTRFGRFALMMEGNMYQEDVAAFVLNLTMNALLQIDRRALYCTAPELWYSTYEVRTSVKSFTFAVKGAQRAAVPQEPPWVKFYPAAIPTADLPARVCALTCACPQLMADLLGLSRSSIHEYRRSSASYNSGKNTQFFLWLHFPALCCFPYLRIPALDRFTQIAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc(C(=O)N2C[C@@H](C)OC[C@H]2Cc2cc(-c3ncccn3)ccc2F)c1-n1nccn1\"], \"target_seq\": \"FAPKDKFNHFYSLIQNAAWYPACGMSLIWADARIGRPSKPRQVTTARLTVLLFVGFHELALDALNYTSVCLVEVNMKSVGWLVAEWRPIVCVRIVPGVSYAPRCATGDCLIECDKLLRSSSWLIFETQSVVSIALCSVHILPKLGGVDLLLLMSHISVYRSAPKYNLKHSFERVSALKEAALGFPRVSVYVHSRILLAAGKSLRLVSIEPFVTLSSLWVPVDAWLIMPQARPVIMVEAVVGFAGPYVSLVYVLSKQGSQMRPLSTRFGRFALMMEGNMYQEDVAAFVLNLTMNALLQIDRRALYCTAPELWYSTYEVRTSVKSFTFAVKGAQRAAVPQEPPWVKFYPAAIPTADLPARVCALTCACPQLMADLLGLSRSSIHEYRRSSASYNSGKNTQFFLWLHFPALCCFPYLRIPALDRFTQIAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP' is the designated sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP\", \"result\": [{\"smile\": \"Cc1cccc(C(=O)N2C[C@@H](C)OC[C@H]2Cc2cc(-c3ncccn3)ccc2F)c1-n1nccn1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cccc(C(=O)N2C[C@@H](C)OC[C@H]2Cc2cc(-c3ncccn3)ccc2F)c1-n1nccn1' can interact with the protein 'MEPSATPGAQMGVPPGSREPSPVPPDYEDEFLRYLWRDYLYPKQYEWVLIAAYVAVFVVALVGNTLVCLAVWRNHHMRTVTNYFIVNLSLADVLVTAICLPASLLVDITESWLFGHALCKVIPYLQAVSVSVAVLTLSFIALDRWYAICHPLLFKSTARRARGSILGIWAVSLAIMVPQAAVMECSSVLPELANRTRLFSVCDERWADDLYPKIYHSCFFIVTYLAPLGLMAMAYFQIFRKLWGRQIPGTTSALVRNWKRPSDQLGDLEQGLSGEPQPRARAFLAEVKQMRARRKTAKMLMVVLLVFALCYLPISVLNVLKRVFGMFRQASDREAVYACFTFSHWLVYANSAANPIIYNFLSGKFREQFKAAFSCCLPGLGPCGSLKAPSPRSSASHKSLSLQSRCSISKISEHVVLTSVTTVLP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it for the drug '[NH4+].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Al+3]' and target protein 'KDCCVNLFYEPESIYTEVPILVAKNRELNPNLPLDGVSAERAPGMSKICVEGHEGPTWETARELYDKEFPGILIQYSPEDGSICEEGGSVALQPDVIYRTQRDIKVLYLDEYVSSPALSIYYGHMEFVHVSFSEGCDTMTWSSGVKDSHDKEPTTDHYSGNVLKGSMEVIKQELNSLCACKAHMFPNAVPVVRHTDSRASQSDKSWVNDQELLGVVIQEETKKHVDLNDKAQAVNSVDAHPAKGYRTAVSVVMALPSQLDERLTEMLGSLAMLVGDPFDLENGKIPGHSTPFRDEPKLESLAPAGNDMCDPSSKDPIIDDLKKVMTDFVVGNKCSLGVRFKDKSKISKRNSPMYSKLSLVPVDCYDDGLNLRNIKWLDDPRDIGSAMHVLKKIQNSKDVQCTQYTHLVNRVSTSPKAILKTKGGSYTVSPHNIPWRMCPIGVRESGLISDFDRTILKEAECYLQLKWVIPPLRRTVNLLRKEYCRLRTYYREALSPWPLANLLSEESDSYFADNHDLENVCSHCPYHHAEESEVSKTRLKNLLQASGAGNAWVEVMHLKQYMLGVKGVPPTLTGEGAMSMVLETPWPAMMKAYADERSVLATHVLPEYSVGTTKDVDSSRGTGEGWEPIQYLMRFFPNYIAVTLLPAVAPSEDKILPRIITTHGNAEMSRVLGCMLLFSLHMQVSKWPDSYLLACEGGNRRTKFGPQLSSLPAVEQNLNPTQEKKSLALWRISETDRTALAYTLPVASQVPDPSILLWRPRWWQARDAHTPADDSSAETGFQVVSVSLLAFKSPSKGCLQMFPSTTMGRGREEVNIPRPYLEAAAAAAAAAAA' to have an interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[NH4+].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Al+3]\"], \"target_seq\": \"KDCCVNLFYEPESIYTEVPILVAKNRELNPNLPLDGVSAERAPGMSKICVEGHEGPTWETARELYDKEFPGILIQYSPEDGSICEEGGSVALQPDVIYRTQRDIKVLYLDEYVSSPALSIYYGHMEFVHVSFSEGCDTMTWSSGVKDSHDKEPTTDHYSGNVLKGSMEVIKQELNSLCACKAHMFPNAVPVVRHTDSRASQSDKSWVNDQELLGVVIQEETKKHVDLNDKAQAVNSVDAHPAKGYRTAVSVVMALPSQLDERLTEMLGSLAMLVGDPFDLENGKIPGHSTPFRDEPKLESLAPAGNDMCDPSSKDPIIDDLKKVMTDFVVGNKCSLGVRFKDKSKISKRNSPMYSKLSLVPVDCYDDGLNLRNIKWLDDPRDIGSAMHVLKKIQNSKDVQCTQYTHLVNRVSTSPKAILKTKGGSYTVSPHNIPWRMCPIGVRESGLISDFDRTILKEAECYLQLKWVIPPLRRTVNLLRKEYCRLRTYYREALSPWPLANLLSEESDSYFADNHDLENVCSHCPYHHAEESEVSKTRLKNLLQASGAGNAWVEVMHLKQYMLGVKGVPPTLTGEGAMSMVLETPWPAMMKAYADERSVLATHVLPEYSVGTTKDVDSSRGTGEGWEPIQYLMRFFPNYIAVTLLPAVAPSEDKILPRIITTHGNAEMSRVLGCMLLFSLHMQVSKWPDSYLLACEGGNRRTKFGPQLSSLPAVEQNLNPTQEKKSLALWRISETDRTALAYTLPVASQVPDPSILLWRPRWWQARDAHTPADDSSAETGFQVVSVSLLAFKSPSKGCLQMFPSTTMGRGREEVNIPRPYLEAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The protein's correct sequence is 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\", \"result\": [{\"smile\": \"[NH4+].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Al+3]\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[NH4+].[O-]S(=O)(=O)[O-].[O-]S(=O)(=O)[O-].[Al+3]' can not interact with the protein 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'CC1C\\2CCC(/C2=C/C3(C(CC(=C3C(C1O)OC4C(C(C(C(O4)COC(C)(C)C=C)O)OC(=O)C)O)C(C)COC(=O)C)O)C)COC' have a chance to interact with the target protein 'YMPTAPLIVEWNSSMMMMVRAHSCFSGGLAPISNAWFSPEPSANQSKSALVFEIAKFETIQITQKNNNERGFITSNDEECEIEIGIENQDNSRIYSLIMISVMAEAIIMNELILFTSNSRKPSPGISKEMAPQLIGRLVILYELPLLFFGIETESLKIVSWCLVISHISLFGKKELFSSRMMKVPGRPAYYTISGQTFGNKPPPGEKHTDPQNQISMTENSATTGDEVTLECYLLIGISIESAEAPYIQPTQGEVEKNQLEIIMKLPILFKPKFKAGLPLSSELVPMGCIEPNREVTAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1C\\2CCC(/C2=C/C3(C(CC(=C3C(C1O)OC4C(C(C(C(O4)COC(C)(C)C=C)O)OC(=O)C)O)C(C)COC(=O)C)O)C)COC\"], \"target_seq\": \"YMPTAPLIVEWNSSMMMMVRAHSCFSGGLAPISNAWFSPEPSANQSKSALVFEIAKFETIQITQKNNNERGFITSNDEECEIEIGIENQDNSRIYSLIMISVMAEAIIMNELILFTSNSRKPSPGISKEMAPQLIGRLVILYELPLLFFGIETESLKIVSWCLVISHISLFGKKELFSSRMMKVPGRPAYYTISGQTFGNKPPPGEKHTDPQNQISMTENSATTGDEVTLECYLLIGISIESAEAPYIQPTQGEVEKNQLEIIMKLPILFKPKFKAGLPLSSELVPMGCIEPNREVTAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MTTPRNSVNGTFPAEPMKGPIAMQSGPKPLFRRMSSLVGPTQSFFMRESKTLGAVQIMNGLFHIALGGLLMIPAGIYAPICVTVWYPLWGGIMYIISGSLLAATEKNSRKCLVKGKMIMNSLSLFAAISGMILSIMDILNIKISHFLKMESLNFIRAHTPYINIYNCEPANPSEKNSPSTQYCYSIQSLFLGILSVMLIFAFFQELVIAGIVENEWKRTCSRPKSNIVLLSAEEKKEQTIEIKEEVVGLTETSSQPKNEEDIEIIPIQEEEEEETETNFPEPPQDQESSPIENDSSP'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTTPRNSVNGTFPAEPMKGPIAMQSGPKPLFRRMSSLVGPTQSFFMRESKTLGAVQIMNGLFHIALGGLLMIPAGIYAPICVTVWYPLWGGIMYIISGSLLAATEKNSRKCLVKGKMIMNSLSLFAAISGMILSIMDILNIKISHFLKMESLNFIRAHTPYINIYNCEPANPSEKNSPSTQYCYSIQSLFLGILSVMLIFAFFQELVIAGIVENEWKRTCSRPKSNIVLLSAEEKKEQTIEIKEEVVGLTETSSQPKNEEDIEIIPIQEEEEEETETNFPEPPQDQESSPIENDSSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTTPRNSVNGTFPAEPMKGPIAMQSGPKPLFRRMSSLVGPTQSFFMRESKTLGAVQIMNGLFHIALGGLLMIPAGIYAPICVTVWYPLWGGIMYIISGSLLAATEKNSRKCLVKGKMIMNSLSLFAAISGMILSIMDILNIKISHFLKMESLNFIRAHTPYINIYNCEPANPSEKNSPSTQYCYSIQSLFLGILSVMLIFAFFQELVIAGIVENEWKRTCSRPKSNIVLLSAEEKKEQTIEIKEEVVGLTETSSQPKNEEDIEIIPIQEEEEEETETNFPEPPQDQESSPIENDSSP\", \"result\": [{\"smile\": \"CC1C\\2CCC(/C2=C/C3(C(CC(=C3C(C1O)OC4C(C(C(C(O4)COC(C)(C)C=C)O)OC(=O)C)O)C(C)COC(=O)C)O)C)COC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1C\\2CCC(/C2=C/C3(C(CC(=C3C(C1O)OC4C(C(C(C(O4)COC(C)(C)C=C)O)OC(=O)C)O)C(C)COC(=O)C)O)C)COC' can not interact with the protein 'MTTPRNSVNGTFPAEPMKGPIAMQSGPKPLFRRMSSLVGPTQSFFMRESKTLGAVQIMNGLFHIALGGLLMIPAGIYAPICVTVWYPLWGGIMYIISGSLLAATEKNSRKCLVKGKMIMNSLSLFAAISGMILSIMDILNIKISHFLKMESLNFIRAHTPYINIYNCEPANPSEKNSPSTQYCYSIQSLFLGILSVMLIFAFFQELVIAGIVENEWKRTCSRPKSNIVLLSAEEKKEQTIEIKEEVVGLTETSSQPKNEEDIEIIPIQEEEEEETETNFPEPPQDQESSPIENDSSP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' can interact with the target protein 'IPTSCRENLFRRMAIVNNDKYKDEEPTVIKTVSPVPPMLLTYITYPQINALKQHSETVADCANRYNYSEDEAEINHVEILKHYEMFNDDLTTASTDKSVTDSNMAMTVQLECTHEYYNNLKFGVGVVRNLLLAQIEESLAAVQMRGGYGEQLFGLEGNLPQKVLQGDTFFRSLTIILEQSEFPESLKIWKEIEIFDYISHLTLLEGRPPTLEFRFICDTTAALHGEKEQRNSPVRCELVEKLSDKGAWLISLIQQQHSLLEEELGIKKHEIKMIPDRTEGNEAKLGFQRRKKAQSDLGTPLSRFMLQFTAKARKVHETVVCVEFLSISKEHEQTYVFVMQFESGVQLGLPLMKAVMLQGESTEPSTNVIDADKVLFDLEAVYDRLRLKTKAPTKAPDRTFFANLPQGWHWYLKHLKLRSELGHSKQGGMAHNQAERQPHKAFTNIAGKMGVTEQDLMQELPLSLTTSLKDKVKIEINNVVERYYSPLDGMGAFTKVLSTALHKMILGITAIFVAGLTAYMRKYRNSIGGIMALIHFRTLGSKFLEVGRDILGVIYKDSELTSLVALRHEKNHDIIHIIKLDDSTDLKHGHWEILPNKHYDMMEKRHYRCEGIKMEYVGTKWLLNAYEVGCPNIEVSSREKITKVSNLKGYKSKRTPDGQRNQSEMEISVICVILIYYYWTFEHEDRVVVGTYFECNCKIKRIDYGERIEHDALLFINSLFEDLVGSTEKAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"target_seq\": \"IPTSCRENLFRRMAIVNNDKYKDEEPTVIKTVSPVPPMLLTYITYPQINALKQHSETVADCANRYNYSEDEAEINHVEILKHYEMFNDDLTTASTDKSVTDSNMAMTVQLECTHEYYNNLKFGVGVVRNLLLAQIEESLAAVQMRGGYGEQLFGLEGNLPQKVLQGDTFFRSLTIILEQSEFPESLKIWKEIEIFDYISHLTLLEGRPPTLEFRFICDTTAALHGEKEQRNSPVRCELVEKLSDKGAWLISLIQQQHSLLEEELGIKKHEIKMIPDRTEGNEAKLGFQRRKKAQSDLGTPLSRFMLQFTAKARKVHETVVCVEFLSISKEHEQTYVFVMQFESGVQLGLPLMKAVMLQGESTEPSTNVIDADKVLFDLEAVYDRLRLKTKAPTKAPDRTFFANLPQGWHWYLKHLKLRSELGHSKQGGMAHNQAERQPHKAFTNIAGKMGVTEQDLMQELPLSLTTSLKDKVKIEINNVVERYYSPLDGMGAFTKVLSTALHKMILGITAIFVAGLTAYMRKYRNSIGGIMALIHFRTLGSKFLEVGRDILGVIYKDSELTSLVALRHEKNHDIIHIIKLDDSTDLKHGHWEILPNKHYDMMEKRHYRCEGIKMEYVGTKWLLNAYEVGCPNIEVSSREKITKVSNLKGYKSKRTPDGQRNQSEMEISVICVILIYYYWTFEHEDRVVVGTYFECNCKIKRIDYGERIEHDALLFINSLFEDLVGSTEKAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL' represents the valid sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL\", \"result\": [{\"smile\": \"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' can not interact with the protein 'MQSTSNHLWLLSDILGQGATANVFRGRHKKTGDLFAIKVFNNISFLRPVDVQMREFEVLKKLNHKNIVKLFAIEEETTTRHKVLIMEFCPCGSLYTVLEEPSNAYGLPESEFLIVLRDVVGGMNHLRENGIVHRDIKPGNIMRVIGEDGQSVYKLTDFGAARELEDDEQFVSLYGTEEYLHPDMYERAVLRKDHQKKYGATVDLWSIGVTFYHAATGSLPFRPFEGPRRNKEVMYKIITGKPSGAISGVQKAENGPIDWSGDMPVSCSLSRGLQVLLTPVLANILEADQEKCWGFDQFFAETSDILHRMVIHVFSLQQMTAHKIYIHSYNTATIFHELVYKQTKIISSNQELIYEGRRLVLEPGRLAQHFPKTTEENPIFVVSREPLNTIGLIYEKISLPKVHPRYDLDGDASMAKAITGVVCYACRIASTLLLYQELMRKGIRWLIELIKDDYNETVHKKTEVVITLDFCIRNIEKTVKVYEKLMKINLEAAELGEISDIHTKLLRLSSSQGTIETSLQDIDSRLSPGGSLADAWAHQEGTHPKDRNVEKLQVLLNCMTEIYYQFKKDKAERRLAYNEEQIHKFDKQKLYYHATKAMTHFTDECVKKYEAFLNKSEEWIRKMLHLRKQLLSLTNQCFDIEEEVSKYQEYTNELQETLPQKMFTASSGIKHTMTPIYPSSNTLVEMTLGMKKLKEEMEGVVKELAENNHILERFGSLTMDGGLRNVDCL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'COc1ccc(-c2nn3c(-c4cccs4)nnc3s2)cc1OC' is likely to interact with the target protein 'ADVDAGRGLDGSLGKANSLETNLIVLVELHPAEGGYGPAASLTWAKMYLEHCVMRKGASSSLLLHEYQLLTLVRSRWNRVEHVFGVLSREPQQLSFFSVLLLPGPVSAVDYDLWPVRPSFCDRSGEGKAACQVAELAATGVVFWPTQVVVQQEIDQQSRGDPTAGEPVGARSGGIGYREEALPRFGLYFLTQAVPGDMDRLEPIQVDEACTKPEEGFRTTASAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(-c2nn3c(-c4cccs4)nnc3s2)cc1OC\"], \"target_seq\": \"ADVDAGRGLDGSLGKANSLETNLIVLVELHPAEGGYGPAASLTWAKMYLEHCVMRKGASSSLLLHEYQLLTLVRSRWNRVEHVFGVLSREPQQLSFFSVLLLPGPVSAVDYDLWPVRPSFCDRSGEGKAACQVAELAATGVVFWPTQVVVQQEIDQQSRGDPTAGEPVGARSGGIGYREEALPRFGLYFLTQAVPGDMDRLEPIQVDEACTKPEEGFRTTASAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MERIEGASVGRCAASPYLRPLTLHYRQNGAQKSWDFMKTHDSVTVLLFNSSRRSLVLVKQFRPAVYAGEVERRFPGSLAAVDQDGPRELQPALPGSAGVTVELCAGLVDQPGLSLEEVACKEAWEECGYHLAPSDLRRVATYWSGVGLTGSRQTMFYTEVTDAQRSGPGGGLVEEGELIEVVHLPLEGAQAFADDPDIPKTLGVIFGVSWFLSQVAPNLDLQ' is the sequence most proper for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MERIEGASVGRCAASPYLRPLTLHYRQNGAQKSWDFMKTHDSVTVLLFNSSRRSLVLVKQFRPAVYAGEVERRFPGSLAAVDQDGPRELQPALPGSAGVTVELCAGLVDQPGLSLEEVACKEAWEECGYHLAPSDLRRVATYWSGVGLTGSRQTMFYTEVTDAQRSGPGGGLVEEGELIEVVHLPLEGAQAFADDPDIPKTLGVIFGVSWFLSQVAPNLDLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MERIEGASVGRCAASPYLRPLTLHYRQNGAQKSWDFMKTHDSVTVLLFNSSRRSLVLVKQFRPAVYAGEVERRFPGSLAAVDQDGPRELQPALPGSAGVTVELCAGLVDQPGLSLEEVACKEAWEECGYHLAPSDLRRVATYWSGVGLTGSRQTMFYTEVTDAQRSGPGGGLVEEGELIEVVHLPLEGAQAFADDPDIPKTLGVIFGVSWFLSQVAPNLDLQ\", \"result\": [{\"smile\": \"COc1ccc(-c2nn3c(-c4cccs4)nnc3s2)cc1OC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc(-c2nn3c(-c4cccs4)nnc3s2)cc1OC' can not interact with the protein 'MERIEGASVGRCAASPYLRPLTLHYRQNGAQKSWDFMKTHDSVTVLLFNSSRRSLVLVKQFRPAVYAGEVERRFPGSLAAVDQDGPRELQPALPGSAGVTVELCAGLVDQPGLSLEEVACKEAWEECGYHLAPSDLRRVATYWSGVGLTGSRQTMFYTEVTDAQRSGPGGGLVEEGELIEVVHLPLEGAQAFADDPDIPKTLGVIFGVSWFLSQVAPNLDLQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the chances of the drug 'Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1' interacting with the target protein 'WTGVTQYEETQYDVDNKAGMPSIVEEISIIGTAQTTVMTYGQGDFHKTVTFVRYNVEDNMQYIFGSLNGGFLDQLLNQSFNIHEDWVFTRMRQFLTTACPWALAPYFYECAASGRVMGYVKTNHHVPEYFYDQGTCQHIPFIPNSFRTTMEAEPPLTKGSSGNHSYKDLAEIKPSNVGILKPNLFGTWNTPHLDALNNAKLIVGTRWIFETIADICVNPTGDDKGTNIRPLDSLQHSRHGPVIQGLPTDWSQGICAPLSVYDTGGGYAFLCVRRELKLGIVGIILNNALNRTSPAMTIAWTTRLHYGVKLFSCQYYDDYEKGMLYDDQAKTVFETREAYYAVNAFIGIRPPQVSIRYDQFPHSFYNKQAPWNLERQLCLDVAAFLLTKTGTWTTMMWETAIQPNPITYPLTHWLEENKVEPDGVKKEMIFAFVDGERVNNHEELGSFNVRKGLFAKAWGTQSDYSNPIIQAQYRRPLRELMGRLALKKITLLVQENPTCVENFDGIDYWQHLTAFAIWGDSIDVKSGSLYPTYIGGLAISGLWPILMLAMFDENDWKPVRKSEMITFVMPIIPPCSHSASGGFEDSSLNELWKCQTENFYCEQKRSAQEPNYNGRWIGEEEQLLNMLPRGSHDPFWDIVIEPQISNVVAYNHPKAGPTNRQIMQECAEFEPNHNPSEMVFALNHAAGYDGKLTQLLEVFGMFFLPGETPEEMRLFYMNTDLMIYFRIKTNPWTSLYDRPKERRWYSFIQTISELGFIRDEGNCLGDFLTPQYVEQTTDSEWTRVPMRPGGTYNLAPVDPRHNNNRVTLKPMYIGFIFAVPPYGYVRYVPQSKKIKCREGYYYKITPGSSLDNIIVFTPITISGDVTPFYGEPYRLWLLSTRVLVHGNALGMSAIHDTLTKKLKDRSTVMWLTPFEIPSRKSFITPGPITLKILPNKPLQNWIEIKTQITVFYLQTKNHYSIEIDGGIPPTPNDIHKSNNEWTVKLAASATFVLDISQHTFGTSSVSKFLLLTWPLKPDQDDRKQFALNNFEFPRSADWTVQITAFTCNRDLFSFRQKVTPLGDATNYGVHYDSASTYDDFPILKATRDVVKVPRVEGFYNNESFIETNALAGKYLTEPEPNKGLVVVGQNPSYAPGGFFSGALPMEPLKHEGIPPRLVQGGFIRETKRHNGFEEFWIAAYFIPDLESGYDAQPVIEHTYAEIKKDFEVKPDSTETTDDTIKMMQTELTVSGSTTGIPTNVFTDHGSDDLLVWGVNHGRSRNAWRTYHQPWVPDTVPGYVFIEVAEREAAIFIFHQYVQETPLHPTRSRKAFLLCNFNAGIVQFLLMDSTFQSFPTKDPEWRIIIRGGWYVIGYNDRGRGMQSAGADSGYALKRLILNGWPTYIQVPYDYGKGDSMRSSPTGEDRYVSRPLENTYTITYHKGEIRFYSYLEVARDFQEQWLLVSGKTILEHIDSNYAYGFTLYQFRGHSMLGCNPAYGSLVYVRPGRHWKFITTCTLVVGTEVSYASLSNISVVITNNTVGPRSYATCENPNIMERFLQEIYWPNAITWYSDEENNLNEDHAPIRWNFRNVFLTVDSISNNNVAELGMYWGTVIDDTPSNAYNTIFITGDNYTTYDNLRFLNENEFPLISIRYLIKLNLVPPQLIIIRGGLNLFNFILDPSLTDEQGTEQGAYPAITRGEKWQHLMTTVNYVMMTRGAGAQTPCTVKIEYALKQWSIQGIETYAEPRQTEKMGPPITGTQIGRDFENMMFPSLFMYLHHKDLGGKLPCPWRMDNDTHLSGMITFYTLEYSNWEKFSGNYQPGPQLTPDILVYILRSLEDQSNSTPDHNEFRGNEVSLSHLGAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\"], \"target_seq\": \"WTGVTQYEETQYDVDNKAGMPSIVEEISIIGTAQTTVMTYGQGDFHKTVTFVRYNVEDNMQYIFGSLNGGFLDQLLNQSFNIHEDWVFTRMRQFLTTACPWALAPYFYECAASGRVMGYVKTNHHVPEYFYDQGTCQHIPFIPNSFRTTMEAEPPLTKGSSGNHSYKDLAEIKPSNVGILKPNLFGTWNTPHLDALNNAKLIVGTRWIFETIADICVNPTGDDKGTNIRPLDSLQHSRHGPVIQGLPTDWSQGICAPLSVYDTGGGYAFLCVRRELKLGIVGIILNNALNRTSPAMTIAWTTRLHYGVKLFSCQYYDDYEKGMLYDDQAKTVFETREAYYAVNAFIGIRPPQVSIRYDQFPHSFYNKQAPWNLERQLCLDVAAFLLTKTGTWTTMMWETAIQPNPITYPLTHWLEENKVEPDGVKKEMIFAFVDGERVNNHEELGSFNVRKGLFAKAWGTQSDYSNPIIQAQYRRPLRELMGRLALKKITLLVQENPTCVENFDGIDYWQHLTAFAIWGDSIDVKSGSLYPTYIGGLAISGLWPILMLAMFDENDWKPVRKSEMITFVMPIIPPCSHSASGGFEDSSLNELWKCQTENFYCEQKRSAQEPNYNGRWIGEEEQLLNMLPRGSHDPFWDIVIEPQISNVVAYNHPKAGPTNRQIMQECAEFEPNHNPSEMVFALNHAAGYDGKLTQLLEVFGMFFLPGETPEEMRLFYMNTDLMIYFRIKTNPWTSLYDRPKERRWYSFIQTISELGFIRDEGNCLGDFLTPQYVEQTTDSEWTRVPMRPGGTYNLAPVDPRHNNNRVTLKPMYIGFIFAVPPYGYVRYVPQSKKIKCREGYYYKITPGSSLDNIIVFTPITISGDVTPFYGEPYRLWLLSTRVLVHGNALGMSAIHDTLTKKLKDRSTVMWLTPFEIPSRKSFITPGPITLKILPNKPLQNWIEIKTQITVFYLQTKNHYSIEIDGGIPPTPNDIHKSNNEWTVKLAASATFVLDISQHTFGTSSVSKFLLLTWPLKPDQDDRKQFALNNFEFPRSADWTVQITAFTCNRDLFSFRQKVTPLGDATNYGVHYDSASTYDDFPILKATRDVVKVPRVEGFYNNESFIETNALAGKYLTEPEPNKGLVVVGQNPSYAPGGFFSGALPMEPLKHEGIPPRLVQGGFIRETKRHNGFEEFWIAAYFIPDLESGYDAQPVIEHTYAEIKKDFEVKPDSTETTDDTIKMMQTELTVSGSTTGIPTNVFTDHGSDDLLVWGVNHGRSRNAWRTYHQPWVPDTVPGYVFIEVAEREAAIFIFHQYVQETPLHPTRSRKAFLLCNFNAGIVQFLLMDSTFQSFPTKDPEWRIIIRGGWYVIGYNDRGRGMQSAGADSGYALKRLILNGWPTYIQVPYDYGKGDSMRSSPTGEDRYVSRPLENTYTITYHKGEIRFYSYLEVARDFQEQWLLVSGKTILEHIDSNYAYGFTLYQFRGHSMLGCNPAYGSLVYVRPGRHWKFITTCTLVVGTEVSYASLSNISVVITNNTVGPRSYATCENPNIMERFLQEIYWPNAITWYSDEENNLNEDHAPIRWNFRNVFLTVDSISNNNVAELGMYWGTVIDDTPSNAYNTIFITGDNYTTYDNLRFLNENEFPLISIRYLIKLNLVPPQLIIIRGGLNLFNFILDPSLTDEQGTEQGAYPAITRGEKWQHLMTTVNYVMMTRGAGAQTPCTVKIEYALKQWSIQGIETYAEPRQTEKMGPPITGTQIGRDFENMMFPSLFMYLHHKDLGGKLPCPWRMDNDTHLSGMITFYTLEYSNWEKFSGNYQPGPQLTPDILVYILRSLEDQSNSTPDHNEFRGNEVSLSHLGAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The correct sequence associated with the protein is 'MAKKKFSALEISLIVLFIIVTAIAIALVTVLATKVPAVEEIKSPTPTSNSTPTSTPTSTSTPTSTSTPSPGKCPPEQGEPINERINCIPEQHPTKAICEERGCCWRPWNNTVIPWCFFADNHGYNAESITNENAGLKATLNRIPSPTLFGEDIKSVILTTQTQTGNRFRFKITDPNNKRYEVPHQFVKEETGIPAADTLYDVQVSENPFSIKVIRKSNNKVLCDTSVGPLLYSNQYLQISTRLPSEYIYGFGGHIHKRFRHDLYWKTWPIFTRDEIPGDNNHNLYGHQTFFMGIGDTSGKSYGVFLMNSNAMEVFIQPTPIITYRVTGGILDFYIFLGDTPEQVVQQYQEVHWRPAMPAYWNLGFQLSRWNYGSLDTVSEVVRRNREAGIPYDAQVTDIDYMEDHKEFTYDRVKFNGLPEFAQDLHNHGKYIIILDPAISINKRANGAEYQTYVRGNEKNVWVNESDGTTPLIGEVWPGLTVYPDFTNPQTIEWWANECNLFHQQVEYDGLWIDMNEVSSFIQGSLNLKGVLLIVLNYPPFTPGILDKVMYSKTLCMDAVQHWGKQYDVHSLYGYSMAIATEQAVERVFPNKRSFILTRSTFGGSGRHANHWLGDNTASWEQMEWSITGMLEFGIFGMPLVGATSCGFLADTTEELCRRWMQLGAFYPFSRNHNAEGYMEQDPAYFGQDSSRHYLTIRYTLLPFLYTLFYRAHMFGETVARPFLYEFYDDTNSWIEDTQFLWGPALLITPVLRPGVENVSAYIPNATWYDYETGIKRPWRKERINMYLPGDKIGLHLRGGYIIPTQEPDVTTTASRKNPLGLIVALDDNQAAKGELFWDDGESKDSIEKKMYILYTFSVSNNELVLNCTHSSYAEGTSLAFKTIKVLGLREDVRSITVGENDQQMATHTNFTFDSANKILSITALNFNLAGSFIVRWCRTFSDNEKFTCYPDVGTATEGTCTQRGCLWQPVSGLSNVPPYYFPPENNPYTLTSIQPLPTGITAELQLNPPNARIKLPSNPISTLRVGVKYHPNDMLQFKIYDAQHKRYEVPVPLNIPDTPTSSNERLYDVEIKENPFGIQVRRRSSGKLIWDSRLPGFGFNDQFIQISTRLPSNYLYGFGEVEHTAFKRDLNWHTWGMFTRDQPPGYKLNSYGFHPYYMALENEGNAHGVLLLNSNGMDVTFQPTPALTYRTIGGILDFYMFLGPTPEIATRQYHEVIGFPVMPPYWALGFQLCRYGYRNTSEIEQLYNDMVAANIPYDVQYTDINYMERQLDFTIGERFKTLPEFVDRIRKDGMKYIVILAPAISGNETQPYPAFERGIQKDVFVKWPNTNDICWPKVWPDLPNVTIDETITEDEAVNASRAHVAFPDFFRNSTLEWWAREIYDFYNEKMKFDGLWIDMNEPSSFGIQMGGKVLNECRRMMTLNYPPVFSPELRVKEGEGASISEAMCMETEHILIDGSSVLQYDVHNLYGWSQVKPTLDALQNTTGLRGIVISRSTYPTTGRWGGHWLGDNYTTWDNLEKSLIGMLELNLFGIPYIGADICGVFHDSGYPSLYFVGIQVGAFYPYPRESPTINFTRSQDPVSWMKLLLQMSKKVLEIRYTLLPYFYTQMHEAHAHGGTVIRPLMHEFFDDKETWEIYKQFLWGPAFMVTPVVEPFRTSVTGYVPKARWFDYHTGADIKLKGILHTFSAPFDTINLHVRGGYILPCQEPARNTHLSRQNYMKLIVAADDNQMAQGTLFGDDGESIDTYERGQYTSIQFNLNQTTLTSTVLANGYKNKQEMRLGSIHIWGKGTLRISNANLVYGGRKHQPPFTQEEAKETLIFDLKNMNVTLDEPIQITWS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAKKKFSALEISLIVLFIIVTAIAIALVTVLATKVPAVEEIKSPTPTSNSTPTSTPTSTSTPTSTSTPSPGKCPPEQGEPINERINCIPEQHPTKAICEERGCCWRPWNNTVIPWCFFADNHGYNAESITNENAGLKATLNRIPSPTLFGEDIKSVILTTQTQTGNRFRFKITDPNNKRYEVPHQFVKEETGIPAADTLYDVQVSENPFSIKVIRKSNNKVLCDTSVGPLLYSNQYLQISTRLPSEYIYGFGGHIHKRFRHDLYWKTWPIFTRDEIPGDNNHNLYGHQTFFMGIGDTSGKSYGVFLMNSNAMEVFIQPTPIITYRVTGGILDFYIFLGDTPEQVVQQYQEVHWRPAMPAYWNLGFQLSRWNYGSLDTVSEVVRRNREAGIPYDAQVTDIDYMEDHKEFTYDRVKFNGLPEFAQDLHNHGKYIIILDPAISINKRANGAEYQTYVRGNEKNVWVNESDGTTPLIGEVWPGLTVYPDFTNPQTIEWWANECNLFHQQVEYDGLWIDMNEVSSFIQGSLNLKGVLLIVLNYPPFTPGILDKVMYSKTLCMDAVQHWGKQYDVHSLYGYSMAIATEQAVERVFPNKRSFILTRSTFGGSGRHANHWLGDNTASWEQMEWSITGMLEFGIFGMPLVGATSCGFLADTTEELCRRWMQLGAFYPFSRNHNAEGYMEQDPAYFGQDSSRHYLTIRYTLLPFLYTLFYRAHMFGETVARPFLYEFYDDTNSWIEDTQFLWGPALLITPVLRPGVENVSAYIPNATWYDYETGIKRPWRKERINMYLPGDKIGLHLRGGYIIPTQEPDVTTTASRKNPLGLIVALDDNQAAKGELFWDDGESKDSIEKKMYILYTFSVSNNELVLNCTHSSYAEGTSLAFKTIKVLGLREDVRSITVGENDQQMATHTNFTFDSANKILSITALNFNLAGSFIVRWCRTFSDNEKFTCYPDVGTATEGTCTQRGCLWQPVSGLSNVPPYYFPPENNPYTLTSIQPLPTGITAELQLNPPNARIKLPSNPISTLRVGVKYHPNDMLQFKIYDAQHKRYEVPVPLNIPDTPTSSNERLYDVEIKENPFGIQVRRRSSGKLIWDSRLPGFGFNDQFIQISTRLPSNYLYGFGEVEHTAFKRDLNWHTWGMFTRDQPPGYKLNSYGFHPYYMALENEGNAHGVLLLNSNGMDVTFQPTPALTYRTIGGILDFYMFLGPTPEIATRQYHEVIGFPVMPPYWALGFQLCRYGYRNTSEIEQLYNDMVAANIPYDVQYTDINYMERQLDFTIGERFKTLPEFVDRIRKDGMKYIVILAPAISGNETQPYPAFERGIQKDVFVKWPNTNDICWPKVWPDLPNVTIDETITEDEAVNASRAHVAFPDFFRNSTLEWWAREIYDFYNEKMKFDGLWIDMNEPSSFGIQMGGKVLNECRRMMTLNYPPVFSPELRVKEGEGASISEAMCMETEHILIDGSSVLQYDVHNLYGWSQVKPTLDALQNTTGLRGIVISRSTYPTTGRWGGHWLGDNYTTWDNLEKSLIGMLELNLFGIPYIGADICGVFHDSGYPSLYFVGIQVGAFYPYPRESPTINFTRSQDPVSWMKLLLQMSKKVLEIRYTLLPYFYTQMHEAHAHGGTVIRPLMHEFFDDKETWEIYKQFLWGPAFMVTPVVEPFRTSVTGYVPKARWFDYHTGADIKLKGILHTFSAPFDTINLHVRGGYILPCQEPARNTHLSRQNYMKLIVAADDNQMAQGTLFGDDGESIDTYERGQYTSIQFNLNQTTLTSTVLANGYKNKQEMRLGSIHIWGKGTLRISNANLVYGGRKHQPPFTQEEAKETLIFDLKNMNVTLDEPIQITWS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAKKKFSALEISLIVLFIIVTAIAIALVTVLATKVPAVEEIKSPTPTSNSTPTSTPTSTSTPTSTSTPSPGKCPPEQGEPINERINCIPEQHPTKAICEERGCCWRPWNNTVIPWCFFADNHGYNAESITNENAGLKATLNRIPSPTLFGEDIKSVILTTQTQTGNRFRFKITDPNNKRYEVPHQFVKEETGIPAADTLYDVQVSENPFSIKVIRKSNNKVLCDTSVGPLLYSNQYLQISTRLPSEYIYGFGGHIHKRFRHDLYWKTWPIFTRDEIPGDNNHNLYGHQTFFMGIGDTSGKSYGVFLMNSNAMEVFIQPTPIITYRVTGGILDFYIFLGDTPEQVVQQYQEVHWRPAMPAYWNLGFQLSRWNYGSLDTVSEVVRRNREAGIPYDAQVTDIDYMEDHKEFTYDRVKFNGLPEFAQDLHNHGKYIIILDPAISINKRANGAEYQTYVRGNEKNVWVNESDGTTPLIGEVWPGLTVYPDFTNPQTIEWWANECNLFHQQVEYDGLWIDMNEVSSFIQGSLNLKGVLLIVLNYPPFTPGILDKVMYSKTLCMDAVQHWGKQYDVHSLYGYSMAIATEQAVERVFPNKRSFILTRSTFGGSGRHANHWLGDNTASWEQMEWSITGMLEFGIFGMPLVGATSCGFLADTTEELCRRWMQLGAFYPFSRNHNAEGYMEQDPAYFGQDSSRHYLTIRYTLLPFLYTLFYRAHMFGETVARPFLYEFYDDTNSWIEDTQFLWGPALLITPVLRPGVENVSAYIPNATWYDYETGIKRPWRKERINMYLPGDKIGLHLRGGYIIPTQEPDVTTTASRKNPLGLIVALDDNQAAKGELFWDDGESKDSIEKKMYILYTFSVSNNELVLNCTHSSYAEGTSLAFKTIKVLGLREDVRSITVGENDQQMATHTNFTFDSANKILSITALNFNLAGSFIVRWCRTFSDNEKFTCYPDVGTATEGTCTQRGCLWQPVSGLSNVPPYYFPPENNPYTLTSIQPLPTGITAELQLNPPNARIKLPSNPISTLRVGVKYHPNDMLQFKIYDAQHKRYEVPVPLNIPDTPTSSNERLYDVEIKENPFGIQVRRRSSGKLIWDSRLPGFGFNDQFIQISTRLPSNYLYGFGEVEHTAFKRDLNWHTWGMFTRDQPPGYKLNSYGFHPYYMALENEGNAHGVLLLNSNGMDVTFQPTPALTYRTIGGILDFYMFLGPTPEIATRQYHEVIGFPVMPPYWALGFQLCRYGYRNTSEIEQLYNDMVAANIPYDVQYTDINYMERQLDFTIGERFKTLPEFVDRIRKDGMKYIVILAPAISGNETQPYPAFERGIQKDVFVKWPNTNDICWPKVWPDLPNVTIDETITEDEAVNASRAHVAFPDFFRNSTLEWWAREIYDFYNEKMKFDGLWIDMNEPSSFGIQMGGKVLNECRRMMTLNYPPVFSPELRVKEGEGASISEAMCMETEHILIDGSSVLQYDVHNLYGWSQVKPTLDALQNTTGLRGIVISRSTYPTTGRWGGHWLGDNYTTWDNLEKSLIGMLELNLFGIPYIGADICGVFHDSGYPSLYFVGIQVGAFYPYPRESPTINFTRSQDPVSWMKLLLQMSKKVLEIRYTLLPYFYTQMHEAHAHGGTVIRPLMHEFFDDKETWEIYKQFLWGPAFMVTPVVEPFRTSVTGYVPKARWFDYHTGADIKLKGILHTFSAPFDTINLHVRGGYILPCQEPARNTHLSRQNYMKLIVAADDNQMAQGTLFGDDGESIDTYERGQYTSIQFNLNQTTLTSTVLANGYKNKQEMRLGSIHIWGKGTLRISNANLVYGGRKHQPPFTQEEAKETLIFDLKNMNVTLDEPIQITWS\", \"result\": [{\"smile\": \"Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'Oc1ccc(/C=C/c2cc(O)cc(O)c2)cc1' can not interact with the protein 'MAKKKFSALEISLIVLFIIVTAIAIALVTVLATKVPAVEEIKSPTPTSNSTPTSTPTSTSTPTSTSTPSPGKCPPEQGEPINERINCIPEQHPTKAICEERGCCWRPWNNTVIPWCFFADNHGYNAESITNENAGLKATLNRIPSPTLFGEDIKSVILTTQTQTGNRFRFKITDPNNKRYEVPHQFVKEETGIPAADTLYDVQVSENPFSIKVIRKSNNKVLCDTSVGPLLYSNQYLQISTRLPSEYIYGFGGHIHKRFRHDLYWKTWPIFTRDEIPGDNNHNLYGHQTFFMGIGDTSGKSYGVFLMNSNAMEVFIQPTPIITYRVTGGILDFYIFLGDTPEQVVQQYQEVHWRPAMPAYWNLGFQLSRWNYGSLDTVSEVVRRNREAGIPYDAQVTDIDYMEDHKEFTYDRVKFNGLPEFAQDLHNHGKYIIILDPAISINKRANGAEYQTYVRGNEKNVWVNESDGTTPLIGEVWPGLTVYPDFTNPQTIEWWANECNLFHQQVEYDGLWIDMNEVSSFIQGSLNLKGVLLIVLNYPPFTPGILDKVMYSKTLCMDAVQHWGKQYDVHSLYGYSMAIATEQAVERVFPNKRSFILTRSTFGGSGRHANHWLGDNTASWEQMEWSITGMLEFGIFGMPLVGATSCGFLADTTEELCRRWMQLGAFYPFSRNHNAEGYMEQDPAYFGQDSSRHYLTIRYTLLPFLYTLFYRAHMFGETVARPFLYEFYDDTNSWIEDTQFLWGPALLITPVLRPGVENVSAYIPNATWYDYETGIKRPWRKERINMYLPGDKIGLHLRGGYIIPTQEPDVTTTASRKNPLGLIVALDDNQAAKGELFWDDGESKDSIEKKMYILYTFSVSNNELVLNCTHSSYAEGTSLAFKTIKVLGLREDVRSITVGENDQQMATHTNFTFDSANKILSITALNFNLAGSFIVRWCRTFSDNEKFTCYPDVGTATEGTCTQRGCLWQPVSGLSNVPPYYFPPENNPYTLTSIQPLPTGITAELQLNPPNARIKLPSNPISTLRVGVKYHPNDMLQFKIYDAQHKRYEVPVPLNIPDTPTSSNERLYDVEIKENPFGIQVRRRSSGKLIWDSRLPGFGFNDQFIQISTRLPSNYLYGFGEVEHTAFKRDLNWHTWGMFTRDQPPGYKLNSYGFHPYYMALENEGNAHGVLLLNSNGMDVTFQPTPALTYRTIGGILDFYMFLGPTPEIATRQYHEVIGFPVMPPYWALGFQLCRYGYRNTSEIEQLYNDMVAANIPYDVQYTDINYMERQLDFTIGERFKTLPEFVDRIRKDGMKYIVILAPAISGNETQPYPAFERGIQKDVFVKWPNTNDICWPKVWPDLPNVTIDETITEDEAVNASRAHVAFPDFFRNSTLEWWAREIYDFYNEKMKFDGLWIDMNEPSSFGIQMGGKVLNECRRMMTLNYPPVFSPELRVKEGEGASISEAMCMETEHILIDGSSVLQYDVHNLYGWSQVKPTLDALQNTTGLRGIVISRSTYPTTGRWGGHWLGDNYTTWDNLEKSLIGMLELNLFGIPYIGADICGVFHDSGYPSLYFVGIQVGAFYPYPRESPTINFTRSQDPVSWMKLLLQMSKKVLEIRYTLLPYFYTQMHEAHAHGGTVIRPLMHEFFDDKETWEIYKQFLWGPAFMVTPVVEPFRTSVTGYVPKARWFDYHTGADIKLKGILHTFSAPFDTINLHVRGGYILPCQEPARNTHLSRQNYMKLIVAADDNQMAQGTLFGDDGESIDTYERGQYTSIQFNLNQTTLTSTVLANGYKNKQEMRLGSIHIWGKGTLRISNANLVYGGRKHQPPFTQEEAKETLIFDLKNMNVTLDEPIQITWS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the drug 'COC(=O)C1=CC=C2C(NC(=O)\\C2=C(/NC2=CC=C(C=C2)N(C)C(=O)CN2CCN(C)CC2)C2=CC=CC=C2)=C1''s potential to interact with the target protein 'FCMIPGLNFNFGRKVLIIRNGELYGSGKRYGADLLLLQDEDGDRAVMSGKRSGIVEPTGTRDFGPYAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)C1=CC=C2C(NC(=O)\\C2=C(/NC2=CC=C(C=C2)N(C)C(=O)CN2CCN(C)CC2)C2=CC=CC=C2)=C1\"], \"target_seq\": \"FCMIPGLNFNFGRKVLIIRNGELYGSGKRYGADLLLLQDEDGDRAVMSGKRSGIVEPTGTRDFGPYAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MTGLSMDGGGSPKGDVDPFYYDYETVRNGGLIFAGLAFIVGLLILLSRRFRCGGNKKRRQINEDEP' is the right match for the protein’s sequence. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTGLSMDGGGSPKGDVDPFYYDYETVRNGGLIFAGLAFIVGLLILLSRRFRCGGNKKRRQINEDEP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTGLSMDGGGSPKGDVDPFYYDYETVRNGGLIFAGLAFIVGLLILLSRRFRCGGNKKRRQINEDEP\", \"result\": [{\"smile\": \"COC(=O)C1=CC=C2C(NC(=O)\\C2=C(/NC2=CC=C(C=C2)N(C)C(=O)CN2CCN(C)CC2)C2=CC=CC=C2)=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC(=O)C1=CC=C2C(NC(=O)\\C2=C(/NC2=CC=C(C=C2)N(C)C(=O)CN2CCN(C)CC2)C2=CC=CC=C2)=C1' can not interact with the protein 'MTGLSMDGGGSPKGDVDPFYYDYETVRNGGLIFAGLAFIVGLLILLSRRFRCGGNKKRRQINEDEP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'C(C(CO)O)O' likely form an interaction with the target protein 'YKMIFKVLGRKTNPVAGNTPIEYQDLESSWNTGSGAQAFSKELSGWASDKPLANPEVGPSLGGALSKVKLKRVAPEKKKAGEAGNLKLGRACAIGDKRILIESHSIYQIPAVVAPRNYFRNKSELLVGCLEEGTNASLEYIPQLQKSGVKLFSWAGYISDFSQSAIANISYKQVTCFEITNVASRNELVAHVIIILGPQNSSKDGVAVVTGSEVRTHWVMVTKCEKGTLLPSQVVYSWGAAEGTTGQLFLMSVMFGGVRAALAKSLHFIEGISGGDLNGHAGVTDHFPNNAVEFCTAKVVPWIAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(CO)O)O\"], \"target_seq\": \"YKMIFKVLGRKTNPVAGNTPIEYQDLESSWNTGSGAQAFSKELSGWASDKPLANPEVGPSLGGALSKVKLKRVAPEKKKAGEAGNLKLGRACAIGDKRILIESHSIYQIPAVVAPRNYFRNKSELLVGCLEEGTNASLEYIPQLQKSGVKLFSWAGYISDFSQSAIANISYKQVTCFEITNVASRNELVAHVIIILGPQNSSKDGVAVVTGSEVRTHWVMVTKCEKGTLLPSQVVYSWGAAEGTTGQLFLMSVMFGGVRAALAKSLHFIEGISGGDLNGHAGVTDHFPNNAVEFCTAKVVPWIAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MASWAKGRSYLAPGLLQGQVAIVTGGATGIGKAIVKELLELGSNVVIASRKLERLKSAADELQANLPPTKQARVIPIQCNIRNEEEVNNLVKSTLDTFGKINFLVNNGGGQFLSPAEHISSKGWHAVLETNLTGTFYMCKAVYSSWMKEHGGSIVNIIVPTKAGFPLAVHSGAARAGVYNLTKSLALEWACSGIRINCVAPGVIYSQTAVENYGSWGQSFFEGSFQKIPAKRIGVPEEVSSVVCFLLSPAASFITGQSVDVDGGRSLYTHSYEVPDHDNWPKGAGDLSVVKKMKETFKEKAKL' is the sequence intended for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASWAKGRSYLAPGLLQGQVAIVTGGATGIGKAIVKELLELGSNVVIASRKLERLKSAADELQANLPPTKQARVIPIQCNIRNEEEVNNLVKSTLDTFGKINFLVNNGGGQFLSPAEHISSKGWHAVLETNLTGTFYMCKAVYSSWMKEHGGSIVNIIVPTKAGFPLAVHSGAARAGVYNLTKSLALEWACSGIRINCVAPGVIYSQTAVENYGSWGQSFFEGSFQKIPAKRIGVPEEVSSVVCFLLSPAASFITGQSVDVDGGRSLYTHSYEVPDHDNWPKGAGDLSVVKKMKETFKEKAKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MASWAKGRSYLAPGLLQGQVAIVTGGATGIGKAIVKELLELGSNVVIASRKLERLKSAADELQANLPPTKQARVIPIQCNIRNEEEVNNLVKSTLDTFGKINFLVNNGGGQFLSPAEHISSKGWHAVLETNLTGTFYMCKAVYSSWMKEHGGSIVNIIVPTKAGFPLAVHSGAARAGVYNLTKSLALEWACSGIRINCVAPGVIYSQTAVENYGSWGQSFFEGSFQKIPAKRIGVPEEVSSVVCFLLSPAASFITGQSVDVDGGRSLYTHSYEVPDHDNWPKGAGDLSVVKKMKETFKEKAKL\", \"result\": [{\"smile\": \"C(C(CO)O)O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(C(CO)O)O' can interact with the protein 'MASWAKGRSYLAPGLLQGQVAIVTGGATGIGKAIVKELLELGSNVVIASRKLERLKSAADELQANLPPTKQARVIPIQCNIRNEEEVNNLVKSTLDTFGKINFLVNNGGGQFLSPAEHISSKGWHAVLETNLTGTFYMCKAVYSSWMKEHGGSIVNIIVPTKAGFPLAVHSGAARAGVYNLTKSLALEWACSGIRINCVAPGVIYSQTAVENYGSWGQSFFEGSFQKIPAKRIGVPEEVSSVVCFLLSPAASFITGQSVDVDGGRSLYTHSYEVPDHDNWPKGAGDLSVVKKMKETFKEKAKL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think the drug '[H][C@](C)(C1=CC=CN1)C1=C2C=C(NC(N)=O)C=CC2=NC1=O' will have any interaction with the target protein 'NVRIGAAPNGRNNDAPEDHEAGLDIECKLNVIHRKTEYPDPMNIQYIKLFSNALLFDETEEWCQGLQLFILFDRRDDAKYFRGDEKGVIFMFNIFAGGATILRVICSKTPESEDPNRQKLTRAFERTLGIGYLEEKFYELIFNKMLQERLFPKEWAWPNILLDKKNIKNENEIEQAELYDVTRDSSLALGRHSTPRRKFKIFLEGNGKWIEEENLIMRGHNEFVEGWRFSNRNPQRLRRGKQKDMGDSVMLVGHKNTVYVATIKKMLYTFHVSRERPPAIDLSNEDLYSVTGLLTKDDSSYVMICQREIVPQKGEKGLKNFSIGDERSLKGEADDGDRKERICVPTYTQPSKLLGCPTMEPMDEAERLPTSEVEILLDEEVNEYILKAKLRFEASEDGVDAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@](C)(C1=CC=CN1)C1=C2C=C(NC(N)=O)C=CC2=NC1=O\"], \"target_seq\": \"NVRIGAAPNGRNNDAPEDHEAGLDIECKLNVIHRKTEYPDPMNIQYIKLFSNALLFDETEEWCQGLQLFILFDRRDDAKYFRGDEKGVIFMFNIFAGGATILRVICSKTPESEDPNRQKLTRAFERTLGIGYLEEKFYELIFNKMLQERLFPKEWAWPNILLDKKNIKNENEIEQAELYDVTRDSSLALGRHSTPRRKFKIFLEGNGKWIEEENLIMRGHNEFVEGWRFSNRNPQRLRRGKQKDMGDSVMLVGHKNTVYVATIKKMLYTFHVSRERPPAIDLSNEDLYSVTGLLTKDDSSYVMICQREIVPQKGEKGLKNFSIGDERSLKGEADDGDRKERICVPTYTQPSKLLGCPTMEPMDEAERLPTSEVEILLDEEVNEYILKAKLRFEASEDGVDAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MDEDGGGEGGGVPEDLSLEEREELLDIRRRKKELIDDIERLKYEIAEVMTEIDNLTSVEESKTTQRNKQIAMGRKKFNMDPKKGIQFLIENDLLQSSPEDVAQFLYKGEGLNKTVIGDYLGERDEFNIKVLQAFVELHEFADLNLVQALRQFLWSFRLPGEAQKIDRMMEAFASRYCLCNPGVFQSTDTCYVLSFAIIMLNTSLHNHNVRDKPTAERFIAMNRGINEGGDLPEELLRNLYESIKNEPFKIPEDDGNDLTHTFFNPDREGWLLKLGGGRVKTWKRRWFILTDNCLYYFEYTTDKEPRGIIPLENLSIREVEDPRKPNCFELYNPSHKGQVIKACKTEADGRVVEGNHVVYRISAPSPEEKEEWMKSIKASISRDPFYDMLATRKRRIANKK'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDEDGGGEGGGVPEDLSLEEREELLDIRRRKKELIDDIERLKYEIAEVMTEIDNLTSVEESKTTQRNKQIAMGRKKFNMDPKKGIQFLIENDLLQSSPEDVAQFLYKGEGLNKTVIGDYLGERDEFNIKVLQAFVELHEFADLNLVQALRQFLWSFRLPGEAQKIDRMMEAFASRYCLCNPGVFQSTDTCYVLSFAIIMLNTSLHNHNVRDKPTAERFIAMNRGINEGGDLPEELLRNLYESIKNEPFKIPEDDGNDLTHTFFNPDREGWLLKLGGGRVKTWKRRWFILTDNCLYYFEYTTDKEPRGIIPLENLSIREVEDPRKPNCFELYNPSHKGQVIKACKTEADGRVVEGNHVVYRISAPSPEEKEEWMKSIKASISRDPFYDMLATRKRRIANKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDEDGGGEGGGVPEDLSLEEREELLDIRRRKKELIDDIERLKYEIAEVMTEIDNLTSVEESKTTQRNKQIAMGRKKFNMDPKKGIQFLIENDLLQSSPEDVAQFLYKGEGLNKTVIGDYLGERDEFNIKVLQAFVELHEFADLNLVQALRQFLWSFRLPGEAQKIDRMMEAFASRYCLCNPGVFQSTDTCYVLSFAIIMLNTSLHNHNVRDKPTAERFIAMNRGINEGGDLPEELLRNLYESIKNEPFKIPEDDGNDLTHTFFNPDREGWLLKLGGGRVKTWKRRWFILTDNCLYYFEYTTDKEPRGIIPLENLSIREVEDPRKPNCFELYNPSHKGQVIKACKTEADGRVVEGNHVVYRISAPSPEEKEEWMKSIKASISRDPFYDMLATRKRRIANKK\", \"result\": [{\"smile\": \"[H][C@](C)(C1=CC=CN1)C1=C2C=C(NC(N)=O)C=CC2=NC1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@](C)(C1=CC=CN1)C1=C2C=C(NC(N)=O)C=CC2=NC1=O' can not interact with the protein 'MDEDGGGEGGGVPEDLSLEEREELLDIRRRKKELIDDIERLKYEIAEVMTEIDNLTSVEESKTTQRNKQIAMGRKKFNMDPKKGIQFLIENDLLQSSPEDVAQFLYKGEGLNKTVIGDYLGERDEFNIKVLQAFVELHEFADLNLVQALRQFLWSFRLPGEAQKIDRMMEAFASRYCLCNPGVFQSTDTCYVLSFAIIMLNTSLHNHNVRDKPTAERFIAMNRGINEGGDLPEELLRNLYESIKNEPFKIPEDDGNDLTHTFFNPDREGWLLKLGGGRVKTWKRRWFILTDNCLYYFEYTTDKEPRGIIPLENLSIREVEDPRKPNCFELYNPSHKGQVIKACKTEADGRVVEGNHVVYRISAPSPEEKEEWMKSIKASISRDPFYDMLATRKRRIANKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the chances of the drug 'N#Cc1ccccc1Cn1c(N2CCC[C@@H](N)C2)ncc(-c2ccoc2)c1=O' interacting with the target protein 'HYKDKYEETYELKLLVEKHRVFGVIPFESPVPFYSNILSPEFQDIWVESAIRRTKLMREHLLYEDGHKLADQWGRIDTLSETLGITNQPHGLPKRWSIFEMFNGREEITSGQQYRESPALIEISYEDDRGPNHEDDQALQDGLSRPVDPAIRKEKPYKCLGGRCIILLPRFTLSNYILCILIPGIDRHTGCKAINKQISEEPWYSAMMIHWHFPIRNGRLAKKKKCATYYAYAFEYPLLSEKYRSEPSKYEAVWHDEDSHDGQTAHPWFDLVPYAELLLISEENRGKGEEKGNTYDYSVITDKADRLRLCGDPRVPSFLMRLKSFGELSVARILSPVNPDTRLKSFDTLEPTNGYYDMVAMDADSIEAMCLERVQSTITDIYRLKWGSPFKMFRYQEFFILELMFCQSYVGSAISLGRITYGKASGIEDTDLGIRFAEVDMENVQLYRKRLGQTFAEPNLTDHHILVGTGYYQPRPATEYIAVQKQYGFHQQFLLVFIEGIAHYRQRYERPIGESGSKICNQLKRVSQEYPHYVEHMVHEGLCERVGTPERMTVLLPHTKTINMVGSSDLFQKEHESSGRKFKRANATTEWQVQVGAGFMVEELTSTMPLSEDIPDGWDPSALRTIRIPSANHLPESIELEYHESITLIIDAIGMYCDHVLYITLRNFGAYNKACRVRNSVLEVFFQSNPNLVVIIPGRGISIVKIKKGLYNKDAGYVWMSVQTRLSETNPIKPLFGYKGEMYAEHGLMVEERDILSKLEFADSAVFTIDLEYFSDPSIQLAFDLLNIEHFEGNGERADIDVNVDWVKSLHYPKSFLSFDVPDPEEGAVVTQFSFFGKEISWIMLQSTRGQHGTHGWYGFIALVSCPEAPYVYAPTDSFYETTSSDLTQYIPGLIEMKAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N#Cc1ccccc1Cn1c(N2CCC[C@@H](N)C2)ncc(-c2ccoc2)c1=O\"], \"target_seq\": \"HYKDKYEETYELKLLVEKHRVFGVIPFESPVPFYSNILSPEFQDIWVESAIRRTKLMREHLLYEDGHKLADQWGRIDTLSETLGITNQPHGLPKRWSIFEMFNGREEITSGQQYRESPALIEISYEDDRGPNHEDDQALQDGLSRPVDPAIRKEKPYKCLGGRCIILLPRFTLSNYILCILIPGIDRHTGCKAINKQISEEPWYSAMMIHWHFPIRNGRLAKKKKCATYYAYAFEYPLLSEKYRSEPSKYEAVWHDEDSHDGQTAHPWFDLVPYAELLLISEENRGKGEEKGNTYDYSVITDKADRLRLCGDPRVPSFLMRLKSFGELSVARILSPVNPDTRLKSFDTLEPTNGYYDMVAMDADSIEAMCLERVQSTITDIYRLKWGSPFKMFRYQEFFILELMFCQSYVGSAISLGRITYGKASGIEDTDLGIRFAEVDMENVQLYRKRLGQTFAEPNLTDHHILVGTGYYQPRPATEYIAVQKQYGFHQQFLLVFIEGIAHYRQRYERPIGESGSKICNQLKRVSQEYPHYVEHMVHEGLCERVGTPERMTVLLPHTKTINMVGSSDLFQKEHESSGRKFKRANATTEWQVQVGAGFMVEELTSTMPLSEDIPDGWDPSALRTIRIPSANHLPESIELEYHESITLIIDAIGMYCDHVLYITLRNFGAYNKACRVRNSVLEVFFQSNPNLVVIIPGRGISIVKIKKGLYNKDAGYVWMSVQTRLSETNPIKPLFGYKGEMYAEHGLMVEERDILSKLEFADSAVFTIDLEYFSDPSIQLAFDLLNIEHFEGNGERADIDVNVDWVKSLHYPKSFLSFDVPDPEEGAVVTQFSFFGKEISWIMLQSTRGQHGTHGWYGFIALVSCPEAPYVYAPTDSFYETTSSDLTQYIPGLIEMKAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MWKRSEQMKIKSGKCNMAAAMETEQLGVEIFETADCEENIESQDRPKLEPFYVERYSWSQLKKLLADTRKYHGYMMAKAPHDFMFVKRNDPDGPHSDRIYYLAMSGENRENTLFYSEIPKTINRAAVLMLSWKPLLDLFQATLDYGMYSREEELLRERKRIGTVGIASYDYHQGSGTFLFQAGSGIYHVKDGGPQGFTQQPLRPNLVETSCPNIRMDPKLCPADPDWIAFIHSNDIWISNIVTREERRLTYVHNELANMEEDARSAGVATFVLQEEFDRYSGYWWCPKAETTPSGGKILRILYEENDESEVEIIHVTSPMLETRRADSFRYPKTGTANPKVTFKMSEIMIDAEGRIIDVIDKELIQPFEILFEGVEYIARAGWTPEGKYAWSILLDRSQTRLQIVLISPELFIPVEDDVMERQRLIESVPDSVTPLIIYEETTDIWINIHDIFHVFPQSHEEEIEFIFASECKTGFRHLYKITSILKESKYKRSSGGLPAPSDFKCPIKEEIAITSGEWEVLGRHGSNIQVDEVRRLVYFEGTKDSPLEHHLYVVSYVNPGEVTRLTDRGYSHSCCISQHCDFFISKYSNQKNPHCVSLYKLSSPEDDPTCKTKEFWATILDSAGPLPDYTPPEIFSFESTTGFTLYGMLYKPHDLQPGKKYPTVLFIYGGPQVQLVNNRFKGVKYFRLNTLASLGYVVVVIDNRGSCHRGLKFEGAFKYKMGQIEIDDQVEGLQYLASRYDFIDLDRVGIHGWSYGGYLSLMALMQRSDIFRVAIAGAPVTLWIFYDTGYTERYMGHPDQNEQGYYLGSVAMQAEKFPSEPNRLLLLHGFLDENVHFAHTSILLSFLVRAGKPYDLQIYPQERHSIRVPESGEHYELHLLHYLQENLGSRIAALKVI' represents the valid sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MWKRSEQMKIKSGKCNMAAAMETEQLGVEIFETADCEENIESQDRPKLEPFYVERYSWSQLKKLLADTRKYHGYMMAKAPHDFMFVKRNDPDGPHSDRIYYLAMSGENRENTLFYSEIPKTINRAAVLMLSWKPLLDLFQATLDYGMYSREEELLRERKRIGTVGIASYDYHQGSGTFLFQAGSGIYHVKDGGPQGFTQQPLRPNLVETSCPNIRMDPKLCPADPDWIAFIHSNDIWISNIVTREERRLTYVHNELANMEEDARSAGVATFVLQEEFDRYSGYWWCPKAETTPSGGKILRILYEENDESEVEIIHVTSPMLETRRADSFRYPKTGTANPKVTFKMSEIMIDAEGRIIDVIDKELIQPFEILFEGVEYIARAGWTPEGKYAWSILLDRSQTRLQIVLISPELFIPVEDDVMERQRLIESVPDSVTPLIIYEETTDIWINIHDIFHVFPQSHEEEIEFIFASECKTGFRHLYKITSILKESKYKRSSGGLPAPSDFKCPIKEEIAITSGEWEVLGRHGSNIQVDEVRRLVYFEGTKDSPLEHHLYVVSYVNPGEVTRLTDRGYSHSCCISQHCDFFISKYSNQKNPHCVSLYKLSSPEDDPTCKTKEFWATILDSAGPLPDYTPPEIFSFESTTGFTLYGMLYKPHDLQPGKKYPTVLFIYGGPQVQLVNNRFKGVKYFRLNTLASLGYVVVVIDNRGSCHRGLKFEGAFKYKMGQIEIDDQVEGLQYLASRYDFIDLDRVGIHGWSYGGYLSLMALMQRSDIFRVAIAGAPVTLWIFYDTGYTERYMGHPDQNEQGYYLGSVAMQAEKFPSEPNRLLLLHGFLDENVHFAHTSILLSFLVRAGKPYDLQIYPQERHSIRVPESGEHYELHLLHYLQENLGSRIAALKVI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWKRSEQMKIKSGKCNMAAAMETEQLGVEIFETADCEENIESQDRPKLEPFYVERYSWSQLKKLLADTRKYHGYMMAKAPHDFMFVKRNDPDGPHSDRIYYLAMSGENRENTLFYSEIPKTINRAAVLMLSWKPLLDLFQATLDYGMYSREEELLRERKRIGTVGIASYDYHQGSGTFLFQAGSGIYHVKDGGPQGFTQQPLRPNLVETSCPNIRMDPKLCPADPDWIAFIHSNDIWISNIVTREERRLTYVHNELANMEEDARSAGVATFVLQEEFDRYSGYWWCPKAETTPSGGKILRILYEENDESEVEIIHVTSPMLETRRADSFRYPKTGTANPKVTFKMSEIMIDAEGRIIDVIDKELIQPFEILFEGVEYIARAGWTPEGKYAWSILLDRSQTRLQIVLISPELFIPVEDDVMERQRLIESVPDSVTPLIIYEETTDIWINIHDIFHVFPQSHEEEIEFIFASECKTGFRHLYKITSILKESKYKRSSGGLPAPSDFKCPIKEEIAITSGEWEVLGRHGSNIQVDEVRRLVYFEGTKDSPLEHHLYVVSYVNPGEVTRLTDRGYSHSCCISQHCDFFISKYSNQKNPHCVSLYKLSSPEDDPTCKTKEFWATILDSAGPLPDYTPPEIFSFESTTGFTLYGMLYKPHDLQPGKKYPTVLFIYGGPQVQLVNNRFKGVKYFRLNTLASLGYVVVVIDNRGSCHRGLKFEGAFKYKMGQIEIDDQVEGLQYLASRYDFIDLDRVGIHGWSYGGYLSLMALMQRSDIFRVAIAGAPVTLWIFYDTGYTERYMGHPDQNEQGYYLGSVAMQAEKFPSEPNRLLLLHGFLDENVHFAHTSILLSFLVRAGKPYDLQIYPQERHSIRVPESGEHYELHLLHYLQENLGSRIAALKVI\", \"result\": [{\"smile\": \"N#Cc1ccccc1Cn1c(N2CCC[C@@H](N)C2)ncc(-c2ccoc2)c1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N#Cc1ccccc1Cn1c(N2CCC[C@@H](N)C2)ncc(-c2ccoc2)c1=O' can not interact with the protein 'MWKRSEQMKIKSGKCNMAAAMETEQLGVEIFETADCEENIESQDRPKLEPFYVERYSWSQLKKLLADTRKYHGYMMAKAPHDFMFVKRNDPDGPHSDRIYYLAMSGENRENTLFYSEIPKTINRAAVLMLSWKPLLDLFQATLDYGMYSREEELLRERKRIGTVGIASYDYHQGSGTFLFQAGSGIYHVKDGGPQGFTQQPLRPNLVETSCPNIRMDPKLCPADPDWIAFIHSNDIWISNIVTREERRLTYVHNELANMEEDARSAGVATFVLQEEFDRYSGYWWCPKAETTPSGGKILRILYEENDESEVEIIHVTSPMLETRRADSFRYPKTGTANPKVTFKMSEIMIDAEGRIIDVIDKELIQPFEILFEGVEYIARAGWTPEGKYAWSILLDRSQTRLQIVLISPELFIPVEDDVMERQRLIESVPDSVTPLIIYEETTDIWINIHDIFHVFPQSHEEEIEFIFASECKTGFRHLYKITSILKESKYKRSSGGLPAPSDFKCPIKEEIAITSGEWEVLGRHGSNIQVDEVRRLVYFEGTKDSPLEHHLYVVSYVNPGEVTRLTDRGYSHSCCISQHCDFFISKYSNQKNPHCVSLYKLSSPEDDPTCKTKEFWATILDSAGPLPDYTPPEIFSFESTTGFTLYGMLYKPHDLQPGKKYPTVLFIYGGPQVQLVNNRFKGVKYFRLNTLASLGYVVVVIDNRGSCHRGLKFEGAFKYKMGQIEIDDQVEGLQYLASRYDFIDLDRVGIHGWSYGGYLSLMALMQRSDIFRVAIAGAPVTLWIFYDTGYTERYMGHPDQNEQGYYLGSVAMQAEKFPSEPNRLLLLHGFLDENVHFAHTSILLSFLVRAGKPYDLQIYPQERHSIRVPESGEHYELHLLHYLQENLGSRIAALKVI'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the drug 'O=C1NC(=O)c2ccc(I)cc2C1=CNCc1ccc(-c2ccoc2)c(O)c1''s potential to interact with the target protein 'PSLYSTVATYTRLNDTKEVMPANYHDSNCGGLSDLFVFYIYNGPLLSGLNELNSYKRCIKVAIDSHLNIVIRQLRSNRKVWLNTLMFAAVCRLEHIAVHALKAACLLPLDGPISPDPPNSQLTYGLIDNCKEGSRRTQECDAEAAAHLKGESKHRISGKESNSSGQYREGNRKFQVISRPLWLILCLHLFNADCNAFFWNLVKKLKGTRLECSEGTNPVGEGYALELCLEEGCEVVSGSGDLDPKNVNVCLILGSIPKLKCGDNGDRSDMQHGDQTIRGLSMYDEWADDALLQKVIPANNLFDNWIPRDPIQTLRAVNVYSLCPLMKTPPQPYEFGDKSSEGHCALFLKLIGELVSCLPCVILILAGLLEICGRYINTSWIVNPLLQFFTDHWSPKLHISLQVNRRPHTKMLQNEEGGPTSQDICVHVNLKSDGMNCTVTGRSYKLGTETIKCGKTQSCQIIPRARLANGEKLGHCQKSMGCIVIEPERPIPELTLPQSSQKPMLYQNEYFPRNFCGEDQTWNEFSGRGVCIRQVRKVQSSRSLLYRQSCLKDMSQKLNSEKNDVGRRRAVLRDDRFMNNQTMFETGELHSSVNRAYACWTNALTMCTSVIEHEDCGIVLHFTGISRISGNANAEEETVQCGCNAVEAGLADTFHETKEMSSNVSLGKHYGDGILRHAGSISPTFESIICCIFPSKTFVSSDLQDADRVARSAFPNDEAGMPIGLDEAPTKALNKKAICRTDSVTWPCGGMLCLQQGPINLQQPVGGLAPDKTSLKCLHIYMSYVERASQDKRTTRIKCVIPIGVGINQMECATDMLCACARASIPREICGYRDEVFHQSIPASVSATPQNDTNLPWKPRYQDGSKEVGTEFGYLHYIWFPTLGTAESEMFECMQVSDNKDCCEAVKDLLRPFCIVGDVLTISTGNEVMRNQKVGKGSAVTYADEGLFFYRPGIDPGGKPTTGIKPVNAKVSAVLFVMLLNPDAEYVGEPNLRQMTPQIQCPPHEEMCPLGVPVHPEPNINEACIGLPPISPTCIGAQGQEPPSSIHPRTCKTWDCYALNAHFLADIPSGDLNLVICEEPSPDPNVDYVKTALECATTKQECAEKYNHQKLSHTVCCYRPKARPFQKDELLYTNNSNITAPSDKKSRKGHLTDRESLRGEEPRVTQYALQKECYVTVFYAAHRGIELCLGAELFKSIVDIENKKADFVAGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1NC(=O)c2ccc(I)cc2C1=CNCc1ccc(-c2ccoc2)c(O)c1\"], \"target_seq\": \"PSLYSTVATYTRLNDTKEVMPANYHDSNCGGLSDLFVFYIYNGPLLSGLNELNSYKRCIKVAIDSHLNIVIRQLRSNRKVWLNTLMFAAVCRLEHIAVHALKAACLLPLDGPISPDPPNSQLTYGLIDNCKEGSRRTQECDAEAAAHLKGESKHRISGKESNSSGQYREGNRKFQVISRPLWLILCLHLFNADCNAFFWNLVKKLKGTRLECSEGTNPVGEGYALELCLEEGCEVVSGSGDLDPKNVNVCLILGSIPKLKCGDNGDRSDMQHGDQTIRGLSMYDEWADDALLQKVIPANNLFDNWIPRDPIQTLRAVNVYSLCPLMKTPPQPYEFGDKSSEGHCALFLKLIGELVSCLPCVILILAGLLEICGRYINTSWIVNPLLQFFTDHWSPKLHISLQVNRRPHTKMLQNEEGGPTSQDICVHVNLKSDGMNCTVTGRSYKLGTETIKCGKTQSCQIIPRARLANGEKLGHCQKSMGCIVIEPERPIPELTLPQSSQKPMLYQNEYFPRNFCGEDQTWNEFSGRGVCIRQVRKVQSSRSLLYRQSCLKDMSQKLNSEKNDVGRRRAVLRDDRFMNNQTMFETGELHSSVNRAYACWTNALTMCTSVIEHEDCGIVLHFTGISRISGNANAEEETVQCGCNAVEAGLADTFHETKEMSSNVSLGKHYGDGILRHAGSISPTFESIICCIFPSKTFVSSDLQDADRVARSAFPNDEAGMPIGLDEAPTKALNKKAICRTDSVTWPCGGMLCLQQGPINLQQPVGGLAPDKTSLKCLHIYMSYVERASQDKRTTRIKCVIPIGVGINQMECATDMLCACARASIPREICGYRDEVFHQSIPASVSATPQNDTNLPWKPRYQDGSKEVGTEFGYLHYIWFPTLGTAESEMFECMQVSDNKDCCEAVKDLLRPFCIVGDVLTISTGNEVMRNQKVGKGSAVTYADEGLFFYRPGIDPGGKPTTGIKPVNAKVSAVLFVMLLNPDAEYVGEPNLRQMTPQIQCPPHEEMCPLGVPVHPEPNINEACIGLPPISPTCIGAQGQEPPSSIHPRTCKTWDCYALNAHFLADIPSGDLNLVICEEPSPDPNVDYVKTALECATTKQECAEKYNHQKLSHTVCCYRPKARPFQKDELLYTNNSNITAPSDKKSRKGHLTDRESLRGEEPRVTQYALQKECYVTVFYAAHRGIELCLGAELFKSIVDIENKKADFVAGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The proper sequence to use with the protein is 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"O=C1NC(=O)c2ccc(I)cc2C1=CNCc1ccc(-c2ccoc2)c(O)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C1NC(=O)c2ccc(I)cc2C1=CNCc1ccc(-c2ccoc2)c(O)c1' can not interact with the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12' will interact with the target protein 'GELLAGEDQTSENDIMCEKESEQTEECTARRLIELCLPKVFFTIFPKQQVILIDTTAKLRANCLYFLQDCEESMTSPMSLPEPKSGHVAQRRSLVLTFDKTVHSDMYLCIDTEQNDVLAQDRKGVCGRSEMIRIAEIYSSMRGEEYASFLVRCLMKNIPMVMISMEPLMERFMRYVANSSRHEPEQNQEAEFARTGIYASMLDCKWKNQKVLRVEDDLGQSPNYVIGPLTKCLRTRAILKVQEIEERRLDTRGASGVIPNRMNYDLVVFCKFVQGHEIYRKLCEYVFLVSLEIKVRYVRCNFDPFPTTPHTRAMATHILNLFVQAREEDYAVHKLKDFLVDMDLFDPEEFRFINEAALTAAKNMKWVRMFEQFELSTATIEKRHACEITEYQNPRLRVNDMGKSPKDARSGLPDMASITFLSHRKSPKSSTAGADDSHCYAEITPLFWRPSEDPEENGERAWDDINPCGNHPSKPPKTQGRPLKVVPAELFLGRKMVIKIELKWYITKCLIKNLRSPVLRVEQYIGNSIKAFELDDLMAVSIPEHLSPEEKQSPDDSWNSTPNQRLRRCIIPMDIQAEINDAEKYSDLRSHTMAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12\"], \"target_seq\": \"GELLAGEDQTSENDIMCEKESEQTEECTARRLIELCLPKVFFTIFPKQQVILIDTTAKLRANCLYFLQDCEESMTSPMSLPEPKSGHVAQRRSLVLTFDKTVHSDMYLCIDTEQNDVLAQDRKGVCGRSEMIRIAEIYSSMRGEEYASFLVRCLMKNIPMVMISMEPLMERFMRYVANSSRHEPEQNQEAEFARTGIYASMLDCKWKNQKVLRVEDDLGQSPNYVIGPLTKCLRTRAILKVQEIEERRLDTRGASGVIPNRMNYDLVVFCKFVQGHEIYRKLCEYVFLVSLEIKVRYVRCNFDPFPTTPHTRAMATHILNLFVQAREEDYAVHKLKDFLVDMDLFDPEEFRFINEAALTAAKNMKWVRMFEQFELSTATIEKRHACEITEYQNPRLRVNDMGKSPKDARSGLPDMASITFLSHRKSPKSSTAGADDSHCYAEITPLFWRPSEDPEENGERAWDDINPCGNHPSKPPKTQGRPLKVVPAELFLGRKMVIKIELKWYITKCLIKNLRSPVLRVEQYIGNSIKAFELDDLMAVSIPEHLSPEEKQSPDDSWNSTPNQRLRRCIIPMDIQAEINDAEKYSDLRSHTMAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The proper sequence for the protein is 'MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA\", \"result\": [{\"smile\": \"CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCc1nn(C)c2c(=O)[nH]c(-c3cc(S(=O)(=O)N4CCN(C)CC4)ccc3OCC)nc12' can not interact with the protein 'MGSGSSSYRPKAIYLDIDGRIQKVIFSKYCNSSDIMDLFCIATGLPRNTTISLLTTDDAMVSIDPTMPANSERTPYKVRPVAIKQLSAGVEDKRTTSRGQSAERPLRDRRVVGLEQPRREGAFESGQVEPRPREPQGCYQEGQRIPPEREELIQSVLAQVAEQFSRAFKINELKAEVANHLAVLEKRVELEGLKVVEIEKCKSDIKKMREELAARSSRTNCPCKYSFLDNHKKLTPRRDVPTYPKYLLSPETIEALRKPTFDVWLWEPNEMLSCLEHMYHDLGLVRDFSINPVTLRRWLFCVHDNYRNNPFHNFRHCFCVAQMMYSMVWLCSLQEKFSQTDILILMTAAICHDLDHPGYNNTYQINARTELAVRYNDISPLENHHCAVAFQILAEPECNIFSNIPPDGFKQIRQGMITLILATDMARHAEIMDSFKEKMENFDYSNEEHMTLLKMILIKCCDISNEVRPMEVAEPWVDCLLEEYFMQSDREKSEGLPVAPFMDRDKVTKATAQIGFIKFVLIPMFETVTKLFPMVEEIMLQPLWESRDRYEELKRIDDAMKELQKKTDSLTSGATEKSRERSRDVKNSEGDCA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug '[H][C@@]12CC3=CN(C)C4=C3C(=CC=C4)C1=C[C@H](CN2C)C(=O)NC(CC)CO' and the target protein 'KNSMSNKDIEFFLFSESWNCYTYHKLQPKVMGVDLSCTGTSIIPSQVSMNNFCESKPKNNDKLTHLASFFWLKTCMNLWQLRLPFMLCAVRRSILAKVFYRIKGTLLSVSFHIDTFAVLAYSCVVAPIITIFITNSILGTKDAVSVNVGKVALATLHISYPDLSGLASMCVIVRAFNVVLEVKCMTICISKSSAQFVKVIREEIIMVSAIPKLKNSFYNQNTGAISTETNVALDKVLEQDLFNDKLPNSSQNNQHCIPASSSAYFQGQNAENGKNRPTGLLTSFEELPLKAKLHESYFAYFLLTLIAMRDVYELTLSLQLDQWMNVYDMVKSWSKFQTLLLILMIQSQEDLSVTYSQCASSLLLSISITIAPDDTSSSLCPSESYCSGSDNVLVTTKNSVVGARSYELAFLGGSQAIIINNLGTWVEQLPKAMDDSAFIMKFFRIDGIGRLWLGLELLNCRVLIRATDCFMAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]12CC3=CN(C)C4=C3C(=CC=C4)C1=C[C@H](CN2C)C(=O)NC(CC)CO\"], \"target_seq\": \"KNSMSNKDIEFFLFSESWNCYTYHKLQPKVMGVDLSCTGTSIIPSQVSMNNFCESKPKNNDKLTHLASFFWLKTCMNLWQLRLPFMLCAVRRSILAKVFYRIKGTLLSVSFHIDTFAVLAYSCVVAPIITIFITNSILGTKDAVSVNVGKVALATLHISYPDLSGLASMCVIVRAFNVVLEVKCMTICISKSSAQFVKVIREEIIMVSAIPKLKNSFYNQNTGAISTETNVALDKVLEQDLFNDKLPNSSQNNQHCIPASSSAYFQGQNAENGKNRPTGLLTSFEELPLKAKLHESYFAYFLLTLIAMRDVYELTLSLQLDQWMNVYDMVKSWSKFQTLLLILMIQSQEDLSVTYSQCASSLLLSISITIAPDDTSSSLCPSESYCSGSDNVLVTTKNSVVGARSYELAFLGGSQAIIINNLGTWVEQLPKAMDDSAFIMKFFRIDGIGRLWLGLELLNCRVLIRATDCFMAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV\", \"result\": [{\"smile\": \"[H][C@@]12CC3=CN(C)C4=C3C(=CC=C4)C1=C[C@H](CN2C)C(=O)NC(CC)CO\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@]12CC3=CN(C)C4=C3C(=CC=C4)C1=C[C@H](CN2C)C(=O)NC(CC)CO' can interact with the protein 'MDILCEENTSLSSTTNSLMQLNDDTRLYSNDFNSGEANTSDAFNWTVDSENRTNLSCEGCLSPSCLSLLHLQEKNWSALLTAVVIILTIAGNILVIMAVSLEKKLQNATNYFLMSLAIADMLLGFLVMPVSMLTILYGYRWPLPSKLCAVWIYLDVLFSTASIMHLCAISLDRYVAIQNPIHHSRFNSRTKAFLKIIAVWTISVGISMPIPVFGLQDDSKVFKEGSCLLADDNFVLIGSFVSFFIPLTIMVITYFLTIKSLQKEATLCVSDLGTRAKLASFSFLPQSSLSSEKLFQRSIHREPGSYTGRRTMQSISNEQKACKVLGIVFFLFVVMWCPFFITNIMAVICKESCNEDVIGALLNVFVWIGYLSSAVNPLVYTLFNKTYRSAFSRYIQCQYKENKKPLQLILVNTIPALAYKSSQLQMGQKKNSKQDAKTTDNDCSMVALGKQHSEEASKDNSDGVNEKVSCV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood that the drug 'COc1cc2c(cc1C(F)(F)F)N(C(=O)Nc1cc(OC(F)(F)F)cc(-c3cccnc3)c1)CC2' will engage with the target protein 'WVEVQLLLGIEQNLPQEQVHRSYLCTHTHGPEFLLFVTISTSCGEFPELMAVAWRTFDSLARLTPTAGLVRPDNFFLFAHRRGDLAITLTLVVVLFEKLLLNLGHAQAYLGMLLLDSQQRLLLAGSALRECFHGVELEDTQFEIADPHTELKGKARGFVPHTLPHMNPHAAGMTAFKVEREIPGGTLKNSIFPLSLHGVDRDTVLDKVYHRGEFPMDLRDWAHRGSIFCALVLIDGQHVDEPNEQSPWLLPRLGRSDRPFFGSGGVPVSGFPELSVATIVMAVDQARVKILTTEAKRDRAVEVPGLRALLINFRPVVDVALPRRQAVQDAQHPYVFSQFIPKLSAESADAASSDAALLVFLFQGFDPYGFALNDEPLVRLEVTPRPKALTARNETTGETFQRAWPFMLPRVHNGELNCMFPPEIYQLFVFLFLVANFLWGLIQARVQKSRGQRLRRMLPSPKVVESPCAGFGLPGTRERPELSARMWTPVRDRPAIQAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(cc1C(F)(F)F)N(C(=O)Nc1cc(OC(F)(F)F)cc(-c3cccnc3)c1)CC2\"], \"target_seq\": \"WVEVQLLLGIEQNLPQEQVHRSYLCTHTHGPEFLLFVTISTSCGEFPELMAVAWRTFDSLARLTPTAGLVRPDNFFLFAHRRGDLAITLTLVVVLFEKLLLNLGHAQAYLGMLLLDSQQRLLLAGSALRECFHGVELEDTQFEIADPHTELKGKARGFVPHTLPHMNPHAAGMTAFKVEREIPGGTLKNSIFPLSLHGVDRDTVLDKVYHRGEFPMDLRDWAHRGSIFCALVLIDGQHVDEPNEQSPWLLPRLGRSDRPFFGSGGVPVSGFPELSVATIVMAVDQARVKILTTEAKRDRAVEVPGLRALLINFRPVVDVALPRRQAVQDAQHPYVFSQFIPKLSAESADAASSDAALLVFLFQGFDPYGFALNDEPLVRLEVTPRPKALTARNETTGETFQRAWPFMLPRVHNGELNCMFPPEIYQLFVFLFLVANFLWGLIQARVQKSRGQRLRRMLPSPKVVESPCAGFGLPGTRERPELSARMWTPVRDRPAIQAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR' is the sequence that’s right for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"COc1cc2c(cc1C(F)(F)F)N(C(=O)Nc1cc(OC(F)(F)F)cc(-c3cccnc3)c1)CC2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1cc2c(cc1C(F)(F)F)N(C(=O)Nc1cc(OC(F)(F)F)cc(-c3cccnc3)c1)CC2' can not interact with the protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction regarding the drug 'O=c1ccc2ccc(OCc3ccc(Br)cc3)cc2o1''s interaction with the target protein 'FKWATKVIVRVGGEPTTAPDLEEKATMLNVYGDLVLIEYRNGGWYQGTPAYEQKMQIGEVTCLYQGDTPLQAVAVRPHSLIDTKERMNIFLQRMWHTHKVLRWALEPVYMCGACATGVLGDARPKSEGEGGETILGDDVFKGLPNSYQYEGVPSVYAIWNYSAMAIHPLYGTLGPPTEFGVEVPFLPPRLGINRPEVELPRHLESISAELRGVLFGMRITAYFASKGWAGEKQILHYILFLYDAETGTFTEQSALHDIELVLTSRIANVCVTVVPVIISKMLRGYMLDIQFALASTDSLTFDEERNVIKTLSLGHTDHEELKKRCRNGPVHLTERKVCRVNTYYVLKDITVEIVKAEMIEGPSTSNKKYMKEDCTGGRCLMKPALEPDFRKNDGAEVLIGTMLSIRPRGGAAGRKLYNASDKWEPWRMNEKYPEKVARLLTPWKTRHKPLRSNPLFEPVLSYQDTGFTNVIIGCYEGHALMLVVAEILRKREMGEAILLWVGTAAHTLKRYDWENVVQKQAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ccc2ccc(OCc3ccc(Br)cc3)cc2o1\"], \"target_seq\": \"FKWATKVIVRVGGEPTTAPDLEEKATMLNVYGDLVLIEYRNGGWYQGTPAYEQKMQIGEVTCLYQGDTPLQAVAVRPHSLIDTKERMNIFLQRMWHTHKVLRWALEPVYMCGACATGVLGDARPKSEGEGGETILGDDVFKGLPNSYQYEGVPSVYAIWNYSAMAIHPLYGTLGPPTEFGVEVPFLPPRLGINRPEVELPRHLESISAELRGVLFGMRITAYFASKGWAGEKQILHYILFLYDAETGTFTEQSALHDIELVLTSRIANVCVTVVPVIISKMLRGYMLDIQFALASTDSLTFDEERNVIKTLSLGHTDHEELKKRCRNGPVHLTERKVCRVNTYYVLKDITVEIVKAEMIEGPSTSNKKYMKEDCTGGRCLMKPALEPDFRKNDGAEVLIGTMLSIRPRGGAAGRKLYNASDKWEPWRMNEKYPEKVARLLTPWKTRHKPLRSNPLFEPVLSYQDTGFTNVIIGCYEGHALMLVVAEILRKREMGEAILLWVGTAAHTLKRYDWENVVQKQAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MSNKCDVVVVGGGISGMAAAKLLHDSGLNVVVLEARDRVGGRTYTLRNQKVKYVDLGGSYVGPTQNRILRLAKELGLETYKVNEVERLIHHVKGKSYPFRGPFPPVWNPITYLDHNNFWRTMDDMGREIPSDAPWKAPLAEEWDNMTMKELLDKLCWTESAKQLATLFVNLCVTAETHEVSALWFLWYVKQCGGTTRIISTTNGGQERKFVGGSGQVSERIMDLLGDRVKLERPVIYIDQTRENVLVETLNHEMYEAKYVISAIPPTLGMKIHFNPPLPMMRNQMITRVPLGSVIKCIVYYKEPFWRKKDYCGTMIIDGEEAPVAYTLDDTKPEGNYAAIMGFILAHKARKLARLTKEERLKKLCELYAKVLGSLEALEPVHYEEKNWCEEQYSGGCYTTYFPPGILTQYGRVLRQPVDRIYFAGTETATHWSGYMEGAVEAGERAAREILHAMGKIPEDEIWQSEPESVDVPAQPITTTFLERHLPSVPGLLRLIGLTTIFSATALGFLAHKRGLLVRV' represents the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSNKCDVVVVGGGISGMAAAKLLHDSGLNVVVLEARDRVGGRTYTLRNQKVKYVDLGGSYVGPTQNRILRLAKELGLETYKVNEVERLIHHVKGKSYPFRGPFPPVWNPITYLDHNNFWRTMDDMGREIPSDAPWKAPLAEEWDNMTMKELLDKLCWTESAKQLATLFVNLCVTAETHEVSALWFLWYVKQCGGTTRIISTTNGGQERKFVGGSGQVSERIMDLLGDRVKLERPVIYIDQTRENVLVETLNHEMYEAKYVISAIPPTLGMKIHFNPPLPMMRNQMITRVPLGSVIKCIVYYKEPFWRKKDYCGTMIIDGEEAPVAYTLDDTKPEGNYAAIMGFILAHKARKLARLTKEERLKKLCELYAKVLGSLEALEPVHYEEKNWCEEQYSGGCYTTYFPPGILTQYGRVLRQPVDRIYFAGTETATHWSGYMEGAVEAGERAAREILHAMGKIPEDEIWQSEPESVDVPAQPITTTFLERHLPSVPGLLRLIGLTTIFSATALGFLAHKRGLLVRV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSNKCDVVVVGGGISGMAAAKLLHDSGLNVVVLEARDRVGGRTYTLRNQKVKYVDLGGSYVGPTQNRILRLAKELGLETYKVNEVERLIHHVKGKSYPFRGPFPPVWNPITYLDHNNFWRTMDDMGREIPSDAPWKAPLAEEWDNMTMKELLDKLCWTESAKQLATLFVNLCVTAETHEVSALWFLWYVKQCGGTTRIISTTNGGQERKFVGGSGQVSERIMDLLGDRVKLERPVIYIDQTRENVLVETLNHEMYEAKYVISAIPPTLGMKIHFNPPLPMMRNQMITRVPLGSVIKCIVYYKEPFWRKKDYCGTMIIDGEEAPVAYTLDDTKPEGNYAAIMGFILAHKARKLARLTKEERLKKLCELYAKVLGSLEALEPVHYEEKNWCEEQYSGGCYTTYFPPGILTQYGRVLRQPVDRIYFAGTETATHWSGYMEGAVEAGERAAREILHAMGKIPEDEIWQSEPESVDVPAQPITTTFLERHLPSVPGLLRLIGLTTIFSATALGFLAHKRGLLVRV\", \"result\": [{\"smile\": \"O=c1ccc2ccc(OCc3ccc(Br)cc3)cc2o1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=c1ccc2ccc(OCc3ccc(Br)cc3)cc2o1' can interact with the protein 'MSNKCDVVVVGGGISGMAAAKLLHDSGLNVVVLEARDRVGGRTYTLRNQKVKYVDLGGSYVGPTQNRILRLAKELGLETYKVNEVERLIHHVKGKSYPFRGPFPPVWNPITYLDHNNFWRTMDDMGREIPSDAPWKAPLAEEWDNMTMKELLDKLCWTESAKQLATLFVNLCVTAETHEVSALWFLWYVKQCGGTTRIISTTNGGQERKFVGGSGQVSERIMDLLGDRVKLERPVIYIDQTRENVLVETLNHEMYEAKYVISAIPPTLGMKIHFNPPLPMMRNQMITRVPLGSVIKCIVYYKEPFWRKKDYCGTMIIDGEEAPVAYTLDDTKPEGNYAAIMGFILAHKARKLARLTKEERLKKLCELYAKVLGSLEALEPVHYEEKNWCEEQYSGGCYTTYFPPGILTQYGRVLRQPVDRIYFAGTETATHWSGYMEGAVEAGERAAREILHAMGKIPEDEIWQSEPESVDVPAQPITTTFLERHLPSVPGLLRLIGLTTIFSATALGFLAHKRGLLVRV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you predict about the interaction of the drug 'CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4' with the target protein 'PKCPQSILAGNSAILYPAFLAVEKKRDAFQSCGGPALDQPRTVALLLPWLYSHGGVEGPSFKGFGTNGGILVLGHPYIVDEYMDRIMNFEGELKLNLETEVVEHDLEEHSDEYDRELRSVDKLYVMEDPDRLMLITPVSDVRERLKPAASTHELGGVRKVGDAIAKKSWCDPVVKEPQGNPLEDQTEEDLDIRSAAPFAKWLMPKTAMGALQMLHIGYHAPEVGCFPSGAVGGTSMTGMLLVDNSGAETLSFEYVMAVGNTSPAGLDTFSEVFIWIRPHPSDRNTSSPLDMYLALVKDQLMDPWLAVRRLNGTCHRIRSPHESIGTFVDMLVESTVFACLSALRGASPRGPFSVTDDTGALDLSKHGKRKAWLYVRALAPKQLEESPLKQNEVRSFSLSGTCVCAFYASAFRITRSYALVLKYVVKPPYDQEPVATSRLLGCLYFKASASEDRLKKKLLQIADKSDVNTATLTGESNLPAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4\"], \"target_seq\": \"PKCPQSILAGNSAILYPAFLAVEKKRDAFQSCGGPALDQPRTVALLLPWLYSHGGVEGPSFKGFGTNGGILVLGHPYIVDEYMDRIMNFEGELKLNLETEVVEHDLEEHSDEYDRELRSVDKLYVMEDPDRLMLITPVSDVRERLKPAASTHELGGVRKVGDAIAKKSWCDPVVKEPQGNPLEDQTEEDLDIRSAAPFAKWLMPKTAMGALQMLHIGYHAPEVGCFPSGAVGGTSMTGMLLVDNSGAETLSFEYVMAVGNTSPAGLDTFSEVFIWIRPHPSDRNTSSPLDMYLALVKDQLMDPWLAVRRLNGTCHRIRSPHESIGTFVDMLVESTVFACLSALRGASPRGPFSVTDDTGALDLSKHGKRKAWLYVRALAPKQLEESPLKQNEVRSFSLSGTCVCAFYASAFRITRSYALVLKYVVKPPYDQEPVATSRLLGCLYFKASASEDRLKKKLLQIADKSDVNTATLTGESNLPAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'TRALPEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT' is the sequence meant for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"TRALPEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"TRALPEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\", \"result\": [{\"smile\": \"CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=CC=N1)C(=O)NC2=C3C(=CC(=C2OC)Cl)C4=C(N3)C=NC=C4' can not interact with the protein 'TRALPEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the drug 'COC1=CC2=C(C=CN=C2C=C1OCCCN3CCOCC3)OC4=C(C=C(C=C4)NC(=O)C5(CC5)C(=O)NC6=CC=C(C=C6)F)F' is likely to engage with the target protein 'PSLFYQECVQLPDVFTMGRCSTDLIRVCQLPQEAGHRVINKRRGKARVFKNIKEKDKKGKLVARVLILRPKVLRRPCDNGQIKFVKGDDSVCELKKDFGKRWWSDAELAPTAPPWLRHVINSIGISPVVYSSPKLFGQTYGNTQRLANSRSLESPGMINKTIPAEDLDVVFMMMKIEQAPTPYPLFSGPEIITSANSMTMMALADSTKVLNVLETHYELHADVATATPDISDMYLIVSEVKIEDKAQDTMVASIEHIQSMGFFLGTGELSTDFFDVRYKSFEPVLVEPSHRNEKRKPMMSEQFKNDHMISTMYECEDADDPLKSRYIAELRIERLEFPKMGASVGTNAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COC1=CC2=C(C=CN=C2C=C1OCCCN3CCOCC3)OC4=C(C=C(C=C4)NC(=O)C5(CC5)C(=O)NC6=CC=C(C=C6)F)F\"], \"target_seq\": \"PSLFYQECVQLPDVFTMGRCSTDLIRVCQLPQEAGHRVINKRRGKARVFKNIKEKDKKGKLVARVLILRPKVLRRPCDNGQIKFVKGDDSVCELKKDFGKRWWSDAELAPTAPPWLRHVINSIGISPVVYSSPKLFGQTYGNTQRLANSRSLESPGMINKTIPAEDLDVVFMMMKIEQAPTPYPLFSGPEIITSANSMTMMALADSTKVLNVLETHYELHADVATATPDISDMYLIVSEVKIEDKAQDTMVASIEHIQSMGFFLGTGELSTDFFDVRYKSFEPVLVEPSHRNEKRKPMMSEQFKNDHMISTMYECEDADDPLKSRYIAELRIERLEFPKMGASVGTNAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\", \"result\": [{\"smile\": \"COC1=CC2=C(C=CN=C2C=C1OCCCN3CCOCC3)OC4=C(C=C(C=C4)NC(=O)C5(CC5)C(=O)NC6=CC=C(C=C6)F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'COC1=CC2=C(C=CN=C2C=C1OCCCN3CCOCC3)OC4=C(C=C(C=C4)NC(=O)C5(CC5)C(=O)NC6=CC=C(C=C6)F)F' can not interact with the protein 'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the drug 'O=C(CCCCCCc1ccccc1)c1ncc(-c2cc(F)ccn2)o1''s interaction with the target protein 'IERVTQEAVFVKLTARRKVPYSLPEPYKPGARARRQTSRVLESLLLPAGTVLQGSHIEMEKRMWHPTKGLADFTVMMSAVVLRSRVLDASLLATLSLVLKDMVFNVKAGRDCAVGYHLNLGLEHGCFGGSLVYPAALTVSQQVACERRMVQSLNDLGQVSWAFQLPSVLLTPRERFPSQGNDNQRAGLSWGPIRLFQLRPFALHGPAKKVPMACCFSYLDAEKDQGDLPATIPTESEVRVGMEEEPWGSQLYQLKVCMGTASCLLVLDNSAAEGQGGAAPVIQDSPVGGIEHQAKAPPLGDRYQFGQNGDLPLSYGALGVPKLQVNPKLFNMAGVTFAMESTGCSVAQKVRPLWVFLYTIYTDELYDGAGSKLTVKRFGDPFSFAPACEELVSIRTGTYKRCACLEDIGLPAFASNSLGDALAGTGTDVWVSPKPEGVCGMMLTVVSSYVLRNKEMRPLESELYTNLARLKGRRALSPFEQSGGQNSLLPDLELLCSQPVDLRLKQLEWFPRGAGHNNTGPCATLVPAVLLTHAFSGPLDLAMPYLEDETSSLLARVVLKLGWWVYCSFQKVLSSTAGCAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(CCCCCCc1ccccc1)c1ncc(-c2cc(F)ccn2)o1\"], \"target_seq\": \"IERVTQEAVFVKLTARRKVPYSLPEPYKPGARARRQTSRVLESLLLPAGTVLQGSHIEMEKRMWHPTKGLADFTVMMSAVVLRSRVLDASLLATLSLVLKDMVFNVKAGRDCAVGYHLNLGLEHGCFGGSLVYPAALTVSQQVACERRMVQSLNDLGQVSWAFQLPSVLLTPRERFPSQGNDNQRAGLSWGPIRLFQLRPFALHGPAKKVPMACCFSYLDAEKDQGDLPATIPTESEVRVGMEEEPWGSQLYQLKVCMGTASCLLVLDNSAAEGQGGAAPVIQDSPVGGIEHQAKAPPLGDRYQFGQNGDLPLSYGALGVPKLQVNPKLFNMAGVTFAMESTGCSVAQKVRPLWVFLYTIYTDELYDGAGSKLTVKRFGDPFSFAPACEELVSIRTGTYKRCACLEDIGLPAFASNSLGDALAGTGTDVWVSPKPEGVCGMMLTVVSSYVLRNKEMRPLESELYTNLARLKGRRALSPFEQSGGQNSLLPDLELLCSQPVDLRLKQLEWFPRGAGHNNTGPCATLVPAVLLTHAFSGPLDLAMPYLEDETSSLLARVVLKLGWWVYCSFQKVLSSTAGCAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The valid sequence for the protein is 'MVQYELWAALPGASGVALACCFVAAAVALRWSGRRTARGAVVRARQRQRAGLENMDRAAQRFRLQNPDLDSEALLALPLPQLVQKLHSRELAPEAVLFTYVGKAWEVNKGTNCVTSYLADCETQLSQAPRQGLLYGVPVSLKECFTYKGQDSTLGLSLNEGVPAECDSVVVHVLKLQGAVPFVHTNVPQSMFSYDCSNPLFGQTVNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSSFCGICGLKPTGNRLSKSGLKGCVYGQEAVRLSVGPMARDVESLALCLRALLCEDMFRLDPTVPPLPFREEVYTSSQPLRVGYYETDNYTMPSPAMRRAVLETKQSLEAAGHTLVPFLPSNIPHALETLSTGGLFSDGGHTFLQNFKGDFVDPCLGDLVSILKLPQWLKGLLAFLVKPLLPRLSAFLSNMKSRSAGKLWELQHEIEVYRKTVIAQWRALDLDVVLTPMLAPALDLNAPGRATGAVSYTMLYNCLDFPAGVVPVTTVTAEDEAQMEHYRGYFGDIWDKMLQKGMKKSVGLPVAVQCVALPWQEELCLRFMREVERLMTPEKQSS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVQYELWAALPGASGVALACCFVAAAVALRWSGRRTARGAVVRARQRQRAGLENMDRAAQRFRLQNPDLDSEALLALPLPQLVQKLHSRELAPEAVLFTYVGKAWEVNKGTNCVTSYLADCETQLSQAPRQGLLYGVPVSLKECFTYKGQDSTLGLSLNEGVPAECDSVVVHVLKLQGAVPFVHTNVPQSMFSYDCSNPLFGQTVNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSSFCGICGLKPTGNRLSKSGLKGCVYGQEAVRLSVGPMARDVESLALCLRALLCEDMFRLDPTVPPLPFREEVYTSSQPLRVGYYETDNYTMPSPAMRRAVLETKQSLEAAGHTLVPFLPSNIPHALETLSTGGLFSDGGHTFLQNFKGDFVDPCLGDLVSILKLPQWLKGLLAFLVKPLLPRLSAFLSNMKSRSAGKLWELQHEIEVYRKTVIAQWRALDLDVVLTPMLAPALDLNAPGRATGAVSYTMLYNCLDFPAGVVPVTTVTAEDEAQMEHYRGYFGDIWDKMLQKGMKKSVGLPVAVQCVALPWQEELCLRFMREVERLMTPEKQSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVQYELWAALPGASGVALACCFVAAAVALRWSGRRTARGAVVRARQRQRAGLENMDRAAQRFRLQNPDLDSEALLALPLPQLVQKLHSRELAPEAVLFTYVGKAWEVNKGTNCVTSYLADCETQLSQAPRQGLLYGVPVSLKECFTYKGQDSTLGLSLNEGVPAECDSVVVHVLKLQGAVPFVHTNVPQSMFSYDCSNPLFGQTVNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSSFCGICGLKPTGNRLSKSGLKGCVYGQEAVRLSVGPMARDVESLALCLRALLCEDMFRLDPTVPPLPFREEVYTSSQPLRVGYYETDNYTMPSPAMRRAVLETKQSLEAAGHTLVPFLPSNIPHALETLSTGGLFSDGGHTFLQNFKGDFVDPCLGDLVSILKLPQWLKGLLAFLVKPLLPRLSAFLSNMKSRSAGKLWELQHEIEVYRKTVIAQWRALDLDVVLTPMLAPALDLNAPGRATGAVSYTMLYNCLDFPAGVVPVTTVTAEDEAQMEHYRGYFGDIWDKMLQKGMKKSVGLPVAVQCVALPWQEELCLRFMREVERLMTPEKQSS\", \"result\": [{\"smile\": \"O=C(CCCCCCc1ccccc1)c1ncc(-c2cc(F)ccn2)o1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(CCCCCCc1ccccc1)c1ncc(-c2cc(F)ccn2)o1' can interact with the protein 'MVQYELWAALPGASGVALACCFVAAAVALRWSGRRTARGAVVRARQRQRAGLENMDRAAQRFRLQNPDLDSEALLALPLPQLVQKLHSRELAPEAVLFTYVGKAWEVNKGTNCVTSYLADCETQLSQAPRQGLLYGVPVSLKECFTYKGQDSTLGLSLNEGVPAECDSVVVHVLKLQGAVPFVHTNVPQSMFSYDCSNPLFGQTVNPWKSSKSPGGSSGGEGALIGSGGSPLGLGTDIGGSIRFPSSFCGICGLKPTGNRLSKSGLKGCVYGQEAVRLSVGPMARDVESLALCLRALLCEDMFRLDPTVPPLPFREEVYTSSQPLRVGYYETDNYTMPSPAMRRAVLETKQSLEAAGHTLVPFLPSNIPHALETLSTGGLFSDGGHTFLQNFKGDFVDPCLGDLVSILKLPQWLKGLLAFLVKPLLPRLSAFLSNMKSRSAGKLWELQHEIEVYRKTVIAQWRALDLDVVLTPMLAPALDLNAPGRATGAVSYTMLYNCLDFPAGVVPVTTVTAEDEAQMEHYRGYFGDIWDKMLQKGMKKSVGLPVAVQCVALPWQEELCLRFMREVERLMTPEKQSS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug '[H][C@]1(O)CC[C@@]2([H])[C@]3([H])CC[C@]4([H])CC(=O)CC[C@]4(C)[C@@]3([H])CC[C@]12C' interacting with the target protein 'GWQATSHVGNPLSREYMLDRKKIVQYPVMNIHTKPNGVTQTSAALTRRAFFRCFVGDVRNAEGQQALQVNAKVFAVHNPCLYRLPMPTQALIKLFGCYFGVLTPAYLFLVRKFVAMRGFIPQIAPLLDPMLPEALDWLEVKWYDGSQKAIGDQKCPQSERVSLQIISAGAYIIAPRSGEEAYATPAPIAAAENRLHGMEALSERALTLYVVWQLWGRTDFLMTPLPSELEFGLHCFPGQYPGLLREQLKPELQWNESSITLDRYQQAGRVFEFCLHAHLWFPGELRAREALYHFLHTRFGVIRTHRPKVAAPDSRAYESLMYVLKLLPVKYMRYNASVIKARIEAEMKGPDDDHLLQVFQSLNQMQQSAAFGVPQGPVPWRDPNERAQRASLGKTADFRRSLELVQEITVLRRDLHLTALQLGGHKQGGQKLLQRMLSAGLHGQGYGLKMLCKKVELSYEFEPIDSLVPFQLDSDGEEAPKVEFSFWLQVVPFGPTATEVHLTINRLRYQQLKAALDLIGFEFAPKLRAGMAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@]1(O)CC[C@@]2([H])[C@]3([H])CC[C@]4([H])CC(=O)CC[C@]4(C)[C@@]3([H])CC[C@]12C\"], \"target_seq\": \"GWQATSHVGNPLSREYMLDRKKIVQYPVMNIHTKPNGVTQTSAALTRRAFFRCFVGDVRNAEGQQALQVNAKVFAVHNPCLYRLPMPTQALIKLFGCYFGVLTPAYLFLVRKFVAMRGFIPQIAPLLDPMLPEALDWLEVKWYDGSQKAIGDQKCPQSERVSLQIISAGAYIIAPRSGEEAYATPAPIAAAENRLHGMEALSERALTLYVVWQLWGRTDFLMTPLPSELEFGLHCFPGQYPGLLREQLKPELQWNESSITLDRYQQAGRVFEFCLHAHLWFPGELRAREALYHFLHTRFGVIRTHRPKVAAPDSRAYESLMYVLKLLPVKYMRYNASVIKARIEAEMKGPDDDHLLQVFQSLNQMQQSAAFGVPQGPVPWRDPNERAQRASLGKTADFRRSLELVQEITVLRRDLHLTALQLGGHKQGGQKLLQRMLSAGLHGQGYGLKMLCKKVELSYEFEPIDSLVPFQLDSDGEEAPKVEFSFWLQVVPFGPTATEVHLTINRLRYQQLKAALDLIGFEFAPKLRAGMAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MAALGCARLRWALRGAGRGLCPHGARAKAAIPAALPSDKATGAPGAGPGVRRRQRSLEEIPRLGQLRFFFQLFVQGYALQLHQLQVLYKAKYGPMWMSYLGPQMHVNLASAPLLEQVMRQEGKYPVRNDMELWKEHRDQHDLTYGPFTTEGHHWYQLRQALNQRLLKPAEAALYTDAFNEVIDDFMTRLDQLRAESASGNQVSDMAQLFYYFALEAICYILFEKRIGCLQRSIPEDTVTFVRSIGLMFQNSLYATFLPKWTRPVLPFWKRYLDGWNAIFSFGKKLIDEKLEDMEAQLQAAGPDGIQVSGYLHFLLASGQLSPREAMGSLPELLMAGVDTTSNTLTWALYHLSKDPEIQEALHEEVVGVVPAGQVPQHKDFAHMPLLKAVLKETLRLYPVVPTNSRIIEKEIEVDGFLFPKNTQFVFCHYVVSRDPTAFSEPESFQPHRWLRNSQPATPRIQHPFGSVPFGYGVRACLGRRIAELEMQLLLARLIQKYKVVLAPETGELKSVARIVLVPNKKVGLQFLQRQC'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAALGCARLRWALRGAGRGLCPHGARAKAAIPAALPSDKATGAPGAGPGVRRRQRSLEEIPRLGQLRFFFQLFVQGYALQLHQLQVLYKAKYGPMWMSYLGPQMHVNLASAPLLEQVMRQEGKYPVRNDMELWKEHRDQHDLTYGPFTTEGHHWYQLRQALNQRLLKPAEAALYTDAFNEVIDDFMTRLDQLRAESASGNQVSDMAQLFYYFALEAICYILFEKRIGCLQRSIPEDTVTFVRSIGLMFQNSLYATFLPKWTRPVLPFWKRYLDGWNAIFSFGKKLIDEKLEDMEAQLQAAGPDGIQVSGYLHFLLASGQLSPREAMGSLPELLMAGVDTTSNTLTWALYHLSKDPEIQEALHEEVVGVVPAGQVPQHKDFAHMPLLKAVLKETLRLYPVVPTNSRIIEKEIEVDGFLFPKNTQFVFCHYVVSRDPTAFSEPESFQPHRWLRNSQPATPRIQHPFGSVPFGYGVRACLGRRIAELEMQLLLARLIQKYKVVLAPETGELKSVARIVLVPNKKVGLQFLQRQC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAALGCARLRWALRGAGRGLCPHGARAKAAIPAALPSDKATGAPGAGPGVRRRQRSLEEIPRLGQLRFFFQLFVQGYALQLHQLQVLYKAKYGPMWMSYLGPQMHVNLASAPLLEQVMRQEGKYPVRNDMELWKEHRDQHDLTYGPFTTEGHHWYQLRQALNQRLLKPAEAALYTDAFNEVIDDFMTRLDQLRAESASGNQVSDMAQLFYYFALEAICYILFEKRIGCLQRSIPEDTVTFVRSIGLMFQNSLYATFLPKWTRPVLPFWKRYLDGWNAIFSFGKKLIDEKLEDMEAQLQAAGPDGIQVSGYLHFLLASGQLSPREAMGSLPELLMAGVDTTSNTLTWALYHLSKDPEIQEALHEEVVGVVPAGQVPQHKDFAHMPLLKAVLKETLRLYPVVPTNSRIIEKEIEVDGFLFPKNTQFVFCHYVVSRDPTAFSEPESFQPHRWLRNSQPATPRIQHPFGSVPFGYGVRACLGRRIAELEMQLLLARLIQKYKVVLAPETGELKSVARIVLVPNKKVGLQFLQRQC\", \"result\": [{\"smile\": \"[H][C@]1(O)CC[C@@]2([H])[C@]3([H])CC[C@]4([H])CC(=O)CC[C@]4(C)[C@@]3([H])CC[C@]12C\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@]1(O)CC[C@@]2([H])[C@]3([H])CC[C@]4([H])CC(=O)CC[C@]4(C)[C@@]3([H])CC[C@]12C' can not interact with the protein 'MAALGCARLRWALRGAGRGLCPHGARAKAAIPAALPSDKATGAPGAGPGVRRRQRSLEEIPRLGQLRFFFQLFVQGYALQLHQLQVLYKAKYGPMWMSYLGPQMHVNLASAPLLEQVMRQEGKYPVRNDMELWKEHRDQHDLTYGPFTTEGHHWYQLRQALNQRLLKPAEAALYTDAFNEVIDDFMTRLDQLRAESASGNQVSDMAQLFYYFALEAICYILFEKRIGCLQRSIPEDTVTFVRSIGLMFQNSLYATFLPKWTRPVLPFWKRYLDGWNAIFSFGKKLIDEKLEDMEAQLQAAGPDGIQVSGYLHFLLASGQLSPREAMGSLPELLMAGVDTTSNTLTWALYHLSKDPEIQEALHEEVVGVVPAGQVPQHKDFAHMPLLKAVLKETLRLYPVVPTNSRIIEKEIEVDGFLFPKNTQFVFCHYVVSRDPTAFSEPESFQPHRWLRNSQPATPRIQHPFGSVPFGYGVRACLGRRIAELEMQLLLARLIQKYKVVLAPETGELKSVARIVLVPNKKVGLQFLQRQC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the drug 'CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O' is likely to interact with the target protein 'RESGLARARAAPAKLCEVLHYIQIRMDSREADLRSPVLSLKGKIADSDLTTTSLLIDVNSGYEKGATGLDARMNVELGSPVTNMRPGANEFLEIATIRYILHGENGSCEKRHCRPELGLLCGLSVKSNSSVDTCALDFPIMRNLSSALNRFSANSGGNRVVVMSKCIYECARYLPRLTHVMFRTCFYITQKLDFMAQVFNTAHVFEGFALSKKVAESLDKEIFITMNAEPHKVLCSDQLMILGEINNRVEFCHKATFQQAHGLGGLEGTGVTWAKQLVKPSVDIGYLGISYYYVRQDPFLYHLVKMPDFGGYNCCFRSSENKSKGNRVIADFNSDMTKQVIAFHGASLSLAELSRLKILLTQLNALLLEQVESDLPEKSKVESQKPEDLVRMTYFISPTRILVRIEKSGMISHGKSRKGVKAANMDAIKGADLWPVTRLETKAVLANEMNVIQPLHARADDADPPIQLDSSRHCIGHLDSMRKVRIENQDDAVLVIAYIEGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\"], \"target_seq\": \"RESGLARARAAPAKLCEVLHYIQIRMDSREADLRSPVLSLKGKIADSDLTTTSLLIDVNSGYEKGATGLDARMNVELGSPVTNMRPGANEFLEIATIRYILHGENGSCEKRHCRPELGLLCGLSVKSNSSVDTCALDFPIMRNLSSALNRFSANSGGNRVVVMSKCIYECARYLPRLTHVMFRTCFYITQKLDFMAQVFNTAHVFEGFALSKKVAESLDKEIFITMNAEPHKVLCSDQLMILGEINNRVEFCHKATFQQAHGLGGLEGTGVTWAKQLVKPSVDIGYLGISYYYVRQDPFLYHLVKMPDFGGYNCCFRSSENKSKGNRVIADFNSDMTKQVIAFHGASLSLAELSRLKILLTQLNALLLEQVESDLPEKSKVESQKPEDLVRMTYFISPTRILVRIEKSGMISHGKSRKGVKAANMDAIKGADLWPVTRLETKAVLANEMNVIQPLHARADDADPPIQLDSSRHCIGHLDSMRKVRIENQDDAVLVIAYIEGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MNRESFAAGERLVSPAYVRQGCEARRSHEHLIRLLLEKGKCPENGWDESTLELFLHELAIMDSNNFLGNCGVGEREGRVASALVARRHYRFIHGIGRSGDISAVQPKAAGSSLLNKITNSLVLDIIKLAGVHTVANCFVVPMATGMSLTLCFLTLRHKRPKAKYIIWPRIDQKSCFKSMITAGFEPVVIENVLEGDELRTDLKAVEAKVQELGPDCILCIHSTTSCFAPRVPDRLEELAVICANYDIPHIVNNAYGVQSSKCMHLIQQGARVGRIDAFVQSLDKNFMVPVGGAIIAGFNDSFIQEISKMYPGRASASPSLDVLITLLSLGSNGYKKLLKERKEMFSYLSNQIKKLSEAYNERLLHTPHNPISLAMTLKTLDEHRDKAVTQLGSMLFTRQVSGARVVPLGSMQTVSGYTFRGFMSHTNNYPCAYLNAASAIGMKMQDVDLFIKRLDRCLKAVRKERSKESDDNYDKTEDVDIEEMALKLDNVLLDTYQDASS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MNRESFAAGERLVSPAYVRQGCEARRSHEHLIRLLLEKGKCPENGWDESTLELFLHELAIMDSNNFLGNCGVGEREGRVASALVARRHYRFIHGIGRSGDISAVQPKAAGSSLLNKITNSLVLDIIKLAGVHTVANCFVVPMATGMSLTLCFLTLRHKRPKAKYIIWPRIDQKSCFKSMITAGFEPVVIENVLEGDELRTDLKAVEAKVQELGPDCILCIHSTTSCFAPRVPDRLEELAVICANYDIPHIVNNAYGVQSSKCMHLIQQGARVGRIDAFVQSLDKNFMVPVGGAIIAGFNDSFIQEISKMYPGRASASPSLDVLITLLSLGSNGYKKLLKERKEMFSYLSNQIKKLSEAYNERLLHTPHNPISLAMTLKTLDEHRDKAVTQLGSMLFTRQVSGARVVPLGSMQTVSGYTFRGFMSHTNNYPCAYLNAASAIGMKMQDVDLFIKRLDRCLKAVRKERSKESDDNYDKTEDVDIEEMALKLDNVLLDTYQDASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNRESFAAGERLVSPAYVRQGCEARRSHEHLIRLLLEKGKCPENGWDESTLELFLHELAIMDSNNFLGNCGVGEREGRVASALVARRHYRFIHGIGRSGDISAVQPKAAGSSLLNKITNSLVLDIIKLAGVHTVANCFVVPMATGMSLTLCFLTLRHKRPKAKYIIWPRIDQKSCFKSMITAGFEPVVIENVLEGDELRTDLKAVEAKVQELGPDCILCIHSTTSCFAPRVPDRLEELAVICANYDIPHIVNNAYGVQSSKCMHLIQQGARVGRIDAFVQSLDKNFMVPVGGAIIAGFNDSFIQEISKMYPGRASASPSLDVLITLLSLGSNGYKKLLKERKEMFSYLSNQIKKLSEAYNERLLHTPHNPISLAMTLKTLDEHRDKAVTQLGSMLFTRQVSGARVVPLGSMQTVSGYTFRGFMSHTNNYPCAYLNAASAIGMKMQDVDLFIKRLDRCLKAVRKERSKESDDNYDKTEDVDIEEMALKLDNVLLDTYQDASS\", \"result\": [{\"smile\": \"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O' can not interact with the protein 'MNRESFAAGERLVSPAYVRQGCEARRSHEHLIRLLLEKGKCPENGWDESTLELFLHELAIMDSNNFLGNCGVGEREGRVASALVARRHYRFIHGIGRSGDISAVQPKAAGSSLLNKITNSLVLDIIKLAGVHTVANCFVVPMATGMSLTLCFLTLRHKRPKAKYIIWPRIDQKSCFKSMITAGFEPVVIENVLEGDELRTDLKAVEAKVQELGPDCILCIHSTTSCFAPRVPDRLEELAVICANYDIPHIVNNAYGVQSSKCMHLIQQGARVGRIDAFVQSLDKNFMVPVGGAIIAGFNDSFIQEISKMYPGRASASPSLDVLITLLSLGSNGYKKLLKERKEMFSYLSNQIKKLSEAYNERLLHTPHNPISLAMTLKTLDEHRDKAVTQLGSMLFTRQVSGARVVPLGSMQTVSGYTFRGFMSHTNNYPCAYLNAASAIGMKMQDVDLFIKRLDRCLKAVRKERSKESDDNYDKTEDVDIEEMALKLDNVLLDTYQDASS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction between the drug 'C1CNCCC1NC(=O)C2=C(C=NN2)NC(=O)C3=C(C=CC=C3Cl)Cl' and the target protein 'SDFGRKPDRVNKNGPSRMEQEGILNPYLYMTVKMETNEYVMVWSSAQQTVYMAYFPMSDSDNLSPNNTRFSQNENKKLSSSITTKSYKFAQNKKASEHAFRIISGSVRKFYVGNQKCDTEINPLTSQLDDNSWRVVHLIGVDTLRITSSENWARAADQEDKILSRIYEVQKTSEHSISYTLPQTPRTSHESMPTKQAIARITDSMTLGGFTEVCTSSFQNISDRAQIQGPCSTDRPYVIYLMLKTHYEDVIGPTGYSKEKAYIRISPMPSGAQTKGKDLLDLFCDVFNPLYDIWMHLQMKQQVFSTTKTIFFNLLVSHENYKLSPEYAEFAVERVICSSHADDFISIFILYRPVPGYAADSLQSTLAPGLLRTGSVSRTTTSDSASVRNVSLLHSIAKGGFFTKFSGSRPPVNSSNEKTSPHSICWAFSKGTIIWTGIPTLQSTTDVLSERPKQVNVKFCGFEISKIHLYAPTGYGTMKPTFIFEQLSLFLKGLNNNEDHKSSLTVNPLKCKEKFIKRALSEPRKMMTLNTITESHLTDGERPPKESRSETKVERKQLKNHTNRTTTMDIVELEPDTMPRANGGTWGIGRALIGVMKSMQNRTYDHALSISMHHGKYNLEWNTADDLKRFRCQVANLRLTHQEASVYFGDAPSPRYDLYIVSGNTTISNKAEQNKAQEDNAVLGQTNNEDLRMKDLPNSSLTKNISHRLGEFTKDSSNVDIAKEDLCEHRARPGNGCIFHSAPPGNGGPVEGEQKLIPHLQKFQVESQRLSPTCYFFPEWKGSLIKEMHEKSYNGPPSAESSVGRSSINYRTNSDSYEISFDAKEPSEENKYVVQYGGHCTPILPINFASIIHSEKDDRFKAKSSLVSSPGLVLTHLKDERGYQKQLSGAAREEFRSMSYKDVKNHSSMLSPSAPNSQLYLAPIPQSLMASDSLKQGQLSGLMRNGNVLPLSRAALGNVHTALNKVSYEYAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C1CNCCC1NC(=O)C2=C(C=NN2)NC(=O)C3=C(C=CC=C3Cl)Cl\"], \"target_seq\": \"SDFGRKPDRVNKNGPSRMEQEGILNPYLYMTVKMETNEYVMVWSSAQQTVYMAYFPMSDSDNLSPNNTRFSQNENKKLSSSITTKSYKFAQNKKASEHAFRIISGSVRKFYVGNQKCDTEINPLTSQLDDNSWRVVHLIGVDTLRITSSENWARAADQEDKILSRIYEVQKTSEHSISYTLPQTPRTSHESMPTKQAIARITDSMTLGGFTEVCTSSFQNISDRAQIQGPCSTDRPYVIYLMLKTHYEDVIGPTGYSKEKAYIRISPMPSGAQTKGKDLLDLFCDVFNPLYDIWMHLQMKQQVFSTTKTIFFNLLVSHENYKLSPEYAEFAVERVICSSHADDFISIFILYRPVPGYAADSLQSTLAPGLLRTGSVSRTTTSDSASVRNVSLLHSIAKGGFFTKFSGSRPPVNSSNEKTSPHSICWAFSKGTIIWTGIPTLQSTTDVLSERPKQVNVKFCGFEISKIHLYAPTGYGTMKPTFIFEQLSLFLKGLNNNEDHKSSLTVNPLKCKEKFIKRALSEPRKMMTLNTITESHLTDGERPPKESRSETKVERKQLKNHTNRTTTMDIVELEPDTMPRANGGTWGIGRALIGVMKSMQNRTYDHALSISMHHGKYNLEWNTADDLKRFRCQVANLRLTHQEASVYFGDAPSPRYDLYIVSGNTTISNKAEQNKAQEDNAVLGQTNNEDLRMKDLPNSSLTKNISHRLGEFTKDSSNVDIAKEDLCEHRARPGNGCIFHSAPPGNGGPVEGEQKLIPHLQKFQVESQRLSPTCYFFPEWKGSLIKEMHEKSYNGPPSAESSVGRSSINYRTNSDSYEISFDAKEPSEENKYVVQYGGHCTPILPINFASIIHSEKDDRFKAKSSLVSSPGLVLTHLKDERGYQKQLSGAAREEFRSMSYKDVKNHSSMLSPSAPNSQLYLAPIPQSLMASDSLKQGQLSGLMRNGNVLPLSRAALGNVHTALNKVSYEYAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH\", \"result\": [{\"smile\": \"C1CNCCC1NC(=O)C2=C(C=NN2)NC(=O)C3=C(C=CC=C3Cl)Cl\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'C1CNCCC1NC(=O)C2=C(C=NN2)NC(=O)C3=C(C=CC=C3Cl)Cl' can not interact with the protein 'MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' is expected to interact with the target protein 'AQLNETCPLVGGRLSNAGATLMKSQGLHLFGGEASFDTPMTYRASGQQVTLAIAIMKSIHFKFRINVLQDMHNRTNLSLHVKEIPPYVLGMPPVVAKNDELEIIDGLLWKKKETKFGCVIGTPYLPTLSCLSQKFECCRYRPPQKNQEVVAKYLKKKLIHGRGAWKELPKHFMLFAWEEHNPFVQDCSSPSCFLEEQTPWIEPLSPAEKHILSSPADEDLTHIIETNPMKGPPAQLECTSEDNNPFAGASVFALQVANDICSIKKRRCEFPIALKKQQKVKLCQPIEGLQLIEKSWDLDQPLEDLTHKSAATHLDAAADRERQTRSNRWSRSHPILGVGVEIRGNKCAAAFSLYTNSLGPKSIGTYDAGSDPLTRFITEMVSGNDYAFLSTFYALEEFLFDTLEKGNCTIQVEGFSDKIIEDHGYSINAGTKDHDEYVPCDSLEGVDNGITIDLHFRSKIGTFEAVPRVTSKKWYQPLTCNERPSPTRAAIFSHHLKQLNNADEGQKNRLNLINEGFVSHPGLIYTCQNTSQHIGYAMKNPVNSCFDFKATDSSNDFRVMPMLRPLAKDSRKADERKFYRGHIEPDVPMGNPALTLHYSHITQGRQYQMKDIFTTNLEPTIPKYLMPVFTRSVLSEPNALKRCFSTFIFYTFHAITPDDMEKRVLVLTDSGRKHPPEGEAAGSPIPSLGEDKRNISVAEVQTMARLVVDKVADEPLKQFEALQGKEVLDWRSIPVTSFESGKMCTIIIHKSPYLQVPAEIIEIIITAENISMTDAKNQGWTLLDLMLDGGVLQRSLVFLPWKYSRVKRKALLLFVHSSYHARGNAVAQYKPKQTPNGNLAAVPLKDAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\"], \"target_seq\": \"AQLNETCPLVGGRLSNAGATLMKSQGLHLFGGEASFDTPMTYRASGQQVTLAIAIMKSIHFKFRINVLQDMHNRTNLSLHVKEIPPYVLGMPPVVAKNDELEIIDGLLWKKKETKFGCVIGTPYLPTLSCLSQKFECCRYRPPQKNQEVVAKYLKKKLIHGRGAWKELPKHFMLFAWEEHNPFVQDCSSPSCFLEEQTPWIEPLSPAEKHILSSPADEDLTHIIETNPMKGPPAQLECTSEDNNPFAGASVFALQVANDICSIKKRRCEFPIALKKQQKVKLCQPIEGLQLIEKSWDLDQPLEDLTHKSAATHLDAAADRERQTRSNRWSRSHPILGVGVEIRGNKCAAAFSLYTNSLGPKSIGTYDAGSDPLTRFITEMVSGNDYAFLSTFYALEEFLFDTLEKGNCTIQVEGFSDKIIEDHGYSINAGTKDHDEYVPCDSLEGVDNGITIDLHFRSKIGTFEAVPRVTSKKWYQPLTCNERPSPTRAAIFSHHLKQLNNADEGQKNRLNLINEGFVSHPGLIYTCQNTSQHIGYAMKNPVNSCFDFKATDSSNDFRVMPMLRPLAKDSRKADERKFYRGHIEPDVPMGNPALTLHYSHITQGRQYQMKDIFTTNLEPTIPKYLMPVFTRSVLSEPNALKRCFSTFIFYTFHAITPDDMEKRVLVLTDSGRKHPPEGEAAGSPIPSLGEDKRNISVAEVQTMARLVVDKVADEPLKQFEALQGKEVLDWRSIPVTSFESGKMCTIIIHKSPYLQVPAEIIEIIITAENISMTDAKNQGWTLLDLMLDGGVLQRSLVFLPWKYSRVKRKALLLFVHSSYHARGNAVAQYKPKQTPNGNLAAVPLKDAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY' works as the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY\", \"result\": [{\"smile\": \"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' can not interact with the protein 'MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction probability for the drug 'N[C@H]1C(O)O[C@H](CO)[C@@H](O)[C@@H]1O' and the target protein 'EKLAFRPSGEALIRYDEIQESLECATEDHMAGEFELAHLHYIRGHYDSDVLRDAPDQVDLLYIPVTKGFPDLRPRKDSLGSFRESLLKIVGLFELFDRQDRVATPLPSQRISDQLAGMATDMAPNAPRPTAASALKPEPGGKPASGDQQGLRAKGVVGTPTTLGYAQFPVHPCQLGSRVPLSRDVRYIGSLVRRHGCPSTEKGTKSVPEFDGLGGTLEHQAATLKTSKPCKSLQPKELQLVPLKMELKCGVEKIRPNLAGQQLREVQFGLQRHTSRFVKPQHSASYNLEERGMEPKQLQDSSVAMALPLGSIQPEGHSALCVRAMQEILEVNKFVIGDLRAAKRSIQCGEMLKSITQHGGNTLANGTGSPSLFDGRKPHLTGRGSHLKNYGQPPQYKVMSPLGADLGGRQAVEKPPGVPKLLTYTVFCPSGKFGITHNHASEDMFPAAGTVGGPTTGLHTTETDRGTEDWVEEPALGVEATAALQADLKLASLNARSLYDQKSGGGGSLGGSEIADQEILEDDLPNLSSVLAGRGLRLKSVSVPQDGPGPYLNGEKEGNVTVPLEYHHDNGASSGEPYIPGQDVIHVIMAQRNYPEESERAGVADWQQPFMLGLCEAVNASSHLGDVESALGQHSTTFDLALERKDMQATTPEKGALETLRAHLGLYLPATGRPACAAGALPPSAVMQSLEEKPYVASTCYALGALVSPRALVVPVKMLEADLNSGTYAMAAYSATTVKEVHLFIHPEPPYLALQNKRPSSHSEARDDVPGLNESEPLLPESPVDQGLRNGLRGETHAPERGSTLLALGYFVGQQFPSLHEFTCEDDDGGRLAVRAGQRGIDGAGGDAGKVLTLGSYHDLELMYDPRARRSLTPGSSAQSTPEDLRLLRRPGLHATIEGFAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@H]1C(O)O[C@H](CO)[C@@H](O)[C@@H]1O\"], \"target_seq\": \"EKLAFRPSGEALIRYDEIQESLECATEDHMAGEFELAHLHYIRGHYDSDVLRDAPDQVDLLYIPVTKGFPDLRPRKDSLGSFRESLLKIVGLFELFDRQDRVATPLPSQRISDQLAGMATDMAPNAPRPTAASALKPEPGGKPASGDQQGLRAKGVVGTPTTLGYAQFPVHPCQLGSRVPLSRDVRYIGSLVRRHGCPSTEKGTKSVPEFDGLGGTLEHQAATLKTSKPCKSLQPKELQLVPLKMELKCGVEKIRPNLAGQQLREVQFGLQRHTSRFVKPQHSASYNLEERGMEPKQLQDSSVAMALPLGSIQPEGHSALCVRAMQEILEVNKFVIGDLRAAKRSIQCGEMLKSITQHGGNTLANGTGSPSLFDGRKPHLTGRGSHLKNYGQPPQYKVMSPLGADLGGRQAVEKPPGVPKLLTYTVFCPSGKFGITHNHASEDMFPAAGTVGGPTTGLHTTETDRGTEDWVEEPALGVEATAALQADLKLASLNARSLYDQKSGGGGSLGGSEIADQEILEDDLPNLSSVLAGRGLRLKSVSVPQDGPGPYLNGEKEGNVTVPLEYHHDNGASSGEPYIPGQDVIHVIMAQRNYPEESERAGVADWQQPFMLGLCEAVNASSHLGDVESALGQHSTTFDLALERKDMQATTPEKGALETLRAHLGLYLPATGRPACAAGALPPSAVMQSLEEKPYVASTCYALGALVSPRALVVPVKMLEADLNSGTYAMAAYSATTVKEVHLFIHPEPPYLALQNKRPSSHSEARDDVPGLNESEPLLPESPVDQGLRNGLRGETHAPERGSTLLALGYFVGQQFPSLHEFTCEDDDGGRLAVRAGQRGIDGAGGDAGKVLTLGSYHDLELMYDPRARRSLTPGSSAQSTPEDLRLLRRPGLHATIEGFAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The correct expression for the protein is 'MESCYNPGLDGIIEYDDFKLNSSIVEPKEPAPETADGPYLVIVEQPKQRGFRFRYGCEGPSHGGLPGASSEKGRKTYPTVKICNYEGPAKIEVDLVTHSDPPRAHAHSLVGKQCSELGICAVSVGPKDMTAQFNNLGVLHVTKKNMMGTMIQKLQRQRLRSRPQGLTEAEQRELEQEAKELKKVMDLSIVRLRFSAFLRASDGSFSLPLKPVISQPIHDSKSPGASNLKISRMDKTAGSVRGGDEVYLLCDKVQKDDIEVRFYEDDENGWQAFGDFSPTDVHKQYAIVFRTPPYHKMKIERPVTVFLQLKRKRGGDVSDSKQFTYYPLVEDKEEVQRKRRKALPTFSQPFGGGSHMGGGSGGAAGGYGGAGGGGSLGFFPSSLAYSPYQSGAGPMGCYPGGGGGAQMAATVPSRDSGEEAAEPSAPSRTPQCEPQAPEMLQRAREYNARLFGLAQRSARALLDYGVTADARALLAGQRHLLTAQDENGDTPLHLAIIHGQTSVIEQIVYVIHHAQDLGVVNLTNHLHQTPLHLAVITGQTSVVSFLLRVGADPALLDRHGDSAMHLALRAGAGAPELLRALLQSGAPAVPQLLHMPDFEGLYPVHLAVRARSPECLDLLVDSGAEVEATERQGGRTALHLATEMEELGLVTHLVTKLRANVNARTFAGNTPLHLAAGLGYPTLTRLLLKAGADIHAENEEPLCPLPSPPTSDSDSDSEGPEKDTRSSFRGHTPLDLTCSTKVKTLLLNAAQNTMEPPLTPPSPAGPGLSLGDTALQNLEQLLDGPEAQGSWAELAERLGLRSLVDTYRQTTSPSGSLLRSYELAGGDLAGLLEALSDMGLEEGVRLLRGPETRDKLPSTAEVKEDSAYGSQSVEQEAEKLGPPPEPPGGLCHGHPQPQVH'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MESCYNPGLDGIIEYDDFKLNSSIVEPKEPAPETADGPYLVIVEQPKQRGFRFRYGCEGPSHGGLPGASSEKGRKTYPTVKICNYEGPAKIEVDLVTHSDPPRAHAHSLVGKQCSELGICAVSVGPKDMTAQFNNLGVLHVTKKNMMGTMIQKLQRQRLRSRPQGLTEAEQRELEQEAKELKKVMDLSIVRLRFSAFLRASDGSFSLPLKPVISQPIHDSKSPGASNLKISRMDKTAGSVRGGDEVYLLCDKVQKDDIEVRFYEDDENGWQAFGDFSPTDVHKQYAIVFRTPPYHKMKIERPVTVFLQLKRKRGGDVSDSKQFTYYPLVEDKEEVQRKRRKALPTFSQPFGGGSHMGGGSGGAAGGYGGAGGGGSLGFFPSSLAYSPYQSGAGPMGCYPGGGGGAQMAATVPSRDSGEEAAEPSAPSRTPQCEPQAPEMLQRAREYNARLFGLAQRSARALLDYGVTADARALLAGQRHLLTAQDENGDTPLHLAIIHGQTSVIEQIVYVIHHAQDLGVVNLTNHLHQTPLHLAVITGQTSVVSFLLRVGADPALLDRHGDSAMHLALRAGAGAPELLRALLQSGAPAVPQLLHMPDFEGLYPVHLAVRARSPECLDLLVDSGAEVEATERQGGRTALHLATEMEELGLVTHLVTKLRANVNARTFAGNTPLHLAAGLGYPTLTRLLLKAGADIHAENEEPLCPLPSPPTSDSDSDSEGPEKDTRSSFRGHTPLDLTCSTKVKTLLLNAAQNTMEPPLTPPSPAGPGLSLGDTALQNLEQLLDGPEAQGSWAELAERLGLRSLVDTYRQTTSPSGSLLRSYELAGGDLAGLLEALSDMGLEEGVRLLRGPETRDKLPSTAEVKEDSAYGSQSVEQEAEKLGPPPEPPGGLCHGHPQPQVH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MESCYNPGLDGIIEYDDFKLNSSIVEPKEPAPETADGPYLVIVEQPKQRGFRFRYGCEGPSHGGLPGASSEKGRKTYPTVKICNYEGPAKIEVDLVTHSDPPRAHAHSLVGKQCSELGICAVSVGPKDMTAQFNNLGVLHVTKKNMMGTMIQKLQRQRLRSRPQGLTEAEQRELEQEAKELKKVMDLSIVRLRFSAFLRASDGSFSLPLKPVISQPIHDSKSPGASNLKISRMDKTAGSVRGGDEVYLLCDKVQKDDIEVRFYEDDENGWQAFGDFSPTDVHKQYAIVFRTPPYHKMKIERPVTVFLQLKRKRGGDVSDSKQFTYYPLVEDKEEVQRKRRKALPTFSQPFGGGSHMGGGSGGAAGGYGGAGGGGSLGFFPSSLAYSPYQSGAGPMGCYPGGGGGAQMAATVPSRDSGEEAAEPSAPSRTPQCEPQAPEMLQRAREYNARLFGLAQRSARALLDYGVTADARALLAGQRHLLTAQDENGDTPLHLAIIHGQTSVIEQIVYVIHHAQDLGVVNLTNHLHQTPLHLAVITGQTSVVSFLLRVGADPALLDRHGDSAMHLALRAGAGAPELLRALLQSGAPAVPQLLHMPDFEGLYPVHLAVRARSPECLDLLVDSGAEVEATERQGGRTALHLATEMEELGLVTHLVTKLRANVNARTFAGNTPLHLAAGLGYPTLTRLLLKAGADIHAENEEPLCPLPSPPTSDSDSDSEGPEKDTRSSFRGHTPLDLTCSTKVKTLLLNAAQNTMEPPLTPPSPAGPGLSLGDTALQNLEQLLDGPEAQGSWAELAERLGLRSLVDTYRQTTSPSGSLLRSYELAGGDLAGLLEALSDMGLEEGVRLLRGPETRDKLPSTAEVKEDSAYGSQSVEQEAEKLGPPPEPPGGLCHGHPQPQVH\", \"result\": [{\"smile\": \"N[C@H]1C(O)O[C@H](CO)[C@@H](O)[C@@H]1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@H]1C(O)O[C@H](CO)[C@@H](O)[C@@H]1O' can interact with the protein 'MESCYNPGLDGIIEYDDFKLNSSIVEPKEPAPETADGPYLVIVEQPKQRGFRFRYGCEGPSHGGLPGASSEKGRKTYPTVKICNYEGPAKIEVDLVTHSDPPRAHAHSLVGKQCSELGICAVSVGPKDMTAQFNNLGVLHVTKKNMMGTMIQKLQRQRLRSRPQGLTEAEQRELEQEAKELKKVMDLSIVRLRFSAFLRASDGSFSLPLKPVISQPIHDSKSPGASNLKISRMDKTAGSVRGGDEVYLLCDKVQKDDIEVRFYEDDENGWQAFGDFSPTDVHKQYAIVFRTPPYHKMKIERPVTVFLQLKRKRGGDVSDSKQFTYYPLVEDKEEVQRKRRKALPTFSQPFGGGSHMGGGSGGAAGGYGGAGGGGSLGFFPSSLAYSPYQSGAGPMGCYPGGGGGAQMAATVPSRDSGEEAAEPSAPSRTPQCEPQAPEMLQRAREYNARLFGLAQRSARALLDYGVTADARALLAGQRHLLTAQDENGDTPLHLAIIHGQTSVIEQIVYVIHHAQDLGVVNLTNHLHQTPLHLAVITGQTSVVSFLLRVGADPALLDRHGDSAMHLALRAGAGAPELLRALLQSGAPAVPQLLHMPDFEGLYPVHLAVRARSPECLDLLVDSGAEVEATERQGGRTALHLATEMEELGLVTHLVTKLRANVNARTFAGNTPLHLAAGLGYPTLTRLLLKAGADIHAENEEPLCPLPSPPTSDSDSDSEGPEKDTRSSFRGHTPLDLTCSTKVKTLLLNAAQNTMEPPLTPPSPAGPGLSLGDTALQNLEQLLDGPEAQGSWAELAERLGLRSLVDTYRQTTSPSGSLLRSYELAGGDLAGLLEALSDMGLEEGVRLLRGPETRDKLPSTAEVKEDSAYGSQSVEQEAEKLGPPPEPPGGLCHGHPQPQVH'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the drug 'CC(N)CC1=CC=CC=C1' is likely to engage with the target protein 'INKWGLNSLAADSVMFVASQPYNDEHHIASSSDLTSPSMLYQGLGEAEAWSLIGPSYRRVSGASMQMLLIFIILIYIKLMGIRPENGRIILLCSAILRMAMKIIIEALLICMGIFSAHGVIGANILEDILVAPPTAVGFAFVTFMGVGMPVKLTLTIGVGSGVLEEQRMLGARMQWLLPHVYGPGSGASIAIYFLITVTSVGLFNQSSDPLFENKYSQLIDPATCSSIRCPGWKGTGSSILTRLDYFFSVTEVCKCCLDMYPGVQMGDNFVSSLLLMDTIIQILFLVNVFFATNAGSSDALLVAAEHPMGDRTMIEMVGNAHMFWLVTLMGQYTIMAFCVRYALGFPNKYQAGATPLFIKGIPIGDREMFNISIYKGPYLVAPQNAAYSIKKCQPSLSLVGFKRILPLIDIFLTESDCIVAALFALTIGNMIAAEAADKMPLNPEPLPTGLDHASPSKPSSDVTFVTQSVGEILVPMATLVGIQLLMAAVSEFRNIIALLATYYIATAKVFLFSAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC(N)CC1=CC=CC=C1\"], \"target_seq\": \"INKWGLNSLAADSVMFVASQPYNDEHHIASSSDLTSPSMLYQGLGEAEAWSLIGPSYRRVSGASMQMLLIFIILIYIKLMGIRPENGRIILLCSAILRMAMKIIIEALLICMGIFSAHGVIGANILEDILVAPPTAVGFAFVTFMGVGMPVKLTLTIGVGSGVLEEQRMLGARMQWLLPHVYGPGSGASIAIYFLITVTSVGLFNQSSDPLFENKYSQLIDPATCSSIRCPGWKGTGSSILTRLDYFFSVTEVCKCCLDMYPGVQMGDNFVSSLLLMDTIIQILFLVNVFFATNAGSSDALLVAAEHPMGDRTMIEMVGNAHMFWLVTLMGQYTIMAFCVRYALGFPNKYQAGATPLFIKGIPIGDREMFNISIYKGPYLVAPQNAAYSIKKCQPSLSLVGFKRILPLIDIFLTESDCIVAALFALTIGNMIAAEAADKMPLNPEPLPTGLDHASPSKPSSDVTFVTQSVGEILVPMATLVGIQLLMAAVSEFRNIIALLATYYIATAKVFLFSAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "The sequence that matches the protein is 'MALSELALVRWLQESRRSRKLILFIVFLALLLDNMLLTVVVPIIPSYLYSIKHEKNATEIQTARPVHTASISDSFQSIFSYYDNSTMVTGNATRDLTLHQTATQHMVTNASAVPSDCPSEDKDLLNENVQVGLLFASKATVQLITNPFIGLLTNRIGYPIPIFAGFCIMFVSTIMFAFSSSYAFLLIARSLQGIGSSCSSVAGMGMLASVYTDDEERGNVMGIALGGLAMGVLVGPPFGSVLYEFVGKTAPFLVLAALVLLDGAIQLFVLQPSRVQPESQKGTPLTTLLKDPYILIAAGSICFANMGIAMLEPALPIWMMETMCSRKWQLGVAFLPASISYLIGTNIFGILAHKMGRWLCALLGMIIVGVSILCIPFAKNIYGLIAPNFGVGFAIGMVDSSMMPIMGYLVDLRHVSVYGSVYAIADVAFCMGYAIGPSAGGAIAKAIGFPWLMTIIGIIDILFAPLCFFLRSPPAKEEKMAILMDHNCPIKTKMYTQNNIQSYPIGEDEESESD'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALSELALVRWLQESRRSRKLILFIVFLALLLDNMLLTVVVPIIPSYLYSIKHEKNATEIQTARPVHTASISDSFQSIFSYYDNSTMVTGNATRDLTLHQTATQHMVTNASAVPSDCPSEDKDLLNENVQVGLLFASKATVQLITNPFIGLLTNRIGYPIPIFAGFCIMFVSTIMFAFSSSYAFLLIARSLQGIGSSCSSVAGMGMLASVYTDDEERGNVMGIALGGLAMGVLVGPPFGSVLYEFVGKTAPFLVLAALVLLDGAIQLFVLQPSRVQPESQKGTPLTTLLKDPYILIAAGSICFANMGIAMLEPALPIWMMETMCSRKWQLGVAFLPASISYLIGTNIFGILAHKMGRWLCALLGMIIVGVSILCIPFAKNIYGLIAPNFGVGFAIGMVDSSMMPIMGYLVDLRHVSVYGSVYAIADVAFCMGYAIGPSAGGAIAKAIGFPWLMTIIGIIDILFAPLCFFLRSPPAKEEKMAILMDHNCPIKTKMYTQNNIQSYPIGEDEESESD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALSELALVRWLQESRRSRKLILFIVFLALLLDNMLLTVVVPIIPSYLYSIKHEKNATEIQTARPVHTASISDSFQSIFSYYDNSTMVTGNATRDLTLHQTATQHMVTNASAVPSDCPSEDKDLLNENVQVGLLFASKATVQLITNPFIGLLTNRIGYPIPIFAGFCIMFVSTIMFAFSSSYAFLLIARSLQGIGSSCSSVAGMGMLASVYTDDEERGNVMGIALGGLAMGVLVGPPFGSVLYEFVGKTAPFLVLAALVLLDGAIQLFVLQPSRVQPESQKGTPLTTLLKDPYILIAAGSICFANMGIAMLEPALPIWMMETMCSRKWQLGVAFLPASISYLIGTNIFGILAHKMGRWLCALLGMIIVGVSILCIPFAKNIYGLIAPNFGVGFAIGMVDSSMMPIMGYLVDLRHVSVYGSVYAIADVAFCMGYAIGPSAGGAIAKAIGFPWLMTIIGIIDILFAPLCFFLRSPPAKEEKMAILMDHNCPIKTKMYTQNNIQSYPIGEDEESESD\", \"result\": [{\"smile\": \"CC(N)CC1=CC=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC(N)CC1=CC=CC=C1' can interact with the protein 'MALSELALVRWLQESRRSRKLILFIVFLALLLDNMLLTVVVPIIPSYLYSIKHEKNATEIQTARPVHTASISDSFQSIFSYYDNSTMVTGNATRDLTLHQTATQHMVTNASAVPSDCPSEDKDLLNENVQVGLLFASKATVQLITNPFIGLLTNRIGYPIPIFAGFCIMFVSTIMFAFSSSYAFLLIARSLQGIGSSCSSVAGMGMLASVYTDDEERGNVMGIALGGLAMGVLVGPPFGSVLYEFVGKTAPFLVLAALVLLDGAIQLFVLQPSRVQPESQKGTPLTTLLKDPYILIAAGSICFANMGIAMLEPALPIWMMETMCSRKWQLGVAFLPASISYLIGTNIFGILAHKMGRWLCALLGMIIVGVSILCIPFAKNIYGLIAPNFGVGFAIGMVDSSMMPIMGYLVDLRHVSVYGSVYAIADVAFCMGYAIGPSAGGAIAKAIGFPWLMTIIGIIDILFAPLCFFLRSPPAKEEKMAILMDHNCPIKTKMYTQNNIQSYPIGEDEESESD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an expected interaction between the drug 'CCSC(=NC1=CC=CC=C1)N' and the target protein 'GSLFWKHEPVVIDGPGADSAEAMGALNPARRFSRYLSDVFISRTNEIWRNAFSKFRVEFKKRSAIAGLRQKHLPHCMWVHGKELMNLEIQDGFLGSENVSTEHLLGYASGTPGEKPGLRCWHFESKHFQLPHESALEMRLGYLVKNAQNDTFTFESDYLLNLRALSRDANMARDLQQLQGDEKLEKKGAVHVLVDGVTGTGLAQKVWVWEIGHESNRASLGLYKKWPLVLYKFDPGDLHERKRSEVSTTHNAHVYQLTRVKALTQPNERIEHFLHLAKVGRNMGTDYPKINDSFVTQKSEDDANGTLPRYPDSTYYNKQPPIECKEKLNTLDQRRQVSEKGGIVLVEGGHESLDLKQPYQQASKTGIPLRHVGFGCGDFFRFTFKRDTIGVAMYHGAISDPWETSRMPSGRENEGSDLKFGWFKPPFGLELTLKVRHDYKAYQSEEATAVRYAHEMGIAHQFEDSRFQYGEYTGDRRCRGDYPTAQQQMSATVSPDHVGVLDHEAYVLCKDGISSTNKYQVFGFPFDQHLLIEFRFISESLITLFKICAPYSGYNKERQPLVGDMNIKLGQGSEQEGQFDELPAGVMALTRPQKVKDLSVTLEPVLMRTGRWMESISPPTLPPAKTSLRQMKLVTSDTNKQDEAFSVENDSLGTVDPPQKNFKDFHGITKQVIAVEAKLLFEDKLIFFKFSRVYKSFGQNALEPTLPEPTGVQPAVSYESQTGIHFGPVVIKGRSGETNPQMILYWGSHLVEMQRTIKADRPKREPSNRLVVTVNDVVVEPKRAICRDREIEEANSGICEAQVRGLLKSPYVVAETYHFPYPSPLSGNEHLLGCLGIPETEKDQVPFGYCLWVRIMYRPAVKRFLIYCAMVDIETNFCTTLSQLDVKFSMEINGEPETTVAKMSLGDVLREAGETDPLTAGSDDGQDQNPTEALAAEGVGLSTYLPKYPHISVNCEPPKAQNRIWGGSLPQIANSWAKHDRDEGQMGLQGESLATWVGCELLQKHRKLVPMKFLFALNSDINLACVLRPGVLGIRGVLQSNEGPATGIGPCQEQILIGKSRTPDQLRVAVGLDRGWSAPRFQAIRIFNMKDVFTVGTSTESVGAAGRQIKIPTTKASENHEKALPSIVPPYNDVDFRMKGQPGNPKSDVEKSVKYAQINRSWHFSQDVFSKDSKGKIELEADPTARNSIRIFVDYLQEATEQATGWSEKLDTRTSIRCRSCYQIWNDGSISLIMDNPSPANIMKIDIAQELKFQTLAGIEEVTFYSVPKKLMSFVQTLWNVIKIEDTWLRCNHTRQAESKPTGLPQPVCEEPPAANAGGILQHKVPHLTLLRVNQMTRASEMERACLPSELCALVKKSNVVFVPVRKSLDYANVIKPGHPDPYLTVCAELIPSLAGEELSTNNKYLPGLRRISVSDLDIPNQFRVNGARHGVKLAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCSC(=NC1=CC=CC=C1)N\"], \"target_seq\": \"GSLFWKHEPVVIDGPGADSAEAMGALNPARRFSRYLSDVFISRTNEIWRNAFSKFRVEFKKRSAIAGLRQKHLPHCMWVHGKELMNLEIQDGFLGSENVSTEHLLGYASGTPGEKPGLRCWHFESKHFQLPHESALEMRLGYLVKNAQNDTFTFESDYLLNLRALSRDANMARDLQQLQGDEKLEKKGAVHVLVDGVTGTGLAQKVWVWEIGHESNRASLGLYKKWPLVLYKFDPGDLHERKRSEVSTTHNAHVYQLTRVKALTQPNERIEHFLHLAKVGRNMGTDYPKINDSFVTQKSEDDANGTLPRYPDSTYYNKQPPIECKEKLNTLDQRRQVSEKGGIVLVEGGHESLDLKQPYQQASKTGIPLRHVGFGCGDFFRFTFKRDTIGVAMYHGAISDPWETSRMPSGRENEGSDLKFGWFKPPFGLELTLKVRHDYKAYQSEEATAVRYAHEMGIAHQFEDSRFQYGEYTGDRRCRGDYPTAQQQMSATVSPDHVGVLDHEAYVLCKDGISSTNKYQVFGFPFDQHLLIEFRFISESLITLFKICAPYSGYNKERQPLVGDMNIKLGQGSEQEGQFDELPAGVMALTRPQKVKDLSVTLEPVLMRTGRWMESISPPTLPPAKTSLRQMKLVTSDTNKQDEAFSVENDSLGTVDPPQKNFKDFHGITKQVIAVEAKLLFEDKLIFFKFSRVYKSFGQNALEPTLPEPTGVQPAVSYESQTGIHFGPVVIKGRSGETNPQMILYWGSHLVEMQRTIKADRPKREPSNRLVVTVNDVVVEPKRAICRDREIEEANSGICEAQVRGLLKSPYVVAETYHFPYPSPLSGNEHLLGCLGIPETEKDQVPFGYCLWVRIMYRPAVKRFLIYCAMVDIETNFCTTLSQLDVKFSMEINGEPETTVAKMSLGDVLREAGETDPLTAGSDDGQDQNPTEALAAEGVGLSTYLPKYPHISVNCEPPKAQNRIWGGSLPQIANSWAKHDRDEGQMGLQGESLATWVGCELLQKHRKLVPMKFLFALNSDINLACVLRPGVLGIRGVLQSNEGPATGIGPCQEQILIGKSRTPDQLRVAVGLDRGWSAPRFQAIRIFNMKDVFTVGTSTESVGAAGRQIKIPTTKASENHEKALPSIVPPYNDVDFRMKGQPGNPKSDVEKSVKYAQINRSWHFSQDVFSKDSKGKIELEADPTARNSIRIFVDYLQEATEQATGWSEKLDTRTSIRCRSCYQIWNDGSISLIMDNPSPANIMKIDIAQELKFQTLAGIEEVTFYSVPKKLMSFVQTLWNVIKIEDTWLRCNHTRQAESKPTGLPQPVCEEPPAANAGGILQHKVPHLTLLRVNQMTRASEMERACLPSELCALVKKSNVVFVPVRKSLDYANVIKPGHPDPYLTVCAELIPSLAGEELSTNNKYLPGLRRISVSDLDIPNQFRVNGARHGVKLAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MEDHMFGVQQIQPNVISVRLFKRKVGGLGFLVKERVSKPPVIISDLIRGGAAEQSGLIQAGDIILAVNGRPLVDLSYDSALEVLRGIASETHVVLILRGPEGFTTHLETTFTGDGTPKTIRVTQPLGPPTKAVDLSHQPPAGKEQPLAVDGASGPGNGPQHAYDDGQEAGSLPHANGLAPRPPGQDPAKKATRVSLQGRGENNELLKEIEPVLSLLTSGSRGVKGGAPAKAEMKDMGIQVDRDLDGKSHKPLPLGVENDRVFNDLWGKGNVPVVLNNPYSEKEQPPTSGKQSPTKNGSPSKCPRFLKVKNWETEVVLTDTLHLKSTLETGCTEYICMGSIMHPSQHARRPEDVRTKGQLFPLAKEFIDQYYSSIKRFGSKAHMERLEEVNKEIDTTSTYQLKDTELIYGAKHAWRNASRCVGRIQWSKLQVFDARDCTTAHGMFNYICNHVKYATNKGNLRSAITIFPQRTDGKHDFRVWNSQLIRYAGYKQPDGSTLGDPANVQFTEICIQQGWKPPRGRFDVLPLLLQANGNDPELFQIPPELVLEVPIRHPKFEWFKDLGLKWYGLPAVSNMLLEIGGLEFSACPFSGWYMGTEIGVRDYCDNSRYNILEEVAKKMNLDMRKTSSLWKDQALVEINIAVLYSFQSDKVTIVDHHSATESFIKHMENEYRCRGGCPADWVWIVPPMSGSITPVFHQEMLNYRLTPSFEYQPDPWNTHVWKGTNGTPTKRRAIGFKKLAEAVKFSAKLMGQAMAKRVKATILYATETGKSQAYAKTLCEIFKHAFDAKVMSMEEYDIVHLEHETLVLVVTSTFGNGDPPENGEKFGCALMEMRHPNSVQEERKSYKVRFNSVSSYSDSQKSSGDGPDLRDNFESAGPLANVRFSVFGLGSRAYPHFCAFGHAVDTLLEELGGERILKMREGDELCGQEEAFRTWAKKVFKAACDVFCVGDDVNIEKANNSLISNDRSWKRNKFRLTFVAEAPELTQGLSNVHKKRVSAARLLSRQNLQSPKSSRSTIFVRLHTNGSQELQYQPGDHLGVFPGNHEDLVNALIERLEDAPPVNQMVKVELLEERNTALGVISNWTDELRLPPCTIFQAFKYYLDITTPPTPLQLQQFASLATSEKEKQRLLVLSKGLQEYEEWKWGKNPTIVEVLEEFPSIQMPATLLLTQLSLLQPRYYSISSSPDMYPDEVHLTVAIVSYRTRDGEGPIHHGVCSSWLNRIQADELVPCFVRGAPSFHLPRNPQVPCILVGPGTGIAPFRSFWQQRQFDIQHKGMNPCPMVLVFGCRQSKIDHIYREETLQAKNKGVFRELYTAYSREPDKPKKYVQDILQEQLAESVYRALKEQGGHIYVCGDVTMAADVLKAIQRIMTQQGKLSAEDAGVFISRMRDDNRYHEDIFGVTLRTYEVTNRLRSESIAFIEESKKDTDEVFSS'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEDHMFGVQQIQPNVISVRLFKRKVGGLGFLVKERVSKPPVIISDLIRGGAAEQSGLIQAGDIILAVNGRPLVDLSYDSALEVLRGIASETHVVLILRGPEGFTTHLETTFTGDGTPKTIRVTQPLGPPTKAVDLSHQPPAGKEQPLAVDGASGPGNGPQHAYDDGQEAGSLPHANGLAPRPPGQDPAKKATRVSLQGRGENNELLKEIEPVLSLLTSGSRGVKGGAPAKAEMKDMGIQVDRDLDGKSHKPLPLGVENDRVFNDLWGKGNVPVVLNNPYSEKEQPPTSGKQSPTKNGSPSKCPRFLKVKNWETEVVLTDTLHLKSTLETGCTEYICMGSIMHPSQHARRPEDVRTKGQLFPLAKEFIDQYYSSIKRFGSKAHMERLEEVNKEIDTTSTYQLKDTELIYGAKHAWRNASRCVGRIQWSKLQVFDARDCTTAHGMFNYICNHVKYATNKGNLRSAITIFPQRTDGKHDFRVWNSQLIRYAGYKQPDGSTLGDPANVQFTEICIQQGWKPPRGRFDVLPLLLQANGNDPELFQIPPELVLEVPIRHPKFEWFKDLGLKWYGLPAVSNMLLEIGGLEFSACPFSGWYMGTEIGVRDYCDNSRYNILEEVAKKMNLDMRKTSSLWKDQALVEINIAVLYSFQSDKVTIVDHHSATESFIKHMENEYRCRGGCPADWVWIVPPMSGSITPVFHQEMLNYRLTPSFEYQPDPWNTHVWKGTNGTPTKRRAIGFKKLAEAVKFSAKLMGQAMAKRVKATILYATETGKSQAYAKTLCEIFKHAFDAKVMSMEEYDIVHLEHETLVLVVTSTFGNGDPPENGEKFGCALMEMRHPNSVQEERKSYKVRFNSVSSYSDSQKSSGDGPDLRDNFESAGPLANVRFSVFGLGSRAYPHFCAFGHAVDTLLEELGGERILKMREGDELCGQEEAFRTWAKKVFKAACDVFCVGDDVNIEKANNSLISNDRSWKRNKFRLTFVAEAPELTQGLSNVHKKRVSAARLLSRQNLQSPKSSRSTIFVRLHTNGSQELQYQPGDHLGVFPGNHEDLVNALIERLEDAPPVNQMVKVELLEERNTALGVISNWTDELRLPPCTIFQAFKYYLDITTPPTPLQLQQFASLATSEKEKQRLLVLSKGLQEYEEWKWGKNPTIVEVLEEFPSIQMPATLLLTQLSLLQPRYYSISSSPDMYPDEVHLTVAIVSYRTRDGEGPIHHGVCSSWLNRIQADELVPCFVRGAPSFHLPRNPQVPCILVGPGTGIAPFRSFWQQRQFDIQHKGMNPCPMVLVFGCRQSKIDHIYREETLQAKNKGVFRELYTAYSREPDKPKKYVQDILQEQLAESVYRALKEQGGHIYVCGDVTMAADVLKAIQRIMTQQGKLSAEDAGVFISRMRDDNRYHEDIFGVTLRTYEVTNRLRSESIAFIEESKKDTDEVFSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEDHMFGVQQIQPNVISVRLFKRKVGGLGFLVKERVSKPPVIISDLIRGGAAEQSGLIQAGDIILAVNGRPLVDLSYDSALEVLRGIASETHVVLILRGPEGFTTHLETTFTGDGTPKTIRVTQPLGPPTKAVDLSHQPPAGKEQPLAVDGASGPGNGPQHAYDDGQEAGSLPHANGLAPRPPGQDPAKKATRVSLQGRGENNELLKEIEPVLSLLTSGSRGVKGGAPAKAEMKDMGIQVDRDLDGKSHKPLPLGVENDRVFNDLWGKGNVPVVLNNPYSEKEQPPTSGKQSPTKNGSPSKCPRFLKVKNWETEVVLTDTLHLKSTLETGCTEYICMGSIMHPSQHARRPEDVRTKGQLFPLAKEFIDQYYSSIKRFGSKAHMERLEEVNKEIDTTSTYQLKDTELIYGAKHAWRNASRCVGRIQWSKLQVFDARDCTTAHGMFNYICNHVKYATNKGNLRSAITIFPQRTDGKHDFRVWNSQLIRYAGYKQPDGSTLGDPANVQFTEICIQQGWKPPRGRFDVLPLLLQANGNDPELFQIPPELVLEVPIRHPKFEWFKDLGLKWYGLPAVSNMLLEIGGLEFSACPFSGWYMGTEIGVRDYCDNSRYNILEEVAKKMNLDMRKTSSLWKDQALVEINIAVLYSFQSDKVTIVDHHSATESFIKHMENEYRCRGGCPADWVWIVPPMSGSITPVFHQEMLNYRLTPSFEYQPDPWNTHVWKGTNGTPTKRRAIGFKKLAEAVKFSAKLMGQAMAKRVKATILYATETGKSQAYAKTLCEIFKHAFDAKVMSMEEYDIVHLEHETLVLVVTSTFGNGDPPENGEKFGCALMEMRHPNSVQEERKSYKVRFNSVSSYSDSQKSSGDGPDLRDNFESAGPLANVRFSVFGLGSRAYPHFCAFGHAVDTLLEELGGERILKMREGDELCGQEEAFRTWAKKVFKAACDVFCVGDDVNIEKANNSLISNDRSWKRNKFRLTFVAEAPELTQGLSNVHKKRVSAARLLSRQNLQSPKSSRSTIFVRLHTNGSQELQYQPGDHLGVFPGNHEDLVNALIERLEDAPPVNQMVKVELLEERNTALGVISNWTDELRLPPCTIFQAFKYYLDITTPPTPLQLQQFASLATSEKEKQRLLVLSKGLQEYEEWKWGKNPTIVEVLEEFPSIQMPATLLLTQLSLLQPRYYSISSSPDMYPDEVHLTVAIVSYRTRDGEGPIHHGVCSSWLNRIQADELVPCFVRGAPSFHLPRNPQVPCILVGPGTGIAPFRSFWQQRQFDIQHKGMNPCPMVLVFGCRQSKIDHIYREETLQAKNKGVFRELYTAYSREPDKPKKYVQDILQEQLAESVYRALKEQGGHIYVCGDVTMAADVLKAIQRIMTQQGKLSAEDAGVFISRMRDDNRYHEDIFGVTLRTYEVTNRLRSESIAFIEESKKDTDEVFSS\", \"result\": [{\"smile\": \"CCSC(=NC1=CC=CC=C1)N\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCSC(=NC1=CC=CC=C1)N' can interact with the protein 'MEDHMFGVQQIQPNVISVRLFKRKVGGLGFLVKERVSKPPVIISDLIRGGAAEQSGLIQAGDIILAVNGRPLVDLSYDSALEVLRGIASETHVVLILRGPEGFTTHLETTFTGDGTPKTIRVTQPLGPPTKAVDLSHQPPAGKEQPLAVDGASGPGNGPQHAYDDGQEAGSLPHANGLAPRPPGQDPAKKATRVSLQGRGENNELLKEIEPVLSLLTSGSRGVKGGAPAKAEMKDMGIQVDRDLDGKSHKPLPLGVENDRVFNDLWGKGNVPVVLNNPYSEKEQPPTSGKQSPTKNGSPSKCPRFLKVKNWETEVVLTDTLHLKSTLETGCTEYICMGSIMHPSQHARRPEDVRTKGQLFPLAKEFIDQYYSSIKRFGSKAHMERLEEVNKEIDTTSTYQLKDTELIYGAKHAWRNASRCVGRIQWSKLQVFDARDCTTAHGMFNYICNHVKYATNKGNLRSAITIFPQRTDGKHDFRVWNSQLIRYAGYKQPDGSTLGDPANVQFTEICIQQGWKPPRGRFDVLPLLLQANGNDPELFQIPPELVLEVPIRHPKFEWFKDLGLKWYGLPAVSNMLLEIGGLEFSACPFSGWYMGTEIGVRDYCDNSRYNILEEVAKKMNLDMRKTSSLWKDQALVEINIAVLYSFQSDKVTIVDHHSATESFIKHMENEYRCRGGCPADWVWIVPPMSGSITPVFHQEMLNYRLTPSFEYQPDPWNTHVWKGTNGTPTKRRAIGFKKLAEAVKFSAKLMGQAMAKRVKATILYATETGKSQAYAKTLCEIFKHAFDAKVMSMEEYDIVHLEHETLVLVVTSTFGNGDPPENGEKFGCALMEMRHPNSVQEERKSYKVRFNSVSSYSDSQKSSGDGPDLRDNFESAGPLANVRFSVFGLGSRAYPHFCAFGHAVDTLLEELGGERILKMREGDELCGQEEAFRTWAKKVFKAACDVFCVGDDVNIEKANNSLISNDRSWKRNKFRLTFVAEAPELTQGLSNVHKKRVSAARLLSRQNLQSPKSSRSTIFVRLHTNGSQELQYQPGDHLGVFPGNHEDLVNALIERLEDAPPVNQMVKVELLEERNTALGVISNWTDELRLPPCTIFQAFKYYLDITTPPTPLQLQQFASLATSEKEKQRLLVLSKGLQEYEEWKWGKNPTIVEVLEEFPSIQMPATLLLTQLSLLQPRYYSISSSPDMYPDEVHLTVAIVSYRTRDGEGPIHHGVCSSWLNRIQADELVPCFVRGAPSFHLPRNPQVPCILVGPGTGIAPFRSFWQQRQFDIQHKGMNPCPMVLVFGCRQSKIDHIYREETLQAKNKGVFRELYTAYSREPDKPKKYVQDILQEQLAESVYRALKEQGGHIYVCGDVTMAADVLKAIQRIMTQQGKLSAEDAGVFISRMRDDNRYHEDIFGVTLRTYEVTNRLRSESIAFIEESKKDTDEVFSS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the drug 'O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1cc(F)cc(-n2nccn2)c1' and the target protein 'AQEETAPRGYAVEFKKFVPLALFVNRNGRAFTKMIFPGTTYRLLLWPLYLGLFPDILTYPKIGPSVMKVDEDPFNILTPKDDLLQFHVKSQWFPEKKEPLKKTVDEEIFCSTSPLIKVTVINFTREDSPPHTFFTLKGDQETSGNRFVMRQKLSLLSNQYIPELSAGDVIIIGEATCLLQCPGKMSAILHNSLARITEEDAFYREEVALYKYEATHNVTFRLSSIVEGKATPITCSYMYLEKDYAKKPLLTEFLSGNIIGVYFLVTNDVGGKMDVPVMVQDFPDLGLREMLRWPRSNEVIVIDGLTPRELSKLPVVCHIQLQLDKTPNGLPVFKEIFIGKPRKEPGDVDNSNSMELDDKKQIGMDPLGDDVFCLVVIYSIREHLLKPLLMMGEQSNTIAQMSIVTMLKYFSNLHQTLPERAFFRVVLLVQFRFMKLMDAISRGVFIPMSASKEIFFCSLPSKYRLLKRKLAFNVPKSSGVKVLLPDYEHPATVSMMGNWKVLFAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1cc(F)cc(-n2nccn2)c1\"], \"target_seq\": \"AQEETAPRGYAVEFKKFVPLALFVNRNGRAFTKMIFPGTTYRLLLWPLYLGLFPDILTYPKIGPSVMKVDEDPFNILTPKDDLLQFHVKSQWFPEKKEPLKKTVDEEIFCSTSPLIKVTVINFTREDSPPHTFFTLKGDQETSGNRFVMRQKLSLLSNQYIPELSAGDVIIIGEATCLLQCPGKMSAILHNSLARITEEDAFYREEVALYKYEATHNVTFRLSSIVEGKATPITCSYMYLEKDYAKKPLLTEFLSGNIIGVYFLVTNDVGGKMDVPVMVQDFPDLGLREMLRWPRSNEVIVIDGLTPRELSKLPVVCHIQLQLDKTPNGLPVFKEIFIGKPRKEPGDVDNSNSMELDDKKQIGMDPLGDDVFCLVVIYSIREHLLKPLLMMGEQSNTIAQMSIVTMLKYFSNLHQTLPERAFFRVVLLVQFRFMKLMDAISRGVFIPMSASKEIFFCSLPSKYRLLKRKLAFNVPKSSGVKVLLPDYEHPATVSMMGNWKVLFAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1cc(F)cc(-n2nccn2)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(c1ccccc1-n1nccn1)N1CCOC[C@H]1Cc1cc(F)cc(-n2nccn2)c1' can not interact with the protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you see the drug 'CNC(=O)[C@@H](NC(=O)[C@H](CCCc1ccccc1)[C@H](C)N(O)C=O)C(C)(C)C' interacting with the target protein 'PHHGRGQDDRDHPTIRQSPVWAAVQIMDQRDGLYVKSGDVTLCKWSRTPAKYGLRRDGGVCAAWETYGVCGAGFPVLAREDADRAKCTKAAASYALFLAPRPCDRDLTMTQRGTITAYQAKVDSGDEVIPKEPFVTMCGLKTWRPDFNRAGFVSDDSRNFDSELTGCLTGDPRYGTTLDGDTLTKEVFPRVKNIRLFYFPLTHSELLALTTVPFGPADMPNSAPGGRSDPPYVPRSETRLEIAARCRVDTLKVFWFPKEIGRTSGEGEIDLPALGYPQQGRFDPTCKEHRCGAPGMDKHFDTGPPGGPLTVVFLDQARPMLSSPRTFGTLDLLQGPSALPVQGEQVDRGAYSLAASQRWSFPAGEACHYPLSGSAVPPPFTHPKPFGFGAQNHLCLIAAFYHAHYFLGGIQTEGLRQRPNLFSFCHTFSVVKIFPSKSDDTSGKDQDPGLFISYGSLRGVTLEGRAPFDNFGGGRYLLWRMAFVSPFADVTLELGPYLVLADFLPTRYLYCTQTVLDMPSRDKDNFNPLGDPTCVPFVGTVEVCAPPRAQTGYFEYTSLVRRGDDWNECLIDWRDEKEVHARSVFALYYSASLQDFSWAAFPDASGMQLNTWTELGRPWLEDSGSDALQRFFFYPFRERVTCEAEGQCYLNYVFTKDSGRSWLNPPYPLDSTIDGLPSTLHGKQTDLFTVTQRGGNGASGGSYDWEQAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)[C@@H](NC(=O)[C@H](CCCc1ccccc1)[C@H](C)N(O)C=O)C(C)(C)C\"], \"target_seq\": \"PHHGRGQDDRDHPTIRQSPVWAAVQIMDQRDGLYVKSGDVTLCKWSRTPAKYGLRRDGGVCAAWETYGVCGAGFPVLAREDADRAKCTKAAASYALFLAPRPCDRDLTMTQRGTITAYQAKVDSGDEVIPKEPFVTMCGLKTWRPDFNRAGFVSDDSRNFDSELTGCLTGDPRYGTTLDGDTLTKEVFPRVKNIRLFYFPLTHSELLALTTVPFGPADMPNSAPGGRSDPPYVPRSETRLEIAARCRVDTLKVFWFPKEIGRTSGEGEIDLPALGYPQQGRFDPTCKEHRCGAPGMDKHFDTGPPGGPLTVVFLDQARPMLSSPRTFGTLDLLQGPSALPVQGEQVDRGAYSLAASQRWSFPAGEACHYPLSGSAVPPPFTHPKPFGFGAQNHLCLIAAFYHAHYFLGGIQTEGLRQRPNLFSFCHTFSVVKIFPSKSDDTSGKDQDPGLFISYGSLRGVTLEGRAPFDNFGGGRYLLWRMAFVSPFADVTLELGPYLVLADFLPTRYLYCTQTVLDMPSRDKDNFNPLGDPTCVPFVGTVEVCAPPRAQTGYFEYTSLVRRGDDWNECLIDWRDEKEVHARSVFALYYSASLQDFSWAAFPDASGMQLNTWTELGRPWLEDSGSDALQRFFFYPFRERVTCEAEGQCYLNYVFTKDSGRSWLNPPYPLDSTIDGLPSTLHGKQTDLFTVTQRGGNGASGGSYDWEQAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MSLWQPLVLVLLVLGCCFAAPRQRQSTLVLFPGDLRTNLTDRQLAEEYLYRYGYTRVAEMRGESKSLGPALLLLQKQLSLPETGELDSATLKAMRTPRCGVPDLGRFQTFEGDLKWHHHNITYWIQNYSEDLPRAVIDDAFARAFALWSAVTPLTFTRVYSRDADIVIQFGVAEHGDGYPFDGKDGLLAHAFPPGPGIQGDAHFDDDELWSLGKGVVVPTRFGNADGAACHFPFIFEGRSYSACTTDGRSDGLPWCSTTANYDTDDRFGFCPSERLYTQDGNADGKPCQFPFIFQGQSYSACTTDGRSDGYRWCATTANYDRDKLFGFCPTRADSTVMGGNSAGELCVFPFTFLGKEYSTCTSEGRGDGRLWCATTSNFDSDKKWGFCPDQGYSLFLVAAHEFGHALGLDHSSVPEALMYPMYRFTEGPPLHKDDVNGIRHLYGPRPEPEPRPPTTTTPQPTAPPTVCPTGPPTVHPSERPTAGPTGPPSAGPTGPPTAGPSTATTVPLSPVDDACNVNIFDAIAEIGNQLYLFKDGKYWRFSEGRGSRPQGPFLIADKWPALPRKLDSVFEERLSKKLFFFSGRQVWVYTGASVLGPRRLDKLGLGADVAQVTGALRSGRGKMLLFSGRRLWRFDVKAQMVDPRSASEVDRMFPGVPLDTHDVFQYREKAYFCQDRFYWRVSSRSELNQVDQVGYVTYDILQCPED' is the sequence to use for the protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSLWQPLVLVLLVLGCCFAAPRQRQSTLVLFPGDLRTNLTDRQLAEEYLYRYGYTRVAEMRGESKSLGPALLLLQKQLSLPETGELDSATLKAMRTPRCGVPDLGRFQTFEGDLKWHHHNITYWIQNYSEDLPRAVIDDAFARAFALWSAVTPLTFTRVYSRDADIVIQFGVAEHGDGYPFDGKDGLLAHAFPPGPGIQGDAHFDDDELWSLGKGVVVPTRFGNADGAACHFPFIFEGRSYSACTTDGRSDGLPWCSTTANYDTDDRFGFCPSERLYTQDGNADGKPCQFPFIFQGQSYSACTTDGRSDGYRWCATTANYDRDKLFGFCPTRADSTVMGGNSAGELCVFPFTFLGKEYSTCTSEGRGDGRLWCATTSNFDSDKKWGFCPDQGYSLFLVAAHEFGHALGLDHSSVPEALMYPMYRFTEGPPLHKDDVNGIRHLYGPRPEPEPRPPTTTTPQPTAPPTVCPTGPPTVHPSERPTAGPTGPPSAGPTGPPTAGPSTATTVPLSPVDDACNVNIFDAIAEIGNQLYLFKDGKYWRFSEGRGSRPQGPFLIADKWPALPRKLDSVFEERLSKKLFFFSGRQVWVYTGASVLGPRRLDKLGLGADVAQVTGALRSGRGKMLLFSGRRLWRFDVKAQMVDPRSASEVDRMFPGVPLDTHDVFQYREKAYFCQDRFYWRVSSRSELNQVDQVGYVTYDILQCPED\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSLWQPLVLVLLVLGCCFAAPRQRQSTLVLFPGDLRTNLTDRQLAEEYLYRYGYTRVAEMRGESKSLGPALLLLQKQLSLPETGELDSATLKAMRTPRCGVPDLGRFQTFEGDLKWHHHNITYWIQNYSEDLPRAVIDDAFARAFALWSAVTPLTFTRVYSRDADIVIQFGVAEHGDGYPFDGKDGLLAHAFPPGPGIQGDAHFDDDELWSLGKGVVVPTRFGNADGAACHFPFIFEGRSYSACTTDGRSDGLPWCSTTANYDTDDRFGFCPSERLYTQDGNADGKPCQFPFIFQGQSYSACTTDGRSDGYRWCATTANYDRDKLFGFCPTRADSTVMGGNSAGELCVFPFTFLGKEYSTCTSEGRGDGRLWCATTSNFDSDKKWGFCPDQGYSLFLVAAHEFGHALGLDHSSVPEALMYPMYRFTEGPPLHKDDVNGIRHLYGPRPEPEPRPPTTTTPQPTAPPTVCPTGPPTVHPSERPTAGPTGPPSAGPTGPPTAGPSTATTVPLSPVDDACNVNIFDAIAEIGNQLYLFKDGKYWRFSEGRGSRPQGPFLIADKWPALPRKLDSVFEERLSKKLFFFSGRQVWVYTGASVLGPRRLDKLGLGADVAQVTGALRSGRGKMLLFSGRRLWRFDVKAQMVDPRSASEVDRMFPGVPLDTHDVFQYREKAYFCQDRFYWRVSSRSELNQVDQVGYVTYDILQCPED\", \"result\": [{\"smile\": \"CNC(=O)[C@@H](NC(=O)[C@H](CCCc1ccccc1)[C@H](C)N(O)C=O)C(C)(C)C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CNC(=O)[C@@H](NC(=O)[C@H](CCCc1ccccc1)[C@H](C)N(O)C=O)C(C)(C)C' can interact with the protein 'MSLWQPLVLVLLVLGCCFAAPRQRQSTLVLFPGDLRTNLTDRQLAEEYLYRYGYTRVAEMRGESKSLGPALLLLQKQLSLPETGELDSATLKAMRTPRCGVPDLGRFQTFEGDLKWHHHNITYWIQNYSEDLPRAVIDDAFARAFALWSAVTPLTFTRVYSRDADIVIQFGVAEHGDGYPFDGKDGLLAHAFPPGPGIQGDAHFDDDELWSLGKGVVVPTRFGNADGAACHFPFIFEGRSYSACTTDGRSDGLPWCSTTANYDTDDRFGFCPSERLYTQDGNADGKPCQFPFIFQGQSYSACTTDGRSDGYRWCATTANYDRDKLFGFCPTRADSTVMGGNSAGELCVFPFTFLGKEYSTCTSEGRGDGRLWCATTSNFDSDKKWGFCPDQGYSLFLVAAHEFGHALGLDHSSVPEALMYPMYRFTEGPPLHKDDVNGIRHLYGPRPEPEPRPPTTTTPQPTAPPTVCPTGPPTVHPSERPTAGPTGPPSAGPTGPPTAGPSTATTVPLSPVDDACNVNIFDAIAEIGNQLYLFKDGKYWRFSEGRGSRPQGPFLIADKWPALPRKLDSVFEERLSKKLFFFSGRQVWVYTGASVLGPRRLDKLGLGADVAQVTGALRSGRGKMLLFSGRRLWRFDVKAQMVDPRSASEVDRMFPGVPLDTHDVFQYREKAYFCQDRFYWRVSSRSELNQVDQVGYVTYDILQCPED'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'CCCCC(=O)OCC(=O)[C@]1(C[C@@H](C2=C(C3=C(C(=C2C1)O)C(=O)C4=C(C3=O)C(=CC=C4)OC)O)O[C@H]5C[C@@H]([C@@H]([C@@H](O5)C)O)NC(=O)C(F)(F)F)O' and the target protein 'QDESEPEKLVHYTRGDFENTNLEQWSTAMGYKVGQGTEGFFDNNTPKDTLKLQATIVIWNLAKYKAAKGATAFDLEPCLVKGKESTVATKNNNEGVGGDGGDNSLTPDLDAGLATEYTTRAPNTNFMTKANKLKGGGISLYAGKQHSIDGLLFYLNGLEAYSKTTLAIETWRYGVGSEIVGSNYGIGNTGVSSEVFKAFLTTYCSSRSHTNNQFADTGMRFLKLEDRFGLTSKTKTNLKISVLIQASKLFGQKPIGLFALGGFVRYKNKSATLLTITDADKWGAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCC(=O)OCC(=O)[C@]1(C[C@@H](C2=C(C3=C(C(=C2C1)O)C(=O)C4=C(C3=O)C(=CC=C4)OC)O)O[C@H]5C[C@@H]([C@@H]([C@@H](O5)C)O)NC(=O)C(F)(F)F)O\"], \"target_seq\": \"QDESEPEKLVHYTRGDFENTNLEQWSTAMGYKVGQGTEGFFDNNTPKDTLKLQATIVIWNLAKYKAAKGATAFDLEPCLVKGKESTVATKNNNEGVGGDGGDNSLTPDLDAGLATEYTTRAPNTNFMTKANKLKGGGISLYAGKQHSIDGLLFYLNGLEAYSKTTLAIETWRYGVGSEIVGSNYGIGNTGVSSEVFKAFLTTYCSSRSHTNNQFADTGMRFLKLEDRFGLTSKTKTNLKISVLIQASKLFGQKPIGLFALGGFVRYKNKSATLLTITDADKWGAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MAVPPTYADLGKSARDVFTKGYGFGLIKLDLKTKSENGLEFTSSGSANTETTKVTGSLETKYRWTEYGLTFTEKWNTDNTLGTEITVEDQLARGLKLTFDSSFSPNTGKKNAKIKTGYKREHINLGCDMDFDIAGPSIRGALVLGYEGWLAGYQMNFETAKSRVTQSNFAVGYKTDEFQLHTNVNDGTEFGGSIYQKVNKKLETAVNLAWTAGNSNTRFGIAAKYQIDPDACFSAKVNNSSLIGLGYTQTLKPGIKLTLSALLDGKNVNAGGHKLGLGLEFQA'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAVPPTYADLGKSARDVFTKGYGFGLIKLDLKTKSENGLEFTSSGSANTETTKVTGSLETKYRWTEYGLTFTEKWNTDNTLGTEITVEDQLARGLKLTFDSSFSPNTGKKNAKIKTGYKREHINLGCDMDFDIAGPSIRGALVLGYEGWLAGYQMNFETAKSRVTQSNFAVGYKTDEFQLHTNVNDGTEFGGSIYQKVNKKLETAVNLAWTAGNSNTRFGIAAKYQIDPDACFSAKVNNSSLIGLGYTQTLKPGIKLTLSALLDGKNVNAGGHKLGLGLEFQA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAVPPTYADLGKSARDVFTKGYGFGLIKLDLKTKSENGLEFTSSGSANTETTKVTGSLETKYRWTEYGLTFTEKWNTDNTLGTEITVEDQLARGLKLTFDSSFSPNTGKKNAKIKTGYKREHINLGCDMDFDIAGPSIRGALVLGYEGWLAGYQMNFETAKSRVTQSNFAVGYKTDEFQLHTNVNDGTEFGGSIYQKVNKKLETAVNLAWTAGNSNTRFGIAAKYQIDPDACFSAKVNNSSLIGLGYTQTLKPGIKLTLSALLDGKNVNAGGHKLGLGLEFQA\", \"result\": [{\"smile\": \"CCCCC(=O)OCC(=O)[C@]1(C[C@@H](C2=C(C3=C(C(=C2C1)O)C(=O)C4=C(C3=O)C(=CC=C4)OC)O)O[C@H]5C[C@@H]([C@@H]([C@@H](O5)C)O)NC(=O)C(F)(F)F)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCCC(=O)OCC(=O)[C@]1(C[C@@H](C2=C(C3=C(C(=C2C1)O)C(=O)C4=C(C3=O)C(=CC=C4)OC)O)O[C@H]5C[C@@H]([C@@H]([C@@H](O5)C)O)NC(=O)C(F)(F)F)O' can not interact with the protein 'MAVPPTYADLGKSARDVFTKGYGFGLIKLDLKTKSENGLEFTSSGSANTETTKVTGSLETKYRWTEYGLTFTEKWNTDNTLGTEITVEDQLARGLKLTFDSSFSPNTGKKNAKIKTGYKREHINLGCDMDFDIAGPSIRGALVLGYEGWLAGYQMNFETAKSRVTQSNFAVGYKTDEFQLHTNVNDGTEFGGSIYQKVNKKLETAVNLAWTAGNSNTRFGIAAKYQIDPDACFSAKVNNSSLIGLGYTQTLKPGIKLTLSALLDGKNVNAGGHKLGLGLEFQA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O' probably interact with the target protein 'KSFTAVKDRMVSSTTVLVASSREYVGEAVFSSDKHGTKGKSAFAGAKSAEDHAPQKSHNHLLDAGLATCHLGDLAHVLLYRHTVEKFNLFPLPHGTLANAVLLPAPAPVTHDWALMAYMHKAAVVLDKLPLTSVKFLADAFNAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O\"], \"target_seq\": \"KSFTAVKDRMVSSTTVLVASSREYVGEAVFSSDKHGTKGKSAFAGAKSAEDHAPQKSHNHLLDAGLATCHLGDLAHVLLYRHTVEKFNLFPLPHGTLANAVLLPAPAPVTHDWALMAYMHKAAVVLDKLPLTSVKFLADAFNAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR' is the valid sequence for this protein. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR\", \"result\": [{\"smile\": \"OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug 'OC(=O)C1=CC(=CC(=C1)C(O)=O)C(O)=O' can interact with the protein 'MVLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide insights on whether the drug '[H]N([H])S(=O)(=O)C1=CC=C(C=C1)C(=O)N([H])C[C@@H](C)CN1C=CC2=CC=CC=C12' will interact with the target protein 'SFKAMSMQFIIMSASHIRSFASPRLPSYRISDLTHFITRFLETDTLTNFYFQNSLADIGEISWMIMSLATKVRFPGIVIFFSIIFLVRWELHDVNLRCSERSNKAHRKMWDMFSRPNNPKSTLRIDHLFLWCDKTLHLDRVDLLVKFINITPVSEFSMTPLKIDAVPSWAVVIRQYFKTLSTEIGVRKTIIQSSLVLIQNKRSRQGDWNLAGTMLSDAEVDGNTSKREIPPGICLHAQIVVIRKDKVYHHRSTYYRMESGNTEKVYSQFRLLTILVEDNDLDFQTTKSEPAATVAKRWASTVPRFDQSHHRIMGVDTVGGCTLDSAYTINHLAMSELTFMSMMSCVSNETNLILSFMMHIQWHCRVMVKFDSKTGKSYFDNMPDDYDYVENSYVVQWAYFNFFDYCAQSLSFVFAVSVSLIVSAVMRFNLFPEYMFEKYLMYEKYLSSISDGSSFYTYGFEVVLGLRAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H]N([H])S(=O)(=O)C1=CC=C(C=C1)C(=O)N([H])C[C@@H](C)CN1C=CC2=CC=CC=C12\"], \"target_seq\": \"SFKAMSMQFIIMSASHIRSFASPRLPSYRISDLTHFITRFLETDTLTNFYFQNSLADIGEISWMIMSLATKVRFPGIVIFFSIIFLVRWELHDVNLRCSERSNKAHRKMWDMFSRPNNPKSTLRIDHLFLWCDKTLHLDRVDLLVKFINITPVSEFSMTPLKIDAVPSWAVVIRQYFKTLSTEIGVRKTIIQSSLVLIQNKRSRQGDWNLAGTMLSDAEVDGNTSKREIPPGICLHAQIVVIRKDKVYHHRSTYYRMESGNTEKVYSQFRLLTILVEDNDLDFQTTKSEPAATVAKRWASTVPRFDQSHHRIMGVDTVGGCTLDSAYTINHLAMSELTFMSMMSCVSNETNLILSFMMHIQWHCRVMVKFDSKTGKSYFDNMPDDYDYVENSYVVQWAYFNFFDYCAQSLSFVFAVSVSLIVSAVMRFNLFPEYMFEKYLMYEKYLSSISDGSSFYTYGFEVVLGLRAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The protein sequence is probably wrong, please provide the correct sequence."
            },
            {
                "from": "human",
                "value": "'MVLAFQLVSFTYIWIILKPNVCAASNIKMTHQRCSSSMKQTCKQETRMKKDDSTKARPQKYEQLLHIEDNDFAMRPGFGGSPVPVGIDVHVESIDSISETNMDFTMTFYLRHYWKDERLSFPSTANKSMTFDHRLTRKIWVPDIFFVHSKRSFIHDTTMENIMLRVHPDGNVLLSLRITVSAMCFMDFSRFPLDTQNCSLELESYAYNEDDLMLYWKHGNKSLNTEEHMSLSQFFIEDFSASSGLAFYSSTGWYNRLFINFVLRRHVFFFVLQTYFPAILMVMLSWVSFWIDRRAVPARVSLGITTVLTMSTIITAVSASMPQVSYLKAVDVYLWVSSLFVFLSVIEYAAVNYLTTVEERKQFKKTGKISRMYNIDAVQAMAFDGCYHDSEIDMDQTSLSLNSEDFMRRKSICSPSTDSSRIKRRKSLGGHVGRIILENNHVIDTYSRILFPIVYILFNLFYWGVYV'. MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVLAFQLVSFTYIWIILKPNVCAASNIKMTHQRCSSSMKQTCKQETRMKKDDSTKARPQKYEQLLHIEDNDFAMRPGFGGSPVPVGIDVHVESIDSISETNMDFTMTFYLRHYWKDERLSFPSTANKSMTFDHRLTRKIWVPDIFFVHSKRSFIHDTTMENIMLRVHPDGNVLLSLRITVSAMCFMDFSRFPLDTQNCSLELESYAYNEDDLMLYWKHGNKSLNTEEHMSLSQFFIEDFSASSGLAFYSSTGWYNRLFINFVLRRHVFFFVLQTYFPAILMVMLSWVSFWIDRRAVPARVSLGITTVLTMSTIITAVSASMPQVSYLKAVDVYLWVSSLFVFLSVIEYAAVNYLTTVEERKQFKKTGKISRMYNIDAVQAMAFDGCYHDSEIDMDQTSLSLNSEDFMRRKSICSPSTDSSRIKRRKSLGGHVGRIILENNHVIDTYSRILFPIVYILFNLFYWGVYV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVLAFQLVSFTYIWIILKPNVCAASNIKMTHQRCSSSMKQTCKQETRMKKDDSTKARPQKYEQLLHIEDNDFAMRPGFGGSPVPVGIDVHVESIDSISETNMDFTMTFYLRHYWKDERLSFPSTANKSMTFDHRLTRKIWVPDIFFVHSKRSFIHDTTMENIMLRVHPDGNVLLSLRITVSAMCFMDFSRFPLDTQNCSLELESYAYNEDDLMLYWKHGNKSLNTEEHMSLSQFFIEDFSASSGLAFYSSTGWYNRLFINFVLRRHVFFFVLQTYFPAILMVMLSWVSFWIDRRAVPARVSLGITTVLTMSTIITAVSASMPQVSYLKAVDVYLWVSSLFVFLSVIEYAAVNYLTTVEERKQFKKTGKISRMYNIDAVQAMAFDGCYHDSEIDMDQTSLSLNSEDFMRRKSICSPSTDSSRIKRRKSLGGHVGRIILENNHVIDTYSRILFPIVYILFNLFYWGVYV\", \"result\": [{\"smile\": \"[H]N([H])S(=O)(=O)C1=CC=C(C=C1)C(=O)N([H])C[C@@H](C)CN1C=CC2=CC=CC=C12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The drug '[H]N([H])S(=O)(=O)C1=CC=C(C=C1)C(=O)N([H])C[C@@H](C)CN1C=CC2=CC=CC=C12' can not interact with the protein 'MVLAFQLVSFTYIWIILKPNVCAASNIKMTHQRCSSSMKQTCKQETRMKKDDSTKARPQKYEQLLHIEDNDFAMRPGFGGSPVPVGIDVHVESIDSISETNMDFTMTFYLRHYWKDERLSFPSTANKSMTFDHRLTRKIWVPDIFFVHSKRSFIHDTTMENIMLRVHPDGNVLLSLRITVSAMCFMDFSRFPLDTQNCSLELESYAYNEDDLMLYWKHGNKSLNTEEHMSLSQFFIEDFSASSGLAFYSSTGWYNRLFINFVLRRHVFFFVLQTYFPAILMVMLSWVSFWIDRRAVPARVSLGITTVLTMSTIITAVSASMPQVSYLKAVDVYLWVSSLFVFLSVIEYAAVNYLTTVEERKQFKKTGKISRMYNIDAVQAMAFDGCYHDSEIDMDQTSLSLNSEDFMRRKSICSPSTDSSRIKRRKSLGGHVGRIILENNHVIDTYSRILFPIVYILFNLFYWGVYV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the generated drugs will interact with the target protein 'MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS\", \"result\": [{\"smile\": \"CN1CCN(CC1)CC/C=C\\2/C3=CC=CC=C3SC4=C2C=C(C=C4)S(=O)(=O)N(C)C\", \"value\": 1.0}, {\"smile\": \"O=C(O)Cn1c2c(c3cc(F)ccc31)CN(C(=O)CCc1cccc3ccccc13)CC2\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CN1CCN(CC1)CC/C=C\\2/C3=CC=CC=C3SC4=C2C=C(C=C4)S(=O)(=O)N(C)C' and 'O=C(O)Cn1c2c(c3cc(F)ccc31)CN(C(=O)CCc1cccc3ccccc13)CC2' can interact with the protein 'MSANATLKPLCPILEQMSRLQSHSNTSIRYIDHAAVLLHGLASLLGLVENGVILFVVGCRMRQTVVTTWVLHLALSDLLASASLPFFTYFLAVGHSWELGTTFCKLHSSIFFLNMFASGFLLSAISLDRCLQVVRPVWAQNHRTVAAAHKVCLVLWALAVLNTVPYFVFRDTISRLDGRIMCYYNVLLLNPGPDRDATCNSRQVALAVSKFLLAFLVPLAIIASSHAAVSLRLQHRGRRRPGRFVRLVAAVVAAFALCWGPYHVFSLLEARAHANPGLRPLVWRGLPFVTSLAFFNSVANPVLYVLTCPDMLRKLRRSLRTVLESVLVDDSELGGAGSSRRRRTSSTARSASPLALCSRPEEPRGPARLLGWLLGSCAASPQTGPLNRALSSTSS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the prediction for the optimized drugs' interaction with the target protein 'MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV\", \"result\": [{\"smile\": \"CN[C@H]1CC[C@](CCOc2cc(F)c(S(=O)(=O)Nc3ncns3)cc2F)(c2ccccc2)CC1\", \"value\": 0.0}, {\"smile\": \"CC[C@H](C)[C@@H]1NC(=O)[C@@H]2CSSC[C@@H]3NC(=O)[C@H](C(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H]4CCCN4C(=O)[C@H](CCCCN)NC(=O)[C@H](CSSC[C@H](NC(=O)[C@@H](N)CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](Cc4ccccc4)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCCN)C(=O)N2)NC(=O)[C@H](CSSC[C@@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](Cc2ccc(O)cc2)C(=O)N[C@H](C(=O)N[C@@H](Cc2ccccc2)C(N)=O)C(C)C)NC(=O)[C@H](Cc2c[nH]c4ccccc24)NC(=O)[C@H](CCCCN)NC(=O)[C@H](Cc2cnc[nH]2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC3=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]2CCCN2C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN[C@H]1CC[C@](CCOc2cc(F)c(S(=O)(=O)Nc3ncns3)cc2F)(c2ccccc2)CC1' nor 'CC[C@H](C)[C@@H]1NC(=O)[C@@H]2CSSC[C@@H]3NC(=O)[C@H](C(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H]4CCCN4C(=O)[C@H](CCCCN)NC(=O)[C@H](CSSC[C@H](NC(=O)[C@@H](N)CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)NCC(=O)N[C@@H](Cc4ccccc4)C(=O)N[C@@H](CCSC)C(=O)N[C@@H](CCCNC(=N)N)C(=O)N[C@@H](CCCCN)C(=O)N2)NC(=O)[C@H](CSSC[C@@H](C(=O)N[C@@H](CCCCN)C(=O)N[C@@H](Cc2ccc(O)cc2)C(=O)N[C@H](C(=O)N[C@@H](Cc2ccccc2)C(N)=O)C(C)C)NC(=O)[C@H](Cc2c[nH]c4ccccc24)NC(=O)[C@H](CCCCN)NC(=O)[C@H](Cc2cnc[nH]2)NC(=O)[C@H]([C@@H](C)O)NC(=O)[C@H](CCCNC(=N)N)NC(=O)[C@H](CO)NC3=O)NC(=O)[C@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H]2CCCN2C1=O' can interact with the protein 'MANFLLPRGTSSFRRFTRESLAAIEKRMAEKQARGSTTLQESREGLPEEEAPRPQLDLQASKKLPDLYGNPPQELIGEPLEDLDPFYSTQKTFIVLNKGKTIFRFSATNALYVLSPFHPIRRAAVKILVHSLFNMLIMCTILTNCVFMAQHDPPPWTKYVEYTFTAIYTFESLVKILARGFCLHAFTFLRDPWNWLDFSVIIMAYTTEFVDLGNVSALRTFRVLRALKTISVISGLKTIVGALIQSVKKLADVMVLTVFCLSVFALIGLQLFMGNLRHKCVRNFTALNGTNGSVEADGLVWESLDLYLSDPENYLLKNGTSDVLLCGNSSDAGTCPEGYRCLKAGENPDHGYTSFDSFAWAFLALFRLMTQDCWERLYQQTLRSAGKIYMIFFMLVIFLGSFYLVNLILAVVAMAYEEQNQATIAETEEKEKRFQEAMEMLKKEHEALTIRGVDTVSRSSLEMSPLAPVNSHERRSKRRKRMSSGTEECGEDRLPKSDSEDGPRAMNHLSLTRGLSRTSMKPRSSRGSIFTFRRRDLGSEADFADDENSTAGESESHHTSLLVPWPLRRTSAQGQPSPGTSAPGHALHGKKNSTVDCNGVVSLLGAGDPEATSPGSHLLRPVMLEHPPDTTTPSEEPGGPQMLTSQAPCVDGFEEPGARQRALSAVSVLTSALEELEESRHKCPPCWNRLAQRYLIWECCPLWMSIKQGVKLVVMDPFTDLTITMCIVLNTLFMALEHYNMTSEFEEMLQVGNLVFTGIFTAEMTFKIIALDPYYYFQQGWNIFDSIIVILSLMELGLSRMSNLSVLRSFRLLRVFKLAKSWPTLNTLIKIIGNSVGALGNLTLVLAIIVFIFAVVGMQLFGKNYSELRDSDSGLLPRWHMMDFFHAFLIIFRILCGEWIETMWDCMEVSGQSLCLLVFLLVMVIGNLVVLNLFLALLLSSFSADNLTAPDEDREMNNLQLALARIQRGLRFVKRTTWDFCCGLLRQRPQKPAALAAQGQLPSCIATPYSPPPPETEKVPPTRKETRFEEGEQPGQGTPGDPEPVCVPIAVAESDTDDQEEDEENSLGTEEESSKQQESQPVSGGPEAPPDSRTWSQVSATASSEAEASASQADWRQQWKAEPQAPGCGETPEDSCSEGSTADMTNTAELLEQIPDLGQDVKDPEDCFTEGCVRRCPCCAVDTTQAPGKVWWRLRKTCYHIVEHSWFETFIIFMILLSSGALAFEDIYLEERKTIKVLLEYADKMFTYVFVLEMLLKWVAYGFKKYFTNAWCWLDFLIVDVSLVSLVANTLGFAEMGPIKSLRTLRALRPLRALSRFEGMRVVVNALVGAIPSIMNVLLVCLIFWLIFSIMGVNLFAGKFGRCINQTEGDLPLNYTIVNNKSQCESLNLTGELYWTKVKVNFDNVGAGYLALLQVATFKGWMDIMYAAVDSRGYEEQPQWEYNLYMYIYFVIFIIFGSFFTLNLFIGVIIDNFNQQKKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPRPLNKYQGFIFDIVTKQAFDVTIMFLICLNMVTMMVETDDQSPEKINILAKINLLFVAIFTGECIVKLAALRHYYFTNSWNIFDFVVVILSIVGTVLSDIIQKYFFSPTLFRVIRLARIGRILRLIRGAKGIRTLLFALMMSLPALFNIGLLLFLVMFIYSIFGMANFAYVKWEAGIDDMFNFQTFANSMLCLFQITTSAGWDGLLSPILNTGPPYCDPTLPNSNGSRGDCGSPAVGILFFTTYIIISFLIVVNMYIAIILENFSVATEESTEPLSEDDFDMFYEIWEKFDPEATQFIEYSVLSDFADALSEPLRIAKPNQISLINMDLPMVSGDRIHCMDILFAFTKRVLGESGEMDALKIQMEEKFMAANPSKISYEPITTTLRRKHEEVSAMVIQRAFRRHLLQRSLKHASFLFRQQAGSGLSEEDAPEREGLIAYVMSENFSRPLGPPSSSSISSTSFPPSYDSVTRATSDNLQVRGSDYSHSEDLADFPPSPDRDRESIV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the optimized drugs interact with the target protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ', based on your analysis? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"O=c1cc(N2CCOCC2)oc2c(-c3cccc4c3sc3ccccc34)cccc12\", \"value\": 1.0}, {\"smile\": \"Cc1ccc2c(c1)C(=O)N(C)c1cnc(Nc3cccc(S(=O)(=O)N4CCC(O)CC4)c3)nc1N2C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'O=c1cc(N2CCOCC2)oc2c(-c3cccc4c3sc3ccccc34)cccc12' and 'Cc1ccc2c(c1)C(=O)N(C)c1cnc(Nc3cccc(S(=O)(=O)N4CCC(O)CC4)c3)nc1N2C' can interact with the protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the optimized drugs is likely to interact with the target protein 'MAESELMHIHSLAEHYLQYVLQVPAFESAPSQACRVLQRVAFSVQKEVEKNLKSYLDDFHVESIDTARIIFNQVMEKEFEDGIINWGRIVTIFAFGGVLLKKLPQEQIALDVCAYKQVSSFVAEFIMNNTGEWIRQNGGWEDGFIKKFEPKSGWLTFLQMTGQIWEMLFLLK'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAESELMHIHSLAEHYLQYVLQVPAFESAPSQACRVLQRVAFSVQKEVEKNLKSYLDDFHVESIDTARIIFNQVMEKEFEDGIINWGRIVTIFAFGGVLLKKLPQEQIALDVCAYKQVSSFVAEFIMNNTGEWIRQNGGWEDGFIKKFEPKSGWLTFLQMTGQIWEMLFLLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAESELMHIHSLAEHYLQYVLQVPAFESAPSQACRVLQRVAFSVQKEVEKNLKSYLDDFHVESIDTARIIFNQVMEKEFEDGIINWGRIVTIFAFGGVLLKKLPQEQIALDVCAYKQVSSFVAEFIMNNTGEWIRQNGGWEDGFIKKFEPKSGWLTFLQMTGQIWEMLFLLK\", \"result\": [{\"smile\": \"Cc1nc2ccccc2n1C1CCN(CC(=O)Nc2ccccc2Br)CC1\", \"value\": 0.0}, {\"smile\": \"O=C(Nc1cnn(C23CC4CC(CC(C4)C2)C3)c1)c1cnn2c(C(F)(F)F)cc(-c3ccco3)nc12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cc1nc2ccccc2n1C1CCN(CC(=O)Nc2ccccc2Br)CC1' nor 'O=C(Nc1cnn(C23CC4CC(CC(C4)C2)C3)c1)c1cnn2c(C(F)(F)F)cc(-c3ccco3)nc12' can interact with the protein 'MAESELMHIHSLAEHYLQYVLQVPAFESAPSQACRVLQRVAFSVQKEVEKNLKSYLDDFHVESIDTARIIFNQVMEKEFEDGIINWGRIVTIFAFGGVLLKKLPQEQIALDVCAYKQVSSFVAEFIMNNTGEWIRQNGGWEDGFIKKFEPKSGWLTFLQMTGQIWEMLFLLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you expect the generated drugs to have an interaction with the target protein 'MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\", \"result\": [{\"smile\": \"CN(c1ncnc2[nH]ccc12)[C@H]1C[C@@H](NS(=O)(=O)CCCC(F)(F)F)C1\", \"value\": 0.0}, {\"smile\": \"COc1cc(F)c(-c2nc3c(NCC4CCNCC4)c(Cl)cnc3[nH]2)cc1OC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN(c1ncnc2[nH]ccc12)[C@H]1C[C@@H](NS(=O)(=O)CCCC(F)(F)F)C1' nor 'COc1cc(F)c(-c2nc3c(NCC4CCNCC4)c(Cl)cnc3[nH]2)cc1OC' can interact with the protein 'MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine if the generated drugs will interact with the target protein 'MRALWVLGLCCVLLTFGSVRADDEVDVDGTVEEDLGKSREGSRTDDEVVQREEEAIQLDGLNASQIRELREKSEKFAFQAEVNRMMKLIINSLYKNKEIFLRELISNASDALDKIRLISLTDENALSGNEELTVKIKCDKEKNLLHVTDTGVGMTREELVKNLGTIAKSGTSEFLNKMTEAQEDGQSTSELIGQFGVGFYSAFLVADKVIVTSKHNNDTQHIWESDSNEFSVIADPRGNTLGRGTTITLVLKEEASDYLELDTIKNLVKKYSQFINFPIYVWSSKTETVEEPMEEEEAAKEEKEESDDEAAVEEEEEEKKPKTKKVEKTVWDWELMNDIKPIWQRPSKEVEEDEYKAFYKSFSKESDDPMAYIHFTAEGEVTFKSILFVPTSAPRGLFDEYGSKKSDYIKLYVRRVFITDDFHDMMPKYLNFVKGVVDSDDLPLNVSRETLQQHKLLKVIRKKLVRKTLDMIKKIADDKYNDTFWKEFGTNIKLGVIEDHSNRTRLAKLLRFQSSHHPTDITSLDQYVERMKEKQDKIYFMAGSSRKEAESSPFVERLLKKGYEVIYLTEPVDEYCIQALPEFDGKRFQNVAKEGVKFDESEKTKESREAVEKEFEPLLNWMKDKALKDKIEKAVVSQRLTESPCALVASQYGWSGNMERIMKAQAYQTGKDISTNYYASQKKTFEINPRHPLIRDMLRRIKEDEDDKTVLDLAVVLFETATLRSGYLLPDTKAYGDRIERMLRLSLNIDPDAKVEEEPEEEPEETAEDTTEDTEQDEDEEMDVGTDEEEETAKESTAEKDEL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MRALWVLGLCCVLLTFGSVRADDEVDVDGTVEEDLGKSREGSRTDDEVVQREEEAIQLDGLNASQIRELREKSEKFAFQAEVNRMMKLIINSLYKNKEIFLRELISNASDALDKIRLISLTDENALSGNEELTVKIKCDKEKNLLHVTDTGVGMTREELVKNLGTIAKSGTSEFLNKMTEAQEDGQSTSELIGQFGVGFYSAFLVADKVIVTSKHNNDTQHIWESDSNEFSVIADPRGNTLGRGTTITLVLKEEASDYLELDTIKNLVKKYSQFINFPIYVWSSKTETVEEPMEEEEAAKEEKEESDDEAAVEEEEEEKKPKTKKVEKTVWDWELMNDIKPIWQRPSKEVEEDEYKAFYKSFSKESDDPMAYIHFTAEGEVTFKSILFVPTSAPRGLFDEYGSKKSDYIKLYVRRVFITDDFHDMMPKYLNFVKGVVDSDDLPLNVSRETLQQHKLLKVIRKKLVRKTLDMIKKIADDKYNDTFWKEFGTNIKLGVIEDHSNRTRLAKLLRFQSSHHPTDITSLDQYVERMKEKQDKIYFMAGSSRKEAESSPFVERLLKKGYEVIYLTEPVDEYCIQALPEFDGKRFQNVAKEGVKFDESEKTKESREAVEKEFEPLLNWMKDKALKDKIEKAVVSQRLTESPCALVASQYGWSGNMERIMKAQAYQTGKDISTNYYASQKKTFEINPRHPLIRDMLRRIKEDEDDKTVLDLAVVLFETATLRSGYLLPDTKAYGDRIERMLRLSLNIDPDAKVEEEPEEEPEETAEDTTEDTEQDEDEEMDVGTDEEEETAKESTAEKDEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRALWVLGLCCVLLTFGSVRADDEVDVDGTVEEDLGKSREGSRTDDEVVQREEEAIQLDGLNASQIRELREKSEKFAFQAEVNRMMKLIINSLYKNKEIFLRELISNASDALDKIRLISLTDENALSGNEELTVKIKCDKEKNLLHVTDTGVGMTREELVKNLGTIAKSGTSEFLNKMTEAQEDGQSTSELIGQFGVGFYSAFLVADKVIVTSKHNNDTQHIWESDSNEFSVIADPRGNTLGRGTTITLVLKEEASDYLELDTIKNLVKKYSQFINFPIYVWSSKTETVEEPMEEEEAAKEEKEESDDEAAVEEEEEEKKPKTKKVEKTVWDWELMNDIKPIWQRPSKEVEEDEYKAFYKSFSKESDDPMAYIHFTAEGEVTFKSILFVPTSAPRGLFDEYGSKKSDYIKLYVRRVFITDDFHDMMPKYLNFVKGVVDSDDLPLNVSRETLQQHKLLKVIRKKLVRKTLDMIKKIADDKYNDTFWKEFGTNIKLGVIEDHSNRTRLAKLLRFQSSHHPTDITSLDQYVERMKEKQDKIYFMAGSSRKEAESSPFVERLLKKGYEVIYLTEPVDEYCIQALPEFDGKRFQNVAKEGVKFDESEKTKESREAVEKEFEPLLNWMKDKALKDKIEKAVVSQRLTESPCALVASQYGWSGNMERIMKAQAYQTGKDISTNYYASQKKTFEINPRHPLIRDMLRRIKEDEDDKTVLDLAVVLFETATLRSGYLLPDTKAYGDRIERMLRLSLNIDPDAKVEEEPEEEPEETAEDTTEDTEQDEDEEMDVGTDEEEETAKESTAEKDEL\", \"result\": [{\"smile\": \"C1=NC2=C(C(=N1)N)N=CN2[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O\", \"value\": 1.0}, {\"smile\": \"CC(C)(CC1CC2=C(C1)C=CC=C2)NC[C@@H](O)COC1=CC(CCC(O)=O)=CC(F)=C1F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'C1=NC2=C(C(=N1)N)N=CN2[C@H]3[C@@H]([C@@H]([C@H](O3)COP(=O)(O)OP(=O)(O)OP(=O)(O)O)O)O' can interact with the protein, while the second one 'CC(C)(CC1CC2=C(C1)C=CC=C2)NC[C@@H](O)COC1=CC(CCC(O)=O)=CC(F)=C1F' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the likelihood that the generated drugs will engage with the target protein 'MNTSAPPAVSPNITVLAPGKGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASNASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRARELAALQGSETPGKGGGSSSSSERSQPGAEGSPETPPGRCCRCCRAPRLLQAYSWKEEEEEDEGSMESLTSSEGEEPGSEVVIKMPMVDPEAQAPTKQPPRSSPNTVKRPTKKGRDRAGKGQKPRGKEQLAKRKTFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDKRRWRKIPKRPGSVHRTPSRQC'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MNTSAPPAVSPNITVLAPGKGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASNASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRARELAALQGSETPGKGGGSSSSSERSQPGAEGSPETPPGRCCRCCRAPRLLQAYSWKEEEEEDEGSMESLTSSEGEEPGSEVVIKMPMVDPEAQAPTKQPPRSSPNTVKRPTKKGRDRAGKGQKPRGKEQLAKRKTFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDKRRWRKIPKRPGSVHRTPSRQC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNTSAPPAVSPNITVLAPGKGPWQVAFIGITTGLLSLATVTGNLLVLISFKVNTELKTVNNYFLLSLACADLIIGTFSMNLYTTYLLMGHWALGTLACDLWLALDYVASNASVMNLLLISFDRYFSVTRPLSYRAKRTPRRAALMIGLAWLVSFVLWAPAILFWQYLVGERTVLAGQCYIQFLSQPIITFGTAMAAFYLPVTVMCTLYWRIYRETENRARELAALQGSETPGKGGGSSSSSERSQPGAEGSPETPPGRCCRCCRAPRLLQAYSWKEEEEEDEGSMESLTSSEGEEPGSEVVIKMPMVDPEAQAPTKQPPRSSPNTVKRPTKKGRDRAGKGQKPRGKEQLAKRKTFSLVKEKKAARTLSAILLAFILTWTPYNIMVLVSTFCKDCVPETLWELGYWLCYVNSTINPMCYALCNKAFRDTFRLLLLCRWDKRRWRKIPKRPGSVHRTPSRQC\", \"result\": [{\"smile\": \"CC(C)[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2\", \"value\": 1.0}, {\"smile\": \"CCCCOc1nsnc1C1=CCCN(C)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CC(C)[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2' can interact with the protein, while the second one 'CCCCOc1nsnc1C1=CCCN(C)C1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if there will be an interaction between the generated drugs and the target protein 'MKTKLNIYNMQFLLFVFLVWDPARLVLANIQEDEAKNNITIFTRILDRLLDGYDNRLRPGLGDSITEVFTNIYVTSFGPVSDTDMEYTIDVFFRQKWKDERLKFKGPMNILRLNNLMASKIWTPDTFFHNGKKSVAHNMTMPNKLLRIQDDGTLLYTMRLTVQAECPMHLEDFPMDAHSCPLKFGSYAYTTSEVTYIWTYNASDSVQVAPDGSRLNQYDLLGQSIGKETIKSSTGEYTVMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKSVVNDKKKEKASVMIQNNAYAVAVANYAPNLSKDPVLSTISKSATTPEPNKKPENKPAEAKKTFNSVSKIDRMSRIVFPVLFGTFNLVYWATYLNREPVLGVSP'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKTKLNIYNMQFLLFVFLVWDPARLVLANIQEDEAKNNITIFTRILDRLLDGYDNRLRPGLGDSITEVFTNIYVTSFGPVSDTDMEYTIDVFFRQKWKDERLKFKGPMNILRLNNLMASKIWTPDTFFHNGKKSVAHNMTMPNKLLRIQDDGTLLYTMRLTVQAECPMHLEDFPMDAHSCPLKFGSYAYTTSEVTYIWTYNASDSVQVAPDGSRLNQYDLLGQSIGKETIKSSTGEYTVMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKSVVNDKKKEKASVMIQNNAYAVAVANYAPNLSKDPVLSTISKSATTPEPNKKPENKPAEAKKTFNSVSKIDRMSRIVFPVLFGTFNLVYWATYLNREPVLGVSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKTKLNIYNMQFLLFVFLVWDPARLVLANIQEDEAKNNITIFTRILDRLLDGYDNRLRPGLGDSITEVFTNIYVTSFGPVSDTDMEYTIDVFFRQKWKDERLKFKGPMNILRLNNLMASKIWTPDTFFHNGKKSVAHNMTMPNKLLRIQDDGTLLYTMRLTVQAECPMHLEDFPMDAHSCPLKFGSYAYTTSEVTYIWTYNASDSVQVAPDGSRLNQYDLLGQSIGKETIKSSTGEYTVMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKSVVNDKKKEKASVMIQNNAYAVAVANYAPNLSKDPVLSTISKSATTPEPNKKPENKPAEAKKTFNSVSKIDRMSRIVFPVLFGTFNLVYWATYLNREPVLGVSP\", \"result\": [{\"smile\": \"[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC[C@@]2([H])C[C@](C)(O)CC[C@]12C\", \"value\": 1.0}, {\"smile\": \"FC(F)OC(F)(F)C(F)Cl\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs '[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@@]1([H])[C@@]2([H])CC[C@@]2([H])C[C@](C)(O)CC[C@]12C' and 'FC(F)OC(F)(F)C(F)Cl' can interact with the protein 'MKTKLNIYNMQFLLFVFLVWDPARLVLANIQEDEAKNNITIFTRILDRLLDGYDNRLRPGLGDSITEVFTNIYVTSFGPVSDTDMEYTIDVFFRQKWKDERLKFKGPMNILRLNNLMASKIWTPDTFFHNGKKSVAHNMTMPNKLLRIQDDGTLLYTMRLTVQAECPMHLEDFPMDAHSCPLKFGSYAYTTSEVTYIWTYNASDSVQVAPDGSRLNQYDLLGQSIGKETIKSSTGEYTVMTAHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRGWAWDGKSVVNDKKKEKASVMIQNNAYAVAVANYAPNLSKDPVLSTISKSATTPEPNKKPENKPAEAKKTFNSVSKIDRMSRIVFPVLFGTFNLVYWATYLNREPVLGVSP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "If the generated drugs can interact with the target protein 'MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MQSKVLLAVALWLCVETRAASVGLPSVSLDLPRLSIQKDILTIKANTTLQITCRGQRDLDWLWPNNQSGSEQRVEVTECSDGLFCKTLTIPKVIGNDTGAYKCFYRETDLASVIYVYVQDYRSPFIASVSDQHGVVYITENKNKTVVIPCLGSISNLNVSLCARYPEKRFVPDGNRISWDSKKGFTIPSYMISYAGMVFCEAKINDESYQSIMYIVVVVGYRIYDVVLSPSHGIELSVGEKLVLNCTARTELNVGIDFNWEYPSSKHQHKKLVNRDLKTQSGSEMKKFLSTLTIDGVTRSDQGLYTCAASSGLMTKKNSTFVRVHEKPFVAFGSGMESLVEATVGERVRIPAKYLGYPPPEIKWYKNGIPLESNHTIKAGHVLTIMEVSERDTGNYTVILTNPISKEKQSHVVSLVVYVPPQIGEKSLISPVDSYQYGTTQTLTCTVYAIPPPHHIHWYWQLEEECANEPSQAVSVTNPYPCEEWRSVEDFQGGNKIEVNKNQFALIEGKNKTVSTLVIQAANVSALYKCEAVNKVGRGERVISFHVTRGPEITLQPDMQPTEQESVSLWCTADRSTFENLTWYKLGPQPLPIHVGELPTPVCKNLDTLWKLNATMFSNSTNDILIMELKNASLQDQGDYVCLAQDRKTKKRHCVVRQLTVLERVAPTITGNLENQTTSIGESIEVSCTASGNPPPQIMWFKDNETLVEDSGIVLKDGNRNLTIRRVRKEDEGLYTCQACSVLGCAKVEAFFIIEGAQEKTNLEIIILVGTAVIAMFFWLLLVIILRTVKRANGGELKTGYLSIVMDPDELPLDEHCERLPYDASKWEFPRDRLKLGKPLGRGAFGQVIEADAFGIDKTATCRTVAVKMLKEGATHSEHRALMSELKILIHIGHHLNVVNLLGACTKPGGPLMVIVEFCKFGNLSTYLRSKRNEFVPYKTKGARFRQGKDYVGAIPVDLKRRLDSITSSQSSASSGFVEEKSLSDVEEEEAPEDLYKDFLTLEHLICYSFQVAKGMEFLASRKCIHRDLAARNILLSEKNVVKICDFGLARDIYKDPDYVRKGDARLPLKWMAPETIFDRVYTIQSDVWSFGVLLWEIFSLGASPYPGVKIDEEFCRRLKEGTRMRAPDYTTPEMYQTMLDCWHGEPSQRPTFSELVEHLGNLLQANAQQDGKDYIVLPISETLSMEEDSGLSLPTSPVSCMEEEEVCDPKFHYDNTAGISQYLQNSKRKSRPVSVKTFEDIPLEEPEVKVIPDDNQTDSGMVLASEELKTLEDRTKLSPSFGGMVPSKSRESVASEGSNQTSGYQSGYHSDDTDTTVYSSEEAELLKLIEIGVQTGSTAQILQPDSGTTLSSPPV\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCCn1ccnn1\", \"value\": 1.0}, {\"smile\": \"Cc1cc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)nc(C)n1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCCn1ccnn1' can interact with the protein, while the second one 'Cc1cc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)nc(C)n1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a chance the generated drugs will interact with the target protein 'MLLLLVLLIGASGINAVVYKEVPIQANTDTLEYVHTVWRHGDRTPAELLFPDDITKWPEGLGELTEQGAAQQYRLGQWLKRRYGSWLGEKFNRNAIYIRSSDYNRTLMSAQANMAGLFPPKYPIAGGLMWQPIPVHTISKPTDKELYEEASCPTAEIEMNAQWKSTKANGIRKKFARELSFFSQKLNLPNMELKATWRIFDNLFCEKQNNITWPSWMNSSIFERVDQLYNEVSQLEFHTDTLRRLRGGTLLEEIFHRFSDKASGSLGKEAKFYAYSAHDSTIAALLATLGVFYDIYPKYATCLLIEMHKLANETRLIRVFHKNETDIDRLIEYSIPGCDDPCTLQKLGDDLKKYFPEDWEAECGLKTSFQFIYLVIISILVISTVCSCTMLFVEKQKRKILRFPVDGLRDDTAPMLGGDDSD'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLLLLVLLIGASGINAVVYKEVPIQANTDTLEYVHTVWRHGDRTPAELLFPDDITKWPEGLGELTEQGAAQQYRLGQWLKRRYGSWLGEKFNRNAIYIRSSDYNRTLMSAQANMAGLFPPKYPIAGGLMWQPIPVHTISKPTDKELYEEASCPTAEIEMNAQWKSTKANGIRKKFARELSFFSQKLNLPNMELKATWRIFDNLFCEKQNNITWPSWMNSSIFERVDQLYNEVSQLEFHTDTLRRLRGGTLLEEIFHRFSDKASGSLGKEAKFYAYSAHDSTIAALLATLGVFYDIYPKYATCLLIEMHKLANETRLIRVFHKNETDIDRLIEYSIPGCDDPCTLQKLGDDLKKYFPEDWEAECGLKTSFQFIYLVIISILVISTVCSCTMLFVEKQKRKILRFPVDGLRDDTAPMLGGDDSD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLLLLVLLIGASGINAVVYKEVPIQANTDTLEYVHTVWRHGDRTPAELLFPDDITKWPEGLGELTEQGAAQQYRLGQWLKRRYGSWLGEKFNRNAIYIRSSDYNRTLMSAQANMAGLFPPKYPIAGGLMWQPIPVHTISKPTDKELYEEASCPTAEIEMNAQWKSTKANGIRKKFARELSFFSQKLNLPNMELKATWRIFDNLFCEKQNNITWPSWMNSSIFERVDQLYNEVSQLEFHTDTLRRLRGGTLLEEIFHRFSDKASGSLGKEAKFYAYSAHDSTIAALLATLGVFYDIYPKYATCLLIEMHKLANETRLIRVFHKNETDIDRLIEYSIPGCDDPCTLQKLGDDLKKYFPEDWEAECGLKTSFQFIYLVIISILVISTVCSCTMLFVEKQKRKILRFPVDGLRDDTAPMLGGDDSD\", \"result\": [{\"smile\": \"CC12CCC(=O)C=C1CCC3C2CCC4(C3CC[C@]4(C#C)O)C\", \"value\": 0.0}, {\"smile\": \"C(C(C(=O)O)S)(C(=O)O)S\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC12CCC(=O)C=C1CCC3C2CCC4(C3CC[C@]4(C#C)O)C' nor 'C(C(C(=O)O)S)(C(=O)O)S' can interact with the protein 'MLLLLVLLIGASGINAVVYKEVPIQANTDTLEYVHTVWRHGDRTPAELLFPDDITKWPEGLGELTEQGAAQQYRLGQWLKRRYGSWLGEKFNRNAIYIRSSDYNRTLMSAQANMAGLFPPKYPIAGGLMWQPIPVHTISKPTDKELYEEASCPTAEIEMNAQWKSTKANGIRKKFARELSFFSQKLNLPNMELKATWRIFDNLFCEKQNNITWPSWMNSSIFERVDQLYNEVSQLEFHTDTLRRLRGGTLLEEIFHRFSDKASGSLGKEAKFYAYSAHDSTIAALLATLGVFYDIYPKYATCLLIEMHKLANETRLIRVFHKNETDIDRLIEYSIPGCDDPCTLQKLGDDLKKYFPEDWEAECGLKTSFQFIYLVIISILVISTVCSCTMLFVEKQKRKILRFPVDGLRDDTAPMLGGDDSD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction between the optimized drugs and the target protein 'MEPLFPASTPSWNASSPGAASGGGDNRTLVGPAPSAGARAVLVPVLYLLVCAAGLGGNTLVIYVVLRFAKMKTVTNIYILNLAVADVLYMLGLPFLATQNAASFWPFGPVLCRLVMTLDGVNQFTSVFCLTVMSVDRYLAVVHPLSSARWRRPRVAKLASAAAWVLSLCMSLPLLVFADVQEGGTCNASWPEPVGLWGAVFIIYTAVLGFFAPLLVICLCYLLIVVKVRAAGVRVGCVRRRSERKVTRMVLVVVLVFAGCWLPFFTVNIVNLAVALPQEPASAGLYFFVVILSYANSCANPVLYGFLSDNFRQSFQKVLCLRKGSGAKDADATEPRPDRIRQQQEATPPAHRAAANGLMQTSKL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEPLFPASTPSWNASSPGAASGGGDNRTLVGPAPSAGARAVLVPVLYLLVCAAGLGGNTLVIYVVLRFAKMKTVTNIYILNLAVADVLYMLGLPFLATQNAASFWPFGPVLCRLVMTLDGVNQFTSVFCLTVMSVDRYLAVVHPLSSARWRRPRVAKLASAAAWVLSLCMSLPLLVFADVQEGGTCNASWPEPVGLWGAVFIIYTAVLGFFAPLLVICLCYLLIVVKVRAAGVRVGCVRRRSERKVTRMVLVVVLVFAGCWLPFFTVNIVNLAVALPQEPASAGLYFFVVILSYANSCANPVLYGFLSDNFRQSFQKVLCLRKGSGAKDADATEPRPDRIRQQQEATPPAHRAAANGLMQTSKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEPLFPASTPSWNASSPGAASGGGDNRTLVGPAPSAGARAVLVPVLYLLVCAAGLGGNTLVIYVVLRFAKMKTVTNIYILNLAVADVLYMLGLPFLATQNAASFWPFGPVLCRLVMTLDGVNQFTSVFCLTVMSVDRYLAVVHPLSSARWRRPRVAKLASAAAWVLSLCMSLPLLVFADVQEGGTCNASWPEPVGLWGAVFIIYTAVLGFFAPLLVICLCYLLIVVKVRAAGVRVGCVRRRSERKVTRMVLVVVLVFAGCWLPFFTVNIVNLAVALPQEPASAGLYFFVVILSYANSCANPVLYGFLSDNFRQSFQKVLCLRKGSGAKDADATEPRPDRIRQQQEATPPAHRAAANGLMQTSKL\", \"result\": [{\"smile\": \"Cn1cc([C@@]2(c3cc(C(=O)O)ccn3)N[C@@H](c3nc(-c4ccc(F)cc4)c[nH]3)Cc3c2[nH]c2ccccc32)cn1\", \"value\": 0.0}, {\"smile\": \"Cc1nc([C@]2(c3cnn(C)c3)N[C@@H](c3nc(-c4ccc(F)cn4)c[nH]3)Cc3c2[nH]c2ccccc32)no1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cn1cc([C@@]2(c3cc(C(=O)O)ccn3)N[C@@H](c3nc(-c4ccc(F)cc4)c[nH]3)Cc3c2[nH]c2ccccc32)cn1' nor 'Cc1nc([C@]2(c3cnn(C)c3)N[C@@H](c3nc(-c4ccc(F)cn4)c[nH]3)Cc3c2[nH]c2ccccc32)no1' can interact with the protein 'MEPLFPASTPSWNASSPGAASGGGDNRTLVGPAPSAGARAVLVPVLYLLVCAAGLGGNTLVIYVVLRFAKMKTVTNIYILNLAVADVLYMLGLPFLATQNAASFWPFGPVLCRLVMTLDGVNQFTSVFCLTVMSVDRYLAVVHPLSSARWRRPRVAKLASAAAWVLSLCMSLPLLVFADVQEGGTCNASWPEPVGLWGAVFIIYTAVLGFFAPLLVICLCYLLIVVKVRAAGVRVGCVRRRSERKVTRMVLVVVLVFAGCWLPFFTVNIVNLAVALPQEPASAGLYFFVVILSYANSCANPVLYGFLSDNFRQSFQKVLCLRKGSGAKDADATEPRPDRIRQQQEATPPAHRAAANGLMQTSKL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the optimized drugs is expected to interact with the target protein 'MNEVSVIKEGWLHKRGEYIKTWRPRYFLLKSDGSFIGYKERPEAPDQTLPPLNNFSVAECQLMKTERPRPNTFVIRCLQWTTVIERTFHVDSPDEREEWMRAIQMVANSLKQRAPGEDPMDYKCGSPSDSSTTEEMEVAVSKARAKVTMNDFDYLKLLGKGTFGKVILVREKATGRYYAMKILRKEVIIAKDEVAHTVTESRVLQNTRHPFLTALKYAFQTHDRLCFVMEYANGGELFFHLSRERVFTEERARFYGAEIVSALEYLHSRDVVYRDIKLENLMLDKDGHIKITDFGLCKEGISDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHERLFELILMEEIRFPRTLSPEAKSLLAGLLKKDPKQRLGGGPSDAKEVMEHRFFLSINWQDVVQKKLLPPFKPQVTSEVDTRYFDDEFTAQSITITPPDRYDSLGLLELDQRTHFPQFSYSASIRE'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MNEVSVIKEGWLHKRGEYIKTWRPRYFLLKSDGSFIGYKERPEAPDQTLPPLNNFSVAECQLMKTERPRPNTFVIRCLQWTTVIERTFHVDSPDEREEWMRAIQMVANSLKQRAPGEDPMDYKCGSPSDSSTTEEMEVAVSKARAKVTMNDFDYLKLLGKGTFGKVILVREKATGRYYAMKILRKEVIIAKDEVAHTVTESRVLQNTRHPFLTALKYAFQTHDRLCFVMEYANGGELFFHLSRERVFTEERARFYGAEIVSALEYLHSRDVVYRDIKLENLMLDKDGHIKITDFGLCKEGISDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHERLFELILMEEIRFPRTLSPEAKSLLAGLLKKDPKQRLGGGPSDAKEVMEHRFFLSINWQDVVQKKLLPPFKPQVTSEVDTRYFDDEFTAQSITITPPDRYDSLGLLELDQRTHFPQFSYSASIRE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNEVSVIKEGWLHKRGEYIKTWRPRYFLLKSDGSFIGYKERPEAPDQTLPPLNNFSVAECQLMKTERPRPNTFVIRCLQWTTVIERTFHVDSPDEREEWMRAIQMVANSLKQRAPGEDPMDYKCGSPSDSSTTEEMEVAVSKARAKVTMNDFDYLKLLGKGTFGKVILVREKATGRYYAMKILRKEVIIAKDEVAHTVTESRVLQNTRHPFLTALKYAFQTHDRLCFVMEYANGGELFFHLSRERVFTEERARFYGAEIVSALEYLHSRDVVYRDIKLENLMLDKDGHIKITDFGLCKEGISDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHERLFELILMEEIRFPRTLSPEAKSLLAGLLKKDPKQRLGGGPSDAKEVMEHRFFLSINWQDVVQKKLLPPFKPQVTSEVDTRYFDDEFTAQSITITPPDRYDSLGLLELDQRTHFPQFSYSASIRE\", \"result\": [{\"smile\": \"Cc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3ccn12\", \"value\": 1.0}, {\"smile\": \"C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'Cc1nnc2c3cc(-c4ccccc4)c(-c4ccc(CN5CCC(c6nnc(-c7ccccn7)[nH]6)CC5)cc4)nc3ccn12' can interact with the protein, while the second one 'C1CCC(C1)C(CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3.OP(=O)(O)O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your prediction regarding the optimized drugs' interaction with the target protein 'MIITQTSHCYMTSLGILFLINILPGTTGQGESRRQEPGDFVKQDIGGLSPKHAPDIPDDSTDNITIFTRILDRLLDGYDNRLRPGLGDAVTEVKTDIYVTSFGPVSDTDMEYTIDVFFRQTWHDERLKFDGPMKILPLNNLLASKIWTPDTFFHNGKKSVAHNMTTPNKLLRLVDNGTLLYTMRLTIHAECPMHLEDFPMDVHACPLKFGSYAYTTAEVVYSWTLGKNKSVEVAQDGSRLNQYDLLGHVVGTEIIRSSTGEYVVMTTHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRSWAWEGKKVPEALEMKKKTPAAPAKKTSTTFNIVGTTYPINLAKDTEFSTISKGAAPSASSTPTIIASPKATYVQDSPTETKTYNSVSKVDKISRIIFPVLFAIFNLVYWATYVNRESAIKGMIRKQ'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MIITQTSHCYMTSLGILFLINILPGTTGQGESRRQEPGDFVKQDIGGLSPKHAPDIPDDSTDNITIFTRILDRLLDGYDNRLRPGLGDAVTEVKTDIYVTSFGPVSDTDMEYTIDVFFRQTWHDERLKFDGPMKILPLNNLLASKIWTPDTFFHNGKKSVAHNMTTPNKLLRLVDNGTLLYTMRLTIHAECPMHLEDFPMDVHACPLKFGSYAYTTAEVVYSWTLGKNKSVEVAQDGSRLNQYDLLGHVVGTEIIRSSTGEYVVMTTHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRSWAWEGKKVPEALEMKKKTPAAPAKKTSTTFNIVGTTYPINLAKDTEFSTISKGAAPSASSTPTIIASPKATYVQDSPTETKTYNSVSKVDKISRIIFPVLFAIFNLVYWATYVNRESAIKGMIRKQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MIITQTSHCYMTSLGILFLINILPGTTGQGESRRQEPGDFVKQDIGGLSPKHAPDIPDDSTDNITIFTRILDRLLDGYDNRLRPGLGDAVTEVKTDIYVTSFGPVSDTDMEYTIDVFFRQTWHDERLKFDGPMKILPLNNLLASKIWTPDTFFHNGKKSVAHNMTTPNKLLRLVDNGTLLYTMRLTIHAECPMHLEDFPMDVHACPLKFGSYAYTTAEVVYSWTLGKNKSVEVAQDGSRLNQYDLLGHVVGTEIIRSSTGEYVVMTTHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRSWAWEGKKVPEALEMKKKTPAAPAKKTSTTFNIVGTTYPINLAKDTEFSTISKGAAPSASSTPTIIASPKATYVQDSPTETKTYNSVSKVDKISRIIFPVLFAIFNLVYWATYVNRESAIKGMIRKQ\", \"result\": [{\"smile\": \"[O-][N+](=O)C1=CC2=C(NC(=O)CN=C2C2=CC=CC=C2)C=C1\", \"value\": 1.0}, {\"smile\": \"[H]C(Cl)=CC(O)(CC)C#C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs '[O-][N+](=O)C1=CC2=C(NC(=O)CN=C2C2=CC=CC=C2)C=C1' and '[H]C(Cl)=CC(O)(CC)C#C' can interact with the protein 'MIITQTSHCYMTSLGILFLINILPGTTGQGESRRQEPGDFVKQDIGGLSPKHAPDIPDDSTDNITIFTRILDRLLDGYDNRLRPGLGDAVTEVKTDIYVTSFGPVSDTDMEYTIDVFFRQTWHDERLKFDGPMKILPLNNLLASKIWTPDTFFHNGKKSVAHNMTTPNKLLRLVDNGTLLYTMRLTIHAECPMHLEDFPMDVHACPLKFGSYAYTTAEVVYSWTLGKNKSVEVAQDGSRLNQYDLLGHVVGTEIIRSSTGEYVVMTTHFHLKRKIGYFVIQTYLPCIMTVILSQVSFWLNRESVPARTVFGVTTVLTMTTLSISARNSLPKVAYATAMDWFIAVCYAFVFSALIEFATVNYFTKRSWAWEGKKVPEALEMKKKTPAAPAKKTSTTFNIVGTTYPINLAKDTEFSTISKGAAPSASSTPTIIASPKATYVQDSPTETKTYNSVSKVDKISRIIFPVLFAIFNLVYWATYVNRESAIKGMIRKQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you assess the possibility of the optimized drugs interacting with the target protein 'MWELVALLLLTLAYLFWPKRRCPGAKYPKSLLSLPLVGSLPFLPRHGHMHNNFFKLQKKYGPIYSVRMGTKTTVIVGHHQLAKEVLIKKGKDFSGRPQMATLDIASNNRKGIAFADSGAHWQLHRRLAMATFALFKDGDQKLEKIICQEISTLCDMLATHNGQSIDISFPVFVAVTNVISLICFNTSYKNGDPELNVIQNYNEGIIDNLSKDSLVDLVPWLKIFPNKTLEKLKSHVKIRNDLLNKILENYKEKFRSDSITNMLDTLMQAKMNSDNGNAGPDQDSELLSDNHILTTIGDIFGAGVETTTSVVKWTLAFLLHNPQVKKKLYEEIDQNVGFSRTPTISDRNRLLLLEATIREVLRLRPVAPMLIPHKANVDSSIGEFAVDKGTEVIINLWALHHNEKEWHQPDQFMPERFLNPAGTQLISPSVSYLPFGAGPRSCIGEILARQELFLIMAWLLQRFDLEVPDDGQLPSLEGIPKVVFLIDSFKVKIKVRQAWREAQAEGST'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MWELVALLLLTLAYLFWPKRRCPGAKYPKSLLSLPLVGSLPFLPRHGHMHNNFFKLQKKYGPIYSVRMGTKTTVIVGHHQLAKEVLIKKGKDFSGRPQMATLDIASNNRKGIAFADSGAHWQLHRRLAMATFALFKDGDQKLEKIICQEISTLCDMLATHNGQSIDISFPVFVAVTNVISLICFNTSYKNGDPELNVIQNYNEGIIDNLSKDSLVDLVPWLKIFPNKTLEKLKSHVKIRNDLLNKILENYKEKFRSDSITNMLDTLMQAKMNSDNGNAGPDQDSELLSDNHILTTIGDIFGAGVETTTSVVKWTLAFLLHNPQVKKKLYEEIDQNVGFSRTPTISDRNRLLLLEATIREVLRLRPVAPMLIPHKANVDSSIGEFAVDKGTEVIINLWALHHNEKEWHQPDQFMPERFLNPAGTQLISPSVSYLPFGAGPRSCIGEILARQELFLIMAWLLQRFDLEVPDDGQLPSLEGIPKVVFLIDSFKVKIKVRQAWREAQAEGST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MWELVALLLLTLAYLFWPKRRCPGAKYPKSLLSLPLVGSLPFLPRHGHMHNNFFKLQKKYGPIYSVRMGTKTTVIVGHHQLAKEVLIKKGKDFSGRPQMATLDIASNNRKGIAFADSGAHWQLHRRLAMATFALFKDGDQKLEKIICQEISTLCDMLATHNGQSIDISFPVFVAVTNVISLICFNTSYKNGDPELNVIQNYNEGIIDNLSKDSLVDLVPWLKIFPNKTLEKLKSHVKIRNDLLNKILENYKEKFRSDSITNMLDTLMQAKMNSDNGNAGPDQDSELLSDNHILTTIGDIFGAGVETTTSVVKWTLAFLLHNPQVKKKLYEEIDQNVGFSRTPTISDRNRLLLLEATIREVLRLRPVAPMLIPHKANVDSSIGEFAVDKGTEVIINLWALHHNEKEWHQPDQFMPERFLNPAGTQLISPSVSYLPFGAGPRSCIGEILARQELFLIMAWLLQRFDLEVPDDGQLPSLEGIPKVVFLIDSFKVKIKVRQAWREAQAEGST\", \"result\": [{\"smile\": \"Cc1nnc2n1-c1ccc(-c3cncc([C@](C)(O)C(F)(F)F)c3)cc1CC2\", \"value\": 0.0}, {\"smile\": \"CNC(=O)c1ccc2cc([C@@]3(O)CCn4cncc43)ccc2c1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CNC(=O)c1ccc2cc([C@@]3(O)CCn4cncc43)ccc2c1' can interact with the protein, while the first one 'Cc1nnc2n1-c1ccc(-c3cncc([C@](C)(O)C(F)(F)F)c3)cc1CC2' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think the generated drugs will have any interaction with the target protein 'MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR\", \"result\": [{\"smile\": \"CC(C(=O)O)O.CN1CCN(CC1)C2=CC3=C(C=C2)NC(=C4C(=C5C(=NC4=O)C=CC=C5F)N)N3.O\", \"value\": 0.0}, {\"smile\": \"CCOP(=O)(CCn1cc(-c2ccc(-c3nc(-c4ccc(F)cc4)c(-c4cc[nH]c(=O)c4)[nH]3)cc2)nn1)OCC\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CCOP(=O)(CCn1cc(-c2ccc(-c3nc(-c4ccc(F)cc4)c(-c4cc[nH]c(=O)c4)[nH]3)cc2)nn1)OCC' can interact with the protein, while the first one 'CC(C(=O)O)O.CN1CCN(CC1)C2=CC3=C(C=C2)NC(=C4C(=C5C(=NC4=O)C=CC=C5F)N)N3.O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide insights on whether the generated drugs will interact with the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"C[C@H](Nc1cc(-c2sc(C3CCN(C)CC3)nc2-c2ccc(F)cc2)ccn1)c1ccccc1\", \"value\": 0.0}, {\"smile\": \"CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C[C@H](Nc1cc(-c2sc(C3CCN(C)CC3)nc2-c2ccc(F)cc2)ccn1)c1ccccc1' nor 'CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC' can interact with the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your take on the generated drugs' potential interaction with the target protein 'MPRHHAGGEEGGAAGLWVKSGAAAAAAGGGRLGSGMKDVESGRGRVLLNSAAARGDGLLLLGTRAATLGGGGGGLRESRRGKQGARMSLLGKPLSYTSSQSCRRNVKYRRVQNYLYNVLERPRGWAFIYHAFVFLLVFGCLILSVFSTIPEHTKLASSCLLILEFVMIVVFGLEFIIRIWSAGCCCRYRGWQGRLRFARKPFCVIDTIVLIASIAVVSAKTQGNIFATSALRSLRFLQILRMVRMDRRGGTWKLLGSVVYAHSKELITAWYIGFLVLIFSSFLVYLVEKDANKEFSTYADALWWGTITLTTIGYGDKTPLTWLGRLLSAGFALLGISFFALPAGILGSGFALKVQEQHRQKHFEKRRNPAANLIQCVWRSYAADEKSVSIATWKPHLKALHTCSPTKKEQGEASSSQKLSFKERVRMASPRGQSIKSRQASVGDRRSPSTDITAEGSPTKVQKSWSFNDRTRFRPSLRLKSSQPKPVIDADTALGTDDVYDEKGCQCDVSVEDLTPPLKTVIRAIRIMKFHVAKRKFKETLRPYDVKDVIEQYSAGHLDMLCRIKSLQTRVDQILGKGQITSDKKSREKITAEHETTDDLSMLGRVVKVEKQVQSIESKLDCLLDIYQQVLRKGSASALALASFQIPPFECEQTSDYQSPVDSKDLSGSAQNSGCLSRSTSANISRGLQFILTPNEFSAQTFYALSPTMHSQATQVPISQSDGSAVAATNTIANQINTAPKPAAPTTLQIPPPLPAIKHLPRPETLHPNPAGLQESISDVTTCLVASKENVQVAQSNLTKDRSMRKSFDMGGETLLSVCPMVPKDLGKSLSVQNLIRSTEELNIQLSGSESSGSRGSQDFYPKWRESKLFITDEEVGPEETETDTFDAAPQPAREAAFASDSLRTGRSRSSQSICKAGESTDALSLPHVKLK'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPRHHAGGEEGGAAGLWVKSGAAAAAAGGGRLGSGMKDVESGRGRVLLNSAAARGDGLLLLGTRAATLGGGGGGLRESRRGKQGARMSLLGKPLSYTSSQSCRRNVKYRRVQNYLYNVLERPRGWAFIYHAFVFLLVFGCLILSVFSTIPEHTKLASSCLLILEFVMIVVFGLEFIIRIWSAGCCCRYRGWQGRLRFARKPFCVIDTIVLIASIAVVSAKTQGNIFATSALRSLRFLQILRMVRMDRRGGTWKLLGSVVYAHSKELITAWYIGFLVLIFSSFLVYLVEKDANKEFSTYADALWWGTITLTTIGYGDKTPLTWLGRLLSAGFALLGISFFALPAGILGSGFALKVQEQHRQKHFEKRRNPAANLIQCVWRSYAADEKSVSIATWKPHLKALHTCSPTKKEQGEASSSQKLSFKERVRMASPRGQSIKSRQASVGDRRSPSTDITAEGSPTKVQKSWSFNDRTRFRPSLRLKSSQPKPVIDADTALGTDDVYDEKGCQCDVSVEDLTPPLKTVIRAIRIMKFHVAKRKFKETLRPYDVKDVIEQYSAGHLDMLCRIKSLQTRVDQILGKGQITSDKKSREKITAEHETTDDLSMLGRVVKVEKQVQSIESKLDCLLDIYQQVLRKGSASALALASFQIPPFECEQTSDYQSPVDSKDLSGSAQNSGCLSRSTSANISRGLQFILTPNEFSAQTFYALSPTMHSQATQVPISQSDGSAVAATNTIANQINTAPKPAAPTTLQIPPPLPAIKHLPRPETLHPNPAGLQESISDVTTCLVASKENVQVAQSNLTKDRSMRKSFDMGGETLLSVCPMVPKDLGKSLSVQNLIRSTEELNIQLSGSESSGSRGSQDFYPKWRESKLFITDEEVGPEETETDTFDAAPQPAREAAFASDSLRTGRSRSSQSICKAGESTDALSLPHVKLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPRHHAGGEEGGAAGLWVKSGAAAAAAGGGRLGSGMKDVESGRGRVLLNSAAARGDGLLLLGTRAATLGGGGGGLRESRRGKQGARMSLLGKPLSYTSSQSCRRNVKYRRVQNYLYNVLERPRGWAFIYHAFVFLLVFGCLILSVFSTIPEHTKLASSCLLILEFVMIVVFGLEFIIRIWSAGCCCRYRGWQGRLRFARKPFCVIDTIVLIASIAVVSAKTQGNIFATSALRSLRFLQILRMVRMDRRGGTWKLLGSVVYAHSKELITAWYIGFLVLIFSSFLVYLVEKDANKEFSTYADALWWGTITLTTIGYGDKTPLTWLGRLLSAGFALLGISFFALPAGILGSGFALKVQEQHRQKHFEKRRNPAANLIQCVWRSYAADEKSVSIATWKPHLKALHTCSPTKKEQGEASSSQKLSFKERVRMASPRGQSIKSRQASVGDRRSPSTDITAEGSPTKVQKSWSFNDRTRFRPSLRLKSSQPKPVIDADTALGTDDVYDEKGCQCDVSVEDLTPPLKTVIRAIRIMKFHVAKRKFKETLRPYDVKDVIEQYSAGHLDMLCRIKSLQTRVDQILGKGQITSDKKSREKITAEHETTDDLSMLGRVVKVEKQVQSIESKLDCLLDIYQQVLRKGSASALALASFQIPPFECEQTSDYQSPVDSKDLSGSAQNSGCLSRSTSANISRGLQFILTPNEFSAQTFYALSPTMHSQATQVPISQSDGSAVAATNTIANQINTAPKPAAPTTLQIPPPLPAIKHLPRPETLHPNPAGLQESISDVTTCLVASKENVQVAQSNLTKDRSMRKSFDMGGETLLSVCPMVPKDLGKSLSVQNLIRSTEELNIQLSGSESSGSRGSQDFYPKWRESKLFITDEEVGPEETETDTFDAAPQPAREAAFASDSLRTGRSRSSQSICKAGESTDALSLPHVKLK\", \"result\": [{\"smile\": \"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1CCCO1\", \"value\": 0.0}, {\"smile\": \"[H][C@](C)(CC)[C@]([H])(NC(=O)[C@@]([H])(NC(=O)[C@@]1([H])O[C@]1([H])C(=O)OCC)[C@@]([H])(C)CC)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1CCCO1' nor '[H][C@](C)(CC)[C@]([H])(NC(=O)[C@@]([H])(NC(=O)[C@@]1([H])O[C@]1([H])C(=O)OCC)[C@@]([H])(C)CC)C(O)=O' can interact with the protein 'MPRHHAGGEEGGAAGLWVKSGAAAAAAGGGRLGSGMKDVESGRGRVLLNSAAARGDGLLLLGTRAATLGGGGGGLRESRRGKQGARMSLLGKPLSYTSSQSCRRNVKYRRVQNYLYNVLERPRGWAFIYHAFVFLLVFGCLILSVFSTIPEHTKLASSCLLILEFVMIVVFGLEFIIRIWSAGCCCRYRGWQGRLRFARKPFCVIDTIVLIASIAVVSAKTQGNIFATSALRSLRFLQILRMVRMDRRGGTWKLLGSVVYAHSKELITAWYIGFLVLIFSSFLVYLVEKDANKEFSTYADALWWGTITLTTIGYGDKTPLTWLGRLLSAGFALLGISFFALPAGILGSGFALKVQEQHRQKHFEKRRNPAANLIQCVWRSYAADEKSVSIATWKPHLKALHTCSPTKKEQGEASSSQKLSFKERVRMASPRGQSIKSRQASVGDRRSPSTDITAEGSPTKVQKSWSFNDRTRFRPSLRLKSSQPKPVIDADTALGTDDVYDEKGCQCDVSVEDLTPPLKTVIRAIRIMKFHVAKRKFKETLRPYDVKDVIEQYSAGHLDMLCRIKSLQTRVDQILGKGQITSDKKSREKITAEHETTDDLSMLGRVVKVEKQVQSIESKLDCLLDIYQQVLRKGSASALALASFQIPPFECEQTSDYQSPVDSKDLSGSAQNSGCLSRSTSANISRGLQFILTPNEFSAQTFYALSPTMHSQATQVPISQSDGSAVAATNTIANQINTAPKPAAPTTLQIPPPLPAIKHLPRPETLHPNPAGLQESISDVTTCLVASKENVQVAQSNLTKDRSMRKSFDMGGETLLSVCPMVPKDLGKSLSVQNLIRSTEELNIQLSGSESSGSRGSQDFYPKWRESKLFITDEEVGPEETETDTFDAAPQPAREAAFASDSLRTGRSRSSQSICKAGESTDALSLPHVKLK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the chances of the generated drugs interacting with the target protein 'MSKSKCSVGLMSSVVAPAKEPNAVGPKEVELILVKEQNGVQLTSSTLTNPRQSPVEAQDRETWGKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVWKICPILKGVGFTVILISLYVGFFYNVIIAWALHYLFSSFTTELPWIHCNNSWNSPNCSDAHPGDSSGDSSGLNDTFGTTPAAEYFERGVLHLHQSHGIDDLGPPRWQLTACLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLTALLLRGVTLPGAIDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYNKFTNNCYRDAIVTTSINSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEAIATLPLSSAWAVVFFIMLLTLGIDSAMGGMESVITGLIDEFQLLHRHRELFTLFIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVGQFSDDIQQMTGQRPSLYWRLCWKLVSPCFLLFVVVVSIVTFRPPHYGAYIFPDWANALGWVIATSSMAMVPIYAAYKFCSLPGSFREKLAYAIAPEKDRELVDRGEVRQFTLRHWLKV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSKSKCSVGLMSSVVAPAKEPNAVGPKEVELILVKEQNGVQLTSSTLTNPRQSPVEAQDRETWGKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVWKICPILKGVGFTVILISLYVGFFYNVIIAWALHYLFSSFTTELPWIHCNNSWNSPNCSDAHPGDSSGDSSGLNDTFGTTPAAEYFERGVLHLHQSHGIDDLGPPRWQLTACLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLTALLLRGVTLPGAIDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYNKFTNNCYRDAIVTTSINSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEAIATLPLSSAWAVVFFIMLLTLGIDSAMGGMESVITGLIDEFQLLHRHRELFTLFIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVGQFSDDIQQMTGQRPSLYWRLCWKLVSPCFLLFVVVVSIVTFRPPHYGAYIFPDWANALGWVIATSSMAMVPIYAAYKFCSLPGSFREKLAYAIAPEKDRELVDRGEVRQFTLRHWLKV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSKSKCSVGLMSSVVAPAKEPNAVGPKEVELILVKEQNGVQLTSSTLTNPRQSPVEAQDRETWGKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVWKICPILKGVGFTVILISLYVGFFYNVIIAWALHYLFSSFTTELPWIHCNNSWNSPNCSDAHPGDSSGDSSGLNDTFGTTPAAEYFERGVLHLHQSHGIDDLGPPRWQLTACLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLTALLLRGVTLPGAIDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYNKFTNNCYRDAIVTTSINSLTSFSSGFVVFSFLGYMAQKHSVPIGDVAKDGPGLIFIIYPEAIATLPLSSAWAVVFFIMLLTLGIDSAMGGMESVITGLIDEFQLLHRHRELFTLFIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVGQFSDDIQQMTGQRPSLYWRLCWKLVSPCFLLFVVVVSIVTFRPPHYGAYIFPDWANALGWVIATSSMAMVPIYAAYKFCSLPGSFREKLAYAIAPEKDRELVDRGEVRQFTLRHWLKV\", \"result\": [{\"smile\": \"CNCc1cc(C(=O)N(C)C)ccc1Oc1ccc(Cl)cc1OC\", \"value\": 0.0}, {\"smile\": \"O=C1CCCN1CC1(O)CCN(c2nc(Oc3cccc(F)c3)cc(C(F)(F)F)n2)CC1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'O=C1CCCN1CC1(O)CCN(c2nc(Oc3cccc(F)c3)cc(C(F)(F)F)n2)CC1' can interact with the protein, while the first one 'CNCc1cc(C(=O)N(C)C)ccc1Oc1ccc(Cl)cc1OC' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the generated drugs likely interact with the target protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\", \"value\": 0.0}, {\"smile\": \"CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' nor 'CNC(=O)C1=NC=CC(=C1)OC2=CC=C(C=C2)NC(=O)NC3=CC(=C(C=C3)Cl)C(F)(F)F' can interact with the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the anticipated interaction between the optimized drugs and the target protein 'MADSRDPASDQMQHWKEQRAAQKADVLTTGAGNPVGDKLNVITVGPRGPLLVQDVVFTDEMAHFDRERIPERVVHAKGAGAFGYFEVTHDITKYSKAKVFEHIGKKTPIAVRFSTVAGESGSADTVRDPRGFAVKFYTEDGNWDLVGNNTPIFFIRDPILFPSFIHSQKRNPQTHLKDPDMVWDFWSLRPESLHQVSFLFSDRGIPDGHRHMNGYGSHTFKLVNANGEAVYCKFHYKTDQGIKNLSVEDAARLSQEDPDYGIRDLFNAIATGKYPSWTFYIQVMTFNQAETFPFNPFDLTKVWPHKDYPLIPVGKLVLNRNPVNYFAEVEQIAFDPSNMPPGIEASPDKMLQGRLFAYPDTHRHRLGPNYLHIPVNCPYRARVANYQRDGPMCMQDNQGGAPNYYPNSFGAPEQQPSALEHSIQYSGEVRRFNTANDDNVTQVRAFYVNVLNEEQRKRLCENIAGHLKDAQIFIQKKAVKNFTEVHPDYGSHIQALLDKYNAEKPKNAIHTFVQSGSHLAAREKANL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MADSRDPASDQMQHWKEQRAAQKADVLTTGAGNPVGDKLNVITVGPRGPLLVQDVVFTDEMAHFDRERIPERVVHAKGAGAFGYFEVTHDITKYSKAKVFEHIGKKTPIAVRFSTVAGESGSADTVRDPRGFAVKFYTEDGNWDLVGNNTPIFFIRDPILFPSFIHSQKRNPQTHLKDPDMVWDFWSLRPESLHQVSFLFSDRGIPDGHRHMNGYGSHTFKLVNANGEAVYCKFHYKTDQGIKNLSVEDAARLSQEDPDYGIRDLFNAIATGKYPSWTFYIQVMTFNQAETFPFNPFDLTKVWPHKDYPLIPVGKLVLNRNPVNYFAEVEQIAFDPSNMPPGIEASPDKMLQGRLFAYPDTHRHRLGPNYLHIPVNCPYRARVANYQRDGPMCMQDNQGGAPNYYPNSFGAPEQQPSALEHSIQYSGEVRRFNTANDDNVTQVRAFYVNVLNEEQRKRLCENIAGHLKDAQIFIQKKAVKNFTEVHPDYGSHIQALLDKYNAEKPKNAIHTFVQSGSHLAAREKANL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MADSRDPASDQMQHWKEQRAAQKADVLTTGAGNPVGDKLNVITVGPRGPLLVQDVVFTDEMAHFDRERIPERVVHAKGAGAFGYFEVTHDITKYSKAKVFEHIGKKTPIAVRFSTVAGESGSADTVRDPRGFAVKFYTEDGNWDLVGNNTPIFFIRDPILFPSFIHSQKRNPQTHLKDPDMVWDFWSLRPESLHQVSFLFSDRGIPDGHRHMNGYGSHTFKLVNANGEAVYCKFHYKTDQGIKNLSVEDAARLSQEDPDYGIRDLFNAIATGKYPSWTFYIQVMTFNQAETFPFNPFDLTKVWPHKDYPLIPVGKLVLNRNPVNYFAEVEQIAFDPSNMPPGIEASPDKMLQGRLFAYPDTHRHRLGPNYLHIPVNCPYRARVANYQRDGPMCMQDNQGGAPNYYPNSFGAPEQQPSALEHSIQYSGEVRRFNTANDDNVTQVRAFYVNVLNEEQRKRLCENIAGHLKDAQIFIQKKAVKNFTEVHPDYGSHIQALLDKYNAEKPKNAIHTFVQSGSHLAAREKANL\", \"result\": [{\"smile\": \"N[C@@H](CCC(=O)N[C@@H](CS)C(O)=O)C(O)=O\", \"value\": 0.0}, {\"smile\": \"CN1CC[C@]23C4C(=O)CC[C@]2([C@H]1CC5=C3C(=C(C=C5)OC)O4)O.Cl\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'N[C@@H](CCC(=O)N[C@@H](CS)C(O)=O)C(O)=O' nor 'CN1CC[C@]23C4C(=O)CC[C@]2([C@H]1CC5=C3C(=C(C=C5)OC)O4)O.Cl' can interact with the protein 'MADSRDPASDQMQHWKEQRAAQKADVLTTGAGNPVGDKLNVITVGPRGPLLVQDVVFTDEMAHFDRERIPERVVHAKGAGAFGYFEVTHDITKYSKAKVFEHIGKKTPIAVRFSTVAGESGSADTVRDPRGFAVKFYTEDGNWDLVGNNTPIFFIRDPILFPSFIHSQKRNPQTHLKDPDMVWDFWSLRPESLHQVSFLFSDRGIPDGHRHMNGYGSHTFKLVNANGEAVYCKFHYKTDQGIKNLSVEDAARLSQEDPDYGIRDLFNAIATGKYPSWTFYIQVMTFNQAETFPFNPFDLTKVWPHKDYPLIPVGKLVLNRNPVNYFAEVEQIAFDPSNMPPGIEASPDKMLQGRLFAYPDTHRHRLGPNYLHIPVNCPYRARVANYQRDGPMCMQDNQGGAPNYYPNSFGAPEQQPSALEHSIQYSGEVRRFNTANDDNVTQVRAFYVNVLNEEQRKRLCENIAGHLKDAQIFIQKKAVKNFTEVHPDYGSHIQALLDKYNAEKPKNAIHTFVQSGSHLAAREKANL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you see the generated drugs interacting with the target protein 'MTYAESTRSAVVYSSIVPPPRTPVGPPMLKDGVRKGSASQPLQPKNGANSLDYWSKSEDEKSGITCYYSRHNIQFPPPQRKMKPVKPNPVGRSTINGFTPSTSSPQNGLRYPPFPQNTPESVMSPPVGIYRNIPAMPRAKLTKDEKNGGKMNRDGGGENPKNVVWGGTSRKDDDTASTPLNSFSANASIEKKRSTARRKPRWARCFQTLFCCVTPPREIEKIQSSQRTNSTNNNHQNGRPSTPTNTGPPIQLITQVHRDGTVTGLPTTGQACQQNGSGDGVTPYDKIANDSVGTINEKPLLPPLLPQDSNKKCLVIDLDETLVHSSFKPVKNPDFVIPVEIDGVEHQVYVLKRPYVDEFLAKVGEHFECILFTASLAKYADPVADLLDKKRVFRGRLFREACVFHKGNYVKDLSRLGRNLNQTLIIDNSPASYAFHPENAVPVTTWFDDPSDTELLDILPSLEHLNGFSSIYDLYRPEEGPQSELLNHCSC'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MTYAESTRSAVVYSSIVPPPRTPVGPPMLKDGVRKGSASQPLQPKNGANSLDYWSKSEDEKSGITCYYSRHNIQFPPPQRKMKPVKPNPVGRSTINGFTPSTSSPQNGLRYPPFPQNTPESVMSPPVGIYRNIPAMPRAKLTKDEKNGGKMNRDGGGENPKNVVWGGTSRKDDDTASTPLNSFSANASIEKKRSTARRKPRWARCFQTLFCCVTPPREIEKIQSSQRTNSTNNNHQNGRPSTPTNTGPPIQLITQVHRDGTVTGLPTTGQACQQNGSGDGVTPYDKIANDSVGTINEKPLLPPLLPQDSNKKCLVIDLDETLVHSSFKPVKNPDFVIPVEIDGVEHQVYVLKRPYVDEFLAKVGEHFECILFTASLAKYADPVADLLDKKRVFRGRLFREACVFHKGNYVKDLSRLGRNLNQTLIIDNSPASYAFHPENAVPVTTWFDDPSDTELLDILPSLEHLNGFSSIYDLYRPEEGPQSELLNHCSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTYAESTRSAVVYSSIVPPPRTPVGPPMLKDGVRKGSASQPLQPKNGANSLDYWSKSEDEKSGITCYYSRHNIQFPPPQRKMKPVKPNPVGRSTINGFTPSTSSPQNGLRYPPFPQNTPESVMSPPVGIYRNIPAMPRAKLTKDEKNGGKMNRDGGGENPKNVVWGGTSRKDDDTASTPLNSFSANASIEKKRSTARRKPRWARCFQTLFCCVTPPREIEKIQSSQRTNSTNNNHQNGRPSTPTNTGPPIQLITQVHRDGTVTGLPTTGQACQQNGSGDGVTPYDKIANDSVGTINEKPLLPPLLPQDSNKKCLVIDLDETLVHSSFKPVKNPDFVIPVEIDGVEHQVYVLKRPYVDEFLAKVGEHFECILFTASLAKYADPVADLLDKKRVFRGRLFREACVFHKGNYVKDLSRLGRNLNQTLIIDNSPASYAFHPENAVPVTTWFDDPSDTELLDILPSLEHLNGFSSIYDLYRPEEGPQSELLNHCSC\", \"result\": [{\"smile\": \"C([C@@H](C(=O)O)N)OP(=O)(O)O\", \"value\": 1.0}, {\"smile\": \"[Mg+2]\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'C([C@@H](C(=O)O)N)OP(=O)(O)O' and '[Mg+2]' can interact with the protein 'MTYAESTRSAVVYSSIVPPPRTPVGPPMLKDGVRKGSASQPLQPKNGANSLDYWSKSEDEKSGITCYYSRHNIQFPPPQRKMKPVKPNPVGRSTINGFTPSTSSPQNGLRYPPFPQNTPESVMSPPVGIYRNIPAMPRAKLTKDEKNGGKMNRDGGGENPKNVVWGGTSRKDDDTASTPLNSFSANASIEKKRSTARRKPRWARCFQTLFCCVTPPREIEKIQSSQRTNSTNNNHQNGRPSTPTNTGPPIQLITQVHRDGTVTGLPTTGQACQQNGSGDGVTPYDKIANDSVGTINEKPLLPPLLPQDSNKKCLVIDLDETLVHSSFKPVKNPDFVIPVEIDGVEHQVYVLKRPYVDEFLAKVGEHFECILFTASLAKYADPVADLLDKKRVFRGRLFREACVFHKGNYVKDLSRLGRNLNQTLIIDNSPASYAFHPENAVPVTTWFDDPSDTELLDILPSLEHLNGFSSIYDLYRPEEGPQSELLNHCSC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your analysis of the optimized drugs' interaction with the target protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\", \"result\": [{\"smile\": \"C[C@@H]1COCCN1c1cc(C2(S(C)(=O)=O)CC2)nc(-c2cccc3[nH]ccc23)n1\", \"value\": 0.0}, {\"smile\": \"C[C@H]1CN(S(=O)(=O)C[C@]23CC[C@H](C[C@@H]2O)C3(C)C)CCN1c1ncc(C(F)(F)F)cc1F\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'C[C@@H]1COCCN1c1cc(C2(S(C)(=O)=O)CC2)nc(-c2cccc3[nH]ccc23)n1' nor 'C[C@H]1CN(S(=O)(=O)C[C@]23CC[C@H](C[C@@H]2O)C3(C)C)CCN1c1ncc(C(F)(F)F)cc1F' can interact with the protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the likelihood of interaction between the generated drugs and the target protein 'MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDPFVVLVLCLSCLLLLSIWRQSSGRGKLPPGPTPLPVIGNILQIDIKDVSKSLTNLSKIYGPVFTLYFGLERMVVLHGYEVVKEALIDLGEEFSGRGHFPLAERANRGFGIVFSNGKRWKEIRRFSLMTLRNFGMGKRSIEDRVQEEARCLVEELRKTKASPCDPTFILGCAPCNVICSIIFQKRFDYKDQQFLNLMEKLNENIRIVSTPWIQICNNFPTIIDYFPGTHNKLLKNLAFMESDILEKVKEHQESMDINNPRDFIDCFLIKMEKEKQNQQSEFTIENLVITAADLLGAGTETTSTTLRYALLLLLKHPEVTAKVQEEIERVVGRNRSPCMQDRGHMPYTDAVVHEVQRYIDLIPTSLPHAVTCDVKFRNYLIPKGTTILTSLTSVLHDNKEFPNPEMFDPRHFLDEGGNFKKSNYFMPFSAGKRICVGEGLARMELFLFLTFILQNFNLKSLIDPKDLDTTPVVNGFASVPPFYQLCFIPV\", \"result\": [{\"smile\": \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\", \"value\": 1.0}, {\"smile\": \"Cc1ccc(C(=O)N(CCCN)[C@@H](c2nc3cc(Cl)ccc3c(=O)n2Cc2ccccc2)C(C)C)cc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1' can interact with the protein, while the second one 'Cc1ccc(C(=O)N(CCCN)[C@@H](c2nc3cc(Cl)ccc3c(=O)n2Cc2ccccc2)C(C)C)cc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the optimized drugs with the target protein 'MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV\", \"result\": [{\"smile\": \"Cc1cc(-c2[nH]c3ccc(C4CCN(Cc5csnn5)CC4)cc3c2C(C)C)cn2ncnc12\", \"value\": 1.0}, {\"smile\": \"Cc1c(-c2[nH]c3ccc(C4CCN(c5ncccn5)CC4)cc3c2C(C)C)cn2ncnc2c1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'Cc1cc(-c2[nH]c3ccc(C4CCN(Cc5csnn5)CC4)cc3c2C(C)C)cn2ncnc12' and 'Cc1c(-c2[nH]c3ccc(C4CCN(c5ncccn5)CC4)cc3c2C(C)C)cn2ncnc2c1C' can interact with the protein 'MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you foresee any interaction between the generated drugs and the target protein 'MAADSQDIYRNLSADDYEAAPIVVDSVCPVCEEDGETRIMCTSIPYYRAVILMSFECPHCGHKNNEIQSGEAVQEHGTLIVLRVQKPEDLRRQLVKSEYASIEVPELQLEIPHKSQPGEVTTVEGVLERVHRGLSQDQEKRRLLDPEGASQIDAYLQKITSCMELGETWTLRLRDPTGNCYIQNPDVRHVDPRCIISHYHRNLDERKLLALADDNEEEEEVEPSAAAPEFKSYEDAKQEVLHFATDCPNCHGPTEVKMKPTDIPFFQTVIIMSLACDRCGYKSNEVKSGGAIRDQGCRMSVKLEKDLDLARDVLKTDTCALSIPEIDLEVGGNALCGRFTTIEGLLTATKEQLDAQSSFFMGDSAQTGEKSAVTTFLEKLDDIIALRLPATIILDDPTGCSYVQSLTAPMDDPRLTKEFYTRTYEQNDELGINDMKVENYGELDALAEEDEPHEA'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAADSQDIYRNLSADDYEAAPIVVDSVCPVCEEDGETRIMCTSIPYYRAVILMSFECPHCGHKNNEIQSGEAVQEHGTLIVLRVQKPEDLRRQLVKSEYASIEVPELQLEIPHKSQPGEVTTVEGVLERVHRGLSQDQEKRRLLDPEGASQIDAYLQKITSCMELGETWTLRLRDPTGNCYIQNPDVRHVDPRCIISHYHRNLDERKLLALADDNEEEEEVEPSAAAPEFKSYEDAKQEVLHFATDCPNCHGPTEVKMKPTDIPFFQTVIIMSLACDRCGYKSNEVKSGGAIRDQGCRMSVKLEKDLDLARDVLKTDTCALSIPEIDLEVGGNALCGRFTTIEGLLTATKEQLDAQSSFFMGDSAQTGEKSAVTTFLEKLDDIIALRLPATIILDDPTGCSYVQSLTAPMDDPRLTKEFYTRTYEQNDELGINDMKVENYGELDALAEEDEPHEA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAADSQDIYRNLSADDYEAAPIVVDSVCPVCEEDGETRIMCTSIPYYRAVILMSFECPHCGHKNNEIQSGEAVQEHGTLIVLRVQKPEDLRRQLVKSEYASIEVPELQLEIPHKSQPGEVTTVEGVLERVHRGLSQDQEKRRLLDPEGASQIDAYLQKITSCMELGETWTLRLRDPTGNCYIQNPDVRHVDPRCIISHYHRNLDERKLLALADDNEEEEEVEPSAAAPEFKSYEDAKQEVLHFATDCPNCHGPTEVKMKPTDIPFFQTVIIMSLACDRCGYKSNEVKSGGAIRDQGCRMSVKLEKDLDLARDVLKTDTCALSIPEIDLEVGGNALCGRFTTIEGLLTATKEQLDAQSSFFMGDSAQTGEKSAVTTFLEKLDDIIALRLPATIILDDPTGCSYVQSLTAPMDDPRLTKEFYTRTYEQNDELGINDMKVENYGELDALAEEDEPHEA\", \"result\": [{\"smile\": \"CCC(=O)O[C@H]1CC[C@@H]2[C@@]1(CC[C@H]3[C@H]2CCC4=CC(=O)CC[C@]34C)C\", \"value\": 0.0}, {\"smile\": \"CC[N+](CC)(CC)CC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCC(=O)O[C@H]1CC[C@@H]2[C@@]1(CC[C@H]3[C@H]2CCC4=CC(=O)CC[C@]34C)C' nor 'CC[N+](CC)(CC)CC' can interact with the protein 'MAADSQDIYRNLSADDYEAAPIVVDSVCPVCEEDGETRIMCTSIPYYRAVILMSFECPHCGHKNNEIQSGEAVQEHGTLIVLRVQKPEDLRRQLVKSEYASIEVPELQLEIPHKSQPGEVTTVEGVLERVHRGLSQDQEKRRLLDPEGASQIDAYLQKITSCMELGETWTLRLRDPTGNCYIQNPDVRHVDPRCIISHYHRNLDERKLLALADDNEEEEEVEPSAAAPEFKSYEDAKQEVLHFATDCPNCHGPTEVKMKPTDIPFFQTVIIMSLACDRCGYKSNEVKSGGAIRDQGCRMSVKLEKDLDLARDVLKTDTCALSIPEIDLEVGGNALCGRFTTIEGLLTATKEQLDAQSSFFMGDSAQTGEKSAVTTFLEKLDDIIALRLPATIILDDPTGCSYVQSLTAPMDDPRLTKEFYTRTYEQNDELGINDMKVENYGELDALAEEDEPHEA'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the generated drugs will engage with the target protein 'MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\", \"result\": [{\"smile\": \"CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC\", \"value\": 0.0}, {\"smile\": \"CN(c1ncnc2[nH]ccc12)[C@H]1C[C@@H](NS(=O)(=O)NCC2CC2)C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CN1C2=NC(=NC=C2C=C(C1=O)C3=C(C=CC=C3Cl)Cl)NC4=CC(=CC=C4)SC' nor 'CN(c1ncnc2[nH]ccc12)[C@H]1C[C@@H](NS(=O)(=O)NCC2CC2)C1' can interact with the protein 'MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the expected outcome for the generated drugs interacting with the target protein 'MDDKPNPEALSDSSERLFSFGVIADVQFADLEDGFNFQGTRRRYYRHSLLHLQGAIEDWNNESSMPCCVLQLGDIIDGYNAQYNASKKSLELVMDMFKRLKVPVHHTWGNHEFYNFSREYLTHSKLNTKFLEDQIVHHPETMPSEDYYAYHFVPFPKFRFILLDAYDLSVLGVDQSSPKYEQCMKILREHNPNTELNSPQGLSEPQFVQFNGGFSQEQLNWLNEVLTFSDTNQEKVVIVSHLPIYPDASDNVCLAWNYRDALAVIWSHECVVCFFAGHTHDGGYSEDPFGVYHVNLEGVIETAPDSQAFGTVHVYPDKMMLKGRGRVPDRIMNYKKERAFHC'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MDDKPNPEALSDSSERLFSFGVIADVQFADLEDGFNFQGTRRRYYRHSLLHLQGAIEDWNNESSMPCCVLQLGDIIDGYNAQYNASKKSLELVMDMFKRLKVPVHHTWGNHEFYNFSREYLTHSKLNTKFLEDQIVHHPETMPSEDYYAYHFVPFPKFRFILLDAYDLSVLGVDQSSPKYEQCMKILREHNPNTELNSPQGLSEPQFVQFNGGFSQEQLNWLNEVLTFSDTNQEKVVIVSHLPIYPDASDNVCLAWNYRDALAVIWSHECVVCFFAGHTHDGGYSEDPFGVYHVNLEGVIETAPDSQAFGTVHVYPDKMMLKGRGRVPDRIMNYKKERAFHC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MDDKPNPEALSDSSERLFSFGVIADVQFADLEDGFNFQGTRRRYYRHSLLHLQGAIEDWNNESSMPCCVLQLGDIIDGYNAQYNASKKSLELVMDMFKRLKVPVHHTWGNHEFYNFSREYLTHSKLNTKFLEDQIVHHPETMPSEDYYAYHFVPFPKFRFILLDAYDLSVLGVDQSSPKYEQCMKILREHNPNTELNSPQGLSEPQFVQFNGGFSQEQLNWLNEVLTFSDTNQEKVVIVSHLPIYPDASDNVCLAWNYRDALAVIWSHECVVCFFAGHTHDGGYSEDPFGVYHVNLEGVIETAPDSQAFGTVHVYPDKMMLKGRGRVPDRIMNYKKERAFHC\", \"result\": [{\"smile\": \"C[C@@H]([C@H](C(=O)O)N)C(=O)O\", \"value\": 1.0}, {\"smile\": \"CCO\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'C[C@@H]([C@H](C(=O)O)N)C(=O)O' and 'CCO' can interact with the protein 'MDDKPNPEALSDSSERLFSFGVIADVQFADLEDGFNFQGTRRRYYRHSLLHLQGAIEDWNNESSMPCCVLQLGDIIDGYNAQYNASKKSLELVMDMFKRLKVPVHHTWGNHEFYNFSREYLTHSKLNTKFLEDQIVHHPETMPSEDYYAYHFVPFPKFRFILLDAYDLSVLGVDQSSPKYEQCMKILREHNPNTELNSPQGLSEPQFVQFNGGFSQEQLNWLNEVLTFSDTNQEKVVIVSHLPIYPDASDNVCLAWNYRDALAVIWSHECVVCFFAGHTHDGGYSEDPFGVYHVNLEGVIETAPDSQAFGTVHVYPDKMMLKGRGRVPDRIMNYKKERAFHC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the generated drugs interact with the target protein 'MGSLQPDAGNASWNGTEAPGGGARATPYSLQVTLTLVCLAGLLMLLTVFGNVLVIIAVFTSRALKAPQNLFLVSLASADILVATLVIPFSLANEVMGYWYFGKAWCEIYLALDVLFCTSSIVHLCAISLDRYWSITQAIEYNLKRTPRRIKAIIITVWVISAVISFPPLISIEKKGGGGGPQPAEPRCEINDQKWYVISSCIGSFFAPCLIMILVYVRIYQIAKRRTRVPPSRRGPDAVAAPPGGTERRPNGLGPERSAGPGGAEAEPLPTQLNGAPGEPAPAGPRDTDALDLEESSSSDHAERPPGPRRPERGPRGKGKARASQVKPGDSLPRRGPGATGIGTPAAGPGEERVGAAKASRWRGRQNREKRFTFVLAVVIGVFVVCWFPFFFTYTLTAVGCSVPRTLFKFFFWFGYCNSSLNPVIYTIFNHDFRRAFKKILCRGDRKRIV', based on your evaluation? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGSLQPDAGNASWNGTEAPGGGARATPYSLQVTLTLVCLAGLLMLLTVFGNVLVIIAVFTSRALKAPQNLFLVSLASADILVATLVIPFSLANEVMGYWYFGKAWCEIYLALDVLFCTSSIVHLCAISLDRYWSITQAIEYNLKRTPRRIKAIIITVWVISAVISFPPLISIEKKGGGGGPQPAEPRCEINDQKWYVISSCIGSFFAPCLIMILVYVRIYQIAKRRTRVPPSRRGPDAVAAPPGGTERRPNGLGPERSAGPGGAEAEPLPTQLNGAPGEPAPAGPRDTDALDLEESSSSDHAERPPGPRRPERGPRGKGKARASQVKPGDSLPRRGPGATGIGTPAAGPGEERVGAAKASRWRGRQNREKRFTFVLAVVIGVFVVCWFPFFFTYTLTAVGCSVPRTLFKFFFWFGYCNSSLNPVIYTIFNHDFRRAFKKILCRGDRKRIV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGSLQPDAGNASWNGTEAPGGGARATPYSLQVTLTLVCLAGLLMLLTVFGNVLVIIAVFTSRALKAPQNLFLVSLASADILVATLVIPFSLANEVMGYWYFGKAWCEIYLALDVLFCTSSIVHLCAISLDRYWSITQAIEYNLKRTPRRIKAIIITVWVISAVISFPPLISIEKKGGGGGPQPAEPRCEINDQKWYVISSCIGSFFAPCLIMILVYVRIYQIAKRRTRVPPSRRGPDAVAAPPGGTERRPNGLGPERSAGPGGAEAEPLPTQLNGAPGEPAPAGPRDTDALDLEESSSSDHAERPPGPRRPERGPRGKGKARASQVKPGDSLPRRGPGATGIGTPAAGPGEERVGAAKASRWRGRQNREKRFTFVLAVVIGVFVVCWFPFFFTYTLTAVGCSVPRTLFKFFFWFGYCNSSLNPVIYTIFNHDFRRAFKKILCRGDRKRIV\", \"result\": [{\"smile\": \"CNCCCN1C2=CC=CC=C2CCC2=CC=CC=C12\", \"value\": 1.0}, {\"smile\": \"CCC1=NN(CCCN2CCN(CC2)C2=CC(Cl)=CC=C2)C(=O)N1CCOC1=CC=CC=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CNCCCN1C2=CC=CC=C2CCC2=CC=CC=C12' and 'CCC1=NN(CCCN2CCN(CC2)C2=CC(Cl)=CC=C2)C(=O)N1CCOC1=CC=CC=C1' can interact with the protein 'MGSLQPDAGNASWNGTEAPGGGARATPYSLQVTLTLVCLAGLLMLLTVFGNVLVIIAVFTSRALKAPQNLFLVSLASADILVATLVIPFSLANEVMGYWYFGKAWCEIYLALDVLFCTSSIVHLCAISLDRYWSITQAIEYNLKRTPRRIKAIIITVWVISAVISFPPLISIEKKGGGGGPQPAEPRCEINDQKWYVISSCIGSFFAPCLIMILVYVRIYQIAKRRTRVPPSRRGPDAVAAPPGGTERRPNGLGPERSAGPGGAEAEPLPTQLNGAPGEPAPAGPRDTDALDLEESSSSDHAERPPGPRRPERGPRGKGKARASQVKPGDSLPRRGPGATGIGTPAAGPGEERVGAAKASRWRGRQNREKRFTFVLAVVIGVFVVCWFPFFFTYTLTAVGCSVPRTLFKFFFWFGYCNSSLNPVIYTIFNHDFRRAFKKILCRGDRKRIV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the optimized drugs and the target protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\", \"result\": [{\"smile\": \"C#CCN(Cc1nc2c(c(=O)[nH]1)COCC2)C(=O)CN1CCC(C(=O)c2ccc(F)cc2)CC1\", \"value\": 0.0}, {\"smile\": \"CC(C)N1CC2=NNC(=O)c3cccc4[nH]c(c2c34)C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CC(C)N1CC2=NNC(=O)c3cccc4[nH]c(c2c34)C1' can interact with the protein, while the first one 'C#CCN(Cc1nc2c(c(=O)[nH]1)COCC2)C(=O)CN1CCC(C(=O)c2ccc(F)cc2)CC1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely do you think it is that the generated drugs will interact with the target protein 'MSLKWMSVFLLMQLSCYFSSGSCGKVLVWPTEYSHWINMKTILEELVQRGHEVIVLTSSASILVNASKSSAIKLEVYPTSLTKNDLEDFFMKMFDRWTYSISKNTFWSYFSQLQELCWEYSDYNIKLCEDAVLNKKLMRKLQESKFDVLLADAVNPCGELLAELLNIPFLYSLRFSVGYTVEKNGGGFLFPPSYVPVVMSELSDQMIFMERIKNMIYMLYFDFWFQAYDLKKWDQFYSEVLGRPTTLFETMGKAEMWLIRTYWDFEFPRPFLPNVDFVGGLHCKPAKPLPKEMEEFVQSSGENGIVVFSLGSMISNMSEESANMIASALAQIPQKVLWRFDGKKPNTLGSNTRLYKWLPQNDLLGHPKTKAFITHGGTNGIYEAIYHGIPMVGIPLFADQHDNIAHMKAKGAALSVDIRTMSSRDLLNALKSVINDPIYKENIMKLSRIHHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHNLTWIQYHSLDVIAFLLACVATMIFMITKCCLFCFRKLAKTGKKKKRD'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSLKWMSVFLLMQLSCYFSSGSCGKVLVWPTEYSHWINMKTILEELVQRGHEVIVLTSSASILVNASKSSAIKLEVYPTSLTKNDLEDFFMKMFDRWTYSISKNTFWSYFSQLQELCWEYSDYNIKLCEDAVLNKKLMRKLQESKFDVLLADAVNPCGELLAELLNIPFLYSLRFSVGYTVEKNGGGFLFPPSYVPVVMSELSDQMIFMERIKNMIYMLYFDFWFQAYDLKKWDQFYSEVLGRPTTLFETMGKAEMWLIRTYWDFEFPRPFLPNVDFVGGLHCKPAKPLPKEMEEFVQSSGENGIVVFSLGSMISNMSEESANMIASALAQIPQKVLWRFDGKKPNTLGSNTRLYKWLPQNDLLGHPKTKAFITHGGTNGIYEAIYHGIPMVGIPLFADQHDNIAHMKAKGAALSVDIRTMSSRDLLNALKSVINDPIYKENIMKLSRIHHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHNLTWIQYHSLDVIAFLLACVATMIFMITKCCLFCFRKLAKTGKKKKRD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSLKWMSVFLLMQLSCYFSSGSCGKVLVWPTEYSHWINMKTILEELVQRGHEVIVLTSSASILVNASKSSAIKLEVYPTSLTKNDLEDFFMKMFDRWTYSISKNTFWSYFSQLQELCWEYSDYNIKLCEDAVLNKKLMRKLQESKFDVLLADAVNPCGELLAELLNIPFLYSLRFSVGYTVEKNGGGFLFPPSYVPVVMSELSDQMIFMERIKNMIYMLYFDFWFQAYDLKKWDQFYSEVLGRPTTLFETMGKAEMWLIRTYWDFEFPRPFLPNVDFVGGLHCKPAKPLPKEMEEFVQSSGENGIVVFSLGSMISNMSEESANMIASALAQIPQKVLWRFDGKKPNTLGSNTRLYKWLPQNDLLGHPKTKAFITHGGTNGIYEAIYHGIPMVGIPLFADQHDNIAHMKAKGAALSVDIRTMSSRDLLNALKSVINDPIYKENIMKLSRIHHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHNLTWIQYHSLDVIAFLLACVATMIFMITKCCLFCFRKLAKTGKKKKRD\", \"result\": [{\"smile\": \"CC(C)(C(=O)C1=CN=CC=C1)C1=CC=CN=C1\", \"value\": 0.0}, {\"smile\": \"CC(C)NC1=C(N=CC=C1)N1CCN(CC1)C(=O)C1=CC2=C(N1)C=CC(NS(C)(=O)=O)=C2\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC(C)(C(=O)C1=CN=CC=C1)C1=CC=CN=C1' nor 'CC(C)NC1=C(N=CC=C1)N1CCN(CC1)C(=O)C1=CC2=C(N1)C=CC(NS(C)(=O)=O)=C2' can interact with the protein 'MSLKWMSVFLLMQLSCYFSSGSCGKVLVWPTEYSHWINMKTILEELVQRGHEVIVLTSSASILVNASKSSAIKLEVYPTSLTKNDLEDFFMKMFDRWTYSISKNTFWSYFSQLQELCWEYSDYNIKLCEDAVLNKKLMRKLQESKFDVLLADAVNPCGELLAELLNIPFLYSLRFSVGYTVEKNGGGFLFPPSYVPVVMSELSDQMIFMERIKNMIYMLYFDFWFQAYDLKKWDQFYSEVLGRPTTLFETMGKAEMWLIRTYWDFEFPRPFLPNVDFVGGLHCKPAKPLPKEMEEFVQSSGENGIVVFSLGSMISNMSEESANMIASALAQIPQKVLWRFDGKKPNTLGSNTRLYKWLPQNDLLGHPKTKAFITHGGTNGIYEAIYHGIPMVGIPLFADQHDNIAHMKAKGAALSVDIRTMSSRDLLNALKSVINDPIYKENIMKLSRIHHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHNLTWIQYHSLDVIAFLLACVATMIFMITKCCLFCFRKLAKTGKKKKRD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your estimation of the optimized drugs' interaction with the target protein 'MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESPTMASVPALQLTPANPPPPEVSNPKKPGRVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQELVVTIPKNSHKKGAKLAALQGSVTSAHQVPAVSSVSHTALYTPPPEIPTTVLNIPHPSVISSPLLKSLHSAGPPLLAVTAAPPAQPLAKKKGVKRKADTTTPTPTAILAPGSPASPPGSLEPKAARLPPMRRESGRPIKPPRKDLPDSQQQHQSSKKGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPDEPLEPGPLPVSTAMPPGLAKSSSESSSEESSSESSSEEEEEEDEEDEEEEESESSDSEEERAHRLAELQEQLRAVHEQLAALSQGPISKPKRKREKKEKKKKRKAEKHRGRAGADEDDKGPRAPRPPQPKKSKKASGSGGGSAALGPSGFGPSGGSGTKLPKKATKTAPPALPTGYDSEEEEESRPMSYDEKRQLSLDINKLPGEKLGRVVHIIQAREPSLRDSNPEEIEIDFETLKPSTLRELERYVLSCLRKKPRKPYTIKKPVGKTKEELALEKKRELEKRLQDVSGQLNSTKKPPKKANEKTESSSAQQVAVSRLSASSSSSDSSSSSSSSSSSDTSDSDSG'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESPTMASVPALQLTPANPPPPEVSNPKKPGRVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQELVVTIPKNSHKKGAKLAALQGSVTSAHQVPAVSSVSHTALYTPPPEIPTTVLNIPHPSVISSPLLKSLHSAGPPLLAVTAAPPAQPLAKKKGVKRKADTTTPTPTAILAPGSPASPPGSLEPKAARLPPMRRESGRPIKPPRKDLPDSQQQHQSSKKGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPDEPLEPGPLPVSTAMPPGLAKSSSESSSEESSSESSSEEEEEEDEEDEEEEESESSDSEEERAHRLAELQEQLRAVHEQLAALSQGPISKPKRKREKKEKKKKRKAEKHRGRAGADEDDKGPRAPRPPQPKKSKKASGSGGGSAALGPSGFGPSGGSGTKLPKKATKTAPPALPTGYDSEEEEESRPMSYDEKRQLSLDINKLPGEKLGRVVHIIQAREPSLRDSNPEEIEIDFETLKPSTLRELERYVLSCLRKKPRKPYTIKKPVGKTKEELALEKKRELEKRLQDVSGQLNSTKKPPKKANEKTESSSAQQVAVSRLSASSSSSDSSSSSSSSSSSDTSDSDSG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESPTMASVPALQLTPANPPPPEVSNPKKPGRVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQELVVTIPKNSHKKGAKLAALQGSVTSAHQVPAVSSVSHTALYTPPPEIPTTVLNIPHPSVISSPLLKSLHSAGPPLLAVTAAPPAQPLAKKKGVKRKADTTTPTPTAILAPGSPASPPGSLEPKAARLPPMRRESGRPIKPPRKDLPDSQQQHQSSKKGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPDEPLEPGPLPVSTAMPPGLAKSSSESSSEESSSESSSEEEEEEDEEDEEEEESESSDSEEERAHRLAELQEQLRAVHEQLAALSQGPISKPKRKREKKEKKKKRKAEKHRGRAGADEDDKGPRAPRPPQPKKSKKASGSGGGSAALGPSGFGPSGGSGTKLPKKATKTAPPALPTGYDSEEEEESRPMSYDEKRQLSLDINKLPGEKLGRVVHIIQAREPSLRDSNPEEIEIDFETLKPSTLRELERYVLSCLRKKPRKPYTIKKPVGKTKEELALEKKRELEKRLQDVSGQLNSTKKPPKKANEKTESSSAQQVAVSRLSASSSSSDSSSSSSSSSSSDTSDSDSG\", \"result\": [{\"smile\": \"COc1ncccc1-n1nc(C)c2c1C(=O)N(c1cc(C)c3nnc(C)n3c1)C2c1ccc(Cl)cc1F\", \"value\": 1.0}, {\"smile\": \"COc1ncccc1-n1nc(C)c2c1C(=O)N(c1cc(C)c3nnc(C)n3c1)C2c1ccc(F)cc1F\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'COc1ncccc1-n1nc(C)c2c1C(=O)N(c1cc(C)c3nnc(C)n3c1)C2c1ccc(Cl)cc1F' and 'COc1ncccc1-n1nc(C)c2c1C(=O)N(c1cc(C)c3nnc(C)n3c1)C2c1ccc(F)cc1F' can interact with the protein 'MLQNVTPHNKLPGEGNAGLLGLGPEAAAPGKRIRKPSLLYEGFESPTMASVPALQLTPANPPPPEVSNPKKPGRVTNQLQYLHKVVMKALWKHQFAWPFRQPVDAVKLGLPDYHKIIKQPMDMGTIKRRLENNYYWAASECMQDFNTMFTNCYIYNKPTDDIVLMAQTLEKIFLQKVASMPQEEQELVVTIPKNSHKKGAKLAALQGSVTSAHQVPAVSSVSHTALYTPPPEIPTTVLNIPHPSVISSPLLKSLHSAGPPLLAVTAAPPAQPLAKKKGVKRKADTTTPTPTAILAPGSPASPPGSLEPKAARLPPMRRESGRPIKPPRKDLPDSQQQHQSSKKGKLSEQLKHCNGILKELLSKKHAAYAWPFYKPVDASALGLHDYHDIIKHPMDLSTVKRKMENRDYRDAQEFAADVRLMFSNCYKYNPPDHDVVAMARKLQDVFEFRYAKMPDEPLEPGPLPVSTAMPPGLAKSSSESSSEESSSESSSEEEEEEDEEDEEEEESESSDSEEERAHRLAELQEQLRAVHEQLAALSQGPISKPKRKREKKEKKKKRKAEKHRGRAGADEDDKGPRAPRPPQPKKSKKASGSGGGSAALGPSGFGPSGGSGTKLPKKATKTAPPALPTGYDSEEEEESRPMSYDEKRQLSLDINKLPGEKLGRVVHIIQAREPSLRDSNPEEIEIDFETLKPSTLRELERYVLSCLRKKPRKPYTIKKPVGKTKEELALEKKRELEKRLQDVSGQLNSTKKPPKKANEKTESSSAQQVAVSRLSASSSSSDSSSSSSSSSSSDTSDSDSG'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction on whether the optimized drugs will interact with the target protein 'MAVESQGGRPLVLGLLLCVLGPVVSHAGKILLIPVDGSHWLSMLGAIQQLQQRGHEIVVLAPDASLYIRDGAFYTLKTYPVPFQREDVKESFVSLGHNVFENDSFLQRVIKTYKKIKKDSAMLLSGCSHLLHNKELMASLAESSFDVMLTDPFLPCSPIVAQYLSLPTVFFLHALPCSLEFEATQCPNPFSYVPRPLSSHSDHMTFLQRVKNMLIAFSQNFLCDVVYSPYATLASEFLQREVTVQDLLSSASVWLFRSDFVKDYPRPIMPNMVFVGGINCLHQNPLSQEFEAYINASGEHGIVVFSLGSMVSEIPEKKAMAIADALGKIPQTVLWRYTGTRPSNLANNTILVKWLPQNDLLGHPMTRAFITHAGSHGVYESICNGVPMVMMPLFGDQMDNAKRMETKGAGVTLNVLEMTSEDLENALKAVINDKSYKENIMRLSSLHKDRPVEPLDLAVFWVEFVMRHKGAPHLRPAAHDLTWYQYHSLDVIGFLLAVVLTVAFITFKCCAYGYRKCLGKKGRVKKAHKSKTH'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAVESQGGRPLVLGLLLCVLGPVVSHAGKILLIPVDGSHWLSMLGAIQQLQQRGHEIVVLAPDASLYIRDGAFYTLKTYPVPFQREDVKESFVSLGHNVFENDSFLQRVIKTYKKIKKDSAMLLSGCSHLLHNKELMASLAESSFDVMLTDPFLPCSPIVAQYLSLPTVFFLHALPCSLEFEATQCPNPFSYVPRPLSSHSDHMTFLQRVKNMLIAFSQNFLCDVVYSPYATLASEFLQREVTVQDLLSSASVWLFRSDFVKDYPRPIMPNMVFVGGINCLHQNPLSQEFEAYINASGEHGIVVFSLGSMVSEIPEKKAMAIADALGKIPQTVLWRYTGTRPSNLANNTILVKWLPQNDLLGHPMTRAFITHAGSHGVYESICNGVPMVMMPLFGDQMDNAKRMETKGAGVTLNVLEMTSEDLENALKAVINDKSYKENIMRLSSLHKDRPVEPLDLAVFWVEFVMRHKGAPHLRPAAHDLTWYQYHSLDVIGFLLAVVLTVAFITFKCCAYGYRKCLGKKGRVKKAHKSKTH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAVESQGGRPLVLGLLLCVLGPVVSHAGKILLIPVDGSHWLSMLGAIQQLQQRGHEIVVLAPDASLYIRDGAFYTLKTYPVPFQREDVKESFVSLGHNVFENDSFLQRVIKTYKKIKKDSAMLLSGCSHLLHNKELMASLAESSFDVMLTDPFLPCSPIVAQYLSLPTVFFLHALPCSLEFEATQCPNPFSYVPRPLSSHSDHMTFLQRVKNMLIAFSQNFLCDVVYSPYATLASEFLQREVTVQDLLSSASVWLFRSDFVKDYPRPIMPNMVFVGGINCLHQNPLSQEFEAYINASGEHGIVVFSLGSMVSEIPEKKAMAIADALGKIPQTVLWRYTGTRPSNLANNTILVKWLPQNDLLGHPMTRAFITHAGSHGVYESICNGVPMVMMPLFGDQMDNAKRMETKGAGVTLNVLEMTSEDLENALKAVINDKSYKENIMRLSSLHKDRPVEPLDLAVFWVEFVMRHKGAPHLRPAAHDLTWYQYHSLDVIGFLLAVVLTVAFITFKCCAYGYRKCLGKKGRVKKAHKSKTH\", \"result\": [{\"smile\": \"COC1=C(C\\C=C(/C)CCC(=O)OCCN2CCOCC2)C(O)=C2C(=O)OCC2=C1C\", \"value\": 1.0}, {\"smile\": \"N[C@@H](CC1=CC=CC(O)=C1)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'COC1=C(C\\C=C(/C)CCC(=O)OCCN2CCOCC2)C(O)=C2C(=O)OCC2=C1C' can interact with the protein, while the second one 'N[C@@H](CC1=CC=CC(O)=C1)C(O)=O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the chance that the generated drugs interacts with the target protein 'MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MGLEALVPLAVIVAIFLLLVDLMHRRQRWAARYPPGPLPLPGLGNLLHVDFQNTPYCFDQLRRRFGDVFSLQLAWTPVVVLNGLAAVREALVTHGEDTADRPPVPITQILGFGPRSQGVFLARYGPAWREQRRFSVSTLRNLGLGKKSLEQWVTEEAACLCAAFANHSGRPFRPNGLLDKAVSNVIASLTCGRRFEYDDPRFLRLLDLAQEGLKEESGFLREVLNAVPVLLHIPALAGKVLRFQKAFLTQLDELLTEHRMTWDPAQPPRDLTEAFLAEMEKAKGNPESSFNDENLRIVVADLFSAGMVTTSTTLAWGLLLMILHPDVQRRVQQEIDDVIGQVRRPEMGDQAHMPYTTAVIHEVQRFGDIVPLGVTHMTSRDIEVQGFRIPKGTTLITNLSSVLKDEAVWEKPFRFHPEHFLDAQGHFVKPEAFLPFSAGRRACLGEPLARMELFLFFTSLLQHFSFSVPTGQPRPSHHGVFAFLVSPSPYELCAVPR\", \"result\": [{\"smile\": \"CSC1=CC2=C(SC3=CC=CC=C3N2CCC2CCCCN2C)C=C1\", \"value\": 1.0}, {\"smile\": \"Cc1cc(C(=O)O)nn1Cc1cc(Br)ccc1OCc1ccccc1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'CSC1=CC2=C(SC3=CC=CC=C3N2CCC2CCCCN2C)C=C1' can interact with the protein, while the second one 'Cc1cc(C(=O)O)nn1Cc1cc(Br)ccc1OCc1ccccc1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction expected between the optimized drugs and the target protein 'MEQPQEEAPEVREEEEKEEVAEAEGAPELNGGPQHALPSSSYTDLSRSSSPPSLLDQLQMGCDGASCGSLNMECRVCGDKASGFHYGVHACEGCKGFFRRTIRMKLEYEKCERSCKIQKKNRNKCQYCRFQKCLALGMSHNAIRFGRMPEAEKRKLVAGLTANEGSQYNPQVADLKAFSKHIYNAYLKNFNMTKKKARSILTGKASHTAPFVIHDIETLWQAEKGLVWKQLVNGLPPYKEISVHVFYRCQCTTVETVRELTEFAKSIPSFSSLFLNDQVTLLKYGVHEAIFAMLASIVNKDGLLVANGSGFVTREFLRSLRKPFSDIIEPKFEFAVKFNALELDDSDLALFIAAIILCGDRPGLMNVPRVEAIQDTILRALEFHLQANHPDAQYLFPKLLQKMADLRQLVTEHAQMMQRIKKTETETSLHPLLQEIYKDMY'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEQPQEEAPEVREEEEKEEVAEAEGAPELNGGPQHALPSSSYTDLSRSSSPPSLLDQLQMGCDGASCGSLNMECRVCGDKASGFHYGVHACEGCKGFFRRTIRMKLEYEKCERSCKIQKKNRNKCQYCRFQKCLALGMSHNAIRFGRMPEAEKRKLVAGLTANEGSQYNPQVADLKAFSKHIYNAYLKNFNMTKKKARSILTGKASHTAPFVIHDIETLWQAEKGLVWKQLVNGLPPYKEISVHVFYRCQCTTVETVRELTEFAKSIPSFSSLFLNDQVTLLKYGVHEAIFAMLASIVNKDGLLVANGSGFVTREFLRSLRKPFSDIIEPKFEFAVKFNALELDDSDLALFIAAIILCGDRPGLMNVPRVEAIQDTILRALEFHLQANHPDAQYLFPKLLQKMADLRQLVTEHAQMMQRIKKTETETSLHPLLQEIYKDMY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEQPQEEAPEVREEEEKEEVAEAEGAPELNGGPQHALPSSSYTDLSRSSSPPSLLDQLQMGCDGASCGSLNMECRVCGDKASGFHYGVHACEGCKGFFRRTIRMKLEYEKCERSCKIQKKNRNKCQYCRFQKCLALGMSHNAIRFGRMPEAEKRKLVAGLTANEGSQYNPQVADLKAFSKHIYNAYLKNFNMTKKKARSILTGKASHTAPFVIHDIETLWQAEKGLVWKQLVNGLPPYKEISVHVFYRCQCTTVETVRELTEFAKSIPSFSSLFLNDQVTLLKYGVHEAIFAMLASIVNKDGLLVANGSGFVTREFLRSLRKPFSDIIEPKFEFAVKFNALELDDSDLALFIAAIILCGDRPGLMNVPRVEAIQDTILRALEFHLQANHPDAQYLFPKLLQKMADLRQLVTEHAQMMQRIKKTETETSLHPLLQEIYKDMY\", \"result\": [{\"smile\": \"COc1ccc2c(-n3c(=O)n(Cc4cccc([C@@]5(C)OC(O)=NC5=O)c4)c4ccccc43)noc2c1\", \"value\": 0.0}, {\"smile\": \"CCCc1cc(C=C2CCCCC2)ccc1OCCCOc1cccc(-c2sc(=O)[nH]c2O)c1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'COc1ccc2c(-n3c(=O)n(Cc4cccc([C@@]5(C)OC(O)=NC5=O)c4)c4ccccc43)noc2c1' nor 'CCCc1cc(C=C2CCCCC2)ccc1OCCCOc1cccc(-c2sc(=O)[nH]c2O)c1' can interact with the protein 'MEQPQEEAPEVREEEEKEEVAEAEGAPELNGGPQHALPSSSYTDLSRSSSPPSLLDQLQMGCDGASCGSLNMECRVCGDKASGFHYGVHACEGCKGFFRRTIRMKLEYEKCERSCKIQKKNRNKCQYCRFQKCLALGMSHNAIRFGRMPEAEKRKLVAGLTANEGSQYNPQVADLKAFSKHIYNAYLKNFNMTKKKARSILTGKASHTAPFVIHDIETLWQAEKGLVWKQLVNGLPPYKEISVHVFYRCQCTTVETVRELTEFAKSIPSFSSLFLNDQVTLLKYGVHEAIFAMLASIVNKDGLLVANGSGFVTREFLRSLRKPFSDIIEPKFEFAVKFNALELDDSDLALFIAAIILCGDRPGLMNVPRVEAIQDTILRALEFHLQANHPDAQYLFPKLLQKMADLRQLVTEHAQMMQRIKKTETETSLHPLLQEIYKDMY'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge whether the generated drugs will interact with the target protein 'MSPLGRFSAVAQSRRQLNNVLRRFASNEAPSSVVVPGVRPIERIRNIGISAHIDSGKTTVTERILYYAGRIDSMHEVRGKDDVGATMDFMDLERQRGITIQSAATYVDWHGTNINIIDTPGHVDFTVEVERALRVLDGAVLVLCGVGGVQSQTFTVNRQLARYNVPFICFVNKMDRNGATPLKALDGLRNKLNHNAALIHLPIGKDSNFNGIVDLVEGHALYYEGEGGLIVRKDEIPKDLRVEAEDRRQELIEHIANVDETLGEMFLNDQTPNVQQIHEAIRRTVVKRAFVPVLSGSALKNKGVQTMINSVVKYLPDPSEVVNRATVKTETTGDEKGIILSPKRNNDKPFVGLAFKLEAGKYGQLTYFRVYQGQLSKGDTVYASRDGRKVRVQRLVRMHAADMEEITTAYAGDICATFGLDCHSGETFSTDQNLAPHCESMHIPEPVISMAIKPVNRKDADNFIKALTRFTKEDPTFRREYNQEAKETIVSGMGELHLEIYAQRMKSEYNCPVELGKPTVAYRECLGSPYKFHFRHKKQTGGQGQFGEIEGVIDPLPSDRNTVVEFSDETFGNNIPKNLFPALKKGLDAIVAEGPLIKSRIAGIHVRIQDGSTHAVDSTEIAMINTMQNMMRESFEKANWLLLEPIMKVEATVPTEFQGNVVTSLTQRNALITTTDSTEGYATVICEAPLSDMFGYTSELRSLTEGKGEFSMEYSRYAPTTLEAQDRVQAEWRQLHGIADPNEKGKKKKK'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSPLGRFSAVAQSRRQLNNVLRRFASNEAPSSVVVPGVRPIERIRNIGISAHIDSGKTTVTERILYYAGRIDSMHEVRGKDDVGATMDFMDLERQRGITIQSAATYVDWHGTNINIIDTPGHVDFTVEVERALRVLDGAVLVLCGVGGVQSQTFTVNRQLARYNVPFICFVNKMDRNGATPLKALDGLRNKLNHNAALIHLPIGKDSNFNGIVDLVEGHALYYEGEGGLIVRKDEIPKDLRVEAEDRRQELIEHIANVDETLGEMFLNDQTPNVQQIHEAIRRTVVKRAFVPVLSGSALKNKGVQTMINSVVKYLPDPSEVVNRATVKTETTGDEKGIILSPKRNNDKPFVGLAFKLEAGKYGQLTYFRVYQGQLSKGDTVYASRDGRKVRVQRLVRMHAADMEEITTAYAGDICATFGLDCHSGETFSTDQNLAPHCESMHIPEPVISMAIKPVNRKDADNFIKALTRFTKEDPTFRREYNQEAKETIVSGMGELHLEIYAQRMKSEYNCPVELGKPTVAYRECLGSPYKFHFRHKKQTGGQGQFGEIEGVIDPLPSDRNTVVEFSDETFGNNIPKNLFPALKKGLDAIVAEGPLIKSRIAGIHVRIQDGSTHAVDSTEIAMINTMQNMMRESFEKANWLLLEPIMKVEATVPTEFQGNVVTSLTQRNALITTTDSTEGYATVICEAPLSDMFGYTSELRSLTEGKGEFSMEYSRYAPTTLEAQDRVQAEWRQLHGIADPNEKGKKKKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSPLGRFSAVAQSRRQLNNVLRRFASNEAPSSVVVPGVRPIERIRNIGISAHIDSGKTTVTERILYYAGRIDSMHEVRGKDDVGATMDFMDLERQRGITIQSAATYVDWHGTNINIIDTPGHVDFTVEVERALRVLDGAVLVLCGVGGVQSQTFTVNRQLARYNVPFICFVNKMDRNGATPLKALDGLRNKLNHNAALIHLPIGKDSNFNGIVDLVEGHALYYEGEGGLIVRKDEIPKDLRVEAEDRRQELIEHIANVDETLGEMFLNDQTPNVQQIHEAIRRTVVKRAFVPVLSGSALKNKGVQTMINSVVKYLPDPSEVVNRATVKTETTGDEKGIILSPKRNNDKPFVGLAFKLEAGKYGQLTYFRVYQGQLSKGDTVYASRDGRKVRVQRLVRMHAADMEEITTAYAGDICATFGLDCHSGETFSTDQNLAPHCESMHIPEPVISMAIKPVNRKDADNFIKALTRFTKEDPTFRREYNQEAKETIVSGMGELHLEIYAQRMKSEYNCPVELGKPTVAYRECLGSPYKFHFRHKKQTGGQGQFGEIEGVIDPLPSDRNTVVEFSDETFGNNIPKNLFPALKKGLDAIVAEGPLIKSRIAGIHVRIQDGSTHAVDSTEIAMINTMQNMMRESFEKANWLLLEPIMKVEATVPTEFQGNVVTSLTQRNALITTTDSTEGYATVICEAPLSDMFGYTSELRSLTEGKGEFSMEYSRYAPTTLEAQDRVQAEWRQLHGIADPNEKGKKKKK\", \"result\": [{\"smile\": \"CCCCCCCCCCCCCCCC(=O)OCC(COC(=O)CCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCC\", \"value\": 0.0}, {\"smile\": \"C(C(Cl)(Cl)Cl)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCCCCCCCCCCCCCCC(=O)OCC(COC(=O)CCCCCCCCCCCCCCC)OC(=O)CCCCCCCCCCCCCCC' nor 'C(C(Cl)(Cl)Cl)O' can interact with the protein 'MSPLGRFSAVAQSRRQLNNVLRRFASNEAPSSVVVPGVRPIERIRNIGISAHIDSGKTTVTERILYYAGRIDSMHEVRGKDDVGATMDFMDLERQRGITIQSAATYVDWHGTNINIIDTPGHVDFTVEVERALRVLDGAVLVLCGVGGVQSQTFTVNRQLARYNVPFICFVNKMDRNGATPLKALDGLRNKLNHNAALIHLPIGKDSNFNGIVDLVEGHALYYEGEGGLIVRKDEIPKDLRVEAEDRRQELIEHIANVDETLGEMFLNDQTPNVQQIHEAIRRTVVKRAFVPVLSGSALKNKGVQTMINSVVKYLPDPSEVVNRATVKTETTGDEKGIILSPKRNNDKPFVGLAFKLEAGKYGQLTYFRVYQGQLSKGDTVYASRDGRKVRVQRLVRMHAADMEEITTAYAGDICATFGLDCHSGETFSTDQNLAPHCESMHIPEPVISMAIKPVNRKDADNFIKALTRFTKEDPTFRREYNQEAKETIVSGMGELHLEIYAQRMKSEYNCPVELGKPTVAYRECLGSPYKFHFRHKKQTGGQGQFGEIEGVIDPLPSDRNTVVEFSDETFGNNIPKNLFPALKKGLDAIVAEGPLIKSRIAGIHVRIQDGSTHAVDSTEIAMINTMQNMMRESFEKANWLLLEPIMKVEATVPTEFQGNVVTSLTQRNALITTTDSTEGYATVICEAPLSDMFGYTSELRSLTEGKGEFSMEYSRYAPTTLEAQDRVQAEWRQLHGIADPNEKGKKKKK'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction between the generated drugs and the target protein 'MAAMRKALPRRLVGLASLRAVSTSSMGTLPKRVKIVEVGPRDGLQNEKNIVSTPVKIKLIDMLSEAGLSVIETTSFVSPKWVPQMGDHTEVLKGIQKFPGINYPVLTPNLKGFEAAVAAGAKEVVIFGAASELFTKKNINCSIEESFQRFDAILKAAQSANISVRGYVSCALGCPYEGKISPAKVAEVTKKFYSMGCYEISLGDTIGVGTPGIMKDMLSAVMQEVPLAALAVHCHDTYGQALANTLMALQMGVSVVDSSVAGLGGCPYAQGASGNLATEDLVYMLEGLGIHTGVNLQKLLEAGNFICQALNRKTSSKVAQATCKL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAAMRKALPRRLVGLASLRAVSTSSMGTLPKRVKIVEVGPRDGLQNEKNIVSTPVKIKLIDMLSEAGLSVIETTSFVSPKWVPQMGDHTEVLKGIQKFPGINYPVLTPNLKGFEAAVAAGAKEVVIFGAASELFTKKNINCSIEESFQRFDAILKAAQSANISVRGYVSCALGCPYEGKISPAKVAEVTKKFYSMGCYEISLGDTIGVGTPGIMKDMLSAVMQEVPLAALAVHCHDTYGQALANTLMALQMGVSVVDSSVAGLGGCPYAQGASGNLATEDLVYMLEGLGIHTGVNLQKLLEAGNFICQALNRKTSSKVAQATCKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAAMRKALPRRLVGLASLRAVSTSSMGTLPKRVKIVEVGPRDGLQNEKNIVSTPVKIKLIDMLSEAGLSVIETTSFVSPKWVPQMGDHTEVLKGIQKFPGINYPVLTPNLKGFEAAVAAGAKEVVIFGAASELFTKKNINCSIEESFQRFDAILKAAQSANISVRGYVSCALGCPYEGKISPAKVAEVTKKFYSMGCYEISLGDTIGVGTPGIMKDMLSAVMQEVPLAALAVHCHDTYGQALANTLMALQMGVSVVDSSVAGLGGCPYAQGASGNLATEDLVYMLEGLGIHTGVNLQKLLEAGNFICQALNRKTSSKVAQATCKL\", \"result\": [{\"smile\": \"CC1=CC(OCC(O)CNC(C)(C)C)=C(Cl)C=C1\", \"value\": 0.0}, {\"smile\": \"CC(C)CC(N(C)C)C1(CCC1)C1=CC=C(Cl)C=C1\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC1=CC(OCC(O)CNC(C)(C)C)=C(Cl)C=C1' nor 'CC(C)CC(N(C)C)C1(CCC1)C1=CC=C(Cl)C=C1' can interact with the protein 'MAAMRKALPRRLVGLASLRAVSTSSMGTLPKRVKIVEVGPRDGLQNEKNIVSTPVKIKLIDMLSEAGLSVIETTSFVSPKWVPQMGDHTEVLKGIQKFPGINYPVLTPNLKGFEAAVAAGAKEVVIFGAASELFTKKNINCSIEESFQRFDAILKAAQSANISVRGYVSCALGCPYEGKISPAKVAEVTKKFYSMGCYEISLGDTIGVGTPGIMKDMLSAVMQEVPLAALAVHCHDTYGQALANTLMALQMGVSVVDSSVAGLGGCPYAQGASGNLATEDLVYMLEGLGIHTGVNLQKLLEAGNFICQALNRKTSSKVAQATCKL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of interaction between the optimized drugs and the target protein 'MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVKDLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVKDLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVKDLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD\", \"result\": [{\"smile\": \"CC(C)c1cc(-n2nnc(C(=O)NCCCCCCC(=O)NO)c2-c2ccc(CN3CCOCC3)cc2)c(O)cc1O\", \"value\": 1.0}, {\"smile\": \"COCCOC1=NC(N)=NC(C)=C1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC(C)c1cc(-n2nnc(C(=O)NCCCCCCC(=O)NO)c2-c2ccc(CN3CCOCC3)cc2)c(O)cc1O' and 'COCCOC1=NC(N)=NC(C)=C1' can interact with the protein 'MPEETQTQDQPMEEEEVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYESLTDPSKLDSGKELHINLIPNKQDRTLTIVDTGIGMTKADLINNLGTIAKSGTKAFMEALQAGADISMIGQFGVGFYSAYLVAEKVTVITKHNDDEQYAWESSAGGSFTVRTDTGEPMGRGTKVILHLKEDQTEYLEERRIKEIVKKHSQFIGYPITLFVEKERDKEVSDDEAEEKEDKEEEKEKEEKESEDKPEIEDVGSDEEEEKKDGDKKKKKKIKEKYIDQEELNKTKPIWTRNPDDITNEEYGEFYKSLTNDWEDHLAVKHFSVEGQLEFRALLFVPRRAPFDLFENRKKKNNIKLYVRRVFIMDNCEELIPEYLNFIRGVVDSEDLPLNISREMLQQSKILKVIRKNLVKKCLELFTELAEDKENYKKFYEQFSKNIKLGIHEDSQNRKKLSELLRYYTSASGDEMVSLKDYCTRMKENQKHIYYITGETKDQVANSAFVERLRKHGLEVIYMIEPIDEYCVQQLKEFEGKTLVSVTKEGLELPEDEEEKKKQEEKKTKFENLCKIMKDILEKKVEKVVVSNRLVTSPCCIVTSTYGWTANMERIMKAQALRDNSTMGYMAAKKHLEINPDHSIIETLRQKAEADKNDKSVKDLVILLYETALLSSGFSLEDPQTHANRIYRMIKLGLGIDEDDPTADDTSAAVTEEMPPLEGDDDTSRMEEVD'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the generated drugs possibly engage with the target protein 'MFSPWKISMFLSVREDSVPTTASFSADMLNVTLQGPTLNGTFAQSKCPQVEWLGWLNTIQPPFLWVLFVLATLENIFVLSVFCLHKSSCTVAEIYLGNLAAADLILACGLPFWAITISNNFDWLFGETLCRVVNAIISMNLYSSICFLMLVSIDRYLALVKTMSMGRMRGVRWAKLYSLVIWGCTLLLSSPMLVFRTMKEYSDEGHNVTACVISYPSLIWEVFTNMLLNVVGFLLPLSVITFCTMQIMQVLRNNEMQKFKEIQTERRATVLVLVVLLLFIICWLPFQISTFLDTLHRLGILSSCQDERIIDVITQIASFMAYSNSCLNPLVYVIVGKRFRKKSWEVYQGVCQKGGCRSEPIQMENSMGTLRTSISVERQIHKLQDWAGSRQ'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MFSPWKISMFLSVREDSVPTTASFSADMLNVTLQGPTLNGTFAQSKCPQVEWLGWLNTIQPPFLWVLFVLATLENIFVLSVFCLHKSSCTVAEIYLGNLAAADLILACGLPFWAITISNNFDWLFGETLCRVVNAIISMNLYSSICFLMLVSIDRYLALVKTMSMGRMRGVRWAKLYSLVIWGCTLLLSSPMLVFRTMKEYSDEGHNVTACVISYPSLIWEVFTNMLLNVVGFLLPLSVITFCTMQIMQVLRNNEMQKFKEIQTERRATVLVLVVLLLFIICWLPFQISTFLDTLHRLGILSSCQDERIIDVITQIASFMAYSNSCLNPLVYVIVGKRFRKKSWEVYQGVCQKGGCRSEPIQMENSMGTLRTSISVERQIHKLQDWAGSRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MFSPWKISMFLSVREDSVPTTASFSADMLNVTLQGPTLNGTFAQSKCPQVEWLGWLNTIQPPFLWVLFVLATLENIFVLSVFCLHKSSCTVAEIYLGNLAAADLILACGLPFWAITISNNFDWLFGETLCRVVNAIISMNLYSSICFLMLVSIDRYLALVKTMSMGRMRGVRWAKLYSLVIWGCTLLLSSPMLVFRTMKEYSDEGHNVTACVISYPSLIWEVFTNMLLNVVGFLLPLSVITFCTMQIMQVLRNNEMQKFKEIQTERRATVLVLVVLLLFIICWLPFQISTFLDTLHRLGILSSCQDERIIDVITQIASFMAYSNSCLNPLVYVIVGKRFRKKSWEVYQGVCQKGGCRSEPIQMENSMGTLRTSISVERQIHKLQDWAGSRQ\", \"result\": [{\"smile\": \"NCC1=C(C=CC=C1)C1=CC=CC=C1\", \"value\": 0.0}, {\"smile\": \"CCCCCCCCCCCCCCCCCC(=O)OC[C@@H](CO[P@](O)(=O)OCCN)OC(=O)CCCCCCCCCCCCCCCCC\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'NCC1=C(C=CC=C1)C1=CC=CC=C1' nor 'CCCCCCCCCCCCCCCCCC(=O)OC[C@@H](CO[P@](O)(=O)OCCN)OC(=O)CCCCCCCCCCCCCCCCC' can interact with the protein 'MFSPWKISMFLSVREDSVPTTASFSADMLNVTLQGPTLNGTFAQSKCPQVEWLGWLNTIQPPFLWVLFVLATLENIFVLSVFCLHKSSCTVAEIYLGNLAAADLILACGLPFWAITISNNFDWLFGETLCRVVNAIISMNLYSSICFLMLVSIDRYLALVKTMSMGRMRGVRWAKLYSLVIWGCTLLLSSPMLVFRTMKEYSDEGHNVTACVISYPSLIWEVFTNMLLNVVGFLLPLSVITFCTMQIMQVLRNNEMQKFKEIQTERRATVLVLVVLLLFIICWLPFQISTFLDTLHRLGILSSCQDERIIDVITQIASFMAYSNSCLNPLVYVIVGKRFRKKSWEVYQGVCQKGGCRSEPIQMENSMGTLRTSISVERQIHKLQDWAGSRQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you think there will be an interaction of the optimized drugs with the target protein 'MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV\", \"result\": [{\"smile\": \"COc1cc(-c2[nH]c3ccc(C4CCN(C(=O)CCN5CCCC5=O)CC4)cc3c2C(C)C)cn2ncnc12\", \"value\": 1.0}, {\"smile\": \"Cc1c(-c2[nH]c3ccc(C4CCN(C(=O)OCCN5CCOCC5)CC4)cc3c2C(C)C)cn2ncnc2c1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'COc1cc(-c2[nH]c3ccc(C4CCN(C(=O)CCN5CCCC5=O)CC4)cc3c2C(C)C)cn2ncnc12' and 'Cc1c(-c2[nH]c3ccc(C4CCN(C(=O)OCCN5CCOCC5)CC4)cc3c2C(C)C)cn2ncnc2c1C' can interact with the protein 'MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you infer whether the optimized drugs is likely to interact with the target protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\", \"result\": [{\"smile\": \"CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5\", \"value\": 0.0}, {\"smile\": \"CC1CCN(CC1N(C)C2=NC=NC3=C2C=CN3)C(=O)CC#N\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC1=CC2=C(N1)C=CC(=C2F)OC3=NC=NC4=CC(=C(C=C43)OC)OCCCN5CCCC5' nor 'CC1CCN(CC1N(C)C2=NC=NC3=C2C=CN3)C(=O)CC#N' can interact with the protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the probability that the optimized drugs will have an interaction with the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\", \"result\": [{\"smile\": \"COc1cnc2c(NCc3nnc4ccc(-c5cc(F)cc(F)c5)nn34)ccnc2c1\", \"value\": 0.0}, {\"smile\": \"CC1=C(C)C2=C(CCC(C)(COC3=CC=C(CC4SC(=O)NC4=O)C=C3)O2)C(C)=C1O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'CC1=C(C)C2=C(CCC(C)(COC3=CC=C(CC4SC(=O)NC4=O)C=C3)O2)C(C)=C1O' can interact with the protein, while the first one 'COc1cnc2c(NCc3nnc4ccc(-c5cc(F)cc(F)c5)nn34)ccnc2c1' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the generated drugs interacting with the target protein 'MSAEGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFSDGQEARPEEIGWLNGYNETTGERGDFPGTYVEYIGRKKISPPTPKPRPPRPLPVAPGSSKTEADVEQQALTLPDLAEQFAPPDIAPPLLIKLVEAIEKKGLECSTLYRTQSSSNLAELRQLLDCDTPSVDLEMIDVHVLADAFKRYLLDLPNPVIPAAVYSEMISLAPEVQSSEEYIQLLKKLIRSPSIPHQYWLTLQYLLKHFFKLSQTSSKNLLNARVLSEIFSPMLFRFSAASSDNTENLIKVIEILISTEWNERQPAPALPPKPPKPTTVANNGMNNNMSLQDAEWYWGDISREEVNEKLRDTADGTFLVRDASTKMHGDYTLTLRKGGNNKLIKIFHRDGKYGFSDPLTFSSVVELINHYRNESLAQYNPKLDVKLLYPVSKYQQDQVVKEDNIEAVGKKLHEYNTQFQEKSREYDRLYEEYTRTSQEIQMKRTAIEAFNETIKIFEEQCQTQERYSKEYIEKFKREGNEKEIQRIMHNYDKLKSRISEIIDSRRRLEEDLKKQAAEYREIDKRMNSIKPDLIQLRKTRDQYLMWLTQKGVRQKKLNEWLGNENTEDQYSLVEDDEDLPHHDEKTWNVGSSNRNKAENLLRGKRDGTFLVRESSKQGCYACSVVVDGEVKHCVINKTATGYGFAEPYNLYSSLKELVLHYQHTSLVQHNDSLNVTLAYPVYAQQRR'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSAEGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFSDGQEARPEEIGWLNGYNETTGERGDFPGTYVEYIGRKKISPPTPKPRPPRPLPVAPGSSKTEADVEQQALTLPDLAEQFAPPDIAPPLLIKLVEAIEKKGLECSTLYRTQSSSNLAELRQLLDCDTPSVDLEMIDVHVLADAFKRYLLDLPNPVIPAAVYSEMISLAPEVQSSEEYIQLLKKLIRSPSIPHQYWLTLQYLLKHFFKLSQTSSKNLLNARVLSEIFSPMLFRFSAASSDNTENLIKVIEILISTEWNERQPAPALPPKPPKPTTVANNGMNNNMSLQDAEWYWGDISREEVNEKLRDTADGTFLVRDASTKMHGDYTLTLRKGGNNKLIKIFHRDGKYGFSDPLTFSSVVELINHYRNESLAQYNPKLDVKLLYPVSKYQQDQVVKEDNIEAVGKKLHEYNTQFQEKSREYDRLYEEYTRTSQEIQMKRTAIEAFNETIKIFEEQCQTQERYSKEYIEKFKREGNEKEIQRIMHNYDKLKSRISEIIDSRRRLEEDLKKQAAEYREIDKRMNSIKPDLIQLRKTRDQYLMWLTQKGVRQKKLNEWLGNENTEDQYSLVEDDEDLPHHDEKTWNVGSSNRNKAENLLRGKRDGTFLVRESSKQGCYACSVVVDGEVKHCVINKTATGYGFAEPYNLYSSLKELVLHYQHTSLVQHNDSLNVTLAYPVYAQQRR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSAEGYQYRALYDYKKEREEDIDLHLGDILTVNKGSLVALGFSDGQEARPEEIGWLNGYNETTGERGDFPGTYVEYIGRKKISPPTPKPRPPRPLPVAPGSSKTEADVEQQALTLPDLAEQFAPPDIAPPLLIKLVEAIEKKGLECSTLYRTQSSSNLAELRQLLDCDTPSVDLEMIDVHVLADAFKRYLLDLPNPVIPAAVYSEMISLAPEVQSSEEYIQLLKKLIRSPSIPHQYWLTLQYLLKHFFKLSQTSSKNLLNARVLSEIFSPMLFRFSAASSDNTENLIKVIEILISTEWNERQPAPALPPKPPKPTTVANNGMNNNMSLQDAEWYWGDISREEVNEKLRDTADGTFLVRDASTKMHGDYTLTLRKGGNNKLIKIFHRDGKYGFSDPLTFSSVVELINHYRNESLAQYNPKLDVKLLYPVSKYQQDQVVKEDNIEAVGKKLHEYNTQFQEKSREYDRLYEEYTRTSQEIQMKRTAIEAFNETIKIFEEQCQTQERYSKEYIEKFKREGNEKEIQRIMHNYDKLKSRISEIIDSRRRLEEDLKKQAAEYREIDKRMNSIKPDLIQLRKTRDQYLMWLTQKGVRQKKLNEWLGNENTEDQYSLVEDDEDLPHHDEKTWNVGSSNRNKAENLLRGKRDGTFLVRESSKQGCYACSVVVDGEVKHCVINKTATGYGFAEPYNLYSSLKELVLHYQHTSLVQHNDSLNVTLAYPVYAQQRR\", \"result\": [{\"smile\": \"Nc1nc2cc(-c3ccc4ncc(C(=O)N5CCOCC5)n4c3)ccc2o1\", \"value\": 1.0}, {\"smile\": \"CCN1CC(C)(C)Oc2nc(N3CCOC[C@@H]3C)nc(-c3ccc(NC(=O)Nc4cc(OC)ccn4)cc3)c2C1=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The first drug 'Nc1nc2cc(-c3ccc4ncc(C(=O)N5CCOCC5)n4c3)ccc2o1' can interact with the protein, while the second one 'CCN1CC(C)(C)Oc2nc(N3CCOC[C@@H]3C)nc(-c3ccc(NC(=O)Nc4cc(OC)ccn4)cc3)c2C1=O' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the generated drugs and target protein 'MTCVSFHTPPLLRRYSSILQNLFFFFLLHIPISSTVKQLTVPGQIVLGGLFPIHEAGRNASHQCGKIKADQGVQRMVAMLFALEKVNRDRQLLPQASLGAQILDTCSVDSYALEQTLEFIKSVMSNGDGVTCADGSTGSYTRQPVVAVVGAAGSQVSVMVASMLQLFKIPQVSYSSTGAELSEKPRFAFFSRVVPPDNLQAQVMARVIGALEWTYVHAIADTGSYGERGMDSFRAAAAENGICIDGDVQKISRRWTEKNFRDLLIRMHRTRKARGVVMFVDEDNLKRLLKTLDLLVAEGHTELDRHFWFVASDSWGIKQSVVRGLEHRTYGAITIAPMVREETGYLEYFRSLSPKGFVFLEEFFEYLGCSATVDVKTFGDCFDMVNITLKQESYVPFVVDTVKIIAKAISMYIEDDCGKIPFHKCTLAQSGFRGERLQRYYRNMSLIKNEPALIDANGDGIGRYDVFQLDINGVYQKVGKWRSTDDFLSVEVEKIRHAFKTAHGERPMSVCSTDCPRGHYRAYQDQTCCWACIPCDTSTSIHNETSCEECAVGMVPDRTLHFCVPIPPVSMQWDTTWSLIPAAFSTLGIASTIFVVSVFLKFSNTPVIMASGRELCYCMMSGIGMCYTLTFFLVSQPTVITCSMTRILMGLSMSAIYAAIITKTNRLARVFKPDSAQRPRFITPKAQVGICMGIVSVQLIGTFVWILFDPPGTMIVFPTRTEAVLTCKATTSHLLISLLYNILLIVACTVYAFKTRKIPENFNETRHIGFTMYSTCILWLAFGPIYFATQSDFRIQITSLCMCISLSGTVALICFFAPKVYIVLFQPYKNVRTRQSAVGRLVNQQMRFMSQLTYNPDGCNSYQPMSSNQSYKPSISAEESSHTSNAQVQQPTRAIPPHVIEQLAASLPISDKNDLVKKLSLQDKFFNNNNNNENSEEIRRRRPSSVHTVGAVTNGSSVAHIPPRSYTDEPKSSMIRQDTAKSRTSLAESHQVDLILEEIAADTNSTFL' interact, based on your prediction? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MTCVSFHTPPLLRRYSSILQNLFFFFLLHIPISSTVKQLTVPGQIVLGGLFPIHEAGRNASHQCGKIKADQGVQRMVAMLFALEKVNRDRQLLPQASLGAQILDTCSVDSYALEQTLEFIKSVMSNGDGVTCADGSTGSYTRQPVVAVVGAAGSQVSVMVASMLQLFKIPQVSYSSTGAELSEKPRFAFFSRVVPPDNLQAQVMARVIGALEWTYVHAIADTGSYGERGMDSFRAAAAENGICIDGDVQKISRRWTEKNFRDLLIRMHRTRKARGVVMFVDEDNLKRLLKTLDLLVAEGHTELDRHFWFVASDSWGIKQSVVRGLEHRTYGAITIAPMVREETGYLEYFRSLSPKGFVFLEEFFEYLGCSATVDVKTFGDCFDMVNITLKQESYVPFVVDTVKIIAKAISMYIEDDCGKIPFHKCTLAQSGFRGERLQRYYRNMSLIKNEPALIDANGDGIGRYDVFQLDINGVYQKVGKWRSTDDFLSVEVEKIRHAFKTAHGERPMSVCSTDCPRGHYRAYQDQTCCWACIPCDTSTSIHNETSCEECAVGMVPDRTLHFCVPIPPVSMQWDTTWSLIPAAFSTLGIASTIFVVSVFLKFSNTPVIMASGRELCYCMMSGIGMCYTLTFFLVSQPTVITCSMTRILMGLSMSAIYAAIITKTNRLARVFKPDSAQRPRFITPKAQVGICMGIVSVQLIGTFVWILFDPPGTMIVFPTRTEAVLTCKATTSHLLISLLYNILLIVACTVYAFKTRKIPENFNETRHIGFTMYSTCILWLAFGPIYFATQSDFRIQITSLCMCISLSGTVALICFFAPKVYIVLFQPYKNVRTRQSAVGRLVNQQMRFMSQLTYNPDGCNSYQPMSSNQSYKPSISAEESSHTSNAQVQQPTRAIPPHVIEQLAASLPISDKNDLVKKLSLQDKFFNNNNNNENSEEIRRRRPSSVHTVGAVTNGSSVAHIPPRSYTDEPKSSMIRQDTAKSRTSLAESHQVDLILEEIAADTNSTFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MTCVSFHTPPLLRRYSSILQNLFFFFLLHIPISSTVKQLTVPGQIVLGGLFPIHEAGRNASHQCGKIKADQGVQRMVAMLFALEKVNRDRQLLPQASLGAQILDTCSVDSYALEQTLEFIKSVMSNGDGVTCADGSTGSYTRQPVVAVVGAAGSQVSVMVASMLQLFKIPQVSYSSTGAELSEKPRFAFFSRVVPPDNLQAQVMARVIGALEWTYVHAIADTGSYGERGMDSFRAAAAENGICIDGDVQKISRRWTEKNFRDLLIRMHRTRKARGVVMFVDEDNLKRLLKTLDLLVAEGHTELDRHFWFVASDSWGIKQSVVRGLEHRTYGAITIAPMVREETGYLEYFRSLSPKGFVFLEEFFEYLGCSATVDVKTFGDCFDMVNITLKQESYVPFVVDTVKIIAKAISMYIEDDCGKIPFHKCTLAQSGFRGERLQRYYRNMSLIKNEPALIDANGDGIGRYDVFQLDINGVYQKVGKWRSTDDFLSVEVEKIRHAFKTAHGERPMSVCSTDCPRGHYRAYQDQTCCWACIPCDTSTSIHNETSCEECAVGMVPDRTLHFCVPIPPVSMQWDTTWSLIPAAFSTLGIASTIFVVSVFLKFSNTPVIMASGRELCYCMMSGIGMCYTLTFFLVSQPTVITCSMTRILMGLSMSAIYAAIITKTNRLARVFKPDSAQRPRFITPKAQVGICMGIVSVQLIGTFVWILFDPPGTMIVFPTRTEAVLTCKATTSHLLISLLYNILLIVACTVYAFKTRKIPENFNETRHIGFTMYSTCILWLAFGPIYFATQSDFRIQITSLCMCISLSGTVALICFFAPKVYIVLFQPYKNVRTRQSAVGRLVNQQMRFMSQLTYNPDGCNSYQPMSSNQSYKPSISAEESSHTSNAQVQQPTRAIPPHVIEQLAASLPISDKNDLVKKLSLQDKFFNNNNNNENSEEIRRRRPSSVHTVGAVTNGSSVAHIPPRSYTDEPKSSMIRQDTAKSRTSLAESHQVDLILEEIAADTNSTFL\", \"result\": [{\"smile\": \"CC(=O)NC(CCSC)C(=O)O\", \"value\": 0.0}, {\"smile\": \"CC(CCO)O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CC(=O)NC(CCSC)C(=O)O' nor 'CC(CCO)O' can interact with the protein 'MTCVSFHTPPLLRRYSSILQNLFFFFLLHIPISSTVKQLTVPGQIVLGGLFPIHEAGRNASHQCGKIKADQGVQRMVAMLFALEKVNRDRQLLPQASLGAQILDTCSVDSYALEQTLEFIKSVMSNGDGVTCADGSTGSYTRQPVVAVVGAAGSQVSVMVASMLQLFKIPQVSYSSTGAELSEKPRFAFFSRVVPPDNLQAQVMARVIGALEWTYVHAIADTGSYGERGMDSFRAAAAENGICIDGDVQKISRRWTEKNFRDLLIRMHRTRKARGVVMFVDEDNLKRLLKTLDLLVAEGHTELDRHFWFVASDSWGIKQSVVRGLEHRTYGAITIAPMVREETGYLEYFRSLSPKGFVFLEEFFEYLGCSATVDVKTFGDCFDMVNITLKQESYVPFVVDTVKIIAKAISMYIEDDCGKIPFHKCTLAQSGFRGERLQRYYRNMSLIKNEPALIDANGDGIGRYDVFQLDINGVYQKVGKWRSTDDFLSVEVEKIRHAFKTAHGERPMSVCSTDCPRGHYRAYQDQTCCWACIPCDTSTSIHNETSCEECAVGMVPDRTLHFCVPIPPVSMQWDTTWSLIPAAFSTLGIASTIFVVSVFLKFSNTPVIMASGRELCYCMMSGIGMCYTLTFFLVSQPTVITCSMTRILMGLSMSAIYAAIITKTNRLARVFKPDSAQRPRFITPKAQVGICMGIVSVQLIGTFVWILFDPPGTMIVFPTRTEAVLTCKATTSHLLISLLYNILLIVACTVYAFKTRKIPENFNETRHIGFTMYSTCILWLAFGPIYFATQSDFRIQITSLCMCISLSGTVALICFFAPKVYIVLFQPYKNVRTRQSAVGRLVNQQMRFMSQLTYNPDGCNSYQPMSSNQSYKPSISAEESSHTSNAQVQQPTRAIPPHVIEQLAASLPISDKNDLVKKLSLQDKFFNNNNNNENSEEIRRRRPSSVHTVGAVTNGSSVAHIPPRSYTDEPKSSMIRQDTAKSRTSLAESHQVDLILEEIAADTNSTFL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the likelihood of interaction between the generated drugs and the target protein 'MSVKWTSVILLIQLSFCFSSGNCGKVLVWAAEYSHWMNIKTILDELIQRGHEVTVLASSASILFDPNNSSALKIEIYPTSLTKTELENFIMQQIKRWSDLPKDTFWLYFSQVQEIMSIFGDITRKFCKDVVSNKKFMKKVQESRFDVIFADAIFPCSELLAELFNIPFVYSLSFSPGYTFEKHSGGFIFPPSYVPVVMSELTDQMTFMERVKNMIYVLYFDFWFEIFDMKKWDQFYSEVLGRPTTLSETMGKADVWLIRNSWNFQFPYPLLPNVDFVGGLHCKPAKPLPKEMEDFVQSSGENGVVVFSLGSMVSNMTEERANVIASALAQIPQKVLWRFDGNKPDTLGLNTRLYKWIPQNDLLGHPKTRAFITHGGANGIYEAIYHGIPMVGIPLFADQPDNIAHMKARGAAVRVDFNTMSSTDLLNALKRVINDPSYKENVMKLSRIQHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHDLTWFQYHSLDVIGFLLVCVATVIFIVTKCCLFCFWKFARKAKKGKND'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSVKWTSVILLIQLSFCFSSGNCGKVLVWAAEYSHWMNIKTILDELIQRGHEVTVLASSASILFDPNNSSALKIEIYPTSLTKTELENFIMQQIKRWSDLPKDTFWLYFSQVQEIMSIFGDITRKFCKDVVSNKKFMKKVQESRFDVIFADAIFPCSELLAELFNIPFVYSLSFSPGYTFEKHSGGFIFPPSYVPVVMSELTDQMTFMERVKNMIYVLYFDFWFEIFDMKKWDQFYSEVLGRPTTLSETMGKADVWLIRNSWNFQFPYPLLPNVDFVGGLHCKPAKPLPKEMEDFVQSSGENGVVVFSLGSMVSNMTEERANVIASALAQIPQKVLWRFDGNKPDTLGLNTRLYKWIPQNDLLGHPKTRAFITHGGANGIYEAIYHGIPMVGIPLFADQPDNIAHMKARGAAVRVDFNTMSSTDLLNALKRVINDPSYKENVMKLSRIQHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHDLTWFQYHSLDVIGFLLVCVATVIFIVTKCCLFCFWKFARKAKKGKND\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MSVKWTSVILLIQLSFCFSSGNCGKVLVWAAEYSHWMNIKTILDELIQRGHEVTVLASSASILFDPNNSSALKIEIYPTSLTKTELENFIMQQIKRWSDLPKDTFWLYFSQVQEIMSIFGDITRKFCKDVVSNKKFMKKVQESRFDVIFADAIFPCSELLAELFNIPFVYSLSFSPGYTFEKHSGGFIFPPSYVPVVMSELTDQMTFMERVKNMIYVLYFDFWFEIFDMKKWDQFYSEVLGRPTTLSETMGKADVWLIRNSWNFQFPYPLLPNVDFVGGLHCKPAKPLPKEMEDFVQSSGENGVVVFSLGSMVSNMTEERANVIASALAQIPQKVLWRFDGNKPDTLGLNTRLYKWIPQNDLLGHPKTRAFITHGGANGIYEAIYHGIPMVGIPLFADQPDNIAHMKARGAAVRVDFNTMSSTDLLNALKRVINDPSYKENVMKLSRIQHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHDLTWFQYHSLDVIGFLLVCVATVIFIVTKCCLFCFWKFARKAKKGKND\", \"result\": [{\"smile\": \"CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1\", \"value\": 1.0}, {\"smile\": \"CC1=CN([C@H]2C[C@H](N=[N+]=[N-])[C@@H](CO)O2)C(=O)NC1=O\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1' and 'CC1=CN([C@H]2C[C@H](N=[N+]=[N-])[C@@H](CO)O2)C(=O)NC1=O' can interact with the protein 'MSVKWTSVILLIQLSFCFSSGNCGKVLVWAAEYSHWMNIKTILDELIQRGHEVTVLASSASILFDPNNSSALKIEIYPTSLTKTELENFIMQQIKRWSDLPKDTFWLYFSQVQEIMSIFGDITRKFCKDVVSNKKFMKKVQESRFDVIFADAIFPCSELLAELFNIPFVYSLSFSPGYTFEKHSGGFIFPPSYVPVVMSELTDQMTFMERVKNMIYVLYFDFWFEIFDMKKWDQFYSEVLGRPTTLSETMGKADVWLIRNSWNFQFPYPLLPNVDFVGGLHCKPAKPLPKEMEDFVQSSGENGVVVFSLGSMVSNMTEERANVIASALAQIPQKVLWRFDGNKPDTLGLNTRLYKWIPQNDLLGHPKTRAFITHGGANGIYEAIYHGIPMVGIPLFADQPDNIAHMKARGAAVRVDFNTMSSTDLLNALKRVINDPSYKENVMKLSRIQHDQPVKPLDRAVFWIEFVMRHKGAKHLRVAAHDLTWFQYHSLDVIGFLLVCVATVIFIVTKCCLFCFWKFARKAKKGKND'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the optimized drugs' potential to interact with the target protein 'MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV\", \"result\": [{\"smile\": \"Cc1ccc(C(=O)NC2CC2)cc1-c1ccc(C(=O)NCC2CC2)cc1\", \"value\": 0.0}, {\"smile\": \"CN(C)CC1CCN2C=C(C3=CC=CC=C32)C4=C(C5=CN(CCO1)C6=CC=CC=C65)C(=O)NC4=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cc1ccc(C(=O)NC2CC2)cc1-c1ccc(C(=O)NCC2CC2)cc1' nor 'CN(C)CC1CCN2C=C(C3=CC=CC=C32)C4=C(C5=CN(CCO1)C6=CC=CC=C65)C(=O)NC4=O' can interact with the protein 'MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you project whether the optimized drugs will interact with the target protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS\", \"result\": [{\"smile\": \"CCN(CC)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1\", \"value\": 0.0}, {\"smile\": \"CC(C)[C@@H]1C(=O)N(S(C)(=O)=O)[C@@H]2CCN(C(=O)C3CN(CC(C)(C)C)C3)[C@@H]12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'CCN(CC)Cc1ccc(C(=O)Nc2ccc(Cl)cc2C(=O)Nc2ccc(Cl)cn2)cc1' nor 'CC(C)[C@@H]1C(=O)N(S(C)(=O)=O)[C@@H]2CCN(C(=O)C3CN(CC(C)(C)C)C3)[C@@H]12' can interact with the protein 'MNPLLILTFVAAALAAPFDDDDKIVGGYNCEENSVPYQVSLNSGYHFCGGSLINEQWVVSAGHCYKSRIQVRLGEHNIEVLEGNEQFINAAKIIRHPQYDRKTLNNDIMLIKLSSRAVINARVSTISLPTAPPATGTKCLISGWGNTASSGADYPDELQCLDAPVLSQAKCEASYPGKITSNMFCVGFLEGGKDSCQGDSGGPVVCNGQLQGVVSWGDGCAQKNKPGVYTKVYNYVKWIKNTIAANS'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your forecast for the optimized drugs' interaction with the target protein 'MEPFVVLVLCLSFMLLFSLWRQSCRRRKLPPGPTPLPIIGNMLQIDVKDICKSFTNFSKVYGPVFTVYFGMNPIVVFHGYEAVKEALIDNGEEFSGRGNSPISQRITKGLGIISSNGKRWKEIRRFSLTTLRNFGMGKRSIEDRVQEEAHCLVEELRKTKASPCDPTFILGCAPCNVICSVVFQKRFDYKDQNFLTLMKRFNENFRILNSPWIQVCNNFPLLIDCFPGTHNKVLKNVALTRSYIREKVKEHQASLDVNNPRDFIDCFLIKMEQEKDNQKSEFNIENLVGTVADLFVAGTETTSTTLRYGLLLLLKHPEVTAKVQEEIDHVIGRHRSPCMQDRSHMPYTDAVVHEIQRYSDLVPTGVPHAVTTDTKFRNYLIPKGTTIMALLTSVLHDDKEFPNPNIFDPGHFLDKNGNFKKSDYFMPFSAGKRICAGEGLARMELFLFLTTILQNFNLKSVDDLKNLNTTAVTKGIVSLPPSYQICFIPV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEPFVVLVLCLSFMLLFSLWRQSCRRRKLPPGPTPLPIIGNMLQIDVKDICKSFTNFSKVYGPVFTVYFGMNPIVVFHGYEAVKEALIDNGEEFSGRGNSPISQRITKGLGIISSNGKRWKEIRRFSLTTLRNFGMGKRSIEDRVQEEAHCLVEELRKTKASPCDPTFILGCAPCNVICSVVFQKRFDYKDQNFLTLMKRFNENFRILNSPWIQVCNNFPLLIDCFPGTHNKVLKNVALTRSYIREKVKEHQASLDVNNPRDFIDCFLIKMEQEKDNQKSEFNIENLVGTVADLFVAGTETTSTTLRYGLLLLLKHPEVTAKVQEEIDHVIGRHRSPCMQDRSHMPYTDAVVHEIQRYSDLVPTGVPHAVTTDTKFRNYLIPKGTTIMALLTSVLHDDKEFPNPNIFDPGHFLDKNGNFKKSDYFMPFSAGKRICAGEGLARMELFLFLTTILQNFNLKSVDDLKNLNTTAVTKGIVSLPPSYQICFIPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MEPFVVLVLCLSFMLLFSLWRQSCRRRKLPPGPTPLPIIGNMLQIDVKDICKSFTNFSKVYGPVFTVYFGMNPIVVFHGYEAVKEALIDNGEEFSGRGNSPISQRITKGLGIISSNGKRWKEIRRFSLTTLRNFGMGKRSIEDRVQEEAHCLVEELRKTKASPCDPTFILGCAPCNVICSVVFQKRFDYKDQNFLTLMKRFNENFRILNSPWIQVCNNFPLLIDCFPGTHNKVLKNVALTRSYIREKVKEHQASLDVNNPRDFIDCFLIKMEQEKDNQKSEFNIENLVGTVADLFVAGTETTSTTLRYGLLLLLKHPEVTAKVQEEIDHVIGRHRSPCMQDRSHMPYTDAVVHEIQRYSDLVPTGVPHAVTTDTKFRNYLIPKGTTIMALLTSVLHDDKEFPNPNIFDPGHFLDKNGNFKKSDYFMPFSAGKRICAGEGLARMELFLFLTTILQNFNLKSVDDLKNLNTTAVTKGIVSLPPSYQICFIPV\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1NC(=O)[C@H]1CCCN1.Cl\", \"value\": 0.0}, {\"smile\": \"C[C@H](CC1=CC=CC=C1)N(C)CC#C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "The second drug 'C[C@H](CC1=CC=CC=C1)N(C)CC#C' can interact with the protein, while the first one 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1NC(=O)[C@H]1CCCN1.Cl' can't."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a possibility of the generated drugs interacting with the target protein 'MAADQRPKADTLALRQRLISSSCRLFFPEDPVKIVRAQGQYMYDEQGAEYIDCISNVAHVGHCHPLVVQAAHEQNQVLNTNSRYLHDNIVDYAQRLSETLPEQLCVFYFLNSGSEANDLALRLARHYTGHQDVVVLDHAYHGHLSSLIDISPYKFRNLDGQKEWVHVAPLPDTYRGPYREDHPNPAMAYANEVKRVVSSAQEKGRKIAAFFAESLPSVGGQIIPPAGYFSQVAEHIRKAGGVFVADEIQVGFGRVGKHFWAFQLQGKDFVPDIVTMGKSIGNGHPVACVAATQPVARAFEATGVEYFNTFGGSPVSCAVGLAVLNVLEKEQLQDHATSVGSFLMQLLGQQKIKHPIVGDVRGVGLFIGVDLIKDEATRTPATEEAAYLVSRLKENYVLLSTDGPGRNILKFKPPMCFSLDNARQVVAKLDAILTDMEEKVRSCETLRLQP'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAADQRPKADTLALRQRLISSSCRLFFPEDPVKIVRAQGQYMYDEQGAEYIDCISNVAHVGHCHPLVVQAAHEQNQVLNTNSRYLHDNIVDYAQRLSETLPEQLCVFYFLNSGSEANDLALRLARHYTGHQDVVVLDHAYHGHLSSLIDISPYKFRNLDGQKEWVHVAPLPDTYRGPYREDHPNPAMAYANEVKRVVSSAQEKGRKIAAFFAESLPSVGGQIIPPAGYFSQVAEHIRKAGGVFVADEIQVGFGRVGKHFWAFQLQGKDFVPDIVTMGKSIGNGHPVACVAATQPVARAFEATGVEYFNTFGGSPVSCAVGLAVLNVLEKEQLQDHATSVGSFLMQLLGQQKIKHPIVGDVRGVGLFIGVDLIKDEATRTPATEEAAYLVSRLKENYVLLSTDGPGRNILKFKPPMCFSLDNARQVVAKLDAILTDMEEKVRSCETLRLQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MAADQRPKADTLALRQRLISSSCRLFFPEDPVKIVRAQGQYMYDEQGAEYIDCISNVAHVGHCHPLVVQAAHEQNQVLNTNSRYLHDNIVDYAQRLSETLPEQLCVFYFLNSGSEANDLALRLARHYTGHQDVVVLDHAYHGHLSSLIDISPYKFRNLDGQKEWVHVAPLPDTYRGPYREDHPNPAMAYANEVKRVVSSAQEKGRKIAAFFAESLPSVGGQIIPPAGYFSQVAEHIRKAGGVFVADEIQVGFGRVGKHFWAFQLQGKDFVPDIVTMGKSIGNGHPVACVAATQPVARAFEATGVEYFNTFGGSPVSCAVGLAVLNVLEKEQLQDHATSVGSFLMQLLGQQKIKHPIVGDVRGVGLFIGVDLIKDEATRTPATEEAAYLVSRLKENYVLLSTDGPGRNILKFKPPMCFSLDNARQVVAKLDAILTDMEEKVRSCETLRLQP\", \"result\": [{\"smile\": \"OB(O)CCC1=CC=CC=C1\", \"value\": 0.0}, {\"smile\": \"NCCCC[C@H](N)C(O)=O\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'OB(O)CCC1=CC=CC=C1' nor 'NCCCC[C@H](N)C(O)=O' can interact with the protein 'MAADQRPKADTLALRQRLISSSCRLFFPEDPVKIVRAQGQYMYDEQGAEYIDCISNVAHVGHCHPLVVQAAHEQNQVLNTNSRYLHDNIVDYAQRLSETLPEQLCVFYFLNSGSEANDLALRLARHYTGHQDVVVLDHAYHGHLSSLIDISPYKFRNLDGQKEWVHVAPLPDTYRGPYREDHPNPAMAYANEVKRVVSSAQEKGRKIAAFFAESLPSVGGQIIPPAGYFSQVAEHIRKAGGVFVADEIQVGFGRVGKHFWAFQLQGKDFVPDIVTMGKSIGNGHPVACVAATQPVARAFEATGVEYFNTFGGSPVSCAVGLAVLNVLEKEQLQDHATSVGSFLMQLLGQQKIKHPIVGDVRGVGLFIGVDLIKDEATRTPATEEAAYLVSRLKENYVLLSTDGPGRNILKFKPPMCFSLDNARQVVAKLDAILTDMEEKVRSCETLRLQP'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the generated drugs is likely to engage with the target protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\": \"Cc1nccc(-c2c[nH]c3cnccc23)n1\", \"value\": 0.0}, {\"smile\": \"O=C(O)c1nc2ccccc2c2[nH]c3c(Cl)cccc3c12\", \"value\": 0.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Neither 'Cc1nccc(-c2c[nH]c3cnccc23)n1' nor 'O=C(O)c1nc2ccccc2c2[nH]c3c(Cl)cccc3c12' can interact with the protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you predict about the interaction of the optimized drugs with the target protein 'MADDQGCIEEQGVEDSANEDSVDAKPDRSSFVPSLFSKKKKNVTMRSIKTTRDRVPTYQYNMNFEKLGKCIIINNKNFDKVTGMGVRNGTDKDAEALFKCFRSLGFDVIVYNDCSCAKMQDLLKKASEEDHTNAACFACILLSHGEENVIYGKDGVTPIKDLTAHFRGDRCKTLLEKPKLFFIQACRGTELDDGIQADSGPINDTDANPRYKIPVEADFLFAYSTVPGYYSWRSPGRGSWFVQALCSILEEHGKDLEIMQILTRVNDRVARHFESQSDDPHFHEKKQIPCVVSMLTKELYFSQ'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_classification_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MADDQGCIEEQGVEDSANEDSVDAKPDRSSFVPSLFSKKKKNVTMRSIKTTRDRVPTYQYNMNFEKLGKCIIINNKNFDKVTGMGVRNGTDKDAEALFKCFRSLGFDVIVYNDCSCAKMQDLLKKASEEDHTNAACFACILLSHGEENVIYGKDGVTPIKDLTAHFRGDRCKTLLEKPKLFFIQACRGTELDDGIQADSGPINDTDANPRYKIPVEADFLFAYSTVPGYYSWRSPGRGSWFVQALCSILEEHGKDLEIMQILTRVNDRVARHFESQSDDPHFHEKKQIPCVVSMLTKELYFSQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the probability that drug SMILES and target will interact.\", \"target_seq\": \"MADDQGCIEEQGVEDSANEDSVDAKPDRSSFVPSLFSKKKKNVTMRSIKTTRDRVPTYQYNMNFEKLGKCIIINNKNFDKVTGMGVRNGTDKDAEALFKCFRSLGFDVIVYNDCSCAKMQDLLKKASEEDHTNAACFACILLSHGEENVIYGKDGVTPIKDLTAHFRGDRCKTLLEKPKLFFIQACRGTELDDGIQADSGPINDTDANPRYKIPVEADFLFAYSTVPGYYSWRSPGRGSWFVQALCSILEEHGKDLEIMQILTRVNDRVARHFESQSDDPHFHEKKQIPCVVSMLTKELYFSQ\", \"result\": [{\"smile\": \"CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C=O)CC(=O)O)C(C)C\", \"value\": 1.0}, {\"smile\": \"COC[C@@H]1CCCN1S(=O)(=O)c1ccc2c(c1)C(=O)C(=O)N2Cc1ccc(OCC2CO2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['output__smiles', 'user_target_sequence', 'drug_target_affinity_classification_result']))"
            },
            {
                "from": "gpt",
                "value": "Both the two drugs 'CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@H](C(=O)N[C@H](C=O)CC(=O)O)C(C)C' and 'COC[C@@H]1CCCN1S(=O)(=O)c1ccc2c(c1)C(=O)C(=O)N2Cc1ccc(OCC2CO2)cc1' can interact with the protein 'MADDQGCIEEQGVEDSANEDSVDAKPDRSSFVPSLFSKKKKNVTMRSIKTTRDRVPTYQYNMNFEKLGKCIIINNKNFDKVTGMGVRNGTDKDAEALFKCFRSLGFDVIVYNDCSCAKMQDLLKKASEEDHTNAACFACILLSHGEENVIYGKDGVTPIKDLTAHFRGDRCKTLLEKPKLFFIQACRGTELDDGIQADSGPINDTDANPRYKIPVEADFLFAYSTVPGYYSWRSPGRGSWFVQALCSILEEHGKDLEIMQILTRVNDRVARHFESQSDDPHFHEKKQIPCVVSMLTKELYFSQ'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_classification_predict\", \"description\": \"Predict drug-target reaction probability using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression level of 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' on the cell '1524418'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F\"], \"cell_name\": \"1524418\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.977702]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)[C@H](COC2=CC3=C(C=C2)NC(=O)N3)NC(=O)C4=CC=CN(C4=O)CC5=CC(=C(C=C5)F)F' and organelle '1524418' is 2.977702."
            },
            {
                "from": "human",
                "value": "Create a set of compounds targeting a z-score of -6.190392 for '1659819'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC(N=CC=C(C)C)=NCCN', 'CC=CC(=[O+]CC)C(CC)CCCCCC', 'CC=CC=C(C)CC(CCC)[O+]=Cc1ccn[nH]1', 'C=CCC(C)CCC(OC)C(=C)CCC(=C)C(C)CC', 'CCNC1C(CC)[N+]1=CC=CC(C)CC', 'CCNN=NC=C(C=CN)CCC(C)C', 'C=C(N)CCC(C)O', 'CC(=CCNNNC=C1CCNC1)CN', 'CCC(C)OCCC(C)C(C)=C(C[O+]1C(C)=CC1CC)C1=C(C(C)C)C=C1C(C)C', 'CCC=CC=CCCC(CC)CCC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(N=CC=C(C)C)=NCCN', 'CC=CC(=[O+]CC)C(CC)CCCCCC', 'CC=CC=C(C)CC(CCC)[O+]=Cc1ccn[nH]1', 'C=CCC(C)CCC(OC)C(=C)CCC(=C)C(C)CC', 'CCNC1C(CC)[N+]1=CC=CC(C)CC', 'CCNN=NC=C(C=CN)CCC(C)C', 'C=C(N)CCC(C)O', 'CC(=CCNNNC=C1CCNC1)CN', 'CCC(C)OCCC(C)C(C)=C(C[O+]1C(C)=CC1CC)C1=C(C(C)C)C=C1C(C)C', 'CCC=CC=CCCC(CC)CCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how strongly 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C' acts on the cell '1330935'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C\"], \"cell_name\": \"1330935\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.773873]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C' and organelle '1330935' is -2.773873."
            },
            {
                "from": "human",
                "value": "Produce a range of molecules with the target z-score of -4.054651 for '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC=CN(CC)NN', 'CC=CCC=[O+]CC(C)CCCN', 'C[O+]=CN=C(C)C(C)CO', 'CCOC[O+](C)OC(C)C(C)CC(C)C', 'CC1CN1N(N)NCC=C(NN)C1=CCCC1', 'CCC=CC(=CC=C(C=CCCC(C)C)CCC)CCC1(C)CC1=C(C)CC(C)C(O)=CC(C)C(C)C', 'CCC(=CC=C1CC1)C(C)(C)C=CCC=C(C)CC=CO', 'C=C(CC)OCC(C)(CC=CCC(C)CC1CO1)CCC(C)CCCCCCCF', 'CBB1BBN1', 'C=CC1=C2CC(=[O+]C1)CCC(ONC(C)N(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CN(CC)NN', 'CC=CCC=[O+]CC(C)CCCN', 'C[O+]=CN=C(C)C(C)CO', 'CCOC[O+](C)OC(C)C(C)CC(C)C', 'CC1CN1N(N)NCC=C(NN)C1=CCCC1', 'CCC=CC(=CC=C(C=CCCC(C)C)CCC)CCC1(C)CC1=C(C)CC(C)C(O)=CC(C)C(C)C', 'CCC(=CC=C1CC1)C(C)(C)C=CCC=C(C)CC=CO', 'C=C(CC)OCC(C)(CC=CCC(C)CC1CO1)CCC(C)CCCCCCCF', 'CBB1BBN1', 'C=CC1=C2CC(=[O+]C1)CCC(ONC(C)N(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' on the cell '907287'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"907287\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.159924]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '907287' is 3.159924."
            },
            {
                "from": "human",
                "value": "Generate a variety of compounds with a z-score of -4.054651 for '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCCNCC(=CC=NCC)C[O+](C)CC(=CNCC)C(C)CC', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1', 'CC=C(CC)C(=CCNC=NC1CCCC1C)CNC(C)(CN)CN(N)CCN', 'C=CC(C)CC(=CCC)C1=C(CC)C1', 'C=C1C(=CCC)CC=CC1CN(C)C', 'CC=CC1=NCN(CC)CN1C(C)C', 'C=NC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=N1', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(CCCC)C1)CCC)N(CC)CC', 'CCC1B=NBB1C', 'C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCNCC(=CC=NCC)C[O+](C)CC(=CNCC)C(C)CC', 'C=CC(C)OCC1=C(CCC)C[N+]2=C(CC)OCCC=CC2=CCCC1', 'CC=C(CC)C(=CCNC=NC1CCCC1C)CNC(C)(CN)CN(N)CCN', 'C=CC(C)CC(=CCC)C1=C(CC)C1', 'C=C1C(=CCC)CC=CC1CN(C)C', 'CC=CC1=NCN(CC)CN1C(C)C', 'C=NC(OCC1CCC(=CC(C)=C(C)C)CC1=C)C1COC=N1', 'C=CC=C(C)C(=C(C)C(C=CC1=CCN(CCCC)C1)CCC)N(CC)CC', 'CCC1B=NBB1C', 'C=CC(N=[N+](C=CC)C=CC(N)NN1CC=C1C)C(=C)CC=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]''s effect on the cell '1287706'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]\"], \"cell_name\": \"1287706\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.528633]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]' and organelle '1287706' is 2.528633."
            },
            {
                "from": "human",
                "value": "Generate compounds that meet a z-score of -4.054651 for '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1', 'C=CCC[N+]1=C[N+](N)=C1', 'C=CCN[N+](C)=CN=CCCCNNN(N)C=C', 'NCC=CC1CC1', 'C=CC1CN(C)C2=CN(CC=O)N=CC(CCC(C)=CC2)C1C', 'C=BCCC(N)N', 'BB(C)C(C)C(N)CC[N+]1=BCB(CN)NNC1=C', 'CN1CC=CCC=BC(NCB=N)CN2B3NB3N12', 'B=C=NC(CNC)[N+](=CCCN)[N+](=CC)CCNC=NC', 'C=CC1NC(C)CC1CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['BCB=C1CBNC=CCCBC2C(NCNNC2N(N)CNC(C)=N)N1', 'C=CCC[N+]1=C[N+](N)=C1', 'C=CCN[N+](C)=CN=CCCCNNN(N)C=C', 'NCC=CC1CC1', 'C=CC1CN(C)C2=CN(CC=O)N=CC(CCC(C)=CC2)C1C', 'C=BCCC(N)N', 'BB(C)C(C)C(N)CC[N+]1=BCB(CN)NNC1=C', 'CN1CC=CCC=BC(NCB=N)CN2B3NB3N12', 'B=C=NC(CNC)[N+](=CCCN)[N+](=CC)CCNC=NC', 'C=CC1NC(C)CC1CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' impacts the growth of the cell '1240224'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\"], \"cell_name\": \"1240224\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.458680]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' and organelle '1240224' is 3.458680."
            },
            {
                "from": "human",
                "value": "Produce molecules matching a z-score of -4.054651 for '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CNC(C)CN=NC1CCC1', 'C=CNCCC(C)C[O+]=[O+]C(=CC(C)C(C)CCC1CC1)C1CC(=CC2CCCC2)C1', 'CC=CN=CC(C)N1C=CC(=CC(N)=CC)C(C)C1', 'CCC(C)(C)C1C=NCCCCCC1', 'CB[N+](=NCC1CNNN(C)N1)C(C)=CC=NC(C)N', 'C=CC(=C)CCC(C)NC=NN(C)N[N+](C)=C1C(=N)NC(C)=[N+](C)CC=C1CC(C)[N+](=C)C=NN', 'CCCC1OCC1C1=C[O+]=CCC(C)NN1CC', 'C=CCC1CCCC(C)CC2=C(C2)C2=C(CC)C(F)C=COCCC(C3=CC(N)C3)OC(C)CC=C(C)C1CC2', 'C=C(C)N1C=NC(CC2(C)CCNC2)=C(C(C)C)C=CNC1NC', 'CN=C(C=CC1CC1)C(C)=CCCC(C)C']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNC(C)CN=NC1CCC1', 'C=CNCCC(C)C[O+]=[O+]C(=CC(C)C(C)CCC1CC1)C1CC(=CC2CCCC2)C1', 'CC=CN=CC(C)N1C=CC(=CC(N)=CC)C(C)C1', 'CCC(C)(C)C1C=NCCCCCC1', 'CB[N+](=NCC1CNNN(C)N1)C(C)=CC=NC(C)N', 'C=CC(=C)CCC(C)NC=NN(C)N[N+](C)=C1C(=N)NC(C)=[N+](C)CC=C1CC(C)[N+](=C)C=NN', 'CCCC1OCC1C1=C[O+]=CCC(C)NN1CC', 'C=CCC1CCCC(C)CC2=C(C2)C2=C(CC)C(F)C=COCCC(C3=CC(N)C3)OC(C)CC=C(C)C1CC2', 'C=C(C)N1C=NC(CC2(C)CCNC2)=C(C(C)C)C=CNC1NC', 'CN=C(C=CC1CC1)C(C)=CCCC(C)C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how effectively 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' inhibits the cell '908120'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\"], \"cell_name\": \"908120\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.693844]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' and organelle '908120' is 1.693844."
            },
            {
                "from": "human",
                "value": "Produce a series of molecules matching a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCB1C=CC=CNCC=CCC2=NC(N)C=CCCC2B(C)CN(N)N1', 'CC(C)C1CC=C(CC(CCF)[O+]=CCN)C1', 'C=CCC(N)CC=C(C)C(NCN)C(C)CNC', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(C)C=CC1CC=C(CCC(C)CCC)C1', 'CCCCC(O)=C1C(C(CC)=NOC)=[N+]1C', 'C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CCCC(F)C(C)=CO', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCB1C=CC=CNCC=CCC2=NC(N)C=CCCC2B(C)CN(N)N1', 'CC(C)C1CC=C(CC(CCF)[O+]=CCN)C1', 'C=CCC(N)CC=C(C)C(NCN)C(C)CNC', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(C)C=CC1CC=C(CCC(C)CCC)C1', 'CCCCC(O)=C1C(C(CC)=NOC)=[N+]1C', 'C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CCCC(F)C(C)=CO', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N''s inhibition rate on the cell '1240226'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1240226\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.562113]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1240226' is 4.562113."
            },
            {
                "from": "human",
                "value": "Please develop molecules with a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCCC(CCCCC(N)(N)CC(NCC)NCCN)C1CC2CC(N2)N1CN', 'CCCC(O)N=CC(C)N(N)CC=CCN', 'CC=CC1C=C1C', 'C=C(CC)CCCC1=CC2N3C(CNC=CCC)OC(CCCCCC(NC)C23C)N=C1', 'CCC=[N+](C)NC[N+](C=C(C)C1=C(CCC2C=CO2)CC1)=CC=CCN', 'C=C(CCCC=CCC[N+](C)(C)O)CCNC', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2CCCC1=C2CC12', 'CC=C(C=CCCC)C=[O+]CC=NCC', 'C=C(N)C1=C2C=CC3CC(C)(O3)C1NC(=CC)C2', 'CCCC1=CN=C1NC=CNCC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCCC(CCCCC(N)(N)CC(NCC)NCCN)C1CC2CC(N2)N1CN', 'CCCC(O)N=CC(C)N(N)CC=CCN', 'CC=CC1C=C1C', 'C=C(CC)CCCC1=CC2N3C(CNC=CCC)OC(CCCCCC(NC)C23C)N=C1', 'CCC=[N+](C)NC[N+](C=C(C)C1=C(CCC2C=CO2)CC1)=CC=CCN', 'C=C(CCCC=CCC[N+](C)(C)O)CCNC', 'CC1C(C2=CC2)C=CC[O+]2OC=CCC(N)N1C=C2CCCC1=C2CC12', 'CC=C(C=CCCC)C=[O+]CC=NCC', 'C=C(N)C1=C2C=CC3CC(C)(O3)C1NC(=CC)C2', 'CCCC1=CN=C1NC=CNCC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' in stopping the cell '906831'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\"], \"cell_name\": \"906831\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.445943]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' and organelle '906831' is 2.445943."
            },
            {
                "from": "human",
                "value": "Produce a series of molecules matching a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCB1C=CC=CNCC=CCC2=NC(N)C=CCCC2B(C)CN(N)N1', 'CC(C)C1CC=C(CC(CCF)[O+]=CCN)C1', 'C=CCC(N)CC=C(C)C(NCN)C(C)CNC', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(C)C=CC1CC=C(CCC(C)CCC)C1', 'CCCCC(O)=C1C(C(CC)=NOC)=[N+]1C', 'C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CCCC(F)C(C)=CO', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC1=CC(OCC(C)=CCCCCCCC)CCCCCCC[N+](C(C)=CC=CC(C)(C)C=CN)=CO1', 'CCB1C=CC=CNCC=CCC2=NC(N)C=CCCC2B(C)CN(N)N1', 'CC(C)C1CC=C(CC(CCF)[O+]=CCN)C1', 'C=CCC(N)CC=C(C)C(NCN)C(C)CNC', 'C=C(C(CC(CCC)=C(C)CNCCC=[N+](C)CC(C)CC)C(C)C(C)CC=CC)[N+](C)(N)NN', 'C=C(C)C=CC1CC=C(CCC(C)CCC)C1', 'CCCCC(O)=C1C(C(CC)=NOC)=[N+]1C', 'C=C1CCCCCC(C(C=C(C)CCCC(C(C)=CC)=C(C)C)=CC2CC2)C=C1CC', 'CCCC(F)C(C)=CO', 'CCCC(C)C1C(CC)CC2(CC)COCCCC1(C)C=CCC1CC2C2(C)CCC12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the inhibition impact of 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' on the cell '907171'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"907171\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.652756]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '907171' is 2.652756."
            },
            {
                "from": "human",
                "value": "Please produce a collection of molecules with a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCNCCC=[O+]C=C1CCC(C)CC1=CC(CC)CCNC', 'CC(N)C=CCC1=C[O+]=C2N=CC1=CCCCCCN=C1C3[O+]=CCC13CN2', 'C=C(C=NCCN)NB(N)CN1C=BN=BC(BNN)B(N)C1', 'CCCC1=CC(CO)C(CC(C)OC(O)CCC)=CC(C)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CC=C(C)CNCC1C=C2CC2C(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2C[O+]=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCNCCC=[O+]C=C1CCC(C)CC1=CC(CC)CCNC', 'CC(N)C=CCC1=C[O+]=C2N=CC1=CCCCCCN=C1C3[O+]=CCC13CN2', 'C=C(C=NCCN)NB(N)CN1C=BN=BC(BNN)B(N)C1', 'CCCC1=CC(CO)C(CC(C)OC(O)CCC)=CC(C)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CC=C(C)CNCC1C=C2CC2C(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2C[O+]=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O''s potential in stopping the cell '910910'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\"], \"cell_name\": \"910910\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.260088]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O' and organelle '910910' is 5.260088."
            },
            {
                "from": "human",
                "value": "Develop a series of molecules matching a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1C=C(C=C2C=CN=C=C(CCC)C(=C)C2)OC(O)C(CCO)=C1', 'CC1=NN(CCC(N)=C(C)O)CCCCC2=C(C)C=C12', 'C=CC(C)C(C=CC)C(C)(N)CCC(C)C(C)=C1COC1', 'CC(=CC1C=[O+]1)C=C1CCC(C)C1', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCN1', 'CC1C=NC(CCC=C2C=CC(C)(C)NC(C)CCN=C=CC2)OCC1', 'CBC(N=BC=CN)=NC', 'CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CC1COC2CCCCC=CCC=CC=C3NCC=CC3(C)CC=C(C1)C2', 'CCC(N)CC=CCCC(CC)NN']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=C(C=C2C=CN=C=C(CCC)C(=C)C2)OC(O)C(CCO)=C1', 'CC1=NN(CCC(N)=C(C)O)CCCCC2=C(C)C=C12', 'C=CC(C)C(C=CC)C(C)(N)CCC(C)C(C)=C1COC1', 'CC(=CC1C=[O+]1)C=C1CCC(C)C1', 'C=CC1(CCC=C2CC=[N+](C)C=CCC2)COCN1', 'CC1C=NC(CCC=C2C=CC(C)(C)NC(C)CCN=C=CC2)OCC1', 'CBC(N=BC=CN)=NC', 'CC=C(CCC(N)C(C)CC)[N+](C)=CCCCC', 'CC1COC2CCCCC=CCC=CC=C3NCC=CC3(C)CC=C(C1)C2', 'CCC(N)CC=CCCC(CC)NN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the degree of inhibitory effect for 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' on the cell '687807'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O\"], \"cell_name\": \"687807\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.995806]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC(C1)C2=NC(=C3N2C=CN=C3N)C4=CC5=C(C=C4)C=CC(=N5)C6=CC=CC=C6)O' and organelle '687807' is 3.995806."
            },
            {
                "from": "human",
                "value": "Produce molecules that achieve a z-score of -6.190392 for '722045'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"722045\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1C2=CC=[N+](C=C(CCO)CCC(C)(N)C(C)CCC(C)=C2)C1C', 'CCCNC(N)C(CCN)CC(C)(CC)C1BC(CC=CB(C)CBBN)BC1(C)CC', 'CC1CC(CN2CCCCOC2)C1C', 'CCC1CCC(=CN)C=NCC2(C)CC(=CNC(C)C=C2C)C2CCCCC1C2', 'CC=C(NCC)C(C)C=CCC', 'CCNC1C(CC)[N+]1=CC=CC(C)CC', 'CC=CNC(CC)=NCC(C=CCC)[N+]1=C(C=CN)NC1', 'C=NC(CN(C=CC(=CNC)C(=N)CCC)C(C)N)NCC(C)(O)NC', 'CCC=CC=NC[N+]1=CC=C1', 'CC=CC(C)C1CC=C(C=C(N)C2C(C)C2C(C)N)CNC1NC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C2=CC=[N+](C=C(CCO)CCC(C)(N)C(C)CCC(C)=C2)C1C', 'CCCNC(N)C(CCN)CC(C)(CC)C1BC(CC=CB(C)CBBN)BC1(C)CC', 'CC1CC(CN2CCCCOC2)C1C', 'CCC1CCC(=CN)C=NCC2(C)CC(=CNC(C)C=C2C)C2CCCCC1C2', 'CC=C(NCC)C(C)C=CCC', 'CCNC1C(CC)[N+]1=CC=CC(C)CC', 'CC=CNC(CC)=NCC(C=CCC)[N+]1=C(C=CN)NC1', 'C=NC(CN(C=CC(=CNC)C(=N)CCC)C(C)N)NCC(C)(O)NC', 'CCC=CC=NC[N+]1=CC=C1', 'CC=CC(C)C1CC=C(C=C(N)C2C(C)C2C(C)N)CNC1NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the level of inhibition 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' exerts on the cell '1322218'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC\"], \"cell_name\": \"1322218\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.538592]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)N=CN2C3=CC(=C(S3)C#N)OCC4=CC=CC=C4S(=O)(=O)C)OC' and organelle '1322218' is 3.538592."
            },
            {
                "from": "human",
                "value": "Create molecules optimized to meet a z-score of -6.190392 for '1659819'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC(C)(C)C(C)N', 'C=C(C)OC(=C)C(C)C1=C(C(C)(C)F)C=[O+]1', 'C=C(C)CC=C[N+]1=CC(N)=CN=NCNNCN1', 'CC1COC=C2C=CC=CCCC(NNN(C)N)=CCCC2=N1', 'CCC=CCC=CC[O+](COO)C(C)C', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'C=C(CC(N)NCCC(C)=N)C(N=CCC=CC)C(N)(CC)C(C)C(C)C[N+](CC1C=CC1)=NC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)(C)C(C)N', 'C=C(C)OC(=C)C(C)C1=C(C(C)(C)F)C=[O+]1', 'C=C(C)CC=C[N+]1=CC(N)=CN=NCNNCN1', 'CC1COC=C2C=CC=CCCC(NNN(C)N)=CCCC2=N1', 'CCC=CCC=CC[O+](COO)C(C)C', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'C=C(CC(N)NCCC(C)=N)C(N=CCC=CC)C(N)(CC)C(C)C(C)C[N+](CC1C=CC1)=NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' restricts the cell '910861'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"910861\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.095557]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '910861' is 1.095557."
            },
            {
                "from": "human",
                "value": "Create molecules with the goal of achieving a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=C(CNBN)[N+](C)=NC', 'CCC(C)C(C)=NC=[O+]C', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CCC1=C(N(CC2CC2(N)C(C)CC)C(C)C(C)C(C)C=CNCN)C(C)CNC(C)CC2(C)CC(C)(C)C12', 'CCC(C(C)=N)N(CCCOO)NC', 'C=CC=[N+](CCN)C(N)=C(C=CC)CNC(C)=NC(C)N', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCNC(CC)CC(=CC)CCO']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=C(CNBN)[N+](C)=NC', 'CCC(C)C(C)=NC=[O+]C', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CCC1=C(N(CC2CC2(N)C(C)CC)C(C)C(C)C(C)C=CNCN)C(C)CNC(C)CC2(C)CC(C)(C)C12', 'CCC(C(C)=N)N(CCCOO)NC', 'C=CC=[N+](CCN)C(N)=C(C=CC)CNC(C)=NC(C)N', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCNC(CC)CC(=CC)CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' inhibits the cell '906791'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"906791\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.882543]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' and organelle '906791' is 4.882543."
            },
            {
                "from": "human",
                "value": "Please generate a series of molecules achieving a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CB1NC1C1C2N(C)N12', 'CN(C=CO)[O+](C)C=C1CCCCC=CO1', 'CC1(C)C=CCC2CC(COO2)OCNCO1', 'C=C(N)C(C)C', 'CC1C2=NCN(C=N2)N1C', 'C[O+]=CC=C(C)C(C)COOO[O+](C)C(C)C', 'CCNCCCOC=C1CCC(C)CC1=CC(CC)CCNC', 'CC=CCCCC(=N)CC', 'CCCCC(C)C=C1C=CCNC(C)(CC[N+](C)(N)CC)N(C=CN)C=NC(CC)C=CC1', 'CCC=C(C)C(C)CCC1CCCNC1C']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CB1NC1C1C2N(C)N12', 'CN(C=CO)[O+](C)C=C1CCCCC=CO1', 'CC1(C)C=CCC2CC(COO2)OCNCO1', 'C=C(N)C(C)C', 'CC1C2=NCN(C=N2)N1C', 'C[O+]=CC=C(C)C(C)COOO[O+](C)C(C)C', 'CCNCCCOC=C1CCC(C)CC1=CC(CC)CCNC', 'CC=CCCCC(=N)CC', 'CCCCC(C)C=C1C=CCNC(C)(CC[N+](C)(N)CC)N(C=CN)C=NC(CC)C=CC1', 'CCC=C(C)C(C)CCC1CCCNC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory impact of 'C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC' on the cell '753614'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC\"], \"cell_name\": \"753614\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.036804]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC' and organelle '753614' is -2.036804."
            },
            {
                "from": "human",
                "value": "Create a collection of molecules with a z-score of -4.054651 in '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC=CC(C)(C(=CCC(C)C)CC)C(C)CC', 'CC=[O+]C1=CCC(C2=CC2)=C[N+](C)(C)CCCC=C1', 'CC=CC=CCCCC(CCN)C1C(C)CNN1NC', 'CC=CCCC1=CCC(C)NNCN1', 'C=C(CC)C1CC(C=CC)NCN1', 'C=C(C=CC(C)(C)COCCC)CC12N=C1C2(C)CCOC', 'C=NC1CC(C)=[N+](CBC)C(C)=CC=CC2NN(C)N(C1)C(=C)C2C', 'CCC(CN)[N+](=C[N+]1=CCC2=CNC(C=C2)[N+]2(C)C=CC(=C1N)CN2)CCNC', 'CCCC(=CC1N=C2CNN(NNNN2)C1N(CN)CNC)C(CC)C(C)N', 'CCC=CC(=CC=[N+]1C=CNC(C)CC1)CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC(C)(C(=CCC(C)C)CC)C(C)CC', 'CC=[O+]C1=CCC(C2=CC2)=C[N+](C)(C)CCCC=C1', 'CC=CC=CCCCC(CCN)C1C(C)CNN1NC', 'CC=CCCC1=CCC(C)NNCN1', 'C=C(CC)C1CC(C=CC)NCN1', 'C=C(C=CC(C)(C)COCCC)CC12N=C1C2(C)CCOC', 'C=NC1CC(C)=[N+](CBC)C(C)=CC=CC2NN(C)N(C1)C(=C)C2C', 'CCC(CN)[N+](=C[N+]1=CCC2=CNC(C=C2)[N+]2(C)C=CC(=C1N)CN2)CCNC', 'CCCC(=CC1N=C2CNN(NNNN2)C1N(CN)CNC)C(CC)C(C)N', 'CCC=CC(=CC=[N+]1C=CNC(C)CC1)CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' on the cell '925343'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F\"], \"cell_name\": \"925343\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.983428]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' and organelle '925343' is 2.983428."
            },
            {
                "from": "human",
                "value": "Please generate molecules achieving a z-score of -4.054651 for the cell line '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC=[N+](CC=CCCCNC(CCCC)CCCC(C)C)CCC', 'C=C(N)C=CN=C1CC1NC', 'C=C(C)C(C)CCCCC=CCC(C)CCCC(C)CC', 'CB(C)n1nc(OBB(N)CN)[nH]1', 'CC=C(CC(NC(C)CNCO[N+]1(C)CC1=N)C1=C(C)C1)C(N)C=CN(C)C=C(C)CCC', 'CCN=[N+](C)NC(C)C', 'COC(CC1CCC1C)C1C=CC1', 'CC=C(C=CCC)CC1CCC=CCC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)CC)CC(C)C=C(C)CC1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=[N+](CC=CCCCNC(CCCC)CCCC(C)C)CCC', 'C=C(N)C=CN=C1CC1NC', 'C=C(C)C(C)CCCCC=CCC(C)CCCC(C)CC', 'CB(C)n1nc(OBB(N)CN)[nH]1', 'CC=C(CC(NC(C)CNCO[N+]1(C)CC1=N)C1=C(C)C1)C(N)C=CN(C)C=C(C)CCC', 'CCN=[N+](C)NC(C)C', 'COC(CC1CCC1C)C1C=CC1', 'CC=C(C=CCC)CC1CCC=CCC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)CC)CC(C)C=C(C)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N''s inhibition rate on the cell '1240226'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1240226\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.562113]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1240226' is 4.562113."
            },
            {
                "from": "human",
                "value": "Create molecules with the goal of achieving a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=C(CNBN)[N+](C)=NC', 'CCC(C)C(C)=NC=[O+]C', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CCC1=C(N(CC2CC2(N)C(C)CC)C(C)C(C)C(C)C=CNCN)C(C)CNC(C)CC2(C)CC(C)(C)C12', 'CCC(C(C)=N)N(CCCOO)NC', 'C=CC=[N+](CCN)C(N)=C(C=CC)CNC(C)=NC(C)N', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCNC(CC)CC(=CC)CCO']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC(=C)C(C)NC=NCC(=N)CC=C1C2N(N)N12', 'C=C(CNBN)[N+](C)=NC', 'CCC(C)C(C)=NC=[O+]C', 'C=C1C=CC(CC(CC2=CC=C2N)NCC)C(C(=C)CN)C1', 'CCC1C=CCCN=C(CC=C(C)N)CC=C(NCNC2CC=CN=CNNC2)C1CNC', 'CC=C(C)C(C=C[O+]=CCCC)CO', 'CCC1=C(N(CC2CC2(N)C(C)CC)C(C)C(C)C(C)C=CNCN)C(C)CNC(C)CC2(C)CC(C)(C)C12', 'CCC(C(C)=N)N(CCCOO)NC', 'C=CC=[N+](CCN)C(N)=C(C=CC)CNC(C)=NC(C)N', 'C=CC(=CC(C)=CC(=C)N)CCC=[O+]CCNC(CC)CC(=CC)CCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the degree of inhibition for 'CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]' against the cell '906861'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]\"], \"cell_name\": \"906861\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-5.921183]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=[N+](C2=C(N1CCOC)C(=O)C3=CC=CC=C3C2=O)CC4=NC=CN=C4.[Br-]' and organelle '906861' is -5.921183."
            },
            {
                "from": "human",
                "value": "Design molecules that reach a z-score of -4.054651 for '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CB(N=N)C(=CCC(N)(B=B[N+]1=CC=CCB1)B(N)N)C(C)N', 'C=CC(C)C(C)CCC1=C=C=C1', 'BCBN(NCB(N)N)C1=BB(CN2CB3CCCC32)B1', 'CBBNB(C)BB(CCN)N=NN1CC=NN1', 'BB1BB(NCN)C(B=N)B1', 'CC(S)CC=C1CO1', 'C=CC[N+]1=CC[N+]2=N[N+]3=C(BB(C)CN(C)NC(NBCN)NCBC23)N1', 'BNC(B)N=[N+](C)C=NC', 'C=NCC1CC=CC2CC(=CC1)CC2C', 'C=C=C1CC1C1C2CCNC(C)C1CC([O+](C)N=CN)C2']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CB(N=N)C(=CCC(N)(B=B[N+]1=CC=CCB1)B(N)N)C(C)N', 'C=CC(C)C(C)CCC1=C=C=C1', 'BCBN(NCB(N)N)C1=BB(CN2CB3CCCC32)B1', 'CBBNB(C)BB(CCN)N=NN1CC=NN1', 'BB1BB(NCN)C(B=N)B1', 'CC(S)CC=C1CO1', 'C=CC[N+]1=CC[N+]2=N[N+]3=C(BB(C)CN(C)NC(NBCN)NCBC23)N1', 'BNC(B)N=[N+](C)C=NC', 'C=NCC1CC=CC2CC(=CC1)CC2C', 'C=C=C1CC1C1C2CCNC(C)C1CC([O+](C)N=CN)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C''s effectiveness in inhibiting the cell '724828'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"724828\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.851710]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '724828' is 2.851710."
            },
            {
                "from": "human",
                "value": "Generate molecules with a z-score of -4.054651 for the cell line '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=CC=C1CCCC(F)C(C)=C(CCCC)CC1=CC', 'CC=CNC(CN)=NCC(C=CCC)[N+]1=C(C=NN)NC1', 'C=C(C)C(C)CCCCC=CNC(C)CCCN(C)CC', 'CCCC1CCC(C)C2C=CCC(=CC1C)C=CCNC(C)(CC[N+](C)(C)CC)C(C=CN)C=N2', 'CC=C(C=NCN=NCC)C(N)=CCCC', 'C=C(NC)N(C)NCN(C)C', 'C=C(C)CC(C)=CCCC(=NC)C1=CC(CC)=CC=C(CC=CCC(C)=CCN)C2CC1C2', 'CC(C1=NCCCNCN1C)C1(N)CC2=CC(=B2)[N+](NCC(N)N)=N1', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3C)C2=C(O)C(C)OCCC1', 'CCC1=CC(CN)C(C2(N)CC(CC)C2)NCC1C']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=CC=C1CCCC(F)C(C)=C(CCCC)CC1=CC', 'CC=CNC(CN)=NCC(C=CCC)[N+]1=C(C=NN)NC1', 'C=C(C)C(C)CCCCC=CNC(C)CCCN(C)CC', 'CCCC1CCC(C)C2C=CCC(=CC1C)C=CCNC(C)(CC[N+](C)(C)CC)C(C=CN)C=N2', 'CC=C(C=NCN=NCC)C(N)=CCCC', 'C=C(NC)N(C)NCN(C)C', 'C=C(C)CC(C)=CCCC(=NC)C1=CC(CC)=CC=C(CC=CCC(C)=CCN)C2CC1C2', 'CC(C1=NCCCNCN1C)C1(N)CC2=CC(=B2)[N+](NCC(N)N)=N1', 'CCCC1=CCC(CC)C2(CC)CC(CCC3CCC(CC)CCC(CC(C)N)CCCCC(N(C)C)[O+]3CC(C)(CC)CC3CC3C)C2=C(O)C(C)OCCC1', 'CCC1=CC(CN)C(C2(N)CC(CC)C2)NCC1C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O''s potential in stopping the cell '910910'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\"], \"cell_name\": \"910910\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.260088]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O' and organelle '910910' is 5.260088."
            },
            {
                "from": "human",
                "value": "Build molecules to reach a z-score of -4.054651 for the cell line '1240182'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' on cell growth in the cell '687820'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"687820\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.381123]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '687820' is 2.381123."
            },
            {
                "from": "human",
                "value": "Develop molecules to match a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=[N+]1C2=CN3C=C(C=[N+]21)NNN(CC)CCC(C)C(CCN)C=N3', 'CCC(=[O+]C(C)CN(C)C=CC=C(NO)NC1CCC1)[N+](C)(C)CC', 'BNB(C)CN(N)NNC', 'C=C(C)C(C)C(O)C1=CC(C)CCC(=CCCCC(C)CCCC)CCCCCC(CC)=C1', 'C=CCOCCC=C(CCC=CC(=C)CC1CC(C=C)C1CC(=C)C)C1=CC1', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'CC=C1CC2=C(C(C)(N)CCN)C(N1)C1(C)CC(N=C2)N1', 'CCCC1=C2C=CCC2C1', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=C(C)CC1C=C2[O+]=CN(C)NN2CCCCCNC(=CC)CN2CN12']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+]1C2=CN3C=C(C=[N+]21)NNN(CC)CCC(C)C(CCN)C=N3', 'CCC(=[O+]C(C)CN(C)C=CC=C(NO)NC1CCC1)[N+](C)(C)CC', 'BNB(C)CN(N)NNC', 'C=C(C)C(C)C(O)C1=CC(C)CCC(=CCCCC(C)CCCC)CCCCCC(CC)=C1', 'C=CCOCCC=C(CCC=CC(=C)CC1CC(C=C)C1CC(=C)C)C1=CC1', 'C=C(C)CCC1(C)CC2CCC=CCCC(C)C=CC3=C4CCCC2C(C)C1=C34', 'CC=C1CC2=C(C(C)(N)CCN)C(N1)C1(C)CC(N=C2)N1', 'CCCC1=C2C=CCC2C1', 'C=C(C)C1(C2=CCC[N+](=CC=CC)NO2)CC(C)=N1', 'C=C(C)CC1C=C2[O+]=CN(C)NN2CCCCCNC(=CC)CN2CN12']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the effect of 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' on restricting the cell '949160'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"949160\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.940421]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '949160' is 1.940421."
            },
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -4.054651 for '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'C=C(F)CC1CC(=CCC2C(C)=C3CCNCC2C(CCN)C3)C(O)C1', 'CC=C(NCN=CCCC)[N+](C=CCC1=CCNCC1)=CC(C)CNC', 'CC=C(C)C(C=CC=CCC)[O+](C)CCN', 'C=[N+](CC(=CCCCC)CCC1CC1)C(F)C1(C)CCCO[O+]=CC=CCCC=CC(C)CC1C', 'C=CCC(C=C)CCC(CC=CC1=CC1)C(C)CC(C)C(=C)N', 'CNC1CN1CC=C1CCN(N)NNNC(N)(N=N)C1', 'CCC=CCCC1CCNC(CC2(N)CCOC=CCC(C)C2)O1', 'C=[N+]=CN=CC1C(=C)NC1C', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'C=C(F)CC1CC(=CCC2C(C)=C3CCNCC2C(CCN)C3)C(O)C1', 'CC=C(NCN=CCCC)[N+](C=CCC1=CCNCC1)=CC(C)CNC', 'CC=C(C)C(C=CC=CCC)[O+](C)CCN', 'C=[N+](CC(=CCCCC)CCC1CC1)C(F)C1(C)CCCO[O+]=CC=CCCC=CC(C)CC1C', 'C=CCC(C=C)CCC(CC=CC1=CC1)C(C)CC(C)C(=C)N', 'CNC1CN1CC=C1CCN(N)NNNC(N)(N=N)C1', 'CCC=CCCC1CCNC(CC2(N)CCOC=CCC(C)C2)O1', 'C=[N+]=CN=CC1C(=C)NC1C', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of inhibition by 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' on the cell '910703'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"], \"cell_name\": \"910703\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.355333]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' and organelle '910703' is 1.355333."
            },
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC(=O)C(C)OC(C)(C)CF', 'CCC(C)=CC=NC=CC1CC1C', 'C=C(C=CC=CC=CC)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=C(CCCC)C(O)C=[O+]OC(C)C1C=CC=CCC1', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=NC=C=CCC1CNCNCCN1', 'C=CC(CCN(C)N(C)CC(=C)CC(C)C)OCCF']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(=O)C(C)OC(C)(C)CF', 'CCC(C)=CC=NC=CC1CC1C', 'C=C(C=CC=CC=CC)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=C(CCCC)C(O)C=[O+]OC(C)C1C=CC=CCC1', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=NC=C=CCC1CNCNCCN1', 'C=CC(CCN(C)N(C)CC(=C)CC(C)C)OCCF']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' can hinder the cell '1240161'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"], \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-4.701002]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' and organelle '1240161' is -4.701002."
            },
            {
                "from": "human",
                "value": "Please produce a collection of molecules with a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCNCCC=[O+]C=C1CCC(C)CC1=CC(CC)CCNC', 'CC(N)C=CCC1=C[O+]=C2N=CC1=CCCCCCN=C1C3[O+]=CCC13CN2', 'C=C(C=NCCN)NB(N)CN1C=BN=BC(BNN)B(N)C1', 'CCCC1=CC(CO)C(CC(C)OC(O)CCC)=CC(C)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CC=C(C)CNCC1C=C2CC2C(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2C[O+]=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCNCCC=[O+]C=C1CCC(C)CC1=CC(CC)CCNC', 'CC(N)C=CCC1=C[O+]=C2N=CC1=CCCCCCN=C1C3[O+]=CCC13CN2', 'C=C(C=NCCN)NB(N)CN1C=BN=BC(BNN)B(N)C1', 'CCCC1=CC(CO)C(CC(C)OC(O)CCC)=CC(C)C1', 'CCC=C(CC(C)C=CN)C1=C(C)C1', 'BB=NNN=[N+](CCC(C)N=N)[N+](=CC)NCNC(C)(N)C(=C)NNCCCC1CCN1CNCC', 'C=C(C=CC1CNN(C=CC(C)N=[N+](C)CC)N=N1)CCC', 'C=C1C=C(C)CC=C(C)CC2=CN(CC1)C2(C)C', 'C=CC(=C(CC)C(=C)CC)C(C)C(C=CC=CC(C)CC=C(C)C)CC', 'CC=C(C)CNCC1C=C2CC2C(C(CCCCC)CC(C)C2C(CC)C=CC3=CC2C[O+]=C=CCC3)CCC(=C2C=C2CC)C=C(CC(C)=CCCC)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C''s capacity to inhibit the cell '1287381'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\"], \"cell_name\": \"1287381\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.241289]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' and organelle '1287381' is 3.241289."
            },
            {
                "from": "human",
                "value": "Provide molecules with a z-score matching -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC(=O)C(C)OC(C)(C)CF', 'CCC(C)=CC=NC=CC1CC1C', 'C=C(C=CC=CC=CC)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=C(CCCC)C(O)C=[O+]OC(C)C1C=CC=CCC1', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=NC=C=CCC1CNCNCCN1', 'C=CC(CCN(C)N(C)CC(=C)CC(C)C)OCCF']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC(=O)C(C)OC(C)(C)CF', 'CCC(C)=CC=NC=CC1CC1C', 'C=C(C=CC=CC=CC)C1=CC=[N+]2C(C=CC[N+]2=O)CC1', 'CC(C=CC=CCO)CCCCCC=C1C=CN1', 'CCCCCC(C)CC1CC2=CC1NC2(C)C', 'CC=C(C)C[O+](C)CC(C)C=C1CC=C1CC(C)N(NN)C12CCC(C=CC(C)=CCC)CC=C1C2', 'C=C(CCCC)C(O)C=[O+]OC(C)C1C=CC=CCC1', 'CC=CCC(=CC)CC(C)c1cc(CC)cc(CC)c1C', 'C=NC=C=CCC1CNCNCCN1', 'C=CC(CCN(C)N(C)CC(=C)CC(C)C)OCCF']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the strength of inhibition by 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' on the cell '1240193'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"], \"cell_name\": \"1240193\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.881502]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' and organelle '1240193' is 0.881502."
            },
            {
                "from": "human",
                "value": "Create molecules optimized for a z-score of -4.054651 for the cell line '1240182'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C', 'CC1=CCC2=C(C2)C(C)C1', 'CC(C)=C=CC(C)CC(C)NC1=C2CC1[N+](C)=CC21C=NO1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(NCCC(=C)C(C)CC=CCCC)C(=O)CNN', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CC=C1OC=C2CC(=C1C)C=CC2C']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C', 'CC1=CCC2=C(C2)C(C)C1', 'CC(C)=C=CC(C)CC(C)NC1=C2CC1[N+](C)=CC21C=NO1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(NCCC(=C)C(C)CC=CCCC)C(=O)CNN', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CC=C1OC=C2CC(=C1C)C=CC2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess how much inhibition 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' causes in the cell '906795'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"], \"cell_name\": \"906795\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.935490]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' and organelle '906795' is 2.935490."
            },
            {
                "from": "human",
                "value": "Generate compounds reaching a z-score of -6.190392 for '1331032'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1331032\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(C)(CN)CCC=C(C)C1', 'COC(C)=CC1CCCCNC1OC(C)O', 'C=CCN(CCC=[N+](C)c1[n+](C)c2nc[n+]1=2)C(N)N', 'CC=C(C=CCCC)C=NCN=NCC', 'C=NC=C(C)NN=C', 'C=C1C=CCC(CCC)CC(C)=CC=CCCC(C)N1', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'C=CC(=C)CCC(C)CC=NN(C)C[N+](C)=C1C(=N)NC(C)=[N+](C)CC=C1CC(C)[N+](=C)C=NN', 'CC[N+](C)=C(C=BNN)CN(C)NCCNC=NC', 'CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CC1=C(C(C)CC)C(CCCOC)=CCCC(C)(CN)CCC=C(C)C1', 'COC(C)=CC1CCCCNC1OC(C)O', 'C=CCN(CCC=[N+](C)c1[n+](C)c2nc[n+]1=2)C(N)N', 'CC=C(C=CCCC)C=NCN=NCC', 'C=NC=C(C)NN=C', 'C=C1C=CCC(CCC)CC(C)=CC=CCCC(C)N1', 'C=C(CN)C(C)C(C)CC(O)CCCCC', 'C=CC(=C)CCC(C)CC=NN(C)C[N+](C)=C1C(=N)NC(C)=[N+](C)CC=C1CC(C)[N+](=C)C=NN', 'CC[N+](C)=C(C=BNN)CN(C)NCCNC=NC', 'CCC=CC1C=CCN(CC(=CCC2CC=COC2)CC)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' reduces the cell '1240153' growth."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\"], \"cell_name\": \"1240153\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.968432]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '1240153' is 2.968432."
            },
            {
                "from": "human",
                "value": "Generate a set of molecules matching a z-score of -6.190392 for '1659819'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=[N+](C)C=C1CNC(C)N(CC2OC2CCC)C(C2C=NN2)=N1', 'C=C(C)C=C(C)C1CC1', 'CCCCC1C2(C3CCNN3)C(CO)=NC3=C(CCN(CNC)C(N)=NCN(CC)NN=NCC2[N+]1(C)C)N3', 'CNCC=[N+](C)CCCC=CN(C)C', 'CCC(CCCN)CC1(C)CC(C)(C)C(C)(C=CCCCCCN(C)C2CC2(C)C)CC(C)NC1CC', 'C=[O+]CC(C=C(C)CCCC=CC)=CC', 'C=[N+](N=BNBCCBC)C(N)CC', 'CC=CC1=CCOC(C)CC=CCC1', 'CB(CNC(C)N)NCC(C)B1BC1', 'C=CCC1C(N)NC=CC=[N+](C)CNC1CCC1=C(C=N)C=C1C=CC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=[N+](C)C=C1CNC(C)N(CC2OC2CCC)C(C2C=NN2)=N1', 'C=C(C)C=C(C)C1CC1', 'CCCCC1C2(C3CCNN3)C(CO)=NC3=C(CCN(CNC)C(N)=NCN(CC)NN=NCC2[N+]1(C)C)N3', 'CNCC=[N+](C)CCCC=CN(C)C', 'CCC(CCCN)CC1(C)CC(C)(C)C(C)(C=CCCCCCN(C)C2CC2(C)C)CC(C)NC1CC', 'C=[O+]CC(C=C(C)CCCC=CC)=CC', 'C=[N+](N=BNBCCBC)C(N)CC', 'CC=CC1=CCOC(C)CC=CCC1', 'CB(CNC(C)N)NCC(C)B1BC1', 'C=CCC1C(N)NC=CC=[N+](C)CNC1CCC1=C(C=N)C=C1C=CC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CN1C(=O)N2C=NC(=C2N=N1)C(=O)N''s inhibitory action on the cell '910698'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C(=O)N2C=NC(=C2N=N1)C(=O)N\"], \"cell_name\": \"910698\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.467144]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C(=O)N2C=NC(=C2N=N1)C(=O)N' and organelle '910698' is 4.467144."
            },
            {
                "from": "human",
                "value": "Please generate molecules achieving a z-score of -4.054651 for the cell line '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CC=[N+](CC=CCCCNC(CCCC)CCCC(C)C)CCC', 'C=C(N)C=CN=C1CC1NC', 'C=C(C)C(C)CCCCC=CCC(C)CCCC(C)CC', 'CB(C)n1nc(OBB(N)CN)[nH]1', 'CC=C(CC(NC(C)CNCO[N+]1(C)CC1=N)C1=C(C)C1)C(N)C=CN(C)C=C(C)CCC', 'CCN=[N+](C)NC(C)C', 'COC(CC1CCC1C)C1C=CC1', 'CC=C(C=CCC)CC1CCC=CCC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)CC)CC(C)C=C(C)CC1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CC=[N+](CC=CCCCNC(CCCC)CCCC(C)C)CCC', 'C=C(N)C=CN=C1CC1NC', 'C=C(C)C(C)CCCCC=CCC(C)CCCC(C)CC', 'CB(C)n1nc(OBB(N)CN)[nH]1', 'CC=C(CC(NC(C)CNCO[N+]1(C)CC1=N)C1=C(C)C1)C(N)C=CN(C)C=C(C)CCC', 'CCN=[N+](C)NC(C)C', 'COC(CC1CCC1C)C1C=CC1', 'CC=C(C=CCC)CC1CCC=CCC1C', 'C=CCCCCC=CCCCCCNNC1NC1N', 'C=C(C)CCC(C)(CF)CC(C=CC)CC1=CCC2(COCC)OCC(C)C(=C2C(F)C(C)(C)CCC(C)CC)CC(C)C=C(C)CC1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' impacts the growth of the cell '1240224'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\"], \"cell_name\": \"1240224\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.458680]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' and organelle '1240224' is 3.458680."
            },
            {
                "from": "human",
                "value": "Design compounds to meet a z-score of -4.054651 for '684059'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684059\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCNCC1C2=NCC(NC3(C)CN=CC=C4C=C(CCC1NC)C4NC3)C([N+]1=C(NC)C=C1)C2C', 'C=C(CCC(C)CN1C2=C([N+](=CC)CC(CC)CC)C(C)(CC)CC21)C1(C)CC1', 'CC(C)=CC1=C(C)CC(C(C)CNCN)CN=CN(NC2C=CC2)C(=CCN)CC1', 'B=C(C)C(CC)N(CCB)NC', 'C=C(C)CC(C=C1[O+]=CN(C)NN1CCC)N1CN1CC(=CC)NC', 'C=S=C1CC(CC2CC2=C)C(C)=COC(C)[N+](C)(CCCCC)N1', 'C=[N+]1CN=CN=[N+](C)CCCC=C(C)N(N=CC(C=CC=CN)CN)CC=NNC1C', 'C=C(C)NCC(C)=CN=CC=[N+]1CNCC=C[O+](OC(=C)C(C)OCCC)C1', 'C[O+]=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'CBCN=C(C)CC(CNCNCC)N(CC=NCNC=NC)C(C)(N)NC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCNCC1C2=NCC(NC3(C)CN=CC=C4C=C(CCC1NC)C4NC3)C([N+]1=C(NC)C=C1)C2C', 'C=C(CCC(C)CN1C2=C([N+](=CC)CC(CC)CC)C(C)(CC)CC21)C1(C)CC1', 'CC(C)=CC1=C(C)CC(C(C)CNCN)CN=CN(NC2C=CC2)C(=CCN)CC1', 'B=C(C)C(CC)N(CCB)NC', 'C=C(C)CC(C=C1[O+]=CN(C)NN1CCC)N1CN1CC(=CC)NC', 'C=S=C1CC(CC2CC2=C)C(C)=COC(C)[N+](C)(CCCCC)N1', 'C=[N+]1CN=CN=[N+](C)CCCC=C(C)N(N=CC(C=CC=CN)CN)CC=NNC1C', 'C=C(C)NCC(C)=CN=CC=[N+]1CNCC=C[O+](OC(=C)C(C)OCCC)C1', 'C[O+]=C(C(C)=CC1=CCC1C)C1=CCCCC[O+]=CCC=C1', 'CBCN=C(C)CC(CNCNCC)N(CC=NCNC=NC)C(C)(N)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' inhibits the cell '906791'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"906791\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.882543]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' and organelle '906791' is 4.882543."
            },
            {
                "from": "human",
                "value": "Produce a range of molecules with the target z-score of -4.054651 for '905957'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"905957\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC=CN(CC)NN', 'CC=CCC=[O+]CC(C)CCCN', 'C[O+]=CN=C(C)C(C)CO', 'CCOC[O+](C)OC(C)C(C)CC(C)C', 'CC1CN1N(N)NCC=C(NN)C1=CCCC1', 'CCC=CC(=CC=C(C=CCCC(C)C)CCC)CCC1(C)CC1=C(C)CC(C)C(O)=CC(C)C(C)C', 'CCC(=CC=C1CC1)C(C)(C)C=CCC=C(C)CC=CO', 'C=C(CC)OCC(C)(CC=CCC(C)CC1CO1)CCC(C)CCCCCCCF', 'CBB1BBN1', 'C=CC1=C2CC(=[O+]C1)CCC(ONC(C)N(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC=CN(CC)NN', 'CC=CCC=[O+]CC(C)CCCN', 'C[O+]=CN=C(C)C(C)CO', 'CCOC[O+](C)OC(C)C(C)CC(C)C', 'CC1CN1N(N)NCC=C(NN)C1=CCCC1', 'CCC=CC(=CC=C(C=CCCC(C)C)CCC)CCC1(C)CC1=C(C)CC(C)C(O)=CC(C)C(C)C', 'CCC(=CC=C1CC1)C(C)(C)C=CCC=C(C)CC=CO', 'C=C(CC)OCC(C)(CC=CCC(C)CC1CO1)CCC(C)CCCCCCCF', 'CBB1BBN1', 'C=CC1=C2CC(=[O+]C1)CCC(ONC(C)N(C=CCCC)C(C(=C)C)C1OCCCC=C1CC)=C(C)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' on the cell '905942'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\"], \"cell_name\": \"905942\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.258509]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' and organelle '905942' is 2.258509."
            },
            {
                "from": "human",
                "value": "Create molecules optimized for a z-score of -4.054651 for the cell line '1240182'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C', 'CC1=CCC2=C(C2)C(C)C1', 'CC(C)=C=CC(C)CC(C)NC1=C2CC1[N+](C)=CC21C=NO1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(NCCC(=C)C(C)CC=CCCC)C(=O)CNN', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CC=C1OC=C2CC(=C1C)C=CC2C']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1CCC(CC(C)CC(C)CC2CC[O+](C)C2=C)=CCOCS(O)=CCC(C)C1', 'C=CC[O+](CCC=C(C)c1c(CCC)c2cc[n+]1=2)CNCC(=C)C', 'CC1=CCC2=C(C2)C(C)C1', 'CC(C)=C=CC(C)CC(C)NC1=C2CC1[N+](C)=CC21C=NO1', 'C=C(C)CCC(C)=CC=CC=C1COCC=C[O+](OC(=C)C(C)OCOC)C1', 'CCCCN=[O+]C(N)=CC=C(C)CC=CCC=CC1CCC1', 'CC=CN=CC(C)N1C=CC(=CC(C)=CC)C(C)C1', 'C=C(NCCC(=C)C(C)CC=CCCC)C(=O)CNN', 'CC(O)C=CC1=C2CCC=C(N)C=C3OC3C(C)CC3C4=CC3CC(NC3C=CC23C=C4)[O+](C)C(C)C1', 'CC=C1OC=C2CC(=C1C)C=CC2C']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition power of 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' against the cell '906841'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl\"], \"cell_name\": \"906841\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.356053]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1CC2=CC(=O)C(=CO2)OCCCCCSC3=C4C=CC(=CC4=NC=C3)C(F)(F)F.Cl.Cl' and organelle '906841' is 4.356053."
            },
            {
                "from": "human",
                "value": "Create a set of molecules that have a z-score of -6.190392 for '1240166'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1240166\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['COC(C)=CC1BCCCNC1', 'CCCC(C=NC)CCC(C)C1N(CC=CCCCCC(CC)CC)N1C(C)C', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1', 'CCC1(C)CCNC(C)(CC(N)C(C)N)CC=CNC1', 'C=C1CC=CC=C2CC(C=CO)=CC(CC)C=NC3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC1=C(C)[N+]2=[N+](CCNC=C1)NC(CCN)CC2', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CCC(C=CC=CCC(F)(CO)NC)=C(N)N1C=CCC1C(C)=CC=CF', 'CCCCC(=CC(C)C=C(C)C(C)N)NC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC(C)=CC1BCCCNC1', 'CCCC(C=NC)CCC(C)C1N(CC=CCCCCC(CC)CC)N1C(C)C', 'CCCCCC=CC(C)C1(C)C[O+](CCC(C)O)C2=CC(=C2)O1', 'CCC1(C)CCNC(C)(CC(N)C(C)N)CC=CNC1', 'C=C1CC=CC=C2CC(C=CO)=CC(CC)C=NC3CC=[O+]CC1(C)C(CC=C3C)C2', 'CC1=C(C)[N+]2=[N+](CCNC=C1)NC(CCN)CC2', 'CCC=CC(C)C(=CC(C)OC)C1C[O+]1C', 'CCC(C)CCCCN(CO)CC1C=CC1', 'CCC(C=CC=CCC(F)(CO)NC)=C(N)N1C=CCC1C(C)=CC=CF', 'CCCCC(=CC(C)C=C(C)C(C)N)NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate how effectively 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' inhibits the cell '908120'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7\"], \"cell_name\": \"908120\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.693844]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CN(CCC1N2C3=CC=CC=C3NC2=O)CC4=CC=C(C=C4)C5=NC6=CC7=C(C=C6N=C5C8=CC=CC=C8)N=CN7' and organelle '908120' is 1.693844."
            },
            {
                "from": "human",
                "value": "Create molecules to achieve the target z-score of -4.054651 for the cell line '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CNBBB1BC[NH+]2B(C3N(CN)BN3N2CNC)C2(CC(N)C1)NCN2N=N', 'CC=C(BCCC)N1CCCB=[N+]2C(BCB=BNNC)NNC12', 'N=[N+]1NCN(NCNCNN)N1', 'BBBC(NC)N1CB1BB', 'CBC1(C)N[N+]2(N)CB(C)CN(C)NCC(=CC)C(N)CCC(=NC)N12', 'CC=CCC(=CC)CC(C(CCC(C)N)NC=CCCC)C1(C)CCCC(N)(N(O)NCC)C1(C)CO', 'BB(CC)C(=C)C(N=NCN)C(C)(NNN(C)CCN)NC(=C)N', 'C=CCC[N+]1=C[N+](N)=C1', 'C=C=CNC1(CC)NC(=C)OCC(C)C(C)(C)N1', 'CB(C)CC1(N)NB(C)CC=C(N)CN(BN)NN(NN)C=CN1N']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CNBBB1BC[NH+]2B(C3N(CN)BN3N2CNC)C2(CC(N)C1)NCN2N=N', 'CC=C(BCCC)N1CCCB=[N+]2C(BCB=BNNC)NNC12', 'N=[N+]1NCN(NCNCNN)N1', 'BBBC(NC)N1CB1BB', 'CBC1(C)N[N+]2(N)CB(C)CN(C)NCC(=CC)C(N)CCC(=NC)N12', 'CC=CCC(=CC)CC(C(CCC(C)N)NC=CCCC)C1(C)CCCC(N)(N(O)NCC)C1(C)CO', 'BB(CC)C(=C)C(N=NCN)C(C)(NNN(C)CCN)NC(=C)N', 'C=CCC[N+]1=C[N+](N)=C1', 'C=C=CNC1(CC)NC(=C)OCC(C)C(C)(C)N1', 'CB(C)CC1(N)NB(C)CC=C(N)CN(BN)NN(NN)C=CN1N']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory potency of 'C/C/1=C\\CC[C@@]2([C@H](O2)[C@@H]3[C@@H](CC1)C(=C)C(=O)O3)C' against the cell '688010'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C/C/1=C\\CC[C@@]2([C@H](O2)[C@@H]3[C@@H](CC1)C(=C)C(=O)O3)C\"], \"cell_name\": \"688010\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.261695]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C/C/1=C\\CC[C@@]2([C@H](O2)[C@@H]3[C@@H](CC1)C(=C)C(=O)O3)C' and organelle '688010' is 1.261695."
            },
            {
                "from": "human",
                "value": "Build molecules to reach a z-score of -4.054651 for the cell line '1240182'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F''s action on the cell '909784'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"909784\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.284095]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '909784' is 3.284095."
            },
            {
                "from": "human",
                "value": "Create molecules optimized to meet a z-score of -6.190392 for '1659819'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CCC(C)(C)C(C)N', 'C=C(C)OC(=C)C(C)C1=C(C(C)(C)F)C=[O+]1', 'C=C(C)CC=C[N+]1=CC(N)=CN=NCNNCN1', 'CC1COC=C2C=CC=CCCC(NNN(C)N)=CCCC2=N1', 'CCC=CCC=CC[O+](COO)C(C)C', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'C=C(CC(N)NCCC(C)=N)C(N=CCC=CC)C(N)(CC)C(C)C(C)C[N+](CC1C=CC1)=NC']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CCC(C)(C)C(C)N', 'C=C(C)OC(=C)C(C)C1=C(C(C)(C)F)C=[O+]1', 'C=C(C)CC=C[N+]1=CC(N)=CN=NCNNCN1', 'CC1COC=C2C=CC=CCCC(NNN(C)N)=CCCC2=N1', 'CCC=CCC=CC[O+](COO)C(C)C', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=C1BNCCN(C(=C)CC)CNN=CNCCC1C', 'C=CCC(C)=C1C=CCC2=C(C=CNNCC(CCC)=C1C=C)C2', 'CNC(CNC=CCC(C)C)=C(C)C1=CC1C', 'C=C(CC(N)NCCC(C)=N)C(N=CCC=CC)C(N)(CC)C(C)C(C)C[N+](CC1C=CC1)=NC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory impact 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' has on the cell '925340'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"925340\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.256628]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '925340' is 5.256628."
            },
            {
                "from": "human",
                "value": "Build molecules to reach a z-score of -4.054651 for the cell line '1240182'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"1240182\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['COC1=CCC(N=CN=CC(C)C(C)C2C(C)C2(C)NN)C1', 'CCC(C)C1CC(CCF)C1', 'C=C(CC)CC(=C)COOCNCCCCN=C1CC1', 'CCCC1C(NCCC2=CC=CC=CCCC2)=CC(C)C(CC=C(C)N)=CCCC=CC1CC', 'C=CC1=[N+](C=CCC)NC12CC=CN(C=N)CCC2(C)C(C)CCC', 'CC(NCNC(=N)CN)NC1N(N)CCC=C2C(CN2C)NNN1C', 'CC1=C2C=CC(NC(CCC=C3C=C3)CC=NCC2)N2C=CC2CC1', 'CCON(C)CCCCC(CON)NC', 'C=CC(C)(C=[N+]1CCC=C[O+]1C1C=CC(CCCC)CC1)CCCC(N)(CC)CC(C)CC', 'C=C1CCCC(C)(C(C)CC=CCCC)C=C(O)OCC1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' in limiting the cell '1331034'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\"], \"cell_name\": \"1331034\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.244552]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' and organelle '1331034' is 2.244552."
            },
            {
                "from": "human",
                "value": "Design molecules that reach a z-score of -4.054651 for '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['CB(N=N)C(=CCC(N)(B=B[N+]1=CC=CCB1)B(N)N)C(C)N', 'C=CC(C)C(C)CCC1=C=C=C1', 'BCBN(NCB(N)N)C1=BB(CN2CB3CCCC32)B1', 'CBBNB(C)BB(CCN)N=NN1CC=NN1', 'BB1BB(NCN)C(B=N)B1', 'CC(S)CC=C1CO1', 'C=CC[N+]1=CC[N+]2=N[N+]3=C(BB(C)CN(C)NC(NBCN)NCBC23)N1', 'BNC(B)N=[N+](C)C=NC', 'C=NCC1CC=CC2CC(=CC1)CC2C', 'C=C=C1CC1C1C2CCNC(C)C1CC([O+](C)N=CN)C2']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['CB(N=N)C(=CCC(N)(B=B[N+]1=CC=CCB1)B(N)N)C(C)N', 'C=CC(C)C(C)CCC1=C=C=C1', 'BCBN(NCB(N)N)C1=BB(CN2CB3CCCC32)B1', 'CBBNB(C)BB(CCN)N=NN1CC=NN1', 'BB1BB(NCN)C(B=N)B1', 'CC(S)CC=C1CO1', 'C=CC[N+]1=CC[N+]2=N[N+]3=C(BB(C)CN(C)NC(NBCN)NCBC23)N1', 'BNC(B)N=[N+](C)C=NC', 'C=NCC1CC=CC2CC(=CC1)CC2C', 'C=C=C1CC1C1C2CCNC(C)C1CC([O+](C)N=CN)C2']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' can inhibit the cell '1240184'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1240184\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.673926]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1240184' is 5.673926."
            },
            {
                "from": "human",
                "value": "Produce molecules that meet a z-score of -4.054651 for '684055'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-4.054651\",\"cell_line\": \"684055\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'C=C(F)CC1CC(=CCC2C(C)=C3CCNCC2C(CCN)C3)C(O)C1', 'CC=C(NCN=CCCC)[N+](C=CCC1=CCNCC1)=CC(C)CNC', 'CC=C(C)C(C=CC=CCC)[O+](C)CCN', 'C=[N+](CC(=CCCCC)CCC1CC1)C(F)C1(C)CCCO[O+]=CC=CCCC=CC(C)CC1C', 'C=CCC(C=C)CCC(CC=CC1=CC1)C(C)CC(C)C(=C)N', 'CNC1CN1CC=C1CCN(N)NNNC(N)(N=N)C1', 'CCC=CCCC1CCNC(CC2(N)CCOC=CCC(C)C2)O1', 'C=[N+]=CN=CC1C(=C)NC1C', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C(C)CC2=CCC1[O+]=C1N=C1CCC2', 'C=C(F)CC1CC(=CCC2C(C)=C3CCNCC2C(CCN)C3)C(O)C1', 'CC=C(NCN=CCCC)[N+](C=CCC1=CCNCC1)=CC(C)CNC', 'CC=C(C)C(C=CC=CCC)[O+](C)CCN', 'C=[N+](CC(=CCCCC)CCC1CC1)C(F)C1(C)CCCO[O+]=CC=CCCC=CC(C)CC1C', 'C=CCC(C=C)CCC(CC=CC1=CC1)C(C)CC(C)C(=C)N', 'CNC1CN1CC=C1CCN(N)NNNC(N)(N=N)C1', 'CCC=CCCC1CCNC(CC2(N)CCOC=CCC(C)C2)O1', 'C=[N+]=CN=CC1C(=C)NC1C', 'CC(BN)=NCBC1CC(CN)B1BCB=BBB=BN']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition effect of 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' on the cell '1503367'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7\"], \"cell_name\": \"1503367\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.326199]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=CN(C5=CC=CC=C54)C6CCN(CC6)CC7=CC=CC=N7' and organelle '1503367' is 2.326199."
            },
            {
                "from": "human",
                "value": "Please provide a series of molecules with a z-score of -6.190392 for '1659819'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_generation\", \"arguments\": {\"z-score\": \"-6.190392\",\"cell_line\": \"1659819\"}}"
            },
            {
                "from": "observation",
                "value": "{\"gen_smiles\": ['C=C1C=CCC(C2CCC=CCC2)CC(C)=CC=CCCC(C)N1', 'C=C(CC(C)CC=NNC(C(C)=CCN=[N+]1CCCC(NCC=CC2CC2)NCCCCN1)=C(N)CCC)C1=CCC1', 'CC(C)=CCCOC(CC(O)CCCN)C1CCC(C)C1', 'CCCNC1C=C2C=C(C2)SCCC1', 'CC[N+](N=NC1CN1C)=C(C)C', 'C=CCC=CN(CCC=CCC(C)CC=C(CC)C(C)(C=CCC)C(C)CC)CC(C(C)C)C(C)C', 'CC(=CN=CC1=CC1)NN=CCCC1CNC1', 'CCC(CCCN)CC1(C)CC(C)(C)C(C)(C=CCCCCCN(C)C2CC2(C)C)CC(C)NC1CC', 'C=C(CC(C)CC)[N+](=C)COCC', 'CCCC1CCC[O+](C)CC(OCO)CCC(O)C1']}"
            },
            {
                "from": "gpt",
                "value": "The molecules that best meet your requirements are as follows: ['C=C1C=CCC(C2CCC=CCC2)CC(C)=CC=CCCC(C)N1', 'C=C(CC(C)CC=NNC(C(C)=CCN=[N+]1CCCC(NCC=CC2CC2)NCCCCN1)=C(N)CCC)C1=CCC1', 'CC(C)=CCCOC(CC(O)CCCN)C1CCC(C)C1', 'CCCNC1C=C2C=C(C2)SCCC1', 'CC[N+](N=NC1CN1C)=C(C)C', 'C=CCC=CN(CCC=CCC(C)CC=C(CC)C(C)(C=CCC)C(C)CC)CC(C(C)C)C(C)C', 'CC(=CN=CC1=CC1)NN=CCCC1CNC1', 'CCC(CCCN)CC1(C)CC(C)(C)C(C)(C=CCCCCCN(C)C2CC2(C)C)CC(C)NC1CC', 'C=C(CC(C)CC)[N+](=C)COCC', 'CCCC1CCC[O+](C)CC(OCO)CCC(O)C1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_generation\", \"description\": \"Generate molecules using a regression model for drug-cell response\", \"parameters\": {\"type\": \"object\", \"properties\": {\"z-score\": {\"type\": \"float\", \"description\": \"Drug response target value, in micromolar, a value greater than 0, the minimum value is about 0.001, and the maximum value is 1000\"}, \"cell_line\": {\"type\": \"string\", \"description\": \"The specified cell line string\"}}}, \"required\": [\"z-score\", \"cell_line\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' impacts the growth of the cell '1240224'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\"], \"cell_name\": \"1240224\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.458680]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' and organelle '1240224' is 3.458680."
            },
            {
                "from": "human",
                "value": "Create optimized versions of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' for a target z-score of 1.384099 in '906814', restricting changes to atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.384099, \"cell_line\": \"906814\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' reduces the cell '1240153' growth."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\"], \"cell_name\": \"1240153\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.968432]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '1240153' is 2.968432."
            },
            {
                "from": "human",
                "value": "Develop improved versions of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to achieve a z-score of -0.233200 for '1240185', limiting modifications to the atoms specified in [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.233200, \"cell_line\": \"1240185\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NO)C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)=C(O)CCCCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the degree of suppression by 'CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O' on the cell '905943'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O\"], \"cell_name\": \"905943\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.161068]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=C(N1)/C=C\\2/C(=C/C(=C/3\\C=C4C=CC=CC4=N3)/N2)OC)C.CS(=O)(=O)O' and organelle '905943' is -2.161068."
            },
            {
                "from": "human",
                "value": "Develop new molecules from 'CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' that meet a z-score of -0.325761 for '910919', focusing on atom modifications within [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.325761, \"cell_line\": \"910919\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C[C@@H](C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COCc1ccccc1CO', 'Cc1cccc(CO)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(CO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=C(C=C(C=C4)Cl)Cl' inhibition on the cell '905958'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=C(C=C(C=C4)Cl)Cl\"], \"cell_name\": \"905958\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.373628]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1C=C(C2=CC=CC=C21)C3=C(C(=O)NC3=O)C4=C(C=C(C=C4)Cl)Cl' and organelle '905958' is 3.373628."
            },
            {
                "from": "human",
                "value": "Generate refined compounds from 'COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O' aiming for a z-score of 2.500650 in '1290905', focusing on atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.500650, \"cell_line\": \"1290905\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CC1(O)C(=O)Nc2c(Cl)ccc(Cl)c21)c1ccc(CO)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CC1(O)C(=O)Nc2c(Cl)ccc(Cl)c21)c1ccc(CO)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the level of 'C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3''s inhibitory effect on the cell '925343'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3\"], \"cell_name\": \"925343\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.457888]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(C1)[C@@H](CC#N)N2C=C(C=N2)C3=C4C=CNC4=NC=N3' and organelle '925343' is 4.457888."
            },
            {
                "from": "human",
                "value": "Generate new compounds based on 'COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O' with a z-score of 0.914856 for '1660034', focusing adjustments within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.914856, \"cell_line\": \"1660034\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC=C(C=C1)C(=O)CC2(C3=C(C=CC(=C3NC2=O)Cl)Cl)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CC1(O)C(=O)Nc2c(Cl)ccc(Cl)c21)c1ccc(CO)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CC1(O)C(=O)Nc2c(Cl)ccc(Cl)c21)c1ccc(CO)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the effect of 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' in stopping the cell '906831'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O\"], \"cell_name\": \"906831\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.445943]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CN(C(=O)N=C1N)[C@H]2[C@H]([C@@H]([C@H](O2)CO)O)O' and organelle '906831' is 2.445943."
            },
            {
                "from": "human",
                "value": "Create optimized molecular structures from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' with a z-score of -0.706978 for '907055', modifying only the atoms listed in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.706978, \"cell_line\": \"907055\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the suppressive impact of 'CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C' on the cell '1298531'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C\"], \"cell_name\": \"1298531\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.223709]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCC1=NC(=C(S1)C2=CC(=NC=C2)NC(=O)C3=CC=CC=C3)C4=CC(=CC=C4)C' and organelle '1298531' is 3.223709."
            },
            {
                "from": "human",
                "value": "Produce optimized derivatives of 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' aiming for a z-score of 1.022200 in '905942', focusing modifications on the atoms listed in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.022200, \"cell_line\": \"905942\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]F)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' inhibits the cell '909262'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\"], \"cell_name\": \"909262\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.274491]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' and organelle '909262' is 2.274491."
            },
            {
                "from": "human",
                "value": "Design optimized molecules based on 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5' to reach a z-score of 1.521055 for '1240131', focusing on the atoms specified in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.521055, \"cell_line\": \"1240131\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=C(C=C3)NC(=O)C4=CC=CC=C4)OCCCN5CCOCC5\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(Nc1ccc(Nc2ncnc3c2C=C2C(O)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C(F)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'CC1C2=Cc3c(Nc4ccc(NC(=O)c5ccccc5)cc4)ncnc3C1C2OCCCN1CCOCC1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCCN5CCOCC5[SH]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCC(N5CCOCC5)[O+]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3cc(OCCCN4CCOCC4)c(CO)cc23)cc1)c1ccccc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(Nc1ccc(Nc2ncnc3c2C=C2C(O)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C(F)C3C2OCCCN2CCOCC2)cc1)c1ccccc1', 'CC1C2=Cc3c(Nc4ccc(NC(=O)c5ccccc5)cc4)ncnc3C1C2OCCCN1CCOCC1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCCN5CCOCC5[SH]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3c2C=C2C4OCCC(N5CCOCC5)[O+]2C34)cc1)c1ccccc1', 'O=C(Nc1ccc(Nc2ncnc3cc(OCCCN4CCOCC4)c(CO)cc23)cc1)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how effectively 'CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O' inhibits the cell '753589'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O\"], \"cell_name\": \"753589\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.905496]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O' and organelle '753589' is 0.905496."
            },
            {
                "from": "human",
                "value": "Generate optimized analogs from 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' with a z-score target of 1.496324 in '910850', making adjustments to atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.496324, \"cell_line\": \"910850\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1CO']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['NC1=NONC1C1=NC2C=NC(Oc3cccc(NC(=O)c4ccc(OCCN5CCOCC5)cc4)c3)CC2N1CO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' can hinder the cell '1240161'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"], \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-4.701002]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' and organelle '1240161' is -4.701002."
            },
            {
                "from": "human",
                "value": "Produce optimized molecules derived from 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' that achieve a z-score of 1.451265 for '735784', concentrating on atoms within [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.451265, \"cell_line\": \"735784\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc(CO)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' on the cell '1240189'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\"], \"cell_name\": \"1240189\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.747780]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' and organelle '1240189' is 4.747780."
            },
            {
                "from": "human",
                "value": "Design 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC'-based molecules with optimized features to reach a z-score of 0.483637 in '906828', optimizing the atoms specified by [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.483637, \"cell_line\": \"906828\", \"mask\": [1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CNC(=O)c1ccc(NC2CC(Nc3ccccc3)C3C(CO)=C=NC3N2)nc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' inhibition potential on the cell '688014'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4\"], \"cell_name\": \"688014\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.490365]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4' and organelle '688014' is 2.490365."
            },
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' aiming for a z-score of 1.581680 in '908476', with changes restricted to the atoms in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.581680, \"cell_line\": \"908476\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how effectively 'CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O' inhibits the cell '753589'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O\"], \"cell_name\": \"753589\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.905496]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC[C@@]1(C[C@@H]2C[C@@](C3=C(CCN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)[C@]78CCN9[C@H]7[C@@](C=CC9)([C@H]([C@@]([C@@H]8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC)O' and organelle '753589' is 0.905496."
            },
            {
                "from": "human",
                "value": "Design new compounds based on 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' to meet a z-score of 0.260880 in '688001', with changes restricted to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.260880, \"cell_line\": \"688001\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' on cell growth in the cell '687820'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"687820\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.381123]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '687820' is 2.381123."
            },
            {
                "from": "human",
                "value": "Generate new molecular forms based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a target z-score of -0.071141 in '907044', with a focus on modifying [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.071141, \"cell_line\": \"907044\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the potency of 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' as an inhibitor for the cell '1240178'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\"], \"cell_name\": \"1240178\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.187011]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' and organelle '1240178' is 3.187011."
            },
            {
                "from": "human",
                "value": "Create specialized versions of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' for a z-score of 0.633030 in '906814', restricting changes to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.633030, \"cell_line\": \"906814\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)C(CO)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(NC2=NC3NC4CCC(N(C)C)C(C4)[O+](O)[NH+]4CNC2C4N3)c1', 'C=CC(=O)Nc1cccc(N2C3=NC(NC4CCC(N(C)C)CC4)NC4C3NC[NH+]4C2CO)c1', 'C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3[O+](CO)N(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' on the cell '925343'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F\"], \"cell_name\": \"925343\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.983428]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' and organelle '925343' is 2.983428."
            },
            {
                "from": "human",
                "value": "Create optimized variants of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to achieve a z-score of 1.993231 in '905957', using [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the atom mask for modification."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.993231, \"cell_line\": \"905957\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how strongly 'C1COCCN1C2=CC(=O)C=C(O2)C3=C4C(=CC=C3)SC5=CC=CC=C5S4' suppresses the cell '687505'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=CC(=O)C=C(O2)C3=C4C(=CC=C3)SC5=CC=CC=C5S4\"], \"cell_name\": \"687505\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.150548]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=O)C=C(O2)C3=C4C(=CC=C3)SC5=CC=CC=C5S4' and organelle '687505' is 4.150548."
            },
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' aiming for a z-score of 1.581680 in '908476', with changes restricted to the atoms in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.581680, \"cell_line\": \"908476\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory rate of 'CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O' on the cell '910933'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O\"], \"cell_name\": \"910933\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-1.028284]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN(CC1=CN=C2C(=N1)C(=NC(=N2)N)N)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(=O)O)C(=O)O' and organelle '910933' is -1.028284."
            },
            {
                "from": "human",
                "value": "Create optimized analogs of 'CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C' targeting a z-score of 0.787292 for '908138', with changes restricted to [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.787292, \"cell_line\": \"908138\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N1C=NC2=C1N=C(N=C2NC3=CC(=CC=C3)NC(=O)C=C)NC4CCC(CC4)N(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(=O)Nc1cccc(NC2=NC(NC3CCCCC3)NC3C2NC[NH+]3C(CO)N(C)C)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how much 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N' reduces growth in the cell '753551'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N\"], \"cell_name\": \"753551\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.881437]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=C(C(=NC=C2OC[C@H]3CCCNC3)C#CC(C)(C)O)N=C1C4=NON=C4N' and organelle '753551' is 3.881437."
            },
            {
                "from": "human",
                "value": "Develop improved analogs of 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' to meet a z-score of 1.946578 in '1298141', modifying atoms as specified in [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.946578, \"cell_line\": \"1298141\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2C(C)CO)c(O)c(O)c1O', 'Cc1cc(S(=O)(=O)c2c(O)cccc2C(C)(C)C)ccc1NC(=O)c1cc(CC=C=C2C=CO2)c(O)c(O)c1O', 'CC(C)(C)c1ccccc1[SH]1(=O)OC2C3=CCc4cc(c(O)c(OO)c4O)C(=O)N(CC=C=C3)c3ccc1cc32', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)C)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=CC(C)=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3c(C(C)CC)cccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)CO', 'C=C=C=C=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccc(C(C)C)cc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC=C=C=C=CCc1cc(C(=O)N(CC)c2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'C=C=CC(=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O)C(C)O', 'CC1C=C=CC1=CCc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the extent of suppression by 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' on the cell '749713'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"749713\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.557942]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C[C@H]1C2=NN=C(N2C3=C(C=C(C=C3)OC)C(=N1)C4=CC=C(C=C4)Cl)C' and organelle '749713' is 2.557942."
            },
            {
                "from": "human",
                "value": "Generate optimized variants of 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' with a target z-score of 0.642109 in '910567', making adjustments to atoms within [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.642109, \"cell_line\": \"910567\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCC(=Cc1ccccn1)C1C2C=C3CN1N=C2CC3Sc1ccccc1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCCCc1ccccc1Sc1ccc2c(c1)N=NC2C=Cc1ccccn1', 'OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1', 'OCC(=Cc1ccccn1)C1C2C=C3CN1N=C2CC3Sc1ccccc1', 'OCOc1cccc(C=CC2N=Nc3cc(Sc4ccccc4)ccc32)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' can hinder the cell '1240161'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4\"], \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-4.701002]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCNC(=O)C1=C(/C(=C/2\\C=C(C(=CC2=O)O)C(C)C)/ON1)C3=CC=C(C=C3)CN4CCOCC4' and organelle '1240161' is -4.701002."
            },
            {
                "from": "human",
                "value": "Develop enhanced molecules based on 'CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O' for a z-score of -0.801363 in '910688', focusing on atoms specified in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.801363, \"cell_line\": \"910688\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)C1=CC=CC=C1CC2=C(C(=C(C(=C2)C(=O)NC3=CC=C(C=C3)S(=O)(=O)C4=CC=CC=C4C(C)(C)C)O)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccccc1S(=O)(=O)c1ccc(NC(=O)c2cc(Cc3ccccc3CCO)c(O)c(O)c2O)cc1', 'C=Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O', 'CC1c2ccccc2Cc2cc(c(O)c(O)c2O)C(=O)N1c1ccc(S(=O)(=O)c2ccccc2C(C)(C)C)cc1', 'C#Cc1ccccc1Cc1cc(C(=O)Nc2ccc(S(=O)(=O)c3ccccc3C(C)(C)C)cc2)c(O)c(O)c1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' against the cell '909721'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"909721\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.763597]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '909721' is 1.763597."
            },
            {
                "from": "human",
                "value": "Produce variants of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' optimized for a z-score of 0.630520 in '1240189', focusing modifications on [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.630520, \"cell_line\": \"1240189\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' restricts the cell '910861'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"910861\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.095557]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '910861' is 1.095557."
            },
            {
                "from": "human",
                "value": "Generate new molecular forms based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a target z-score of -0.071141 in '907044', with a focus on modifying [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": -0.071141, \"cell_line\": \"907044\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(=CCCCCCO)Cc1cccc(O)c1', 'CC(O)Cc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' reduces the cell '1240153' growth."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC\"], \"cell_name\": \"1240153\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.968432]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'COC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC(=C(C(=C3)Br)O)Br)OC' and organelle '1240153' is 2.968432."
            },
            {
                "from": "human",
                "value": "Produce optimized versions of 'CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C' that achieve a z-score of 2.027164 in '753534', focusing on adjustments in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 2.027164, \"cell_line\": \"753534\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CCN1C=C(C(=N1)C2=CC=C(C=C2)NC(=O)N(C)C)C3=C4C=C(NC4=NC=C3)C5=CC(=CC=C5)CN(C)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(c1cccc(C2Cc3c(C4CN(C)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1)N(C)C', 'CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(c1cccc(C2Cc3c(C4CN(C)N=C4c4ccc(NC(=O)N(C)C)cc4)ccnc3N2)c1)N(C)C', 'CN(C)C(=O)Nc1ccc(C2=NN3CC2c2ccnc4c2CC(N4)c2cccc(c2)C(N(C)CO)[OH+]3)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict how much 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' can inhibit the cell '1240184'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1240184\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.673926]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1240184' is 5.673926."
            },
            {
                "from": "human",
                "value": "Create derivatives of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' optimized for a z-score of 0.290607 in '909251', modifying atoms only within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.290607, \"cell_line\": \"909251\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory effect of 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' on the cell '909256'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"909256\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-0.239302]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '909256' is -0.239302."
            },
            {
                "from": "human",
                "value": "Create optimized variants of 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' to achieve a z-score of 1.993231 in '905957', using [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the atom mask for modification."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.993231, \"cell_line\": \"905957\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['Cc1ccc(C2CC(C(=O)NCCCCCCC(=O)NO)NO2)cc1', 'CC1=C(O)C(CCCCNC(=O)C2CC(c3ccc(C)cc3)ON2)CC(NO)O1', 'CCC(C)OC(C)OC(NCCCCCCC(=O)NO)C1CC(c2ccc(C)cc2)ON1', 'O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC=C(CCCCCNC(=O)C1NOC(CC)(c2ccc(C)cc2)C1C)C(=O)N(C)O', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(NCCCCCCC(NO)OCOO)C1CC(c2ccccc2)ON1', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C[OH+]ONC(=O)CCCCCCNC(=O)C1(CC[O+]=CCO)CC(c2ccccc2)ON1', 'C=CC=C(CCCC(OC=O)C(NO)OC)CNC(=O)C1CC(c2ccccc2)ON1', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'CCCC=C(CCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)C(=O)NO', 'O=C1NCCCCCCC(NOCO)OC(O)C12CC(c1ccc(O)cc1)ON2', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1', 'ONC1CCCCCC(O)N2[OH+][O+](COC2C2CC(c3ccccc3)ON2)O1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the strength of inhibition by 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' on the cell '1240193'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"], \"cell_name\": \"1240193\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.881502]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' and organelle '1240193' is 0.881502."
            },
            {
                "from": "human",
                "value": "Synthesize optimized molecules derived from 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' with a target z-score of 1.348766 for '907322', limiting modifications to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.348766, \"cell_line\": \"907322\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)CO)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)CO)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' inhibits the cell '713885' growth."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\"], \"cell_name\": \"713885\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.361746]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' and organelle '713885' is 2.361746."
            },
            {
                "from": "human",
                "value": "Create optimized compounds derived from 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' for a z-score of 1.040100 in '724834', focusing on the atoms listed in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.040100, \"cell_line\": \"724834\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], \"gt\": \"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory level of 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' on the cell '1331025'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\"], \"cell_name\": \"1331025\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.358471]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' and organelle '1331025' is 4.358471."
            },
            {
                "from": "human",
                "value": "Create optimized versions of 'COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O' for a target z-score of 1.384099 in '906814', restricting changes to atoms in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.384099, \"cell_line\": \"906814\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=CC(=CC(=C1OC)OC)/C=C/C(=O)N2CCC=CC2=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['COc1cc(C=CC(=O)N2CCC=CC2=O)cc(C(F)OC)c1', 'COc1cc2cc(c1OC)C(F)C1=CCCN(C(=O)C=C2)C1=O', 'COc1cc(C=CC(=O)N2CCC=CC2=O)cc([OH+]O)c1OC']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition of 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' on the growth of the cell '1298219'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"1298219\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.414053]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '1298219' is 4.414053."
            },
            {
                "from": "human",
                "value": "Generate tailored compounds from 'CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4' targeting a z-score of 0.296694 for '908471', concentrating changes on [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.296694, \"cell_line\": \"908471\", \"mask\": [1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CNC(=O)C1=CC=CC=C1SC2=CC3=C(C=C2)C(=NN3)/C=C/C4=CC=CC=N4\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCc1cccc(Sc2ccc3c(c2)N=NC3C=Cc2ccccn2)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the degree of suppression 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' has on the cell '1240161'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O\"], \"cell_name\": \"1240161\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.024576]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C/C=C\\C(=O)[C@H]([C@H](C/C=C/C2=CC(=CC(=C2C(=O)O1)O)OC)O)O' and organelle '1240161' is 0.024576."
            },
            {
                "from": "human",
                "value": "Design fine-tuned molecules from 'CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO' for a z-score of 0.716661 in '907061', using [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] as the mask for changes."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.716661, \"cell_line\": \"907061\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)[C@@H](C1=CC=CC=C1)C(=O)NC2=CC=C(C=C2)C(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=CC(C(=O)Nc1ccc(C(=O)NO)cc1)c1ccccc1', 'O=C(NO)c1ccc(NC(=O)C(CCO)c2ccccc2)cc1', 'O=C(NO)c1ccc(N(CCO)C(=O)Cc2ccccc2)cc1', 'O=C(NO)c1ccc(NC(=O)C(OCO)c2ccccc2)cc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict 'C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F''s suppression level on the cell '905946'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F\"], \"cell_name\": \"905946\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.981734]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CC=N2)CNC3=C(SC=C3)C(=O)NC4=CC=C(C=C4)OC(F)(F)F' and organelle '905946' is 2.981734."
            },
            {
                "from": "human",
                "value": "Synthesize optimized molecules derived from 'CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O' with a target z-score of 1.348766 for '907322', limiting modifications to atoms in [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.348766, \"cell_line\": \"907322\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)N(CCCNC(=O)NC1=CC=C(C=C1)C(C)(C)C)C[C@@H]2[C@H]([C@H]([C@@H](O2)N3C=C(C4=C3N=CN=C4N)Br)O)O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)CO)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(C)(C)c1ccc(NC(=O)NCCCN(CCO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CC(C)(C)c1ccc(NC(=O)NCCCN(CO)CC2OC(N3CC(Br)c4c(N)ncnc43)C(O)C2O)cc1', 'CN(CCCNC(=O)Nc1ccc(C(C)(C)CO)cc1)CC1OC(N2CC(Br)c3c(N)ncnc32)C(O)C1O']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' on the cell '946354'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"946354\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.294461]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '946354' is 0.294461."
            },
            {
                "from": "human",
                "value": "Produce improved molecules based on 'CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1' with a target z-score of 0.725471 in '753596', using [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0] for guidance on optimizations."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.725471, \"cell_line\": \"753596\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], \"gt\": \"CCCCC(C=O)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CC(C)C)NC(=O)OCC1=CC=CC=C1\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['CC(O)c1cccc(CCO)c1', 'COOc1ccccc1CO', 'CC(O)Cc1cccc(O)c1', 'COCc1cccc(O)c1', 'CCc1cccc(CO)c1', 'OCCOc1ccccc1', 'CCCC(C)c1cccc(CCO)c1', 'CC(C)c1cccc(OO)c1', 'C[O+](O)c1ccccc1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the strength of suppression by 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' in the cell '724831'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F\"], \"cell_name\": \"724831\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.046294]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=NC(=C(S1)C2=NC(=NC=C2)N)C3=C(C(=CC=C3)NS(=O)(=O)C4=C(C=CC=C4F)F)F' and organelle '724831' is 2.046294."
            },
            {
                "from": "human",
                "value": "Create derivatives of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' optimized for a z-score of 0.290607 in '909251', modifying atoms only within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.290607, \"cell_line\": \"909251\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how much 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' restricts the cell '910861'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"910861\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.095557]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '910861' is 1.095557."
            },
            {
                "from": "human",
                "value": "Create derivatives of 'C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O' optimized for a z-score of 0.290607 in '909251', modifying atoms only within [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 0.290607, \"cell_line\": \"909251\", \"mask\": [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"C=CCC1=CC=C/C(=C/NNC(=O)CN2CCN(CC2)CC3=CC=CC=C3)/C1=O\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(CCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NNC=C1C=CC=C(OCO)C1=O', 'O=C(CN1CCN(Cc2ccccc2)CC1)NN(C=C1C=CC=CC1=O)OCO']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C''s capacity to inhibit the cell '1287381'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C\"], \"cell_name\": \"1287381\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.241289]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCOC1=C(C=C2C(=C1)N=CC(=C2NC3=CC(=C(C=C3)F)Cl)C#N)NC(=O)/C=C/CN(C)C' and organelle '1287381' is 3.241289."
            },
            {
                "from": "human",
                "value": "Generate enhanced molecules from 'CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5' aiming for a z-score of 1.581680 in '908476', with changes restricted to the atoms in [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.581680, \"cell_line\": \"908476\", \"mask\": [1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C#N)C1=CC=C(C=C1)N2C3=C4C=C(C=CC4=NC=C3N(C2=O)C)C5=CC6=CC=CC=C6N=C5\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C=C=C(C=C)N1C(=O)N(C)C2C=NC34C=C(C(c5cnc6ccccc6c5)CC3C21)[O+]4COCCO', 'C=C=C(C=CCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)=CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5cccc(C(F)CC)c5c4)cc3C21', 'C=C=C(C(CC=C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC)C(c4cnc5cc(CCO)ccc5c4)CC3C21', 'C=C=C(C(C)CC)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2C=NC3C=C(CC[O+]=CCO)C(c4cnc5ccccc5c4)CC3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(C(F)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCO)N1C(=O)N(C)C2CN(C(C)O)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=C)N1C(=O)N(C)C2CN(C(C)CC=C)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCC)N1C(=O)N(C)C2CN(C(C)CC)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC=C)N1C(=O)N(C)C2CN(OCO)c3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=COCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CCCCCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21', 'C=C=C(C=CC(C)CCCO)N1C(=O)N(C)C2C=Nc3ccc(-c4cnc5ccccc5c4)cc3C21']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory impact of 'C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC' on the cell '753614'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC\"], \"cell_name\": \"753614\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.036804]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@H]1C[C@@H]([C@@H]([C@H](/C=C(/[C@@H]([C@H](/C=C\\C=C(\\C(=O)NC2=CC(=O)C(=C(C1)C2=O)NCC=C)/C)OC)OC(=O)N)\\C)C)O)OC' and organelle '753614' is -2.036804."
            },
            {
                "from": "human",
                "value": "Produce optimized analogs from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' to reach a z-score of 1.167693 in '687596', with modifications limited to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.167693, \"cell_line\": \"687596\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibitory power of 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' on the cell '907171'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO\"], \"cell_name\": \"907171\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.251658]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCN(CCO)CC3=CC=C(C=C3)/C=C/C(=O)NO' and organelle '907171' is -2.251658."
            },
            {
                "from": "human",
                "value": "Develop optimized molecules based on 'CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO' with a z-score target of 1.137021 for '687561', optimizing atoms as defined by [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.137021, \"cell_line\": \"687561\", \"mask\": [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"CC(C)(C)OC(=O)NC1=CC=C(C=C1)C2=CC(=NO2)C(=O)NCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['O=C(CCCCC(=COCO)CNC(=O)C1CC(c2ccccc2)ON1)NO', 'C=CC(C)=C(CCCCCNC(=O)C1CC(c2ccccc2)ON1)C(NO)OC', 'COC(CCCCCCNC(=O)C1CC(c2ccc(O)cc2)ON1)NO', 'O=C(CCCCCCNC(=O)C1CC(c2ccccc2)ON1)NO', 'CC1=CCC2C(c3ccc(C)cc3)ONC2(C(=O)NCCCCCCC(=O)NO)C1C', 'C=CONC(CC(C)CCCCNC(=O)C1CC(c2ccc(C)cc2)ON1)OC', 'Cc1ccc(C2ONC(C(=O)NCCCCCCC(=O)NO)C2CCO)cc1', 'O=C(NCCCCCC(C(=O)NO)[O+](O)CCCCO)C1CC(c2ccccc2)ON1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the strength of inhibition by 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' on the cell '1240193'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"], \"cell_name\": \"1240193\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.881502]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' and organelle '1240193' is 0.881502."
            },
            {
                "from": "human",
                "value": "Produce optimized analogs from 'COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO' to reach a z-score of 1.167693 in '687596', with modifications limited to [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.167693, \"cell_line\": \"687596\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], \"gt\": \"COC1=C(C=C2C(=C1)N=CN=C2NC3=CC=CC(=C3)C#C)OCCCCCCC(=O)NO\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['C#Cc1cccc(Nc2ncnc3c2C2C(F)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(NO)OC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3c2C2CC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C(O)C(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCC(F)CCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc([OH+]O)c(OCCCCCCC(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3c2C2C4OCCCCCCC(=O)NOC2C4=C3)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCCC(NO)OC)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc(C)c(OCCCCCC(F)C(=O)NO)cc23)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCC(CCCCC(=O)NO)C4)c1', 'C#Cc1cccc(Nc2ncnc3c2C2SC(=C3)C2OCCCCCCC(=O)NO)c1', 'C#Cc1cccc(Nc2ncnc3cc4c(cc23)OCCCCCC(C(=O)NO)[O+]4F)c1', 'C#Cc1cccc(Nc2ncnc3cc(CO)c(OCCCCCCC(=O)NO)cc23)c1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the effect of 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' on restricting the cell '949160'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"949160\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.940421]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '949160' is 1.940421."
            },
            {
                "from": "human",
                "value": "Design optimized forms of 'CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C' with a z-score of 1.287547 in '687561', making adjustments to atoms listed in [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1]."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_optimization\", \"arguments\": {\"z-score\": 1.287547, \"cell_line\": \"687561\", \"mask\": [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1], \"gt\": \"CN(C1=C(C=CC=N1)CNC2=NC(=NC=C2C(F)(F)F)NC3=CC4=C(C=C3)NC(=O)C4)S(=O)(=O)C\"}}"
            },
            {
                "from": "observation",
                "value": "{\"optimized_results\": {\"gen_smiles\": ['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1']}}"
            },
            {
                "from": "gpt",
                "value": "The generated optimized molecules are as follows: ['OCc1ncccc1CNc1ccnc(Nc2ccc3c(c2)CC(OCC(F)F)N3)n1']."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"drug_cell_response_regression_optimization\", \"description\": \"Optimize molecules based on drug-cell response regression.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"cell_line\": {\"type\": \"string\"}, \"mask\": {\"type\": \"array\", \"items\": {\"type\": \"integer\"}}, \"gt\": {\"type\": \"string\"}, \"z-score\": {\"type\": \"number\"}}}, \"required\": [\"cell_line\", \"mask\", \"gt\", \"z-score\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the strength of inhibition by 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' on the cell '1240193'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3\"], \"cell_name\": \"1240193\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.881502]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)(C)C1=CC(=NO1)NC(=O)NC2=CC=C(C=C2)C3=CN4C5=C(C=C(C=C5)OCCN6CCOCC6)SC4=N3' and organelle '1240193' is 0.881502."
            },
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]\", \"reactant2\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]' and '[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC''s impact in hindering the cell '910700'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"], \"cell_name\": \"910700\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.301389]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' and organelle '910700' is 2.301389."
            },
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]\", \"reactant2\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]' and '[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' restricts the cell '687804'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C\"], \"cell_name\": \"687804\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.936405]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' and organelle '687804' is 2.936405."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC''s impact in hindering the cell '910700'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"], \"cell_name\": \"910700\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.301389]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' and organelle '910700' is 2.301389."
            },
            {
                "from": "human",
                "value": "Outline the synthesis method for '[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][Br:7])[O:22][c:21]1[cH:20][c:19]2[c:25]([cH:24][cH:23]1)[N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:16][CH2:17][CH2:18]2'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][Br:7])[O:22][c:21]1[cH:20][c:19]2[c:25]([cH:24][cH:23]1)[N:15]([C:13]([O:12][C:9]([CH3:8])([CH3:10])[CH3:11])=[O:14])[CH2:16][CH2:17][CH2:18]2\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][Br:7]\", \"reactant2\": \"[CH3:8][C:9]([CH3:10])([CH3:11])[O:12][C:13](=[O:14])[N:15]1[CH2:16][CH2:17][CH2:18][c:19]2[cH:20][c:21]([OH:22])[cH:23][cH:24][c:25]21\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[CH2:1][CH2:2][CH2:3][CH2:4][CH2:5][CH2:6][Br:7]' and '[CH3:8][C:9]([CH3:10])([CH3:11])[O:12][C:13](=[O:14])[N:15]1[CH2:16][CH2:17][CH2:18][c:19]2[cH:20][c:21]([OH:22])[cH:23][cH:24][c:25]21'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the effect of 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' in inhibiting the cell '905961'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(CC1)NC(=O)NC[C@H]2CCCO2\"], \"cell_name\": \"905961\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.091447]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' and organelle '905961' is 2.091447."
            },
            {
                "from": "human",
                "value": "Identify the synthesis path of '[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=C1CCC(=O)N1[Br:1]\", \"reactant2\": \"[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=C1CCC(=O)N1[Br:1]' and '[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast how 'CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5' will inhibit the cell '906808'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5\"], \"cell_name\": \"906808\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.937364]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NN1)NC2=NC(=NC(=C2)N3CCN(CC3)C)SC4=CC=C(C=C4)NC(=O)C5CC5' and organelle '906808' is 1.937364."
            },
            {
                "from": "human",
                "value": "Predict the synthesis steps for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1)[NH:21][CH2:20][C:18]([CH3:17])([CH3:19])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1)[NH:21][CH2:20][C:18]([CH3:17])([CH3:19])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1\", \"reactant2\": \"[CH3:17][C:18]([CH3:19])([CH2:20][NH2:21])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1' and '[CH3:17][C:18]([CH3:19])([CH2:20][NH2:21])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the suppression rate of 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' on the cell '910921'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N\"], \"cell_name\": \"910921\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.690142]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C[C@@]1(CCCN1)C2=NC3=C(C=CC=C3N2)C(=O)N' and organelle '910921' is 1.690142."
            },
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\", \"reactant2\": \"[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1' and '[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the effect of 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4' in inhibiting the cell '909262'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4\"], \"cell_name\": \"909262\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.850009]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)N=C(S2)C(C#N)C3=NC(=NC=C3)NCCC4=CN=CC=C4' and organelle '909262' is 3.850009."
            },
            {
                "from": "human",
                "value": "Provide the synthesis path of '[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the reduction caused by 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' on the cell '1240167'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"1240167\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.577745]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '1240167' is 2.577745."
            },
            {
                "from": "human",
                "value": "Analyze the synthesis path of '[NH2:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH2:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC(C)(C)OC(=O)[NH:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC(C)(C)OC(=O)[NH:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how strongly 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C' acts on the cell '1330935'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C\"], \"cell_name\": \"1330935\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-2.773873]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1(CC2=C(C(=O)C1)C(=NN2C3=CC(=C(C=C3)C(=O)N)NC4CCC(CC4)O)C(F)(F)F)C' and organelle '1330935' is -2.773873."
            },
            {
                "from": "human",
                "value": "Determine the synthesis trajectory for '[C:1]1(=[O:2])[CH:3]2[O:4][c:5]3[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]3[CH:13]=[C:14]2[CH2:15][N:16]1[C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]1(=[O:2])[CH:3]2[O:4][c:5]3[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]3[CH:13]=[C:14]2[CH2:15][N:16]1[C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CCO[C:1](=[O:2])[CH:3]1[O:4][c:5]2[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]2[CH:13]=[C:14]1[CH2:15][NH:16][C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CCO[C:1](=[O:2])[CH:3]1[O:4][c:5]2[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]2[CH:13]=[C:14]1[CH2:15][NH:16][C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the level at which 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' inhibits the cell '909262'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5\"], \"cell_name\": \"909262\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.274491]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=NC(=NC(=N2)N3C4=CC=CC=C4N=C3C(F)F)N5CCOCC5' and organelle '909262' is 2.274491."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br''s inhibition strength against the cell '687807'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"687807\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.820760]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '687807' is 4.820760."
            },
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][c:16]1[n:17][n:18][n:19][nH:20]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][c:16]1[n:17][n:18][n:19][nH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][C:16]#[N:17]\", \"reactant2\": \"[N-:18]=[N+:19]=[N-:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][C:16]#[N:17]' and '[N-:18]=[N+:19]=[N-:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory action of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' against the cell '909724'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"909724\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.608643]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '909724' is 1.608643."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\", \"reactant2\": \"[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1' and '[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate how much 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' reduces growth in the cell '949167'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6\"], \"cell_name\": \"949167\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.037177]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CCN1C2=CC(=NC=C2N=C1C3=NON=C3N)OC4=CC=CC(=C4)NC(=O)C5=CC=C(C=C5)OCCN6CCOCC6' and organelle '949167' is 2.037177."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\", \"reactant2\": \"[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1' and '[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC''s impact in hindering the cell '910700'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC\"], \"cell_name\": \"910700\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.301389]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(C)S(=O)(=O)C1=CC=CC=C1NC2=CC(=NC3=C2C=CN3)NC4=NC=C(C=C4)C(=O)NC' and organelle '910700' is 2.301389."
            },
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[C:1](=[O:2])([O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[C:1](=[O:2])[O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1\", \"reactant2\": \"[NH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[C:1](=[O:2])[O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[NH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O''s potential in stopping the cell '910910'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O\"], \"cell_name\": \"910910\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.260088]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C/C(=C(\\C#N)/C(=O)NC1=C(C=CC(=C1)Br)Br)/O' and organelle '910910' is 5.260088."
            },
            {
                "from": "human",
                "value": "Analyze how '[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][CH2:8][C@H:9]([OH:10])[C:11](=[O:12])[OH:13]' synthesizes."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([C:3]([F:4])([F:5])[F:6])[NH:7][CH2:8][C@H:9]([OH:10])[C:11](=[O:12])[OH:13]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CCO[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6]\", \"reactant2\": \"[NH2:7][CH2:8][C@H:9]([OH:10])[C:11](=[O:12])[OH:13]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CCO[C:1](=[O:2])[C:3]([F:4])([F:5])[F:6]' and '[NH2:7][CH2:8][C@H:9]([OH:10])[C:11](=[O:12])[OH:13]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the extent of 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F''s action on the cell '909784'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F\"], \"cell_name\": \"909784\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.284095]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C=CC3=C2C=CC(=C3)C4=CC(=NN4C5=CC=C(C=C5)NC(=O)CN)C(F)(F)F' and organelle '909784' is 3.284095."
            },
            {
                "from": "human",
                "value": "Predict the most likely synthesis path for '[NH2:1][C@@H:2]([CH2:3][CH2:4][CH2:5][CH2:6][NH:7][C:8]([O:9][C:10]([CH3:11])([CH3:12])[CH3:13])=[O:14])[C:15](=[O:16])[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[C:22](=[O:23])[O:24][C:25]([CH3:26])([CH3:27])[CH3:28]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH2:1][C@@H:2]([CH2:3][CH2:4][CH2:5][CH2:6][NH:7][C:8]([O:9][C:10]([CH3:11])([CH3:12])[CH3:13])=[O:14])[C:15](=[O:16])[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[C:22](=[O:23])[O:24][C:25]([CH3:26])([CH3:27])[CH3:28]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=C(OCc1ccccc1)[NH:1][C@@H:2]([CH2:3][CH2:4][CH2:5][CH2:6][NH:7][C:8]([O:9][C:10]([CH3:11])([CH3:12])[CH3:13])=[O:14])[C:15](=[O:16])[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[C:22](=[O:23])[O:24][C:25]([CH3:26])([CH3:27])[CH3:28]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=C(OCc1ccccc1)[NH:1][C@@H:2]([CH2:3][CH2:4][CH2:5][CH2:6][NH:7][C:8]([O:9][C:10]([CH3:11])([CH3:12])[CH3:13])=[O:14])[C:15](=[O:16])[N:17]1[CH2:18][CH2:19][CH2:20][C@H:21]1[C:22](=[O:23])[O:24][C:25]([CH3:26])([CH3:27])[CH3:28]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the inhibition rate for 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' on the cell '925343'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F\"], \"cell_name\": \"925343\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.983428]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC(=CC(=C1)C(=O)N)C2=CC(=NC=N2)NC3=CC=C(C=C3)OC(F)(F)F' and organelle '925343' is 2.983428."
            },
            {
                "from": "human",
                "value": "Identify the synthesis steps for '[c:1]1([O:22][CH2:21][c:20]2[c:9]([CH3:8])[o:10][n:11][c:12]2-[c:13]2[cH:14][cH:15][cH:16][c:17]([F:18])[cH:19]2)[cH:2][cH:3][c:4]([Cl:5])[n:6][n:7]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:22][CH2:21][c:20]2[c:9]([CH3:8])[o:10][n:11][c:12]2-[c:13]2[cH:14][cH:15][cH:16][c:17]([F:18])[cH:19]2)[cH:2][cH:3][c:4]([Cl:5])[n:6][n:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[cH:2][cH:3][c:4]([Cl:5])[n:6][n:7]1\", \"reactant2\": \"[CH3:8][c:9]1[o:10][n:11][c:12](-[c:13]2[cH:14][cH:15][cH:16][c:17]([F:18])[cH:19]2)[c:20]1[CH2:21][OH:22]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[cH:2][cH:3][c:4]([Cl:5])[n:6][n:7]1' and '[CH3:8][c:9]1[o:10][n:11][c:12](-[c:13]2[cH:14][cH:15][cH:16][c:17]([F:18])[cH:19]2)[c:20]1[CH2:21][OH:22]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the impact of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' on cell growth in the cell '687820'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"687820\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.381123]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '687820' is 2.381123."
            },
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[OH:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC(C)(C)[O:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC(C)(C)[O:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' on the cell '907287'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"907287\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.159924]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '907287' is 3.159924."
            },
            {
                "from": "human",
                "value": "Provide a synthesis prediction for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH2:6][CH2:7][CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH2:6][CH2:7][CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])/[CH:6]=[CH:7]/[CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][C:4](=[O:5])/[CH:6]=[CH:7]/[CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the reduction caused by 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' on the cell '1240167'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C\"], \"cell_name\": \"1240167\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.577745]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(SC2=C1C(=N[C@H](C3=NN=C(N32)C)CC(=O)OC(C)(C)C)C4=CC=C(C=C4)Cl)C' and organelle '1240167' is 2.577745."
            },
            {
                "from": "human",
                "value": "Determine the synthesis trajectory for '[C:1]1(=[O:2])[CH:3]2[O:4][c:5]3[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]3[CH:13]=[C:14]2[CH2:15][N:16]1[C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]1(=[O:2])[CH:3]2[O:4][c:5]3[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]3[CH:13]=[C:14]2[CH2:15][N:16]1[C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CCO[C:1](=[O:2])[CH:3]1[O:4][c:5]2[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]2[CH:13]=[C:14]1[CH2:15][NH:16][C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CCO[C:1](=[O:2])[CH:3]1[O:4][c:5]2[cH:6][cH:7][cH:8][c:9]([O:10][CH3:11])[c:12]2[CH:13]=[C:14]1[CH2:15][NH:16][C@@H:17]([CH2:18][CH:19]([CH3:20])[CH3:21])[C:22](=[O:23])[O:24][CH3:25]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory level of 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' on the cell '1331025'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N\"], \"cell_name\": \"1331025\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.358471]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CN=C(N1)C2=CN=C(N=C2C3=C(C=C(C=C3)Cl)Cl)NCCNC4=NC=C(C=C4)C#N' and organelle '1331025' is 4.358471."
            },
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\", \"reactant2\": \"OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1' and 'OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the inhibitory strength of 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' against the cell '909721'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5\"], \"cell_name\": \"909721\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [1.763597]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=CC2=C1N/C(=C\\3/C(=CC=NC3=O)NC[C@H](C4=CC(=CC=C4)Cl)O)/N2)N5CCOCC5' and organelle '909721' is 1.763597."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' impacts the growth of the cell '1240224'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\"], \"cell_name\": \"1240224\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.458680]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' and organelle '1240224' is 3.458680."
            },
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\", \"reactant2\": \"OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1' and 'OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition of 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' on the growth of the cell '1298219'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br\"], \"cell_name\": \"1298219\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.414053]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCN(C1)C(=O)NC2=CC=CC(=C2)NC3=NC=C(C(=N3)NCCC4=CN=CN4)Br' and organelle '1298219' is 4.414053."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis procedure for '[C:1](=[O:2])([c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12)[NH:42][C@@H:41]([C:39]([N:38]1[CH2:37][CH2:36][CH:35]([N:34]2[N:33]=[C:32]([c:31]3[cH:30][cH:29][c:28]([O:27][CH3:26])[c:62]([O:63][CH3:64])[cH:61]3)[C@@H:60]3[C@H:55]([C:53]2=[O:54])[CH2:56][CH2:57][CH2:58][CH2:59]3)[CH2:52][CH2:51]1)=[O:40])[CH2:43][c:44]1[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12)[NH:42][C@@H:41]([C:39]([N:38]1[CH2:37][CH2:36][CH:35]([N:34]2[N:33]=[C:32]([c:31]3[cH:30][cH:29][c:28]([O:27][CH3:26])[c:62]([O:63][CH3:64])[cH:61]3)[C@@H:60]3[C@H:55]([C:53]2=[O:54])[CH2:56][CH2:57][CH2:58][CH2:59]3)[CH2:52][CH2:51]1)=[O:40])[CH2:43][c:44]1[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1](=[O:2])[c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12\", \"reactant2\": \"[CH3:26][O:27][c:28]1[cH:29][cH:30][c:31]([C:32]2=[N:33][N:34]([CH:35]3[CH2:36][CH2:37][N:38]([C:39](=[O:40])[C@H:41]([NH2:42])[CH2:43][c:44]4[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]4)[CH2:51][CH2:52]3)[C:53](=[O:54])[C@@H:55]3[CH2:56][CH2:57][CH2:58][CH2:59][C@H:60]23)[cH:61][c:62]1[O:63][CH3:64]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12' and '[CH3:26][O:27][c:28]1[cH:29][cH:30][c:31]([C:32]2=[N:33][N:34]([CH:35]3[CH2:36][CH2:37][N:38]([C:39](=[O:40])[C@H:41]([NH2:42])[CH2:43][c:44]4[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]4)[CH2:51][CH2:52]3)[C:53](=[O:54])[C@@H:55]3[CH2:56][CH2:57][CH2:58][CH2:59][C@H:60]23)[cH:61][c:62]1[O:63][CH3:64]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the extent of suppression caused by 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' on the cell '905942'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O\"], \"cell_name\": \"905942\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.258509]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CC2=C(C=CC(=C2)NS(=O)(=O)C3=CC=CC=C3OC)NC1=O' and organelle '905942' is 2.258509."
            },
            {
                "from": "human",
                "value": "Provide the synthesis path of '[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibition potential of 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' on the cell '906875'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"906875\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.496731]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '906875' is 0.496731."
            },
            {
                "from": "human",
                "value": "Outline the synthesis route for '[C:1](=[O:2])([CH2:3][CH:4]1[NH:5][C:6](=[O:7])[NH:8][C:9]1=[O:10])[NH:28][c:27]1[cH:26][cH:25][c:24]([CH2:23][n:22]2[c:14]([CH:12]([CH3:11])[CH3:13])[n:15][c:16]3[cH:17][cH:18][cH:19][cH:20][c:21]32)[cH:30][cH:29]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([CH2:3][CH:4]1[NH:5][C:6](=[O:7])[NH:8][C:9]1=[O:10])[NH:28][c:27]1[cH:26][cH:25][c:24]([CH2:23][n:22]2[c:14]([CH:12]([CH3:11])[CH3:13])[n:15][c:16]3[cH:17][cH:18][cH:19][cH:20][c:21]32)[cH:30][cH:29]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1](=[O:2])[CH2:3][CH:4]1[NH:5][C:6](=[O:7])[NH:8][C:9]1=[O:10]\", \"reactant2\": \"[CH3:11][CH:12]([CH3:13])[c:14]1[n:15][c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2[n:22]1[CH2:23][c:24]1[cH:25][cH:26][c:27]([NH2:28])[cH:29][cH:30]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1](=[O:2])[CH2:3][CH:4]1[NH:5][C:6](=[O:7])[NH:8][C:9]1=[O:10]' and '[CH3:11][CH:12]([CH3:13])[c:14]1[n:15][c:16]2[cH:17][cH:18][cH:19][cH:20][c:21]2[n:22]1[CH2:23][c:24]1[cH:25][cH:26][c:27]([NH2:28])[cH:29][cH:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the effect of 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' in inhibiting the cell '905961'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CCC(CC1)NC(=O)NC[C@H]2CCCO2\"], \"cell_name\": \"905961\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.091447]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CCC(CC1)NC(=O)NC[C@H]2CCCO2' and organelle '905961' is 2.091447."
            },
            {
                "from": "human",
                "value": "Forecast the synthesis path for '[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][c:9](-[c:10]3[n:11][c:12]([NH:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][N:20]([CH3:21])[CH2:22][CH2:23]4)[n:24][cH:25][c:26]3[Cl:27])[s:28][c:29]2[cH:30]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][c:9](-[c:10]3[n:11][c:12]([NH:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][N:20]([CH3:21])[CH2:22][CH2:23]4)[n:24][cH:25][c:26]3[Cl:27])[s:28][c:29]2[cH:30]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][c:9](-[c:10]3[n:11][c:12]([NH:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][N:20]([CH3:21])[CH2:22][CH2:23]4)[n:24][cH:25][c:26]3[Cl:27])[s:28][c:29]2[cH:30]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]2[cH:8][c:9](-[c:10]3[n:11][c:12]([NH:13][CH2:14][CH2:15][CH2:16][N:17]4[CH2:18][CH2:19][N:20]([CH3:21])[CH2:22][CH2:23]4)[n:24][cH:25][c:26]3[Cl:27])[s:28][c:29]2[cH:30]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory effect of 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' on the cell '909256'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N\"], \"cell_name\": \"909256\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-0.239302]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N' and organelle '909256' is -0.239302."
            },
            {
                "from": "human",
                "value": "Analyze the synthesis process for '[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][c:16]1[n:17][n:18][n:19][nH:20]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][c:16]1[n:17][n:18][n:19][nH:20]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][C:16]#[N:17]\", \"reactant2\": \"[N-:18]=[N+:19]=[N-:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][c:2]1[n:3][c:4]2[c:5]3[cH:6][cH:7][cH:8][cH:9][c:10]3[cH:11][cH:12][n:13]2[c:14]1[CH2:15][C:16]#[N:17]' and '[N-:18]=[N+:19]=[N-:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' impacts the growth of the cell '1240224'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N\"], \"cell_name\": \"1240224\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.458680]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1CC(=O)NC(=O)C1N2CC3=C(C2=O)C=CC=C3N' and organelle '1240224' is 3.458680."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\", \"reactant2\": \"[NH4+:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1' and '[NH4+:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the suppression potential of 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' on the cell '907287'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC\"], \"cell_name\": \"907287\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [3.159924]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)CCCOC2=C(C=C3C(=C2)N=CC(=C3NC4=CC(=C(C=C4Cl)Cl)OC)C#N)OC' and organelle '907287' is 3.159924."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\", \"reactant2\": \"[NH4+:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1' and '[NH4+:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate 'CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]''s effect on the cell '1287706'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]\"], \"cell_name\": \"1287706\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.528633]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCN(CC1)C2=CC=C(C3=NO[N+](=C23)[O-])[N+](=O)[O-]' and organelle '1287706' is 2.528633."
            },
            {
                "from": "human",
                "value": "Predict the steps for synthesizing '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10]\", \"reactant2\": \"[Mg+][CH3:11]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10]' and '[Mg+][CH3:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the effect of 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' in suppressing the cell '687586'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F\"], \"cell_name\": \"687586\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [-0.555627]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1COCCN1C2=CC(=CC(=C2)C3=NC(=NC=C3)NC4=CC=C(C=C4)N5C=NC(=N5)N6CCOCC6)F' and organelle '687586' is -0.555627."
            },
            {
                "from": "human",
                "value": "Identify the synthesis path of '[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=C1CCC(=O)N1[Br:1]\", \"reactant2\": \"[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=C1CCC(=O)N1[Br:1]' and '[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the inhibitory potency of 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' on the cell '946354'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl\"], \"cell_name\": \"946354\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [0.294461]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=CC(=NO1)NC(=O)NC2=C(C=C(C=C2)OC3=C4C=C(C(=CC4=NC=C3)OC)OC)Cl' and organelle '946354' is 0.294461."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis method for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1)[NH:19][c:20]1[cH:21][cH:22][c:23]([CH2:24][CH2:25][c:26]2[cH:27][cH:28][cH:29][cH:30][n:31]2)[n:32][cH:33]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1)[NH:19][c:20]1[cH:21][cH:22][c:23]([CH2:24][CH2:25][c:26]2[cH:27][cH:28][cH:29][cH:30][n:31]2)[n:32][cH:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1\", \"reactant2\": \"[NH2:19][c:20]1[cH:21][cH:22][c:23]([CH2:24][CH2:25][c:26]2[cH:27][cH:28][cH:29][cH:30][n:31]2)[n:32][cH:33]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][c:8]1-[c:9]1[cH:10][cH:11][c:12]([C:13]([F:14])([F:15])[F:16])[cH:17][cH:18]1' and '[NH2:19][c:20]1[cH:21][cH:22][c:23]([CH2:24][CH2:25][c:26]2[cH:27][cH:28][cH:29][cH:30][n:31]2)[n:32][cH:33]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the inhibition caused by 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO' on the cell '908473'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO\"], \"cell_name\": \"908473\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.888686]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CN1CCC2=C(C1)C3=CC=CC=C3N2CC4=CC=C(C=C4)C(=O)NO' and organelle '908473' is 5.888686."
            },
            {
                "from": "human",
                "value": "Forecast the possible synthesis route for '[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]\", \"reactant2\": \"[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]' and '[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine how well 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' restricts the cell '687804'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C\"], \"cell_name\": \"687804\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.936405]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC(=CC=C2)C(C)(C)C#N)NC3=CC4=C(C=C3)N=CN(C4=O)C' and organelle '687804' is 2.936405."
            },
            {
                "from": "human",
                "value": "Suggest the best synthesis steps for '[C:1](=[O:2])([O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1)[NH:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[C:1](=[O:2])[O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1\", \"reactant2\": \"[NH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[C:1](=[O:2])[O:3][c:4]1[cH:5][cH:6][cH:7][cH:8][cH:9]1' and '[NH2:10][c:11]1[cH:12][cH:13][c:14]([C:15]2([OH:16])[CH2:17][CH2:18]2)[c:19]([F:20])[cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Forecast the inhibitory impact 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' has on the cell '925340'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O\"], \"cell_name\": \"925340\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [5.256628]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC(CS(=O)(=O)C1=CC=C(C=C1)F)(C(=O)NC2=CC(=C(C=C2)C#N)C(F)(F)F)O' and organelle '925340' is 5.256628."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis process for '[OH:1][C:2](=[O:3])[C@@H:4]1[CH2:5][CH2:6][C@H:7]2[CH2:8][O:9][C@@H:10]([CH3:11])[C:12](=[O:13])[N:14]2[CH2:15]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[C@@H:4]1[CH2:5][CH2:6][C@H:7]2[CH2:8][O:9][C@@H:10]([CH3:11])[C:12](=[O:13])[N:14]2[CH2:15]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"C[O:1][C:2](=[O:3])[C@@H:4]1[CH2:5][CH2:6][C@H:7]2[CH2:8][O:9][C@@H:10]([CH3:11])[C:12](=[O:13])[N:14]2[CH2:15]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'C[O:1][C:2](=[O:3])[C@@H:4]1[CH2:5][CH2:6][C@H:7]2[CH2:8][O:9][C@@H:10]([CH3:11])[C:12](=[O:13])[N:14]2[CH2:15]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N''s inhibition rate on the cell '1240226'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N\"], \"cell_name\": \"1240226\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.562113]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C(C=C1)C2=NN3C=CC=CC3=C2C4=CC5=C(NN=C5N=N4)N' and organelle '1240226' is 4.562113."
            },
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\", \"reactant2\": \"[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1' and '[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate the inhibition caused by 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' on the cell '1240189'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4\"], \"cell_name\": \"1240189\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [4.747780]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'C1=CC=C2C(=C1)C(=CN2)CCNC3=CC=C(C=C3)NC4=CC=NC=C4' and organelle '1240189' is 4.747780."
            },
            {
                "from": "human",
                "value": "Identify the synthesis path of '[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)(=[O:12])[NH:30][c:29]1[c:21]([NH:20][CH2:19][CH2:18][N:15]([CH2:14][CH3:13])[CH2:16][CH3:17])[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)(=[O:12])[NH:30][c:29]1[c:21]([NH:20][CH2:19][CH2:18][N:15]([CH2:14][CH3:13])[CH2:16][CH3:17])[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)=[O:12]\", \"reactant2\": \"[CH3:13][CH2:14][N:15]([CH2:16][CH3:17])[CH2:18][CH2:19][NH:20][c:21]1[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28][c:29]1[NH2:30]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)=[O:12]' and '[CH3:13][CH2:14][N:15]([CH2:16][CH3:17])[CH2:18][CH2:19][NH:20][c:21]1[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28][c:29]1[NH2:30]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Evaluate how much 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' inhibits the cell '713885' growth."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_cell_response_regression_predict\", \"arguments\": {\"smiles\": [\"CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C\"], \"cell_name\": \"713885\"}}"
            },
            {
                "from": "observation",
                "value": "{\"response_value\": [2.361746]}"
            },
            {
                "from": "gpt",
                "value": "The z-score value between drug 'CC1CC(=O)N(C2=CN=C(N=C2N1C3CCCC3)NC4=C(C=C(C=C4)C(=O)NC5CCN(CC5)C)OC)C' and organelle '713885' is 2.361746."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\", \"reactant2\": \"[NH4+:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1' and '[NH4+:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_cell_response_regression_predict\", \"description\": \"Predict drug-cell response using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"cell_name\": {\"type\": \"string\", \"description\": \"Organelle name string\"}}}, \"required\": [\"smiles\", \"cell_name\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5''s interaction with the target protein 'DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5\"], \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1' and the target protein 'MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\"], \"target_seq\": \"MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12' will engage with the target protein 'MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12\"], \"target_seq\": \"MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12' can interact with the protein."
            },
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the target protein 'MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [4700.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 4700.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine if the drug 'Cc1cc([C@H]2C[C@H]3[C@@H](C)SC(N)=N[C@@]3(c3ccc(F)cc3F)CO2)on1' will interact with the target protein 'MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc([C@H]2C[C@H]3[C@@H](C)SC(N)=N[C@@]3(c3ccc(F)cc3F)CO2)on1\"], \"target_seq\": \"MALSQSVPFSATELLLASAIFCLVFWVLKGLRPRVPKGLKSPPEPWGWPLLGHVLTLGKNPHLALSRMSQRYGDVLQIRIGSTPVLVLSRLDTIRQALVRQGDDFKGRPDLYTSTLITDGQSLTFSTDSGPVWAARRRLAQNALNTFSIASDPASSSSCYLEEHVSKEAKALISRLQELMAGPGHFDPYNQVVVSVANVIGAMCFGQHFPESSDEMLSLVKNTHEFVETASSGNPLDFFPILRYLPNPALQRFKAFNQRFLWFLQKTVQEHYQDFDKNSVRDITGALFKHSKKGPRASGNLIPQEKIVNLVNDIFGAGFDTVTTAISWSLMYLVTKPEIQRKIQKELDTVIGRERRPRLSDRPQLPYLEAFILETFRHSSFLPFTIPHSTTRDTTLNGFYIPKKCCVFVNQWQVNHDPELWEDPSEFRPERFLTADGTAINKPLSEKMMLFGMGKRRCIGEVLAKWEIFLFLAILLQQLEFSVPPGVKVDLTPIYGLTMKHARCEHVQARRFSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cc([C@H]2C[C@H]3[C@@H](C)SC(N)=N[C@@]3(c3ccc(F)cc3F)CO2)on1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What's your estimation of the binding affinity between the drug 'CN1CCN(c2ccc3nc(-c4c(N)c5c(F)cccc5[nH]c4=O)[nH]c3c2)CC1' and the target protein 'MAVPSLWPWGACLPVIFLSLGFGLDTVEVCPSLDIRSEVAELRQLENCSVVEGHLQILLMFTATGEDFRGLSFPRLTQVTDYLLLFRVYGLESLRDLFPNLAVIRGTRLFLGYALVIFEMPHLRDVALPALGAVLRGAVRVEKNQELCHLSTIDWGLLQPAPGANHIVGNKLGEECADVCPGVLGAAGEPCAKTTFSGHTDYRCWTSSHCQRVCPCPHGMACTARGECCHTECLGGCSQPEDPRACVACRHLYFQGACLWACPPGTYQYESWRCVTAERCASLHSVPGRASTFGIHQGSCLAQCPSGFTRNSSSIFCHKCEGLCPKECKVGTKTIDSIQAAQDLVGCTHVEGSLILNLRQGYNLEPQLQHSLGLVETITGFLKIKHSFALVSLGFFKNLKLIRGDAMVDGNYTLYVLDNQNLQQLGSWVAAGLTIPVGKIYFAFNPRLCLEHIYRLEEVTGTRGRQNKAEINPRTNGDRAACQTRTLRFVSNVTEADRILLRWERYEPLEARDLLSFIVYYKESPFQNATEHVGPDACGTQSWNLLDVELPLSRTQEPGVTLASLKPWTQYAVFVRAITLTTEEDSPHQGAQSPIVYLRTLPAAPTVPQDVISTSNSSSHLLVRWKPPTQRNGNLTYYLVLWQRLAEDGDLYLNDYCHRGLRLPTSNNDPRFDGEDGDPEAEMESDCCPCQHPPPGQVLPPLEAQEASFQKKFENFLHNAITIPISPWKVTSINKSPQRDSGRHRRAAGPLRLGGNSSDFEIQEDKVPRERAVLSGLRHFTEYRIDIHACNHAAHTVGCSAATFVFARTMPHREADGIPGKVAWEASSKNSVLLRWLEPPDPNGLILKYEIKYRRLGEEATVLCVSRLRYAKFGGVHLALLPPGNYSARVRATSLAGNGSWTDSVAFYILGPEEEDAGGLHVLLTATPVGLTLLIVLAALGFFYGKKRNRTLYASVNPEYFSASDMYVPDEWEVPREQISIIRELGQGSFGMVYEGLARGLEAGEESTPVALKTVNELASPRECIEFLKEASVMKAFKCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSHLRSLRPEAENNPGLPQPALGEMIQMAGEIADGMAYLAANKFVHRDLAARNCMVSQDFTVKIGDFGMTRDVYETDYYRKGGKGLLPVRWMAPESLKDGIFTTHSDVWSFGVVLWEIVTLAEQPYQGLSNEQVLKFVMDGGVLEELEGCPLQLQELMSRCWQPNPRLRPSFTHILDSIQEELRPSFRLLSFYYSPECRGARGSLPTTDAEPDSSPTPRDCSPQNGGPGH'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(c2ccc3nc(-c4c(N)c5c(F)cccc5[nH]c4=O)[nH]c3c2)CC1\"], \"target_seq\": \"MAVPSLWPWGACLPVIFLSLGFGLDTVEVCPSLDIRSEVAELRQLENCSVVEGHLQILLMFTATGEDFRGLSFPRLTQVTDYLLLFRVYGLESLRDLFPNLAVIRGTRLFLGYALVIFEMPHLRDVALPALGAVLRGAVRVEKNQELCHLSTIDWGLLQPAPGANHIVGNKLGEECADVCPGVLGAAGEPCAKTTFSGHTDYRCWTSSHCQRVCPCPHGMACTARGECCHTECLGGCSQPEDPRACVACRHLYFQGACLWACPPGTYQYESWRCVTAERCASLHSVPGRASTFGIHQGSCLAQCPSGFTRNSSSIFCHKCEGLCPKECKVGTKTIDSIQAAQDLVGCTHVEGSLILNLRQGYNLEPQLQHSLGLVETITGFLKIKHSFALVSLGFFKNLKLIRGDAMVDGNYTLYVLDNQNLQQLGSWVAAGLTIPVGKIYFAFNPRLCLEHIYRLEEVTGTRGRQNKAEINPRTNGDRAACQTRTLRFVSNVTEADRILLRWERYEPLEARDLLSFIVYYKESPFQNATEHVGPDACGTQSWNLLDVELPLSRTQEPGVTLASLKPWTQYAVFVRAITLTTEEDSPHQGAQSPIVYLRTLPAAPTVPQDVISTSNSSSHLLVRWKPPTQRNGNLTYYLVLWQRLAEDGDLYLNDYCHRGLRLPTSNNDPRFDGEDGDPEAEMESDCCPCQHPPPGQVLPPLEAQEASFQKKFENFLHNAITIPISPWKVTSINKSPQRDSGRHRRAAGPLRLGGNSSDFEIQEDKVPRERAVLSGLRHFTEYRIDIHACNHAAHTVGCSAATFVFARTMPHREADGIPGKVAWEASSKNSVLLRWLEPPDPNGLILKYEIKYRRLGEEATVLCVSRLRYAKFGGVHLALLPPGNYSARVRATSLAGNGSWTDSVAFYILGPEEEDAGGLHVLLTATPVGLTLLIVLAALGFFYGKKRNRTLYASVNPEYFSASDMYVPDEWEVPREQISIIRELGQGSFGMVYEGLARGLEAGEESTPVALKTVNELASPRECIEFLKEASVMKAFKCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSHLRSLRPEAENNPGLPQPALGEMIQMAGEIADGMAYLAANKFVHRDLAARNCMVSQDFTVKIGDFGMTRDVYETDYYRKGGKGLLPVRWMAPESLKDGIFTTHSDVWSFGVVLWEIVTLAEQPYQGLSNEQVLKFVMDGGVLEELEGCPLQLQELMSRCWQPNPRLRPSFTHILDSIQEELRPSFRLLSFYYSPECRGARGSLPTTDAEPDSSPTPRDCSPQNGGPGH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1' possibly engage with the target protein 'MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1\"], \"target_seq\": \"MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12' and the target protein 'MSARTPLPTVNERDTENHTSVDGYTEPHIQPTKSSSRQNIPRCRNSITSATDEQPHIGNYRLQKTIGKGNFAKVKLARHVLTGREVAVKIIDKTQLNPTSLQKLFREVRIMKILNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKYIVHRDLKAENLLLDGDMNIKIADFGFSNEFTVGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKLLVLNPIKRGSLEQIMKDRWMNVGHEEEELKPYTEPDPDFNDTKRIDIMVTMGFARDEINDALINQKYDEVMATYILLGRKPPEFEGGESLSSGNLCQRSRPSSDLNNSTLQSPAHLKVQRSISANQKQRRFSDHAGPSIPPAVSYTKRPQANSVESEQKEEWDKDVARKLGSTTVGSKSEMTASPLVGPERKKSSTIPSNNVYSGGSMARRNTYVCERTTDRYVALQNGKDSSLTEMSVSSISSAGSSVASAVPSARPRHQKSMSTSGHPIKVTLPTIKDGSEAYRPGTTQRVPAASPSAHSISTATPDRTRFPRGSSSRSTFHGEQLRERRSVAYNGPPASPSHETGAFAHARRGTSTGIISKITSKFVRRDPSEGEASGRTDTSRSTSGEPKERDKEEGKDSKPRSLRFTWSMKTTSSMDPNDMMREIRKVLDANNCDYEQKERFLLFCVHGDARQDSLVQWEMEVCKLPRLSLNGVRFKRISGTSIAFKNIASKIANELKL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\"], \"target_seq\": \"MSARTPLPTVNERDTENHTSVDGYTEPHIQPTKSSSRQNIPRCRNSITSATDEQPHIGNYRLQKTIGKGNFAKVKLARHVLTGREVAVKIIDKTQLNPTSLQKLFREVRIMKILNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKYIVHRDLKAENLLLDGDMNIKIADFGFSNEFTVGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKLLVLNPIKRGSLEQIMKDRWMNVGHEEEELKPYTEPDPDFNDTKRIDIMVTMGFARDEINDALINQKYDEVMATYILLGRKPPEFEGGESLSSGNLCQRSRPSSDLNNSTLQSPAHLKVQRSISANQKQRRFSDHAGPSIPPAVSYTKRPQANSVESEQKEEWDKDVARKLGSTTVGSKSEMTASPLVGPERKKSSTIPSNNVYSGGSMARRNTYVCERTTDRYVALQNGKDSSLTEMSVSSISSAGSSVASAVPSARPRHQKSMSTSGHPIKVTLPTIKDGSEAYRPGTTQRVPAASPSAHSISTATPDRTRFPRGSSSRSTFHGEQLRERRSVAYNGPPASPSHETGAFAHARRGTSTGIISKITSKFVRRDPSEGEASGRTDTSRSTSGEPKERDKEEGKDSKPRSLRFTWSMKTTSSMDPNDMMREIRKVLDANNCDYEQKERFLLFCVHGDARQDSLVQWEMEVCKLPRLSLNGVRFKRISGTSIAFKNIASKIANELKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you evaluate if the drug '[H][C@@](CC(C)C)(NC(=O)C1=CC=CO1)C(=O)N[C@@]([H])(CC1=CNC2=C1C=CC=C2)P(O)(O)=O' is expected to interact with the target protein 'MSSQVVGIEPLYIKAEPASPDSPKGSSETETEPPVALAPGPAPTRCLPGHKEEEDGEGAGPGEQGGGKLVLSSLPKRLCLVCGDVASGYHYGVASCEACKAFFKRTIQGSIEYSCPASNECEITKRRRKACQACRFTKCLRVGMLKEGVRLDRVRGGRQKYKRRPEVDPLPFPGPFPAGPLAVAGGPRKTAAPVNALVSHLLVVEPEKLYAMPDPAGPDGHLPAVATLCDLFDREIVVTISWAKSIPGFSSLSLSDQMSVLQSVWMEVLVLGVAQRSLPLQDELAFAEDLVLDEEGARAAGLGELGAALLQLVRRLQALRLEREEYVLLKALALANSDSVHIEDAEAVEQLREALHEALLEYEAGRAGPGGGAERRRAGRLLLTLPLLRQTAGKVLAHFYGVKLEGKVPMHKLFLEMLEAMMD'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@](CC(C)C)(NC(=O)C1=CC=CO1)C(=O)N[C@@]([H])(CC1=CNC2=C1C=CC=C2)P(O)(O)=O\"], \"target_seq\": \"MSSQVVGIEPLYIKAEPASPDSPKGSSETETEPPVALAPGPAPTRCLPGHKEEEDGEGAGPGEQGGGKLVLSSLPKRLCLVCGDVASGYHYGVASCEACKAFFKRTIQGSIEYSCPASNECEITKRRRKACQACRFTKCLRVGMLKEGVRLDRVRGGRQKYKRRPEVDPLPFPGPFPAGPLAVAGGPRKTAAPVNALVSHLLVVEPEKLYAMPDPAGPDGHLPAVATLCDLFDREIVVTISWAKSIPGFSSLSLSDQMSVLQSVWMEVLVLGVAQRSLPLQDELAFAEDLVLDEEGARAAGLGELGAALLQLVRRLQALRLEREEYVLLKALALANSDSVHIEDAEAVEQLREALHEALLEYEAGRAGPGGGAERRRAGRLLLTLPLLRQTAGKVLAHFYGVKLEGKVPMHKLFLEMLEAMMD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@](CC(C)C)(NC(=O)C1=CC=CO1)C(=O)N[C@@]([H])(CC1=CNC2=C1C=CC=C2)P(O)(O)=O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [5.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you assess the possibility of the drug 'OS(=O)(=O)OC[C@H]1O[C@@](COS(O)(=O)=O)(O[C@H]2O[C@H](COS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@H](OS(O)(=O)=O)[C@H]2OS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@@H]1OS(O)(=O)=O' interacting with the target protein 'MAAMSLLRRVSVTAVAALSGRPLGTRLGFGGFLTRGFPKAAAPVRHSGDHGKRLFVIRPSRFYDRRFLKLLRFYIALTGIPVAIFITLVNVFIGQAELAEIPEGYVPEHWEYYKHPISRWIARNFYDSPEKIYERTMAVLQIEAEKAELRVKELEVRKLMHVRGDGPWYYYETIDKELIDHSPKATPDN'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"OS(=O)(=O)OC[C@H]1O[C@@](COS(O)(=O)=O)(O[C@H]2O[C@H](COS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@H](OS(O)(=O)=O)[C@H]2OS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@@H]1OS(O)(=O)=O\"], \"target_seq\": \"MAAMSLLRRVSVTAVAALSGRPLGTRLGFGGFLTRGFPKAAAPVRHSGDHGKRLFVIRPSRFYDRRFLKLLRFYIALTGIPVAIFITLVNVFIGQAELAEIPEGYVPEHWEYYKHPISRWIARNFYDSPEKIYERTMAVLQIEAEKAELRVKELEVRKLMHVRGDGPWYYYETIDKELIDHSPKATPDN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'OS(=O)(=O)OC[C@H]1O[C@@](COS(O)(=O)=O)(O[C@H]2O[C@H](COS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@H](OS(O)(=O)=O)[C@H]2OS(O)(=O)=O)[C@@H](OS(O)(=O)=O)[C@@H]1OS(O)(=O)=O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the target protein 'MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\"], \"target_seq\": \"MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess whether the drug 'COc1ccc(C(=O)N2CCc3ccccc32)cc1CSc1nc2cc(F)ccc2n1CC(=O)O' is likely to interact with the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(C(=O)N2CCc3ccccc32)cc1CSc1nc2cc(F)ccc2n1CC(=O)O\"], \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc(C(=O)N2CCc3ccccc32)cc1CSc1nc2cc(F)ccc2n1CC(=O)O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [5.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict if there will be an interaction between the drug 'CCC1C[C@H]2C3CCC4=CC(=O)CCC4[C@H]3CCC2([C@H]1O)C' and the target protein 'MSTVHEILCKLSLEGDHSTPPSAYGSVKAYTNFDAERDALNIETAIKTKGVDEVTIVNILTNRSNAQRQDIAFAYQRRTKKELASALKSALSGHLETVILGLLKTPAQYDASELKASMKGLGTDEDSLIEIICSRTNQELQEINRVYKEMYKTDLEKDIISDTSGDFRKLMVALAKGRRAEDGSVIDYELIDQDARDLYDAGVKRKGTDVPKWISIMTERSVPHLQKVFDRYKSYSPYDMLESIRKEVKGDLENAFLNLVQCIQNKPLYFADRLYDSMKGKGTRDKVLIRIMVSRSEVDMLKIRSEFKRKYGKSLYYYIQQDTKGDYQKALLYLCGGDD'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C[C@H]2C3CCC4=CC(=O)CCC4[C@H]3CCC2([C@H]1O)C\"], \"target_seq\": \"MSTVHEILCKLSLEGDHSTPPSAYGSVKAYTNFDAERDALNIETAIKTKGVDEVTIVNILTNRSNAQRQDIAFAYQRRTKKELASALKSALSGHLETVILGLLKTPAQYDASELKASMKGLGTDEDSLIEIICSRTNQELQEINRVYKEMYKTDLEKDIISDTSGDFRKLMVALAKGRRAEDGSVIDYELIDQDARDLYDAGVKRKGTDVPKWISIMTERSVPHLQKVFDRYKSYSPYDMLESIRKEVKGDLENAFLNLVQCIQNKPLYFADRLYDSMKGKGTRDKVLIRIMVSRSEVDMLKIRSEFKRKYGKSLYYYIQQDTKGDYQKALLYLCGGDD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCC1C[C@H]2C3CCC4=CC(=O)CCC4[C@H]3CCC2([C@H]1O)C' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Assess the predicted binding value of the drug 'Cc1nc(CCC23CC4CC(CC(C4)C2)C3)c(C(=O)Nc2cccc(C(=O)O)c2)[nH]1' with the target protein 'MELLKLNRSVQGPGPGSGSSLCRPGVSLLNSSSAGNLSCDPPRIRGTGTRELEMAIRITLYAVIFLMSVGGNVLIIVVLGLSRRLRTVTNAFLLSLAVSDLLLAVACMPFTLLPNLMGTFIFGTVICKAISYLMGVSVSVSTLNLVAIALERYSAICRPLQARVWQTRSHAARVILATWLLSGLLMVPYPVYTMVQPVGPRVLQCMHRWPSARVQQTWSVLLLLLLFFIPGVVIAVAYGLISRELYLGLHFDGENDSETQSRARNQGGLPGGAAPGPVHQNGGCRPVTSVAGEDSDGCCVQLPRSRLEMTTLTTPTPGPVPGPRPNQAKLLAKKRVVRMLLVIVLLFFLCWLPVYSVNTWRAFDGPGAQRALSGAPISFIHLLSYVSACVNPLVYCFMHRRFRQACLDTCARCCPRPPRARPQPLPDEDPPTPSIASLSRLSYTTISTLGPG'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc(CCC23CC4CC(CC(C4)C2)C3)c(C(=O)Nc2cccc(C(=O)O)c2)[nH]1\"], \"target_seq\": \"MELLKLNRSVQGPGPGSGSSLCRPGVSLLNSSSAGNLSCDPPRIRGTGTRELEMAIRITLYAVIFLMSVGGNVLIIVVLGLSRRLRTVTNAFLLSLAVSDLLLAVACMPFTLLPNLMGTFIFGTVICKAISYLMGVSVSVSTLNLVAIALERYSAICRPLQARVWQTRSHAARVILATWLLSGLLMVPYPVYTMVQPVGPRVLQCMHRWPSARVQQTWSVLLLLLLFFIPGVVIAVAYGLISRELYLGLHFDGENDSETQSRARNQGGLPGGAAPGPVHQNGGCRPVTSVAGEDSDGCCVQLPRSRLEMTTLTTPTPGPVPGPRPNQAKLLAKKRVVRMLLVIVLLFFLCWLPVYSVNTWRAFDGPGAQRALSGAPISFIHLLSYVSACVNPLVYCFMHRRFRQACLDTCARCCPRPPRARPQPLPDEDPPTPSIASLSRLSYTTISTLGPG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [1548.82]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 1548.82."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug 'COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl' interacting with the target protein 'MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl\"], \"target_seq\": \"MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1' and the target protein 'MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\"], \"target_seq\": \"MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1' interact with the target protein 'MVGSLNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSIPEKNRPLKGRINLVLSRELKEPPQGAHFLSRSLDDALKLTEQPELANKVDMVWIVGGSSVYKEAMNHPGHLKLFVTRIMQDFESDTFFPEIDLEKYKLLPEYPGVLSDVQEEKGIKYKFEVYEKND', based on your evaluation?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1\"], \"target_seq\": \"MVGSLNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSIPEKNRPLKGRINLVLSRELKEPPQGAHFLSRSLDDALKLTEQPELANKVDMVWIVGGSSVYKEAMNHPGHLKLFVTRIMQDFESDTFFPEIDLEKYKLLPEYPGVLSDVQEEKGIKYKFEVYEKND\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1''s affinity with the target protein 'MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\"], \"target_seq\": \"MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your take on the drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O''s potential interaction with the target protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\"], \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12'-target protein 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG' interaction?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction expected between the drug 'COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1' and the target protein 'MEPVPSARAELQFSLLANVSDTFPSAFPSASANASGSPGARSASSLALAIAITALYSAVCAVGLLGNVLVMFGIVRYTKLKTATNIYIFNLALADALATSTLPFQSAKYLMETWPFGELLCKAVLSIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPAKAKLINICIWVLASGVGVPIMVMAVTQPRDGAVVCTLQFPSPSWYWDTVTKICVFLFAFVVPILIITVCYGLMLLRLRSVRLLSGSKEKDRSLRRITRMVLVVVGAFVVCWAPIHIFVIVWTLVDINRRDPLVVAALHLCIALGYANSSLNPVLYAFLDENFKRCFRQLCRAPCGGQEPGSLRRPRQATARERVTACTPSDGPGGGAAA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1\"], \"target_seq\": \"MEPVPSARAELQFSLLANVSDTFPSAFPSASANASGSPGARSASSLALAIAITALYSAVCAVGLLGNVLVMFGIVRYTKLKTATNIYIFNLALADALATSTLPFQSAKYLMETWPFGELLCKAVLSIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPAKAKLINICIWVLASGVGVPIMVMAVTQPRDGAVVCTLQFPSPSWYWDTVTKICVFLFAFVVPILIITVCYGLMLLRLRSVRLLSGSKEKDRSLRRITRMVLVVVGAFVVCWAPIHIFVIVWTLVDINRRDPLVVAALHLCIALGYANSSLNPVLYAFLDENFKRCFRQLCRAPCGGQEPGSLRRPRQATARERVTACTPSDGPGGGAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "How would you assess the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21''s affinity for the target protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you foresee any interaction between the drug 'NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1' and the target protein 'MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1\"], \"target_seq\": \"MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the target protein 'MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MERRWPLGLGLVLLLCAPLPPGARAKEVTLMDTSKAQGELGWLLDPPKDGWSEQQQILNGTPLYMYQDCPMQGRRDTDHWLRSNWIYRGEEASRVHVELQFTVRDCKSFPGGAGPLGCKETFNLLYMESDQDVGIQLRRPLFQKVTTVAADQSFTIRDLVSGSVKLNVERCSLGRLTRRGLYLAFHNPGACVALVSVRVFYQRCPETLNGLAQFPDTLPGPAGLVEVAGTCLPHARASPRPSGAPRMHCSPDGEWLVPVGRCHCEPGYEEGGSGEACVACPSGSYRMDMDTPHCLTCPQQSTAESEGATICTCESGHYRAPGEGPQVACTGPPSAPRNLSFSASGTQLSLRWEPPADTGGRQDVRYSVRCSQCQGTAQDGGPCQPCGVGVHFSPGARGLTTPAVHVNGLEPYANYTFNVEAQNGVSGLGSSGHASTSVSISMGHAESLSGLSLRLVKKEPRQLELTWAGSRPRSPGANLTYELHVLNQDEERYQMVLEPRVLLTELQPDTTYIVRVRMLTPLGPGPFSPDHEFRTSPPVSRGLTGGEIVAVIFGLLLGAALLLGILVFRSRRAQRQRQQRQRDRATDVDREDKLWLKPYVDLQAYEDPAQGALDFTRELDPAWLMVDTVIGEGEFGEVYRGTLRLPSQDCKTVAIKTLKDTSPGGQWWNFLREATIMGQFSHPHILHLEGVVTKRKPIMIITEFMENGALDAFLREREDQLVPGQLVAMLQGIASGMNYLSNHNYVHRDLAARNILVNQNLCCKVSDFGLTRLLDDFDGTYETQGGKIPIRWTAPEAIAHRIFTTASDVWSFGIVMWEVLSFGDKPYGEMSNQEVMKSIEDGYRLPPPVDCPAPLYELMKNCWAYDRARRPHFQKLQAHLEQLLANPHSLRTIANFDPRMTLRLPSLSGSDGIPYRTVSEWLESIRMKRYILHFHSAGLDTMECVLELTAEDLTQMGITLPGHQKRILCSIQGFKD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [4700.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 4700.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a possibility of the drug 'CC1=CC=CC(=C1)C1=CC2=C(N1)C=CC(=C2)C(N)=N' interacting with the target protein 'MAALLAAAAVRARILQVSSKVKSSPTWYSASSFSSSVPTVKLFIGGKFVESKSDKWIDIHNPATNEVIGRVPQATKAEMDAAIASCKRAFPAWADTSVLSRQQVLLRYQQLIKENLKEIAKLITLEQGKTLADAEGDVFRGLQVVEHACSVTSLMMGETMPSITKDMDLYSYRLPLGVCAGIAPFNFPAMIPLWMFPMAMVCGNTFLMKPSERVPGATMLLAKLLQDSGAPDGTLNIIHGQHEAVNFICDHPDIKAISFVGSNKAGEYIFERGSRHGKRVQANMGAKNHGVVMPDANKENTLNQLVGAAFGAAGQRCMALSTAVLVGEAKKWLPELVEHAKNLRVNAGDQPGADLGPLITPQAKERVCNLIDSGTKEGASILLDGRKIKVKGYENGNFVGPTIISNVKPNMTCYKEEIFGPVLVVLETETLDEAIQIVNNNPYGNGTAIFTTNGATARKYAHLVDVGQVGVNVPIPVPLPMFSFTGSRSSFRGDTNFYGKQGIQFYTQLKTITSQWKEEDATLSSPAVVMPTMGR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=CC=CC(=C1)C1=CC2=C(N1)C=CC(=C2)C(N)=N\"], \"target_seq\": \"MAALLAAAAVRARILQVSSKVKSSPTWYSASSFSSSVPTVKLFIGGKFVESKSDKWIDIHNPATNEVIGRVPQATKAEMDAAIASCKRAFPAWADTSVLSRQQVLLRYQQLIKENLKEIAKLITLEQGKTLADAEGDVFRGLQVVEHACSVTSLMMGETMPSITKDMDLYSYRLPLGVCAGIAPFNFPAMIPLWMFPMAMVCGNTFLMKPSERVPGATMLLAKLLQDSGAPDGTLNIIHGQHEAVNFICDHPDIKAISFVGSNKAGEYIFERGSRHGKRVQANMGAKNHGVVMPDANKENTLNQLVGAAFGAAGQRCMALSTAVLVGEAKKWLPELVEHAKNLRVNAGDQPGADLGPLITPQAKERVCNLIDSGTKEGASILLDGRKIKVKGYENGNFVGPTIISNVKPNMTCYKEEIFGPVLVVLETETLDEAIQIVNNNPYGNGTAIFTTNGATARKYAHLVDVGQVGVNVPIPVPLPMFSFTGSRSSFRGDTNFYGKQGIQFYTQLKTITSQWKEEDATLSSPAVVMPTMGR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1=CC=CC(=C1)C1=CC2=C(N1)C=CC(=C2)C(N)=N' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you gauge the affinity between the drug 'CSc1ccccc1CNCCCCCCNCCCCCCCCNCCCCCCNCc1ccccc1SC' and the target protein 'MNNSTNSSNNSLALTSPYKTFEVVFIVLVAGSLSLVTIIGNILVMVSIKVNRHLQTVNNYFLFSLACADLIIGVFSMNLYTLYTVIGYWPLGPVVCDLWLALDYVVSNASVMNLLIISFDRYFCVTKPLTYPVKRTTKMAGMMIAAAWVLSFILWAPAILFWQFIVGVRTVEDGECYIQFFSNAAVTFGTAIAAFYLPVIIMTVLYWHISRASKSRIKKDKKEPVANQDPVSPSLVQGRIVKPNNNNMPSSDDGLEHNKIQNGKAPRDPVTENCVQGEEKESSNDSTSVSAVASNMRDDEITQDENTVSTSLGHSKDENSKQTCIRIGTKTPKSDSCTPTNTTVEVVGSSGQNGDEKQNIVARKIVKMTKQPAKKKPPPSREKKVTRTILAILLAFIITWAPYNVMVLINTFCAPCIPNTVWTIGYWLCYINSTINPACYALCNATFKKTFKHLLMCHYKNIGATR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1ccccc1CNCCCCCCNCCCCCCCCNCCCCCCNCc1ccccc1SC\"], \"target_seq\": \"MNNSTNSSNNSLALTSPYKTFEVVFIVLVAGSLSLVTIIGNILVMVSIKVNRHLQTVNNYFLFSLACADLIIGVFSMNLYTLYTVIGYWPLGPVVCDLWLALDYVVSNASVMNLLIISFDRYFCVTKPLTYPVKRTTKMAGMMIAAAWVLSFILWAPAILFWQFIVGVRTVEDGECYIQFFSNAAVTFGTAIAAFYLPVIIMTVLYWHISRASKSRIKKDKKEPVANQDPVSPSLVQGRIVKPNNNNMPSSDDGLEHNKIQNGKAPRDPVTENCVQGEEKESSNDSTSVSAVASNMRDDEITQDENTVSTSLGHSKDENSKQTCIRIGTKTPKSDSCTPTNTTVEVVGSSGQNGDEKQNIVARKIVKMTKQPAKKKPPPSREKKVTRTILAILLAFIITWAPYNVMVLINTFCAPCIPNTVWTIGYWLCYINSTINPACYALCNATFKKTFKHLLMCHYKNIGATR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [26.92]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 26.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast if the drug 'CN(C)C(=O)N[C@H]1CC[C@H](CCN2CCN(CC2)C2=C(Cl)C(Cl)=CC=C2)CC1' will interact with the target protein 'MSLPNSSCLLEDKMCEGNKTTMASPQLMPLVVVLSTICLVTVGLNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLLMSKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLKYRTKTRASATILGAWFLSFLWVIPILGWNHFMQQTSVRREDKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYAKIYKAVRQHCQHRELINRSLPSFSEIKLRPENPKGDAKKPGKESPWEVLKRKPKDAGGGSVLKSPSQTPKEMKSPVVFSQEDDREVDKLYCFPLDIVHMQAAAEGSSRDYVAVNRSHGQLKTDEQGLNTHGASEISEDQMLGDSQSFSRTDSDTTTETAPGKGKLRSGSNTGLDYIKFTWKRLRSHSRQYVSGLHMNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKNCCNEHLHMFTIWLGYINSTLNPLIYPLCNENFKKTFKRILHIRS'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C(=O)N[C@H]1CC[C@H](CCN2CCN(CC2)C2=C(Cl)C(Cl)=CC=C2)CC1\"], \"target_seq\": \"MSLPNSSCLLEDKMCEGNKTTMASPQLMPLVVVLSTICLVTVGLNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLLMSKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLKYRTKTRASATILGAWFLSFLWVIPILGWNHFMQQTSVRREDKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYAKIYKAVRQHCQHRELINRSLPSFSEIKLRPENPKGDAKKPGKESPWEVLKRKPKDAGGGSVLKSPSQTPKEMKSPVVFSQEDDREVDKLYCFPLDIVHMQAAAEGSSRDYVAVNRSHGQLKTDEQGLNTHGASEISEDQMLGDSQSFSRTDSDTTTETAPGKGKLRSGSNTGLDYIKFTWKRLRSHSRQYVSGLHMNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKNCCNEHLHMFTIWLGYINSTLNPLIYPLCNENFKKTFKRILHIRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)C(=O)N[C@H]1CC[C@H](CCN2CCN(CC2)C2=C(Cl)C(Cl)=CC=C2)CC1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "Assess the predicted binding value of the drug 'Cc1nc(CCC23CC4CC(CC(C4)C2)C3)c(C(=O)Nc2cccc(C(=O)O)c2)[nH]1' with the target protein 'MELLKLNRSVQGPGPGSGSSLCRPGVSLLNSSSAGNLSCDPPRIRGTGTRELEMAIRITLYAVIFLMSVGGNVLIIVVLGLSRRLRTVTNAFLLSLAVSDLLLAVACMPFTLLPNLMGTFIFGTVICKAISYLMGVSVSVSTLNLVAIALERYSAICRPLQARVWQTRSHAARVILATWLLSGLLMVPYPVYTMVQPVGPRVLQCMHRWPSARVQQTWSVLLLLLLFFIPGVVIAVAYGLISRELYLGLHFDGENDSETQSRARNQGGLPGGAAPGPVHQNGGCRPVTSVAGEDSDGCCVQLPRSRLEMTTLTTPTPGPVPGPRPNQAKLLAKKRVVRMLLVIVLLFFLCWLPVYSVNTWRAFDGPGAQRALSGAPISFIHLLSYVSACVNPLVYCFMHRRFRQACLDTCARCCPRPPRARPQPLPDEDPPTPSIASLSRLSYTTISTLGPG'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc(CCC23CC4CC(CC(C4)C2)C3)c(C(=O)Nc2cccc(C(=O)O)c2)[nH]1\"], \"target_seq\": \"MELLKLNRSVQGPGPGSGSSLCRPGVSLLNSSSAGNLSCDPPRIRGTGTRELEMAIRITLYAVIFLMSVGGNVLIIVVLGLSRRLRTVTNAFLLSLAVSDLLLAVACMPFTLLPNLMGTFIFGTVICKAISYLMGVSVSVSTLNLVAIALERYSAICRPLQARVWQTRSHAARVILATWLLSGLLMVPYPVYTMVQPVGPRVLQCMHRWPSARVQQTWSVLLLLLLFFIPGVVIAVAYGLISRELYLGLHFDGENDSETQSRARNQGGLPGGAAPGPVHQNGGCRPVTSVAGEDSDGCCVQLPRSRLEMTTLTTPTPGPVPGPRPNQAKLLAKKRVVRMLLVIVLLFFLCWLPVYSVNTWRAFDGPGAQRALSGAPISFIHLLSYVSACVNPLVYCFMHRRFRQACLDTCARCCPRPPRARPQPLPDEDPPTPSIASLSRLSYTTISTLGPG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [1548.82]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 1548.82."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge whether the drug '[H][C@@](O)(COC1=C(C)C=CC(C)=C1)CN1CCOCC1' will interact with the target protein 'MSCINLPTVLPGSPSKTRGQIQVILGPMFSGKSTELMRRVRRFQIAQYKCLVIKYAKDTRYSSSFCTHDRNTMEALPACLLRDVAQEALGVAVIGIDEGQFFPDIVEFCEAMANAGKTVIVAALDGTFQRKPFGAILNLVPLAESVVKLTAVCMECFREAAYTKRLGTEKEVEVIGGADKYHSVCRLCYFKKASGQPAGPDNKENCPVPGKPGEAVAARKLFAPQQILQCSPAN'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@](O)(COC1=C(C)C=CC(C)=C1)CN1CCOCC1\"], \"target_seq\": \"MSCINLPTVLPGSPSKTRGQIQVILGPMFSGKSTELMRRVRRFQIAQYKCLVIKYAKDTRYSSSFCTHDRNTMEALPACLLRDVAQEALGVAVIGIDEGQFFPDIVEFCEAMANAGKTVIVAALDGTFQRKPFGAILNLVPLAESVVKLTAVCMECFREAAYTKRLGTEKEVEVIGGADKYHSVCRLCYFKKASGQPAGPDNKENCPVPGKPGEAVAARKLFAPQQILQCSPAN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@](O)(COC1=C(C)C=CC(C)=C1)CN1CCOCC1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Infer the expected affinity of the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' towards the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\"], \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug 'C(C(CO)O)O' and the target protein 'MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(CO)O)O\"], \"target_seq\": \"MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(C(CO)O)O' can interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the target protein 'MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' and the target protein 'MDYSYDEDLDELCPVCGDKVSGYHYGLLTCESCKGFFKRTVQNNKHYTCTESQSCKIDKTQRKRCPFCRFQKCLTVGMRLEAVRADRMRGGRNKFGPMYKRDRALKQQKKAQIRANGFKLETGPPMGVPPPPPPAPDYVLPPSLHGPEPKGLAAGPPAGPLGDFGAPALPMAVPGAHGPLAGYLYPAFPGRAIKSEYPEPYASPPQPGLPYGYPEPFSGGPNVPELILQLLQLEPDEDQVRARILGCLQEPTKSRPDQPAAFGLLCRMADQTFISIVDWARRCMVFKELEVADQMTLLQNCWSELLVFDHIYRQVQHGKEGSILLVTGQEVELTTVATQAGSLLHSLVLRAQELVLQLLALQLDRQEFVCLKFIILFSLDLKFLNNHILVKDAQEKANAALLDYTLCHYPHCGDKFQQLLLCLVEVRALSMQAKEYLYHKHLGNEMPRNNLLIEMLQAKQT'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\"], \"target_seq\": \"MDYSYDEDLDELCPVCGDKVSGYHYGLLTCESCKGFFKRTVQNNKHYTCTESQSCKIDKTQRKRCPFCRFQKCLTVGMRLEAVRADRMRGGRNKFGPMYKRDRALKQQKKAQIRANGFKLETGPPMGVPPPPPPAPDYVLPPSLHGPEPKGLAAGPPAGPLGDFGAPALPMAVPGAHGPLAGYLYPAFPGRAIKSEYPEPYASPPQPGLPYGYPEPFSGGPNVPELILQLLQLEPDEDQVRARILGCLQEPTKSRPDQPAAFGLLCRMADQTFISIVDWARRCMVFKELEVADQMTLLQNCWSELLVFDHIYRQVQHGKEGSILLVTGQEVELTTVATQAGSLLHSLVLRAQELVLQLLALQLDRQEFVCLKFIILFSLDLKFLNNHILVKDAQEKANAALLDYTLCHYPHCGDKFQQLLLCLVEVRALSMQAKEYLYHKHLGNEMPRNNLLIEMLQAKQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12' and the target protein 'MSARTPLPTVNERDTENHTSVDGYTEPHIQPTKSSSRQNIPRCRNSITSATDEQPHIGNYRLQKTIGKGNFAKVKLARHVLTGREVAVKIIDKTQLNPTSLQKLFREVRIMKILNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKYIVHRDLKAENLLLDGDMNIKIADFGFSNEFTVGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKLLVLNPIKRGSLEQIMKDRWMNVGHEEEELKPYTEPDPDFNDTKRIDIMVTMGFARDEINDALINQKYDEVMATYILLGRKPPEFEGGESLSSGNLCQRSRPSSDLNNSTLQSPAHLKVQRSISANQKQRRFSDHAGPSIPPAVSYTKRPQANSVESEQKEEWDKDVARKLGSTTVGSKSEMTASPLVGPERKKSSTIPSNNVYSGGSMARRNTYVCERTTDRYVALQNGKDSSLTEMSVSSISSAGSSVASAVPSARPRHQKSMSTSGHPIKVTLPTIKDGSEAYRPGTTQRVPAASPSAHSISTATPDRTRFPRGSSSRSTFHGEQLRERRSVAYNGPPASPSHETGAFAHARRGTSTGIISKITSKFVRRDPSEGEASGRTDTSRSTSGEPKERDKEEGKDSKPRSLRFTWSMKTTSSMDPNDMMREIRKVLDANNCDYEQKERFLLFCVHGDARQDSLVQWEMEVCKLPRLSLNGVRFKRISGTSIAFKNIASKIANELKL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\"], \"target_seq\": \"MSARTPLPTVNERDTENHTSVDGYTEPHIQPTKSSSRQNIPRCRNSITSATDEQPHIGNYRLQKTIGKGNFAKVKLARHVLTGREVAVKIIDKTQLNPTSLQKLFREVRIMKILNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKYIVHRDLKAENLLLDGDMNIKIADFGFSNEFTVGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKLLVLNPIKRGSLEQIMKDRWMNVGHEEEELKPYTEPDPDFNDTKRIDIMVTMGFARDEINDALINQKYDEVMATYILLGRKPPEFEGGESLSSGNLCQRSRPSSDLNNSTLQSPAHLKVQRSISANQKQRRFSDHAGPSIPPAVSYTKRPQANSVESEQKEEWDKDVARKLGSTTVGSKSEMTASPLVGPERKKSSTIPSNNVYSGGSMARRNTYVCERTTDRYVALQNGKDSSLTEMSVSSISSAGSSVASAVPSARPRHQKSMSTSGHPIKVTLPTIKDGSEAYRPGTTQRVPAASPSAHSISTATPDRTRFPRGSSSRSTFHGEQLRERRSVAYNGPPASPSHETGAFAHARRGTSTGIISKITSKFVRRDPSEGEASGRTDTSRSTSGEPKERDKEEGKDSKPRSLRFTWSMKTTSSMDPNDMMREIRKVLDANNCDYEQKERFLLFCVHGDARQDSLVQWEMEVCKLPRLSLNGVRFKRISGTSIAFKNIASKIANELKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your forecast regarding the interaction of the drug 'COCC(=O)N1CCC(c2ccc(C(=O)NC(=N)N)cc2C(F)(F)F)CC1' with the target protein 'MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COCC(=O)N1CCC(c2ccc(C(=O)NC(=N)N)cc2C(F)(F)F)CC1\"], \"target_seq\": \"MALIPDLAMETWLLLAVSLVLLYLYGTHSHGLFKKLGIPGPTPLPFLGNILSYHKGFCMFDMECHKKYGKVWGFYDGQQPVLAITDPDMIKTVLVKECYSVFTNRRPFGPVGFMKSAISIAEDEEWKRLRSLLSPTFTSGKLKEMVPIIAQYGDVLVRNLRREAETGKPVTLKDVFGAYSMDVITSTSFGVNIDSLNNPQDPFVENTKKLLRFDFLDPFFLSITVFPFLIPILEVLNICVFPREVTNFLRKSVKRMKESRLEDTQKHRVDFLQLMIDSQNSKETESHKALSDLELVAQSIIFIFAGYETTSSVLSFIMYELATHPDVQQKLQEEIDAVLPNKAPPTYDTVLQMEYLDMVVNETLRLFPIAMRLERVCKKDVEINGMFIPKGVVVMIPSYALHRDPKYWTEPEKFLPERFSKKNKDNIDPYIYTPFGSGPRNCIGMRFALMNMKLALIRVLQNFSFKPCKETQIPLKLSLGGLLQPEKPVVLKVESRDGTVSGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COCC(=O)N1CCC(c2ccc(C(=O)NC(=N)N)cc2C(F)(F)F)CC1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you provide a prediction for the affinity between the drug 'Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12' and the target protein 'MILSSYNTIQSVFCCCCCCSVQKRQMRTQISLSTDEELPEKYTQRRRPWLSQLSNKKQSNTGRVQPSKRKPLPPLPPSEVAEEKIQVKALYDFLPREPCNLALRRAEEYLILEKYNPHWWKARDRLGNEGLIPSNYVTENKITNLEIYEWYHRNITRNQAEHLLRQESKEGAFIVRDSRHLGSYTISVFMGARRSTEAAIKHYQIKKNDSGQWYVAERHAFQSIPELIWYHQHNAAGLMTRLRYPVGLMGSCLPATAGFSYEKWEIDPSELAFIKEIGSGQFGVVHLGEWRSHIQVAIKAINEGSMSEEDFIEEAKVMMKLSHSKLVQLYGVCIQRKPLYIVTEFMENGCLLNYLRENKGKLRKEMLLSVCQDICEGMEYLERNGYIHRDLAARNCLVSSTCIVKISDFGMTRYVLDDEYVSSFGAKFPIKWSPPEVFLFNKYSSKSDVWSFGVLMWEVFTEGKMPFENKSNLQVVEAISEGFRLYRPHLAPMSIYEVMYSCWHEKPEGRPTFAELLRAVTEIAETW'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"MILSSYNTIQSVFCCCCCCSVQKRQMRTQISLSTDEELPEKYTQRRRPWLSQLSNKKQSNTGRVQPSKRKPLPPLPPSEVAEEKIQVKALYDFLPREPCNLALRRAEEYLILEKYNPHWWKARDRLGNEGLIPSNYVTENKITNLEIYEWYHRNITRNQAEHLLRQESKEGAFIVRDSRHLGSYTISVFMGARRSTEAAIKHYQIKKNDSGQWYVAERHAFQSIPELIWYHQHNAAGLMTRLRYPVGLMGSCLPATAGFSYEKWEIDPSELAFIKEIGSGQFGVVHLGEWRSHIQVAIKAINEGSMSEEDFIEEAKVMMKLSHSKLVQLYGVCIQRKPLYIVTEFMENGCLLNYLRENKGKLRKEMLLSVCQDICEGMEYLERNGYIHRDLAARNCLVSSTCIVKISDFGMTRYVLDDEYVSSFGAKFPIKWSPPEVFLFNKYSSKSDVWSFGVLMWEVFTEGKMPFENKSNLQVVEAISEGFRLYRPHLAPMSIYEVMYSCWHEKPEGRPTFAELLRAVTEIAETW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is there a chance the drug 'CCCCP(O)(=O)CCCN' will interact with the target protein 'MWPGILVGGARVASCRYPALGPRLAAHFPAQRPPQRTLQNGLALQRCLHATATRALPLIPIVVEQTGRGERAYDIYSRLLRERIVCVMGPIDDSVASLVIAQLLFLQSESNKKPIHMYINSPGGVVTAGLAIYDTMQYILNPICTWCVGQAASMGSLLLAAGTPGMRHSLPNSRIMIHQPSGGARGQATDIAIQAEEIMKLKKQLYNIYAKHTKQSLQVIESAMERDRYMSPMEAQEFGILDKVLVHPPQDGEDEPTLVQKEPVEAAPAAEPVPAST'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCP(O)(=O)CCCN\"], \"target_seq\": \"MWPGILVGGARVASCRYPALGPRLAAHFPAQRPPQRTLQNGLALQRCLHATATRALPLIPIVVEQTGRGERAYDIYSRLLRERIVCVMGPIDDSVASLVIAQLLFLQSESNKKPIHMYINSPGGVVTAGLAIYDTMQYILNPICTWCVGQAASMGSLLLAAGTPGMRHSLPNSRIMIHQPSGGARGQATDIAIQAEEIMKLKKQLYNIYAKHTKQSLQVIESAMERDRYMSPMEAQEFGILDKVLVHPPQDGEDEPTLVQKEPVEAAPAAEPVPAST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCCP(O)(=O)CCCN' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12'-target protein 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG' interaction?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Do you foresee any interaction between the drug 'NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1' and the target protein 'MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1\"], \"target_seq\": \"MTAPWRRLRSLVWEYWAGLLVCAFWIPDSRGMPHVIRIGGIFEYADGPNAQVMNAEEHAFRFSANIINRNRTLLPNTTLTYDIQRIHFHDSFEATKKACDQLALGVVAIFGPSQGSCTNAVQSICNALEVPHIQLRWKHHPLDNKDTFYVNLYPDYASLSHAILDLVQYLKWRSATVVYDDSTGLIRLQELIMAPSRYNIRLKIRQLPIDSDDSRPLLKEMKRGREFRIIFDCSHTMAAQILKQAMAMGMMTEYYHFIFTTLDLYALDLEPYRYSGVNLTGFRILNVDNPHVSAIVEKWSMERLQAAPRSESGLLDGVMMTDAALLYDAVHIVSVCYQRAPQMTVNSLQCHRHKAWRFGGRFMNFIKEAQWEGLTGRIVFNKTSGLRTDFDLDIISLKEDGLEKVGVWSPADGLNITEVAKGRGPNVTDSLTNRSLIVTTVLEEPFVMFRKSDRTLYGNDRFEGYCIDLLKELAHILGFSYEIRLVEDGKYGAQDDKGQWNGMVKELIDHKADLAVAPLTITHVREKAIDFSKPFMTLGVSILYRKPNGTNPSVFSFLNPLSPDIWMYVLLAYLGVSCVLFVIARFSPYEWYDAHPCNPGSEVVENNFTLLNSFWFGMGSLMQQGSELMPKALSTRIIGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVKDGATMTFFKKSKISTFEKMWAFMSSKPSALVKNNEEGIQRALTADYALLMESTTIEYVTQRNCNLTQIGGLIDSKGYGIGTPMGSPYRDKITIAILQLQEEDKLHIMKEKWWRGSGCPEEENKEASALGIQKIGGIFIVLAAGLVLSVLVAVGEFVYKLRKTAEREQRSFCSTVADEIRFSLTCQRRVKHKPQPPMMVKTDAVINMHTFNDRRLPGKDSMACSTSLAPVFP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=NNC2=C1C=C(N=N2)C1=C2C=CC=CN2N=C1C1=CC=CC=C1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "How do you project the affinity value for the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1' and the target protein 'MPSRAEDYEVLYTIGTGSYGRCQKIRRKSDGKILVWKELDYGSMTEAEKQMLVSEVNLLRELKHPNIVRYYDRIIDRTNTTLYIVMEYCEGGDLASVITKGTKERQYLDEEFVLRVMTQLTLALKECHRRSDGGHTVLHRDLKPANVFLDGKQNVKLGDFGLARILNHDTSFAKTFVGTPYYMSPEQMNRMSYNEKSDIWSLGCLLYELCALMPPFTAFSQKELAGKIREGKFRRIPYRYSDELNEIITRMLNLKDYHRPSVEEILENPLIADLVADEQRRNLERRGRQLGEPEKSQDSSPVLSELKLKEIQLQERERALKAREERLEQKEQELCVRERLAEDKLARAENLLKNYSLLKERKFLSLASNPELLNLPSSVIKKKVHFSGESKENIMRSENSESQLTSKSKCKDLKKRLHAAQLRAQALSDIEKNYQLKSRQILGMR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\"], \"target_seq\": \"MPSRAEDYEVLYTIGTGSYGRCQKIRRKSDGKILVWKELDYGSMTEAEKQMLVSEVNLLRELKHPNIVRYYDRIIDRTNTTLYIVMEYCEGGDLASVITKGTKERQYLDEEFVLRVMTQLTLALKECHRRSDGGHTVLHRDLKPANVFLDGKQNVKLGDFGLARILNHDTSFAKTFVGTPYYMSPEQMNRMSYNEKSDIWSLGCLLYELCALMPPFTAFSQKELAGKIREGKFRRIPYRYSDELNEIITRMLNLKDYHRPSVEEILENPLIADLVADEQRRNLERRGRQLGEPEKSQDSSPVLSELKLKEIQLQERERALKAREERLEQKEQELCVRERLAEDKLARAENLLKNYSLLKERKFLSLASNPELLNLPSSVIKKKVHFSGESKENIMRSENSESQLTSKSKCKDLKKRLHAAQLRAQALSDIEKNYQLKSRQILGMR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you predict about the interaction of the drug 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' with the target protein 'MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F\"], \"target_seq\": \"MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CCCS(=O)(=O)NC1=C(C(=C(C=C1)F)C(=O)C2=CNC3=NC=C(C=C23)Cl)F' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you outline the affinity between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the target protein 'MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the chance that the drug '[H][C@@]1(OC(=O)C(O)=C1O)[C@@H](O)CO' interacts with the target protein 'MAGRVPSLLVLLVFPSSCLAFRSPLSVFKRFKETTRPFSNECLGTTRPVVPIDSSDFALDIRMPGVTPKQSDTYFCMSMRIPVDEEAFVIDFKPRASMDTVHHMLLFGCNMPSSTGSYWFCDEGTCTDKANILYAWARNAPPTRLPKGVGFRVGGETGSKYFVLQVHYGDISAFRDNNKDCSGVSLHLTRLPQPLIAGMYLMMSVDTVIPAGEKVVNSDISCHYKNYPMHVFAYRVHTHHLGKVVSGYRVRNGQWTLIGRQSPQLPQAFYPVGHPVDVSFGDLLAARCVFTGEGRTEATHIGGTSSDEMCNLYIMYYMEAKHAVSFMTCTQNVAPDMFRTIPPEANIPIPVKSDMVMMHEHHKETEYKDKIPLLQQPKREEEEVLDQGDFYSLLSKLLGEREDVVHVHKYNPTEKAESESDLVAEIANVVQKKDLGRSDAREGAEHERGNAILVRDRIHKFHRLVSTLRPPESRVFSLQQPPPGEGTWEPEHTGDFHMEEALDWPGVYLLPGQVSGVALDPKNNLVIFHRGDHVWDGNSFDSKFVYQQIGLGPIEEDTILVIDPNNAAVLQSSGKNLFYLPHGLSIDKDGNYWVTDVALHQVFKLDPNNKEGPVLILGRSMQPGSDQNHFCQPTDVAVDPGTGAIYVSDGYCNSRIVQFSPSGKFITQWGEESSGSSPLPGQFTVPHSLALVPLLGQLCVADRENGRIQCFKTDTKEFVREIKHSSFGRNVFAISYIPGLLFAVNGKPHFGDQEPVQGFVMNFSNGEIIDIFKPVRKHFDMPHDIVASEDGTVYIGDAHTNTVWKFTLTEKLEHRSVKKAGIEVQEIKEAEAVVETKMENKPTSSELQKMQEKQKLIKEPGSGVPVVLITTLLVIPVVVLLAIAIFIRWKKSRAFGDSEHKLETSSGRVLGRFRGKGSGGLNLGNFFASRKGYSRKGFDRLSTEGSDQEKEDDGSESEEEYSAPLPALAPSSS'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@]1(OC(=O)C(O)=C1O)[C@@H](O)CO\"], \"target_seq\": \"MAGRVPSLLVLLVFPSSCLAFRSPLSVFKRFKETTRPFSNECLGTTRPVVPIDSSDFALDIRMPGVTPKQSDTYFCMSMRIPVDEEAFVIDFKPRASMDTVHHMLLFGCNMPSSTGSYWFCDEGTCTDKANILYAWARNAPPTRLPKGVGFRVGGETGSKYFVLQVHYGDISAFRDNNKDCSGVSLHLTRLPQPLIAGMYLMMSVDTVIPAGEKVVNSDISCHYKNYPMHVFAYRVHTHHLGKVVSGYRVRNGQWTLIGRQSPQLPQAFYPVGHPVDVSFGDLLAARCVFTGEGRTEATHIGGTSSDEMCNLYIMYYMEAKHAVSFMTCTQNVAPDMFRTIPPEANIPIPVKSDMVMMHEHHKETEYKDKIPLLQQPKREEEEVLDQGDFYSLLSKLLGEREDVVHVHKYNPTEKAESESDLVAEIANVVQKKDLGRSDAREGAEHERGNAILVRDRIHKFHRLVSTLRPPESRVFSLQQPPPGEGTWEPEHTGDFHMEEALDWPGVYLLPGQVSGVALDPKNNLVIFHRGDHVWDGNSFDSKFVYQQIGLGPIEEDTILVIDPNNAAVLQSSGKNLFYLPHGLSIDKDGNYWVTDVALHQVFKLDPNNKEGPVLILGRSMQPGSDQNHFCQPTDVAVDPGTGAIYVSDGYCNSRIVQFSPSGKFITQWGEESSGSSPLPGQFTVPHSLALVPLLGQLCVADRENGRIQCFKTDTKEFVREIKHSSFGRNVFAISYIPGLLFAVNGKPHFGDQEPVQGFVMNFSNGEIIDIFKPVRKHFDMPHDIVASEDGTVYIGDAHTNTVWKFTLTEKLEHRSVKKAGIEVQEIKEAEAVVETKMENKPTSSELQKMQEKQKLIKEPGSGVPVVLITTLLVIPVVVLLAIAIFIRWKKSRAFGDSEHKLETSSGRVLGRFRGKGSGGLNLGNFFASRKGYSRKGFDRLSTEGSDQEKEDDGSESEEEYSAPLPALAPSSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@]1(OC(=O)C(O)=C1O)[C@@H](O)CO' can interact with the protein."
            },
            {
                "from": "human",
                "value": "How do you project the affinity value for the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1' and the target protein 'MPSRAEDYEVLYTIGTGSYGRCQKIRRKSDGKILVWKELDYGSMTEAEKQMLVSEVNLLRELKHPNIVRYYDRIIDRTNTTLYIVMEYCEGGDLASVITKGTKERQYLDEEFVLRVMTQLTLALKECHRRSDGGHTVLHRDLKPANVFLDGKQNVKLGDFGLARILNHDTSFAKTFVGTPYYMSPEQMNRMSYNEKSDIWSLGCLLYELCALMPPFTAFSQKELAGKIREGKFRRIPYRYSDELNEIITRMLNLKDYHRPSVEEILENPLIADLVADEQRRNLERRGRQLGEPEKSQDSSPVLSELKLKEIQLQERERALKAREERLEQKEQELCVRERLAEDKLARAENLLKNYSLLKERKFLSLASNPELLNLPSSVIKKKVHFSGESKENIMRSENSESQLTSKSKCKDLKKRLHAAQLRAQALSDIEKNYQLKSRQILGMR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\"], \"target_seq\": \"MPSRAEDYEVLYTIGTGSYGRCQKIRRKSDGKILVWKELDYGSMTEAEKQMLVSEVNLLRELKHPNIVRYYDRIIDRTNTTLYIVMEYCEGGDLASVITKGTKERQYLDEEFVLRVMTQLTLALKECHRRSDGGHTVLHRDLKPANVFLDGKQNVKLGDFGLARILNHDTSFAKTFVGTPYYMSPEQMNRMSYNEKSDIWSLGCLLYELCALMPPFTAFSQKELAGKIREGKFRRIPYRYSDELNEIITRMLNLKDYHRPSVEEILENPLIADLVADEQRRNLERRGRQLGEPEKSQDSSPVLSELKLKEIQLQERERALKAREERLEQKEQELCVRERLAEDKLARAENLLKNYSLLKERKFLSLASNPELLNLPSSVIKKKVHFSGESKENIMRSENSESQLTSKSKCKDLKKRLHAAQLRAQALSDIEKNYQLKSRQILGMR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the drug 'CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C' is likely to engage with the target protein 'MNPFHASCWNTSAELLNKSWNKEFAYQTASVVDTVILPSMIGIICSTGLVGNILIVFTIIRSRKKTVPDIYICNLAVADLVHIVGMPFLIHQWARGGEWVFGGPLCTIITSLDTCNQFACSAIMTVMSVDRYFALVQPFRLTRWRTRYKTIRINLGLWAASFILALPVWVYSKVIKFKDGVESCAFDLTSPDDVLWYTLYLTITTFFFPLPLILVCYILILCYTWEMYQQNKDARCCNPSVPKQRVMKLTKMVLVLVVVFILSAAPYHVIQLVNLQMEQPTLAFYVGYYLSICLSYASSSINPFLYILLSGNFQKRLPQIQRRATEKEINNMGNTLKSHF'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C\"], \"target_seq\": \"MNPFHASCWNTSAELLNKSWNKEFAYQTASVVDTVILPSMIGIICSTGLVGNILIVFTIIRSRKKTVPDIYICNLAVADLVHIVGMPFLIHQWARGGEWVFGGPLCTIITSLDTCNQFACSAIMTVMSVDRYFALVQPFRLTRWRTRYKTIRINLGLWAASFILALPVWVYSKVIKFKDGVESCAFDLTSPDDVLWYTLYLTITTFFFPLPLILVCYILILCYTWEMYQQNKDARCCNPSVPKQRVMKLTKMVLVLVVVFILSAAPYHVIQLVNLQMEQPTLAFYVGYYLSICLSYASSSINPFLYILLSGNFQKRLPQIQRRATEKEINNMGNTLKSHF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' with the target protein 'MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\"], \"target_seq\": \"MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [83.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 83.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the chances of the drug 'COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl' interacting with the target protein 'MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl\"], \"target_seq\": \"MSRSLLLWFLLFLLLLPPLPVLLADPGAPTPVNPCCYYPCQHQGICVRFGLDRYQCDCTRTGYSGPNCTIPGLWTWLRNSLRPSPSFTHFLLTHGRWFWEFVNATFIREMLMRLVLTVRSNLIPSPPTYNSAHDYISWESFSNVSYYTRILPSVPKDCPTPMGTKGKKQLPDAQLLARRFLLRRKFIPDPQGTNLMFAFFAQHFTHQFFKTSGKMGPGFTKALGHGVDLGHIYGDNLERQYQLRLFKDGKLKYQVLDGEMYPPSVEEAPVLMHYPRGIPPQSQMAVGQEVFGLLPGLMLYATLWLREHNRVCDLLKAEHPTWGDEQLFQTTRLILIGETIKIVIEEYVQQLSGYFLQLKFDPELLFGVQFQYRNRIAMEFNHLYHWHPLMPDSFKVGSQEYSYEQFLFNTSMLVDYGVEALVDAFSRQIAGRIGGGRNMDHHILHVAVDVIRESREMRLQPFNEYRKRFGMKPYTSFQELVGEKEMAAELEELYGDIDALEFYPGLLLEKCHPNSIFGESMIEIGAPFSLKGLLGNPICSPEYWKPSTFGGEVGFNIVKTATLKKLVCLNTKTCPYVSFRVPDASQDDGPAVERPSTEL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1c(Cl)cc(C2=C(c3ccc(S(N)(=O)=O)cc3)CCC2)cc1Cl' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the target protein 'MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction between the drug 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' and the target protein 'MDYSYDEDLDELCPVCGDKVSGYHYGLLTCESCKGFFKRTVQNNKHYTCTESQSCKIDKTQRKRCPFCRFQKCLTVGMRLEAVRADRMRGGRNKFGPMYKRDRALKQQKKAQIRANGFKLETGPPMGVPPPPPPAPDYVLPPSLHGPEPKGLAAGPPAGPLGDFGAPALPMAVPGAHGPLAGYLYPAFPGRAIKSEYPEPYASPPQPGLPYGYPEPFSGGPNVPELILQLLQLEPDEDQVRARILGCLQEPTKSRPDQPAAFGLLCRMADQTFISIVDWARRCMVFKELEVADQMTLLQNCWSELLVFDHIYRQVQHGKEGSILLVTGQEVELTTVATQAGSLLHSLVLRAQELVLQLLALQLDRQEFVCLKFIILFSLDLKFLNNHILVKDAQEKANAALLDYTLCHYPHCGDKFQQLLLCLVEVRALSMQAKEYLYHKHLGNEMPRNNLLIEMLQAKQT'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\"], \"target_seq\": \"MDYSYDEDLDELCPVCGDKVSGYHYGLLTCESCKGFFKRTVQNNKHYTCTESQSCKIDKTQRKRCPFCRFQKCLTVGMRLEAVRADRMRGGRNKFGPMYKRDRALKQQKKAQIRANGFKLETGPPMGVPPPPPPAPDYVLPPSLHGPEPKGLAAGPPAGPLGDFGAPALPMAVPGAHGPLAGYLYPAFPGRAIKSEYPEPYASPPQPGLPYGYPEPFSGGPNVPELILQLLQLEPDEDQVRARILGCLQEPTKSRPDQPAAFGLLCRMADQTFISIVDWARRCMVFKELEVADQMTLLQNCWSELLVFDHIYRQVQHGKEGSILLVTGQEVELTTVATQAGSLLHSLVLRAQELVLQLLALQLDRQEFVCLKFIILFSLDLKFLNNHILVKDAQEKANAALLDYTLCHYPHCGDKFQQLLLCLVEVRALSMQAKEYLYHKHLGNEMPRNNLLIEMLQAKQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drug 'CSCC[C@H](NC(=O)[C@@H](N)CC(N)=O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)NCC(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@@H](CC(C)C)C(=O)O)[C@H](C)O)C(C)C' for the target protein 'MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTLTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSIVYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPLHLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAKVSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCVRCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSCC[C@H](NC(=O)[C@@H](N)CC(N)=O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)N[C@@H](CCC(N)=O)C(=O)N[C@@H](CCCCN)C(=O)N[C@H](C(=O)NCC(=O)N[C@H](C(=O)N1CCC[C@H]1C(=O)N[C@@H](CC(C)C)C(=O)O)[C@H](C)O)C(C)C\"], \"target_seq\": \"MNRGVPFRHLLLVLQLALLPAATQGKKVVLGKKGDTVELTCTASQKKSIQFHWKNSNQIKILGNQGSFLTKGPSKLNDRADSRRSLWDQGNFPLIIKNLKIEDSDTYICEVEDQKEEVQLLVFGLTANSDTHLLQGQSLTLTLESPPGSSPSVQCRSPRGKNIQGGKTLSVSQLELQDSGTWTCTVLQNQKKVEFKIDIVVLAFQKASSIVYKKEGEQVEFSFPLAFTVEKLTGSGELWWQAERASSSKSWITFDLKNKEVSVKRVTQDPKLQMGKKLPLHLTLPQALPQYAGSGNLTLALEAKTGKLHQEVNLVVMRATQLQKNLTCEVWGPTSPKLMLSLKLENKEAKVSKREKAVWVLNPEAGMWQCLLSDSGQVLLESNIKVLPTWSTPVQPMALIVLGGVAGLLLFIGLGIFFCVRCRHRRRQAERMSQIKRLLSEKKTCQCPHRFQKTCSPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [6000000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 6000000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'O=c1ccoc2cc(OCc3ccc(F)cc3)ccc12' and target protein 'MENQEKASIAGHMFDVVVIGGGISGLSAAKLLTEYGVSVLVLEARDRVGGRTYTIRNEHVDYVDVGGAYVGPTQNRILRLSKELGIETYKVNVSERLVQYVKGKTYPFRGAFPPVWNPIAYLDYNNLWRTIDNMGKEIPTDAPWEAQHADKWDKMTMKELIDKICWTKTARRFAYLFVNINVTSEPHEVSALWFLWYVKQCGGTTRIFSVTNGGQERKFVGGSGQVSERIMDLLGDQVKLNHPVTHVDQSSDNIIIETLNHEHYECKYVINAIPPTLTAKIHFRPELPAERNQLIQRLPMGAVIKCMMYYKEAFWKKKDYCGCMIIEDEDAPISITLDDTKPDGSLPAIMGFILARKADRLAKLHKEIRKKKICELYAKVLGSQEALHPVHYEEKNWCEEQYSGGCYTAYFPPGIMTQYGRVIRQPVGRIFFAGTETATKWSGYMEGAVEAGERAAREVLNGLGKVTEKDIWVQEPESKDVPAVEITHTFWERNLPSVSGLLKIIGFSTSVTALGFVLYKYKLLPRS' interact, based on your prediction?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ccoc2cc(OCc3ccc(F)cc3)ccc12\"], \"target_seq\": \"MENQEKASIAGHMFDVVVIGGGISGLSAAKLLTEYGVSVLVLEARDRVGGRTYTIRNEHVDYVDVGGAYVGPTQNRILRLSKELGIETYKVNVSERLVQYVKGKTYPFRGAFPPVWNPIAYLDYNNLWRTIDNMGKEIPTDAPWEAQHADKWDKMTMKELIDKICWTKTARRFAYLFVNINVTSEPHEVSALWFLWYVKQCGGTTRIFSVTNGGQERKFVGGSGQVSERIMDLLGDQVKLNHPVTHVDQSSDNIIIETLNHEHYECKYVINAIPPTLTAKIHFRPELPAERNQLIQRLPMGAVIKCMMYYKEAFWKKKDYCGCMIIEDEDAPISITLDDTKPDGSLPAIMGFILARKADRLAKLHKEIRKKKICELYAKVLGSQEALHPVHYEEKNWCEEQYSGGCYTAYFPPGIMTQYGRVIRQPVGRIFFAGTETATKWSGYMEGAVEAGERAAREVLNGLGKVTEKDIWVQEPESKDVPAVEITHTFWERNLPSVSGLLKIIGFSTSVTALGFVLYKYKLLPRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=c1ccoc2cc(OCc3ccc(F)cc3)ccc12' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you gauge the affinity between the drug 'CSc1ccccc1CNCCCCCCNCCCCCCCCNCCCCCCNCc1ccccc1SC' and the target protein 'MNNSTNSSNNSLALTSPYKTFEVVFIVLVAGSLSLVTIIGNILVMVSIKVNRHLQTVNNYFLFSLACADLIIGVFSMNLYTLYTVIGYWPLGPVVCDLWLALDYVVSNASVMNLLIISFDRYFCVTKPLTYPVKRTTKMAGMMIAAAWVLSFILWAPAILFWQFIVGVRTVEDGECYIQFFSNAAVTFGTAIAAFYLPVIIMTVLYWHISRASKSRIKKDKKEPVANQDPVSPSLVQGRIVKPNNNNMPSSDDGLEHNKIQNGKAPRDPVTENCVQGEEKESSNDSTSVSAVASNMRDDEITQDENTVSTSLGHSKDENSKQTCIRIGTKTPKSDSCTPTNTTVEVVGSSGQNGDEKQNIVARKIVKMTKQPAKKKPPPSREKKVTRTILAILLAFIITWAPYNVMVLINTFCAPCIPNTVWTIGYWLCYINSTINPACYALCNATFKKTFKHLLMCHYKNIGATR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1ccccc1CNCCCCCCNCCCCCCCCNCCCCCCNCc1ccccc1SC\"], \"target_seq\": \"MNNSTNSSNNSLALTSPYKTFEVVFIVLVAGSLSLVTIIGNILVMVSIKVNRHLQTVNNYFLFSLACADLIIGVFSMNLYTLYTVIGYWPLGPVVCDLWLALDYVVSNASVMNLLIISFDRYFCVTKPLTYPVKRTTKMAGMMIAAAWVLSFILWAPAILFWQFIVGVRTVEDGECYIQFFSNAAVTFGTAIAAFYLPVIIMTVLYWHISRASKSRIKKDKKEPVANQDPVSPSLVQGRIVKPNNNNMPSSDDGLEHNKIQNGKAPRDPVTENCVQGEEKESSNDSTSVSAVASNMRDDEITQDENTVSTSLGHSKDENSKQTCIRIGTKTPKSDSCTPTNTTVEVVGSSGQNGDEKQNIVARKIVKMTKQPAKKKPPPSREKKVTRTILAILLAFIITWAPYNVMVLINTFCAPCIPNTVWTIGYWLCYINSTINPACYALCNATFKKTFKHLLMCHYKNIGATR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [26.92]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 26.92."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction between the drug 'N[C@@H](CN1C=C(I)C(=O)NC1=O)C(O)=O' and the target protein 'MPAYHSSLMDPDTKLIGNMALLPIRSQFKGPAPRETKDTDIVDEAIYYFKANVFFKNYEIKNEADRTLIYITLYISECLKKLQKCNSKSQGEKEMYTLGITNFPIPGEPGFPLNAIYAKPANKQEDEVMRAYLQQLRQETGLRLCEKVFDPQNDKPSKWWTCFVKRQFMNKSLSGPGQ'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](CN1C=C(I)C(=O)NC1=O)C(O)=O\"], \"target_seq\": \"MPAYHSSLMDPDTKLIGNMALLPIRSQFKGPAPRETKDTDIVDEAIYYFKANVFFKNYEIKNEADRTLIYITLYISECLKKLQKCNSKSQGEKEMYTLGITNFPIPGEPGFPLNAIYAKPANKQEDEVMRAYLQQLRQETGLRLCEKVFDPQNDKPSKWWTCFVKRQFMNKSLSGPGQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'N[C@@H](CN1C=C(I)C(=O)NC1=O)C(O)=O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Determine the affinity value of the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' for the target protein 'MERKVLALQARKKRTKAKKDKAQRKSETQHRGSAPHSESDLPEQEEEILGSDDDEQEDPNDYCKGGYHLVKIGDLFNGRYHVIRKLGWGHFSTVWLSWDIQGKKFVAMKVVKSAEHYTETALDEIRLLKSVRNSDPNDPNREMVVQLLDDFKISGVNGTHICMVFEVLGHHLLKWIIKSNYQGLPLPCVKKIIQQVLQGLDYLHTKCRIIHTDIKPENILLSVNEQYIRRLAAEATEWQRSGAPPPSGSAVSTAPQPKPADKMSKNKKKKLKKKQKRQAELLEKRMQEIEEMEKESGPGQKRPNKQEESESPVERPLKENPPNKMTQEKLEESSTIGQDQTLMERDTEGGAAEINCNGVIEVINYTQNSNNETLRHKEDLHNANDCDVQNLNQESSFLSSQNGDSSTSQETDSCTPITSEVSDTMVCQSSSTVGQSFSEQHISQLQESIRAEIPCEDEQEQEHNGPLDNKGKSTAGNFLVNPLEPKNAEKLKVKIADLGNACWVHKHFTEDIQTRQYRSLEVLIGSGYNTPADIWSTACMAFELATGDYLFEPHSGEEYTRDEDHIALIIELLGKVPRKLIVAGKYSKEFFTKKGDLKHITKLKPWGLFEVLVEKYEWSQEEAAGFTDFLLPMLELIPEKRATAAECLRHPWLNS'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"MERKVLALQARKKRTKAKKDKAQRKSETQHRGSAPHSESDLPEQEEEILGSDDDEQEDPNDYCKGGYHLVKIGDLFNGRYHVIRKLGWGHFSTVWLSWDIQGKKFVAMKVVKSAEHYTETALDEIRLLKSVRNSDPNDPNREMVVQLLDDFKISGVNGTHICMVFEVLGHHLLKWIIKSNYQGLPLPCVKKIIQQVLQGLDYLHTKCRIIHTDIKPENILLSVNEQYIRRLAAEATEWQRSGAPPPSGSAVSTAPQPKPADKMSKNKKKKLKKKQKRQAELLEKRMQEIEEMEKESGPGQKRPNKQEESESPVERPLKENPPNKMTQEKLEESSTIGQDQTLMERDTEGGAAEINCNGVIEVINYTQNSNNETLRHKEDLHNANDCDVQNLNQESSFLSSQNGDSSTSQETDSCTPITSEVSDTMVCQSSSTVGQSFSEQHISQLQESIRAEIPCEDEQEQEHNGPLDNKGKSTAGNFLVNPLEPKNAEKLKVKIADLGNACWVHKHFTEDIQTRQYRSLEVLIGSGYNTPADIWSTACMAFELATGDYLFEPHSGEEYTRDEDHIALIIELLGKVPRKLIVAGKYSKEFFTKKGDLKHITKLKPWGLFEVLVEKYEWSQEEAAGFTDFLLPMLELIPEKRATAAECLRHPWLNS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you analyze whether the drug 'CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C' is likely to engage with the target protein 'MNPFHASCWNTSAELLNKSWNKEFAYQTASVVDTVILPSMIGIICSTGLVGNILIVFTIIRSRKKTVPDIYICNLAVADLVHIVGMPFLIHQWARGGEWVFGGPLCTIITSLDTCNQFACSAIMTVMSVDRYFALVQPFRLTRWRTRYKTIRINLGLWAASFILALPVWVYSKVIKFKDGVESCAFDLTSPDDVLWYTLYLTITTFFFPLPLILVCYILILCYTWEMYQQNKDARCCNPSVPKQRVMKLTKMVLVLVVVFILSAAPYHVIQLVNLQMEQPTLAFYVGYYLSICLSYASSSINPFLYILLSGNFQKRLPQIQRRATEKEINNMGNTLKSHF'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C\"], \"target_seq\": \"MNPFHASCWNTSAELLNKSWNKEFAYQTASVVDTVILPSMIGIICSTGLVGNILIVFTIIRSRKKTVPDIYICNLAVADLVHIVGMPFLIHQWARGGEWVFGGPLCTIITSLDTCNQFACSAIMTVMSVDRYFALVQPFRLTRWRTRYKTIRINLGLWAASFILALPVWVYSKVIKFKDGVESCAFDLTSPDDVLWYTLYLTITTFFFPLPLILVCYILILCYTWEMYQQNKDARCCNPSVPKQRVMKLTKMVLVLVVVFILSAAPYHVIQLVNLQMEQPTLAFYVGYYLSICLSYASSSINPFLYILLSGNFQKRLPQIQRRATEKEINNMGNTLKSHF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN(C)CCN(C)c1nc2ccc(NC(=O)COc3ccc(C(F)(F)F)cc3)cc2n1C' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the target protein 'MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug 'NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1' interact with the target protein 'MVGSLNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSIPEKNRPLKGRINLVLSRELKEPPQGAHFLSRSLDDALKLTEQPELANKVDMVWIVGGSSVYKEAMNHPGHLKLFVTRIMQDFESDTFFPEIDLEKYKLLPEYPGVLSDVQEEKGIKYKFEVYEKND', based on your evaluation?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1\"], \"target_seq\": \"MVGSLNCIVAVSQNMGIGKNGDLPWPPLRNEFRYFQRMTTTSSVEGKQNLVIMGKKTWFSIPEKNRPLKGRINLVLSRELKEPPQGAHFLSRSLDDALKLTEQPELANKVDMVWIVGGSSVYKEAMNHPGHLKLFVTRIMQDFESDTFFPEIDLEKYKLLPEYPGVLSDVQEEKGIKYKFEVYEKND\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'NC1=NC2=NC=C(CC(CC#C)C3=CC=C(C=C3)C(=O)N[C@@H](CCC(O)=O)C(O)=O)N=C2C(N)=N1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccc(-c2ccccc2)cc1' and the target protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccc(-c2ccccc2)cc1\"], \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [49000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 49000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug 'C(C(CO)O)O' and the target protein 'MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(CO)O)O\"], \"target_seq\": \"MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(C(CO)O)O' can interact with the protein."
            },
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the target protein 'MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\"], \"target_seq\": \"MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction on whether the drug 'C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O[C@@H]2[C@H](O[C@H]([C@@H]([C@H]2O)O)O[C@@H]3[C@H](O[C@@H]([C@@H]([C@H]3O)O)O)CO)CO)O)O)O)O' will interact with the target protein 'MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O[C@@H]2[C@H](O[C@H]([C@@H]([C@H]2O)O)O[C@@H]3[C@H](O[C@@H]([C@@H]([C@H]3O)O)O)CO)CO)O)O)O)O\"], \"target_seq\": \"MVFPMWTLKRQILILFNIILISKLLGARWFPKTLPCDVTLDVPKNHVIVDCTDKHLTEIPGGIPTNTTNLTLTINHIPDISPASFHRLDHLVEIDFRCNCVPIPLGSKNNMCIKRLQIKPRSFSGLTYLKSLYLDGNQLLEIPQGLPPSLQLLSLEANNIFSIRKENLTELANIEILYLGQNCYYRNPCYVSYSIEKDAFLNLTKLKVLSLKDNNVTAVPTVLPSTLTELYLYNNMIAKIQEDDFNNLNQLQILDLSGNCPRCYNAPFPCAPCKNNSPLQIPVNAFDALTELKVLRLHSNSLQHVPPRWFKNINKLQELDLSQNFLAKEIGDAKFLHFLPSLIQLDLSFNFELQVYRASMNLSQAFSSLKSLKILRIRGYVFKELKSFNLSPLHNLQNLEVLDLGTNFIKIANLSMFKQFKRLKVIDLSVNKISPSGDSSEVGFCSNARTSVESYEPQVLEQLHYFRYDKYARSCRFKNKEASFMSVNESCYKYGQTLDLSKNSIFFVKSSDFQHLSFLKCLNLSGNLISQTLNGSEFQPLAELRYLDFSNNRLDLLHSTAFEELHKLEVLDISSNSHYFQSEGITHMLNFTKNLKVLQKLMMNDNDISSSTSRTMESESLRTLEFRGNHLDVLWREGDNRYLQLFKNLLKLEELDISKNSLSFLPSGVFDGMPPNLKNLSLAKNGLKSFSWKKLQCLKNLETLDLSHNQLTTVPERLSNCSRSLKNLILKNNQIRSLTKYFLQDAFQLRYLDLSSNKIQMIQKTSFPENVLNNLKMLLLHHNRFLCTCDAVWFVWWVNHTEVTIPYLATDVTCVGPGAHKGQSVISLDLYTCELDLTNLILFSLSISVSLFLMVMMTASHLYFWDVWYIYHFCKAKIKGYQRLISPDCCYDAFIVYDTKDPAVTEWVLAELVAKLEDPREKHFNLCLEERDWLPGQPVLENLSQSIQLSKKTVFVMTDKYAKTENFKIAFYLSHQRLMDEKVDVIILIFLEKPFQKSKFLQLRKRLCGSSVLEWPTNPQAHPYFWQCLKNALATDNHVAYSQVFKETV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C([C@@H]1[C@H]([C@@H]([C@H]([C@@H](O1)O[C@@H]2[C@H](O[C@H]([C@@H]([C@H]2O)O)O[C@@H]3[C@H](O[C@@H]([C@@H]([C@H]3O)O)O)CO)CO)O)O)O)O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug 'CCCOc1ccc2c(c1)c(-c1c(C)n(C(=O)c3ccc(OC)cc3)[nH]c1=O)cn2C(C)=O' to the target protein 'MAENLLDGPPNPKRAKLSSPGFSANDSTDFGSLFDLENDLPDELIPNGGELGLLNSGNLVPDAASKHKQLSELLRGGSGSSINPGIGNVSASSPVQQGLGGQAQGQPNSANMASLSAMGKSPLSQGDSSAPSLPKQAASTSGPTPAASQALNPQAQKQVGLATSSPATSQTGPGICMNANFNQTHPGLLNSNSGHSLINQASQGQAQVMNGSLGAAGRGRGAGMPYPTPAMQGASSSVLAETLTQVSPQMTGHAGLNTAQAGGMAKMGITGNTSPFGQPFSQAGGQPMGATGVNPQLASKQSMVNSLPTFPTDIKNTSVTNVPNMSQMQTSVGIVPTQAIATGPTADPEKRKLIQQQLVLLLHAHKCQRREQANGEVRACSLPHCRTMKNVLNHMTHCQAGKACQVAHCASSRQIISHWKNCTRHDCPVCLPLKNASDKRNQQTILGSPASGIQNTIGSVGTGQQNATSLSNPNPIDPSSMQRAYAALGLPYMNQPQTQLQPQVPGQQPAQPQTHQQMRTLNPLGNNPMNIPAGGITTDQQPPNLISESALPTSLGATNPLMNDGSNSGNIGTLSTIPTAAPPSSTGVRKGWHEHVTQDLRSHLVHKLVQAIFPTPDPAALKDRRMENLVAYAKKVEGDMYESANSRDEYYHLLAEKIYKIQKELEEKRRSRLHKQGILGNQPALPAPGAQPPVIPQAQPVRPPNGPLSLPVNRMQVSQGMNSFNPMSLGNVQLPQAPMGPRAASPMNHSVQMNSMGSVPGMAISPSRMPQPPNMMGAHTNNMMAQAPAQSQFLPQNQFPSSSGAMSVGMGQPPAQTGVSQGQVPGAALPNPLNMLGPQASQLPCPPVTQSPLHPTPPPASTAAGMPSLQHTTPPGMTPPQPAAPTQPSTPVSSSGQTPTPTPGSVPSATQTQSTPTVQAAAQAQVTPQPQTPVQPPSVATPQSSQQQPTPVHAQPPGTPLSQAAASIDNRVPTPSSVASAETNSQQPGPDVPVLEMKTETQAEDTEPDPGESKGEPRSEMMEEDLQGASQVKEETDIAEQKSEPMEVDEKKPEVKVEVKEEEESSSNGTASQSTSPSQPRKKIFKPEELRQALMPTLEALYRQDPESLPFRQPVDPQLLGIPDYFDIVKNPMDLSTIKRKLDTGQYQEPWQYVDDVWLMFNNAWLYNRKTSRVYKFCSKLAEVFEQEIDPVMQSLGYCCGRKYEFSPQTLCCYGKQLCTIPRDAAYYSYQNRYHFCEKCFTEIQGENVTLGDDPSQPQTTISKDQFEKKKNDTLDPEPFVDCKECGRKMHQICVLHYDIIWPSGFVCDNCLKKTGRPRKENKFSAKRLQTTRLGNHLEDRVNKFLRRQNHPEAGEVFVRVVASSDKTVEVKPGMKSRFVDSGEMSESFPYRTKALFAFEEIDGVDVCFFGMHVQEYGSDCPPPNTRRVYISYLDSIHFFRPRCLRTAVYHEILIGYLEYVKKLGYVTGHIWACPPSEGDDYIFHCHPPDQKIPKPKRLQEWYKKMLDKAFAERIIHDYKDIFKQATEDRLTSAKELPYFEGDFWPNVLEESIKELEQEEEERKKEESTAASETTEGSQGDSKNAKKKNNKKTNKNKSSISRANKKKPSMPNVSNDLSQKLYATMEKHKEVFFVIHLHAGPVINTLPPIVDPDPLLSCDLMDGRDAFLTLARDKHWEFSSLRRSKWSTLCMLVELHTQGQDRFVYTCNECKHHVETRWHCTVCEDYDLCINCYNTKSHAHKMVKWGLGLDDEGSSQGEPQSKSPQESRRLSIQRCIQSLVHACQCRNANCSLPSCQKMKRVVQHTKGCKRKTNGGCPVCKQLIALCCYHAKHCQENKCPVPFCLNIKHKLRQQQIQHRLQQAQLMRRRMATMNTRNVPQQSLPSPTSAPPGTPTQQPSTPQTPQPPAQPQPSPVSMSPAGFPSVARTQPPTTVSTGKPTSQVPAPPPPAQPPPAAVEAARQIEREAQQQQHLYRVNINNSMPPGRTGMGTPGSQMAPVSLNVPRPNQVSGPVMPSMPPGQWQQAPLPQQQPMPGLPRPVISMQAQAAVAGPRMPSVQPPRSISPSALQDLLRTLKSPSSPQQQQQVLNILKSNPQLMAAFIKQRTAKYVANQPGMQPQPGLQSQPGMQPQPGMHQQPSLQNLNAMQAGVPRPGVPPQQQAMGGLNPQGQALNIMNPGHNPNMASMNPQYREMLRRQLLQQQQQQQQQQQQQQQQQQGSAGMAGGMAGHGQFQQPQGPGGYPPAMQQQQRMQQHLPLQGSSMGQMAAQMGQLGQMGQPGLGADSTPNIQQALQQRILQQQQMKQQIGSPGQPNPMSPQQHMLSGQPQASHLPGQQIATSLSNQVRSPAPVQSPRPQSQPPHSSPSPRIQPQPSPHHVSPQTGSPHPGLAVTMASSIDQGHLGNPEQSAMLPQLNTPSRSALSSELSLVGDTTGDTLEKFVEGL'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCOc1ccc2c(c1)c(-c1c(C)n(C(=O)c3ccc(OC)cc3)[nH]c1=O)cn2C(C)=O\"], \"target_seq\": \"MAENLLDGPPNPKRAKLSSPGFSANDSTDFGSLFDLENDLPDELIPNGGELGLLNSGNLVPDAASKHKQLSELLRGGSGSSINPGIGNVSASSPVQQGLGGQAQGQPNSANMASLSAMGKSPLSQGDSSAPSLPKQAASTSGPTPAASQALNPQAQKQVGLATSSPATSQTGPGICMNANFNQTHPGLLNSNSGHSLINQASQGQAQVMNGSLGAAGRGRGAGMPYPTPAMQGASSSVLAETLTQVSPQMTGHAGLNTAQAGGMAKMGITGNTSPFGQPFSQAGGQPMGATGVNPQLASKQSMVNSLPTFPTDIKNTSVTNVPNMSQMQTSVGIVPTQAIATGPTADPEKRKLIQQQLVLLLHAHKCQRREQANGEVRACSLPHCRTMKNVLNHMTHCQAGKACQVAHCASSRQIISHWKNCTRHDCPVCLPLKNASDKRNQQTILGSPASGIQNTIGSVGTGQQNATSLSNPNPIDPSSMQRAYAALGLPYMNQPQTQLQPQVPGQQPAQPQTHQQMRTLNPLGNNPMNIPAGGITTDQQPPNLISESALPTSLGATNPLMNDGSNSGNIGTLSTIPTAAPPSSTGVRKGWHEHVTQDLRSHLVHKLVQAIFPTPDPAALKDRRMENLVAYAKKVEGDMYESANSRDEYYHLLAEKIYKIQKELEEKRRSRLHKQGILGNQPALPAPGAQPPVIPQAQPVRPPNGPLSLPVNRMQVSQGMNSFNPMSLGNVQLPQAPMGPRAASPMNHSVQMNSMGSVPGMAISPSRMPQPPNMMGAHTNNMMAQAPAQSQFLPQNQFPSSSGAMSVGMGQPPAQTGVSQGQVPGAALPNPLNMLGPQASQLPCPPVTQSPLHPTPPPASTAAGMPSLQHTTPPGMTPPQPAAPTQPSTPVSSSGQTPTPTPGSVPSATQTQSTPTVQAAAQAQVTPQPQTPVQPPSVATPQSSQQQPTPVHAQPPGTPLSQAAASIDNRVPTPSSVASAETNSQQPGPDVPVLEMKTETQAEDTEPDPGESKGEPRSEMMEEDLQGASQVKEETDIAEQKSEPMEVDEKKPEVKVEVKEEEESSSNGTASQSTSPSQPRKKIFKPEELRQALMPTLEALYRQDPESLPFRQPVDPQLLGIPDYFDIVKNPMDLSTIKRKLDTGQYQEPWQYVDDVWLMFNNAWLYNRKTSRVYKFCSKLAEVFEQEIDPVMQSLGYCCGRKYEFSPQTLCCYGKQLCTIPRDAAYYSYQNRYHFCEKCFTEIQGENVTLGDDPSQPQTTISKDQFEKKKNDTLDPEPFVDCKECGRKMHQICVLHYDIIWPSGFVCDNCLKKTGRPRKENKFSAKRLQTTRLGNHLEDRVNKFLRRQNHPEAGEVFVRVVASSDKTVEVKPGMKSRFVDSGEMSESFPYRTKALFAFEEIDGVDVCFFGMHVQEYGSDCPPPNTRRVYISYLDSIHFFRPRCLRTAVYHEILIGYLEYVKKLGYVTGHIWACPPSEGDDYIFHCHPPDQKIPKPKRLQEWYKKMLDKAFAERIIHDYKDIFKQATEDRLTSAKELPYFEGDFWPNVLEESIKELEQEEEERKKEESTAASETTEGSQGDSKNAKKKNNKKTNKNKSSISRANKKKPSMPNVSNDLSQKLYATMEKHKEVFFVIHLHAGPVINTLPPIVDPDPLLSCDLMDGRDAFLTLARDKHWEFSSLRRSKWSTLCMLVELHTQGQDRFVYTCNECKHHVETRWHCTVCEDYDLCINCYNTKSHAHKMVKWGLGLDDEGSSQGEPQSKSPQESRRLSIQRCIQSLVHACQCRNANCSLPSCQKMKRVVQHTKGCKRKTNGGCPVCKQLIALCCYHAKHCQENKCPVPFCLNIKHKLRQQQIQHRLQQAQLMRRRMATMNTRNVPQQSLPSPTSAPPGTPTQQPSTPQTPQPPAQPQPSPVSMSPAGFPSVARTQPPTTVSTGKPTSQVPAPPPPAQPPPAAVEAARQIEREAQQQQHLYRVNINNSMPPGRTGMGTPGSQMAPVSLNVPRPNQVSGPVMPSMPPGQWQQAPLPQQQPMPGLPRPVISMQAQAAVAGPRMPSVQPPRSISPSALQDLLRTLKSPSSPQQQQQVLNILKSNPQLMAAFIKQRTAKYVANQPGMQPQPGLQSQPGMQPQPGMHQQPSLQNLNAMQAGVPRPGVPPQQQAMGGLNPQGQALNIMNPGHNPNMASMNPQYREMLRRQLLQQQQQQQQQQQQQQQQQQGSAGMAGGMAGHGQFQQPQGPGGYPPAMQQQQRMQQHLPLQGSSMGQMAAQMGQLGQMGQPGLGADSTPNIQQALQQRILQQQQMKQQIGSPGQPNPMSPQQHMLSGQPQASHLPGQQIATSLSNQVRSPAPVQSPRPQSQPPHSSPSPRIQPQPSPHHVSPQTGSPHPGLAVTMASSIDQGHLGNPEQSAMLPQLNTPSRSALSSELSLVGDTTGDTLEKFVEGL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [9800.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 9800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your take on the drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O''s potential interaction with the target protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O\"], \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CC1(C(=O)NC2=C(O1)C=CC(=N2)NC3=NC(=NC=C3F)NC4=CC(=C(C(=C4)OC)OC)OC)C.C1=CC=C(C=C1)S(=O)(=O)O' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "Predict the affinity value between the drug 'Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1' and the target protein 'MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\"], \"target_seq\": \"MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [76.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 76.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Is it probable that the drug 'Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12' will engage with the target protein 'MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12\"], \"target_seq\": \"MKSGSGGGSPTSLWGLLFLSAALSLWPTSGEICGPGIDIRNDYQQLKRLENCTVIEGYLHILLISKAEDYRSYRFPKLTVITEYLLLFRVAGLESLGDLFPNLTVIRGWKLFYNYALVIFEMTNLKDIGLYNLRNITRGAIRIEKNADLCYLSTVDWSLILDAVSNNYIVGNKPPKECGDLCPGTMEEKPMCEKTTINNEYNYRCWTTNRCQKMCPSTCGKRACTENNECCHPECLGSCSAPDNDTACVACRHYYYAGVCVPACPPNTYRFEGWRCVDRDFCANILSAESSDSEGFVIHDGECMQECPSGFIRNGSQSMYCIPCEGPCPKVCEEEKKTKTIDSVTSAQMLQGCTIFKGNLLINIRRGNNIASELENFMGLIEVVTGYVKIRHSHALVSLSFLKNLRLILGEEQLEGNYSFYVLDNQNLQQLWDWDHRNLTIKAGKMYFAFNPKLCVSEIYRMEEVTGTKGRQSKGDINTRNNGERASCESDVLHFTSTTTSKNRIIITWHRYRPPDYRDLISFTVYYKEAPFKNVTEYDGQDACGSNSWNMVDVDLPPNKDVEPGILLHGLKPWTQYAVYVKAVTLTMVENDHIRGAKSEILYIRTNASVPSIPLDVLSASNSSSQLIVKWNPPSLPNGNLSYYIVRWQRQPQDGYLYRHNYCSKDKIPIRKYADGTIDIEEVTENPKTEVCGGEKGPCCACPKTEAEKQAEKEEAEYRKVFENFLHNSIFVPRPERKRRDVMQVANTTMSSRSRNTTAADTYNITDPEELETEYPFFESRVDNKERTVISNLRPFTLYRIDIHSCNHEAEKLGCSASNFVFARTMPAEGADDIPGPVTWEPRPENSIFLKWPEPENPNGLILMYEIKYGSQVEDQRECVSRQEYRKYGGAKLNRLNPGNYTARIQATSLSGNGSWTDPVFFYVQAKTGYENFIHLIIALPVAVLLIVGGLVIMLYVFHRKRNNSRLGNGVLYASVNPEYFSAADVYVPDEWEVAREKITMSRELGQGSFGMVYEGVAKGVVKDEPETRVAIKTVNEAASMRERIEFLNEASVMKEFNCHHVVRLLGVVSQGQPTLVIMELMTRGDLKSYLRSLRPEMENNPVLAPPSLSKMIQMAGEIADGMAYLNANKFVHRDLAARNCMVAEDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMSPESLKDGVFTTYSDVWSFGVVLWEIATLAEQPYQGLSNEQVLRFVMEGGLLDKPDNCPDMLFELMRMCWQYNPKMRPSFLEIISSIKEEMEPGFREVSFYYSEENKLPEPEELDLEPENMESVPLDPSASSSSLPLPDRHSGHKAENGPGPGVLVLRASFDERQPYAHMNGGRKNERALPLPQSSTC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'Cc1cc(C2CCN(CCCF)CC2)cc2[nH]c(-c3c(NC[C@@H](O)c4cccc(Cl)c4)cc[nH]c3=O)nc12' can interact with the protein."
            },
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drug 'O=C([O-])[C@H]1/C(=C/CO)O[C@@H]2CC(=O)N21' for the target protein 'MMRKSLCCALLLGISCSALATPVSEKQLAEVVANTITPLMKAQSVPGMAVAVIYQGKPHYYTFGKADIAANKPVTPQTLFELGSISKTFTGVLGGDAIARGEISLDDAVTRYWPQLTGKQWQGIRMLDLATYTAGGLPLQVPDEVTDNASLLRFYQNWQPQWKPGTTRLYANASIGLFGALAVKPSGMPYEQAMTTRVLKPLKLDHTWINVPKAEEAHYAWGYRDGKAVRVSPGMLDAQAYGVKTNVQDMANWVMANMAPENVADASLKQGIALAQSRYWRIGSMYQGLGWEMLNWPVEANTVVEGSDSKVALAPLPVAEVNPPAPPVKASWVHKTGSTGGFGSYVAFIPEKQIGIVMLANTSYPNPARVEAAYHILEALQ'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C([O-])[C@H]1/C(=C/CO)O[C@@H]2CC(=O)N21\"], \"target_seq\": \"MMRKSLCCALLLGISCSALATPVSEKQLAEVVANTITPLMKAQSVPGMAVAVIYQGKPHYYTFGKADIAANKPVTPQTLFELGSISKTFTGVLGGDAIARGEISLDDAVTRYWPQLTGKQWQGIRMLDLATYTAGGLPLQVPDEVTDNASLLRFYQNWQPQWKPGTTRLYANASIGLFGALAVKPSGMPYEQAMTTRVLKPLKLDHTWINVPKAEEAHYAWGYRDGKAVRVSPGMLDAQAYGVKTNVQDMANWVMANMAPENVADASLKQGIALAQSRYWRIGSMYQGLGWEMLNWPVEANTVVEGSDSKVALAPLPVAEVNPPAPPVKASWVHKTGSTGGFGSYVAFIPEKQIGIVMLANTSYPNPARVEAAYHILEALQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [400.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you predict if there will be an interaction between the drug 'C(C(CO)O)O' and the target protein 'MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"C(C(CO)O)O\"], \"target_seq\": \"MFNSKRIGNVALKTVRAPRESSFVDYITDLNARKQLQHSYPTDLLSKCSEDLRQLPPYVYAGFDPTAESLHIGNLLILVNLIRAQQFGIRPIALIGEFTASIGDPSGKKSERGLLAGDVIMHNSRKVTDQICKIFENAPGSSEKPIIVNNNDWLGKISLRDFLRECKNMQVGKMLRMNTIKNRLEVGLSYTEFSYQTMQAFDWYTLSEKYGCRFQLGGYDQLGHLDFGAHYIKKMMNQAFAAGVCFPILTDSTGAKLGKSEGGGALWLDATKTSPFHFYQFFAQLHDDKAEELLLLFSLQDIEHIRDVLKNHRSNLGQWIAQRELAAEITRIVHGKEGLEVAMRCTKAMFGAKKADLSGLSRSEVLQLFRTTIDLKKENVATMGQLADASRLGSGKGHLLMQQGAFSVNGEKKRSPSESIADVFLENASDLTLVCWGKRGYQLVRWV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'C(C(CO)O)O' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the predicted affinity value for the drug 'COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl' and the target protein 'MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\"], \"target_seq\": \"MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [3000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 3000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will the drug '[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1' possibly engage with the target protein 'MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1\"], \"target_seq\": \"MAFMKKYLLPILGLFMAYYYYSANEEFRPEMLQGKKVIVTGASKGIGREMAYHLAKMGAHVVVTARSKETLQKVVSHCLELGAASAHYIAGTMEDMTFAEQFVAQAGKLMGGLDMLILNHITNTSLNLFHDDIHHVRKSMEVNFLSYVVLTVAALPMLKQSNGSIVVVSSLAGKVAYPMVAAYSASKFALDGFFSSIRKEYSVSRVNVSITLCVLGLIDTETAMKAVSGIVHMQAAPKEECALEIIKGGALRQEEVYYDSSLWTTLLIRNPCRKILEFLYSTSYNMDRFINK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug '[H][C@@](C)(NC1=NC(=O)[C@@](C)(S1)C(C)(C)O)C1=CC=C(F)C=C1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "Can you outline the affinity between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the target protein 'MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug 'NCC(O)=O''s interaction with the target protein 'MMLPLQGAQMLQMLEKSLRKSLPASLKVYGTVFHINHGNPFNLKAVVDKWPDFNTVVVCPQEQDMTDDLDHYTNTYQIYSKDPQNCQEFLGSPELINWKQHLQIQSSQPSLNEAIQNLAAIKSFKVKQTQRILYMAAETAKELTPFLLKSKILSPNGGKPKAINQEMFKLSSMDVTHAHLVNKFWHFGGNERSQRFIERCIQTFPTCCLLGPEGTPVCWDLMDQTGEMRMAGTLPEYRLHGLVTYVIYSHAQKLGKLGFPVYSHVDYSNEAMQKMSYTLQHVPIPRSWNQWNCVPL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"NCC(O)=O\"], \"target_seq\": \"MMLPLQGAQMLQMLEKSLRKSLPASLKVYGTVFHINHGNPFNLKAVVDKWPDFNTVVVCPQEQDMTDDLDHYTNTYQIYSKDPQNCQEFLGSPELINWKQHLQIQSSQPSLNEAIQNLAAIKSFKVKQTQRILYMAAETAKELTPFLLKSKILSPNGGKPKAINQEMFKLSSMDVTHAHLVNKFWHFGGNERSQRFIERCIQTFPTCCLLGPEGTPVCWDLMDQTGEMRMAGTLPEYRLHGLVTYVIYSHAQKLGKLGFPVYSHVDYSNEAMQKMSYTLQHVPIPRSWNQWNCVPL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'NCC(O)=O' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccc(-c2ccccc2)cc1' and the target protein 'MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccc(-c2ccccc2)cc1\"], \"target_seq\": \"MTSTGQDSTTTRQRRSRQNPQSPPQDSSVTSKRNIKKGAVPRSIPNLAEVKKKGKMKKLGQAMEEDLIVGLQGMDLNLEAEALAGTGLVLDEQLNEFHCLWDDSFPEGPERLHAIKEQLIQEGLLDRCVSFQARFAEKEELMLVHSLEYIDLMETTQYMNEGELRVLADTYDSVYLHPNSYSCACLASGSVLRLVDAVLGAEIRNGMAIIRPPGHHAQHSLMDGYCMFNHVAVAARYAQQKHRIRRVLIVDWDVHHGQGTQFTFDQDPSVLYFSIHRYEQGRFWPHLKASNWSTTGFGQGQGYTINVPWNQVGMRDADYIAAFLHVLLPVALEFQPQLVLVAAGFDALQGDPKGEMAATPAGFAQLTHLLMGLAGGKLILSLEGGYNLRALAEGVSASLHTLLGDPCPMLESPGAPCRSAQASVSCALEALEPFWEVLVRSTETVERDNMEEDNVEESEEEGPWEPPVLPILTWPVLQSRTGLVYDQNMMNHCNLWDSHHPEVPQRILRIMCRLEELGLAGRCLTLTPRPATEAELLTCHSAEYVGHLRATEKMKTRELHRESSNFDSIYICPSTFACAQLATGAACRLVEAVLSGEVLNGAAVVRPPGHHAEQDAACGFCFFNSVAVAARHAQTISGHALRILIVDWDVHHGNGTQHMFEDDPSVLYVSLHRYDHGTFFPMGDEGASSQIGRAAGTGFTVNVAWNGPRMGDADYLAAWHRLVLPIAYEFNPELVLVSAGFDAARGDPLGGCQVSPEGYAHLTHLLMGLASGRIILILEGGYNLTSISESMAACTRSLLGDPPPLLTLPRPPLSGALASITETIQVHRRYWRSLRVMKVEDREGPSSSKLVTKKAPQPAKPRLAERMTTREKKVLEAGMGKVTSASFGEESTPGQTNSETAVVALTQDQPSEAATGGATLAQTISEAAIGGAMLGQTTSEEAVGGATPDQTTSEETVGGAILDQTTSEDAVGGATLGQTTSEEAVGGATLAQTTSEAAMEGATLDQTTSEEAPGGTELIQTPLASSTDHQTPPTSPVQGTTPQISPSTLIGSLRTLELGSESQGASESQAPGEENLLGEAAGGQDMADSMLMQGSRGLTDQAIFYAVTPLPWCPHLVAVCPIPAAGLDVTQPCGDCGTIQENWVCLSCYQVYCGRYINGHMLQHHGNSGHPLVLSYIDLSAWCYYCQAYVHHQALLDVKNIAHQNKFGEDMPHPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [49000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 49000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Will there be an interaction expected between the drug 'COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1' and the target protein 'MEPVPSARAELQFSLLANVSDTFPSAFPSASANASGSPGARSASSLALAIAITALYSAVCAVGLLGNVLVMFGIVRYTKLKTATNIYIFNLALADALATSTLPFQSAKYLMETWPFGELLCKAVLSIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPAKAKLINICIWVLASGVGVPIMVMAVTQPRDGAVVCTLQFPSPSWYWDTVTKICVFLFAFVVPILIITVCYGLMLLRLRSVRLLSGSKEKDRSLRRITRMVLVVVGAFVVCWAPIHIFVIVWTLVDINRRDPLVVAALHLCIALGYANSSLNPVLYAFLDENFKRCFRQLCRAPCGGQEPGSLRRPRQATARERVTACTPSDGPGGGAAA'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1\"], \"target_seq\": \"MEPVPSARAELQFSLLANVSDTFPSAFPSASANASGSPGARSASSLALAIAITALYSAVCAVGLLGNVLVMFGIVRYTKLKTATNIYIFNLALADALATSTLPFQSAKYLMETWPFGELLCKAVLSIDYYNMFTSIFTLTMMSVDRYIAVCHPVKALDFRTPAKAKLINICIWVLASGVGVPIMVMAVTQPRDGAVVCTLQFPSPSWYWDTVTKICVFLFAFVVPILIITVCYGLMLLRLRSVRLLSGSKEKDRSLRRITRMVLVVVGAFVVCWAPIHIFVIVWTLVDINRRDPLVVAALHLCIALGYANSSLNPVLYAFLDENFKRCFRQLCRAPCGGQEPGSLRRPRQATARERVTACTPSDGPGGGAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'COc1ccc2[nH]c3c(c2c1)C1(c2cccc(O)c2)CCN(C)C(C3)C1' can interact with the protein."
            },
            {
                "from": "human",
                "value": "What affinity value is expected between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein 'MWLKVGGLLRGTGGQLGQTVGWPCGALGPGPHRWGPCGGSWAQKFYQDGPGRGLGEEDIRRAREARPRKTPRPQLSDRSRERKVPASRISRLANFGGLAVGLGLGVLAEMAKKSMPGGRLQSEGGSGLDSSPFLSEANAERIVQTLCTVRGAALKVGQMLSIQDNSFISPQLQHIFERVRQSADFMPRWQMLRVLEEELGRDWQAKVASLEEVPFAAASIGQVHQGLLRDGTEVAVKIQYPGIAQSIQSDVQNLLAVLKMSAALPAGLFAEQSLQALQQELAWECDYRREAACAQNFRQLLANDPFFRVPAVVKELCTTRVLGMELAGGVPLDQCQGLSQDLRNQICFQLLTLCLRELFEFRFMQTDPNWANFLYDASSHQVTLLDFGASREFGTEFTDHYIEVVKAAADGDRDCVLQKSRDLKFLTGFETKAFSDAHVEAVMILGEPFATQGPYDFGSGETARRIQDLIPVLLRHRLCPPPEETYALHRKLAGAFLACAHLRAHIACRDLFQDTYHRYWASRQPDAATAGSLPTKGDSWVDPS'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"MWLKVGGLLRGTGGQLGQTVGWPCGALGPGPHRWGPCGGSWAQKFYQDGPGRGLGEEDIRRAREARPRKTPRPQLSDRSRERKVPASRISRLANFGGLAVGLGLGVLAEMAKKSMPGGRLQSEGGSGLDSSPFLSEANAERIVQTLCTVRGAALKVGQMLSIQDNSFISPQLQHIFERVRQSADFMPRWQMLRVLEEELGRDWQAKVASLEEVPFAAASIGQVHQGLLRDGTEVAVKIQYPGIAQSIQSDVQNLLAVLKMSAALPAGLFAEQSLQALQQELAWECDYRREAACAQNFRQLLANDPFFRVPAVVKELCTTRVLGMELAGGVPLDQCQGLSQDLRNQICFQLLTLCLRELFEFRFMQTDPNWANFLYDASSHQVTLLDFGASREFGTEFTDHYIEVVKAAADGDRDCVLQKSRDLKFLTGFETKAFSDAHVEAVMILGEPFATQGPYDFGSGETARRIQDLIPVLLRHRLCPPPEETYALHRKLAGAFLACAHLRAHIACRDLFQDTYHRYWASRQPDAATAGSLPTKGDSWVDPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [10000.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the prediction for the drug 'CN1C2CCC1[C@@H](c1nnc(-c3ccccc3)s1)C(c1ccc(Cl)cc1)C2''s interaction with the target protein 'MSKSKCSVGPMSSVVAPAKESNAVGPREVELILVKEQNGVQLTNSTLINPPQTPVEAQERETWSKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVWKICPVLKGVGFTVILISFYVGFFYNVIIAWALHYFFSSFTMDLPWIHCNNTWNSPNCSDAHASNSSDGLGLNDTFGTTPAAEYFERGVLHLHQSRGIDDLGPPRWQLTACLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLTALLLRGVTLPGAMDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYNKFTNNCYRDAIITTSINSLTSFSSGFVVFSFLGYMAQKHNVPIRDVATDGPGLIFIIYPEAIATLPLSSAWAAVFFLMLLTLGIDSAMGGMESVITGLVDEFQLLHRHRELFTLGIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVQQFSDDIKQMTGQRPNLYWRLCWKLVSPCFLLYVVVVSIVTFRPPHYGAYIFPDWANALGWIIATSSMAMVPIYATYKFCSLPGSFREKLAYAITPEKDHQLVDRGEVRQFTLRHWLLL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C2CCC1[C@@H](c1nnc(-c3ccccc3)s1)C(c1ccc(Cl)cc1)C2\"], \"target_seq\": \"MSKSKCSVGPMSSVVAPAKESNAVGPREVELILVKEQNGVQLTNSTLINPPQTPVEAQERETWSKKIDFLLSVIGFAVDLANVWRFPYLCYKNGGGAFLVPYLLFMVIAGMPLFYMELALGQFNREGAAGVWKICPVLKGVGFTVILISFYVGFFYNVIIAWALHYFFSSFTMDLPWIHCNNTWNSPNCSDAHASNSSDGLGLNDTFGTTPAAEYFERGVLHLHQSRGIDDLGPPRWQLTACLVLVIVLLYFSLWKGVKTSGKVVWITATMPYVVLTALLLRGVTLPGAMDGIRAYLSVDFYRLCEASVWIDAATQVCFSLGVGFGVLIAFSSYNKFTNNCYRDAIITTSINSLTSFSSGFVVFSFLGYMAQKHNVPIRDVATDGPGLIFIIYPEAIATLPLSSAWAAVFFLMLLTLGIDSAMGGMESVITGLVDEFQLLHRHRELFTLGIVLATFLLSLFCVTNGGIYVFTLLDHFAAGTSILFGVLIEAIGVAWFYGVQQFSDDIKQMTGQRPNLYWRLCWKLVSPCFLLYVVVVSIVTFRPPHYGAYIFPDWANALGWIIATSSMAMVPIYATYKFCSLPGSFREKLAYAITPEKDHQLVDRGEVRQFTLRHWLLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [1.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'CN1C2CCC1[C@@H](c1nnc(-c3ccccc3)s1)C(c1ccc(Cl)cc1)C2' can interact with the protein."
            },
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [5.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome for the drug 'O=C(Nc1cc(-c2ccccc2)ccc1O)c1ccccc1' interacting with the target protein 'MDLRVGQRPPVEPPPEPTLLALQRPQRLHHHLFLAGLQQQRSVEPMRLSMDTPMPELQVGPQEQELRQLLHKDKSKRSAVASSVVKQKLAEVILKKQQAALERTVHPNSPGIPYRTLEPLETEGATRSMLSSFLPPVPSLPSDPPEHFPLRKTVSEPNLKLRYKPKKSLERRKNPLLRKESAPPSLRRRPAETLGDSSPSSSSTPASGCSSPNDSEHGPNPILGSEALLGQRLRLQETSVAPFALPTVSLLPAITLGLPAPARADSDRRTHPTLGPRGPILGSPHTPLFLPHGLEPEAGGTLPSRLQPILLLDPSGSHAPLLTVPGLGPLPFHFAQSLMTTERLSGSGLHWPLSRTRSEPLPPSATAPPPPGPMQPRLEQLKTHVQVIKRSAKPSEKPRLRQIPSAEDLETDGGGPGQVVDDGLEHRELGHGQPEARGPAPLQQHPQVLLWEQQRLAGRLPRGSTGDTVLLPLAQGGHRPLSRAQSSPAAPASLSAPEPASQARVLSSSETPARTLPFTTGLIYDSVMLKHQCSCGDNSRHPEHAGRIQSIWSRLQERGLRSQCECLRGRKASLEELQSVHSERHVLLYGTNPLSRLKLDNGKLAGLLAQRMFVMLPCGGVGVDTDTIWNELHSSNAARWAAGSVTDLAFKVASRELKNGFAVVRPPGHHADHSTAMGFCFFNSVAIACRQLQQQSKASKILIVDWDVHHGNGTQQTFYQDPSVLYISLHRHDDGNFFPGSGAVDEVGAGSGEGFNVNVAWAGGLDPPMGDPEYLAAFRIVVMPIAREFSPDLVLVSAGFDAAEGHPAPLGGYHVSAKCFGYMTQQLMNLAGGAVVLALEGGHDLTAICDASEACVAALLGNRVDPLSEEGWKQKPNLNAIRSLEAVIRVHSKYWGCMQRLASCPDSWVPRVPGADKEEVEAVTALASLSVGILAEDRPSEQLVEEEEPMNL'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_classification_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(Nc1cc(-c2ccccc2)ccc1O)c1ccccc1\"], \"target_seq\": \"MDLRVGQRPPVEPPPEPTLLALQRPQRLHHHLFLAGLQQQRSVEPMRLSMDTPMPELQVGPQEQELRQLLHKDKSKRSAVASSVVKQKLAEVILKKQQAALERTVHPNSPGIPYRTLEPLETEGATRSMLSSFLPPVPSLPSDPPEHFPLRKTVSEPNLKLRYKPKKSLERRKNPLLRKESAPPSLRRRPAETLGDSSPSSSSTPASGCSSPNDSEHGPNPILGSEALLGQRLRLQETSVAPFALPTVSLLPAITLGLPAPARADSDRRTHPTLGPRGPILGSPHTPLFLPHGLEPEAGGTLPSRLQPILLLDPSGSHAPLLTVPGLGPLPFHFAQSLMTTERLSGSGLHWPLSRTRSEPLPPSATAPPPPGPMQPRLEQLKTHVQVIKRSAKPSEKPRLRQIPSAEDLETDGGGPGQVVDDGLEHRELGHGQPEARGPAPLQQHPQVLLWEQQRLAGRLPRGSTGDTVLLPLAQGGHRPLSRAQSSPAAPASLSAPEPASQARVLSSSETPARTLPFTTGLIYDSVMLKHQCSCGDNSRHPEHAGRIQSIWSRLQERGLRSQCECLRGRKASLEELQSVHSERHVLLYGTNPLSRLKLDNGKLAGLLAQRMFVMLPCGGVGVDTDTIWNELHSSNAARWAAGSVTDLAFKVASRELKNGFAVVRPPGHHADHSTAMGFCFFNSVAIACRQLQQQSKASKILIVDWDVHHGNGTQQTFYQDPSVLYISLHRHDDGNFFPGSGAVDEVGAGSGEGFNVNVAWAGGLDPPMGDPEYLAAFRIVVMPIAREFSPDLVLVSAGFDAAEGHPAPLGGYHVSAKCFGYMTQQLMNLAGGAVVLALEGGHDLTAICDASEACVAALLGNRVDPLSEEGWKQKPNLNAIRSLEAVIRVHSKYWGCMQRLASCPDSWVPRVPGADKEEVEAVTALASLSVGILAEDRPSEQLVEEEEPMNL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_classification\": [0.0]}"
            },
            {
                "from": "gpt",
                "value": "The drug 'O=C(Nc1cc(-c2ccccc2)ccc1O)c1ccccc1' can not interact with the protein."
            },
            {
                "from": "human",
                "value": "What is the affinity value you predict for the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein 'MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH'?"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"affinity_value\": [1300.0]}"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug and the protein is 1300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_classification_predict\", \"description\": \"Predict drug-target classification affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}, {\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects could potentially arise from drug1 'CNC1(C)C2CCC(C2)C1(C)C' and drug2 'CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NOC(=O)N1)C(=CC=C2)C(=O)OCC1=C(C)OC(=O)O1' interactions based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CNC1(C)C2CCC(C2)C1(C)C\", \"CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NOC(=O)N1)C(=CC=C2)C(=O)OCC1=C(C)OC(=O)O1\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CNC1(C)C2CCC(C2)C1(C)C\", \"smile2\": \"CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NOC(=O)N1)C(=CC=C2)C(=O)OCC1=C(C)OC(=O)O1\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the risk or severity of adverse effects can be increased when 'CNC1(C)C2CCC(C2)C1(C)C' is combined with 'CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NOC(=O)N1)C(=CC=C2)C(=O)OCC1=C(C)OC(=O)O1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the risk or severity of adverse effects can be increased when 'CNC1(C)C2CCC(C2)C1(C)C' is combined with 'CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NOC(=O)N1)C(=CC=C2)C(=O)OCC1=C(C)OC(=O)O1'."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis path of '[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:21]2[CH2:20][CH2:19][CH2:18][CH:17]([C:14]([F:13])([F:15])[F:16])[CH2:22]2)[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1\", \"reactant2\": \"[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[cH:2][c:3]([O:4][CH2:5][C:6]#[C:7][CH2:8][CH3:9])[n:10][cH:11][n:12]1' and '[F:13][C:14]([F:15])([F:16])[CH:17]1[CH2:18][CH2:19][CH2:20][NH:21][CH2:22]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect side effects from drug1 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1' and drug2 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' to manifest based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\", \"C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\", \"smile2\": \"C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be decreased when combined with 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be decreased when combined with 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1'."
            },
            {
                "from": "human",
                "value": "Deduce the most probable synthesis method for '[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[n:14]2[cH:13][c:12]([CH3:11])[n:16][cH:15]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1\", \"reactant2\": \"[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'F[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH3:7])=[O:8])[cH:9][cH:10]1' and '[CH3:11][c:12]1[cH:13][nH:14][cH:15][n:16]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of side effects occurring from drug1 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1' and drug2 '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN[C@@H](C)[C@@H](O)C1=CC=CC=C1\", \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN[C@@H](C)[C@@H](O)C1=CC=CC=C1\", \"smile2\": \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' can be decreased when combined with 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' can be decreased when combined with 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1'."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\", \"reactant2\": \"[NH4+:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1' and '[NH4+:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might drug1 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1' and drug2 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' affect the patient in terms of side effects based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1\", \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1\", \"smile2\": \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' can be decreased when combined with 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' can be decreased when combined with 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1'."
            },
            {
                "from": "human",
                "value": "Forecast the possible synthesis route for '[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]\", \"reactant2\": \"[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]' and '[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you think will result from drug1 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' and drug2 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"smile2\": \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'."
            },
            {
                "from": "human",
                "value": "Predict the synthesis process for '[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CN(C)[CH:1]=[O:2]\", \"reactant2\": \"[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you think will result from drug1 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' and drug2 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"smile2\": \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'."
            },
            {
                "from": "human",
                "value": "Suggest the synthesis route for '[nH:1]1[c:2]2[n:3][cH:4][cH:5][c:6]([CH2:7][N:8]3[c:9]4[cH:10][cH:11][cH:12][c:13]([NH:14][C:15]([NH:16][c:17]5[c:18]([O:19][CH3:20])[cH:21][cH:22][c:23]([Cl:24])[cH:25]5)=[O:26])[c:27]4[CH2:28][CH2:29]3)[c:30]2[cH:31][cH:32]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[nH:1]1[c:2]2[n:3][cH:4][cH:5][c:6]([CH2:7][N:8]3[c:9]4[cH:10][cH:11][cH:12][c:13]([NH:14][C:15]([NH:16][c:17]5[c:18]([O:19][CH3:20])[cH:21][cH:22][c:23]([Cl:24])[cH:25]5)=[O:26])[c:27]4[CH2:28][CH2:29]3)[c:30]2[cH:31][cH:32]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC(C)(C)OC(=O)[n:1]1[c:2]2[n:3][cH:4][cH:5][c:6]([CH2:7][N:8]3[c:9]4[cH:10][cH:11][cH:12][c:13]([NH:14][C:15]([NH:16][c:17]5[c:18]([O:19][CH3:20])[cH:21][cH:22][c:23]([Cl:24])[cH:25]5)=[O:26])[c:27]4[CH2:28][CH2:29]3)[c:30]2[cH:31][cH:32]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC(C)(C)OC(=O)[n:1]1[c:2]2[n:3][cH:4][cH:5][c:6]([CH2:7][N:8]3[c:9]4[cH:10][cH:11][cH:12][c:13]([NH:14][C:15]([NH:16][c:17]5[c:18]([O:19][CH3:20])[cH:21][cH:22][c:23]([Cl:24])[cH:25]5)=[O:26])[c:27]4[CH2:28][CH2:29]3)[c:30]2[cH:31][cH:32]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect side effects from drug1 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1' and drug2 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' to manifest based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\", \"C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\", \"smile2\": \"C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be decreased when combined with 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'C[C@@H](OC[C@]1(CC[C@]2(CCC(=O)N2)CN1)C1=CC=CC=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be decreased when combined with 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1'."
            },
            {
                "from": "human",
                "value": "Predict the synthesis process for '[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CN(C)[CH:1]=[O:2]\", \"reactant2\": \"[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the side effects of drug1 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' and drug2 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O' will manifest based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"smile2\": \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that drug1 'CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1' and drug2 'CC(C)(C)C1=CC(=C(O)C=C1NC(=O)C1=CNC2=CC=CC=C2C1=O)C(C)(C)C' will cause side effects based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1\", \"CC(C)(C)C1=CC(=C(O)C=C1NC(=O)C1=CNC2=CC=CC=C2C1=O)C(C)(C)C\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1\", \"smile2\": \"CC(C)(C)C1=CC(=C(O)C=C1NC(=O)C1=CNC2=CC=CC=C2C1=O)C(C)(C)C\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the serum concentration of 'CC(C)(C)C1=CC(=C(O)C=C1NC(=O)C1=CNC2=CC=CC=C2C1=O)C(C)(C)C' can be increased when it is combined with 'CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the serum concentration of 'CC(C)(C)C1=CC(=C(O)C=C1NC(=O)C1=CNC2=CC=CC=C2C1=O)C(C)(C)C' can be increased when it is combined with 'CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1'."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([NH2:20])[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1\", \"reactant2\": \"[NH4+:20]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Cl[c:1]1[c:2]2[n:3][cH:4][n:5]([CH2:6][N:7]3[C:8](=[O:9])[CH2:10][CH:11]([CH2:12][CH2:13][CH3:14])[CH2:15]3)[c:16]2[n:17][cH:18][n:19]1' and '[NH4+:20]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think the reactions of drug1 '[H][C@@]12OC3=C(OC(C)=O)C=CC4=C3[C@@]11CCN(C)[C@]([H])(C4)[C@]1([H])C=C[C@@H]2OC(C)=O' and drug2 'CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2' will affect side effects based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12OC3=C(OC(C)=O)C=CC4=C3[C@@]11CCN(C)[C@]([H])(C4)[C@]1([H])C=C[C@@H]2OC(C)=O\", \"CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"[H][C@@]12OC3=C(OC(C)=O)C=CC4=C3[C@@]11CCN(C)[C@]([H])(C4)[C@]1([H])C=C[C@@H]2OC(C)=O\", \"smile2\": \"CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the risk or severity of adverse effects can be increased when '[H][C@@]12OC3=C(OC(C)=O)C=CC4=C3[C@@]11CCN(C)[C@]([H])(C4)[C@]1([H])C=C[C@@H]2OC(C)=O' is combined with 'CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the risk or severity of adverse effects can be increased when '[H][C@@]12OC3=C(OC(C)=O)C=CC4=C3[C@@]11CCN(C)[C@]([H])(C4)[C@]1([H])C=C[C@@H]2OC(C)=O' is combined with 'CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2'."
            },
            {
                "from": "human",
                "value": "Deduce the best synthesis route for '[CH3:1][C:2]1=[CH:3][CH2:4][C@@H:5]2[C:6]([C:7](=[O:8])[O:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)=[CH:17][O:18][C@@H:19]([O:20][C:24]([NH:23][CH3:22])=[O:25])[C@@H:21]12'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][C:2]1=[CH:3][CH2:4][C@@H:5]2[C:6]([C:7](=[O:8])[O:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)=[CH:17][O:18][C@@H:19]([O:20][C:24]([NH:23][CH3:22])=[O:25])[C@@H:21]12\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][C:2]1=[CH:3][CH2:4][C@@H:5]2[C:6]([C:7](=[O:8])[O:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)=[CH:17][O:18][C@H:19]([OH:20])[C@@H:21]12\", \"reactant2\": \"[CH3:22][N:23]=[C:24]=[O:25]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][C:2]1=[CH:3][CH2:4][C@@H:5]2[C:6]([C:7](=[O:8])[O:9][CH2:10][c:11]3[cH:12][cH:13][cH:14][cH:15][cH:16]3)=[CH:17][O:18][C@H:19]([OH:20])[C@@H:21]12' and '[CH3:22][N:23]=[C:24]=[O:25]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse reactions could stem from drug1 'CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(O)=O' and drug2 'COC1=CC=C(C=C1)C(CN(C)C)C1(O)CCCCC1' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(O)=O\", \"COC1=CC=C(C=C1)C(CN(C)C)C1(O)CCCCC1\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(O)=O\", \"smile2\": \"COC1=CC=C(C=C1)C(CN(C)C)C1(O)CCCCC1\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'COC1=CC=C(C=C1)C(CN(C)C)C1(O)CCCCC1' can be decreased when combined with 'CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'COC1=CC=C(C=C1)C(CN(C)C)C1(O)CCCCC1' can be decreased when combined with 'CC(C=CC1=C(C)CCCC1(C)C)=CC=CC(C)=CC(O)=O'."
            },
            {
                "from": "human",
                "value": "Calculate the most efficient synthesis path for '[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\", \"reactant2\": \"[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1' and '[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the possible side effects resulting from drug1 'CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O' and drug2 'CC1=C(C)N=C(NS(=O)(=O)C2=CC=C(N)C=C2)O1' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\", \"CC1=C(C)N=C(NS(=O)(=O)C2=CC=C(N)C=C2)O1\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\", \"smile2\": \"CC1=C(C)N=C(NS(=O)(=O)C2=CC=C(N)C=C2)O1\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'CC1=C(C)N=C(NS(=O)(=O)C2=CC=C(N)C=C2)O1' can be increased when combined with 'CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'CC1=C(C)N=C(NS(=O)(=O)C2=CC=C(N)C=C2)O1' can be increased when combined with 'CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O'."
            },
            {
                "from": "human",
                "value": "Predict the synthesis method for '[CH3:1][N:7]([CH2:6][CH2:5][CH2:4][CH:3]=[CH2:2])[S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][N:7]([CH2:6][CH2:5][CH2:4][CH:3]=[CH2:2])[S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"I[CH3:1]\", \"reactant2\": \"[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][NH:7][S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'I[CH3:1]' and '[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][NH:7][S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the side effects of drug1 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' and drug2 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O' will manifest based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"smile2\": \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'."
            },
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[OH:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC(C)(C)[O:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC(C)(C)[O:1][C:2]([CH2:3][O:4][c:5]1[cH:6][cH:7][c:8]([CH2:9][CH2:10][CH3:11])[cH:12][cH:13]1)=[O:14]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you think will result from drug1 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' and drug2 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC\", \"smile2\": \"CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)[N+]([O-])=O)C(=O)OC' may increase the hypotensive activities of 'CCCCCCCC(=O)CC[C@H]1[C@H](O)C[C@H](O)[C@@H]1C\\C=C/CCCC(O)=O'."
            },
            {
                "from": "human",
                "value": "Predict the synthesis path of '[NH:1]1[CH2:2][CH2:3][c:4]2[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]3[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]3)[n:17]2)[CH2:18][CH2:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH:1]1[CH2:2][CH2:3][c:4]2[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]3[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]3)[n:17]2)[CH2:18][CH2:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"c1ccc(C[N:1]2[CH2:2][CH2:3][c:4]3[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]4[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]4)[n:17]3)[CH2:18][CH2:19]2)cc1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'c1ccc(C[N:1]2[CH2:2][CH2:3][c:4]3[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]4[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]4)[n:17]3)[CH2:18][CH2:19]2)cc1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the side effects of drug1 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' and drug2 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O' will manifest based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O\", \"smile2\": \"C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, 'CN1C(C(=O)NC2=NC=CC=C2)=C(O)C2=C(C=CC=C2)S1(=O)=O' may increase the neuroexcitatory activities of 'C[C@H]1CN(C[C@@H](C)N1)C1=C(F)C(N)=C2C(=O)C(=CN(C3CC3)C2=C1F)C(O)=O'."
            },
            {
                "from": "human",
                "value": "Calculate the most efficient synthesis path for '[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\", \"reactant2\": \"[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1' and '[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might drug1 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1' and drug2 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' affect the patient in terms of side effects based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1\", \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1\", \"smile2\": \"OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' can be decreased when combined with 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'OC[C@@H](NC(=O)C(Cl)Cl)[C@H](O)C1=CC=C(C=C1)[N+]([O-])=O' can be decreased when combined with 'CC(=O)OC1=CC2=C(CCN(C2)C(C(=O)C2CC2)C2=CC=CC=C2F)S1'."
            },
            {
                "from": "human",
                "value": "Forecast the possible synthesis route for '[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[F:1][C:2]([F:3])([F:4])[CH2:5][NH:6][C:7](=[S:8])[NH:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]\", \"reactant2\": \"[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[F:1][C:2]([F:3])([F:4])[CH2:5][N:6]=[C:7]=[S:8]' and '[NH2:9][c:10]1[cH:11][cH:12][n:13][c:14]([S:15][CH2:16][C:17]#[C:18][CH2:19][N:20]2[C:21](=[O:22])[c:23]3[cH:24][cH:25][cH:26][cH:27][c:28]3[C:29]2=[O:30])[n:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please comment on the potential side effects of drug1 '[H][C@]12[C@H](C[C@H](O)C=C1C=C[C@H](C)[C@@H]2CC[C@@H](O)C[C@@H](O)CC(O)=O)OC(=O)[C@@H](C)CC' and drug2 'OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]12[C@H](C[C@H](O)C=C1C=C[C@H](C)[C@@H]2CC[C@@H](O)C[C@@H](O)CC(O)=O)OC(=O)[C@@H](C)CC\", \"OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"[H][C@]12[C@H](C[C@H](O)C=C1C=C[C@H](C)[C@@H]2CC[C@@H](O)C[C@@H](O)CC(O)=O)OC(=O)[C@@H](C)CC\", \"smile2\": \"OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the serum concentration of 'OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2' can be increased when it is combined with '[H][C@]12[C@H](C[C@H](O)C=C1C=C[C@H](C)[C@@H]2CC[C@@H](O)C[C@@H](O)CC(O)=O)OC(=O)[C@@H](C)CC'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the serum concentration of 'OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2' can be increased when it is combined with '[H][C@]12[C@H](C[C@H](O)C=C1C=C[C@H](C)[C@@H]2CC[C@@H](O)C[C@@H](O)CC(O)=O)OC(=O)[C@@H](C)CC'."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis path of '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C@H:16]3[NH:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][c:4]1[cH:5][cH:6][c:7]([F:8])[c:9](-[c:10]2[cH:11][c:12]([CH3:13])[n:14][c:15]([C:16]3=[N:17][C@:18]4([CH2:19][CH2:20]3)[CH2:21][CH2:22][N:23]([CH3:24])[C:25]4=[O:26])[n:27]2)[cH:28]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of side effects occurring from drug1 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1' and drug2 '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN[C@@H](C)[C@@H](O)C1=CC=CC=C1\", \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN[C@@H](C)[C@@H](O)C1=CC=CC=C1\", \"smile2\": \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' can be decreased when combined with 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' can be decreased when combined with 'CN[C@@H](C)[C@@H](O)C1=CC=CC=C1'."
            },
            {
                "from": "human",
                "value": "Analyze the synthesis path of '[NH2:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH2:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC(C)(C)OC(=O)[NH:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC(C)(C)OC(=O)[NH:1][CH2:2][CH2:3][C:4]([NH:5][c:6]1[cH:7][cH:8][c:9]([CH3:10])[cH:11][c:12]1[OH:13])=[O:14]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please detail the expected side effects from drug1 'COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C' and drug2 'CCN(CC)CCCC(C)NC1=C2C=CC(Cl)=CC2=NC=C1' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C\", \"CCN(CC)CCCC(C)NC1=C2C=CC(Cl)=CC2=NC=C1\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C\", \"smile2\": \"CCN(CC)CCCC(C)NC1=C2C=CC(Cl)=CC2=NC=C1\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the metabolism of 'CCN(CC)CCCC(C)NC1=C2C=CC(Cl)=CC2=NC=C1' can be decreased when combined with 'COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the metabolism of 'CCN(CC)CCCC(C)NC1=C2C=CC(Cl)=CC2=NC=C1' can be decreased when combined with 'COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C'."
            },
            {
                "from": "human",
                "value": "Forecast the most probable synthesis route for '[OH:1][C:2](=[O:3])[C@@H:4]1[CH2:5][C:6](=[O:7])[CH2:8][C@H:9]1[C:10](=[O:11])[O:12][CH2:13][CH3:14]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[C@@H:4]1[CH2:5][C:6](=[O:7])[CH2:8][C@H:9]1[C:10](=[O:11])[O:12][CH2:13][CH3:14]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CC[O:1][C:2](=[O:3])[C@@H:4]1[CH2:5][C:6](=[O:7])[CH2:8][C@H:9]1[C:10](=[O:11])[O:12][CH2:13][CH3:14]\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CC[O:1][C:2](=[O:3])[C@@H:4]1[CH2:5][C:6](=[O:7])[CH2:8][C@H:9]1[C:10](=[O:11])[O:12][CH2:13][CH3:14]' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide insight into the side effects of drug1 'CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1C' and drug2 'O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1' based on drugbank."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1C\", \"O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1\"]], \"task_list\": [\"drugbank\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1C\", \"smile2\": \"O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1\", \"valid\": true, \"tasks\": [{\"task_name\": \"drugbank\", \"result\": \"the risk or severity of adverse effects can be increased when 'CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1C' is combined with 'O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1'.\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the drugbank prediction, the risk or severity of adverse effects can be increased when 'CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1C' is combined with 'O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1'."
            },
            {
                "from": "human",
                "value": "Predict the synthesis route for '[C:1](=[O:2])([c:3]1[n:4][c:5]([C:6]#[N:7])[c:8]2[cH:9][cH:10][c:11]([O:12][CH:13]3[CH2:14][CH2:15][CH2:16][CH2:17][CH2:18]3)[cH:19][c:20]2[c:21]1[OH:22])[NH:23][CH2:24][C:25](=[O:26])[OH:27]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[n:4][c:5]([C:6]#[N:7])[c:8]2[cH:9][cH:10][c:11]([O:12][CH:13]3[CH2:14][CH2:15][CH2:16][CH2:17][CH2:18]3)[cH:19][c:20]2[c:21]1[OH:22])[NH:23][CH2:24][C:25](=[O:26])[OH:27]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CCCCO[C:1](=[O:2])[c:3]1[n:4][c:5]([C:6]#[N:7])[c:8]2[cH:9][cH:10][c:11]([O:12][CH:13]3[CH2:14][CH2:15][CH2:16][CH2:17][CH2:18]3)[cH:19][c:20]2[c:21]1[OH:22]\", \"reactant2\": \"[NH2:23][CH2:24][C:25](=[O:26])[OH:27]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CCCCO[C:1](=[O:2])[c:3]1[n:4][c:5]([C:6]#[N:7])[c:8]2[cH:9][cH:10][c:11]([O:12][CH:13]3[CH2:14][CH2:15][CH2:16][CH2:17][CH2:18]3)[cH:19][c:20]2[c:21]1[OH:22]' and '[NH2:23][CH2:24][C:25](=[O:26])[OH:27]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the side effects we might see with drug1 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' and drug2 'C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\", \"C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\", \"smile2\": \"C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"postmenopausal bleeding\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' and 'C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3' may cause postmenopausal bleeding when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis strategy for '[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:8]2[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]2)[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1\", \"reactant2\": \"OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([CH:5]=[O:6])[s:7]1' and 'OB(O)[c:8]1[cH:9][cH:10][c:11]([CH2:12][OH:13])[cH:14][cH:15]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely are side effects with drug1 'CC(C)C(CCCN(C)CCC1=CC(=C(C=C1)OC)OC)(C#N)C2=CC(=C(C=C2)OC)OC' and drug2 'COC1=C(C=C2C(=C1)CC(C2=O)CC3CCN(CC3)CC4=CC=CC=C4)OC' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)C(CCCN(C)CCC1=CC(=C(C=C1)OC)OC)(C#N)C2=CC(=C(C=C2)OC)OC\", \"COC1=C(C=C2C(=C1)CC(C2=O)CC3CCN(CC3)CC4=CC=CC=C4)OC\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(C)C(CCCN(C)CCC1=CC(=C(C=C1)OC)OC)(C#N)C2=CC(=C(C=C2)OC)OC\", \"smile2\": \"COC1=C(C=C2C(=C1)CC(C2=O)CC3CCN(CC3)CC4=CC=CC=C4)OC\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"Hepatitis C\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CC(C)C(CCCN(C)CCC1=CC(=C(C=C1)OC)OC)(C#N)C2=CC(=C(C=C2)OC)OC' and 'COC1=C(C=C2C(=C1)CC(C2=O)CC3CCN(CC3)CC4=CC=CC=C4)OC' may cause Hepatitis C when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis steps for '[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1)[NH:21][CH2:20][C:18]([CH3:17])([CH3:19])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH2:1]([c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1)[NH:21][CH2:20][C:18]([CH3:17])([CH3:19])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1\", \"reactant2\": \"[CH3:17][C:18]([CH3:19])([CH2:20][NH2:21])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=[CH:1][c:2]1[cH:3][cH:4][cH:5][c:6](-[c:7]2[n:8][o:9][c:10]([C:11]([F:12])([F:13])[F:14])[n:15]2)[cH:16]1' and '[CH3:17][C:18]([CH3:19])([CH2:20][NH2:21])[c:22]1[cH:23][o:24][c:25](-[c:26]2[cH:27][cH:28][c:29]([Cl:30])[cH:31][cH:32]2)[n:33]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from drug1 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and drug2 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"smile2\": \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"Drug hypersensitivity\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' may cause Drug hypersensitivity when interacting."
            },
            {
                "from": "human",
                "value": "Forecast the synthesis method for '[c:1]1(-[c:12]2[cH:13][cH:14][c:15]([C:16]([F:17])([F:18])[F:19])[cH:20][cH:21]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1(-[c:12]2[cH:13][cH:14][c:15]([C:16]([F:17])([F:18])[F:19])[cH:20][cH:21]2)[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1\", \"reactant2\": \"OB(O)[c:12]1[cH:13][cH:14][c:15]([C:16]([F:17])([F:18])[F:19])[cH:20][cH:21]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[cH:2][cH:3][c:4]([C:5]([O:6][CH2:7][CH3:8])=[O:9])[cH:10][cH:11]1' and 'OB(O)[c:12]1[cH:13][cH:14][c:15]([C:16]([F:17])([F:18])[F:19])[cH:20][cH:21]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant are the side effects anticipated from drug1 'CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O' and drug2 '[Cl-].[K+]' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O\", \"[Cl-].[K+]\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O\", \"smile2\": \"[Cl-].[K+]\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"multiple sclerosis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O' and '[Cl-].[K+]' may cause multiple sclerosis when interacting."
            },
            {
                "from": "human",
                "value": "Provide a synthesis prediction for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH2:6][CH2:7][CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH2:6][CH2:7][CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])/[CH:6]=[CH:7]/[CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:1][CH2:2][O:3][C:4](=[O:5])/[CH:6]=[CH:7]/[CH:8]1[CH2:9][CH2:10][C:11]([F:12])([F:13])[CH2:14][CH2:15]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect side effects from drug1 'C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O' and drug2 'CC(C)(C)NCC(C1=NC(=C(C=C1)O)CO)O' to manifest based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O\", \"CC(C)(C)NCC(C1=NC(=C(C=C1)O)CO)O\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O\", \"smile2\": \"CC(C)(C)NCC(C1=NC(=C(C=C1)O)CO)O\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"myelitis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1CC1N2C=C(C(=O)C3=CC(=C(C=C32)N4CCNCC4)F)C(=O)O' and 'CC(C)(C)NCC(C1=NC(=C(C=C1)O)CO)O' may cause myelitis when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis path of '[NH:1]1[CH2:2][CH2:3][c:4]2[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]3[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]3)[n:17]2)[CH2:18][CH2:19]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[NH:1]1[CH2:2][CH2:3][c:4]2[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]3[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]3)[n:17]2)[CH2:18][CH2:19]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"c1ccc(C[N:1]2[CH2:2][CH2:3][c:4]3[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]4[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]4)[n:17]3)[CH2:18][CH2:19]2)cc1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'c1ccc(C[N:1]2[CH2:2][CH2:3][c:4]3[c:5]([cH:6][n:7][c:8]([CH2:9][c:10]4[cH:11][cH:12][cH:13][c:14]([Cl:15])[cH:16]4)[n:17]3)[CH2:18][CH2:19]2)cc1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects do you expect from drug1 'C1=NC2=C(N1COCCO)NC(=NC2=O)N' and drug2 'C(CC(O)(P(=O)(O)O)P(=O)(O)O)CN' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=NC2=C(N1COCCO)NC(=NC2=O)N\", \"C(CC(O)(P(=O)(O)O)P(=O)(O)O)CN\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1=NC2=C(N1COCCO)NC(=NC2=O)N\", \"smile2\": \"C(CC(O)(P(=O)(O)O)P(=O)(O)O)CN\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"abnormal movements\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1=NC2=C(N1COCCO)NC(=NC2=O)N' and 'C(CC(O)(P(=O)(O)O)P(=O)(O)O)CN' may cause abnormal movements when interacting."
            },
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]\", \"reactant2\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]' and '[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please explain the possible side effects of drug1 'CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O' and drug2 'CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\", \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\", \"smile2\": \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"polyarthritis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O' and 'CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC' may cause polyarthritis when interacting."
            },
            {
                "from": "human",
                "value": "Calculate the most efficient synthesis path for '[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([N:30]2[CH2:29][CH:28]([F:27])[CH2:31]2)[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1\", \"reactant2\": \"[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'Br[c:1]1[n:2][cH:3][cH:4][c:5]([C:6]([NH:7][c:8]2[s:9][c:10]3[c:11]([CH:12]4[CH2:13][O:14][CH2:15][CH2:16][O:17]4)[cH:18][cH:19][c:20]([O:21][CH3:22])[c:23]3[n:24]2)=[O:25])[cH:26]1' and '[F:27][CH:28]1[CH2:29][NH:30][CH2:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide a forecast of the side effects from drug1 'CN(C)C(=N)N=C(N)N' and drug2 'CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)C(=N)N=C(N)N\", \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CN(C)C(=N)N=C(N)N\", \"smile2\": \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"lower GI bleeding\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CN(C)C(=N)N=C(N)N' and 'CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C' may cause lower GI bleeding when interacting."
            },
            {
                "from": "human",
                "value": "Provide the synthesis path of '[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[OH:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1\", \"reactant2\": \"\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'C[O:1][C:2](=[O:3])[c:4]1[cH:5][cH:6][c:7]([O:8][CH2:9][CH2:10][CH2:11][O:12]/[N:13]=[CH:14]/[c:15]2[cH:16][cH:17][c:18]([C:19]([CH3:20])([CH3:21])[CH3:22])[cH:23][cH:24]2)[cH:25][c:26]1[NH:27][C:28](=[O:29])[c:30]1[cH:31][cH:32][c:33](-[c:34]2[cH:35][cH:36][cH:37][cH:38][cH:39]2)[cH:40][cH:41]1' and ''."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the possible side effects resulting from drug1 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O' and drug2 'CCCC1=NC2=C(N1CC3=CC=C(C=C3)C4=CC=CC=C4C(=O)O)C=C(C=C2C)C5=NC6=CC=CC=C6N5C' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O\", \"CCCC1=NC2=C(N1CC3=CC=C(C=C3)C4=CC=CC=C4C(=O)O)C=C(C=C2C)C5=NC6=CC=CC=C6N5C\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O\", \"smile2\": \"CCCC1=NC2=C(N1CC3=CC=C(C=C3)C4=CC=CC=C4C(=O)O)C=C(C=C2C)C5=NC6=CC=CC=C6N5C\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"als\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O' and 'CCCC1=NC2=C(N1CC3=CC=C(C=C3)C4=CC=CC=C4C(=O)O)C=C(C=C2C)C5=NC6=CC=CC=C6N5C' may cause als when interacting."
            },
            {
                "from": "human",
                "value": "Estimate the synthesis method for '[CH3:1][N:9]([CH2:8][CH2:7][N:4]([CH2:3][CH3:2])[CH2:5][CH3:6])[c:10]1[cH:11][cH:12][c:13](-[n:14]2[cH:15][cH:16][c:17]([O:18][CH2:19][c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:26][c:27]2=[O:28])[cH:29][cH:30]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][N:9]([CH2:8][CH2:7][N:4]([CH2:3][CH3:2])[CH2:5][CH3:6])[c:10]1[cH:11][cH:12][c:13](-[n:14]2[cH:15][cH:16][c:17]([O:18][CH2:19][c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:26][c:27]2=[O:28])[cH:29][cH:30]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=[CH2:1]\", \"reactant2\": \"[CH3:2][CH2:3][N:4]([CH2:5][CH3:6])[CH2:7][CH2:8][NH:9][c:10]1[cH:11][cH:12][c:13](-[n:14]2[cH:15][cH:16][c:17]([O:18][CH2:19][c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:26][c:27]2=[O:28])[cH:29][cH:30]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=[CH2:1]' and '[CH3:2][CH2:3][N:4]([CH2:5][CH3:6])[CH2:7][CH2:8][NH:9][c:10]1[cH:11][cH:12][c:13](-[n:14]2[cH:15][cH:16][c:17]([O:18][CH2:19][c:20]3[cH:21][cH:22][cH:23][cH:24][cH:25]3)[cH:26][c:27]2=[O:28])[cH:29][cH:30]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the side effects of drug1 'CC(=O)NC(CS)C(=O)O' and drug2 'CCOC(=O)C1=C(NC(=C(C1C2=CC=CC=C2Cl)C(=O)OC)C)COCCN' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(=O)NC(CS)C(=O)O\", \"CCOC(=O)C1=C(NC(=C(C1C2=CC=CC=C2Cl)C(=O)OC)C)COCCN\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(=O)NC(CS)C(=O)O\", \"smile2\": \"CCOC(=O)C1=C(NC(=C(C1C2=CC=CC=C2Cl)C(=O)OC)C)COCCN\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"Bladder inflammation\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CC(=O)NC(CS)C(=O)O' and 'CCOC(=O)C1=C(NC(=C(C1C2=CC=CC=C2Cl)C(=O)OC)C)COCCN' may cause Bladder inflammation when interacting."
            },
            {
                "from": "human",
                "value": "Predict the steps for synthesizing '[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10])[CH3:11]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10]\", \"reactant2\": \"[Mg+][CH3:11]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CON(C)[C:1](=[O:2])[c:3]1[cH:4][cH:5][c:6]([CH3:7])[n:8][c:9]1[Cl:10]' and '[Mg+][CH3:11]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with drug1 '[O-]S(=O)(=O)[O-]' and drug2 'CN1C2CCC1CC(C2)OC(=O)C(CO)C3=CC=CC=C3' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[O-]S(=O)(=O)[O-]\", \"CN1C2CCC1CC(C2)OC(=O)C(CO)C3=CC=CC=C3\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"[O-]S(=O)(=O)[O-]\", \"smile2\": \"CN1C2CCC1CC(C2)OC(=O)C(CO)C3=CC=CC=C3\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"Splenic infarction\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs '[O-]S(=O)(=O)[O-]' and 'CN1C2CCC1CC(C2)OC(=O)C(CO)C3=CC=CC=C3' may cause Splenic infarction when interacting."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1\", \"reactant2\": \"[NH2:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1' and '[NH2:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your prediction on the side effects of drug1 'CC(C)(C1=CC=C(C=C1)C(CCCN2CCC(CC2)C(C3=CC=CC=C3)(C4=CC=CC=C4)O)O)C(=O)O' and drug2 'COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3' interactions based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C1=CC=C(C=C1)C(CCCN2CCC(CC2)C(C3=CC=CC=C3)(C4=CC=CC=C4)O)O)C(=O)O\", \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(C)(C1=CC=C(C=C1)C(CCCN2CCC(CC2)C(C3=CC=CC=C3)(C4=CC=CC=C4)O)O)C(=O)O\", \"smile2\": \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"kidney transplant\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CC(C)(C1=CC=C(C=C1)C(CCCN2CCC(CC2)C(C3=CC=CC=C3)(C4=CC=CC=C4)O)O)C(=O)O' and 'COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3' may cause kidney transplant when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis method for '[CH3:1][N:7]([CH2:6][CH2:5][CH2:4][CH:3]=[CH2:2])[S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][N:7]([CH2:6][CH2:5][CH2:4][CH:3]=[CH2:2])[S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"I[CH3:1]\", \"reactant2\": \"[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][NH:7][S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'I[CH3:1]' and '[CH2:2]=[CH:3][CH2:4][CH2:5][CH2:6][NH:7][S:8](=[O:9])(=[O:10])[CH2:11][CH2:12][C:13](=[O:14])[O:15][CH3:16]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from drug1 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and drug2 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"smile2\": \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"Drug hypersensitivity\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' may cause Drug hypersensitivity when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis process for '[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CN(C)[CH:1]=[O:2]\", \"reactant2\": \"[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide insight into the side effects of drug1 'CCNC(=O)N(CCCN(C)C)C(=O)C1CC2C(CC3=CNC4=CC=CC2=C34)N(C1)CC=C' and drug2 'C1=CC=C(C=C1)C(C2=CC=CC=C2)S(=O)CC(=O)N' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCNC(=O)N(CCCN(C)C)C(=O)C1CC2C(CC3=CNC4=CC=CC2=C34)N(C1)CC=C\", \"C1=CC=C(C=C1)C(C2=CC=CC=C2)S(=O)CC(=O)N\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CCNC(=O)N(CCCN(C)C)C(=O)C1CC2C(CC3=CNC4=CC=CC2=C34)N(C1)CC=C\", \"smile2\": \"C1=CC=C(C=C1)C(C2=CC=CC=C2)S(=O)CC(=O)N\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"dysmenorrhea\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CCNC(=O)N(CCCN(C)C)C(=O)C1CC2C(CC3=CNC4=CC=CC2=C34)N(C1)CC=C' and 'C1=CC=C(C=C1)C(C2=CC=CC=C2)S(=O)CC(=O)N' may cause dysmenorrhea when interacting."
            },
            {
                "from": "human",
                "value": "Identify the synthesis path of '[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)(=[O:12])[NH:30][c:29]1[c:21]([NH:20][CH2:19][CH2:18][N:15]([CH2:14][CH3:13])[CH2:16][CH3:17])[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)(=[O:12])[NH:30][c:29]1[c:21]([NH:20][CH2:19][CH2:18][N:15]([CH2:14][CH3:13])[CH2:16][CH3:17])[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)=[O:12]\", \"reactant2\": \"[CH3:13][CH2:14][N:15]([CH2:16][CH3:17])[CH2:18][CH2:19][NH:20][c:21]1[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28][c:29]1[NH2:30]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1]([CH2:2][c:3]1[cH:4][cH:5][c:6]([O:7][CH2:8][CH3:9])[cH:10][cH:11]1)=[O:12]' and '[CH3:13][CH2:14][N:15]([CH2:16][CH3:17])[CH2:18][CH2:19][NH:20][c:21]1[cH:22][cH:23][c:24]([N+:25](=[O:26])[O-:27])[cH:28][c:29]1[NH2:30]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of side effects occurring from drug1 'CC(=O)NC1=CC=C(C=C1)O' and drug2 'C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(=O)NC1=CC=C(C=C1)O\", \"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC(=O)NC1=CC=C(C=C1)O\", \"smile2\": \"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"duodenitis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CC(=O)NC1=CC=C(C=C1)O' and 'C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N' may cause duodenitis when interacting."
            },
            {
                "from": "human",
                "value": "Deduce the synthesis path for '[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1)[NH:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1\", \"reactant2\": \"[NH2:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][cH:5][cH:6][cH:7][cH:8]1' and '[NH2:9][c:10]1[cH:11][n:12][c:13]([O:14][CH2:15][C:16]([F:17])([F:18])[F:19])[c:20](-[c:21]2[cH:22][cH:23][c:24]([Cl:25])[cH:26][cH:27]2)[cH:28]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects should we anticipate from drug1 'CON=C(C1=CC=CO1)C(=O)NC2C3N(C2=O)C(=C(CS3)COC(=O)N)C(=O)[O-]' and drug2 'C(N)(N)(Cl)Cl' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CON=C(C1=CC=CO1)C(=O)NC2C3N(C2=O)C(=C(CS3)COC(=O)N)C(=O)[O-]\", \"C(N)(N)(Cl)Cl\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CON=C(C1=CC=CO1)C(=O)NC2C3N(C2=O)C(=C(CS3)COC(=O)N)C(=O)[O-]\", \"smile2\": \"C(N)(N)(Cl)Cl\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"folliculitis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CON=C(C1=CC=CO1)C(=O)NC2C3N(C2=O)C(=C(CS3)COC(=O)N)C(=O)[O-]' and 'C(N)(N)(Cl)Cl' may cause folliculitis when interacting."
            },
            {
                "from": "human",
                "value": "Identify the most efficient synthesis process for '[c:1]1([O:30][C:28]([c:27]2[cH:26][cH:25][c:24]([CH2:23][NH:22][C:20](=[NH:19])[NH2:21])[o:31]2)=[O:29])[cH:2][cH:3][c:4]2[cH:5][c:6]([C:7](=[NH:8])[NH2:9])[cH:10][cH:11][c:12]2[cH:13]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[c:1]1([O:30][C:28]([c:27]2[cH:26][cH:25][c:24]([CH2:23][NH:22][C:20](=[NH:19])[NH2:21])[o:31]2)=[O:29])[cH:2][cH:3][c:4]2[cH:5][c:6]([C:7](=[NH:8])[NH2:9])[cH:10][cH:11][c:12]2[cH:13]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CS(=O)(=O)O[c:1]1[cH:2][cH:3][c:4]2[cH:5][c:6]([C:7](=[NH:8])[NH2:9])[cH:10][cH:11][c:12]2[cH:13]1\", \"reactant2\": \"[NH:19]=[C:20]([NH2:21])[NH:22][CH2:23][c:24]1[cH:25][cH:26][c:27]([C:28](=[O:29])[OH:30])[o:31]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CS(=O)(=O)O[c:1]1[cH:2][cH:3][c:4]2[cH:5][c:6]([C:7](=[NH:8])[NH2:9])[cH:10][cH:11][c:12]2[cH:13]1' and '[NH:19]=[C:20]([NH2:21])[NH:22][CH2:23][c:24]1[cH:25][cH:26][c:27]([C:28](=[O:29])[OH:30])[o:31]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential side effects should be considered for drug1 'C1CNP(=O)(OC1)N(CCCl)CCCl' and drug2 'C1=NC2=C(N=C(N=C2N1C3C(C(C(O3)CO)O)F)Cl)N' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CNP(=O)(OC1)N(CCCl)CCCl\", \"C1=NC2=C(N=C(N=C2N1C3C(C(C(O3)CO)O)F)Cl)N\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1CNP(=O)(OC1)N(CCCl)CCCl\", \"smile2\": \"C1=NC2=C(N=C(N=C2N1C3C(C(C(O3)CO)O)F)Cl)N\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"histoplasmosis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1CNP(=O)(OC1)N(CCCl)CCCl' and 'C1=NC2=C(N=C(N=C2N1C3C(C(C(O3)CO)O)F)Cl)N' may cause histoplasmosis when interacting."
            },
            {
                "from": "human",
                "value": "Predict the synthesis process for '[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH:1](=[O:2])[c:10]1[c:8]([F:9])[cH:7][cH:6][c:5]([O:4][CH3:3])[c:11]1[Cl:12]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"CN(C)[CH:1]=[O:2]\", \"reactant2\": \"[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'CN(C)[CH:1]=[O:2]' and '[CH3:3][O:4][c:5]1[cH:6][cH:7][c:8]([F:9])[cH:10][c:11]1[Cl:12]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the side effects we might see with drug1 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' and drug2 'C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\", \"C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\", \"smile2\": \"C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"postmenopausal bleeding\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' and 'C1=CC=C(C=C1)C2(C(=O)N(C(=O)N2)COP(=O)(O)O)C3=CC=CC=C3' may cause postmenopausal bleeding when interacting."
            },
            {
                "from": "human",
                "value": "Calculate the synthesis procedure for '[C:1](=[O:2])([c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12)[NH:42][C@@H:41]([C:39]([N:38]1[CH2:37][CH2:36][CH:35]([N:34]2[N:33]=[C:32]([c:31]3[cH:30][cH:29][c:28]([O:27][CH3:26])[c:62]([O:63][CH3:64])[cH:61]3)[C@@H:60]3[C@H:55]([C:53]2=[O:54])[CH2:56][CH2:57][CH2:58][CH2:59]3)[CH2:52][CH2:51]1)=[O:40])[CH2:43][c:44]1[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[C:1](=[O:2])([c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12)[NH:42][C@@H:41]([C:39]([N:38]1[CH2:37][CH2:36][CH:35]([N:34]2[N:33]=[C:32]([c:31]3[cH:30][cH:29][c:28]([O:27][CH3:26])[c:62]([O:63][CH3:64])[cH:61]3)[C@@H:60]3[C@H:55]([C:53]2=[O:54])[CH2:56][CH2:57][CH2:58][CH2:59]3)[CH2:52][CH2:51]1)=[O:40])[CH2:43][c:44]1[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O[C:1](=[O:2])[c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12\", \"reactant2\": \"[CH3:26][O:27][c:28]1[cH:29][cH:30][c:31]([C:32]2=[N:33][N:34]([CH:35]3[CH2:36][CH2:37][N:38]([C:39](=[O:40])[C@H:41]([NH2:42])[CH2:43][c:44]4[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]4)[CH2:51][CH2:52]3)[C:53](=[O:54])[C@@H:55]3[CH2:56][CH2:57][CH2:58][CH2:59][C@H:60]23)[cH:61][c:62]1[O:63][CH3:64]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O[C:1](=[O:2])[c:3]1[cH:4][nH:5][c:6]2[c:7](-[c:8]3[c:9]([O:10][CH2:11][CH:12]4[CH2:13][CH2:14]4)[cH:15][cH:16][c:17]4[c:18]3[O:19][CH2:20][O:21]4)[n:22][cH:23][n:24][c:25]12' and '[CH3:26][O:27][c:28]1[cH:29][cH:30][c:31]([C:32]2=[N:33][N:34]([CH:35]3[CH2:36][CH2:37][N:38]([C:39](=[O:40])[C@H:41]([NH2:42])[CH2:43][c:44]4[cH:45][cH:46][cH:47][c:48]([Cl:49])[cH:50]4)[CH2:51][CH2:52]3)[C:53](=[O:54])[C@@H:55]3[CH2:56][CH2:57][CH2:58][CH2:59][C@H:60]23)[cH:61][c:62]1[O:63][CH3:64]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your insight on the potential side effects of drug1 'C1=NC2=C(N1COCCO)NC(=NC2=O)N' and drug2 'CCCNC1CCC2=C(C1)SC(=N2)N' based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=NC2=C(N1COCCO)NC(=NC2=O)N\", \"CCCNC1CCC2=C(C1)SC(=N2)N\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"C1=NC2=C(N1COCCO)NC(=NC2=O)N\", \"smile2\": \"CCCNC1CCC2=C(C1)SC(=N2)N\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"cirrhosis\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'C1=NC2=C(N1COCCO)NC(=NC2=O)N' and 'CCCNC1CCC2=C(C1)SC(=N2)N' may cause cirrhosis when interacting."
            },
            {
                "from": "human",
                "value": "Identify the synthesis path of '[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[Br:1][CH2:15][c:14]1[cH:13][cH:12][c:11](-[c:10]2[cH:9][c:8]([P:4]([O:3][CH3:2])(=[O:5])[O:6][CH3:7])[c:20]([O:21][C:22]([CH3:23])=[O:24])[cH:19][cH:18]2)[cH:17][cH:16]1\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"O=C1CCC(=O)N1[Br:1]\", \"reactant2\": \"[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are 'O=C1CCC(=O)N1[Br:1]' and '[CH3:2][O:3][P:4](=[O:5])([O:6][CH3:7])[c:8]1[cH:9][c:10](-[c:11]2[cH:12][cH:13][c:14]([CH3:15])[cH:16][cH:17]2)[cH:18][cH:19][c:20]1[O:21][C:22]([CH3:23])=[O:24]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please predict how the reactions to drug1 'CC1CC2C3CCC(C3(CC(C2C4(C1=CC(=O)C=C4)C)O)C)(C(=O)CO)O' and drug2 'CC(C)CC(CC(=O)O)CN' will lead to side effects based on twosides."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1CC2C3CCC(C3(CC(C2C4(C1=CC(=O)C=C4)C)O)C)(C(=O)CO)O\", \"CC(C)CC(CC(=O)O)CN\"]], \"task_list\": [\"twosides\"]}}"
            },
            {
                "from": "observation",
                "value": "[{\"smile1\": \"CC1CC2C3CCC(C3(CC(C2C4(C1=CC(=O)C=C4)C)O)C)(C(=O)CO)O\", \"smile2\": \"CC(C)CC(CC(=O)O)CN\", \"valid\": true, \"tasks\": [{\"task_name\": \"twosides\", \"result\": \"kidney transplant\"}]"
            },
            {
                "from": "gpt",
                "value": "According to the twosides prediction, the two drugs 'CC1CC2C3CCC(C3(CC(C2C4(C1=CC(=O)C=C4)C)O)C)(C(=O)CO)O' and 'CC(C)CC(CC(=O)O)CN' may cause kidney transplant when interacting."
            },
            {
                "from": "human",
                "value": "Suggest the synthesis path for '[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]'."
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"arguments\": {\"smile_list\": [\"[CH3:1][CH2:2][O:3][C:4](=[O:5])[CH:6]1[CH2:7][CH2:8][CH2:9][N:10]1[C:21]([NH:20][c:19]1[cH:18][c:17]([C:15]([O:14][CH:12]([CH3:11])[CH3:13])=[O:16])[c:25]([Cl:26])[cH:24][cH:23]1)=[S:22]\"]}}"
            },
            {
                "from": "observation",
                "value": "{\"reactant1\": \"[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]\", \"reactant2\": \"[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1\"}"
            },
            {
                "from": "gpt",
                "value": "The possibly precursor reactants are '[CH3:11][CH:12]([CH3:13])[O:14][C:15](=[O:16])[c:17]1[cH:18][c:19]([N:20]=[C:21]=[S:22])[cH:23][cH:24][c:25]1[Cl:26]' and '[CH3:1][CH2:2][O:3][C:4](=[O:5])[C@@H:6]1[CH2:7][CH2:8][CH2:9][NH:10]1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"task_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smiles_pairs\", \"task_list\"]}, {\"name\": \"Retrosynthetic_reaction_pathway_prediction\", \"description\": \"Predict the retrosynthetic reaction pathway for molecules.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smile_list\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}, \"required\": [\"smile_list\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the anticipated side effects from [H][C@@]12CC3=C(C=C(O)C=C3)[C@]3(CCCC[C@@]13O)CCN2CC1CCC1 and [H][C@@]12CO[C@@]3(COS(N)(=O)=O)OC(C)(C)O[C@@]3([H])[C@]1([H])OC(C)(C)O2, CC1=CC(=O)N(O)C(=C1)C1CCCCC1 and CCOC(=O)C1=C(C)NC(C)=C(C1C1=C(Cl)C(Cl)=CC=C1)C(=O)OC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC3=C(C=C(O)C=C3)[C@]3(CCCC[C@@]13O)CCN2CC1CCC1\", \"[H][C@@]12CO[C@@]3(COS(N)(=O)=O)OC(C)(C)O[C@@]3([H])[C@]1([H])OC(C)(C)O2\"], [\"CC1=CC(=O)N(O)C(=C1)C1CCCCC1\", \"CCOC(=O)C1=C(C)NC(C)=C(C1C1=C(Cl)C(Cl)=CC=C1)C(=O)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC3=C(C=C(O)C=C3)[C@]3(CCCC[C@@]13O)CCN2CC1CCC1\",\"smile2\": \"[H][C@@]12CC3=C(C=C(O)C=C3)[C@]3(CCCC[C@@]13O)CCN2CC1CCC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CC1=CC(=O)N(O)C(=C1)C1CCCCC1\",\"smile2\": \"CCOC(=O)C1=C(C)NC(C)=C(C1C1=C(Cl)C(Cl)=CC=C1)C(=O)OC\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when '[H][C@@]12CC3=C(C=C(O)C=C3)[C@]3(CCCC[C@@]13O)CCN2CC1CCC1' is combined with '[H][C@@]12CO[C@@]3(COS(N)(=O)=O)OC(C)(C)O[C@@]3([H])[C@]1([H])OC(C)(C)O2'. The risk or severity of adverse effects can be increased when 'CC1=CC(=O)N(O)C(=C1)C1CCCCC1' is combined with 'CCOC(=O)C1=C(C)NC(C)=C(C1C1=C(Cl)C(Cl)=CC=C1)C(=O)OC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please forecast the side effects associated with CCCCNC(=O)NS(=O)(=O)C1=CC=C(C)C=C1 and CNCC1=CC=C(C=C1)C1=C2CCNC(=O)C3=C2C(N1)=CC(F)=C3, O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1 and CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCNC(=O)NS(=O)(=O)C1=CC=C(C)C=C1\", \"CNCC1=CC=C(C=C1)C1=C2CCNC(=O)C3=C2C(N1)=CC(F)=C3\"], [\"O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1\", \"CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCNC(=O)NS(=O)(=O)C1=CC=C(C)C=C1\",\"smile2\": \"CCCCNC(=O)NS(=O)(=O)C1=CC=C(C)C=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1\",\"smile2\": \"CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'CNCC1=CC=C(C=C1)C1=C2CCNC(=O)C3=C2C(N1)=CC(F)=C3' can be decreased when combined with 'CCCCNC(=O)NS(=O)(=O)C1=CC=C(C)C=C1'. The metabolism of 'CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O' can be decreased when combined with 'O=C(O[C@H]1CN2CCC1CC2)N1CCC2=CC=CC=C2[C@@H]1C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects might occur with ClC1=CC=C(COC(CN2C=CN=C2)C2=C(Cl)C=C(Cl)C=C2)C=C1 and OC[C@@H]1CC[C@@H](O1)N1C=NC2=C1NC=NC2=O, [O-][N+](=O)OCCNC(=O)C1=CC=CN=C1 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClC1=CC=C(COC(CN2C=CN=C2)C2=C(Cl)C=C(Cl)C=C2)C=C1\", \"OC[C@@H]1CC[C@@H](O1)N1C=NC2=C1NC=NC2=O\"], [\"[O-][N+](=O)OCCNC(=O)C1=CC=CN=C1\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClC1=CC=C(COC(CN2C=CN=C2)C2=C(Cl)C=C(Cl)C=C2)C=C1\",\"smile2\": \"ClC1=CC=C(COC(CN2C=CN=C2)C2=C(Cl)C=C(Cl)C=C2)C=C1\",\"interact_result\": \"#drug1 can cause a decrease in the absorption of #drug2 resulting in a reduced serum concentration and potentially a decrease in efficacy.\"}, {\"smile1\": \"[O-][N+](=O)OCCNC(=O)C1=CC=CN=C1\",\"smile2\": \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\",\"interact_result\": \"#drug3 may increase the hypotensive activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'ClC1=CC=C(COC(CN2C=CN=C2)C2=C(Cl)C=C(Cl)C=C2)C=C1' can cause a decrease in the absorption of 'OC[C@@H]1CC[C@@H](O1)N1C=NC2=C1NC=NC2=O' resulting in a reduced serum concentration and potentially a decrease in efficacy. '[O-][N+](=O)OCCNC(=O)C1=CC=CN=C1' may increase the hypotensive activities of 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the side effects of OCCN1CCN(CCCN2C3=CC=CC=C3SC3=C2C=C(Cl)C=C3)CC1 and CC\\C(=C(/C1=CC=CC=C1)C1=CC=C(OCCN(C)C)C=C1)C1=CC=CC=C1 will manifest, CCOC(=O)[C@H](CCC1=CC=CC=C1)N[C@H]1CCC2=CC=CC=C2N(CC(O)=O)C1=O and OC(=O)C1=C(NC2=CC=CC(=C2)C(F)(F)F)N=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OCCN1CCN(CCCN2C3=CC=CC=C3SC3=C2C=C(Cl)C=C3)CC1\", \"CC\\C(=C(/C1=CC=CC=C1)C1=CC=C(OCCN(C)C)C=C1)C1=CC=CC=C1\"], [\"CCOC(=O)[C@H](CCC1=CC=CC=C1)N[C@H]1CCC2=CC=CC=C2N(CC(O)=O)C1=O\", \"OC(=O)C1=C(NC2=CC=CC(=C2)C(F)(F)F)N=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OCCN1CCN(CCCN2C3=CC=CC=C3SC3=C2C=C(Cl)C=C3)CC1\",\"smile2\": \"OCCN1CCN(CCCN2C3=CC=CC=C3SC3=C2C=C(Cl)C=C3)CC1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"CCOC(=O)[C@H](CCC1=CC=CC=C1)N[C@H]1CCC2=CC=CC=C2N(CC(O)=O)C1=O\",\"smile2\": \"OC(=O)C1=C(NC2=CC=CC(=C2)C(F)(F)F)N=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'CC\\C(=C(/C1=CC=CC=C1)C1=CC=C(OCCN(C)C)C=C1)C1=CC=CC=C1' can be decreased when combined with 'OCCN1CCN(CCCN2C3=CC=CC=C3SC3=C2C=C(Cl)C=C3)CC1'. The risk or severity of adverse effects can be increased when 'CCOC(=O)[C@H](CCC1=CC=CC=C1)N[C@H]1CCC2=CC=CC=C2N(CC(O)=O)C1=O' is combined with 'OC(=O)C1=C(NC2=CC=CC(=C2)C(F)(F)F)N=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the expected side effects for both CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1 and COC1=C(OC)C=C(C[N+]2(CCOCCC3CCC4CC3C4(C)C)CCOCC2)C(Br)=C1, OC(=O)CCNC(=O)C1=CC=C(C=C1)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O and NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1\", \"COC1=C(OC)C=C(C[N+]2(CCOCCC3CCC4CC3C4(C)C)CCOCC2)C(Br)=C1\"], [\"OC(=O)CCNC(=O)C1=CC=C(C=C1)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O\", \"NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1\",\"smile2\": \"CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1\",\"interact_result\": \"#drug1 may increase the hypotensive activities of #drug2.\"}, {\"smile1\": \"OC(=O)CCNC(=O)C1=CC=C(C=C1)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O\",\"smile2\": \"NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1' may increase the hypotensive activities of 'COC1=C(OC)C=C(C[N+]2(CCOCCC3CCC4CC3C4(C)C)CCOCC2)C(Br)=C1'. The therapeutic efficacy of 'NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F' can be decreased when used in combination with 'OC(=O)CCNC(=O)C1=CC=C(C=C1)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects should we anticipate from CCCNC(C)C(=O)NC1=CC=CC=C1C and CCNC1C2CCC(C2)C1C1=CC=CC=C1, ClC=C(Cl)Cl and CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCNC(C)C(=O)NC1=CC=CC=C1C\", \"CCNC1C2CCC(C2)C1C1=CC=CC=C1\"], [\"ClC=C(Cl)Cl\", \"CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCNC(C)C(=O)NC1=CC=CC=C1C\",\"smile2\": \"CCCNC(C)C(=O)NC1=CC=CC=C1C\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"ClC=C(Cl)Cl\",\"smile2\": \"CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CCCNC(C)C(=O)NC1=CC=CC=C1C' is combined with 'CCNC1C2CCC(C2)C1C1=CC=CC=C1'. The risk or severity of adverse effects can be increased when 'ClC=C(Cl)Cl' is combined with 'CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please predict the adverse reactions of [OH-].[OH-].[Mg++] and [H][C@@]12CC[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C, CN1CCN(CC(=O)N2C3=CC=CC=C3C(=O)NC3=C2N=CC=C3)CC1 and NC1=NC(CC(=O)NC2=CC=C(CCNC[C@H](O)C3=CC=CC=C3)C=C2)=CS1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[OH-].[OH-].[Mg++]\", \"[H][C@@]12CC[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C\"], [\"CN1CCN(CC(=O)N2C3=CC=CC=C3C(=O)NC3=C2N=CC=C3)CC1\", \"NC1=NC(CC(=O)NC2=CC=C(CCNC[C@H](O)C3=CC=CC=C3)C=C2)=CS1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[OH-].[OH-].[Mg++]\",\"smile2\": \"[OH-].[OH-].[Mg++]\",\"interact_result\": \"the bioavailability of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"CN1CCN(CC(=O)N2C3=CC=CC=C3C(=O)NC3=C2N=CC=C3)CC1\",\"smile2\": \"NC1=NC(CC(=O)NC2=CC=C(CCNC[C@H](O)C3=CC=CC=C3)C=C2)=CS1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The bioavailability of '[H][C@@]12CC[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C' can be decreased when combined with '[OH-].[OH-].[Mg++]'. The risk or severity of adverse effects can be increased when 'CN1CCN(CC(=O)N2C3=CC=CC=C3C(=O)NC3=C2N=CC=C3)CC1' is combined with 'NC1=NC(CC(=O)NC2=CC=C(CCNC[C@H](O)C3=CC=CC=C3)C=C2)=CS1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might [Mg++].C[N+](C)(C)CCO.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O and CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O affect the patient in terms of side effects, CC1=NN=C2CN=C(C3=CC=CC=C3Cl)C3=C(C=CC(Cl)=C3)N12 and BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[Mg++].C[N+](C)(C)CCO.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O\", \"CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O\"], [\"CC1=NN=C2CN=C(C3=CC=CC=C3Cl)C3=C(C=CC(Cl)=C3)N12\", \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[Mg++].C[N+](C)(C)CCO.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O\",\"smile2\": \"[Mg++].C[N+](C)(C)CCO.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CC1=NN=C2CN=C(C3=CC=CC=C3Cl)C3=C(C=CC(Cl)=C3)N12\",\"smile2\": \"BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when '[Mg++].C[N+](C)(C)CCO.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O.OC1=CC=CC=C1C([O-])=O' is combined with 'CN1C(C(=O)NC2=CC=CC=N2)=C(O)C2=C(C=C(Cl)S2)S1(=O)=O'. The risk or severity of adverse effects can be increased when 'CC1=NN=C2CN=C(C3=CC=CC=C3Cl)C3=C(C=CC(Cl)=C3)N12' is combined with 'BrC1=CC2=C(NC(=O)CN=C2C2=CC=CC=N2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you foresee for CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F and CCN(CC)CCCC(C)NC1=C2C=C(OC)C=CC2=NC2=C1C=CC(Cl)=C2, CN(C)CCC[C@]1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1 and CC(=O)NC[C@H]1CN(C(=O)O1)C1=CC(F)=C(C=C1)N1CCOCC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\", \"CCN(CC)CCCC(C)NC1=C2C=C(OC)C=CC2=NC2=C1C=CC(Cl)=C2\"], [\"CN(C)CCC[C@]1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1\", \"CC(=O)NC[C@H]1CN(C(=O)O1)C1=CC(F)=C(C=C1)N1CCOCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\",\"smile2\": \"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\",\"interact_result\": \"the serum concentration of #drug2 can be decreased when it is combined with #drug1.\"}, {\"smile1\": \"CN(C)CCC[C@]1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1\",\"smile2\": \"CC(=O)NC[C@H]1CN(C(=O)O1)C1=CC(F)=C(C=C1)N1CCOCC1\",\"interact_result\": \"#drug3 may increase the serotonergic activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of 'CCN(CC)CCCC(C)NC1=C2C=C(OC)C=CC2=NC2=C1C=CC(Cl)=C2' can be decreased when it is combined with 'CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F'. 'CN(C)CCC[C@]1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1' may increase the serotonergic activities of 'CC(=O)NC[C@H]1CN(C(=O)O1)C1=CC(F)=C(C=C1)N1CCOCC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your prediction on the side effects of [H][C@@]1(CC[N+](C)(C)C1)OC(=O)[C@@](O)(C1CCCC1)C1=CC=CC=C1 and CCN(CC)CCOC(=O)C1(CCCCC1)C1CCCCC1 interactions, [H][C@@]12C[C@@H](C)[C@](OC(=O)CC)(C(=O)SCF)[C@@]1(C)C[C@H](O)[C@@]1(F)[C@@]2([H])C[C@H](F)C2=CC(=O)C=C[C@]12C and CN(C)S(=O)(=O)C1=CC2=C(SC3=CC=CC=C3N2CCCN2CCC(CCO)CC2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]1(CC[N+](C)(C)C1)OC(=O)[C@@](O)(C1CCCC1)C1=CC=CC=C1\", \"CCN(CC)CCOC(=O)C1(CCCCC1)C1CCCCC1\"], [\"[H][C@@]12C[C@@H](C)[C@](OC(=O)CC)(C(=O)SCF)[C@@]1(C)C[C@H](O)[C@@]1(F)[C@@]2([H])C[C@H](F)C2=CC(=O)C=C[C@]12C\", \"CN(C)S(=O)(=O)C1=CC2=C(SC3=CC=CC=C3N2CCCN2CCC(CCO)CC2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]1(CC[N+](C)(C)C1)OC(=O)[C@@](O)(C1CCCC1)C1=CC=CC=C1\",\"smile2\": \"[H][C@@]1(CC[N+](C)(C)C1)OC(=O)[C@@](O)(C1CCCC1)C1=CC=CC=C1\",\"interact_result\": \"#drug1 may increase the anticholinergic activities of #drug2.\"}, {\"smile1\": \"[H][C@@]12C[C@@H](C)[C@](OC(=O)CC)(C(=O)SCF)[C@@]1(C)C[C@H](O)[C@@]1(F)[C@@]2([H])C[C@H](F)C2=CC(=O)C=C[C@]12C\",\"smile2\": \"CN(C)S(=O)(=O)C1=CC2=C(SC3=CC=CC=C3N2CCCN2CCC(CCO)CC2)C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[H][C@@]1(CC[N+](C)(C)C1)OC(=O)[C@@](O)(C1CCCC1)C1=CC=CC=C1' may increase the anticholinergic activities of 'CCN(CC)CCOC(=O)C1(CCCCC1)C1CCCCC1'. The risk or severity of adverse effects can be increased when '[H][C@@]12C[C@@H](C)[C@](OC(=O)CC)(C(=O)SCF)[C@@]1(C)C[C@H](O)[C@@]1(F)[C@@]2([H])C[C@H](F)C2=CC(=O)C=C[C@]12C' is combined with 'CN(C)S(=O)(=O)C1=CC2=C(SC3=CC=CC=C3N2CCCN2CCC(CCO)CC2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please evaluate the potential side effects of CC(OC1=C(Cl)C=CC=C1Cl)C1=NCCN1 and CCCN[C@H]1CCC2=C(C1)SC(N)=N2, [Ca++].[Ca++].[Ca++].OC(CC([O-])=O)(CC([O-])=O)C([O-])=O.OC(CC([O-])=O)(CC([O-])=O)C([O-])=O and N[C@@H](CC1=CC(I)=C(OC2=CC(I)=C(O)C=C2)C(I)=C1)C(O)=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(OC1=C(Cl)C=CC=C1Cl)C1=NCCN1\", \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\"], [\"[Ca++].[Ca++].[Ca++].OC(CC([O-])=O)(CC([O-])=O)C([O-])=O.OC(CC([O-])=O)(CC([O-])=O)C([O-])=O\", \"N[C@@H](CC1=CC(I)=C(OC2=CC(I)=C(O)C=C2)C(I)=C1)C(O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(OC1=C(Cl)C=CC=C1Cl)C1=NCCN1\",\"smile2\": \"CC(OC1=C(Cl)C=CC=C1Cl)C1=NCCN1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"[Ca++].[Ca++].[Ca++].OC(CC([O-])=O)(CC([O-])=O)C([O-])=O.OC(CC([O-])=O)(CC([O-])=O)C([O-])=O\",\"smile2\": \"N[C@@H](CC1=CC(I)=C(OC2=CC(I)=C(O)C=C2)C(I)=C1)C(O)=O\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CC(OC1=C(Cl)C=CC=C1Cl)C1=NCCN1' is combined with 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2'. The therapeutic efficacy of 'N[C@@H](CC1=CC(I)=C(OC2=CC(I)=C(O)C=C2)C(I)=C1)C(O)=O' can be decreased when used in combination with '[Ca++].[Ca++].[Ca++].OC(CC([O-])=O)(CC([O-])=O)C([O-])=O.OC(CC([O-])=O)(CC([O-])=O)C([O-])=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the possible side effects resulting from CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O and CNCCC(OC1=CC=C(C=C1)C(F)(F)F)C1=CC=CC=C1, CN(C)C(=O)CC1=C(N=C2C=CC(C)=CN12)C1=CC=C(C)C=C1 and [H][C@]12[C@H](C[C@@H](C)C=C1C=C[C@H](C)[C@@H]2CC[C@@H]1C[C@@H](O)CC(=O)O1)OC(=O)[C@@H](C)CC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O\", \"CNCCC(OC1=CC=C(C=C1)C(F)(F)F)C1=CC=CC=C1\"], [\"CN(C)C(=O)CC1=C(N=C2C=CC(C)=CN12)C1=CC=C(C)C=C1\", \"[H][C@]12[C@H](C[C@@H](C)C=C1C=C[C@H](C)[C@@H]2CC[C@@H]1C[C@@H](O)CC(=O)O1)OC(=O)[C@@H](C)CC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O\",\"smile2\": \"CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}, {\"smile1\": \"CN(C)C(=O)CC1=C(N=C2C=CC(C)=CN12)C1=CC=C(C)C=C1\",\"smile2\": \"[H][C@]12[C@H](C[C@@H](C)C=C1C=C[C@H](C)[C@@H]2CC[C@@H]1C[C@@H](O)CC(=O)O1)OC(=O)[C@@H](C)CC\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of 'CNCCC(OC1=CC=C(C=C1)C(F)(F)F)C1=CC=CC=C1' can be increased when it is combined with 'CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O'. The metabolism of '[H][C@]12[C@H](C[C@@H](C)C=C1C=C[C@H](C)[C@@H]2CC[C@@H]1C[C@@H](O)CC(=O)O1)OC(=O)[C@@H](C)CC' can be decreased when combined with 'CN(C)C(=O)CC1=C(N=C2C=CC(C)=CN12)C1=CC=C(C)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects could arise from the use of [H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12 and [H]\\C1=C([H])\\[C@]2([H])C[C@]2(N=C(O)[C@]2([H])C[C@]([H])(C[C@@]2([H])C(=O)N(C)CCCC1)OC1=C2C=CC(OC)=C(C)C2=NC(=C1)C1=NC(=CS1)C(C)C)C(O)=NS(=O)(=O)C1CC1, NNCCC1=CC=CC=C1 and NNC1=NN=CC2=CC=CC=C12. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\", \"[H]\\C1=C([H])\\[C@]2([H])C[C@]2(N=C(O)[C@]2([H])C[C@]([H])(C[C@@]2([H])C(=O)N(C)CCCC1)OC1=C2C=CC(OC)=C(C)C2=NC(=C1)C1=NC(=CS1)C(C)C)C(O)=NS(=O)(=O)C1CC1\"], [\"NNCCC1=CC=CC=C1\", \"NNC1=NN=CC2=CC=CC=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\",\"smile2\": \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}, {\"smile1\": \"NNCCC1=CC=CC=C1\",\"smile2\": \"NNC1=NN=CC2=CC=CC=C12\",\"interact_result\": \"#drug3 may increase the hypotensive activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of '[H]\\C1=C([H])\\[C@]2([H])C[C@]2(N=C(O)[C@]2([H])C[C@]([H])(C[C@@]2([H])C(=O)N(C)CCCC1)OC1=C2C=CC(OC)=C(C)C2=NC(=C1)C1=NC(=CS1)C(C)C)C(O)=NS(=O)(=O)C1CC1' can be increased when it is combined with '[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12'. 'NNCCC1=CC=CC=C1' may increase the hypotensive activities of 'NNC1=NN=CC2=CC=CC=C12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might OCCCC([O-])=O and CN1C2=C(C=C(C=C2)[N+]([O-])=O)C(=NCC1=O)C1=CC=CC=C1F reactions lead to side effects, CO[C@H]1CN(CCCOC2=CC=C(F)C=C2)CC[C@H]1NC(=O)C1=CC(Cl)=C(N)C=C1OC and [H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OCCCC([O-])=O\", \"CN1C2=C(C=C(C=C2)[N+]([O-])=O)C(=NCC1=O)C1=CC=CC=C1F\"], [\"CO[C@H]1CN(CCCOC2=CC=C(F)C=C2)CC[C@H]1NC(=O)C1=CC(Cl)=C(N)C=C1OC\", \"[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OCCCC([O-])=O\",\"smile2\": \"OCCCC([O-])=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CO[C@H]1CN(CCCOC2=CC=C(F)C=C2)CC[C@H]1NC(=O)C1=CC(Cl)=C(N)C=C1OC\",\"smile2\": \"[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1\",\"interact_result\": \"#drug3 may increase the qtc-prolonging activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'OCCCC([O-])=O' is combined with 'CN1C2=C(C=C(C=C2)[N+]([O-])=O)C(=NCC1=O)C1=CC=CC=C1F'. 'CO[C@H]1CN(CCCOC2=CC=C(F)C=C2)CC[C@H]1NC(=O)C1=CC(Cl)=C(N)C=C1OC' may increase the QTc-prolonging activities of '[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with COCCCOC1=C(OC)C=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=C1 and COC(=O)C(C1CCCCN1)C1=CC=CC=C1, OC(=O)C1=CC(=CC=C1O)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O and CCNC(=O)CCC\\C=C/C[C@H]1[C@@H](O)C[C@@H](O)[C@@H]1\\C=C\\[C@@H](O)CCC1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COCCCOC1=C(OC)C=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=C1\", \"COC(=O)C(C1CCCCN1)C1=CC=CC=C1\"], [\"OC(=O)C1=CC(=CC=C1O)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O\", \"CCNC(=O)CCC\\C=C/C[C@H]1[C@@H](O)C[C@@H](O)[C@@H]1\\C=C\\[C@@H](O)CCC1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COCCCOC1=C(OC)C=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=C1\",\"smile2\": \"COCCCOC1=C(OC)C=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=C1\",\"interact_result\": \"#drug1 may decrease the antihypertensive activities of #drug2.\"}, {\"smile1\": \"OC(=O)C1=CC(=CC=C1O)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O\",\"smile2\": \"CCNC(=O)CCC\\C=C/C[C@H]1[C@@H](O)C[C@@H](O)[C@@H]1\\C=C\\[C@@H](O)CCC1=CC=CC=C1\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'COCCCOC1=C(OC)C=CC(C[C@@H](C[C@H](N)[C@@H](O)C[C@@H](C(C)C)C(=O)NCC(C)(C)C(N)=O)C(C)C)=C1' may decrease the antihypertensive activities of 'COC(=O)C(C1CCCCN1)C1=CC=CC=C1'. The therapeutic efficacy of 'CCNC(=O)CCC\\C=C/C[C@H]1[C@@H](O)C[C@@H](O)[C@@H]1\\C=C\\[C@@H](O)CCC1=CC=CC=C1' can be decreased when used in combination with 'OC(=O)C1=CC(=CC=C1O)\\N=N\\C1=CC=C(O)C(=C1)C(O)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please outline the potential side effects of [H][C@@]12CC[C@@H](C)[C@]3([H])CC[C@@]4(C)OO[C@@]13[C@]([H])(O[C@H](OC)[C@@H]2C)O4 and CC(C)CN(C[C@@H](OP(O)(O)=O)[C@H](CC1=CC=CC=C1)NC(=O)O[C@H]1CCOC1)S(=O)(=O)C1=CC=C(N)C=C1, C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F and OC(=O)C1=CC=CC=C1O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC[C@@H](C)[C@]3([H])CC[C@@]4(C)OO[C@@]13[C@]([H])(O[C@H](OC)[C@@H]2C)O4\", \"CC(C)CN(C[C@@H](OP(O)(O)=O)[C@H](CC1=CC=CC=C1)NC(=O)O[C@H]1CCOC1)S(=O)(=O)C1=CC=C(N)C=C1\"], [\"C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\", \"OC(=O)C1=CC=CC=C1O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC[C@@H](C)[C@]3([H])CC[C@@]4(C)OO[C@@]13[C@]([H])(O[C@H](OC)[C@@H]2C)O4\",\"smile2\": \"[H][C@@]12CC[C@@H](C)[C@]3([H])CC[C@@]4(C)OO[C@@]13[C@]([H])(O[C@H](OC)[C@@H]2C)O4\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\",\"smile2\": \"OC(=O)C1=CC=CC=C1O\",\"interact_result\": \"the metabolism of #drug4 can be increased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'CC(C)CN(C[C@@H](OP(O)(O)=O)[C@H](CC1=CC=CC=C1)NC(=O)O[C@H]1CCOC1)S(=O)(=O)C1=CC=C(N)C=C1' can be decreased when combined with '[H][C@@]12CC[C@@H](C)[C@]3([H])CC[C@@]4(C)OO[C@@]13[C@]([H])(O[C@H](OC)[C@@H]2C)O4'. The metabolism of 'OC(=O)C1=CC=CC=C1O' can be increased when combined with 'C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects do you expect from CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12 and CN1N=C(SC1=NC(C)=O)S(N)(=O)=O, CC1=NC=C(C=C1)C1=C(C=C(Cl)C=N1)C1=CC=C(C=C1)S(C)(=O)=O and [H][C@@]12C[C@@]3([H])[C@]4([H])C[C@H](F)C5=CC(=O)C=C[C@]5(C)[C@@]4([H])[C@@H](O)C[C@]3(C)[C@@]1(OC(C)(C)O2)C(=O)CO. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12\", \"CN1N=C(SC1=NC(C)=O)S(N)(=O)=O\"], [\"CC1=NC=C(C=C1)C1=C(C=C(Cl)C=N1)C1=CC=C(C=C1)S(C)(=O)=O\", \"[H][C@@]12C[C@@]3([H])[C@]4([H])C[C@H](F)C5=CC(=O)C=C[C@]5(C)[C@@]4([H])[C@@H](O)C[C@]3(C)[C@@]1(OC(C)(C)O2)C(=O)CO\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12\",\"smile2\": \"CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CC1=NC=C(C=C1)C1=C(C=C(Cl)C=N1)C1=CC=C(C=C1)S(C)(=O)=O\",\"smile2\": \"[H][C@@]12C[C@@]3([H])[C@]4([H])C[C@H](F)C5=CC(=O)C=C[C@]5(C)[C@@]4([H])[C@@H](O)C[C@]3(C)[C@@]1(OC(C)(C)O2)C(=O)CO\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12' is combined with 'CN1N=C(SC1=NC(C)=O)S(N)(=O)=O'. The risk or severity of adverse effects can be increased when 'CC1=NC=C(C=C1)C1=C(C=C(Cl)C=N1)C1=CC=C(C=C1)S(C)(=O)=O' is combined with '[H][C@@]12C[C@@]3([H])[C@]4([H])C[C@H](F)C5=CC(=O)C=C[C@]5(C)[C@@]4([H])[C@@H](O)C[C@]3(C)[C@@]1(OC(C)(C)O2)C(=O)CO'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think the reactions of CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2 and CCCC(C)C1(CC)C(=O)NC(=S)NC1=O will affect side effects, CC1=CC=C(C=C1)C(=O)C1=CC(=C(O)C(O)=C1)[N+]([O-])=O and COC1=CC=CC=C1OCCNCC(O)COC1=CC=CC2=C1C1=CC=CC=C1N2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2\", \"CCCC(C)C1(CC)C(=O)NC(=S)NC1=O\"], [\"CC1=CC=C(C=C1)C(=O)C1=CC(=C(O)C(O)=C1)[N+]([O-])=O\", \"COC1=CC=CC=C1OCCNCC(O)COC1=CC=CC2=C1C1=CC=CC=C1N2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2\",\"smile2\": \"CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}, {\"smile1\": \"CC1=CC=C(C=C1)C(=O)C1=CC(=C(O)C(O)=C1)[N+]([O-])=O\",\"smile2\": \"COC1=CC=CC=C1OCCNCC(O)COC1=CC=CC2=C1C1=CC=CC=C1N2\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of 'CCCC(C)C1(CC)C(=O)NC(=S)NC1=O' can be increased when it is combined with 'CC1=CC=CC=C1C(=O)NC1=CC(C)=C(C=C1)C(=O)N1CCC[C@@H](O)C2=C1C=CC(Cl)=C2'. The risk or severity of adverse effects can be increased when 'CC1=CC=C(C=C1)C(=O)C1=CC(=C(O)C(O)=C1)[N+]([O-])=O' is combined with 'COC1=CC=CC=C1OCCNCC(O)COC1=CC=CC2=C1C1=CC=CC=C1N2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the predicted side effects of CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12 and [H][C@@]12[C@@H](C)C3=C(C(O)=CC=C3)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])[C@H]2O, CCCNC(C)C(=O)NC1=CC=CC=C1C and CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\", \"[H][C@@]12[C@@H](C)C3=C(C(O)=CC=C3)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])[C@H]2O\"], [\"CCCNC(C)C(=O)NC1=CC=CC=C1C\", \"CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\",\"smile2\": \"CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"CCCNC(C)C(=O)NC1=CC=CC=C1C\",\"smile2\": \"CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of '[H][C@@]12[C@@H](C)C3=C(C(O)=CC=C3)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])[C@H]2O' can be decreased when combined with 'CNCCC=C1C2=CC=CC=C2CCC2=CC=CC=C12'. The risk or severity of adverse effects can be increased when 'CCCNC(C)C(=O)NC1=CC=CC=C1C' is combined with 'CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the side effects we might see with CN(C)C(=N)NC(N)=N and OC(=O)C1=C(O)C=CC(=C1)C1=C(F)C=C(F)C=C1, [H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2 and ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)C(=N)NC(N)=N\", \"OC(=O)C1=C(O)C=CC(=C1)C1=C(F)C=C(F)C=C1\"], [\"[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2\", \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)C(=N)NC(N)=N\",\"smile2\": \"CN(C)C(=N)NC(N)=N\",\"interact_result\": \"#drug1 may increase the hypoglycemic activities of #drug2.\"}, {\"smile1\": \"[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2\",\"smile2\": \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\",\"interact_result\": \"#drug3 may increase the qtc-prolonging activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CN(C)C(=N)NC(N)=N' may increase the hypoglycemic activities of 'OC(=O)C1=C(O)C=CC(=C1)C1=C(F)C=C(F)C=C1'. '[H][C@@]1(C[C@@]2([H])C[C@]3([H])C[C@@]([H])(C1)N2CC3=O)OC(=O)C1=CNC2=C1C=CC=C2' may increase the QTc-prolonging activities of 'ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from [H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1 and [H][C@@]12CC[C@@](O)(C#C)[C@@]1(C)CC[C@]1([H])C3=C(CC(=O)CC3)C[C@@]([H])(C)[C@@]21[H], CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1 and [H][C@@]12CC[C@@](OC(C)=O)(C#C)[C@@]1(CC)CC[C@]1([H])[C@@]3([H])CC\\C(C=C3CC[C@@]21[H])=N/O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1\", \"[H][C@@]12CC[C@@](O)(C#C)[C@@]1(C)CC[C@]1([H])C3=C(CC(=O)CC3)C[C@@]([H])(C)[C@@]21[H]\"], [\"CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1\", \"[H][C@@]12CC[C@@](OC(C)=O)(C#C)[C@@]1(CC)CC[C@]1([H])[C@@]3([H])CC\\C(C=C3CC[C@@]21[H])=N/O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1\",\"smile2\": \"[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1\",\"interact_result\": \"#drug1 may increase the hypotensive activities of #drug2.\"}, {\"smile1\": \"CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1\",\"smile2\": \"[H][C@@]12CC[C@@](OC(C)=O)(C#C)[C@@]1(CC)CC[C@]1([H])[C@@]3([H])CC\\C(C=C3CC[C@@]21[H])=N/O\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[H][C@]12C[C@@H](O)[C@H](CC[C@@H](O)CCCCC)[C@@]1([H])CC1=C(C2)C(OCC(O)=O)=CC=C1' may increase the hypotensive activities of '[H][C@@]12CC[C@@](O)(C#C)[C@@]1(C)CC[C@]1([H])C3=C(CC(=O)CC3)C[C@@]([H])(C)[C@@]21[H]'. The therapeutic efficacy of '[H][C@@]12CC[C@@](OC(C)=O)(C#C)[C@@]1(CC)CC[C@]1([H])[C@@]3([H])CC\\C(C=C3CC[C@@]21[H])=N/O' can be decreased when used in combination with 'CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect the side effects of COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1 and CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12 to differ, CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2 and OC12CC3CC(C1)CC(C3)(C2)NCC(=O)N1CCC[C@H]1C#N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1\", \"CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12\"], [\"CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2\", \"OC12CC3CC(C1)CC(C3)(C2)NCC(=O)N1CCC[C@H]1C#N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1\",\"smile2\": \"COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1\",\"interact_result\": \"the serum concentration of #drug2 can be decreased when it is combined with #drug1.\"}, {\"smile1\": \"CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2\",\"smile2\": \"OC12CC3CC(C1)CC(C3)(C2)NCC(=O)N1CCC[C@H]1C#N\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of 'CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12' can be decreased when it is combined with 'COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1'. The therapeutic efficacy of 'OC12CC3CC(C1)CC(C3)(C2)NCC(=O)N1CCC[C@H]1C#N' can be decreased when used in combination with 'CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential side effects should be considered for [H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@]1([H])[C@@]2([H])C=CC2=CC(=O)CC[C@@]12C and CCCCCCOC(=O)N=C(N)C1=CC=C(NCC2=NC3=C(C=CC(=C3)C(=O)N(CCC(=O)OCC)C3=CC=CC=N3)N2C)C=C1, [H][C@]12[C@H](OC(=O)C3=CC=CC=C3)[C@]3(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)C4=CC=CC=C4)C4=CC=CC=C4)C(C)=C([C@@H](OC(C)=O)C(=O)[C@]1(C)[C@@H](O)C[C@H]1OC[C@@]21OC(C)=O)C3(C)C and COC1=C2N(C=C(C(O)=O)C(=O)C2=CC(F)=C1N1CCNC(C)C1)C1CC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@]1([H])[C@@]2([H])C=CC2=CC(=O)CC[C@@]12C\", \"CCCCCCOC(=O)N=C(N)C1=CC=C(NCC2=NC3=C(C=CC(=C3)C(=O)N(CCC(=O)OCC)C3=CC=CC=N3)N2C)C=C1\"], [\"[H][C@]12[C@H](OC(=O)C3=CC=CC=C3)[C@]3(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)C4=CC=CC=C4)C4=CC=CC=C4)C(C)=C([C@@H](OC(C)=O)C(=O)[C@]1(C)[C@@H](O)C[C@H]1OC[C@@]21OC(C)=O)C3(C)C\", \"COC1=C2N(C=C(C(O)=O)C(=O)C2=CC(F)=C1N1CCNC(C)C1)C1CC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@]1([H])[C@@]2([H])C=CC2=CC(=O)CC[C@@]12C\",\"smile2\": \"[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@]1([H])[C@@]2([H])C=CC2=CC(=O)CC[C@@]12C\",\"interact_result\": \"the therapeutic efficacy of #drug2 can be decreased when used in combination with #drug1.\"}, {\"smile1\": \"[H][C@]12[C@H](OC(=O)C3=CC=CC=C3)[C@]3(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)C4=CC=CC=C4)C4=CC=CC=C4)C(C)=C([C@@H](OC(C)=O)C(=O)[C@]1(C)[C@@H](O)C[C@H]1OC[C@@]21OC(C)=O)C3(C)C\",\"smile2\": \"COC1=C2N(C=C(C(O)=O)C(=O)C2=CC(F)=C1N1CCNC(C)C1)C1CC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The therapeutic efficacy of 'CCCCCCOC(=O)N=C(N)C1=CC=C(NCC2=NC3=C(C=CC(=C3)C(=O)N(CCC(=O)OCC)C3=CC=CC=N3)N2C)C=C1' can be decreased when used in combination with '[H][C@@]12CC[C@H](C(C)=O)[C@@]1(C)CC[C@]1([H])[C@@]2([H])C=CC2=CC(=O)CC[C@@]12C'. The risk or severity of adverse effects can be increased when '[H][C@]12[C@H](OC(=O)C3=CC=CC=C3)[C@]3(O)C[C@H](OC(=O)[C@H](O)[C@@H](NC(=O)C4=CC=CC=C4)C4=CC=CC=C4)C(C)=C([C@@H](OC(C)=O)C(=O)[C@]1(C)[C@@H](O)C[C@H]1OC[C@@]21OC(C)=O)C3(C)C' is combined with 'COC1=C2N(C=C(C(O)=O)C(=O)C2=CC(F)=C1N1CCNC(C)C1)C1CC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the side effects of ClC1=C(NC2=NCCN2)C2=NSN=C2C=C1 and [H][C@@]12CC(=O)CC[C@@]1([H])C(C)(C)OC1=CC(=CC(O)=C21)C(C)(C)CCCCCC, OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl and [H][C@@]12C[C@@H](C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])[C@H](Cl)CC2=CC(=O)C=C[C@]12C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClC1=C(NC2=NCCN2)C2=NSN=C2C=C1\", \"[H][C@@]12CC(=O)CC[C@@]1([H])C(C)(C)OC1=CC(=CC(O)=C21)C(C)(C)CCCCCC\"], [\"OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl\", \"[H][C@@]12C[C@@H](C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])[C@H](Cl)CC2=CC(=O)C=C[C@]12C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClC1=C(NC2=NCCN2)C2=NSN=C2C=C1\",\"smile2\": \"ClC1=C(NC2=NCCN2)C2=NSN=C2C=C1\",\"interact_result\": \"#drug1 may increase the central nervous system depressant (cns depressant) activities of #drug2.\"}, {\"smile1\": \"OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl\",\"smile2\": \"[H][C@@]12C[C@@H](C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])[C@H](Cl)CC2=CC(=O)C=C[C@]12C\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'ClC1=C(NC2=NCCN2)C2=NSN=C2C=C1' may increase the central nervous system depressant (CNS depressant) activities of '[H][C@@]12CC(=O)CC[C@@]1([H])C(C)(C)OC1=CC(=CC(O)=C21)C(C)(C)CCCCCC'. The risk or severity of adverse effects can be increased when 'OC(=O)CC1=CC=CC=C1NC1=C(Cl)C=CC=C1Cl' is combined with '[H][C@@]12C[C@@H](C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@H](O)[C@@]1([H])[C@@]2([H])[C@H](Cl)CC2=CC(=O)C=C[C@]12C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide insight into the side effects of [H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O and CCC1=C(C)NC2=C1C(=O)C(CN1CCOCC1)CC2, COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1 and CC(C)C1=C(C(=O)NC2=CC=CC=C2)C(=C(N1CC[C@@H](O)C[C@@H](O)CC(O)=O)C1=CC=C(F)C=C1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\", \"CCC1=C(C)NC2=C1C(=O)C(CN1CCOCC1)CC2\"], [\"COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\", \"CC(C)C1=C(C(=O)NC2=CC=CC=C2)C(=C(N1CC[C@@H](O)C[C@@H](O)CC(O)=O)C1=CC=C(F)C=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\",\"smile2\": \"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\",\"smile2\": \"CC(C)C1=C(C(=O)NC2=CC=CC=C2)C(=C(N1CC[C@@H](O)C[C@@H](O)CC(O)=O)C1=CC=C(F)C=C1)C1=CC=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when '[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O' is combined with 'CCC1=C(C)NC2=C1C(=O)C(CN1CCOCC1)CC2'. The risk or severity of adverse effects can be increased when 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1' is combined with 'CC(C)C1=C(C(=O)NC2=CC=CC=C2)C(=C(N1CC[C@@H](O)C[C@@H](O)CC(O)=O)C1=CC=C(F)C=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you think will result from CN1CCN(CC2=CC=C(C=C2)C(=O)NC2=CC(NC3=NC=CC(=N3)C3=CN=CC=C3)=C(C)C=C2)CC1 and CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12, ClC1=CC=C2N=C3NC(=O)CN3CC2=C1Cl and CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCN(CC2=CC=C(C=C2)C(=O)NC2=CC(NC3=NC=CC(=N3)C3=CN=CC=C3)=C(C)C=C2)CC1\", \"CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12\"], [\"ClC1=CC=C2N=C3NC(=O)CN3CC2=C1Cl\", \"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1CCN(CC2=CC=C(C=C2)C(=O)NC2=CC(NC3=NC=CC(=N3)C3=CN=CC=C3)=C(C)C=C2)CC1\",\"smile2\": \"CN1CCN(CC2=CC=C(C=C2)C(=O)NC2=CC(NC3=NC=CC(=N3)C3=CN=CC=C3)=C(C)C=C2)CC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"ClC1=CC=C2N=C3NC(=O)CN3CC2=C1Cl\",\"smile2\": \"CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1\",\"interact_result\": \"#drug3 may increase the qtc-prolonging activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CN1CCN(CC2=CC=C(C=C2)C(=O)NC2=CC(NC3=NC=CC(=N3)C3=CN=CC=C3)=C(C)C=C2)CC1' is combined with 'CN1CCN(CC1)C1=NC2=CC(Cl)=CC=C2NC2=CC=CC=C12'. 'ClC1=CC=C2N=C3NC(=O)CN3CC2=C1Cl' may increase the QTc-prolonging activities of 'CCCCCCCCC1=CC=C(CCC(N)(CO)CO)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant are the side effects anticipated from CC1=C2NC(=O)C3=C(N=CC=C3)N(C3CC3)C2=NC=C1 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC, COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C and [H][C@@](C)(O)[C@]([H])(CC)N1N=CN(C1=O)C1=CC=C(C=C1)N1CCN(CC1)C1=CC=C(OC[C@]2([H])CO[C@](CN3C=NC=N3)(C2)C2=C(F)C=C(F)C=C2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=C2NC(=O)C3=C(N=CC=C3)N(C3CC3)C2=NC=C1\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\"], [\"COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C\", \"[H][C@@](C)(O)[C@]([H])(CC)N1N=CN(C1=O)C1=CC=C(C=C1)N1CCN(CC1)C1=CC=C(OC[C@]2([H])CO[C@](CN3C=NC=N3)(C2)C2=C(F)C=C(F)C=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=C2NC(=O)C3=C(N=CC=C3)N(C3CC3)C2=NC=C1\",\"smile2\": \"CC1=C2NC(=O)C3=C(N=CC=C3)N(C3CC3)C2=NC=C1\",\"interact_result\": \"the metabolism of #drug2 can be increased when combined with #drug1.\"}, {\"smile1\": \"COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C\",\"smile2\": \"[H][C@@](C)(O)[C@]([H])(CC)N1N=CN(C1=O)C1=CC=C(C=C1)N1CCN(CC1)C1=CC=C(OC[C@]2([H])CO[C@](CN3C=NC=N3)(C2)C2=C(F)C=C(F)C=C2)C=C1\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC' can be increased when combined with 'CC1=C2NC(=O)C3=C(N=CC=C3)N(C3CC3)C2=NC=C1'. The metabolism of '[H][C@@](C)(O)[C@]([H])(CC)N1N=CN(C1=O)C1=CC=C(C=C1)N1CCN(CC1)C1=CC=C(OC[C@]2([H])CO[C@](CN3C=NC=N3)(C2)C2=C(F)C=C(F)C=C2)C=C1' can be decreased when combined with 'COC1=C(C=C(C=C1C1=CC2=CC=C(NS(C)(=O)=O)C=C2C=C1)N1C=CC(=O)NC1=O)C(C)(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential adverse reactions do you foresee for CC(C)(C)NC[C@H](O)COC1=CC=CC=C1C1CCCC1 and C[C@H](CC1=CC2=C(N(CCCO)CC2)C(=C1)C(N)=O)NCCOC1=CC=CC=C1OCC(F)(F)F, CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1 and COC1=CC=C(CC(C)(C)NC[C@H](O)C2=C3OCC(=O)NC3=CC(O)=C2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NC[C@H](O)COC1=CC=CC=C1C1CCCC1\", \"C[C@H](CC1=CC2=C(N(CCCO)CC2)C(=C1)C(N)=O)NCCOC1=CC=CC=C1OCC(F)(F)F\"], [\"CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1\", \"COC1=CC=C(CC(C)(C)NC[C@H](O)C2=C3OCC(=O)NC3=CC(O)=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NC[C@H](O)COC1=CC=CC=C1C1CCCC1\",\"smile2\": \"CC(C)(C)NC[C@H](O)COC1=CC=CC=C1C1CCCC1\",\"interact_result\": \"#drug1 may increase the orthostatic hypotensive activities of #drug2.\"}, {\"smile1\": \"CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1\",\"smile2\": \"COC1=CC=C(CC(C)(C)NC[C@H](O)C2=C3OCC(=O)NC3=CC(O)=C2)C=C1\",\"interact_result\": \"#drug3 may increase the tachycardic activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC(C)(C)NC[C@H](O)COC1=CC=CC=C1C1CCCC1' may increase the orthostatic hypotensive activities of 'C[C@H](CC1=CC2=C(N(CCCO)CC2)C(=C1)C(N)=O)NCCOC1=CC=CC=C1OCC(F)(F)F'. 'CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1' may increase the tachycardic activities of 'COC1=CC=C(CC(C)(C)NC[C@H](O)C2=C3OCC(=O)NC3=CC(O)=C2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please explain the possible side effects of CCCCC(=O)N(CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1)[C@@H](C(C)C)C(O)=O and CC1=CN(C(=O)C=C1)C1=CC=CC=C1, CCC[C@H]1CN([C@@H](CC)C(N)=O)C(=O)C1 and C\\N=C(\\NCCSCC1=C(C)NC=N1)NC#N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC(=O)N(CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1)[C@@H](C(C)C)C(O)=O\", \"CC1=CN(C(=O)C=C1)C1=CC=CC=C1\"], [\"CCC[C@H]1CN([C@@H](CC)C(N)=O)C(=O)C1\", \"C\\N=C(\\NCCSCC1=C(C)NC=N1)NC#N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC(=O)N(CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1)[C@@H](C(C)C)C(O)=O\",\"smile2\": \"CCCCC(=O)N(CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1)[C@@H](C(C)C)C(O)=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CCC[C@H]1CN([C@@H](CC)C(N)=O)C(=O)C1\",\"smile2\": \"C\\N=C(\\NCCSCC1=C(C)NC=N1)NC#N\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CCCCC(=O)N(CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1)[C@@H](C(C)C)C(O)=O' is combined with 'CC1=CN(C(=O)C=C1)C1=CC=CC=C1'. The metabolism of 'C\\N=C(\\NCCSCC1=C(C)NC=N1)NC#N' can be decreased when combined with 'CCC[C@H]1CN([C@@H](CC)C(N)=O)C(=O)C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely are side effects with CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NN=NN1)C(=CC=C2)C(=O)OC(C)OC(=O)OC1CCCCC1 and COC1=C(OC)C=C(CC2=NC=CC3=CC(OC)=C(OC)C=C23)C=C1, O=C1CCC2=C(N1)C=CC(OCCCCC1=NN=NN1C1CCCCC1)=C2 and OCCN(CCO)CCO.OC(=O)C1=CC=CC=C1O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NN=NN1)C(=CC=C2)C(=O)OC(C)OC(=O)OC1CCCCC1\", \"COC1=C(OC)C=C(CC2=NC=CC3=CC(OC)=C(OC)C=C23)C=C1\"], [\"O=C1CCC2=C(N1)C=CC(OCCCCC1=NN=NN1C1CCCCC1)=C2\", \"OCCN(CCO)CCO.OC(=O)C1=CC=CC=C1O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NN=NN1)C(=CC=C2)C(=O)OC(C)OC(=O)OC1CCCCC1\",\"smile2\": \"CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NN=NN1)C(=CC=C2)C(=O)OC(C)OC(=O)OC1CCCCC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"O=C1CCC2=C(N1)C=CC(OCCCCC1=NN=NN1C1CCCCC1)=C2\",\"smile2\": \"OCCN(CCO)CCO.OC(=O)C1=CC=CC=C1O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CCOC1=NC2=C(N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NN=NN1)C(=CC=C2)C(=O)OC(C)OC(=O)OC1CCCCC1' is combined with 'COC1=C(OC)C=C(CC2=NC=CC3=CC(OC)=C(OC)C=C23)C=C1'. The risk or severity of adverse effects can be increased when 'O=C1CCC2=C(N1)C=CC(OCCCCC1=NN=NN1C1CCCCC1)=C2' is combined with 'OCCN(CCO)CCO.OC(=O)C1=CC=CC=C1O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects should be expected from the reactions of CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F and [H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C, [Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].CO[C@H]1O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]2O[C@H]([C@@H](O[C@H]3O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]4O[C@@H]([C@@H](O[C@H]5O[C@H](COS([O-])(=O)=O)[C@@H](O)[C@H](O)[C@H]5NS([O-])(=O)=O)[C@H](O)[C@H]4O)C(O)=O)[C@H](OS([O-])(=O)=O)[C@H]3NS([O-])(=O)=O)[C@H](O)[C@H]2OS([O-])(=O)=O)C(O)=O)[C@H](O)[C@H]1NS([O-])(=O)=O and C[C@@H](CC1=CC(O)=C(O)C=C1)[C@H](C)CC1=CC(O)=C(O)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\", \"[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C\"], [\"[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].CO[C@H]1O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]2O[C@H]([C@@H](O[C@H]3O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]4O[C@@H]([C@@H](O[C@H]5O[C@H](COS([O-])(=O)=O)[C@@H](O)[C@H](O)[C@H]5NS([O-])(=O)=O)[C@H](O)[C@H]4O)C(O)=O)[C@H](OS([O-])(=O)=O)[C@H]3NS([O-])(=O)=O)[C@H](O)[C@H]2OS([O-])(=O)=O)C(O)=O)[C@H](O)[C@H]1NS([O-])(=O)=O\", \"C[C@@H](CC1=CC(O)=C(O)C=C1)[C@H](C)CC1=CC(O)=C(O)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\",\"smile2\": \"CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F\",\"interact_result\": \"the serum concentration of #drug2 can be decreased when it is combined with #drug1.\"}, {\"smile1\": \"[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].CO[C@H]1O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]2O[C@H]([C@@H](O[C@H]3O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]4O[C@@H]([C@@H](O[C@H]5O[C@H](COS([O-])(=O)=O)[C@@H](O)[C@H](O)[C@H]5NS([O-])(=O)=O)[C@H](O)[C@H]4O)C(O)=O)[C@H](OS([O-])(=O)=O)[C@H]3NS([O-])(=O)=O)[C@H](O)[C@H]2OS([O-])(=O)=O)C(O)=O)[C@H](O)[C@H]1NS([O-])(=O)=O\",\"smile2\": \"C[C@@H](CC1=CC(O)=C(O)C=C1)[C@H](C)CC1=CC(O)=C(O)C=C1\",\"interact_result\": \"#drug3 may increase the anticoagulant activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of '[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C' can be decreased when it is combined with 'CNC(=O)C1=C(F)C=C(C=C1)N1C(=S)N(C(=O)C1(C)C)C1=CC=C(C#N)C(=C1)C(F)(F)F'. '[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].CO[C@H]1O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]2O[C@H]([C@@H](O[C@H]3O[C@H](COS([O-])(=O)=O)[C@@H](O[C@@H]4O[C@@H]([C@@H](O[C@H]5O[C@H](COS([O-])(=O)=O)[C@@H](O)[C@H](O)[C@H]5NS([O-])(=O)=O)[C@H](O)[C@H]4O)C(O)=O)[C@H](OS([O-])(=O)=O)[C@H]3NS([O-])(=O)=O)[C@H](O)[C@H]2OS([O-])(=O)=O)C(O)=O)[C@H](O)[C@H]1NS([O-])(=O)=O' may increase the anticoagulant activities of 'C[C@@H](CC1=CC(O)=C(O)C=C1)[C@H](C)CC1=CC(O)=C(O)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likelihood of side effects occurring from [H][C@](O)(CNC(C)(C)C)COC1=NSN=C1N1CCOCC1 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1, CN1C2=C(C=C(Cl)C=C2)C(=NCC1=O)C1=CC=CC=C1 and CO[C@@H]1[C@@H](C[C@H]2O[C@]1(C)N1C3=C(C=CC=C3)C3=C1C1=C(C4=C(C=CC=C4)N21)C1=C3CNC1=O)N(C)C(=O)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@](O)(CNC(C)(C)C)COC1=NSN=C1N1CCOCC1\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\"], [\"CN1C2=C(C=C(Cl)C=C2)C(=NCC1=O)C1=CC=CC=C1\", \"CO[C@@H]1[C@@H](C[C@H]2O[C@]1(C)N1C3=C(C=CC=C3)C3=C1C1=C(C4=C(C=CC=C4)N21)C1=C3CNC1=O)N(C)C(=O)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@](O)(CNC(C)(C)C)COC1=NSN=C1N1CCOCC1\",\"smile2\": \"[H][C@](O)(CNC(C)(C)C)COC1=NSN=C1N1CCOCC1\",\"interact_result\": \"#drug1 may increase the hypotensive activities of #drug2.\"}, {\"smile1\": \"CN1C2=C(C=C(Cl)C=C2)C(=NCC1=O)C1=CC=CC=C1\",\"smile2\": \"CO[C@@H]1[C@@H](C[C@H]2O[C@]1(C)N1C3=C(C=CC=C3)C3=C1C1=C(C4=C(C=CC=C4)N21)C1=C3CNC1=O)N(C)C(=O)C1=CC=CC=C1\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[H][C@](O)(CNC(C)(C)C)COC1=NSN=C1N1CCOCC1' may increase the hypotensive activities of 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1'. The metabolism of 'CO[C@@H]1[C@@H](C[C@H]2O[C@]1(C)N1C3=C(C=CC=C3)C3=C1C1=C(C4=C(C=CC=C4)N21)C1=C3CNC1=O)N(C)C(=O)C1=CC=CC=C1' can be decreased when combined with 'CN1C2=C(C=C(Cl)C=C2)C(=NCC1=O)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the reactions of [H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C and [H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1 influencing side effects, O=[As]O[As]=O and CC(C)NCC(O)COC1=C(C)C(C)=C(OC(C)=O)C(C)=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C\", \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\"], [\"O=[As]O[As]=O\", \"CC(C)NCC(O)COC1=C(C)C(C)=C(OC(C)=O)C(C)=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C\",\"smile2\": \"[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}, {\"smile1\": \"O=[As]O[As]=O\",\"smile2\": \"CC(C)NCC(O)COC1=C(C)C(C)=C(OC(C)=O)C(C)=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of '[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1' can be increased when it is combined with '[H][C@@]12CC(=C)[C@](O)(C(=O)CO)[C@@]1(C)C[C@]([H])(O)[C@@]1(F)[C@@]2([H])CCC2=CC(=O)C=C[C@]12C'. The risk or severity of adverse effects can be increased when 'O=[As]O[As]=O' is combined with 'CC(C)NCC(O)COC1=C(C)C(C)=C(OC(C)=O)C(C)=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please detail the expected side effects from ClC=C(Cl)Cl and OC(=O)COCCN1CCN(CC1)[C@H](C1=CC=CC=C1)C1=CC=C(Cl)C=C1, [H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)[C@H](N)C1=CC=CC=C1)C(=O)OCOC(=O)C(C)(C)C and CN(N=O)C(=O)N[C@H]1[C@@H](O)O[C@H](CO)[C@@H](O)[C@@H]1O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClC=C(Cl)Cl\", \"OC(=O)COCCN1CCN(CC1)[C@H](C1=CC=CC=C1)C1=CC=C(Cl)C=C1\"], [\"[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)[C@H](N)C1=CC=CC=C1)C(=O)OCOC(=O)C(C)(C)C\", \"CN(N=O)C(=O)N[C@H]1[C@@H](O)O[C@H](CO)[C@@H](O)[C@@H]1O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClC=C(Cl)Cl\",\"smile2\": \"ClC=C(Cl)Cl\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)[C@H](N)C1=CC=CC=C1)C(=O)OCOC(=O)C(C)(C)C\",\"smile2\": \"CN(N=O)C(=O)N[C@H]1[C@@H](O)O[C@H](CO)[C@@H](O)[C@@H]1O\",\"interact_result\": \"the serum concentration of #drug4 can be decreased when it is combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'ClC=C(Cl)Cl' is combined with 'OC(=O)COCCN1CCN(CC1)[C@H](C1=CC=CC=C1)C1=CC=C(Cl)C=C1'. The serum concentration of 'CN(N=O)C(=O)N[C@H]1[C@@H](O)O[C@H](CO)[C@@H](O)[C@@H]1O' can be decreased when it is combined with '[H][C@]12SC(C)(C)[C@@H](N1C(=O)[C@H]2NC(=O)[C@H](N)C1=CC=CC=C1)C(=O)OCOC(=O)C(C)(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your analysis of the potential side effects of CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O and CCC1=NN(CCCN2CCN(CC2)C2=CC(Cl)=CC=C2)C(=O)N1CCOC1=CC=CC=C1, [H]\\C(CCN1CCN(CCO)CC1)=C1/C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F and CC(C)C(=O)OC(C)OC(=O)NCC1(CC(O)=O)CCCCC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\", \"CCC1=NN(CCCN2CCN(CC2)C2=CC(Cl)=CC=C2)C(=O)N1CCOC1=CC=CC=C1\"], [\"[H]\\C(CCN1CCN(CCO)CC1)=C1/C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F\", \"CC(C)C(=O)OC(C)OC(=O)NCC1(CC(O)=O)CCCCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\",\"smile2\": \"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"[H]\\C(CCN1CCN(CCO)CC1)=C1/C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F\",\"smile2\": \"CC(C)C(=O)OC(C)OC(=O)NCC1(CC(O)=O)CCCCC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O' is combined with 'CCC1=NN(CCCN2CCN(CC2)C2=CC(Cl)=CC=C2)C(=O)N1CCOC1=CC=CC=C1'. The risk or severity of adverse effects can be increased when '[H]\\C(CCN1CCN(CCO)CC1)=C1/C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F' is combined with 'CC(C)C(=O)OC(C)OC(=O)NCC1(CC(O)=O)CCCCC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might CN(C)CCCC1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1 and CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1 lead to side effects, [H][C@]12CN(C[C@@]1([H])NCCC2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1OC)C(O)=O and C[C@H]1O[C@H](C[C@H](O)[C@@H]1O[C@H]1C[C@H](O)[C@H](O[C@H]2C[C@H](O)[C@H](O[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O)[C@H]3O)[C@@H](C)O2)[C@@H](C)O1)O[C@H]1CC[C@@]2(C)[C@H](CC[C@@H]3[C@@H]2C[C@@H](O)[C@]2(C)[C@H](CC[C@]32O)C2=CC(=O)OC2)C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1\", \"CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1\"], [\"[H][C@]12CN(C[C@@]1([H])NCCC2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1OC)C(O)=O\", \"C[C@H]1O[C@H](C[C@H](O)[C@@H]1O[C@H]1C[C@H](O)[C@H](O[C@H]2C[C@H](O)[C@H](O[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O)[C@H]3O)[C@@H](C)O2)[C@@H](C)O1)O[C@H]1CC[C@@]2(C)[C@H](CC[C@@H]3[C@@H]2C[C@@H](O)[C@]2(C)[C@H](CC[C@]32O)C2=CC(=O)OC2)C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1\",\"smile2\": \"CN(C)CCCC1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}, {\"smile1\": \"[H][C@]12CN(C[C@@]1([H])NCCC2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1OC)C(O)=O\",\"smile2\": \"C[C@H]1O[C@H](C[C@H](O)[C@@H]1O[C@H]1C[C@H](O)[C@H](O[C@H]2C[C@H](O)[C@H](O[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O)[C@H]3O)[C@@H](C)O2)[C@@H](C)O1)O[C@H]1CC[C@@]2(C)[C@H](CC[C@@H]3[C@@H]2C[C@@H](O)[C@]2(C)[C@H](CC[C@]32O)C2=CC(=O)OC2)C1\",\"interact_result\": \"#drug3 may decrease the cardiotoxic activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CN(C)CCCC1(OCC2=C1C=CC(=C2)C#N)C1=CC=C(F)C=C1' may increase the QTc-prolonging activities of 'CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1'. '[H][C@]12CN(C[C@@]1([H])NCCC2)C1=C(F)C=C2C(=O)C(=CN(C3CC3)C2=C1OC)C(O)=O' may decrease the cardiotoxic activities of 'C[C@H]1O[C@H](C[C@H](O)[C@@H]1O[C@H]1C[C@H](O)[C@H](O[C@H]2C[C@H](O)[C@H](O[C@@H]3O[C@H](CO)[C@@H](O)[C@H](O)[C@H]3O)[C@@H](C)O2)[C@@H](C)O1)O[C@H]1CC[C@@]2(C)[C@H](CC[C@@H]3[C@@H]2C[C@@H](O)[C@]2(C)[C@H](CC[C@]32O)C2=CC(=O)OC2)C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you predict as a result of CN(C1=CC2=NN(C)C(C)=C2C=C1)C1=CC=NC(NC2=CC=C(C)C(=C2)S(N)(=O)=O)=N1 and CNCC[C@@H](OC1=CC=CC=C1C)C1=CC=CC=C1, C[C@H](CC1=CC=CC=C1)N(C)CC#C and CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)COC1=C(C)C=CC=C1C)CC1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C1=CC2=NN(C)C(C)=C2C=C1)C1=CC=NC(NC2=CC=C(C)C(=C2)S(N)(=O)=O)=N1\", \"CNCC[C@@H](OC1=CC=CC=C1C)C1=CC=CC=C1\"], [\"C[C@H](CC1=CC=CC=C1)N(C)CC#C\", \"CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)COC1=C(C)C=CC=C1C)CC1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C1=CC2=NN(C)C(C)=C2C=C1)C1=CC=NC(NC2=CC=C(C)C(=C2)S(N)(=O)=O)=N1\",\"smile2\": \"CN(C1=CC2=NN(C)C(C)=C2C=C1)C1=CC=NC(NC2=CC=C(C)C(=C2)S(N)(=O)=O)=N1\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}, {\"smile1\": \"C[C@H](CC1=CC=CC=C1)N(C)CC#C\",\"smile2\": \"CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)COC1=C(C)C=CC=C1C)CC1=CC=CC=C1\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CN(C1=CC2=NN(C)C(C)=C2C=C1)C1=CC=NC(NC2=CC=C(C)C(=C2)S(N)(=O)=O)=N1' may increase the QTc-prolonging activities of 'CNCC[C@@H](OC1=CC=CC=C1C)C1=CC=CC=C1'. The metabolism of 'CC(C)[C@H](N1CCCNC1=O)C(=O)N[C@H](C[C@H](O)[C@H](CC1=CC=CC=C1)NC(=O)COC1=C(C)C=CC=C1C)CC1=CC=CC=C1' can be decreased when combined with 'C[C@H](CC1=CC=CC=C1)N(C)CC#C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide a forecast of the side effects from O=[As]O[As]=O and CN1CCN(CC1)C1=NC2=CC=CC=C2NC2=C1C=C(C)S2, CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O and [N]=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O=[As]O[As]=O\", \"CN1CCN(CC1)C1=NC2=CC=CC=C2NC2=C1C=C(C)S2\"], [\"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\", \"[N]=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"O=[As]O[As]=O\",\"smile2\": \"O=[As]O[As]=O\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}, {\"smile1\": \"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\",\"smile2\": \"[N]=O\",\"interact_result\": \"#drug3 may increase the hypotensive activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'O=[As]O[As]=O' may increase the QTc-prolonging activities of 'CN1CCN(CC1)C1=NC2=CC=CC=C2NC2=C1C=C(C)S2'. 'CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O' may increase the hypotensive activities of '[N]=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse reactions could stem from CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O and CCCN(CC)C(CC)C(O)=NC1=C(C)C=CC=C1C, CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1 and CC(CN(C)C)CN1C2=CC=CC=C2SC2=C1C=C(C=C2)C#N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O\", \"CCCN(CC)C(CC)C(O)=NC1=C(C)C=CC=C1C\"], [\"CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1\", \"CC(CN(C)C)CN1C2=CC=CC=C2SC2=C1C=C(C=C2)C#N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O\",\"smile2\": \"CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1\",\"smile2\": \"CC(CN(C)C)CN1C2=CC=CC=C2SC2=C1C=C(C=C2)C#N\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CCCC(C)C1(CC=C)C(=O)NC(=S)NC1=O' is combined with 'CCCN(CC)C(CC)C(O)=NC1=C(C)C=CC=C1C'. The risk or severity of adverse effects can be increased when 'CCN(CC)C(=O)[C@@]1(C[C@@H]1CN)C1=CC=CC=C1' is combined with 'CC(CN(C)C)CN1C2=CC=CC=C2SC2=C1C=C(C=C2)C#N'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect side effects from CC1=NOC(NS(=O)(=O)C2=CC=C(N)C=C2)=C1C and COCCCCC(=NOCCN)C1=CC=C(C=C1)C(F)(F)F to manifest, CN(C)CCOC(C1=CC=C(Cl)C=C1)C1=CC=CC=N1 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=NOC(NS(=O)(=O)C2=CC=C(N)C=C2)=C1C\", \"COCCCCC(=NOCCN)C1=CC=C(C=C1)C(F)(F)F\"], [\"CN(C)CCOC(C1=CC=C(Cl)C=C1)C1=CC=CC=N1\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=NOC(NS(=O)(=O)C2=CC=C(N)C=C2)=C1C\",\"smile2\": \"CC1=NOC(NS(=O)(=O)C2=CC=C(N)C=C2)=C1C\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"CN(C)CCOC(C1=CC=C(Cl)C=C1)C1=CC=CC=N1\",\"smile2\": \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'COCCCCC(=NOCCN)C1=CC=C(C=C1)C(F)(F)F' can be decreased when combined with 'CC1=NOC(NS(=O)(=O)C2=CC=C(N)C=C2)=C1C'. The metabolism of 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC(=CC=C1)N(=O)=O)C(=O)OCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=CC=C1' can be decreased when combined with 'CN(C)CCOC(C1=CC=C(Cl)C=C1)C1=CC=CC=N1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the anticipated reactions and side effects of [H][C@]12C[C@@H](OC(=O)C3=CC(OC)=C(OC)C(OC)=C3)[C@H](OC)[C@@H](C(=O)OC)[C@@]1([H])C[C@@]1([H])N(CCC3=C1NC1=C3C=CC(OC)=C1)C2 and CCC(=O)NCC[C@@H]1CCC2=C1C1=C(OCC1)C=C2, CN(CCCCCCCCCCN(C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C and CC(C)(C)NCC(O)COC1=CC=CC2=C1CCC(=O)N2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]12C[C@@H](OC(=O)C3=CC(OC)=C(OC)C(OC)=C3)[C@H](OC)[C@@H](C(=O)OC)[C@@]1([H])C[C@@]1([H])N(CCC3=C1NC1=C3C=CC(OC)=C1)C2\", \"CCC(=O)NCC[C@@H]1CCC2=C1C1=C(OCC1)C=C2\"], [\"CN(CCCCCCCCCCN(C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C\", \"CC(C)(C)NCC(O)COC1=CC=CC2=C1CCC(=O)N2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@]12C[C@@H](OC(=O)C3=CC(OC)=C(OC)C(OC)=C3)[C@H](OC)[C@@H](C(=O)OC)[C@@]1([H])C[C@@]1([H])N(CCC3=C1NC1=C3C=CC(OC)=C1)C2\",\"smile2\": \"[H][C@]12C[C@@H](OC(=O)C3=CC(OC)=C(OC)C(OC)=C3)[C@H](OC)[C@@H](C(=O)OC)[C@@]1([H])C[C@@]1([H])N(CCC3=C1NC1=C3C=CC(OC)=C1)C2\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CN(CCCCCCCCCCN(C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C\",\"smile2\": \"CC(C)(C)NCC(O)COC1=CC=CC2=C1CCC(=O)N2\",\"interact_result\": \"#drug3 may increase the bradycardic activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when '[H][C@]12C[C@@H](OC(=O)C3=CC(OC)=C(OC)C(OC)=C3)[C@H](OC)[C@@H](C(=O)OC)[C@@]1([H])C[C@@]1([H])N(CCC3=C1NC1=C3C=CC(OC)=C1)C2' is combined with 'CCC(=O)NCC[C@@H]1CCC2=C1C1=C(OCC1)C=C2'. 'CN(CCCCCCCCCCN(C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C)C(=O)OC1=CC=CC(=C1)[N+](C)(C)C' may increase the bradycardic activities of 'CC(C)(C)NCC(O)COC1=CC=CC2=C1CCC(=O)N2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please comment on the potential side effects of ClC1=C(CN2CCCC2=N)NC(=O)NC1=O and [H][C@@]12CC[C@@](O)(C#C)[C@@]1(CC)C=CC1=C3CCC(=O)C=C3CC[C@@]21[H], CN(C)CC\\C=C1\\C2=CC=CC=C2COC2=C1C=C(CC(O)=O)C=C2 and OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClC1=C(CN2CCCC2=N)NC(=O)NC1=O\", \"[H][C@@]12CC[C@@](O)(C#C)[C@@]1(CC)C=CC1=C3CCC(=O)C=C3CC[C@@]21[H]\"], [\"CN(C)CC\\C=C1\\C2=CC=CC=C2COC2=C1C=C(CC(O)=O)C=C2\", \"OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClC1=C(CN2CCCC2=N)NC(=O)NC1=O\",\"smile2\": \"ClC1=C(CN2CCCC2=N)NC(=O)NC1=O\",\"interact_result\": \"the therapeutic efficacy of #drug2 can be decreased when used in combination with #drug1.\"}, {\"smile1\": \"CN(C)CC\\C=C1\\C2=CC=CC=C2COC2=C1C=C(CC(O)=O)C=C2\",\"smile2\": \"OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The therapeutic efficacy of '[H][C@@]12CC[C@@](O)(C#C)[C@@]1(CC)C=CC1=C3CCC(=O)C=C3CC[C@@]21[H]' can be decreased when used in combination with 'ClC1=C(CN2CCCC2=N)NC(=O)NC1=O'. The risk or severity of adverse effects can be increased when 'CN(C)CC\\C=C1\\C2=CC=CC=C2COC2=C1C=C(CC(O)=O)C=C2' is combined with 'OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects could potentially arise from OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1 and CN(C)CCC=C1C2=CC=CC=C2CCC2=CC=CC=C12 interactions, CC(C)C1=CC=CC(C(C)C)=C1OCOP(O)(O)=O and CN(C)CCOC1=CC2=CC=CC=C2SC2=CC=C(Cl)C=C12. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\", \"CN(C)CCC=C1C2=CC=CC=C2CCC2=CC=CC=C12\"], [\"CC(C)C1=CC=CC(C(C)C)=C1OCOP(O)(O)=O\", \"CN(C)CCOC1=CC2=CC=CC=C2SC2=CC=C(Cl)C=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\",\"smile2\": \"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\",\"interact_result\": \"the metabolism of #drug2 can be increased when combined with #drug1.\"}, {\"smile1\": \"CC(C)C1=CC=CC(C(C)C)=C1OCOP(O)(O)=O\",\"smile2\": \"CN(C)CCOC1=CC2=CC=CC=C2SC2=CC=C(Cl)C=C12\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'CN(C)CCC=C1C2=CC=CC=C2CCC2=CC=CC=C12' can be increased when combined with 'OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1'. The risk or severity of adverse effects can be increased when 'CC(C)C1=CC=CC(C(C)C)=C1OCOP(O)(O)=O' is combined with 'CN(C)CCOC1=CC2=CC=CC=C2SC2=CC=C(Cl)C=C12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant might the side effects be for OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2 and CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O, CN1CCN(CC1)C1=NC2=CC=CC=C2OC2=C1C=C(Cl)C=C2 and [H][C@@]12CCCC[C@@]11CCN(C)[C@@H]2CC2=C1C=C(O)C=C2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2\", \"CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O\"], [\"CN1CCN(CC1)C1=NC2=CC=CC=C2OC2=C1C=C(Cl)C=C2\", \"[H][C@@]12CCCC[C@@]11CCN(C)[C@@H]2CC2=C1C=C(O)C=C2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2\",\"smile2\": \"OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}, {\"smile1\": \"CN1CCN(CC1)C1=NC2=CC=CC=C2OC2=C1C=C(Cl)C=C2\",\"smile2\": \"[H][C@@]12CCCC[C@@]11CCN(C)[C@@H]2CC2=C1C=C(O)C=C2\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The metabolism of 'CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)C(=O)[C@H](C)[C@@H](O)[C@]1(C)O' can be decreased when combined with 'OC1=CC=C(C=C1)C1=C(C(=O)C2=CC=C(OCCN3CCCCC3)C=C2)C2=C(S1)C=C(O)C=C2'. The risk or severity of adverse effects can be increased when 'CN1CCN(CC1)C1=NC2=CC=CC=C2OC2=C1C=C(Cl)C=C2' is combined with '[H][C@@]12CCCC[C@@]11CCN(C)[C@@H]2CC2=C1C=C(O)C=C2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the side effects of CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F and CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl, [Ca++].[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O.[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O and [H][C@]12CN3C=C(C(=O)NCC4=CC=C(F)C=C4F)C(=O)C(O)=C3C(=O)N1[C@H](C)CCO2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F\", \"CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl\"], [\"[Ca++].[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O.[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O\", \"[H][C@]12CN3C=C(C(=O)NCC4=CC=C(F)C=C4F)C(=O)C(O)=C3C(=O)N1[C@H](C)CCO2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F\",\"smile2\": \"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"[Ca++].[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O.[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O\",\"smile2\": \"[H][C@]12CN3C=C(C(=O)NCC4=CC=C(F)C=C4F)C(=O)C(O)=C3C(=O)N1[C@H](C)CCO2\",\"interact_result\": \"the serum concentration of #drug4 can be decreased when it is combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(F)(F)F' is combined with 'CN1C2=C(C=C(Cl)C=C2)C(=NC(O)C1=O)C1=CC=CC=C1Cl'. The serum concentration of '[H][C@]12CN3C=C(C(=O)NCC4=CC=C(F)C=C4F)C(=O)C(O)=C3C(=O)N1[C@H](C)CCO2' can be decreased when it is combined with '[Ca++].[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O.[H][C@@](O)(CO)[C@@]([H])(O)[C@]([H])(O)[C@@]([H])(O)C([O-])=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please predict how the reactions to CCC1=C(CC)C=C2CC(CC2=C1)NC[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1 and CC1=NC(NC2=NC=C(S2)C(=O)NC2=C(C)C=CC=C2Cl)=CC(=N1)N1CCN(CCO)CC1 will lead to side effects, CC(C)OP(F)(=O)OC(C)C and [H][C@@]12CCN(C[C@@H]1C=C)[C@]([H])(C2)[C@@H](O)C1=C2C=C(OC)C=CC2=NC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1=C(CC)C=C2CC(CC2=C1)NC[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1\", \"CC1=NC(NC2=NC=C(S2)C(=O)NC2=C(C)C=CC=C2Cl)=CC(=N1)N1CCN(CCO)CC1\"], [\"CC(C)OP(F)(=O)OC(C)C\", \"[H][C@@]12CCN(C[C@@H]1C=C)[C@]([H])(C2)[C@@H](O)C1=C2C=C(OC)C=CC2=NC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC1=C(CC)C=C2CC(CC2=C1)NC[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1\",\"smile2\": \"CCC1=C(CC)C=C2CC(CC2=C1)NC[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}, {\"smile1\": \"CC(C)OP(F)(=O)OC(C)C\",\"smile2\": \"[H][C@@]12CCN(C[C@@H]1C=C)[C@]([H])(C2)[C@@H](O)C1=C2C=C(OC)C=CC2=NC=C1\",\"interact_result\": \"the therapeutic efficacy of #drug4 can be decreased when used in combination with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The serum concentration of 'CC1=NC(NC2=NC=C(S2)C(=O)NC2=C(C)C=CC=C2Cl)=CC(=N1)N1CCN(CCO)CC1' can be increased when it is combined with 'CCC1=C(CC)C=C2CC(CC2=C1)NC[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1'. The therapeutic efficacy of '[H][C@@]12CCN(C[C@@H]1C=C)[C@]([H])(C2)[C@@H](O)C1=C2C=C(OC)C=CC2=NC=C1' can be decreased when used in combination with 'CC(C)OP(F)(=O)OC(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the possible side effects you expect from C[C@H]1[C@H]2CC3=CC=C(O)C=C3[C@]1(C)CCN2CC=C(C)C and CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O, CN[C@H]1CC[C@@H](C2=CC(Cl)=C(Cl)C=C2)C2=CC=CC=C12 and CCCSC1=NC2=C(N=NN2[C@@H]2C[C@H](OCCO)[C@@H](O)[C@H]2O)C(N[C@@H]2C[C@H]2C2=CC(F)=C(F)C=C2)=N1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C[C@H]1[C@H]2CC3=CC=C(O)C=C3[C@]1(C)CCN2CC=C(C)C\", \"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O\"], [\"CN[C@H]1CC[C@@H](C2=CC(Cl)=C(Cl)C=C2)C2=CC=CC=C12\", \"CCCSC1=NC2=C(N=NN2[C@@H]2C[C@H](OCCO)[C@@H](O)[C@H]2O)C(N[C@@H]2C[C@H]2C2=CC(F)=C(F)C=C2)=N1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C[C@H]1[C@H]2CC3=CC=C(O)C=C3[C@]1(C)CCN2CC=C(C)C\",\"smile2\": \"C[C@H]1[C@H]2CC3=CC=C(O)C=C3[C@]1(C)CCN2CC=C(C)C\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CN[C@H]1CC[C@@H](C2=CC(Cl)=C(Cl)C=C2)C2=CC=CC=C12\",\"smile2\": \"CCCSC1=NC2=C(N=NN2[C@@H]2C[C@H](OCCO)[C@@H](O)[C@H]2O)C(N[C@@H]2C[C@H]2C2=CC(F)=C(F)C=C2)=N1\",\"interact_result\": \"the metabolism of #drug4 can be decreased when combined with #drug3.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'C[C@H]1[C@H]2CC3=CC=C(O)C=C3[C@]1(C)CCN2CC=C(C)C' is combined with 'CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O'. The metabolism of 'CCCSC1=NC2=C(N=NN2[C@@H]2C[C@H](OCCO)[C@@H](O)[C@H]2O)C(N[C@@H]2C[C@H]2C2=CC(F)=C(F)C=C2)=N1' can be decreased when combined with 'CN[C@H]1CC[C@@H](C2=CC(Cl)=C(Cl)C=C2)C2=CC=CC=C12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the side effects of ClC1=CC=CC(=C1)N1CCN(CCCN2N=C3C=CC=CN3C2=O)CC1 and FC(F)(F)C1=CC(=CC=C1)N1CCN(CCN2C(=O)NC3=CC=CC=C23)CC1 to be, CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O and CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClC1=CC=CC(=C1)N1CCN(CCCN2N=C3C=CC=CN3C2=O)CC1\", \"FC(F)(F)C1=CC(=CC=C1)N1CCN(CCN2C(=O)NC3=CC=CC=C23)CC1\"], [\"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\", \"CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClC1=CC=CC(=C1)N1CCN(CCCN2N=C3C=CC=CN3C2=O)CC1\",\"smile2\": \"ClC1=CC=CC(=C1)N1CCN(CCCN2N=C3C=CC=CN3C2=O)CC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O\",\"smile2\": \"CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'ClC1=CC=CC(=C1)N1CCN(CCCN2N=C3C=CC=CN3C2=O)CC1' is combined with 'FC(F)(F)C1=CC(=CC=C1)N1CCN(CCN2C(=O)NC3=CC=CC=C23)CC1'. The risk or severity of adverse effects can be increased when 'CCC#CC(C)C1(CC=C)C(=O)NC(=O)N(C)C1=O' is combined with 'CN(C)CCOC(C1=CC=CC=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your insight on the potential side effects of FC(F)OC(F)(F)C(F)Cl and COC1=CC=CC=C1OCC(O)COC(N)=O, CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12 and CC[C@H](NC(C)C)[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"FC(F)OC(F)(F)C(F)Cl\", \"COC1=CC=CC=C1OCC(O)COC(N)=O\"], [\"CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12\", \"CC[C@H](NC(C)C)[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"FC(F)OC(F)(F)C(F)Cl\",\"smile2\": \"FC(F)OC(F)(F)C(F)Cl\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}, {\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12\",\"smile2\": \"CC[C@H](NC(C)C)[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug3 is combined with #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The risk or severity of adverse effects can be increased when 'FC(F)OC(F)(F)C(F)Cl' is combined with 'COC1=CC=CC=C1OCC(O)COC(N)=O'. The risk or severity of adverse effects can be increased when 'CN(C)CCCN1C2=CC=CC=C2CCC2=CC=CC=C12' is combined with 'CC[C@H](NC(C)C)[C@H](O)C1=C2C=CC(=O)NC2=C(O)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that CCCCC1C(=O)N(N(C1=O)C1=CC=C(O)C=C1)C1=CC=CC=C1 and CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1 will cause side effects, CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O and O=[As]O[As]=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1C(=O)N(N(C1=O)C1=CC=C(O)C=C1)C1=CC=CC=C1\", \"CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1\"], [\"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\", \"O=[As]O[As]=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC1C(=O)N(N(C1=O)C1=CC=C(O)C=C1)C1=CC=CC=C1\",\"smile2\": \"CCCCC1C(=O)N(N(C1=O)C1=CC=C(O)C=C1)C1=CC=CC=C1\",\"interact_result\": \"#drug1 may increase the neuroexcitatory activities of #drug2.\"}, {\"smile1\": \"CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O\",\"smile2\": \"O=[As]O[As]=O\",\"interact_result\": \"#drug3 may increase the hypotensive activities of #drug4.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CCCCC1C(=O)N(N(C1=O)C1=CC=C(O)C=C1)C1=CC=CC=C1' may increase the neuroexcitatory activities of 'CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1'. 'CCCC(C)C1(CC=C)C(=O)NC(=O)NC1=O' may increase the hypotensive activities of 'O=[As]O[As]=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you predict could occur with CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C and C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\", \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\",\"smile2\": \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\",\"interact_result\": \"edema\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C' and 'C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]' may cause edema when interacting."
            },
            {
                "from": "human",
                "value": "And how significant are the side effects anticipated from mentioned above and 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\", \"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\",\"smile2\": \"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O\",\"interact_result\": \"cerumen impaction\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C' and 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)OC)C)C)O)(C)O' may cause cerumen impaction when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might the effects of C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O and CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C manifest in side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\", \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\",\"smile2\": \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\",\"interact_result\": \"basal cell carcinoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' and 'CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C' may cause basal cell carcinoma when interacting."
            },
            {
                "from": "human",
                "value": "And what is the likelihood of side effects occurring from mentioned above and 'C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\", \"C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\",\"smile2\": \"C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O\",\"interact_result\": \"malignant melanoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' and 'C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O' may cause malignant melanoma when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you believe could result from C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O and CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O\", \"CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O\",\"smile2\": \"CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2\",\"interact_result\": \"vitamin B 12 deficiency\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O' and 'CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2' may cause vitamin B 12 deficiency when interacting."
            },
            {
                "from": "human",
                "value": "And how do you foresee the side effects of mentioned above and 'C1=CC=C(C(=C1)CC(=O)[O-])NC2=C(C=CC=C2Cl)Cl' impacting patients. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O\", \"C1=CC=C(C(=C1)CC(=O)[O-])NC2=C(C=CC=C2Cl)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O\",\"smile2\": \"C1=CC=C(C(=C1)CC(=O)[O-])NC2=C(C=CC=C2Cl)Cl\",\"interact_result\": \"hepatitis B\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C2C(=C1)C(=O)NC2(C3=CC(=C(C=C3)Cl)S(=O)(=O)N)O' and 'C1=CC=C(C(=C1)CC(=O)[O-])NC2=C(C=CC=C2Cl)Cl' may cause hepatitis B when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant might the side effects be for CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C and CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C\", \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C\",\"smile2\": \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\",\"interact_result\": \"antinuclear antibody positive\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C' and 'CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F' may cause antinuclear antibody positive when interacting."
            },
            {
                "from": "human",
                "value": "And please describe the predicted side effects from mentioned above and 'CN1CCN2C(C1)C3=CC=CC=C3CC4=C2N=CC=C4'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C\", \"CN1CCN2C(C1)C3=CC=CC=C3CC4=C2N=CC=C4\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C\",\"smile2\": \"CN1CCN2C(C1)C3=CC=CC=C3CC4=C2N=CC=C4\",\"interact_result\": \"basal cell carcinoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCC1OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C' and 'CN1CCN2C(C1)C3=CC=CC=C3CC4=C2N=CC=C4' may cause basal cell carcinoma when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please describe the predicted side effects from CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3 and C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3\", \"C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3\",\"smile2\": \"C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O\",\"interact_result\": \"drowsiness\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3' and 'C1=CC=NC(=C1)NS(=O)(=O)C2=CC=C(C=C2)NN=C3C=CC(=O)C(=C3)C(=O)O' may cause drowsiness when interacting."
            },
            {
                "from": "human",
                "value": "And what do you think about the side effects of mentioned above and 'CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3\", \"CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3\",\"smile2\": \"CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O\",\"interact_result\": \"patent ductus arteriosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CS(=O)(=O)C1=CC=C(C=C1)C2=C(C(=O)OC2)C3=CC=CC=C3' and 'CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O' may cause patent ductus arteriosis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood of experiencing side effects from CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O and C1=CC2=C(C(=C1)OCC(COC3=CC4=C(C=C3)OC(=CC4=O)C(=O)O)O)C(=O)C=C(O2)C(=O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\", \"C1=CC2=C(C(=C1)OCC(COC3=CC4=C(C=C3)OC(=CC4=O)C(=O)O)O)C(=O)C=C(O2)C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\",\"smile2\": \"C1=CC2=C(C(=C1)OCC(COC3=CC4=C(C=C3)OC(=CC4=O)C(=O)O)O)C(=O)C=C(O2)C(=O)O\",\"interact_result\": \"Fatigue\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O' and 'C1=CC2=C(C(=C1)OCC(COC3=CC4=C(C=C3)OC(=CC4=O)C(=O)O)O)C(=O)C=C(O2)C(=O)O' may cause Fatigue when interacting."
            },
            {
                "from": "human",
                "value": "And what side effects might occur with mentioned above and 'CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CC3=CC=CC=C3CC2C(=O)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\", \"CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CC3=CC=CC=C3CC2C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O\",\"smile2\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CC3=CC=CC=C3CC2C(=O)O\",\"interact_result\": \"chronic obstructive airway disease\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCC(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC(CC(CC(=O)O)O)O)O' and 'CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CC3=CC=CC=C3CC2C(=O)O' may cause chronic obstructive airway disease when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects might occur with CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31 and COC1=CC=CC=C1OCC(COC(=O)N)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31\", \"COC1=CC=CC=C1OCC(COC(=O)N)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31\",\"smile2\": \"COC1=CC=CC=C1OCC(COC(=O)N)O\",\"interact_result\": \"cluster headache\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31' and 'COC1=CC=CC=C1OCC(COC(=O)N)O' may cause cluster headache when interacting."
            },
            {
                "from": "human",
                "value": "And what reactions are anticipated from mentioned above and 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' that might lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31\", \"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31\",\"smile2\": \"C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl\",\"interact_result\": \"hordeolum\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCC=C1C2=CC=CC=C2COC3=CC=CC=C31' and 'C1=CC=C(C(=C1)C2=NC(C(=O)NC3=C2C=C(C=C3)Cl)O)Cl' may cause hordeolum when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the predicted side effects you foresee from CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31 and COC(=O)C(C1CCCCN1)C2=CC=CC=C2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31\", \"COC(=O)C(C1CCCCN1)C2=CC=CC=C2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31\",\"smile2\": \"COC(=O)C(C1CCCCN1)C2=CC=CC=C2\",\"interact_result\": \"distended bladder\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31' and 'COC(=O)C(C1CCCCN1)C2=CC=CC=C2' may cause distended bladder when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'C1CN(CCN1CCOCCO)C2=NC3=CC=CC=C3SC4=CC=CC=C42' lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31\", \"C1CN(CCN1CCOCCO)C2=NC3=CC=CC=C3SC4=CC=CC=C42\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31\",\"smile2\": \"C1CN(CCN1CCOCCO)C2=NC3=CC=CC=C3SC4=CC=CC=C42\",\"interact_result\": \"mediastinal disorder\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCN1C2=CC=CC=C2CCC3=CC=CC=C31' and 'C1CN(CCN1CCOCCO)C2=NC3=CC=CC=C3SC4=CC=CC=C42' may cause mediastinal disorder when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects do you expect from C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3 and CC(C)NCC(COC1=CC=C(C=C1)CCOC)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3\", \"CC(C)NCC(COC1=CC=C(C=C1)CCOC)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3\",\"smile2\": \"CC(C)NCC(COC1=CC=C(C=C1)CCOC)O\",\"interact_result\": \"hyperparathyroidism\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3' and 'CC(C)NCC(COC1=CC=C(C=C1)CCOC)O' may cause hyperparathyroidism when interacting."
            },
            {
                "from": "human",
                "value": "And what side effects do you believe could result from mentioned above and 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3\", \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3\",\"smile2\": \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\",\"interact_result\": \"blepharoptosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC(=CC=C1C#N)C(C2=CC=C(C=C2)C#N)N3C=NC=N3' and 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' may cause blepharoptosis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant are the side effects anticipated from C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N and CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\", \"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\",\"smile2\": \"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\",\"interact_result\": \"myoglobinuria\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N' and 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F' may cause myoglobinuria when interacting."
            },
            {
                "from": "human",
                "value": "And what are the predicted side effects of mentioned above and 'C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\", \"C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N\",\"smile2\": \"C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl\",\"interact_result\": \"hypernatraemia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C2C(=C1)C=CC3=CC=CC=C3N2C(=O)N' and 'C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl' may cause hypernatraemia when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C and CCC1C=C(CC(CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC1=O)O)C)C(=CC4CCC(C(C4)OC)Cl)C)O)C)OC)OC)C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C\", \"CCC1C=C(CC(CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC1=O)O)C)C(=CC4CCC(C(C4)OC)Cl)C)O)C)OC)OC)C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C\",\"smile2\": \"CCC1C=C(CC(CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC1=O)O)C)C(=CC4CCC(C(C4)OC)Cl)C)O)C)OC)OC)C)C\",\"interact_result\": \"cellulitis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C' and 'CCC1C=C(CC(CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC1=O)O)C)C(=CC4CCC(C(C4)OC)Cl)C)O)C)OC)OC)C)C' may cause cellulitis when interacting."
            },
            {
                "from": "human",
                "value": "And what's your prediction on the side effects of mentioned above and 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C' interactions. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C\", \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C\",\"smile2\": \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C\",\"interact_result\": \"Pharyngitis Streptococcal\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1(OC2CC3C4CCC5=CC(=O)C=CC5(C4C(CC3(C2(O1)C(=O)CO)C)O)C)C' and 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C' may cause Pharyngitis Streptococcal when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the possible adverse reactions of CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O and C1CN(CCN1CCCN2C(=O)N3C=CC=CC3=N2)C4=CC(=CC=C4)Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\", \"C1CN(CCN1CCCN2C(=O)N3C=CC=CC3=N2)C4=CC(=CC=C4)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\",\"smile2\": \"C1CN(CCN1CCCN2C(=O)N3C=CC=CC3=N2)C4=CC(=CC=C4)Cl\",\"interact_result\": \"blood in urine\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O' and 'C1CN(CCN1CCCN2C(=O)N3C=CC=CC3=N2)C4=CC(=CC=C4)Cl' may cause blood in urine when interacting."
            },
            {
                "from": "human",
                "value": "And what's your forecast regarding the side effects of mentioned above and 'C1C2CNCC1C3=CC4=NC=CN=C4C=C23' reactions. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\", \"C1C2CNCC1C3=CC4=NC=CN=C4C=C23\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\",\"smile2\": \"C1C2CNCC1C3=CC4=NC=CN=C4C=C23\",\"interact_result\": \"abscess\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O' and 'C1C2CNCC1C3=CC4=NC=CN=C4C=C23' may cause abscess when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please evaluate the side effects associated with C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O and CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O\", \"CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O\",\"smile2\": \"CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2\",\"interact_result\": \"CMV infection\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O' and 'CC1=CC(=C(C=C1)O)C(CCN(C(C)C)C(C)C)C2=CC=CC=C2' may cause CMV infection when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'CC(C)C1=NC(=NC(=C1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)N(C)S(=O)(=O)C' reactions influence overall side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O\", \"CC(C)C1=NC(=NC(=C1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)N(C)S(=O)(=O)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O\",\"smile2\": \"CC(C)C1=NC(=NC(=C1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)N(C)S(=O)(=O)C\",\"interact_result\": \"histoplasmosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1CCN(CC1)CCOC2=CC=C(C=C2)C(=O)C3=C(SC4=C3C=CC(=C4)O)C5=CC=C(C=C5)O' and 'CC(C)C1=NC(=NC(=C1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)N(C)S(=O)(=O)C' may cause histoplasmosis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast on the side effects resulting from COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC and CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\", \"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\",\"smile2\": \"CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O\",\"interact_result\": \"lower GI bleeding\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC' and 'CC(C)NCC(COC1=CC=CC2=CC=CC=C21)O' may cause lower GI bleeding when interacting."
            },
            {
                "from": "human",
                "value": "And please outline the side effects expected from mentioned above and 'CN(C)CCC1=CNC2=C1C=C(C=C2)CN3C=NC=N3'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\", \"CN(C)CCC1=CNC2=C1C=C(C=C2)CN3C=NC=N3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\",\"smile2\": \"CN(C)CCC1=CNC2=C1C=C(C=C2)CN3C=NC=N3\",\"interact_result\": \"gastrointestinal bleed\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC' and 'CN(C)CCC1=CNC2=C1C=C(C=C2)CN3C=NC=N3' may cause gastrointestinal bleed when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How likely is it that CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC and C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O will cause side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\", \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\",\"smile2\": \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\",\"interact_result\": \"emphysema\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC' and 'C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O' may cause emphysema when interacting."
            },
            {
                "from": "human",
                "value": "And what's your forecast on the side effects resulting from mentioned above and 'CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\", \"CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\",\"smile2\": \"CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O\",\"interact_result\": \"blood in urine\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC' and 'CC1C=CC=CCCC=CC=CC=CC=CC(CC(C(C(CC(=O)CC(C(CCC(CC(CC(CC(=O)OC(C(C1O)C)C)O)O)O)O)O)O)C(=O)O)O)OC2C(C(C(C(O2)C)O)N)O' may cause blood in urine when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your view on the side effects related to CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl and CC1=C(C(=O)N2CCCCC2=N1)CCN3CCC(CC3)C4=NOC5=C4C=CC(=C5)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl\", \"CC1=C(C(=O)N2CCCCC2=N1)CCN3CCC(CC3)C4=NOC5=C4C=CC(=C5)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl\",\"smile2\": \"CC1=C(C(=O)N2CCCCC2=N1)CCN3CCC(CC3)C4=NOC5=C4C=CC(=C5)F\",\"interact_result\": \"haemolytic anaemia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl' and 'CC1=C(C(=O)N2CCCCC2=N1)CCN3CCC(CC3)C4=NOC5=C4C=CC(=C5)F' may cause haemolytic anaemia when interacting."
            },
            {
                "from": "human",
                "value": "And what reactions are anticipated from mentioned above and 'CN1CCN(CC1)C(=O)OC2C3=NC=CN=C3C(=O)N2C4=NC=C(C=C4)Cl' that might lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl\", \"CN1CCN(CC1)C(=O)OC2C3=NC=CN=C3C(=O)N2C4=NC=C(C=C4)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl\",\"smile2\": \"CN1CCN(CC1)C(=O)OC2C3=NC=CN=C3C(=O)N2C4=NC=C(C=C4)Cl\",\"interact_result\": \"Clotting\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCN1C2=CC=CC=C2SC3=C1C=C(C=C3)Cl' and 'CN1CCN(CC1)C(=O)OC2C3=NC=CN=C3C(=O)N2C4=NC=C(C=C4)Cl' may cause Clotting when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the side effects of CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl and COC1=CC=CC=C1OCC(COC(=O)N)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"COC1=CC=CC=C1OCC(COC(=O)N)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\",\"smile2\": \"COC1=CC=CC=C1OCC(COC(=O)N)O\",\"interact_result\": \"Apnea\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'COC1=CC=CC=C1OCC(COC(=O)N)O' may cause Apnea when interacting."
            },
            {
                "from": "human",
                "value": "And what adverse effects do you expect from mentioned above and 'CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\",\"smile2\": \"CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O\",\"interact_result\": \"malignant hypertension\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O' may cause malignant hypertension when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think the reactions of CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F and CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F will affect side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\", \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\",\"smile2\": \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\",\"interact_result\": \"coughing blood\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F' and 'CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F' may cause coughing blood when interacting."
            },
            {
                "from": "human",
                "value": "And how do you project the side effects of mentioned above and 'CC(C)(C)NCC(COC1=NSN=C1N2CCOCC2)O' to be. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\", \"CC(C)(C)NCC(COC1=NSN=C1N2CCOCC2)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F\",\"smile2\": \"CC(C)(C)NCC(COC1=NSN=C1N2CCOCC2)O\",\"interact_result\": \"Ejaculation Premature\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=C(C=CN=C1CS(=O)C2=NC3=CC=CC=C3N2)OCC(F)(F)F' and 'CC(C)(C)NCC(COC1=NSN=C1N2CCOCC2)O' may cause Ejaculation Premature when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the anticipated reactions and side effects of CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C and CNC(=O)CN(CCN(CCN(CC(=O)NC)CC(=O)[O-])CC(=O)[O-])CC(=O)[O-].O.[Gd+3]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\", \"CNC(=O)CN(CCN(CCN(CC(=O)NC)CC(=O)[O-])CC(=O)[O-])CC(=O)[O-].O.[Gd+3]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\",\"smile2\": \"CNC(=O)CN(CCN(CCN(CC(=O)NC)CC(=O)[O-])CC(=O)[O-])CC(=O)[O-].O.[Gd+3]\",\"interact_result\": \"esophageal cancer\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C' and 'CNC(=O)CN(CCN(CCN(CC(=O)NC)CC(=O)[O-])CC(=O)[O-])CC(=O)[O-].O.[Gd+3]' may cause esophageal cancer when interacting."
            },
            {
                "from": "human",
                "value": "And how do you foresee the reactions of mentioned above and 'C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O' influencing side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\", \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C\",\"smile2\": \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\",\"interact_result\": \"Deafness neurosensory\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=CC=C(C=C1)C2=C(N3C=C(C=CC3=N2)C)CC(=O)N(C)C' and 'C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O' may cause Deafness neurosensory when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What reactions from C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O and CN1C2=C(C=C(C=C2)Cl)C(=NC(C1=O)O)C3=CC=CC=C3 might lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O\", \"CN1C2=C(C=C(C=C2)Cl)C(=NC(C1=O)O)C3=CC=CC=C3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O\",\"smile2\": \"CN1C2=C(C=C(C=C2)Cl)C(=NC(C1=O)O)C3=CC=CC=C3\",\"interact_result\": \"candidiasis of mouth\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O' and 'CN1C2=C(C=C(C=C2)Cl)C(=NC(C1=O)O)C3=CC=CC=C3' may cause candidiasis of mouth when interacting."
            },
            {
                "from": "human",
                "value": "And what's your view on the side effects related to mentioned above and 'C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O\", \"C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O\",\"smile2\": \"C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3\",\"interact_result\": \"dysmenorrhea\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1C(N(C2=C(N1)NC(=NC2=O)N)C=O)CNC3=CC=C(C=C3)C(=O)NC(CCC(=O)O)C(=O)O' and 'C1=CC=C(C=C1)C2(C(=O)NC(=O)N2)C3=CC=CC=C3' may cause dysmenorrhea when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please evaluate the side effects associated with CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F and C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\",\"interact_result\": \"atrophy of skin\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]' may cause atrophy of skin when interacting."
            },
            {
                "from": "human",
                "value": "And how do you expect side effects from mentioned above and 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' to manifest. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\",\"interact_result\": \"sarcoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' may cause sarcoma when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the predicted side effects of CN1C2=C(C(=O)N(C1=O)C)NC=N2 and CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C2=C(C(=O)N(C1=O)C)NC=N2\", \"CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C2=C(C(=O)N(C1=O)C)NC=N2\",\"smile2\": \"CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C\",\"interact_result\": \"calculus ureteric\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C2=C(C(=O)N(C1=O)C)NC=N2' and 'CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C' may cause calculus ureteric when interacting."
            },
            {
                "from": "human",
                "value": "And how might the side effects vary between mentioned above and 'C1NC2=CC(=C(C=C2S(=O)(=O)N1)S(=O)(=O)N)Cl'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C2=C(C(=O)N(C1=O)C)NC=N2\", \"C1NC2=CC(=C(C=C2S(=O)(=O)N1)S(=O)(=O)N)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C2=C(C(=O)N(C1=O)C)NC=N2\",\"smile2\": \"C1NC2=CC(=C(C=C2S(=O)(=O)N1)S(=O)(=O)N)Cl\",\"interact_result\": \"Back Ache\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C2=C(C(=O)N(C1=O)C)NC=N2' and 'C1NC2=CC(=C(C=C2S(=O)(=O)N1)S(=O)(=O)N)Cl' may cause Back Ache when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think the reactions of CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-] and CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC will affect side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]\", \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]\",\"smile2\": \"CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC\",\"interact_result\": \"Cutaneous mycosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]' and 'CC1=CN=C(C(=C1OC)C)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC' may cause Cutaneous mycosis when interacting."
            },
            {
                "from": "human",
                "value": "And what's your insight into the potential side effects of mentioned above and 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]\", \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]\",\"smile2\": \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\",\"interact_result\": \"acute kidney failure\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C(=NC(=O)C(=N1)[O-])SCC2=C(N3C(C(C3=O)NC(=O)C(=NOC)C4=CSC(=N4)N)SC2)C(=O)[O-]' and 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C' may cause acute kidney failure when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant do you think the side effects from CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C and CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3 will be. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C\", \"CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C\",\"smile2\": \"CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3\",\"interact_result\": \"pharyngeal inflammation\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C' and 'CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3' may cause pharyngeal inflammation when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' affect the patient in terms of side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C\", \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C\",\"smile2\": \"C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O\",\"interact_result\": \"alcohol consumption\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCC1C(=O)N(CC(=O)N(C(C(=O)NC(C(=O)N(C(C(=O)NC(C(=O)NC(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N(C(C(=O)N1)C(C(C)CC=CC)O)C)C(C)C)C)CC(C)C)C)CC(C)C)C)C)C)CC(C)C)C)C(C)C)CC(C)C)C)C' and 'C1=CC(=C(C=C1F)F)C(CN2C=NC=N2)(CN3C=NC=N3)O' may cause alcohol consumption when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects could arise from the use of CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C and CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\", \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\",\"smile2\": \"CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F\",\"interact_result\": \"Lyme Disease\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C' and 'CC1CC2C3CC(C4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)SCF)O)C)O)F)C)F' may cause Lyme Disease when interacting."
            },
            {
                "from": "human",
                "value": "And please predict the adverse reactions of mentioned above and 'CC(C)CC(CC(=O)O)CN'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\", \"CC(C)CC(CC(=O)O)CN\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C\",\"smile2\": \"CC(C)CC(CC(=O)O)CN\",\"interact_result\": \"ankle fracture\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CO)O)C)O)F)C' and 'CC(C)CC(CC(=O)O)CN' may cause ankle fracture when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the side effects we might see with CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O and C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O\",\"interact_result\": \"scotoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'C(C1C(C(C(C(O1)OC(C(CO)O)C(C(=O)CO)O)O)O)O)O' may cause scotoma when interacting."
            },
            {
                "from": "human",
                "value": "And what reactions are anticipated from mentioned above and 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)O)C)C)O)(C)O' that might lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)O)C)C)O)(C)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)O)C)C)O)(C)O\",\"interact_result\": \"birth defect\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'CCC1C(C(C(C(=O)C(CC(C(C(C(C(C(=O)O1)C)OC2CC(C(C(O2)C)O)(C)OC)C)OC3C(C(CC(O3)C)N(C)C)O)(C)O)C)C)O)(C)O' may cause birth defect when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your insight into the potential side effects of CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6 and CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\", \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\",\"smile2\": \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\",\"interact_result\": \"Strabismus\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6' and 'CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C' may cause Strabismus when interacting."
            },
            {
                "from": "human",
                "value": "And how significant might the side effects be for mentioned above and 'CC(C)CC(C(=O)NC(CCCN=C(N)N)C(=O)N1CCCC1C(=O)NNC(=O)N)NC(=O)C(COC(C)(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\", \"CC(C)CC(C(=O)NC(CCCN=C(N)N)C(=O)N1CCCC1C(=O)NNC(=O)N)NC(=O)C(COC(C)(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\",\"smile2\": \"CC(C)CC(C(=O)NC(CCCN=C(N)N)C(=O)N1CCCC1C(=O)NNC(=O)N)NC(=O)C(COC(C)(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6\",\"interact_result\": \"actinic keratosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCNC(=O)C1CCCN1C(=O)C(CCCN=C(N)N)NC(=O)C(CC(C)C)NC(=O)C(CC(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6' and 'CC(C)CC(C(=O)NC(CCCN=C(N)N)C(=O)N1CCCC1C(=O)NNC(=O)N)NC(=O)C(COC(C)(C)C)NC(=O)C(CC2=CC=C(C=C2)O)NC(=O)C(CO)NC(=O)C(CC3=CNC4=CC=CC=C43)NC(=O)C(CC5=CN=CN5)NC(=O)C6CCC(=O)N6' may cause actinic keratosis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant do you think the side effects from CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C and CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C will be. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C\", \"CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C\",\"smile2\": \"CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C\",\"interact_result\": \"atrioventricular block\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C' and 'CNC(=C[N+](=O)[O-])NCCSCC1=CC=C(O1)CN(C)C' may cause atrioventricular block when interacting."
            },
            {
                "from": "human",
                "value": "And how likely is it that mentioned above and 'CCCNC1CCC2=C(C1)SC(=N2)N' will cause side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C\", \"CCCNC1CCC2=C(C1)SC(=N2)N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C\",\"smile2\": \"CCCNC1CCC2=C(C1)SC(=N2)N\",\"interact_result\": \"insulin dependent diabetes mellitus\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C(=O)C1=CC(=CC=C1)Cl)NC(C)(C)C' and 'CCCNC1CCC2=C(C1)SC(=N2)N' may cause insulin dependent diabetes mellitus when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential adverse reactions do you foresee for C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N and COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N\", \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N\",\"smile2\": \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\",\"interact_result\": \"rhabdomyolysis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N' and 'COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3' may cause rhabdomyolysis when interacting."
            },
            {
                "from": "human",
                "value": "And what side effects do you predict could occur with mentioned above and 'CCC1=C(CN(C1=O)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCC(CC3)C)C'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N\", \"CCC1=C(CN(C1=O)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCC(CC3)C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N\",\"smile2\": \"CCC1=C(CN(C1=O)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCC(CC3)C)C\",\"interact_result\": \"obesity\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=C(C=C(C(=C1I)OC2=CC(=C(C(=C2)I)O)I)I)CC(C(=O)O)N' and 'CCC1=C(CN(C1=O)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCC(CC3)C)C' may cause obesity when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects do you expect from CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O and CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CCCC2C(=O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\", \"CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CCCC2C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\",\"smile2\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CCCC2C(=O)O\",\"interact_result\": \"adenocarcinoma\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O' and 'CCOC(=O)C(CCC1=CC=CC=C1)NC(C)C(=O)N2CCCC2C(=O)O' may cause adenocarcinoma when interacting."
            },
            {
                "from": "human",
                "value": "And how might the use of mentioned above and 'C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl' lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\", \"C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\",\"smile2\": \"C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl\",\"interact_result\": \"Bone Fracture Spontaneous\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O' and 'C1=COC(=C1)CNC2=CC(=C(C=C2C(=O)O)S(=O)(=O)N)Cl' may cause Bone Fracture Spontaneous when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How will CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F and CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3 potentially influence side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3\",\"interact_result\": \"Pharyngitis Streptococcal\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'CCC(=O)N(C1CCN(CC1)CCC2=CC=CC=C2)C3=CC=CC=C3' may cause Pharyngitis Streptococcal when interacting."
            },
            {
                "from": "human",
                "value": "And what adverse effects do you expect from mentioned above and 'CN1C(=C(NC2=CC=CC=N2)O)C(=O)C3=CC=CC=C3S1(=O)=O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"CN1C(=C(NC2=CC=CC=N2)O)C(=O)C3=CC=CC=C3S1(=O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"CN1C(=C(NC2=CC=CC=N2)O)C(=O)C3=CC=CC=C3S1(=O)=O\",\"interact_result\": \"Scoliosis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'CN1C(=C(NC2=CC=CC=N2)O)C(=O)C3=CC=CC=C3S1(=O)=O' may cause Scoliosis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you expect the side effects of CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O and CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC to interact. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O\", \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O\",\"smile2\": \"CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC\",\"interact_result\": \"food poisoning\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O' and 'CC1CC(C2C(CC(C(O2)(C(=O)C(=O)N3CCCCC3C(=O)OC(C(C(CC(=O)C(C=C(C1)C)CC=C)O)C)C(=CC4CCC(C(C4)OC)O)C)O)C)OC)OC' may cause food poisoning when interacting."
            },
            {
                "from": "human",
                "value": "And please summarize the expected reactions and side effects of mentioned above and 'CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O\", \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O\",\"smile2\": \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\",\"interact_result\": \"ecchymoses\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1=C(C2=C(C1=CC3=CC=C(C=C3)S(=O)C)C=CC(=C2)F)CC(=O)O' and 'CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C' may cause ecchymoses when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How will C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO and CNC1CCC(C2=CC=CC=C12)C3=CC(=C(C=C3)Cl)Cl potentially influence side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO\", \"CNC1CCC(C2=CC=CC=C12)C3=CC(=C(C=C3)Cl)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO\",\"smile2\": \"CNC1CCC(C2=CC=CC=C12)C3=CC(=C(C=C3)Cl)Cl\",\"interact_result\": \"atrial ectopic beats\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO' and 'CNC1CCC(C2=CC=CC=C12)C3=CC(=C(C=C3)Cl)Cl' may cause atrial ectopic beats when interacting."
            },
            {
                "from": "human",
                "value": "And please predict the adverse reactions of mentioned above and 'CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO\", \"CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO\",\"smile2\": \"CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC\",\"interact_result\": \"psoriatic arthritis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1CC1NC2=C3C(=NC(=N2)N)N(C=N3)C4CC(C=C4)CO' and 'CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC' may cause psoriatic arthritis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think [Ca+2] and CC1COC2=C3N1C=C(C(=O)C3=CC(=C2N4CCN(CC4)C)F)C(=O)O will interact in terms of side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[Ca+2]\", \"CC1COC2=C3N1C=C(C(=O)C3=CC(=C2N4CCN(CC4)C)F)C(=O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[Ca+2]\",\"smile2\": \"CC1COC2=C3N1C=C(C(=O)C3=CC(=C2N4CCN(CC4)C)F)C(=O)O\",\"interact_result\": \"pneumothorax\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs '[Ca+2]' and 'CC1COC2=C3N1C=C(C(=O)C3=CC(=C2N4CCN(CC4)C)F)C(=O)O' may cause pneumothorax when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]' affect the patient in terms of side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[Ca+2]\", \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[Ca+2]\",\"smile2\": \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\",\"interact_result\": \"benign intracranial hypertension\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs '[Ca+2]' and 'C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]' may cause benign intracranial hypertension when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your assessment of the possible adverse reactions from CC(C)CC1=CC=C(C=C1)C(C)C(=O)O and CCC1=NN(C(=O)N1CCOC2=CC=CC=C2)CCCN3CCN(CC3)C4=CC(=CC=C4)Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\", \"CCC1=NN(C(=O)N1CCOC2=CC=CC=C2)CCCN3CCN(CC3)C4=CC(=CC=C4)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\",\"smile2\": \"CCC1=NN(C(=O)N1CCOC2=CC=CC=C2)CCCN3CCN(CC3)C4=CC(=CC=C4)Cl\",\"interact_result\": \"facial palsy\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)CC1=CC=C(C=C1)C(C)C(=O)O' and 'CCC1=NN(C(=O)N1CCOC2=CC=CC=C2)CCCN3CCN(CC3)C4=CC(=CC=C4)Cl' may cause facial palsy when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O' lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\", \"CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)CC1=CC=C(C=C1)C(C)C(=O)O\",\"smile2\": \"CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O\",\"interact_result\": \"Breast Lump\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)CC1=CC=C(C=C1)C(C)C(=O)O' and 'CC1=CN=C(S1)NC(=C2C(=O)C3=CC=CC=C3S(=O)(=O)N2C)O' may cause Breast Lump when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the potential adverse effects of CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O and CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O\", \"CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O\",\"smile2\": \"CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N\",\"interact_result\": \"pneumonia staphylococcal\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O' and 'CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N' may cause pneumonia staphylococcal when interacting."
            },
            {
                "from": "human",
                "value": "And how significant do you think the side effects from mentioned above and 'C[N+]1(C2CC(CC1C3C2O3)OC(=O)C(C4=CC=CS4)(C5=CC=CS5)O)C' will be. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O\", \"C[N+]1(C2CC(CC1C3C2O3)OC(=O)C(C4=CC=CS4)(C5=CC=CS5)O)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O\",\"smile2\": \"C[N+]1(C2CC(CC1C3C2O3)OC(=O)C(C4=CC=CS4)(C5=CC=CS5)O)C\",\"interact_result\": \"dysmenorrhea\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC1C(C(CC(O1)OC2C(OC(CC2O)OC3C(OC(CC3O)OC4CCC5(C(C4)CCC6C5CC(C7(C6(CCC7C8=CC(=O)OC8)O)C)O)C)C)C)O)O' and 'C[N+]1(C2CC(CC1C3C2O3)OC(=O)C(C4=CC=CS4)(C5=CC=CS5)O)C' may cause dysmenorrhea when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated side effects profile for C1=CC=C(C=C1)CCNN and [Li+].[AlH4-]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C(C=C1)CCNN\", \"[Li+].[AlH4-]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C(C=C1)CCNN\",\"smile2\": \"[Li+].[AlH4-]\",\"interact_result\": \"ecchymoses\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C(C=C1)CCNN' and '[Li+].[AlH4-]' may cause ecchymoses when interacting."
            },
            {
                "from": "human",
                "value": "And how might mentioned above and 'C1CN(CCN1CCC2=C(C=C3C(=C2)CC(=O)N3)Cl)C4=NSC5=CC=CC=C54' reactions lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CC=C(C=C1)CCNN\", \"C1CN(CCN1CCC2=C(C=C3C(=C2)CC(=O)N3)Cl)C4=NSC5=CC=CC=C54\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CC=C(C=C1)CCNN\",\"smile2\": \"C1CN(CCN1CCC2=C(C=C3C(=C2)CC(=O)N3)Cl)C4=NSC5=CC=CC=C54\",\"interact_result\": \"exophthalmos\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'C1=CC=C(C=C1)CCNN' and 'C1CN(CCN1CCC2=C(C=C3C(=C2)CC(=O)N3)Cl)C4=NSC5=CC=CC=C54' may cause exophthalmos when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What reactions are anticipated from CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl and C1CN2CCC1C(C2)OC(=O)N3CCC4=CC=CC=C4C3C5=CC=CC=C5 that might lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"C1CN2CCC1C(C2)OC(=O)N3CCC4=CC=CC=C4C3C5=CC=CC=C5\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\",\"smile2\": \"C1CN2CCC1C(C2)OC(=O)N3CCC4=CC=CC=C4C3C5=CC=CC=C5\",\"interact_result\": \"hemiparesis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'C1CN2CCC1C(C2)OC(=O)N3CCC4=CC=CC=C4C3C5=CC=CC=C5' may cause hemiparesis when interacting."
            },
            {
                "from": "human",
                "value": "And what side effects should be expected from the reactions of mentioned above and 'CNC(=C[N+](=O)[O-])NCCSCC1=CSC(=N1)CN(C)C'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\", \"CNC(=C[N+](=O)[O-])NCCSCC1=CSC(=N1)CN(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl\",\"smile2\": \"CNC(=C[N+](=O)[O-])NCCSCC1=CSC(=N1)CN(C)C\",\"interact_result\": \"Alcoholic intoxication\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCCCC1=NC(=C(N1CC2=CC=C(C=C2)C3=CC=CC=C3C4=NNN=N4)CO)Cl' and 'CNC(=C[N+](=O)[O-])NCCSCC1=CSC(=N1)CN(C)C' may cause Alcoholic intoxication when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your outlook on the side effects of CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4 and CC(C)C1=C(C(=C(C(=N1)C(C)C)COC)C2=CC=C(C=C2)F)C=CC(CC(CC(=O)O)O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4\", \"CC(C)C1=C(C(=C(C(=N1)C(C)C)COC)C2=CC=C(C=C2)F)C=CC(CC(CC(=O)O)O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4\",\"smile2\": \"CC(C)C1=C(C(=C(C(=N1)C(C)C)COC)C2=CC=C(C=C2)F)C=CC(CC(CC(=O)O)O)O\",\"interact_result\": \"Arthritis infective\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4' and 'CC(C)C1=C(C(=C(C(=N1)C(C)C)COC)C2=CC=C(C=C2)F)C=CC(CC(CC(=O)O)O)O' may cause Arthritis infective when interacting."
            },
            {
                "from": "human",
                "value": "And how do you expect the side effects of mentioned above and 'CC1=CC=CC=C1S(=O)(=O)NC(=O)C2=CC(=C(C=C2)CC3=CN(C4=C3C=C(C=C4)NC(=O)OC5CCCC5)C)OC' to interact. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4\", \"CC1=CC=CC=C1S(=O)(=O)NC(=O)C2=CC(=C(C=C2)CC3=CN(C4=C3C=C(C=C4)NC(=O)OC5CCCC5)C)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4\",\"smile2\": \"CC1=CC=CC=C1S(=O)(=O)NC(=O)C2=CC(=C(C=C2)CC3=CN(C4=C3C=C(C=C4)NC(=O)OC5CCCC5)C)OC\",\"interact_result\": \"blepharitis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)C1=C(C(=C(N1CCC(CC(CC(=O)O)O)O)C2=CC=C(C=C2)F)C3=CC=CC=C3)C(=O)NC4=CC=CC=C4' and 'CC1=CC=CC=C1S(=O)(=O)NC(=O)C2=CC(=C(C=C2)CC3=CN(C4=C3C=C(C=C4)NC(=O)OC5CCCC5)C)OC' may cause blepharitis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome in terms of side effects from CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O and CCOC(=O)N1CCC(=C2C3=C(CCC4=C2N=CC=C4)C=C(C=C3)Cl)CC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\", \"CCOC(=O)N1CCC(=C2C3=C(CCC4=C2N=CC=C4)C=C(C=C3)Cl)CC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\",\"smile2\": \"CCOC(=O)N1CCC(=C2C3=C(CCC4=C2N=CC=C4)C=C(C=C3)Cl)CC1\",\"interact_result\": \"epilepsy\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O' and 'CCOC(=O)N1CCC(=C2C3=C(CCC4=C2N=CC=C4)C=C(C=C3)Cl)CC1' may cause epilepsy when interacting."
            },
            {
                "from": "human",
                "value": "And please summarize the side effects we might see with mentioned above and 'CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\", \"CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O\",\"smile2\": \"CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O\",\"interact_result\": \"haemolytic anaemia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C(CCC1=CC=CC=C1)NC2CCC3=CC=CC=C3N(C2=O)CC(=O)O' and 'CN1CCC23C4C1CC5=C2C(=C(C=C5)O)OC3C(C=C4)O' may cause haemolytic anaemia when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the predicted side effects of CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C and CC12CCC3C(C1CCC2C(=O)NC(C)(C)C)CCC4C3(C=CC(=O)N4)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\", \"CC12CCC3C(C1CCC2C(=O)NC(C)(C)C)CCC4C3(C=CC(=O)N4)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\",\"smile2\": \"CC12CCC3C(C1CCC2C(=O)NC(C)(C)C)CCC4C3(C=CC(=O)N4)C\",\"interact_result\": \"benign prostatic hyperplasia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C' and 'CC12CCC3C(C1CCC2C(=O)NC(C)(C)C)CCC4C3(C=CC(=O)N4)C' may cause benign prostatic hyperplasia when interacting."
            },
            {
                "from": "human",
                "value": "And please evaluate the potential side effects of mentioned above and 'CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\", \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C\",\"smile2\": \"CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C\",\"interact_result\": \"pancreatic pseudocyst\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCOC(=O)C1=C(NC(=C(C1C2=C(C(=CC=C2)Cl)Cl)C(=O)OC)C)C' and 'CCC(C)(C)C(=O)OC1CC(C=C2C1C(C(C=C2)C)CCC3CC(CC(=O)O3)O)C' may cause pancreatic pseudocyst when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-] and [Cl-].[K+]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]\", \"[Cl-].[K+]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]\",\"smile2\": \"[Cl-].[K+]\",\"interact_result\": \"arteriosclerotic heart disease\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]' and '[Cl-].[K+]' may cause arteriosclerotic heart disease when interacting."
            },
            {
                "from": "human",
                "value": "And what side effects do you predict could occur with mentioned above and '[Cl-].[K+]'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]\", \"[Cl-].[K+]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]\",\"smile2\": \"[Cl-].[K+]\",\"interact_result\": \"agoraphobia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CCN1CCN(C(=O)C1=O)C(=O)NC(C2=CC=CC=C2)C(=O)NC3C4N(C3=O)C(C(S4)(C)C)C(=O)[O-]' and '[Cl-].[K+]' may cause agoraphobia when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide a forecast of the side effects from CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C and CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C\", \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C\",\"smile2\": \"CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C\",\"interact_result\": \"hepatorenal syndrome\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C' and 'CC1CC2C3CCC4=CC(=O)C=CC4(C3(C(CC2(C1(C(=O)CCl)O)C)O)F)C' may cause hepatorenal syndrome when interacting."
            },
            {
                "from": "human",
                "value": "And what are the expected side effects of the interactions between mentioned above and 'CN(C)CC1CCCCC1(C2=CC(=CC=C2)OC)O'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C\", \"CN(C)CC1CCCCC1(C2=CC(=CC=C2)OC)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C\",\"smile2\": \"CN(C)CC1CCCCC1(C2=CC(=CC=C2)OC)O\",\"interact_result\": \"fibroids\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC12CC(=O)C3C(C1CCC2(C(=O)CO)O)CCC4=CC(=O)C=CC34C' and 'CN(C)CC1CCCCC1(C2=CC(=CC=C2)OC)O' may cause fibroids when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide insight into the side effects of COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3 and C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\", \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\",\"smile2\": \"C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]\",\"interact_result\": \"ear ache\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3' and 'C1C(C2C(O1)C(CO2)O[N+](=O)[O-])O[N+](=O)[O-]' may cause ear ache when interacting."
            },
            {
                "from": "human",
                "value": "And how do you predict the side effects of mentioned above and 'COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC' will manifest. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\", \"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3\",\"smile2\": \"COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC\",\"interact_result\": \"neurogenic bladder\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C=C(C=C1)Cl)C(=O)NCCC2=CC=C(C=C2)S(=O)(=O)NC(=O)NC3CCCCC3' and 'COC1=C(C(=NC=C1)CS(=O)C2=NC3=C(N2)C=C(C=C3)OC(F)F)OC' may cause neurogenic bladder when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated side effects profile for CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O and CC(C)CC(CC(=O)O)CN. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"CC(C)CC(CC(=O)O)CN\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"CC(C)CC(CC(=O)O)CN\",\"interact_result\": \"gastroenteritis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'CC(C)CC(CC(=O)O)CN' may cause gastroenteritis when interacting."
            },
            {
                "from": "human",
                "value": "And please outline the potential side effects of mentioned above and 'CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC\",\"interact_result\": \"prostatitis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'CC(C1CCC(C(O1)OC2C(CC(C(C2O)OC3C(C(C(CO3)(C)O)NC)O)N)N)N)NC' may cause prostatitis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the reactions of COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O and CCC1=CC2CC(C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)C78CCN9C7C(C=CC9)(C(C(C8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC influencing side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O\", \"CCC1=CC2CC(C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)C78CCN9C7C(C=CC9)(C(C(C8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O\",\"smile2\": \"CCC1=CC2CC(C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)C78CCN9C7C(C=CC9)(C(C(C8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC\",\"interact_result\": \"dehydration\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O' and 'CCC1=CC2CC(C3=C(CN(C2)C1)C4=CC=CC=C4N3)(C5=C(C=C6C(=C5)C78CCN9C7C(C=CC9)(C(C(C8N6C)(C(=O)OC)O)OC(=O)C)CC)OC)C(=O)OC' may cause dehydration when interacting."
            },
            {
                "from": "human",
                "value": "And what are the anticipated reactions and side effects of mentioned above and 'CON=C1CN(CC1CN)C2=C(C=C3C(=O)C(=CN(C3=N2)C4CC4)C(=O)O)F'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O\", \"CON=C1CN(CC1CN)C2=C(C=C3C(=O)C(=CN(C3=N2)C4CC4)C(=O)O)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O\",\"smile2\": \"CON=C1CN(CC1CN)C2=C(C=C3C(=O)C(=CN(C3=N2)C4CC4)C(=O)O)F\",\"interact_result\": \"elevated triglycerides\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=CC=CC=C1OCCNCC(COC2=CC=CC3=C2C4=CC=CC=C4N3)O' and 'CON=C1CN(CC1CN)C2=C(C=C3C(=O)C(=CN(C3=N2)C4CC4)C(=O)O)F' may cause elevated triglycerides when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F and C(CN)C(O)(P(=O)(O)O)P(=O)(O)O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"C(CN)C(O)(P(=O)(O)O)P(=O)(O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"C(CN)C(O)(P(=O)(O)O)P(=O)(O)O\",\"interact_result\": \"eruption\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'C(CN)C(O)(P(=O)(O)O)P(=O)(O)O' may cause eruption when interacting."
            },
            {
                "from": "human",
                "value": "And how do you expect the side effects of mentioned above and 'CN(C)CCC1=CNC2=C1C=C(C=C2)CC3COC(=O)N3' to interact. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\", \"CN(C)CCC1=CNC2=C1C=C(C=C2)CC3COC(=O)N3\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F\",\"smile2\": \"CN(C)CCC1=CNC2=C1C=C(C=C2)CC3COC(=O)N3\",\"interact_result\": \"bad breath\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN(C)CCCC1(C2=C(CO1)C=C(C=C2)C#N)C3=CC=C(C=C3)F' and 'CN(C)CCC1=CNC2=C1C=C(C=C2)CC3COC(=O)N3' may cause bad breath when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects do you predict as a result of CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O and C1=CC2=C(C=C1OC(F)(F)F)SC(=N2)N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"C1=CC2=C(C=C1OC(F)(F)F)SC(=N2)N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"C1=CC2=C(C=C1OC(F)(F)F)SC(=N2)N\",\"interact_result\": \"respiratory failure\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'C1=CC2=C(C=C1OC(F)(F)F)SC(=N2)N' may cause respiratory failure when interacting."
            },
            {
                "from": "human",
                "value": "And how might the use of mentioned above and 'C1CN(CCN1CCOCC(=O)O)C(C2=CC=CC=C2)C3=CC=C(C=C3)Cl' lead to side effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\", \"C1CN(CCN1CCOCC(=O)O)C(C2=CC=CC=C2)C3=CC=C(C=C3)Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O\",\"smile2\": \"C1CN(CCN1CCOCC(=O)O)C(C2=CC=CC=C2)C3=CC=C(C=C3)Cl\",\"interact_result\": \"dysuria\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CC(C)(C)NCC(C1=CC(=C(C=C1)O)CO)O' and 'C1CN(CCN1CCOCC(=O)O)C(C2=CC=CC=C2)C3=CC=C(C=C3)Cl' may cause dysuria when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the expected reactions and side effects of CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3 and CC1=NC=C2N1C3=C(C=C(C=C3)Cl)C(=NC2)C4=CC=CC=C4F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3\", \"CC1=NC=C2N1C3=C(C=C(C=C3)Cl)C(=NC2)C4=CC=CC=C4F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3\",\"smile2\": \"CC1=NC=C2N1C3=C(C=C(C=C3)Cl)C(=NC2)C4=CC=CC=C4F\",\"interact_result\": \"retinal bleeding\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3' and 'CC1=NC=C2N1C3=C(C=C(C=C3)Cl)C(=NC2)C4=CC=CC=C4F' may cause retinal bleeding when interacting."
            },
            {
                "from": "human",
                "value": "And how do you think the reactions of mentioned above and 'CC1=C(C(CCC1)(C)C)C=CC(=CC=CC(=CC(=O)O)C)C' will lead to adverse effects. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3\", \"CC1=C(C(CCC1)(C)C)C=CC(=CC=CC(=CC(=O)O)C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3\",\"smile2\": \"CC1=C(C(CCC1)(C)C)C=CC(=CC=CC(=CC(=O)O)C)C\",\"interact_result\": \"bronchial spasm\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'CN1C(=O)CN=C(C2=C1C=CC(=C2)Cl)C3=CC=CC=C3' and 'CC1=C(C(CCC1)(C)C)C=CC(=CC=CC(=CC(=O)O)C)C' may cause bronchial spasm when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC and CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC\", \"CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC\",\"smile2\": \"CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N\",\"interact_result\": \"asthenia\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC' and 'CC1=C(C(=NO1)C2=CC=CC=C2)C3=CC=C(C=C3)S(=O)(=O)N' may cause asthenia when interacting."
            },
            {
                "from": "human",
                "value": "And please predict the likely adverse effects of mentioned above and 'C1CC(C1)(C(=O)O)C(=O)O.N.N.[Pt]'. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC\", \"C1CC(C1)(C(=O)O)C(=O)O.N.N.[Pt]\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC\",\"smile2\": \"C1CC(C1)(C(=O)O)C(=O)O.N.N.[Pt]\",\"interact_result\": \"cellulitis\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "The two drugs 'COC1=C(C=C2C(=C1)C(=NC(=N2)N3CCN(CC3)C(=O)C4CCCO4)N)OC' and 'C1CC(C1)(C(=O)O)C(=O)O.N.N.[Pt]' may cause cellulitis when interacting."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What reactions from =HCC(=@C]@)3O@@(=C](C=C)C@C)[(==C=C(C3())C1O)])OOC((C[@C[((]C3C(HO)=(C[H)([4H)C[@@O(]COC(CC(C]@]CC[]H)(1H)O))C[C@]O@)==C)ON4]C)C@C(C(C]@2(C[HC[[][CCH@)(]])@(OC[]3)[==)C21[@C((C)][O)1]C]O)[@CO@C[0000 and Cl[223Ra]Cl might lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"=HCC(=@C]@)3O@@(=C](C=C)C@C)[(==C=C(C3())C1O)])OOC((C[@C[((]C3C(HO)=(C[H)([4H)C[@@O(]COC(CC(C]@]CC[]H)(1H)O))C[C@]O@)==C)ON4]C)C@C(C(C]@2(C[HC[[][CCH@)(]])@(OC[]3)[==)C21[@C((C)][O)1]C]O)[@CO@C[0000\", \"Cl[223Ra]Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "[H][C@@](O)([C@@H](NC(=O)OC(C)(C)C)C1=CC=CC=C1)C(=O)O[C@@]1([H])C[C@@]2(O)[C@@]([H])(OC(=O)C3=CC=CC=C3)[C@]3([H])[C@@]4(CO[C@]4([H])C[C@]([H])(OC)[C@@]3(C)C(=O)[C@]([H])(OC)C(=C1C)C2(C)C)OC(C)=O and Cl[223Ra]Cl. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@](O)([C@@H](NC(=O)OC(C)(C)C)C1=CC=CC=C1)C(=O)O[C@@]1([H])C[C@@]2(O)[C@@]([H])(OC(=O)C3=CC=CC=C3)[C@]3([H])[C@@]4(CO[C@]4([H])C[C@]([H])(OC)[C@@]3(C)C(=O)[C@]([H])(OC)C(=C1C)C2(C)C)OC(C)=O\", \"Cl[223Ra]Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@](O)([C@@H](NC(=O)OC(C)(C)C)C1=CC=CC=C1)C(=O)O[C@@]1([H])C[C@@]2(O)[C@@]([H])(OC(=O)C3=CC=CC=C3)[C@]3([H])[C@@]4(CO[C@]4([H])C[C@]([H])(OC)[C@@]3(C)C(=O)[C@]([H])(OC)C(=C1C)C2(C)C)OC(C)=O\",\"smile2\": \"Cl[223Ra]Cl\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when '[H][C@@](O)([C@@H](NC(=O)OC(C)(C)C)C1=CC=CC=C1)C(=O)O[C@@]1([H])C[C@@]2(O)[C@@]([H])(OC(=O)C3=CC=CC=C3)[C@]3([H])[C@@]4(CO[C@]4([H])C[C@]([H])(OC)[C@@]3(C)C(=O)[C@]([H])(OC)C(=C1C)C2(C)C)OC(C)=O' is combined with 'Cl[223Ra]Cl'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential side effects should be considered for ]C@]@OCOOCHC](C[@]NC)@(()[[H@OCC]1@@)[)HNO@[C((CH@H([N[)](](C[H=@N)[CO@H[C1[@CHC)][CH]CHCHC@O(112][HN@@2]][]OO)C)@HO@CCN]H0000 and CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"]C@]@OCOOCHC](C[@]NC)@(()[[H@OCC]1@@)[)HNO@[C((CH@H([N[)](](C[H=@N)[CO@H[C1[@CHC)][CH]CHCHC@O(112][HN@@2]][]OO)C)@HO@CCN]H0000\", \"CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)CC[C@H]2N)[C@H](O)[C@H]1O[C@H]1O[C@H](CO)[C@@H](O)[C@H](N)[C@H]1O and CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1 are the right smiles for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)CC[C@H]2N)[C@H](O)[C@H]1O[C@H]1O[C@H](CO)[C@@H](O)[C@H](N)[C@H]1O\", \"CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)CC[C@H]2N)[C@H](O)[C@H]1O[C@H]1O[C@H](CO)[C@@H](O)[C@H](N)[C@H]1O\",\"smile2\": \"CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1\",\"interact_result\": \"#drug1 may decrease the excretion rate of #drug2 which could result in a higher serum level.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'NCC[C@H](O)C(=O)N[C@@H]1C[C@H](N)[C@@H](O[C@H]2O[C@H](CN)CC[C@H]2N)[C@H](O)[C@H]1O[C@H]1O[C@H](CO)[C@@H](O)[C@H](N)[C@H]1O' may decrease the excretion rate of 'CC1=C(Cl)C(NC2=CC=CC=C2C(O)=O)=C(Cl)C=C1' which could result in a higher serum level."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please detail the expected side effects associated with 1@CC1C]CNC1=CCCCH=C=1N[0000 and [H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"1@CC1C]CNC1=CCCCH=C=1N[0000\", \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles best reflecting the molecules are CN1CCC[C@H]1C1=CN=CC=C1 and [H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCC[C@H]1C1=CN=CC=C1\", \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1CCC[C@H]1C1=CN=CC=C1\",\"smile2\": \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of '[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1' can be decreased when combined with 'CN1CCC[C@H]1C1=CN=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the anticipated side effects from O2)NCCCF(1CCC=(C(C)F(FNC=C)FCF2=)FOC(C(1O)CCC)(C=)0000 and ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O2)NCCCF(1CCC=(C(C)F(FNC=C)FCF2=)FOC(C(1O)CCC)(C=)0000\", \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right smiles for the drugs are FC(F)(F)COC1=CC(C(=O)NCC2CCCCN2)=C(OCC(F)(F)F)C=C1 and ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"FC(F)(F)COC1=CC(C(=O)NCC2CCCCN2)=C(OCC(F)(F)F)C=C1\", \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"FC(F)(F)COC1=CC(C(=O)NCC2CCCCN2)=C(OCC(F)(F)F)C=C1\",\"smile2\": \"ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'FC(F)(F)COC1=CC(C(=O)NCC2CCCCN2)=C(OCC(F)(F)F)C=C1' may increase the qtc-prolonging activities of 'ClC1=CC2=C(OC3=CC=CC=C3N=C2N2CCNCC2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects are expected from the reactions of NC=CC2OC12=((CC2=C)CCCC(C==CC1))CCCSC=C=2N0000 and OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"NC=CC2OC12=((CC2=C)CCCC(C==CC1))CCCSC=C=2N0000\", \"OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The drugs' smiles should be CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O and OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O\", \"OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O\",\"smile2\": \"OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CN(C)CCCN1C2=CC=CC=C2SC2=C1C=C(C=C2)C(C)=O' is combined with 'OC(=O)COCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood of experiencing side effects from C))]12CC3]@=CC[((N=221C[=13CCCCC]CHC1@[=CHN(O)C2C@CCC0000 and ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C))]12CC3]@=CC[((N=221C[=13CCCCC]CHC1@[=CHN(O)C2C@CCC0000\", \"ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The correct expressions for the drugs are [H][C@]12CCCC3=C1C(=CC=C3)C(=O)N(C2)[C@@H]1CN2CCC1CC2 and ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]12CCCC3=C1C(=CC=C3)C(=O)N(C2)[C@@H]1CN2CCC1CC2\", \"ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@]12CCCC3=C1C(=CC=C3)C(=O)N(C2)[C@@H]1CN2CCC1CC2\",\"smile2\": \"ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'ClC1=CC(Cl)=C(C=C1)[C@@H]1CS\\C(S1)=C(\\C#N)N1C=CN=C1' can be increased when it is combined with '[H][C@]12CCCC3=C1C(=CC=C3)C(=O)N(C2)[C@@H]1CN2CCC1CC2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you think the reactions of OO=CCCON1O(O)C(P11CC==1CC)C1=N=)CCC1CC=(()=C)=CO(=0000 and CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1 will affect side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OO=CCCON1O(O)C(P11CC==1CC)C1=N=)CCC1CC=(()=C)=CO(=0000\", \"CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles that fit the molecules best are OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1 and CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\", \"CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\",\"smile2\": \"CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1\",\"interact_result\": \"the metabolism of #drug2 can be increased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'CCCCC1=NC(Cl)=C(CO)N1CC1=CC=C(C=C1)C1=CC=CC=C1C1=NNN=N1' can be increased when combined with 'OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please explain the possible side effects of )N(C2NCCOC=C2CCNO)(N2CCC)C=C=CCCN((CC(=1N=CO=21)CCC)0000 and CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\")N(C2NCCOC=C2CCNO)(N2CCC)C=C=CCCN((CC(=1N=CO=21)CCC)0000\", \"CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CCN1N=NN(CCN2CCC(COC)(CC2)N(C(=O)CC)C2=CC=CC=C2)C1=O and CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C are the right facial expressions for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCN1N=NN(CCN2CCC(COC)(CC2)N(C(=O)CC)C2=CC=CC=C2)C1=O\", \"CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCN1N=NN(CCN2CCC(COC)(CC2)N(C(=O)CC)C2=CC=CC=C2)C1=O\",\"smile2\": \"CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CCN1N=NN(CCN2CCC(COC)(CC2)N(C(=O)CC)C2=CC=CC=C2)C1=O' is combined with 'CCCCN1CCCCC1C(=O)NC1=C(C)C=CC=C1C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How significant do you think the side effects from 1====CCCNC(C2C(O)NCCCCNC=C)1CCC20000 and CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1 will be. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"1====CCCNC(C2C(O)NCCCCNC=C)1CCC20000\", \"CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "COC1=CC(NC(C)CCCN)=C2N=CC=CC2=C1 and CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC(NC(C)CCCN)=C2N=CC=CC2=C1\", \"CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC(NC(C)CCCN)=C2N=CC=CC2=C1\",\"smile2\": \"CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'COC1=CC(NC(C)CCCN)=C2N=CC=CC2=C1' may increase the qtc-prolonging activities of 'CCC(=O)C(CC(C)N(C)C)(C1=CC=CC=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What adverse effects are likely from the combination of 21=C@C2=OCCOCC=N)41C(2CC(C)C)C=CC(=)CC])C=C2CCCC(=O1O)(31=NOCC=(C1NCCCC)C1C3CC[=NO(40000 and C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"21=C@C2=OCCOCC=N)41C(2CC(C)C)C=CC(=)CC])C=C2CCCC(=O1O)(31=NOCC=(C1NCCCC)C1C3CC[=NO(40000\", \"C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right smiles for the molecules are CCC1=C2CN3C(=CC4=C(COC(=O)[C@]4(O)CC)C3=O)C2=NC2=C1C=C(OC(=O)N1CCC(CC1)N1CCCCC1)C=C2 and C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1=C2CN3C(=CC4=C(COC(=O)[C@]4(O)CC)C3=O)C2=NC2=C1C=C(OC(=O)N1CCC(CC1)N1CCCCC1)C=C2\", \"C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC1=C2CN3C(=CC4=C(COC(=O)[C@]4(O)CC)C3=O)C2=NC2=C1C=C(OC(=O)N1CCC(CC1)N1CCCCC1)C=C2\",\"smile2\": \"C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'C[C@@H](O[C@H]1OCCN(CC2=NC(=O)N(N2)P(O)(O)=O)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be increased when it is combined with 'CCC1=C2CN3C(=CC4=C(COC(=O)[C@]4(O)CC)C3=O)C2=NC2=C1C=C(OC(=O)N1CCC(CC1)N1CCCCC1)C=C2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likelihood of experiencing side effects from CC=C(=C(OC=O1NO1CC2C()C(=N=OC)CNCNCS=(C)O)C(2C1))CCC1C=1C)CCC====(1=C==NCOC0000 and N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC=C(=C(OC=O1NO1CC2C()C(=N=OC)CNCNCS=(C)O)C(2C1))CCC1C=1C)CCC====(1=C==NCOC0000\", \"N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The proper smiles for the moleculess are COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1 and N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1\", \"N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1\",\"smile2\": \"N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N\",\"interact_result\": \"the serum concentration of #drug2 can be decreased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'N#CC1=CC=C(C=C1)C(N1C=NC=N1)C1=CC=C(C=C1)C#N' can be decreased when it is combined with 'COC1=CC=CC=C1OC1=C(NS(=O)(=O)C2=CC=C(C=C2)C(C)(C)C)N=C(N=C1OCCO)C1=NC=CC=N1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with C)]-)N(=CFC1]C1=+=)=CC(C=[N()(O[O)F()C((CCCFO)0000 and COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C)]-)N(=CFC1]C1=+=)=CC(C=[N()(O[O)F()C((CCCFO)0000\", \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC(C)C(=O)NC1=CC(=C(C=C1)[N+]([O-])=O)C(F)(F)F and COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)C(=O)NC1=CC(=C(C=C1)[N+]([O-])=O)C(F)(F)F\", \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)C(=O)NC1=CC(=C(C=C1)[N+]([O-])=O)C(F)(F)F\",\"smile2\": \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1' can be decreased when combined with 'CC(C)C(=O)NC1=CC(=C(C=C1)[N+]([O-])=O)C(F)(F)F'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the side effects of CCOO1)O)(C=C(CC)=O(11NC=OON)1=C)C=C1CC=(CP=(C1C=C=0000 and [H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCOO1)O)(C=C(CC)=O(11NC=OON)1=C)C=C1CC=(CP=(C1C=C=0000\", \"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right look for the drugs are OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1 and [H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\", \"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\",\"smile2\": \"[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O\",\"interact_result\": \"the metabolism of #drug2 can be increased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of '[H][C@@]12CCCN1C(=O)[C@H](CC1=CC=CC=C1)N1C(=O)[C@](C)(NC(=O)[C@H]3CN(C)[C@]4([H])CC5=CNC6=CC=CC(=C56)[C@@]4([H])C3)O[C@@]21O' can be increased when combined with 'OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee CC((3CCl))=CONNO(C#2C=CCC4CCl1)CC(=2)3CC1=COC4)=N=CC=(CCNCNC)(C=CCC0000 and C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F interacting in terms of side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC((3CCl))=CONNO(C#2C=CCC4CCl1)CC(=2)3CC1=COC4)=N=CC=(CCNCNC)(C=CCC0000\", \"C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right smiles for the moleculess are COC1=CC(NC2=C(C=NC3=CC(OCCCN4CCN(C)CC4)=C(OC)C=C23)C#N)=C(Cl)C=C1Cl and C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=CC(NC2=C(C=NC3=CC(OCCCN4CCN(C)CC4)=C(OC)C=C23)C#N)=C(Cl)C=C1Cl\", \"C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=CC(NC2=C(C=NC3=CC(OCCCN4CCN(C)CC4)=C(OC)C=C23)C#N)=C(Cl)C=C1Cl\",\"smile2\": \"C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'C[C@@H](O[C@H]1OCCN(CC2=NNC(=O)N2)[C@H]1C1=CC=C(F)C=C1)C1=CC(=CC(=C1)C(F)(F)F)C(F)(F)F' can be increased when it is combined with 'COC1=CC(NC2=C(C=NC3=CC(OCCCN4CCN(C)CC4)=C(OC)C=C23)C#N)=C(Cl)C=C1Cl'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please forecast the side effects associated with ))(CN=CN)=(C11CCOP==1CO)CCO(C(=OC)C=CCCO==11(1=OCC0000 and CCCN[C@H]1CCC2=C(C1)SC(N)=N2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"))(CN=CN)=(C11CCOP==1CO)CCO(C(=OC)C=CCCO==11(1=OCC0000\", \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles that fit the molecules best are OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1 and CCCN[C@H]1CCC2=C(C1)SC(N)=N2. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\", \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1\",\"smile2\": \"CCCN[C@H]1CCC2=C(C1)SC(N)=N2\",\"interact_result\": \"#drug1 may increase the sedative activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'OP(O)(=O)OCN1C(=O)NC(C1=O)(C1=CC=CC=C1)C1=CC=CC=C1' may increase the sedative activities of 'CCCN[C@H]1CCC2=C(C1)SC(N)=N2'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast regarding the side effects of C[(C]CNC==CCC11C@H1)NCCC=C1CCCOC=C()0000 and CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F reactions. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C[(C]CNC==CCC11C@H1)NCCC=C1CCCOC=C()0000\", \"CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CCCN1CCCC[C@H]1C(=O)NC1=C(C)C=CC=C1C and CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCN1CCCC[C@H]1C(=O)NC1=C(C)C=CC=C1C\", \"CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCCN1CCCC[C@H]1C(=O)NC1=C(C)C=CC=C1C\",\"smile2\": \"CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'CC1=CC=C(C=C1)C1=CC(=NN1C1=CC=C(C=C1)S(N)(=O)=O)C(F)(F)F' can be decreased when combined with 'CCCN1CCCC[C@H]1C(=O)NC1=C(C)C=CC=C1C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome in terms of side effects from (C((CC))N=1=(=CC22=OF2)C1=)2CCFCCCNCC=O)CCC(CC=FNCO0000 and CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"(C((CC))N=1=(=CC22=OF2)C1=)2CCFCCCNCC=O)CCC(CC=FNCO0000\", \"CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "FC(F)(F)C1=CC(NC2=C(C=CC=N2)C(=O)OCCN2CCOCC2)=CC=C1 and CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"FC(F)(F)C1=CC(NC2=C(C=CC=N2)C(=O)OCCN2CCOCC2)=CC=C1\", \"CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"FC(F)(F)C1=CC(NC2=C(C=CC=N2)C(=O)OCCN2CCOCC2)=CC=C1\",\"smile2\": \"CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'FC(F)(F)C1=CC(NC2=C(C=CC=N2)C(=O)OCCN2CCOCC2)=CC=C1' is combined with 'CC(C(O)=O)C1=CC=C(C=C1)C(=O)C1=CC=CS1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with =)+(N)1[C=CC)CCCC1CO]=C(C(0000 and [I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"=)+(N)1[C=CC)CCCC1CO]=C(C(0000\", \"[I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The true smiles for the drugs are CC[N+](C)(C)C1=CC(O)=CC=C1 and [I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC[N+](C)(C)C1=CC(O)=CC=C1\", \"[I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC[N+](C)(C)C1=CC(O)=CC=C1\",\"smile2\": \"[I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC\",\"interact_result\": \"the therapeutic efficacy of #drug2 can be decreased when used in combination with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the therapeutic efficacy of '[I-].[I-].[I-].CC[N+](CC)(CC)CCOC1=CC=CC(OCC[N+](CC)(CC)CC)=C1OCC[N+](CC)(CC)CC' can be decreased when used in combination with 'CC[N+](C)(C)C1=CC(O)=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your outlook on the side effects of CCN=ON=1COCCCC((2C21CO=CCC=C=CN=1)C==1CC()C=(CCC)=C=C2=CC=1)O1=CN2CN0000 and COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCN=ON=1COCCCC((2C21CO=CCC=C=CN=1)C==1CC()C=(CCC)=C=C2=CC=1)O1=CN2CN0000\", \"COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "O=C(NC1=C2OC(=CC(=O)C2=CC=C1)C1=NNN=N1)C1=CC=C(OCCCCC2=CC=CC=C2)C=C1 and COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C are the right smiles for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O=C(NC1=C2OC(=CC(=O)C2=CC=C1)C1=NNN=N1)C1=CC=C(OCCCCC2=CC=CC=C2)C=C1\", \"COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"O=C(NC1=C2OC(=CC(=O)C2=CC=C1)C1=NNN=N1)C1=CC=C(OCCCCC2=CC=CC=C2)C=C1\",\"smile2\": \"COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'COC(=O)N[C@H](C(=O)N[C@@H](CC1=CC=CC=C1)[C@@H](O)CN(CC1=CC=C(C=C1)C1=CC=CC=N1)NC(=O)[C@@H](NC(=O)OC)C(C)(C)C)C(C)(C)C' can be increased when it is combined with 'O=C(NC1=C2OC(=CC(=O)C2=CC=C1)C1=NNN=N1)C1=CC=C(OCCCCC2=CC=CC=C2)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the side effects of C1C=CCOO1(1)N==CCC=1CCOCC)()=N=(C0000 and [H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1 impacting patients. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1C=CCOO1(1)N==CCC=1CCOCC)()=N=(C0000\", \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1 and [H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1\", \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1\",\"smile2\": \"[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1\",\"interact_result\": \"the metabolism of #drug2 can be increased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of '[H][C@@](O)(CN1C[C@@]2([H])CCCC[C@@]2([H])C[C@@]1([H])C(O)=NC(C)(C)C)[C@]([H])(CC1=CC=CC=C1)N=C(O)[C@]([H])(CC(O)=N)NC(=O)C1=NC2=CC=CC=C2C=C1' can be increased when combined with 'CCC1(C(=O)NC(=O)NC1=O)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please summarize the expected reactions and side effects of C33NCCCC3CC==CC1(=CCC)NCN2N=3==O))(=CN3((C==3N1CCC=C2CCNC)=C=0000 and CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C33NCCCC3CC==CC1(=CCC)NCN2N=3==O))(=CN3((C==3N1CCC=C2CCNC)=C=0000\", \"CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC1=NC(C)=C2CCC(=O)N(CC3=CC=C(C=C3)C3=CC=CC=C3C3=NNN=N3)C2=N1 and CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O are the most proper expressionss for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=NC(C)=C2CCC(=O)N(CC3=CC=C(C=C3)C3=CC=CC=C3C3=NNN=N3)C2=N1\", \"CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=NC(C)=C2CCC(=O)N(CC3=CC=C(C=C3)C3=CC=CC=C3C3=NNN=N3)C2=N1\",\"smile2\": \"CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'CC(=O)C1=C(C)C2=CN=C(NC3=NC=C(C=C3)N3CCNCC3)N=C2N(C2CCCC2)C1=O' can be increased when it is combined with 'CC1=NC(C)=C2CCC(=O)N(CC3=CC=C(C=C3)C3=CC=CC=C3C3=NNN=N3)C2=N1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects should be expected from the reactions of )=O=C(COO=CC)CC=1=(COCC10000 and CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\")=O=C(COO=CC)CC=1=(COCC10000\", \"CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC(=O)OC1=CC=CC=C1C(O)=O and CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1 are the proper smile for the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(=O)OC1=CC=CC=C1C(O)=O\", \"CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(=O)OC1=CC=CC=C1C(O)=O\",\"smile2\": \"CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CC(=O)OC1=CC=CC=C1C(O)=O' is combined with 'CC(C)N(CCCCOCC(=O)NS(C)(=O)=O)C1=NC(C2=CC=CC=C2)=C(N=C1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the side effects from ]F[e0000 and CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"]F[e0000\", \"CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The most true smiles for the molecules are [Fe] and CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[Fe]\", \"CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[Fe]\",\"smile2\": \"CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1\",\"interact_result\": \"the serum concentration of #drug2 can be decreased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'CCN1C=C(C(O)=O)C(=O)C2=CC(F)=C(C=C12)N1CCNCC1' can be decreased when it is combined with '[Fe]'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might the use of FCCO()(C(POOCC)(=))C0000 and CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1 lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"FCCO()(C(POOCC)(=))C0000\", \"CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC(C)OP(F)(=O)OC(C)C and CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1 are the chosen smile for the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)OP(F)(=O)OC(C)C\", \"CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)OP(F)(=O)OC(C)C\",\"smile2\": \"CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1\",\"interact_result\": \"the therapeutic efficacy of #drug2 can be decreased when used in combination with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the therapeutic efficacy of 'CCN(CC1=CC=NC=C1)C(=O)C(CO)C1=CC=CC=C1' can be decreased when used in combination with 'CC(C)OP(F)(=O)OC(C)C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What potential adverse reactions do you foresee for 3=NO(CCC@C(CCCN[C2CH4@)===)3C@11([N@[CO=CCCHHC)]C])][]C(CC=24@C0000 and CCCC(CCC)C(O)=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"3=NO(CCC@C(CCCN[C2CH4@)===)3C@11([N@[CO=CCCHHC)]C])][]C(CC=24@C0000\", \"CCCC(CCC)C(O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "[H][C@@]12CC3=CNC4=CC=CC(=C34)C1=C[C@H](CN2C)C(=O)N[C@@H](CC)CO and CCCC(CCC)C(O)=O are the correct smiles for the moleculess. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC3=CNC4=CC=CC(=C34)C1=C[C@H](CN2C)C(=O)N[C@@H](CC)CO\", \"CCCC(CCC)C(O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC3=CNC4=CC=CC(=C34)C1=C[C@H](CN2C)C(=O)N[C@@H](CC)CO\",\"smile2\": \"CCCC(CCC)C(O)=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when '[H][C@@]12CC3=CNC4=CC=CC(=C34)C1=C[C@H](CN2C)C(=O)N[C@@H](CC)CO' is combined with 'CCCC(CCC)C(O)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects are likely with ==1C(C1N][CCCCCCC=)@H0000 and CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"==1C(C1N][CCCCCCC=)@H0000\", \"CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "C[C@H](N)CC1=CC=CC=C1 and CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1 are the correct smiles for the moleculess. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C[C@H](N)CC1=CC=CC=C1\", \"CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C[C@H](N)CC1=CC=CC=C1\",\"smile2\": \"CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'CC(=O)N1CCN(CC1)C1=CC=C(OCC2COC(CN3C=CN=C3)(O2)C2=CC=C(Cl)C=C2Cl)C=C1' can be decreased when combined with 'C[C@H](N)CC1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the possible side effects resulting from CC[=]=CCC2=24=CC)CNC(((C=C1CC1C=((N1CONCO))CO1C4)C)C=3CC@O3=)@O=(C0000 and CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC[=]=CCC2=24=CC)CNC(((C=C1CC1C=((N1CONCO))CO1C4)C)C=3CC@O3=)@O=(C0000\", \"CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The proper smiles for the molecules are CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O and CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O\", \"CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O\",\"smile2\": \"CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'CNCCCC1C2=CC=CC=C2C=CC2=CC=CC=C12' can be increased when it is combined with 'CC[C@@]1(O)C(=O)OCC2=C1C=C1N(CC3=CC4=C(C=CC(O)=C4CN(C)C)N=C13)C2=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the anticipated reactions and side effects of (CCC(OCOC)=CCCNC(CC1C)CC=O)((1)=C=N1C=)=1CC=C0000 and NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"(CCC(OCOC)=CCCNC(CC1C)CC=O)((1)=C=N1C=)=1CC=C0000\", \"NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1 and NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F represent the smiles that fit the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1\", \"NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1\",\"smile2\": \"NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F\",\"interact_result\": \"#drug1 may increase the hypotensive activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC(CCC1=CC=CC=C1)NCC(O)C1=CC(C(N)=O)=C(O)C=C1' may increase the hypotensive activities of 'NS(=O)(=O)C1=CC2=C(NCNS2(=O)=O)C=C1C(F)(F)F'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated side effects profile for C=1C)CC1CNC=C=C=1CC1=N(C=)11=CC=C)1CC1CC(CC(C===C0000 and CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C=1C)CC1CNC=C=C=1CC1=N(C=)11=CC=C)1CC1CC(CC(C===C0000\", \"CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles that defines the molecules are C1=CN(C=N1)C(C1=CC=CC=C1)C1=CC=C(C=C1)C1=CC=CC=C1 and CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1=CN(C=N1)C(C1=CC=CC=C1)C1=CC=C(C=C1)C1=CC=CC=C1\", \"CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"C1=CN(C=N1)C(C1=CC=CC=C1)C1=CC=C(C=C1)C1=CC=CC=C1\",\"smile2\": \"CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'C1=CN(C=N1)C(C1=CC=CC=C1)C1=CC=C(C=C1)C1=CC=CC=C1' is combined with 'CCC1=CC=C(C=C1)C(=O)C(C)CN1CCCCC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated side effects profile for =C1)1CCCC=CCCCCC=1=CCC(1(C=NC)=\\C=C1C/1N0000 and FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"=C1)1CCCC=CCCCCC=1=CCC(1(C=NC)=\\C=C1C/1N0000\", \"FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1 and FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12 are the chosen smile for the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1\", \"FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1\",\"smile2\": \"FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12\",\"interact_result\": \"#drug1 may increase the central nervous system depressant (cns depressant) activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC1=CC=C(C=C1)C(=C/CN1CCCC1)\\C1=CC=CC=N1' may increase the central nervous system depressant (cns depressant) activities of 'FC1=CC=C(C=C1)C(=O)CCCN1CCC(=CC1)N1C(=O)NC2=CC=CC=C12'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects could arise from the use of O@@=CO]=C=(C)](C()=O](@1O)[((O][3CC)@)=C1]1N()COC21CHN[OC=C[))@(CC)(HNC(C(CC(])(CCH)C@)C=C=2[)[30000 and C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O@@=CO]=C=(C)](C()=O](@1O)[((O][3CC)@)=C1]1N()COC21CHN[OC=C[))@(CC)(HNC(C(CC(])(CCH)C@)C=C=2[)[30000\", \"C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles that fit the molecules best are [H][C@@]12CC3=C(C(O)=CC=C3N(C)C)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])C2 and C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC3=C(C(O)=CC=C3N(C)C)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])C2\", \"C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC3=C(C(O)=CC=C3N(C)C)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])C2\",\"smile2\": \"C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when '[H][C@@]12CC3=C(C(O)=CC=C3N(C)C)C(=O)C1=C(O)[C@]1(O)C(=O)C(C(N)=O)=C(O)[C@@H](N(C)C)[C@]1([H])C2' is combined with 'C\\C(\\C=C\\C1=C(C)CCCC1(C)C)=C/C=C/C(/C)=C\\C(O)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the possible adverse reactions of N)N])]=O-=([CC=CC1(C1CNO+O[0000 and O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"N)N])]=O-=([CC=CC1(C1CNO+O[0000\", \"O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right smiles for the molecules are CC1=NC=C(N1CCO)[N+]([O-])=O and O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=NC=C(N1CCO)[N+]([O-])=O\", \"O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=NC=C(N1CCO)[N+]([O-])=O\",\"smile2\": \"O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'O[C@@H]1CC[C@@]2(C)[C@@]([H])(CC[C@@]3(C)[C@@]2([H])[C@H](O)C[C@@]2([H])\\C([C@@H](OC(=O)C)C[C@]32C)=C(/CCC=C(C)C)C(O)=O)[C@@H]1C' can be increased when it is combined with 'CC1=NC=C(N1CCO)[N+]([O-])=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What do you think about the side effects of CO1(1OC)C=)=CCC)())(OCCCC(NC=(C0000 and CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CO1(1OC)C=)=CCC)())(OCCCC(NC=(C0000\", \"CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The correct expressions for the drugs are CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1 and CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1\", \"CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1\",\"smile2\": \"CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CC(C)(C)NCC(O)C1=CC(O)=CC(O)=C1' is combined with 'CC(C)(C)NCC(O)C1=CC(Cl)=C(N)C(Cl)=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected outcome in terms of side effects from O==CCOCC)[)@1C=1C((OOH)@C=(N(])CC0000 and [Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O==CCOCC)[)@1C=1C((OOH)@C=(N(])CC0000\", \"[Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "N[C@@H](CC1=CC(O)=C(O)C=C1)C(O)=O and [Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"N[C@@H](CC1=CC(O)=C(O)C=C1)C(O)=O\", \"[Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"N[C@@H](CC1=CC(O)=C(O)C=C1)C(O)=O\",\"smile2\": \"[Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'N[C@@H](CC1=CC(O)=C(O)C=C1)C(O)=O' is combined with '[Cl-].CN(C)C1=CC2=[S+]C3=C(C=CC(=C3)N(C)C)N=C2C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the side effects of BC=C(rO(C)C@1N[]C1@[C1H)N)C(rHB=]C=1CCCCC0000 and CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O impacting patients. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"BC=C(rO(C)C@1N[]C1@[C1H)N)C(rHB=]C=1CCCCC0000\", \"CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "NC1=C(Br)C=C(Br)C=C1CN[C@H]1CC[C@H](O)CC1 and CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O are the right smiles for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"NC1=C(Br)C=C(Br)C=C1CN[C@H]1CC[C@H](O)CC1\", \"CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"NC1=C(Br)C=C(Br)C=C1CN[C@H]1CC[C@H](O)CC1\",\"smile2\": \"CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'CC(C)C[C@H](NC(=O)[C@H](CC1=CC=CC=C1)NC(=O)C1=CN=CC=N1)B(O)O' can be decreased when combined with 'NC1=C(Br)C=C(Br)C=C1CN[C@H]1CC[C@H](O)CC1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please provide a forecast of the side effects from O1CC)NOCC(1(CC)CN=(C())C=O=C0000 and FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"O1CC)NOCC(1(CC)CN=(C())C=O=C0000\", \"FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The most true smiles for the molecules are CCC(C)C1(CC)C(=O)NC(=O)NC1=O and FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCC(C)C1(CC)C(=O)NC(=O)NC1=O\", \"FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CCC(C)C1(CC)C(=O)NC(=O)NC1=O\",\"smile2\": \"FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CCC(C)C1(CC)C(=O)NC(=O)NC1=O' is combined with 'FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might the side effects vary between l)COC=NONCCC(CClN(=)0000 and COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"l)COC=NONCCC(CClN(=)0000\", \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The right expressionss for the molecules are ClCCNC(=O)N(CCCl)N=O and COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"ClCCNC(=O)N(CCCl)N=O\", \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"ClCCNC(=O)N(CCCl)N=O\",\"smile2\": \"COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'COC1=CC=C(OC2=NC=NC(=C2)N(C)CCOC2=CC=C(CC3SC(=O)NC3=O)C=C2)C=C1' can be decreased when combined with 'ClCCNC(=O)N(CCCl)N=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects might be expected from the reactions of C1(C=(OC1((C((=(CNCC=1CCC)CCC=(N))=CO)C)C=C(CC=CCC))CC=COC1CC)N)0000 and COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C1(C=(OC1((C((=(CNCC=1CCC)CCC=(N))=CO)C)C=C(CC=CCC))CC=COC1CC)N)0000\", \"COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The correct expressions for the drugs are CN(C(=O)CN(CCO)CC(=O)N(C)C(C)(C)CC1=CC=CC=C1)C(C)(C)CC1=CC=CC=C1 and COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN(C(=O)CN(CCO)CC(=O)N(C)C(C)(C)CC1=CC=CC=C1)C(C)(C)CC1=CC=CC=C1\", \"COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN(C(=O)CN(CCO)CC(=O)N(C)C(C)(C)CC1=CC=CC=C1)C(C)(C)CC1=CC=CC=C1\",\"smile2\": \"COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CN(C(=O)CN(CCO)CC(=O)N(C)C(C)(C)CC1=CC=CC=C1)C(C)(C)CC1=CC=CC=C1' is combined with 'COC1=C(OC)C=C2C3CC(=O)C(CC(C)C)CN3CCC2=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the possible adverse reactions of (1[]===H[CC@C]@CC@H2NC[C1H1)[]@C2]=)2[O@C=COC@HCCCCCN2C)[]]=C@C(C(10000 and CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"(1[]===H[CC@C]@CC@H2NC[C1H1)[]@C2]=)2[O@C=COC@HCCCCCN2C)[]]=C@C(C(10000\", \"CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12 and CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O are the chosen smile for the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\", \"CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12\",\"smile2\": \"CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[H][C@]1(C[C@@H]2CC[N@]1C[C@@H]2C=C)[C@H](O)C1=CC=NC2=CC=C(OC)C=C12' may increase the qtc-prolonging activities of 'CC(C)C[C@H](NC(=O)[C@@H](CC1=CC=C(NC(N)=O)C=C1)NC(=O)[C@H](CC1=CC=C(NC(=O)[C@@H]2CC(=O)NC(=O)N2)C=C1)NC(=O)[C@H](CO)NC(=O)[C@@H](CC1=CC=CN=C1)NC(=O)[C@@H](CC1=CC=C(Cl)C=C1)NC(=O)[C@@H](CC1=CC=C2C=CC=CC2=C1)NC(C)=O)C(=O)N[C@@H](CCCCNC(C)C)C(=O)N1CCC[C@H]1C(=O)N[C@H](C)C(N)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the expected side effects for both CCCO@@@])@])@CO(2[C)O(2@()C([(][))@(NCCH()@[H@]CC))CH[C[@CO[H[)@([[C(CC(@N@O[](O[@C@]C@]@C()1[(C)((@C2C@HHHO]1O@(H]CH[]C]C@H)@H=]]O)CC)][]H@CO)O]C@C@2(H]@CH(C[CHC)CC)(]CO[[)C0000 and ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CCCO@@@])@])@CO(2[C)O(2@()C([(][))@(NCCH()@[H@]CC))CH[C[@CO[H[)@([[C(CC(@N@O[](O[@C@]C@]@C()1[(C)((@C2C@HHHO]1O@(H]CH[]C]C@H)@H=]]O)CC)][]H@CO)O]C@C@2(H]@CH(C[CHC)CC)(]CO[[)C0000\", \"ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The drugs' correct smile are CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)CN(C)[C@H](C)[C@@H](O)[C@]1(C)O and ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)CN(C)[C@H](C)[C@@H](O)[C@]1(C)O\", \"ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)CN(C)[C@H](C)[C@@H](O)[C@]1(C)O\",\"smile2\": \"ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of 'ClC1=CC=CC=C1CN1CCC2=C(C1)C=CS2' can be decreased when combined with 'CC[C@H]1OC(=O)[C@H](C)[C@@H](O[C@H]2C[C@@](C)(OC)[C@@H](O)[C@H](C)O2)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)C[C@@H]([C@H]2O)N(C)C)[C@](C)(O)C[C@@H](C)CN(C)[C@H](C)[C@@H](O)[C@]1(C)O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How will (11NC===C)ClCCC=C2=1NC(C(2CN1=C)CC2OC2)CCCCC=CCCC=0000 and NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1 potentially influence side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"(11NC===C)ClCCC=C2=1NC(C(2CN1=C)CC2OC2)CCCCC=CCCC=0000\", \"NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CN1CCCC(CC1)N1N=C(CC2=CC=C(Cl)C=C2)C2=CC=CC=C2C1=O and NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1 are the expressions best matching the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCCC(CC1)N1N=C(CC2=CC=C(Cl)C=C2)C2=CC=CC=C2C1=O\", \"NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1CCCC(CC1)N1N=C(CC2=CC=C(Cl)C=C2)C2=CC=CC=C2C1=O\",\"smile2\": \"NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1\",\"interact_result\": \"#drug1 may increase the central nervous system depressant (cns depressant) activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CN1CCCC(CC1)N1N=C(CC2=CC=C(Cl)C=C2)C2=CC=CC=C2C1=O' may increase the central nervous system depressant (cns depressant) activities of 'NC1=NC(N)=C(N=N1)C1=C(Cl)C(Cl)=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the side effects of N1C=CCCCN(C)C1C(COOO)=)(O2)C1CCN==)NC12(C=(C=CCC1=CN1=0000 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC impacting patients. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"N1C=CCCCN(C)C1C(COOO)=)(O2)C1CCN==)NC12(C=(C=CCC1=CN1=0000\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1 and COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC are the expressions best matching the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1\", \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1\",\"smile2\": \"COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'COC1=C(OC)C=C2C(N)=NC(=NC2=C1)N1CCN(CC1)C(=O)C1=CC=CO1' is combined with 'COC(=O)C1=C(C)NC(C)=C(C1C1=CC=CC=C1[N+]([O-])=O)C(=O)OC'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects might occur with )(COOC==CN)(N)1C==1CCCCC=(O0000 and C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\")(COOC==CN)(N)1C==1CCCCC=(O0000\", \"C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The drugs' smiles should be NC1=CC=C(C=C1)C(=O)NCC(O)=O and C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"NC1=CC=C(C=C1)C(=O)NCC(O)=O\", \"C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"NC1=CC=C(C=C1)C(=O)NCC(O)=O\",\"smile2\": \"C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F\",\"interact_result\": \"the serum concentration of #drug2 can be increased when it is combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the serum concentration of 'C\\C(O)=C(/C#N)C(=O)NC1=CC=C(C=C1)C(F)(F)F' can be increased when it is combined with 'NC1=CC=C(C=C1)C(=O)NCC(O)=O'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your opinion on the side effects of ]((C(]C)1(=H2@CH=1C@C][CC@@)=[C]@C@CC=O)CC)O(2[@[(([@2C@=([)[]C[CO1C)]OOOC)()]C@1CC2C]]HC[)0000 and [H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"]((C(]C)1(=H2@CH=1C@C][CC@@)=[C]@C@CC=O)CC)O(2[@[(([@2C@=([)[]C[CO1C)]OOOC)()]C@1CC2C]]HC[)0000\", \"[H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "[H][C@@]12CC[C@](O)(C(=O)COC(C)=O)[C@@]1(C)CC(=O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C and [H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@@]12CC[C@](O)(C(=O)COC(C)=O)[C@@]1(C)CC(=O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C\", \"[H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@@]12CC[C@](O)(C(=O)COC(C)=O)[C@@]1(C)CC(=O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C\",\"smile2\": \"[H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1\",\"interact_result\": \"the metabolism of #drug2 can be decreased when combined with #drug1.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the metabolism of '[H][C@@]12[C@@H](C)C(=O)[C@H](C)C[C@@](C)(OC)[C@H](O[C@@H]3O[C@H](C)C[C@@H]([C@H]3O)N(C)C)[C@@H](C)C(=O)[C@@H](C)C(=O)O[C@H](CC)[C@@]1(C)OC(=O)N2CCCCN1C=NC(=C1)C1=CC=CN=C1' can be decreased when combined with '[H][C@@]12CC[C@](O)(C(=O)COC(C)=O)[C@@]1(C)CC(=O)[C@@]1([H])[C@@]2([H])CCC2=CC(=O)CC[C@]12C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What side effects should be expected from the reactions of (]CHO(C)=)(@NCC=@HOCC=)C]]N(C))((C(COC1CCC2)OC]))]H2=HCHNCC(1[O[=)=O)1C))(C@]CC)(N==(=)NC)1=NC[(=CNNCC)NCC@)CNCO(CC@C[C@=CO(CC)(1H=1[[OCN((CC=)]C=C)CHO[C11)C=HCCN=O1O1(@CCN(CH)=OC@=@(CC[ON][(@CCC==N@CN@)((]NCN0000 and ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"(]CHO(C)=)(@NCC=@HOCC=)C]]N(C))((C(COC1CCC2)OC]))]H2=HCHNCC(1[O[=)=O)1C))(C@]CC)(N==(=)NC)1=NC[(=CNNCC)NCC@)CNCO(CC@C[C@=CO(CC)(1H=1[[OCN((CC=)]C=C)CHO[C11)C=HCCN=O1O1(@CCN(CH)=OC@=@(CC[ON][(@CCC==N@CN@)((]NCN0000\", \"ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles best reflecting the molecules are CC(C)C[C@H](NC(=O)[C@@H](COC(C)(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=CC=CC=C12)NC(=O)[C@H](CC1=CN=CN1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N[C@@H](CCCN=C(N)N)C(=O)N1CCC[C@H]1C(=O)NNC(N)=O and ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC(C)C[C@H](NC(=O)[C@@H](COC(C)(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=CC=CC=C12)NC(=O)[C@H](CC1=CN=CN1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N[C@@H](CCCN=C(N)N)C(=O)N1CCC[C@H]1C(=O)NNC(N)=O\", \"ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC(C)C[C@H](NC(=O)[C@@H](COC(C)(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=CC=CC=C12)NC(=O)[C@H](CC1=CN=CN1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N[C@@H](CCCN=C(N)N)C(=O)N1CCC[C@H]1C(=O)NNC(N)=O\",\"smile2\": \"ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC(C)C[C@H](NC(=O)[C@@H](COC(C)(C)C)NC(=O)[C@H](CC1=CC=C(O)C=C1)NC(=O)[C@H](CO)NC(=O)[C@H](CC1=CNC2=CC=CC=C12)NC(=O)[C@H](CC1=CN=CN1)NC(=O)[C@@H]1CCC(=O)N1)C(=O)N[C@@H](CCCN=C(N)N)C(=O)N1CCC[C@H]1C(=O)NNC(N)=O' may increase the qtc-prolonging activities of 'ClC1=CC=CC(N2CCN(CCCCOC3=CC4=C(CCC(=O)N4)C=C3)CC2)=C1Cl'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the reactions of C[OC2lCCO=)N=]11=@C1CCN=CCCNNCN=C(=)()ON1C=(C=()C21CH(C1C)@0000 and CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C influencing side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C[OC2lCCO=)N=]11=@C1CCN=CCCNNCN=C(=)()ON1C=(C=()C21CH(C1C)@0000\", \"CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CN1CCN(CC1)C(=O)O[C@@H]1N(C(=O)C2=NC=CN=C12)C1=NC=C(Cl)C=C1 and CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CN1CCN(CC1)C(=O)O[C@@H]1N(C(=O)C2=NC=CN=C12)C1=NC=C(Cl)C=C1\", \"CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CN1CCN(CC1)C(=O)O[C@@H]1N(C(=O)C2=NC=CN=C12)C1=NC=C(Cl)C=C1\",\"smile2\": \"CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'CN1CCN(CC1)C(=O)O[C@@H]1N(C(=O)C2=NC=CN=C12)C1=NC=C(Cl)C=C1' is combined with 'CN1CCCCC1C(=O)NC1=C(C)C=CC=C1C'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the side effects of 1Nl)lCCO(=CC)CCC1)=CC=CC(C(C=)CN(C1HNC(=)[1]=CC=C@C[1CNC)]C1FN=(110000 and OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1 to be. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"1Nl)lCCO(=CC)CCC1)=CC=CC(C(C=)CN(C1HNC(=)[1]=CC=C@C[1CNC)]C1FN=(110000\", \"OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "[H][C@](C)(OC1=CC(=CN=C1N)C1=CN(N=C1)C1CCNCC1)C1=C(Cl)C=CC(F)=C1Cl and OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1 are the chosen smile for the drugs. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[H][C@](C)(OC1=CC(=CN=C1N)C1=CN(N=C1)C1CCNCC1)C1=C(Cl)C=CC(F)=C1Cl\", \"OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[H][C@](C)(OC1=CC(=CN=C1N)C1=CN(N=C1)C1CCNCC1)C1=C(Cl)C=CC(F)=C1Cl\",\"smile2\": \"OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1\",\"interact_result\": \"#drug1 may increase the qtc-prolonging activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[H][C@](C)(OC1=CC(=CN=C1N)C1=CN(N=C1)C1CCNCC1)C1=C(Cl)C=CC(F)=C1Cl' may increase the qtc-prolonging activities of 'OCCOCCN1CCN(CC1)C(C1=CC=CC=C1)C1=CC=C(Cl)C=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your prediction on the side effects of OClC(CC(21C)O()==)==C)CN2S1=NCC=(0000 and NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl interactions. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"OClC(CC(21C)O()==)==C)CN2S1=NCC=(0000\", \"NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2 and NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl are the true facial expressions for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2\", \"NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2\",\"smile2\": \"NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl\",\"interact_result\": \"#drug1 may increase the hypotensive activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'CC1=NS(=O)(=O)C2=C(N1)C=CC(Cl)=C2' may increase the hypotensive activities of 'NC(=N)NC(=O)CC1=C(Cl)C=CC=C1Cl'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What are the predicted side effects of C11C=CO=CCC)=1CCC2)C1NCC1)CCC=C=N2==CC=F(C)(CC)CN(11CCCCCC(F=1(C=0000 and OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"C11C=CO=CCC)=1CCC2)C1NCC1)CCC=C=N2==CC=F(C)(CC)CN(11CCCCCC(F=1(C=0000\", \"OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1 and OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1 are the right smiles for the molecules. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1\", \"OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1\",\"smile2\": \"OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1\",\"interact_result\": \"the risk or severity of adverse effects can be increased when #drug1 is combined with #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "the risk or severity of adverse effects can be increased when 'FC1=CC=C(C=C1)C(CCCN1CCC2(CC1)N(CNC2=O)C1=CC=CC=C1)C1=CC=C(F)C=C1' is combined with 'OC(=O)C1(CCN(CCC(C#N)(C2=CC=CC=C2)C2=CC=CC=C2)CC1)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How might [O]N-N#]+[0000 and CN[C@@H](C)[C@H](O)C1=CC=CC=C1 lead to side effects. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[O]N-N#]+[0000\", \"CN[C@@H](C)[C@H](O)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\": [[\"invalid smile\"]]}"
            },
            {
                "from": "gpt",
                "value": "The smile pairs you entered may be wrong, please give me the correct pairs."
            },
            {
                "from": "human",
                "value": "The smiles that defines the molecules are [O-][N+]#N and CN[C@@H](C)[C@H](O)C1=CC=CC=C1. MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_drug_response_predict\", \"arguments\": {\"smiles_pairs\": [[\"[O-][N+]#N\", \"CN[C@@H](C)[C@H](O)C1=CC=CC=C1\"]]}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predicts the result of the interaction between two drugs\", \"result\":[{\"smile1\": \"[O-][N+]#N\",\"smile2\": \"CN[C@@H](C)[C@H](O)C1=CC=CC=C1\",\"interact_result\": \"#drug1 may increase the arrhythmogenic activities of #drug2.\"}]} MemoryPool(arguments=dict_keys(['user_smile_pairs', 'drug_drug_response_result']))"
            },
            {
                "from": "gpt",
                "value": "'[O-][N+]#N' may increase the arrhythmogenic activities of 'CN[C@@H](C)[C@H](O)C1=CC=CC=C1'."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_drug_response_predict\", \"description\": \"Predict drug-drug interactions based on given drug SMILES pairs.\", \"parameters\": {\"type\": \"object\", \"properties\": {\"smiles_pairs\": {\"type\": \"array\", \"items\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}}}}}, \"required\": [\"smiles_pairs\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1' and the target protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\"], \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\", \"value\": 3000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1' and the protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST' is 3000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity between the drug 'CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N)CCSC)C(=O)O' and the target protein 'MAIYADNSYSIGNTPLVRLKHFGHNGNVVVKIEGRNPSYSVKCRIGANMVWQAEKDGTLTKGKEIVDATSGNTGIALAYVAAARGYKITLTMPETMSLERKRLLCGLGVNLVLTEGAKGMKGAIAKAEEIVASDPSRYVMLKQFENPANPQIHRETTGPEIWKDTDGKVDVVVAGVGTGGSITGISRAIKLDFGKQITSVAVEPVESPVISQTLAGEEVKPGPHKIQGIGAGFIPKNLDLSIIDRVETVDSDTALATARRLMAEEGILAGISSGAAVAAADRLAKLPEFADKLIVVILPSASERYLSTALFEGIEG'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N)CCSC)C(=O)O\"], \"target_seq\": \"MAIYADNSYSIGNTPLVRLKHFGHNGNVVVKIEGRNPSYSVKCRIGANMVWQAEKDGTLTKGKEIVDATSGNTGIALAYVAAARGYKITLTMPETMSLERKRLLCGLGVNLVLTEGAKGMKGAIAKAEEIVASDPSRYVMLKQFENPANPQIHRETTGPEIWKDTDGKVDVVVAGVGTGGSITGISRAIKLDFGKQITSVAVEPVESPVISQTLAGEEVKPGPHKIQGIGAGFIPKNLDLSIIDRVETVDSDTALATARRLMAEEGILAGISSGAAVAAADRLAKLPEFADKLIVVILPSASERYLSTALFEGIEG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAIYADNSYSIGNTPLVRLKHFGHNGNVVVKIEGRNPSYSVKCRIGANMVWQAEKDGTLTKGKEIVDATSGNTGIALAYVAAARGYKITLTMPETMSLERKRLLCGLGVNLVLTEGAKGMKGAIAKAEEIVASDPSRYVMLKQFENPANPQIHRETTGPEIWKDTDGKVDVVVAGVGTGGSITGISRAIKLDFGKQITSVAVEPVESPVISQTLAGEEVKPGPHKIQGIGAGFIPKNLDLSIIDRVETVDSDTALATARRLMAEEGILAGISSGAAVAAADRLAKLPEFADKLIVVILPSASERYLSTALFEGIEG\", \"result\": [{\"smile\": \"CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N)CCSC)C(=O)O\", \"value\": 1023293.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@H](C)[C@H](NC(=O)CNC(=O)[C@H](CC(=O)O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](N)CCSC)C(=O)O' and the protein 'MAIYADNSYSIGNTPLVRLKHFGHNGNVVVKIEGRNPSYSVKCRIGANMVWQAEKDGTLTKGKEIVDATSGNTGIALAYVAAARGYKITLTMPETMSLERKRLLCGLGVNLVLTEGAKGMKGAIAKAEEIVASDPSRYVMLKQFENPANPQIHRETTGPEIWKDTDGKVDVVVAGVGTGGSITGISRAIKLDFGKQITSVAVEPVESPVISQTLAGEEVKPGPHKIQGIGAGFIPKNLDLSIIDRVETVDSDTALATARRLMAEEGILAGISSGAAVAAADRLAKLPEFADKLIVVILPSASERYLSTALFEGIEG' is 1023293.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted affinity value for the drug 'CC[C@H](C)[C@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)[C@H](CCCCN)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N1CCC[C@H]1C(N)=O' and the target protein 'MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](C)[C@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)[C@H](CCCCN)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N1CCC[C@H]1C(N)=O\"], \"target_seq\": \"MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD\", \"result\": [{\"smile\": \"CC[C@H](C)[C@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)[C@H](CCCCN)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N1CCC[C@H]1C(N)=O\", \"value\": 600.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@H](C)[C@H](NC(=O)[C@H](CC(=O)O)NC(=O)[C@H](Cc1c[nH]c2ccccc12)NC(=O)[C@H](CCCCN)NC(=O)[C@@H]1CCCN1C(=O)[C@H](CC(C)C)NC(=O)[C@H](CCSC)NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N1CCC[C@H]1C(N)=O' and the protein 'MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD' is 600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity value of the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' for the target protein 'MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 5200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD' is 5200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the target protein 'MAGIFYFALFSCLFGICDAVTGSRVYPANEVTLLDSRSVQGELGWIASPLEGGWEEVSIMDEKNTPIRTYQVCNVMEPSQNNWLRTDWITREGAQRVYIEIKFTLRDCNSLPGVMGTCKETFNLYYYESDNDKERFIRENQFVKIDTIAADESFTQVDIGDRIMKLNTEIRDVGPLSKKGFYLAFQDVGACIALVSVRVFYKKCPLTVRNLAQFPDTITGADTSSLVEVRGSCVNNSEEKDVPKMYCGADGEWLVPIGNCLCNAGHEERSGECQACKIGYYKALSTDATCAKCPPHSYSVWEGATSCTCDRGFFRADNDAASMPCTRPPSAPLNLISNVNETSVNLEWSSPQNTGGRQDISYNVVCKKCGAGDPSKCRPCGSGVHYTPQQNGLKTTKVSITDLLAHTNYTFEIWAVNGVSKYNPNPDQSVSVTVTTNQAAPSSIALVQAKEVTRYSVALAWLEPDRPNGVILEYEVKYYEKDQNERSYRIVRTAARNTDIKGLNPLTSYVFHVRARTAAGYGDFSEPLEVTTNTVPSRIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSKAKQEADEEKHLNQGVRTYVDPFTYEDPNQAVREFAKEIDASCIKIEKVIGVGEFGEVCSGRLKVPGKREICVAIKTLKAGYTDKQRRDFLSEASIMGQFDHPNIIHLEGVVTKCKPVMIITEYMENGSLDAFLRKNDGRFTVIQLVGMLRGIGSGMKYLSDMSYVHRDLAARNILVNSNLVCKVSDFGMSRVLEDDPEAAYTTRGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVIKAIEEGYRLPPPMDCPIALHQLMLDCWQKERSDRPKFGQIVNMLDKLIRNPNSLKRTGTESSRPNTALLDPSSPEFSAVVSVGDWLQAIKMDRYKDNFTAAGYTTLEAVVHVNQEDLARIGITAITHQNKILSSVQAMRTQMQQMHGRMVPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\"], \"target_seq\": \"MAGIFYFALFSCLFGICDAVTGSRVYPANEVTLLDSRSVQGELGWIASPLEGGWEEVSIMDEKNTPIRTYQVCNVMEPSQNNWLRTDWITREGAQRVYIEIKFTLRDCNSLPGVMGTCKETFNLYYYESDNDKERFIRENQFVKIDTIAADESFTQVDIGDRIMKLNTEIRDVGPLSKKGFYLAFQDVGACIALVSVRVFYKKCPLTVRNLAQFPDTITGADTSSLVEVRGSCVNNSEEKDVPKMYCGADGEWLVPIGNCLCNAGHEERSGECQACKIGYYKALSTDATCAKCPPHSYSVWEGATSCTCDRGFFRADNDAASMPCTRPPSAPLNLISNVNETSVNLEWSSPQNTGGRQDISYNVVCKKCGAGDPSKCRPCGSGVHYTPQQNGLKTTKVSITDLLAHTNYTFEIWAVNGVSKYNPNPDQSVSVTVTTNQAAPSSIALVQAKEVTRYSVALAWLEPDRPNGVILEYEVKYYEKDQNERSYRIVRTAARNTDIKGLNPLTSYVFHVRARTAAGYGDFSEPLEVTTNTVPSRIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSKAKQEADEEKHLNQGVRTYVDPFTYEDPNQAVREFAKEIDASCIKIEKVIGVGEFGEVCSGRLKVPGKREICVAIKTLKAGYTDKQRRDFLSEASIMGQFDHPNIIHLEGVVTKCKPVMIITEYMENGSLDAFLRKNDGRFTVIQLVGMLRGIGSGMKYLSDMSYVHRDLAARNILVNSNLVCKVSDFGMSRVLEDDPEAAYTTRGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVIKAIEEGYRLPPPMDCPIALHQLMLDCWQKERSDRPKFGQIVNMLDKLIRNPNSLKRTGTESSRPNTALLDPSSPEFSAVVSVGDWLQAIKMDRYKDNFTAAGYTTLEAVVHVNQEDLARIGITAITHQNKILSSVQAMRTQMQQMHGRMVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGIFYFALFSCLFGICDAVTGSRVYPANEVTLLDSRSVQGELGWIASPLEGGWEEVSIMDEKNTPIRTYQVCNVMEPSQNNWLRTDWITREGAQRVYIEIKFTLRDCNSLPGVMGTCKETFNLYYYESDNDKERFIRENQFVKIDTIAADESFTQVDIGDRIMKLNTEIRDVGPLSKKGFYLAFQDVGACIALVSVRVFYKKCPLTVRNLAQFPDTITGADTSSLVEVRGSCVNNSEEKDVPKMYCGADGEWLVPIGNCLCNAGHEERSGECQACKIGYYKALSTDATCAKCPPHSYSVWEGATSCTCDRGFFRADNDAASMPCTRPPSAPLNLISNVNETSVNLEWSSPQNTGGRQDISYNVVCKKCGAGDPSKCRPCGSGVHYTPQQNGLKTTKVSITDLLAHTNYTFEIWAVNGVSKYNPNPDQSVSVTVTTNQAAPSSIALVQAKEVTRYSVALAWLEPDRPNGVILEYEVKYYEKDQNERSYRIVRTAARNTDIKGLNPLTSYVFHVRARTAAGYGDFSEPLEVTTNTVPSRIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSKAKQEADEEKHLNQGVRTYVDPFTYEDPNQAVREFAKEIDASCIKIEKVIGVGEFGEVCSGRLKVPGKREICVAIKTLKAGYTDKQRRDFLSEASIMGQFDHPNIIHLEGVVTKCKPVMIITEYMENGSLDAFLRKNDGRFTVIQLVGMLRGIGSGMKYLSDMSYVHRDLAARNILVNSNLVCKVSDFGMSRVLEDDPEAAYTTRGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVIKAIEEGYRLPPPMDCPIALHQLMLDCWQKERSDRPKFGQIVNMLDKLIRNPNSLKRTGTESSRPNTALLDPSSPEFSAVVSVGDWLQAIKMDRYKDNFTAAGYTTLEAVVHVNQEDLARIGITAITHQNKILSSVQAMRTQMQQMHGRMVPV\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'MAGIFYFALFSCLFGICDAVTGSRVYPANEVTLLDSRSVQGELGWIASPLEGGWEEVSIMDEKNTPIRTYQVCNVMEPSQNNWLRTDWITREGAQRVYIEIKFTLRDCNSLPGVMGTCKETFNLYYYESDNDKERFIRENQFVKIDTIAADESFTQVDIGDRIMKLNTEIRDVGPLSKKGFYLAFQDVGACIALVSVRVFYKKCPLTVRNLAQFPDTITGADTSSLVEVRGSCVNNSEEKDVPKMYCGADGEWLVPIGNCLCNAGHEERSGECQACKIGYYKALSTDATCAKCPPHSYSVWEGATSCTCDRGFFRADNDAASMPCTRPPSAPLNLISNVNETSVNLEWSSPQNTGGRQDISYNVVCKKCGAGDPSKCRPCGSGVHYTPQQNGLKTTKVSITDLLAHTNYTFEIWAVNGVSKYNPNPDQSVSVTVTTNQAAPSSIALVQAKEVTRYSVALAWLEPDRPNGVILEYEVKYYEKDQNERSYRIVRTAARNTDIKGLNPLTSYVFHVRARTAAGYGDFSEPLEVTTNTVPSRIIGDGANSTVLLVSVSGSVVLVVILIAAFVISRRRSKYSKAKQEADEEKHLNQGVRTYVDPFTYEDPNQAVREFAKEIDASCIKIEKVIGVGEFGEVCSGRLKVPGKREICVAIKTLKAGYTDKQRRDFLSEASIMGQFDHPNIIHLEGVVTKCKPVMIITEYMENGSLDAFLRKNDGRFTVIQLVGMLRGIGSGMKYLSDMSYVHRDLAARNILVNSNLVCKVSDFGMSRVLEDDPEAAYTTRGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVIKAIEEGYRLPPPMDCPIALHQLMLDCWQKERSDRPKFGQIVNMLDKLIRNPNSLKRTGTESSRPNTALLDPSSPEFSAVVSVGDWLQAIKMDRYKDNFTAAGYTTLEAVVHVNQEDLARIGITAITHQNKILSSVQAMRTQMQQMHGRMVPV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the target protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' to the target protein 'MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIKHELFKEARKYPLHQLLQDESSYIFVSVTQEAEREEFFDETRRLCDLRLFQPFLKVIEPVGNREEKILNREIGFAIGMPVCEFDMVKDPEVQDFRRNILNVCKEAVDLRDLNSPHSRAMYVYPPNVESSPELPKHIYNKLDKGQIIVVIWVIVSPNNDKQKYTLKINHDCVPEQVIAEAIRKKTRSMLLSSEQLKLCVLEYQGKYILKVCGCDEYFLEKYPLSQYKYIRSCIMLGRMPNLMLMAKESLYSQLPMDCFTMPSYSRRISTATPYMNGETSTKSLWVINSALRIKILCATYVNVNIRDIDKIYVRTGIYHGGEPLCDNVNTQRVPCSNPRWNEWLNYDIYIPDLPRAARLCLSICSVKGRKGAKEEHCPLAWGNINLFDYTDTLVSGKMALNLWPVPHGLEDLLNPIGVTGSNPNKETPCLELEFDWFSSVVKFPDMSVIEEHANWSVSREAGFSYSHAGLSNRLARDNELRENDKEQLKAISTRDPLSEITEQEKDFLWSHRHYCVTIPEILPKLLLSVKWNSRDEVAQMYCLVKDWPPIKPEQAMELLDCNYPDPMVRGFAVRCLEKYLTDDKLSQYLIQLVQVLKYEQYLDNLLVRFLLKKALTNQRIGHFFFWHLKSEMHNKTVSQRFGLLLESYCRACGMYLKHLNRQVEAMEKLINLTDILKQEKKDETQKVQMKFLVEQMRRPDFMDALQGFLSPLNPAHQLGNLRLEECRIMSSAKRPLWLNWENPDIMSELLFQNNEIIFKNGDDLRQDMLTLQIIRIMENIWQNQGLDLRMLPYGCLSIGDCVGLIEVVRNSHTIMQIQCKGGLKGALQFNSHTLHQWLKDKNKGEIYDAAIDLFTRSCAGYCVATFILGIGDRHNSNIMVKDDGQLFHIDFGHFLDHKKKKFGYKRERVPFVLTQDFLIVISKGAQECTKTREFERFQEMCYKAYLAIRQHANLFINLFSMMLGSGMPELQSFDDIAYIRKTLALDKTEQEALEYFMKQMNDAHHGGWTTKMDWIFHTIKQHALN'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIKHELFKEARKYPLHQLLQDESSYIFVSVTQEAEREEFFDETRRLCDLRLFQPFLKVIEPVGNREEKILNREIGFAIGMPVCEFDMVKDPEVQDFRRNILNVCKEAVDLRDLNSPHSRAMYVYPPNVESSPELPKHIYNKLDKGQIIVVIWVIVSPNNDKQKYTLKINHDCVPEQVIAEAIRKKTRSMLLSSEQLKLCVLEYQGKYILKVCGCDEYFLEKYPLSQYKYIRSCIMLGRMPNLMLMAKESLYSQLPMDCFTMPSYSRRISTATPYMNGETSTKSLWVINSALRIKILCATYVNVNIRDIDKIYVRTGIYHGGEPLCDNVNTQRVPCSNPRWNEWLNYDIYIPDLPRAARLCLSICSVKGRKGAKEEHCPLAWGNINLFDYTDTLVSGKMALNLWPVPHGLEDLLNPIGVTGSNPNKETPCLELEFDWFSSVVKFPDMSVIEEHANWSVSREAGFSYSHAGLSNRLARDNELRENDKEQLKAISTRDPLSEITEQEKDFLWSHRHYCVTIPEILPKLLLSVKWNSRDEVAQMYCLVKDWPPIKPEQAMELLDCNYPDPMVRGFAVRCLEKYLTDDKLSQYLIQLVQVLKYEQYLDNLLVRFLLKKALTNQRIGHFFFWHLKSEMHNKTVSQRFGLLLESYCRACGMYLKHLNRQVEAMEKLINLTDILKQEKKDETQKVQMKFLVEQMRRPDFMDALQGFLSPLNPAHQLGNLRLEECRIMSSAKRPLWLNWENPDIMSELLFQNNEIIFKNGDDLRQDMLTLQIIRIMENIWQNQGLDLRMLPYGCLSIGDCVGLIEVVRNSHTIMQIQCKGGLKGALQFNSHTLHQWLKDKNKGEIYDAAIDLFTRSCAGYCVATFILGIGDRHNSNIMVKDDGQLFHIDFGHFLDHKKKKFGYKRERVPFVLTQDFLIVISKGAQECTKTREFERFQEMCYKAYLAIRQHANLFINLFSMMLGSGMPELQSFDDIAYIRKTLALDKTEQEALEYFMKQMNDAHHGGWTTKMDWIFHTIKQHALN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIKHELFKEARKYPLHQLLQDESSYIFVSVTQEAEREEFFDETRRLCDLRLFQPFLKVIEPVGNREEKILNREIGFAIGMPVCEFDMVKDPEVQDFRRNILNVCKEAVDLRDLNSPHSRAMYVYPPNVESSPELPKHIYNKLDKGQIIVVIWVIVSPNNDKQKYTLKINHDCVPEQVIAEAIRKKTRSMLLSSEQLKLCVLEYQGKYILKVCGCDEYFLEKYPLSQYKYIRSCIMLGRMPNLMLMAKESLYSQLPMDCFTMPSYSRRISTATPYMNGETSTKSLWVINSALRIKILCATYVNVNIRDIDKIYVRTGIYHGGEPLCDNVNTQRVPCSNPRWNEWLNYDIYIPDLPRAARLCLSICSVKGRKGAKEEHCPLAWGNINLFDYTDTLVSGKMALNLWPVPHGLEDLLNPIGVTGSNPNKETPCLELEFDWFSSVVKFPDMSVIEEHANWSVSREAGFSYSHAGLSNRLARDNELRENDKEQLKAISTRDPLSEITEQEKDFLWSHRHYCVTIPEILPKLLLSVKWNSRDEVAQMYCLVKDWPPIKPEQAMELLDCNYPDPMVRGFAVRCLEKYLTDDKLSQYLIQLVQVLKYEQYLDNLLVRFLLKKALTNQRIGHFFFWHLKSEMHNKTVSQRFGLLLESYCRACGMYLKHLNRQVEAMEKLINLTDILKQEKKDETQKVQMKFLVEQMRRPDFMDALQGFLSPLNPAHQLGNLRLEECRIMSSAKRPLWLNWENPDIMSELLFQNNEIIFKNGDDLRQDMLTLQIIRIMENIWQNQGLDLRMLPYGCLSIGDCVGLIEVVRNSHTIMQIQCKGGLKGALQFNSHTLHQWLKDKNKGEIYDAAIDLFTRSCAGYCVATFILGIGDRHNSNIMVKDDGQLFHIDFGHFLDHKKKKFGYKRERVPFVLTQDFLIVISKGAQECTKTREFERFQEMCYKAYLAIRQHANLFINLFSMMLGSGMPELQSFDDIAYIRKTLALDKTEQEALEYFMKQMNDAHHGGWTTKMDWIFHTIKQHALN\", \"result\": [{\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the protein 'MPPRPSSGELWGIHLMPPRILVECLLPNGMIVTLECLREATLITIKHELFKEARKYPLHQLLQDESSYIFVSVTQEAEREEFFDETRRLCDLRLFQPFLKVIEPVGNREEKILNREIGFAIGMPVCEFDMVKDPEVQDFRRNILNVCKEAVDLRDLNSPHSRAMYVYPPNVESSPELPKHIYNKLDKGQIIVVIWVIVSPNNDKQKYTLKINHDCVPEQVIAEAIRKKTRSMLLSSEQLKLCVLEYQGKYILKVCGCDEYFLEKYPLSQYKYIRSCIMLGRMPNLMLMAKESLYSQLPMDCFTMPSYSRRISTATPYMNGETSTKSLWVINSALRIKILCATYVNVNIRDIDKIYVRTGIYHGGEPLCDNVNTQRVPCSNPRWNEWLNYDIYIPDLPRAARLCLSICSVKGRKGAKEEHCPLAWGNINLFDYTDTLVSGKMALNLWPVPHGLEDLLNPIGVTGSNPNKETPCLELEFDWFSSVVKFPDMSVIEEHANWSVSREAGFSYSHAGLSNRLARDNELRENDKEQLKAISTRDPLSEITEQEKDFLWSHRHYCVTIPEILPKLLLSVKWNSRDEVAQMYCLVKDWPPIKPEQAMELLDCNYPDPMVRGFAVRCLEKYLTDDKLSQYLIQLVQVLKYEQYLDNLLVRFLLKKALTNQRIGHFFFWHLKSEMHNKTVSQRFGLLLESYCRACGMYLKHLNRQVEAMEKLINLTDILKQEKKDETQKVQMKFLVEQMRRPDFMDALQGFLSPLNPAHQLGNLRLEECRIMSSAKRPLWLNWENPDIMSELLFQNNEIIFKNGDDLRQDMLTLQIIRIMENIWQNQGLDLRMLPYGCLSIGDCVGLIEVVRNSHTIMQIQCKGGLKGALQFNSHTLHQWLKDKNKGEIYDAAIDLFTRSCAGYCVATFILGIGDRHNSNIMVKDDGQLFHIDFGHFLDHKKKKFGYKRERVPFVLTQDFLIVISKGAQECTKTREFERFQEMCYKAYLAIRQHANLFINLFSMMLGSGMPELQSFDDIAYIRKTLALDKTEQEALEYFMKQMNDAHHGGWTTKMDWIFHTIKQHALN' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity value you predict for the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the target protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\"], \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the binding affinity between the drug 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1' and the target protein 'MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\"], \"target_seq\": \"MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE\", \"result\": [{\"smile\": \"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1' and the protein 'MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drug 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' for the target protein 'MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP\", \"result\": [{\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein 'MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding affinity for the drug 'COc1ccc(CCNC[C@H](O)COc2ccc(-c3ncc[nH]3)cc2)cc1OC' and the target protein 'MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(CCNC[C@H](O)COc2ccc(-c3ncc[nH]3)cc2)cc1OC\"], \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\", \"result\": [{\"smile\": \"COc1ccc(CCNC[C@H](O)COc2ccc(-c3ncc[nH]3)cc2)cc1OC\", \"value\": 29512.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc(CCNC[C@H](O)COc2ccc(-c3ncc[nH]3)cc2)cc1OC' and the protein 'MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL' is 29512.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an estimate of the affinity between the drug 'CN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(I)c(c34)C[C@H]21' and the target protein 'MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(I)c(c34)C[C@H]21\"], \"target_seq\": \"MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI\", \"result\": [{\"smile\": \"CN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(I)c(c34)C[C@H]21\", \"value\": 5623.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(I)c(c34)C[C@H]21' and the protein 'MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI' is 5623.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drug 'O=C1N(Cc2cccc(-c3ccccn3)c2)CCCC12CCN(c1cnc3ccccc3n1)CC2'-target protein 'MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1N(Cc2cccc(-c3ccccn3)c2)CCCC12CCN(c1cnc3ccccc3n1)CC2\"], \"target_seq\": \"MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW\", \"result\": [{\"smile\": \"O=C1N(Cc2cccc(-c3ccccn3)c2)CCCC12CCN(c1cnc3ccccc3n1)CC2\", \"value\": 175.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C1N(Cc2cccc(-c3ccccn3)c2)CCCC12CCN(c1cnc3ccccc3n1)CC2' and the protein 'MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW' is 175.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the target protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 940.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE' is 940.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value do you project for the drug 'CCCCCCOC(=O)c1cccnc1' and the target protein 'MSALGVTVALLVWAAFLLLVSMWRQVHSSWNLPPGPFPLPIIGNLFQLELKNIPKSFTRLAQRFGPVFTLYVGSQRMVVMHGYKAVKEALLDYKDEFSGRGDLPAFHAHRDRGIIFNNGPTWKDIRRFSLTTLRNYGMGKQGNESRIQREAHFLLEALRKTQGQPFDPTFLIGCAPCNVIADILFRKHFDYNDEKFLRLMYLFNENFHLLSTPWLQLYNNFPSFLHYLPGSHRKVIKNVAEVKEYVSERVKEHHQSLDPNCPRDLTDCLLVEMEKEKHSAERLYTMDGITVTVADLFFAGTETTSTTLRYGLLILMKYPEIEEKLHEEIDRVIGPSRIPAIKDRQEMPYMDAVVHEIQRFITLVPSNLPHEATRDTIFRGYLIPKGTVVVPTLDSVLYDNQEFPDPEKFKPEHFLNENGKFKYSDYFKPFSTGKRVCAGEGLARMELFLLLCAILQHFNLKPLVDPKDIDLSPIHIGFGCIPPRYKLCVIPRS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCCCOC(=O)c1cccnc1\"], \"target_seq\": \"MSALGVTVALLVWAAFLLLVSMWRQVHSSWNLPPGPFPLPIIGNLFQLELKNIPKSFTRLAQRFGPVFTLYVGSQRMVVMHGYKAVKEALLDYKDEFSGRGDLPAFHAHRDRGIIFNNGPTWKDIRRFSLTTLRNYGMGKQGNESRIQREAHFLLEALRKTQGQPFDPTFLIGCAPCNVIADILFRKHFDYNDEKFLRLMYLFNENFHLLSTPWLQLYNNFPSFLHYLPGSHRKVIKNVAEVKEYVSERVKEHHQSLDPNCPRDLTDCLLVEMEKEKHSAERLYTMDGITVTVADLFFAGTETTSTTLRYGLLILMKYPEIEEKLHEEIDRVIGPSRIPAIKDRQEMPYMDAVVHEIQRFITLVPSNLPHEATRDTIFRGYLIPKGTVVVPTLDSVLYDNQEFPDPEKFKPEHFLNENGKFKYSDYFKPFSTGKRVCAGEGLARMELFLLLCAILQHFNLKPLVDPKDIDLSPIHIGFGCIPPRYKLCVIPRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSALGVTVALLVWAAFLLLVSMWRQVHSSWNLPPGPFPLPIIGNLFQLELKNIPKSFTRLAQRFGPVFTLYVGSQRMVVMHGYKAVKEALLDYKDEFSGRGDLPAFHAHRDRGIIFNNGPTWKDIRRFSLTTLRNYGMGKQGNESRIQREAHFLLEALRKTQGQPFDPTFLIGCAPCNVIADILFRKHFDYNDEKFLRLMYLFNENFHLLSTPWLQLYNNFPSFLHYLPGSHRKVIKNVAEVKEYVSERVKEHHQSLDPNCPRDLTDCLLVEMEKEKHSAERLYTMDGITVTVADLFFAGTETTSTTLRYGLLILMKYPEIEEKLHEEIDRVIGPSRIPAIKDRQEMPYMDAVVHEIQRFITLVPSNLPHEATRDTIFRGYLIPKGTVVVPTLDSVLYDNQEFPDPEKFKPEHFLNENGKFKYSDYFKPFSTGKRVCAGEGLARMELFLLLCAILQHFNLKPLVDPKDIDLSPIHIGFGCIPPRYKLCVIPRS\", \"result\": [{\"smile\": \"CCCCCCOC(=O)c1cccnc1\", \"value\": 1400.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCCCOC(=O)c1cccnc1' and the protein 'MSALGVTVALLVWAAFLLLVSMWRQVHSSWNLPPGPFPLPIIGNLFQLELKNIPKSFTRLAQRFGPVFTLYVGSQRMVVMHGYKAVKEALLDYKDEFSGRGDLPAFHAHRDRGIIFNNGPTWKDIRRFSLTTLRNYGMGKQGNESRIQREAHFLLEALRKTQGQPFDPTFLIGCAPCNVIADILFRKHFDYNDEKFLRLMYLFNENFHLLSTPWLQLYNNFPSFLHYLPGSHRKVIKNVAEVKEYVSERVKEHHQSLDPNCPRDLTDCLLVEMEKEKHSAERLYTMDGITVTVADLFFAGTETTSTTLRYGLLILMKYPEIEEKLHEEIDRVIGPSRIPAIKDRQEMPYMDAVVHEIQRFITLVPSNLPHEATRDTIFRGYLIPKGTVVVPTLDSVLYDNQEFPDPEKFKPEHFLNENGKFKYSDYFKPFSTGKRVCAGEGLARMELFLLLCAILQHFNLKPLVDPKDIDLSPIHIGFGCIPPRYKLCVIPRS' is 1400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 1.5}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 1.5."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted binding strength of the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1' with the target protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\"], \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF\", \"result\": [{\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 530.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1' and the protein 'MGNAATAKKGSEVESVKEFLAKAKEDFLKKWENPTQNNAGLEDFERKKTLGTGSFGRVMLVKHKATEQYYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVRLEYAFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDHQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVSDIKTHKWFATTDWIAIYQRKVEAPFIPKFRGSGDTSNFDDYEEEDIRVSITEKCAKEFGEF' is 530.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the affinity value of the drug 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1' toward the target protein 'MCTVVDPRIVRRYLLRRQLGQGAYGIVWKAVDRRTGEVVAIKKIFDAFRDKTDAQRTFREITLLQEFGDHPNIISLLDVIRAENDRDIYLVFEFMDTDLNAVIRKGGLLQDVHVRSIFYQLLRATRFLHSGHVVHRDQKPSNVLLDANCTVKLCDFGLARSLGDLPEGPEDQAVTEYVATRWYRAPEVLLSSHRYTLGVDMWSLGCILGEMLRGRPLFPGTSTLHQLELILETIPPPSEEDLLALGSGCRASVLHQLGSRPRQTLDALLPPDTSPEALDLLRRLLVFAPDKRLSATQALQHPYVQRFHCPSDEWAREADVRPRAHEGVQLSVPEYRSRVYQMILECGGSSGTSREKGPEGVSPSQAHLHKPRADPQLPSRTPVQGPRPRPQSSPGHDPAEHESPRAAKNVPRQNSAPLLQTALLGNGERPPGAKEAPPLTLSLVKPSGRGAAPSLTSQAAAQVANQALIRGDWNRGGGVRVASVQQVPPRLPPEARPGRRMFSTSALQGAQGGARALLGGYSQAYGTVCHSALGHLPLLEGHHV'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1\"], \"target_seq\": \"MCTVVDPRIVRRYLLRRQLGQGAYGIVWKAVDRRTGEVVAIKKIFDAFRDKTDAQRTFREITLLQEFGDHPNIISLLDVIRAENDRDIYLVFEFMDTDLNAVIRKGGLLQDVHVRSIFYQLLRATRFLHSGHVVHRDQKPSNVLLDANCTVKLCDFGLARSLGDLPEGPEDQAVTEYVATRWYRAPEVLLSSHRYTLGVDMWSLGCILGEMLRGRPLFPGTSTLHQLELILETIPPPSEEDLLALGSGCRASVLHQLGSRPRQTLDALLPPDTSPEALDLLRRLLVFAPDKRLSATQALQHPYVQRFHCPSDEWAREADVRPRAHEGVQLSVPEYRSRVYQMILECGGSSGTSREKGPEGVSPSQAHLHKPRADPQLPSRTPVQGPRPRPQSSPGHDPAEHESPRAAKNVPRQNSAPLLQTALLGNGERPPGAKEAPPLTLSLVKPSGRGAAPSLTSQAAAQVANQALIRGDWNRGGGVRVASVQQVPPRLPPEARPGRRMFSTSALQGAQGGARALLGGYSQAYGTVCHSALGHLPLLEGHHV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MCTVVDPRIVRRYLLRRQLGQGAYGIVWKAVDRRTGEVVAIKKIFDAFRDKTDAQRTFREITLLQEFGDHPNIISLLDVIRAENDRDIYLVFEFMDTDLNAVIRKGGLLQDVHVRSIFYQLLRATRFLHSGHVVHRDQKPSNVLLDANCTVKLCDFGLARSLGDLPEGPEDQAVTEYVATRWYRAPEVLLSSHRYTLGVDMWSLGCILGEMLRGRPLFPGTSTLHQLELILETIPPPSEEDLLALGSGCRASVLHQLGSRPRQTLDALLPPDTSPEALDLLRRLLVFAPDKRLSATQALQHPYVQRFHCPSDEWAREADVRPRAHEGVQLSVPEYRSRVYQMILECGGSSGTSREKGPEGVSPSQAHLHKPRADPQLPSRTPVQGPRPRPQSSPGHDPAEHESPRAAKNVPRQNSAPLLQTALLGNGERPPGAKEAPPLTLSLVKPSGRGAAPSLTSQAAAQVANQALIRGDWNRGGGVRVASVQQVPPRLPPEARPGRRMFSTSALQGAQGGARALLGGYSQAYGTVCHSALGHLPLLEGHHV\", \"result\": [{\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1\", \"value\": 640.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1' and the protein 'MCTVVDPRIVRRYLLRRQLGQGAYGIVWKAVDRRTGEVVAIKKIFDAFRDKTDAQRTFREITLLQEFGDHPNIISLLDVIRAENDRDIYLVFEFMDTDLNAVIRKGGLLQDVHVRSIFYQLLRATRFLHSGHVVHRDQKPSNVLLDANCTVKLCDFGLARSLGDLPEGPEDQAVTEYVATRWYRAPEVLLSSHRYTLGVDMWSLGCILGEMLRGRPLFPGTSTLHQLELILETIPPPSEEDLLALGSGCRASVLHQLGSRPRQTLDALLPPDTSPEALDLLRRLLVFAPDKRLSATQALQHPYVQRFHCPSDEWAREADVRPRAHEGVQLSVPEYRSRVYQMILECGGSSGTSREKGPEGVSPSQAHLHKPRADPQLPSRTPVQGPRPRPQSSPGHDPAEHESPRAAKNVPRQNSAPLLQTALLGNGERPPGAKEAPPLTLSLVKPSGRGAAPSLTSQAAAQVANQALIRGDWNRGGGVRVASVQQVPPRLPPEARPGRRMFSTSALQGAQGGARALLGGYSQAYGTVCHSALGHLPLLEGHHV' is 640.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity rating expected for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the target protein 'MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the affinity between the drug 'O=C(/C=C/N1CCc2c(ncnc2NC2CC2)C1)c1ccccc1O' and the target protein 'MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDESRHCSTGSGSASFAHTAPPPAGVNPDLEEPPLKEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(/C=C/N1CCc2c(ncnc2NC2CC2)C1)c1ccccc1O\"], \"target_seq\": \"MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDESRHCSTGSGSASFAHTAPPPAGVNPDLEEPPLKEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDESRHCSTGSGSASFAHTAPPPAGVNPDLEEPPLKEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED\", \"result\": [{\"smile\": \"O=C(/C=C/N1CCc2c(ncnc2NC2CC2)C1)c1ccccc1O\", \"value\": 49.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(/C=C/N1CCc2c(ncnc2NC2CC2)C1)c1ccccc1O' and the protein 'MSTPDPPLGGTPRPGPSPGPGPSPGAMLGPSPGPSPGSAHSMMGPSPGPPSAGHPIPTQGPGGYPQDNMHQMHKPMESMHEKGMSDDPRYNQMKGMGMRSGGHAGMGPPPSPMDQHSQGYPSPLGGSEHASSPVPASGPSSGPQMSSGPGGAPLDGADPQALGQQNRGPTPFNQNQLHQLRAQIMAYKMLARGQPLPDHLQMAVQGKRPMPGMQQQMPTLPPPSVSATGPGPGPGPGPGPGPGPAPPNYSRPHGMGGPNMPPPGPSGVPPGMPGQPPGGPPKPWPEGPMANAAAPTSTPQKLIPPQPTGRPSPAPPAVPPAASPVMPPQTQSPGQPAQPAPMVPLHQKQSRITPIQKPRGLDPVEILQEREYRLQARIAHRIQELENLPGSLAGDLRTKATIELKALRLLNFQRQLRQEVVVCMRRDTALETALNAKAYKRSKRQSLREARITEKLEKQQKIEQERKRRQKHQEYLNSILQHAKDFKEYHRSVTGKIQKLTKAVATYHANTEREQKKENERIEKERMRRLMAEDEEGYRKLIDQKKDKRLAYLLQQTDEYVANLTELVRQHKAAQVAKEKKKKKKKKKAENAEGQTPAIGPDGEPLDETSQMSDLPVKVIHVESGKILTGTDAPKAGQLEAWLEMNPGYEVAPRSDSEESGSEEEEEEEEEEQPQAAQPPTLPVEEKKKIPDPDSDDVSEVDARHIIENAKQDVDDEYGVSQALARGLQSYYAVAHAVTERVDKQSALMVNGVLKQYQIKGLEWLVSLYNNNLNGILADEMGLGKTIQTIALITYLMEHKRINGPFLIIVPLSTLSNWAYEFDKWAPSVVKVSYKGSPAARRAFVPQLRSGKFNVLLTTYEYIIKDKHILAKIRWKYMIVDEGHRMKNHHCKLTQVLNTHYVAPRRLLLTGTPLQNKLPELWALLNFLLPTIFKSCSTFEQWFNAPFAMTGEKVDLNEEETILIIRRLHKVLRPFLLRRLKKEVEAQLPEKVEYVIKCDMSALQRVLYRHMQAKGVLLTDGSEKDKKGKGGTKTLMNTIMQLRKICNHPYMFQHIEESFSEHLGFTGGIVQGLDLYRASGKFELLDRILPKLRATNHKVLLFCQMTSLMTIMEDYFAYRGFKYLRLDGTTKAEDRGMLLKTFNEPGSEYFIFLLSTRAGGLGLNLQSADTVIIFDSDWNPHQDLQAQDRAHRIGQQNEVRVLRLCTVNSVEEKILAAAKYKLNVDQKVIQAGMFDQKSSSHERRAFLQAILEHEEQDESRHCSTGSGSASFAHTAPPPAGVNPDLEEPPLKEEDEVPDDETVNQMIARHEEEFDLFMRMDLDRRREEARNPKRKPRLMEEDELPSWIIKDDAEVERLTCEEEEEKMFGRGSRHRKEVDYSDSLTEKQWLKAIEEGTLEEIEEEVRQKKSSRKRKRDSDAGSSTPTTSTRSRDKDDESKKQKKRGRPPAEKLSPNPPNLTKKMKKIVDAVIKYKDSSSGRQLSEVFIQLPSRKELPEYYELIRKPVDFKKIKERIRNHKYRSLNDLEKDVMLLCQNAQTFNLEGSLIYEDSIVLQSVFTSVRQKIEKEDDSEGEESEEEEEGEEEGSESESRSVKVKIKLGRKEKAQDRLKGGRRRPSRGSRAKPVVSDDDSEEEQEEDRSGSGSEED' is 49.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the affinity between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the target protein 'MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\"], \"target_seq\": \"MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the protein 'MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction strength between the drug 'CCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C' and the target protein 'MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C\"], \"target_seq\": \"MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA\", \"result\": [{\"smile\": \"CCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C\", \"value\": 1000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C' and the protein 'MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA' is 1000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value is expected between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the target protein 'MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted binding value of the drug 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1' with the target protein 'MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA\", \"result\": [{\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1' and the protein 'MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the affinity of the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' toward the target protein 'MDSRAQLWGLALNKRRATLPHPGGSTNLKADPEELFTKLEKIGKGSFGEVFKGIDNRTQKVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYVTKYYGSYLKDTKLWIIMEYLGGGSALDLLEPGPLDETQIATILREILKGLDYLHSEKKIHRDIKAANVLLSEHGEVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDSKADIWSLGITAIELARGEPPHSELHPMKVLFLIPKNNPPTLEGNYSKPLKEFVEACLNKEPSFRPTAKELLKHKFILRNAKKTSYLTELIDRYKRWKAEQSHDDSSSEDSDAETDGQASGGSDSGDWIFTIREKDPKNLENGALQPSDLDRNKMKDIPKRPFSQCLSTIISPLFAELKEKSQACGGNLGSIEELRGAIYLAEEACPGISDTMVAQLVQRLQRYSLSGGGTSSH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"MDSRAQLWGLALNKRRATLPHPGGSTNLKADPEELFTKLEKIGKGSFGEVFKGIDNRTQKVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYVTKYYGSYLKDTKLWIIMEYLGGGSALDLLEPGPLDETQIATILREILKGLDYLHSEKKIHRDIKAANVLLSEHGEVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDSKADIWSLGITAIELARGEPPHSELHPMKVLFLIPKNNPPTLEGNYSKPLKEFVEACLNKEPSFRPTAKELLKHKFILRNAKKTSYLTELIDRYKRWKAEQSHDDSSSEDSDAETDGQASGGSDSGDWIFTIREKDPKNLENGALQPSDLDRNKMKDIPKRPFSQCLSTIISPLFAELKEKSQACGGNLGSIEELRGAIYLAEEACPGISDTMVAQLVQRLQRYSLSGGGTSSH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDSRAQLWGLALNKRRATLPHPGGSTNLKADPEELFTKLEKIGKGSFGEVFKGIDNRTQKVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYVTKYYGSYLKDTKLWIIMEYLGGGSALDLLEPGPLDETQIATILREILKGLDYLHSEKKIHRDIKAANVLLSEHGEVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDSKADIWSLGITAIELARGEPPHSELHPMKVLFLIPKNNPPTLEGNYSKPLKEFVEACLNKEPSFRPTAKELLKHKFILRNAKKTSYLTELIDRYKRWKAEQSHDDSSSEDSDAETDGQASGGSDSGDWIFTIREKDPKNLENGALQPSDLDRNKMKDIPKRPFSQCLSTIISPLFAELKEKSQACGGNLGSIEELRGAIYLAEEACPGISDTMVAQLVQRLQRYSLSGGGTSSH\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MDSRAQLWGLALNKRRATLPHPGGSTNLKADPEELFTKLEKIGKGSFGEVFKGIDNRTQKVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYVTKYYGSYLKDTKLWIIMEYLGGGSALDLLEPGPLDETQIATILREILKGLDYLHSEKKIHRDIKAANVLLSEHGEVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDSKADIWSLGITAIELARGEPPHSELHPMKVLFLIPKNNPPTLEGNYSKPLKEFVEACLNKEPSFRPTAKELLKHKFILRNAKKTSYLTELIDRYKRWKAEQSHDDSSSEDSDAETDGQASGGSDSGDWIFTIREKDPKNLENGALQPSDLDRNKMKDIPKRPFSQCLSTIISPLFAELKEKSQACGGNLGSIEELRGAIYLAEEACPGISDTMVAQLVQRLQRYSLSGGGTSSH' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12' and the target protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12\"], \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12' and the protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug 'CCCCCCCCOCCCc1cnc[nH]1' and the target protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCCCCCOCCCc1cnc[nH]1\"], \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"CCCCCCCCOCCCc1cnc[nH]1\", \"value\": 15848.93}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCCCCCOCCCc1cnc[nH]1' and the protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP' is 15848.93."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the affinity between the drug 'CCCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C' and the target protein 'MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C\"], \"target_seq\": \"MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP\", \"result\": [{\"smile\": \"CCCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C\", \"value\": 1000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCc1cc2c(cc1C(=O)c1ccc(C(=O)O)cc1)C(C)(C)CCC2(C)C' and the protein 'MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP' is 1000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' for the target protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\"], \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the affinity between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein 'MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MAVPFVEDWDLVQTLGEGAYGEVQLAVNRVTEEAVAVKIVDMKRAVDCPENIKKEICINKMLNHENVVKFYGHRREGNIQYLFLEYCSGGELFDRIEPDIGMPEPDAQRFFHQLMAGVVYLHGIGITHRDIKPENLLLDERDNLKISDFGLATVFRYNNRERLLNKMCGTLPYVAPELLKRREFHAEPVDVWSCGIVLTAMLAGELPWDQPSDSCQEYSDWKEKKTYLNPWKKIDSAPLALLHKILVENPSARITIPDIKKDRWYNKPLKKGAKRPRVTSGGVSESPSGFSKHIQSNLDFSPVNSASSEENVKYSSSQPEPRTGLSLWDTSPSYIDKLVQGISFSQPTCPDHMLLNSQLLGTPGSSQNPWQRLVKRMTRFFTKLDADKSYQCLKETCEKLGYQWKKSCMNQVTISTTDRRNNKLIFKVNLLEMDDKILVDFRLSKGDGLEFKRHFLKIKGKLIDIVSSQKIWLPAT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the affinity between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1' and the target protein 'MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1\"], \"target_seq\": \"MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1' and the protein 'MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the target protein 'MELENIVANSLLLKARQGGYGKKSGRSKKWKEILTLPPVSQCSELRHSIEKDYSSLCDKQPIGRRLFRQFCDTKPTLKRHIEFLDAVAEYEVADDEDRSDCGLSILDRFFNDKLAAPLPEIPPDVVTECRLGLKEENPSKKAFEECTRVAHNYLRGEPFEEYQESSYFSQFLQWKWLERQPVTKNTFRHYRVLGKGGFGEVCACQVRATGKMYACKKLQKKRIKKRKGEAMALNEKRILEKVQSRFVVSLAYAYETKDALCLVLTIMNGGDLKFHIYNLGNPGFDEQRAVFYAAELCCGLEDLQRERIVYRDLKPENILLDDRGHIRISDLGLATEIPEGQRVRGRVGTVGYMAPEVVNNEKYTFSPDWWGLGCLIYEMIQGHSPFKKYKEKVKWEEVDQRIKNDTEEYSEKFSEDAKSICRMLLTKNPSKRLGCRGEGAAGVKQHPVFKDINFRRLEANMLEPPFCPDPHAVYCKDVLDIEQFSVVKGIYLDTADEDFYARFATGCVSIPWQNEMIESGCFKDINKSESEEALPLDLDKNIHTPVSRPNRGFFYRLFRRGGCLTMVPSEKEVEPKQC'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\"], \"target_seq\": \"MELENIVANSLLLKARQGGYGKKSGRSKKWKEILTLPPVSQCSELRHSIEKDYSSLCDKQPIGRRLFRQFCDTKPTLKRHIEFLDAVAEYEVADDEDRSDCGLSILDRFFNDKLAAPLPEIPPDVVTECRLGLKEENPSKKAFEECTRVAHNYLRGEPFEEYQESSYFSQFLQWKWLERQPVTKNTFRHYRVLGKGGFGEVCACQVRATGKMYACKKLQKKRIKKRKGEAMALNEKRILEKVQSRFVVSLAYAYETKDALCLVLTIMNGGDLKFHIYNLGNPGFDEQRAVFYAAELCCGLEDLQRERIVYRDLKPENILLDDRGHIRISDLGLATEIPEGQRVRGRVGTVGYMAPEVVNNEKYTFSPDWWGLGCLIYEMIQGHSPFKKYKEKVKWEEVDQRIKNDTEEYSEKFSEDAKSICRMLLTKNPSKRLGCRGEGAAGVKQHPVFKDINFRRLEANMLEPPFCPDPHAVYCKDVLDIEQFSVVKGIYLDTADEDFYARFATGCVSIPWQNEMIESGCFKDINKSESEEALPLDLDKNIHTPVSRPNRGFFYRLFRRGGCLTMVPSEKEVEPKQC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELENIVANSLLLKARQGGYGKKSGRSKKWKEILTLPPVSQCSELRHSIEKDYSSLCDKQPIGRRLFRQFCDTKPTLKRHIEFLDAVAEYEVADDEDRSDCGLSILDRFFNDKLAAPLPEIPPDVVTECRLGLKEENPSKKAFEECTRVAHNYLRGEPFEEYQESSYFSQFLQWKWLERQPVTKNTFRHYRVLGKGGFGEVCACQVRATGKMYACKKLQKKRIKKRKGEAMALNEKRILEKVQSRFVVSLAYAYETKDALCLVLTIMNGGDLKFHIYNLGNPGFDEQRAVFYAAELCCGLEDLQRERIVYRDLKPENILLDDRGHIRISDLGLATEIPEGQRVRGRVGTVGYMAPEVVNNEKYTFSPDWWGLGCLIYEMIQGHSPFKKYKEKVKWEEVDQRIKNDTEEYSEKFSEDAKSICRMLLTKNPSKRLGCRGEGAAGVKQHPVFKDINFRRLEANMLEPPFCPDPHAVYCKDVLDIEQFSVVKGIYLDTADEDFYARFATGCVSIPWQNEMIESGCFKDINKSESEEALPLDLDKNIHTPVSRPNRGFFYRLFRRGGCLTMVPSEKEVEPKQC\", \"result\": [{\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 590.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MELENIVANSLLLKARQGGYGKKSGRSKKWKEILTLPPVSQCSELRHSIEKDYSSLCDKQPIGRRLFRQFCDTKPTLKRHIEFLDAVAEYEVADDEDRSDCGLSILDRFFNDKLAAPLPEIPPDVVTECRLGLKEENPSKKAFEECTRVAHNYLRGEPFEEYQESSYFSQFLQWKWLERQPVTKNTFRHYRVLGKGGFGEVCACQVRATGKMYACKKLQKKRIKKRKGEAMALNEKRILEKVQSRFVVSLAYAYETKDALCLVLTIMNGGDLKFHIYNLGNPGFDEQRAVFYAAELCCGLEDLQRERIVYRDLKPENILLDDRGHIRISDLGLATEIPEGQRVRGRVGTVGYMAPEVVNNEKYTFSPDWWGLGCLIYEMIQGHSPFKKYKEKVKWEEVDQRIKNDTEEYSEKFSEDAKSICRMLLTKNPSKRLGCRGEGAAGVKQHPVFKDINFRRLEANMLEPPFCPDPHAVYCKDVLDIEQFSVVKGIYLDTADEDFYARFATGCVSIPWQNEMIESGCFKDINKSESEEALPLDLDKNIHTPVSRPNRGFFYRLFRRGGCLTMVPSEKEVEPKQC' is 590.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you quantify the predicted affinity for the drug 'CSc1nc(Cl)c(C=O)c(NCc2ccc(S(N)(=O)=O)cc2)n1' and the target protein 'MASPDWGYDDKNGPEQWSKLYPIANGNNQSPVDIKTSETKHDTSLKPISVSYNPATAKEIINVGHSFHVNFEDNDNRSVLKGGPFSDSYRLFQFHFHWGSTNEHGSEHTVDGVKYSAELHVAHWNSAKYSSLAEAASKADGLAVIGVLMKVGEANPKLQKVLDALQAIKTKGKRAPFTNFDPSTLLPSSLDFWTYPGSLTHPPLYESVTWIICKESISVSSEQLAQFRSLLSNVEGDNAVPMQHNNRPTQPLKGRTVRASF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1nc(Cl)c(C=O)c(NCc2ccc(S(N)(=O)=O)cc2)n1\"], \"target_seq\": \"MASPDWGYDDKNGPEQWSKLYPIANGNNQSPVDIKTSETKHDTSLKPISVSYNPATAKEIINVGHSFHVNFEDNDNRSVLKGGPFSDSYRLFQFHFHWGSTNEHGSEHTVDGVKYSAELHVAHWNSAKYSSLAEAASKADGLAVIGVLMKVGEANPKLQKVLDALQAIKTKGKRAPFTNFDPSTLLPSSLDFWTYPGSLTHPPLYESVTWIICKESISVSSEQLAQFRSLLSNVEGDNAVPMQHNNRPTQPLKGRTVRASF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASPDWGYDDKNGPEQWSKLYPIANGNNQSPVDIKTSETKHDTSLKPISVSYNPATAKEIINVGHSFHVNFEDNDNRSVLKGGPFSDSYRLFQFHFHWGSTNEHGSEHTVDGVKYSAELHVAHWNSAKYSSLAEAASKADGLAVIGVLMKVGEANPKLQKVLDALQAIKTKGKRAPFTNFDPSTLLPSSLDFWTYPGSLTHPPLYESVTWIICKESISVSSEQLAQFRSLLSNVEGDNAVPMQHNNRPTQPLKGRTVRASF\", \"result\": [{\"smile\": \"CSc1nc(Cl)c(C=O)c(NCc2ccc(S(N)(=O)=O)cc2)n1\", \"value\": 7.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSc1nc(Cl)c(C=O)c(NCc2ccc(S(N)(=O)=O)cc2)n1' and the protein 'MASPDWGYDDKNGPEQWSKLYPIANGNNQSPVDIKTSETKHDTSLKPISVSYNPATAKEIINVGHSFHVNFEDNDNRSVLKGGPFSDSYRLFQFHFHWGSTNEHGSEHTVDGVKYSAELHVAHWNSAKYSSLAEAASKADGLAVIGVLMKVGEANPKLQKVLDALQAIKTKGKRAPFTNFDPSTLLPSSLDFWTYPGSLTHPPLYESVTWIICKESISVSSEQLAQFRSLLSNVEGDNAVPMQHNNRPTQPLKGRTVRASF' is 7.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' with the target protein 'MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\"], \"target_seq\": \"MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the protein 'MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1''s affinity for the target protein 'MASTRSIELEHFEERDKRPRPGSRRGAPSSSGGSSSSGPKGNGLIPSPAHSAHCSFYRTRTLQALSSEKKAKKARFYRNGDRYFKGLVFAISSDRFRSFDALLIELTRSLSDNVNLPQGVRTIYTIDGSRKVTSLDELLEGESYVCASNEPFRKVDYTKNINPNWSVNIKGGTSRALAAASSVKSEVKESKDFIKPKLVTVIRSGVKPRKAVRILLNKKTAHSFEQVLTDITEAIKLDSGVVKRLCTLDGKQVTCLQDFFGDDDVFIACGPEKFRYAQDDFVLDHSECRVLKSSYSRSSAVKYSGSKSPGPSRRSKSPASVNGTPSSQLSTPKSTKSSSSSPTSPGSFRGLKQISAHGRSSSNVNGGPELDRCISPEGVNGNRCSESSTLLEKYKIGKVIGDGNFAVVKECIDRSTGKEFALKIIDKAKCCGKEHLIENEVSILRRVKHPNIIMLVEEMETATELFLVMELVKGGDLFDAITSSTKYTERDGSAMVYNLANALRYLHGLSIVHRDIKPENLLVCEYPDGTKSLKLGDFGLATVVEGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRSENNLQEDLFDQILAGKLEFPAPYWDNITDSAKELISQMLQVNVEARCTAGQILSHPWVSDDASQENNMQAEVTGKLKQHFNNALPKQNSTTTGVSVIMNTALDKEGQIFCSKHCQDSGRPGMEPISPVPPSVEEIPVPGEAVPAPTPPESPTPHPPPAAPGGERAGTWRRHRD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\"], \"target_seq\": \"MASTRSIELEHFEERDKRPRPGSRRGAPSSSGGSSSSGPKGNGLIPSPAHSAHCSFYRTRTLQALSSEKKAKKARFYRNGDRYFKGLVFAISSDRFRSFDALLIELTRSLSDNVNLPQGVRTIYTIDGSRKVTSLDELLEGESYVCASNEPFRKVDYTKNINPNWSVNIKGGTSRALAAASSVKSEVKESKDFIKPKLVTVIRSGVKPRKAVRILLNKKTAHSFEQVLTDITEAIKLDSGVVKRLCTLDGKQVTCLQDFFGDDDVFIACGPEKFRYAQDDFVLDHSECRVLKSSYSRSSAVKYSGSKSPGPSRRSKSPASVNGTPSSQLSTPKSTKSSSSSPTSPGSFRGLKQISAHGRSSSNVNGGPELDRCISPEGVNGNRCSESSTLLEKYKIGKVIGDGNFAVVKECIDRSTGKEFALKIIDKAKCCGKEHLIENEVSILRRVKHPNIIMLVEEMETATELFLVMELVKGGDLFDAITSSTKYTERDGSAMVYNLANALRYLHGLSIVHRDIKPENLLVCEYPDGTKSLKLGDFGLATVVEGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRSENNLQEDLFDQILAGKLEFPAPYWDNITDSAKELISQMLQVNVEARCTAGQILSHPWVSDDASQENNMQAEVTGKLKQHFNNALPKQNSTTTGVSVIMNTALDKEGQIFCSKHCQDSGRPGMEPISPVPPSVEEIPVPGEAVPAPTPPESPTPHPPPAAPGGERAGTWRRHRD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTRSIELEHFEERDKRPRPGSRRGAPSSSGGSSSSGPKGNGLIPSPAHSAHCSFYRTRTLQALSSEKKAKKARFYRNGDRYFKGLVFAISSDRFRSFDALLIELTRSLSDNVNLPQGVRTIYTIDGSRKVTSLDELLEGESYVCASNEPFRKVDYTKNINPNWSVNIKGGTSRALAAASSVKSEVKESKDFIKPKLVTVIRSGVKPRKAVRILLNKKTAHSFEQVLTDITEAIKLDSGVVKRLCTLDGKQVTCLQDFFGDDDVFIACGPEKFRYAQDDFVLDHSECRVLKSSYSRSSAVKYSGSKSPGPSRRSKSPASVNGTPSSQLSTPKSTKSSSSSPTSPGSFRGLKQISAHGRSSSNVNGGPELDRCISPEGVNGNRCSESSTLLEKYKIGKVIGDGNFAVVKECIDRSTGKEFALKIIDKAKCCGKEHLIENEVSILRRVKHPNIIMLVEEMETATELFLVMELVKGGDLFDAITSSTKYTERDGSAMVYNLANALRYLHGLSIVHRDIKPENLLVCEYPDGTKSLKLGDFGLATVVEGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRSENNLQEDLFDQILAGKLEFPAPYWDNITDSAKELISQMLQVNVEARCTAGQILSHPWVSDDASQENNMQAEVTGKLKQHFNNALPKQNSTTTGVSVIMNTALDKEGQIFCSKHCQDSGRPGMEPISPVPPSVEEIPVPGEAVPAPTPPESPTPHPPPAAPGGERAGTWRRHRD\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1' and the protein 'MASTRSIELEHFEERDKRPRPGSRRGAPSSSGGSSSSGPKGNGLIPSPAHSAHCSFYRTRTLQALSSEKKAKKARFYRNGDRYFKGLVFAISSDRFRSFDALLIELTRSLSDNVNLPQGVRTIYTIDGSRKVTSLDELLEGESYVCASNEPFRKVDYTKNINPNWSVNIKGGTSRALAAASSVKSEVKESKDFIKPKLVTVIRSGVKPRKAVRILLNKKTAHSFEQVLTDITEAIKLDSGVVKRLCTLDGKQVTCLQDFFGDDDVFIACGPEKFRYAQDDFVLDHSECRVLKSSYSRSSAVKYSGSKSPGPSRRSKSPASVNGTPSSQLSTPKSTKSSSSSPTSPGSFRGLKQISAHGRSSSNVNGGPELDRCISPEGVNGNRCSESSTLLEKYKIGKVIGDGNFAVVKECIDRSTGKEFALKIIDKAKCCGKEHLIENEVSILRRVKHPNIIMLVEEMETATELFLVMELVKGGDLFDAITSSTKYTERDGSAMVYNLANALRYLHGLSIVHRDIKPENLLVCEYPDGTKSLKLGDFGLATVVEGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRSENNLQEDLFDQILAGKLEFPAPYWDNITDSAKELISQMLQVNVEARCTAGQILSHPWVSDDASQENNMQAEVTGKLKQHFNNALPKQNSTTTGVSVIMNTALDKEGQIFCSKHCQDSGRPGMEPISPVPPSVEEIPVPGEAVPAPTPPESPTPHPPPAAPGGERAGTWRRHRD' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' and the target protein 'MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH\", \"result\": [{\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' and the protein 'MATITCTRFTEEYQLFEELGKGAFSVVRRCVKVLAGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGHHYLIFDLVTGGELFEDIVAREYYSEADASHCIQQILEAVLHCHQMGVVHRDLKPENLLLASKLKGAAVKLADFGLAIEVEGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDLWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPSKRITAAEALKHPWISHRSTVASCMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSGGKSGGNKKSDGVKESSESTNTTIEDEDTKVRKQEIIKVTEQLIEAISNGDFESYTKMCDPGMTAFEPEALGNLVEGLDFHRFYFENLWSRNSKPVHTTILNPHIHLMGDESACIAYIRITQYLDAGGIPRTAQSEETRVWHRRDGKWQIVHFHRSGAPSVLPH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O''s affinity with the target protein 'MERPPGLRPGAGGPWEMRERLGTGGFGNVCLYQHRELDLKIAIKSCRLELSTKNRERWCHEIQIMKKLNHANVVKACDVPEELNILIHDVPLLAMEYCSGGDLRKLLNKPENCCGLKESQILSLLSDIGSGIRYLHENKIIHRDLKPENIVLQDVGGKIIHKIIDLGYAKDVDQGSLCTSFVGTLQYLAPELFENKPYTATVDYWSFGTMVFECIAGYRPFLHHLQPFTWHEKIKKKDPKCIFACEEMSGEVRFSSHLPQPNSLCSLVVEPMENWLQLMLNWDPQQRGGPVDLTLKQPRCFVLMDHILNLKIVHILNMTSAKIISFLLPPDESLHSLQSRIERETGINTGSQELLSETGISLDPRKPASQCVLDGVRGCDSYMVYLFDKSKTVYEGPFASRSLSDCVNYIVQDSKIQLPIIQLRKVWAEAVHYVSGLKEDYSRLFQGQRAAMLSLLRYNANLTKMKNTLISASQQLKAKLEFFHKSIQLDLERYSEQMTYGISSEKMLKAWKEMEEKAIHYAEVGVIGYLEDQIMSLHAEIMELQKSPYGRRQGDLMESLEQRAIDLYKQLKHRPSDHSYSDSTEMVKIIVHTVQSQDRVLKELFGHLSKLLGCKQKIIDLLPKVEVALSNIKEADNTVMFMQGKRQKEIWHLLKIACTQSSARSLVGSSLEGAVTPQTSAWLPPTSAEHDHSLSCVVTPQDGETSAQMIEENLNCLGHLSTIIHEANEEQGNSMMNLDWSWLTE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"MERPPGLRPGAGGPWEMRERLGTGGFGNVCLYQHRELDLKIAIKSCRLELSTKNRERWCHEIQIMKKLNHANVVKACDVPEELNILIHDVPLLAMEYCSGGDLRKLLNKPENCCGLKESQILSLLSDIGSGIRYLHENKIIHRDLKPENIVLQDVGGKIIHKIIDLGYAKDVDQGSLCTSFVGTLQYLAPELFENKPYTATVDYWSFGTMVFECIAGYRPFLHHLQPFTWHEKIKKKDPKCIFACEEMSGEVRFSSHLPQPNSLCSLVVEPMENWLQLMLNWDPQQRGGPVDLTLKQPRCFVLMDHILNLKIVHILNMTSAKIISFLLPPDESLHSLQSRIERETGINTGSQELLSETGISLDPRKPASQCVLDGVRGCDSYMVYLFDKSKTVYEGPFASRSLSDCVNYIVQDSKIQLPIIQLRKVWAEAVHYVSGLKEDYSRLFQGQRAAMLSLLRYNANLTKMKNTLISASQQLKAKLEFFHKSIQLDLERYSEQMTYGISSEKMLKAWKEMEEKAIHYAEVGVIGYLEDQIMSLHAEIMELQKSPYGRRQGDLMESLEQRAIDLYKQLKHRPSDHSYSDSTEMVKIIVHTVQSQDRVLKELFGHLSKLLGCKQKIIDLLPKVEVALSNIKEADNTVMFMQGKRQKEIWHLLKIACTQSSARSLVGSSLEGAVTPQTSAWLPPTSAEHDHSLSCVVTPQDGETSAQMIEENLNCLGHLSTIIHEANEEQGNSMMNLDWSWLTE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERPPGLRPGAGGPWEMRERLGTGGFGNVCLYQHRELDLKIAIKSCRLELSTKNRERWCHEIQIMKKLNHANVVKACDVPEELNILIHDVPLLAMEYCSGGDLRKLLNKPENCCGLKESQILSLLSDIGSGIRYLHENKIIHRDLKPENIVLQDVGGKIIHKIIDLGYAKDVDQGSLCTSFVGTLQYLAPELFENKPYTATVDYWSFGTMVFECIAGYRPFLHHLQPFTWHEKIKKKDPKCIFACEEMSGEVRFSSHLPQPNSLCSLVVEPMENWLQLMLNWDPQQRGGPVDLTLKQPRCFVLMDHILNLKIVHILNMTSAKIISFLLPPDESLHSLQSRIERETGINTGSQELLSETGISLDPRKPASQCVLDGVRGCDSYMVYLFDKSKTVYEGPFASRSLSDCVNYIVQDSKIQLPIIQLRKVWAEAVHYVSGLKEDYSRLFQGQRAAMLSLLRYNANLTKMKNTLISASQQLKAKLEFFHKSIQLDLERYSEQMTYGISSEKMLKAWKEMEEKAIHYAEVGVIGYLEDQIMSLHAEIMELQKSPYGRRQGDLMESLEQRAIDLYKQLKHRPSDHSYSDSTEMVKIIVHTVQSQDRVLKELFGHLSKLLGCKQKIIDLLPKVEVALSNIKEADNTVMFMQGKRQKEIWHLLKIACTQSSARSLVGSSLEGAVTPQTSAWLPPTSAEHDHSLSCVVTPQDGETSAQMIEENLNCLGHLSTIIHEANEEQGNSMMNLDWSWLTE\", \"result\": [{\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein 'MERPPGLRPGAGGPWEMRERLGTGGFGNVCLYQHRELDLKIAIKSCRLELSTKNRERWCHEIQIMKKLNHANVVKACDVPEELNILIHDVPLLAMEYCSGGDLRKLLNKPENCCGLKESQILSLLSDIGSGIRYLHENKIIHRDLKPENIVLQDVGGKIIHKIIDLGYAKDVDQGSLCTSFVGTLQYLAPELFENKPYTATVDYWSFGTMVFECIAGYRPFLHHLQPFTWHEKIKKKDPKCIFACEEMSGEVRFSSHLPQPNSLCSLVVEPMENWLQLMLNWDPQQRGGPVDLTLKQPRCFVLMDHILNLKIVHILNMTSAKIISFLLPPDESLHSLQSRIERETGINTGSQELLSETGISLDPRKPASQCVLDGVRGCDSYMVYLFDKSKTVYEGPFASRSLSDCVNYIVQDSKIQLPIIQLRKVWAEAVHYVSGLKEDYSRLFQGQRAAMLSLLRYNANLTKMKNTLISASQQLKAKLEFFHKSIQLDLERYSEQMTYGISSEKMLKAWKEMEEKAIHYAEVGVIGYLEDQIMSLHAEIMELQKSPYGRRQGDLMESLEQRAIDLYKQLKHRPSDHSYSDSTEMVKIIVHTVQSQDRVLKELFGHLSKLLGCKQKIIDLLPKVEVALSNIKEADNTVMFMQGKRQKEIWHLLKIACTQSSARSLVGSSLEGAVTPQTSAWLPPTSAEHDHSLSCVVTPQDGETSAQMIEENLNCLGHLSTIIHEANEEQGNSMMNLDWSWLTE' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the expected binding affinity of the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' for the target protein 'MACLHETRTPSPSFGGFVSTLSEASMRKLDPDTSDCTPEKDLTPTHVLQLHEQDAGGPGGAAGSPESRASRVRADEVRLQCQSGSGFLEGLFGCLRPVWTMIGKAYSTEHKQQQEDLWEVPFEEILDLQWVGSGAQGAVFLGRFHGEEVAVKKVRDLKETDIKHLRKLKHPNIITFKGVCTQAPCYCILMEFCAQGQLYEVLRAGRPVTPSLLVDWSMGIAGGMNYLHLHKIIHRDLKSPNMLITYDDVVKISDFGTSKELSDKSTKMSFAGTVAWMAPEVIRNEPVSEKVDIWSFGVVLWELLTGEIPYKDVDSSAIIWGVGSNSLHLPVPSSCPDGFKILLRQCWNSKPRNRPSFRQILLHLDIASADVLSTPQETYFKSQAEWREEVKLHFEKIKSEGTCLHRLEEELVMRRREELRHALDIREHYERKLERANNLYMELNALMLQLELKERELLRREQALERRCPGLLKPHPSRGLLHGNTMEKLIKKRNVPQKLSPHSKRPDILKTESLLPKLDAALSGVGLPGCPKGPPSPGRSRRGKTRHRKASAKGSCGDLPGLRTAVPPHEPGGPGSPGGLGGGPSAWEACPPALRGLHHDLLLRKMSSSSPDLLSAALGSRGRGATGGAGDPGSPPPARGDTPPSEGSAPGSTSPDSPGGAKGEPPPPVGPGEGVGLLGTGREGTSGRGGSRAGSQHLTPAALLYRAAVTRSQKRGISSEEEEGEVDSEVELTSSQRWPQSLNMRQSLSTFSSENPSDGEEGTASEPSPSGTPEVGSTNTDERPDERSDDMCSQGSEIPLDPPPSEVIPGPEPSSLPIPHQELLRERGPPNSEDSDCDSTELDNSNSVDALRPPASLPP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\"], \"target_seq\": \"MACLHETRTPSPSFGGFVSTLSEASMRKLDPDTSDCTPEKDLTPTHVLQLHEQDAGGPGGAAGSPESRASRVRADEVRLQCQSGSGFLEGLFGCLRPVWTMIGKAYSTEHKQQQEDLWEVPFEEILDLQWVGSGAQGAVFLGRFHGEEVAVKKVRDLKETDIKHLRKLKHPNIITFKGVCTQAPCYCILMEFCAQGQLYEVLRAGRPVTPSLLVDWSMGIAGGMNYLHLHKIIHRDLKSPNMLITYDDVVKISDFGTSKELSDKSTKMSFAGTVAWMAPEVIRNEPVSEKVDIWSFGVVLWELLTGEIPYKDVDSSAIIWGVGSNSLHLPVPSSCPDGFKILLRQCWNSKPRNRPSFRQILLHLDIASADVLSTPQETYFKSQAEWREEVKLHFEKIKSEGTCLHRLEEELVMRRREELRHALDIREHYERKLERANNLYMELNALMLQLELKERELLRREQALERRCPGLLKPHPSRGLLHGNTMEKLIKKRNVPQKLSPHSKRPDILKTESLLPKLDAALSGVGLPGCPKGPPSPGRSRRGKTRHRKASAKGSCGDLPGLRTAVPPHEPGGPGSPGGLGGGPSAWEACPPALRGLHHDLLLRKMSSSSPDLLSAALGSRGRGATGGAGDPGSPPPARGDTPPSEGSAPGSTSPDSPGGAKGEPPPPVGPGEGVGLLGTGREGTSGRGGSRAGSQHLTPAALLYRAAVTRSQKRGISSEEEEGEVDSEVELTSSQRWPQSLNMRQSLSTFSSENPSDGEEGTASEPSPSGTPEVGSTNTDERPDERSDDMCSQGSEIPLDPPPSEVIPGPEPSSLPIPHQELLRERGPPNSEDSDCDSTELDNSNSVDALRPPASLPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MACLHETRTPSPSFGGFVSTLSEASMRKLDPDTSDCTPEKDLTPTHVLQLHEQDAGGPGGAAGSPESRASRVRADEVRLQCQSGSGFLEGLFGCLRPVWTMIGKAYSTEHKQQQEDLWEVPFEEILDLQWVGSGAQGAVFLGRFHGEEVAVKKVRDLKETDIKHLRKLKHPNIITFKGVCTQAPCYCILMEFCAQGQLYEVLRAGRPVTPSLLVDWSMGIAGGMNYLHLHKIIHRDLKSPNMLITYDDVVKISDFGTSKELSDKSTKMSFAGTVAWMAPEVIRNEPVSEKVDIWSFGVVLWELLTGEIPYKDVDSSAIIWGVGSNSLHLPVPSSCPDGFKILLRQCWNSKPRNRPSFRQILLHLDIASADVLSTPQETYFKSQAEWREEVKLHFEKIKSEGTCLHRLEEELVMRRREELRHALDIREHYERKLERANNLYMELNALMLQLELKERELLRREQALERRCPGLLKPHPSRGLLHGNTMEKLIKKRNVPQKLSPHSKRPDILKTESLLPKLDAALSGVGLPGCPKGPPSPGRSRRGKTRHRKASAKGSCGDLPGLRTAVPPHEPGGPGSPGGLGGGPSAWEACPPALRGLHHDLLLRKMSSSSPDLLSAALGSRGRGATGGAGDPGSPPPARGDTPPSEGSAPGSTSPDSPGGAKGEPPPPVGPGEGVGLLGTGREGTSGRGGSRAGSQHLTPAALLYRAAVTRSQKRGISSEEEEGEVDSEVELTSSQRWPQSLNMRQSLSTFSSENPSDGEEGTASEPSPSGTPEVGSTNTDERPDERSDDMCSQGSEIPLDPPPSEVIPGPEPSSLPIPHQELLRERGPPNSEDSDCDSTELDNSNSVDALRPPASLPP\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 670.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the protein 'MACLHETRTPSPSFGGFVSTLSEASMRKLDPDTSDCTPEKDLTPTHVLQLHEQDAGGPGGAAGSPESRASRVRADEVRLQCQSGSGFLEGLFGCLRPVWTMIGKAYSTEHKQQQEDLWEVPFEEILDLQWVGSGAQGAVFLGRFHGEEVAVKKVRDLKETDIKHLRKLKHPNIITFKGVCTQAPCYCILMEFCAQGQLYEVLRAGRPVTPSLLVDWSMGIAGGMNYLHLHKIIHRDLKSPNMLITYDDVVKISDFGTSKELSDKSTKMSFAGTVAWMAPEVIRNEPVSEKVDIWSFGVVLWELLTGEIPYKDVDSSAIIWGVGSNSLHLPVPSSCPDGFKILLRQCWNSKPRNRPSFRQILLHLDIASADVLSTPQETYFKSQAEWREEVKLHFEKIKSEGTCLHRLEEELVMRRREELRHALDIREHYERKLERANNLYMELNALMLQLELKERELLRREQALERRCPGLLKPHPSRGLLHGNTMEKLIKKRNVPQKLSPHSKRPDILKTESLLPKLDAALSGVGLPGCPKGPPSPGRSRRGKTRHRKASAKGSCGDLPGLRTAVPPHEPGGPGSPGGLGGGPSAWEACPPALRGLHHDLLLRKMSSSSPDLLSAALGSRGRGATGGAGDPGSPPPARGDTPPSEGSAPGSTSPDSPGGAKGEPPPPVGPGEGVGLLGTGREGTSGRGGSRAGSQHLTPAALLYRAAVTRSQKRGISSEEEEGEVDSEVELTSSQRWPQSLNMRQSLSTFSSENPSDGEEGTASEPSPSGTPEVGSTNTDERPDERSDDMCSQGSEIPLDPPPSEVIPGPEPSSLPIPHQELLRERGPPNSEDSDCDSTELDNSNSVDALRPPASLPP' is 670.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'Nc1nc(N)c2cc([N+](=O)[O-])ccc2n1' and the target protein 'MSRAAARFKIPMPETKADFAFPSLRAFSIVVALDMQHGIGDGESIPWRVPEDMTFFKNQTTLLRNKKPPTEKKRNAVVMGRKTWESVPVKFRPLKGRLNIVLSSKATVEELLAPLPEGQRAAAAQDVVVVNGGLAEALRLLARPLYCSSIETAYCVGGAQVYADAMLSPCIEKLQEVYLTRIYATAPACTRFFPFPPENAATAWDLASSQGRRKSEAEGLEFEICKYVPRNHEERQYLELIDRIMKTGIVKEDRTGVGTISLFGAQMRFSLRDNRLPLLTTKRVFWRGVCEELLWFLRGETSAQLLADKDIHIWDGNGSREFLDSRGLTENKEMDLGPVYGFQWRHFGADYKGFEANYDGEGVDQIKLIVETIKTNPNDRRLLVTAWNPCALQKMALPPCHLLAQFYVNTDTSELSCMLYQRSCDMGLGVPFNIASYALLTILIAKATGLRPGELVHTLGDAHVYRNHVDALKAQLERVPHAFPTLIFKEERQYLEDYELTDMEVIDYVPHPAIKMEMAV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(N)c2cc([N+](=O)[O-])ccc2n1\"], \"target_seq\": \"MSRAAARFKIPMPETKADFAFPSLRAFSIVVALDMQHGIGDGESIPWRVPEDMTFFKNQTTLLRNKKPPTEKKRNAVVMGRKTWESVPVKFRPLKGRLNIVLSSKATVEELLAPLPEGQRAAAAQDVVVVNGGLAEALRLLARPLYCSSIETAYCVGGAQVYADAMLSPCIEKLQEVYLTRIYATAPACTRFFPFPPENAATAWDLASSQGRRKSEAEGLEFEICKYVPRNHEERQYLELIDRIMKTGIVKEDRTGVGTISLFGAQMRFSLRDNRLPLLTTKRVFWRGVCEELLWFLRGETSAQLLADKDIHIWDGNGSREFLDSRGLTENKEMDLGPVYGFQWRHFGADYKGFEANYDGEGVDQIKLIVETIKTNPNDRRLLVTAWNPCALQKMALPPCHLLAQFYVNTDTSELSCMLYQRSCDMGLGVPFNIASYALLTILIAKATGLRPGELVHTLGDAHVYRNHVDALKAQLERVPHAFPTLIFKEERQYLEDYELTDMEVIDYVPHPAIKMEMAV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSRAAARFKIPMPETKADFAFPSLRAFSIVVALDMQHGIGDGESIPWRVPEDMTFFKNQTTLLRNKKPPTEKKRNAVVMGRKTWESVPVKFRPLKGRLNIVLSSKATVEELLAPLPEGQRAAAAQDVVVVNGGLAEALRLLARPLYCSSIETAYCVGGAQVYADAMLSPCIEKLQEVYLTRIYATAPACTRFFPFPPENAATAWDLASSQGRRKSEAEGLEFEICKYVPRNHEERQYLELIDRIMKTGIVKEDRTGVGTISLFGAQMRFSLRDNRLPLLTTKRVFWRGVCEELLWFLRGETSAQLLADKDIHIWDGNGSREFLDSRGLTENKEMDLGPVYGFQWRHFGADYKGFEANYDGEGVDQIKLIVETIKTNPNDRRLLVTAWNPCALQKMALPPCHLLAQFYVNTDTSELSCMLYQRSCDMGLGVPFNIASYALLTILIAKATGLRPGELVHTLGDAHVYRNHVDALKAQLERVPHAFPTLIFKEERQYLEDYELTDMEVIDYVPHPAIKMEMAV\", \"result\": [{\"smile\": \"Nc1nc(N)c2cc([N+](=O)[O-])ccc2n1\", \"value\": 15810.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1nc(N)c2cc([N+](=O)[O-])ccc2n1' and the protein 'MSRAAARFKIPMPETKADFAFPSLRAFSIVVALDMQHGIGDGESIPWRVPEDMTFFKNQTTLLRNKKPPTEKKRNAVVMGRKTWESVPVKFRPLKGRLNIVLSSKATVEELLAPLPEGQRAAAAQDVVVVNGGLAEALRLLARPLYCSSIETAYCVGGAQVYADAMLSPCIEKLQEVYLTRIYATAPACTRFFPFPPENAATAWDLASSQGRRKSEAEGLEFEICKYVPRNHEERQYLELIDRIMKTGIVKEDRTGVGTISLFGAQMRFSLRDNRLPLLTTKRVFWRGVCEELLWFLRGETSAQLLADKDIHIWDGNGSREFLDSRGLTENKEMDLGPVYGFQWRHFGADYKGFEANYDGEGVDQIKLIVETIKTNPNDRRLLVTAWNPCALQKMALPPCHLLAQFYVNTDTSELSCMLYQRSCDMGLGVPFNIASYALLTILIAKATGLRPGELVHTLGDAHVYRNHVDALKAQLERVPHAFPTLIFKEERQYLEDYELTDMEVIDYVPHPAIKMEMAV' is 15810.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value for the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 22.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS' is 22.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated interaction strength between the drug 'N=C(N)N=C(N)Nc1ccc(Cl)cc1C(=O)c1ccccc1' and the target protein 'MSELEKAMVALIDVFHQYSGREGDKHKLKKSELKELINNELSHFLEEIKEQEVVDKVMETLDNDGDGECDFQEFMAFVAMVTTACHEFFEHE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N=C(N)N=C(N)Nc1ccc(Cl)cc1C(=O)c1ccccc1\"], \"target_seq\": \"MSELEKAMVALIDVFHQYSGREGDKHKLKKSELKELINNELSHFLEEIKEQEVVDKVMETLDNDGDGECDFQEFMAFVAMVTTACHEFFEHE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSELEKAMVALIDVFHQYSGREGDKHKLKKSELKELINNELSHFLEEIKEQEVVDKVMETLDNDGDGECDFQEFMAFVAMVTTACHEFFEHE\", \"result\": [{\"smile\": \"N=C(N)N=C(N)Nc1ccc(Cl)cc1C(=O)c1ccccc1\", \"value\": 4500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N=C(N)N=C(N)Nc1ccc(Cl)cc1C(=O)c1ccccc1' and the protein 'MSELEKAMVALIDVFHQYSGREGDKHKLKKSELKELINNELSHFLEEIKEQEVVDKVMETLDNDGDGECDFQEFMAFVAMVTTACHEFFEHE' is 4500.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted binding affinity of the drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' with the target protein 'AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\"], \"target_seq\": \"AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL\", \"result\": [{\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 3.8}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the protein 'AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL' is 3.8."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O''s affinity to be for the target protein 'MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL\", \"result\": [{\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein 'MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1'-target protein 'MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\"], \"target_seq\": \"MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the protein 'MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction for the affinity between the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the target protein 'MGQELCAKTVQPGCSCYHCSEGGEAHSCRRSQPETTEAAFKLTDLKEASCSMTSFHPRGLQAARAQKFKSKRPRSNSDCFQEEDLRQGFQWRKSLPFGAASSYLNLEKLGEGSYATVYKGISRINGQLVALKVISMNAEEGVPFTAIREASLLKGLKHANIVLLHDIIHTKETLTFVFEYMHTDLAQYMSQHPGGLHPHNVRLFMFQLLRGLAYIHHQHVLHRDLKPQNLLISHLGELKLADFGLARAKSIPSQTYSSEVVTLWYRPPDALLGATEYSSELDIWGAGCIFIEMFQGQPLFPGVSNILEQLEKIWEVLGVPTEDTWPGVSKLPNYNPEWFPLPTPRSLHVVWNRLGRVPEAEDLASQMLKGFPRDRVSAQEALVHDYFSALPSQLYQLPDEESLFTVSGVRLKPEMCDLLASYQKGHHPAQFSKCW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\"], \"target_seq\": \"MGQELCAKTVQPGCSCYHCSEGGEAHSCRRSQPETTEAAFKLTDLKEASCSMTSFHPRGLQAARAQKFKSKRPRSNSDCFQEEDLRQGFQWRKSLPFGAASSYLNLEKLGEGSYATVYKGISRINGQLVALKVISMNAEEGVPFTAIREASLLKGLKHANIVLLHDIIHTKETLTFVFEYMHTDLAQYMSQHPGGLHPHNVRLFMFQLLRGLAYIHHQHVLHRDLKPQNLLISHLGELKLADFGLARAKSIPSQTYSSEVVTLWYRPPDALLGATEYSSELDIWGAGCIFIEMFQGQPLFPGVSNILEQLEKIWEVLGVPTEDTWPGVSKLPNYNPEWFPLPTPRSLHVVWNRLGRVPEAEDLASQMLKGFPRDRVSAQEALVHDYFSALPSQLYQLPDEESLFTVSGVRLKPEMCDLLASYQKGHHPAQFSKCW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGQELCAKTVQPGCSCYHCSEGGEAHSCRRSQPETTEAAFKLTDLKEASCSMTSFHPRGLQAARAQKFKSKRPRSNSDCFQEEDLRQGFQWRKSLPFGAASSYLNLEKLGEGSYATVYKGISRINGQLVALKVISMNAEEGVPFTAIREASLLKGLKHANIVLLHDIIHTKETLTFVFEYMHTDLAQYMSQHPGGLHPHNVRLFMFQLLRGLAYIHHQHVLHRDLKPQNLLISHLGELKLADFGLARAKSIPSQTYSSEVVTLWYRPPDALLGATEYSSELDIWGAGCIFIEMFQGQPLFPGVSNILEQLEKIWEVLGVPTEDTWPGVSKLPNYNPEWFPLPTPRSLHVVWNRLGRVPEAEDLASQMLKGFPRDRVSAQEALVHDYFSALPSQLYQLPDEESLFTVSGVRLKPEMCDLLASYQKGHHPAQFSKCW\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the protein 'MGQELCAKTVQPGCSCYHCSEGGEAHSCRRSQPETTEAAFKLTDLKEASCSMTSFHPRGLQAARAQKFKSKRPRSNSDCFQEEDLRQGFQWRKSLPFGAASSYLNLEKLGEGSYATVYKGISRINGQLVALKVISMNAEEGVPFTAIREASLLKGLKHANIVLLHDIIHTKETLTFVFEYMHTDLAQYMSQHPGGLHPHNVRLFMFQLLRGLAYIHHQHVLHRDLKPQNLLISHLGELKLADFGLARAKSIPSQTYSSEVVTLWYRPPDALLGATEYSSELDIWGAGCIFIEMFQGQPLFPGVSNILEQLEKIWEVLGVPTEDTWPGVSKLPNYNPEWFPLPTPRSLHVVWNRLGRVPEAEDLASQMLKGFPRDRVSAQEALVHDYFSALPSQLYQLPDEESLFTVSGVRLKPEMCDLLASYQKGHHPAQFSKCW' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug 'COc1cc2cc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCNCCO' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2cc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCNCCO\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"COc1cc2cc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCNCCO\", \"value\": 5.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2cc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OCCNCCO' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the expected affinity of the drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1' towards the target protein 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\"], \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\", \"result\": [{\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1' and the protein 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the binding affinity between the drug 'O=C(Cc1ccc(-c2cnc3[nH]c(CO)cc3c2)cc1F)Nc1cc(C2(C(F)(F)F)CC2)on1' and the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(Cc1ccc(-c2cnc3[nH]c(CO)cc3c2)cc1F)Nc1cc(C2(C(F)(F)F)CC2)on1\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"O=C(Cc1ccc(-c2cnc3[nH]c(CO)cc3c2)cc1F)Nc1cc(C2(C(F)(F)F)CC2)on1\", \"value\": 5.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(Cc1ccc(-c2cnc3[nH]c(CO)cc3c2)cc1F)Nc1cc(C2(C(F)(F)F)CC2)on1' and the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1''s affinity for the target protein 'MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\", \"value\": 2900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1' and the protein 'MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM' is 2900.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the target protein 'MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein 'MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the target protein 'MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN\", \"result\": [{\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1', 'COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1' with respect to the target protein 'MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\"], \"target_seq\": \"MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 1400.0}, {\"smile\": \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1', 'COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1' and the protein are 1400.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected value for the affinity between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1', 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' and the target protein 'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\"], \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1', 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding strength of the drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' to the target protein 'MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\", \"result\": [{\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 10000.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21', 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1' and the target protein 'MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\"], \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21', 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likely binding affinity for the drug 'COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)on4)c3)c2cc1OCCN1CCN(C)CC1', 'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1' and the target protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)on4)c3)c2cc1OCCN1CCN(C)CC1\", \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\"], \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\": \"COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)on4)c3)c2cc1OCCN1CCN(C)CC1\", \"value\": 375.0}, {\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)on4)c3)c2cc1OCCN1CCN(C)CC1', 'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1' and the protein are 375.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the predicted affinity value between the drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the target protein 'MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 3100.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein are 3100.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O''s affinity towards the target protein 'ERFELGDGRKPVKEEKMEERDLLSDLQDISDSERKTSSAESSSAESGSGSEEEEEEEEEEEEEGSTSEESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALSPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTLWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGKSEIDQINKVFKDLGTPSEKIWPGYSELPAVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\"], \"target_seq\": \"ERFELGDGRKPVKEEKMEERDLLSDLQDISDSERKTSSAESSSAESGSGSEEEEEEEEEEEEEGSTSEESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALSPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTLWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGKSEIDQINKVFKDLGTPSEKIWPGYSELPAVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"ERFELGDGRKPVKEEKMEERDLLSDLQDISDSERKTSSAESSSAESGSGSEEEEEEEEEEEEEGSTSEESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALSPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTLWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGKSEIDQINKVFKDLGTPSEKIWPGYSELPAVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 8.4}, {\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O' and the protein are 8.4 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the projected affinity value between the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the target protein 'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 470.0}, {\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the protein are 470.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the interaction value between the drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O', 'Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1' and the target protein 'MLLRSAGKLNVGTKKEDGESTAPTPRPKVLRCKCHHHCPEDSVNNICSTDGYCFTMIEEDDSGLPVVTSGCLGLEGSDFQCRDTPIPHQRRSIECCTERNECNKDLHPTLPPLKNRDFVDGPIHHRALLISVTVCSLLLVLIILFCYFRYKRQETRPRYSIGLEQDETYIPPGESLRDLIEQSQSSGSGSGLPLLVQRTIAKQIQMVKQIGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDYLKSTTLDAKSMLKLAYSSVSGLCHLHTEIFSTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVKFISDTNEVDIPPNTRVGTKRYMPPEVLDESLNRNHFQSYIMADMYSFGLILWEVARRCVSGGIVEEYQLPYHDLVPSDPSYEDMREIVCIKKLRPSFPNRWSSDECLRQMGKLMTECWAHNPASRLTALRVKKTLAKMSESQDIKL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\"], \"target_seq\": \"MLLRSAGKLNVGTKKEDGESTAPTPRPKVLRCKCHHHCPEDSVNNICSTDGYCFTMIEEDDSGLPVVTSGCLGLEGSDFQCRDTPIPHQRRSIECCTERNECNKDLHPTLPPLKNRDFVDGPIHHRALLISVTVCSLLLVLIILFCYFRYKRQETRPRYSIGLEQDETYIPPGESLRDLIEQSQSSGSGSGLPLLVQRTIAKQIQMVKQIGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDYLKSTTLDAKSMLKLAYSSVSGLCHLHTEIFSTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVKFISDTNEVDIPPNTRVGTKRYMPPEVLDESLNRNHFQSYIMADMYSFGLILWEVARRCVSGGIVEEYQLPYHDLVPSDPSYEDMREIVCIKKLRPSFPNRWSSDECLRQMGKLMTECWAHNPASRLTALRVKKTLAKMSESQDIKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLLRSAGKLNVGTKKEDGESTAPTPRPKVLRCKCHHHCPEDSVNNICSTDGYCFTMIEEDDSGLPVVTSGCLGLEGSDFQCRDTPIPHQRRSIECCTERNECNKDLHPTLPPLKNRDFVDGPIHHRALLISVTVCSLLLVLIILFCYFRYKRQETRPRYSIGLEQDETYIPPGESLRDLIEQSQSSGSGSGLPLLVQRTIAKQIQMVKQIGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDYLKSTTLDAKSMLKLAYSSVSGLCHLHTEIFSTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVKFISDTNEVDIPPNTRVGTKRYMPPEVLDESLNRNHFQSYIMADMYSFGLILWEVARRCVSGGIVEEYQLPYHDLVPSDPSYEDMREIVCIKKLRPSFPNRWSSDECLRQMGKLMTECWAHNPASRLTALRVKKTLAKMSESQDIKL\", \"result\": [{\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 10000.0}, {\"smile\": \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O', 'Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the binding affinity you would expect for the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the target protein 'MGKEPLTLKSIQVAVEELYPNKARALTLAQHSRAPSPRLRSRLFSKALKGDHRCGETETPKSCSEVAGCKAAMRHQGKIPEELSLDDRARTQKKWGRGKWEPEPSSKPPREATLEERHARGEKHLGVEIEKTSGEIIRCEKCKRERELQQSLERERLSLGTSELDMGKGPMYDVEKLVRTRSCRRSPEANPASGEEGWKGDSHRSSPRNPTQELRRPSKSMDKKEDRGPEDQESHAQGAAKAKKDLVEVLPVTEEGLREVKKDTRPMSRSKHGGWLLREHQAGFEKLRRTRGEEKEAEKEKKPCMSGGRRMTLRDDQPAKLEKEPKTRPEENKPERPSGRKPRPMGIIAANVEKHYETGRVIGDGNFAVVKECRHRETRQAYAMKIIDKSRLKGKEDMVDSEILIIQSLSHPNIVKLHEVYETDMEIYLILEYVQGGDLFDAIIESVKFPEPDAALMIMDLCKALVHMHDKSIVHRDLKPENLLVQRNEDKSTTLKLADFGLAKHVVRPIFTVCGTPTYVAPEILSEKGYGLEVDMWAAGVILYILLCGFPPFRSPERDQDELFNIIQLGHFEFLPPYWDNISDAAKDLVSRLLVVDPKKRYTAHQVLQHPWIETAGKTNTVKRQKQVSPSSEGHFRSQHKRVVEQVS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\"], \"target_seq\": \"MGKEPLTLKSIQVAVEELYPNKARALTLAQHSRAPSPRLRSRLFSKALKGDHRCGETETPKSCSEVAGCKAAMRHQGKIPEELSLDDRARTQKKWGRGKWEPEPSSKPPREATLEERHARGEKHLGVEIEKTSGEIIRCEKCKRERELQQSLERERLSLGTSELDMGKGPMYDVEKLVRTRSCRRSPEANPASGEEGWKGDSHRSSPRNPTQELRRPSKSMDKKEDRGPEDQESHAQGAAKAKKDLVEVLPVTEEGLREVKKDTRPMSRSKHGGWLLREHQAGFEKLRRTRGEEKEAEKEKKPCMSGGRRMTLRDDQPAKLEKEPKTRPEENKPERPSGRKPRPMGIIAANVEKHYETGRVIGDGNFAVVKECRHRETRQAYAMKIIDKSRLKGKEDMVDSEILIIQSLSHPNIVKLHEVYETDMEIYLILEYVQGGDLFDAIIESVKFPEPDAALMIMDLCKALVHMHDKSIVHRDLKPENLLVQRNEDKSTTLKLADFGLAKHVVRPIFTVCGTPTYVAPEILSEKGYGLEVDMWAAGVILYILLCGFPPFRSPERDQDELFNIIQLGHFEFLPPYWDNISDAAKDLVSRLLVVDPKKRYTAHQVLQHPWIETAGKTNTVKRQKQVSPSSEGHFRSQHKRVVEQVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGKEPLTLKSIQVAVEELYPNKARALTLAQHSRAPSPRLRSRLFSKALKGDHRCGETETPKSCSEVAGCKAAMRHQGKIPEELSLDDRARTQKKWGRGKWEPEPSSKPPREATLEERHARGEKHLGVEIEKTSGEIIRCEKCKRERELQQSLERERLSLGTSELDMGKGPMYDVEKLVRTRSCRRSPEANPASGEEGWKGDSHRSSPRNPTQELRRPSKSMDKKEDRGPEDQESHAQGAAKAKKDLVEVLPVTEEGLREVKKDTRPMSRSKHGGWLLREHQAGFEKLRRTRGEEKEAEKEKKPCMSGGRRMTLRDDQPAKLEKEPKTRPEENKPERPSGRKPRPMGIIAANVEKHYETGRVIGDGNFAVVKECRHRETRQAYAMKIIDKSRLKGKEDMVDSEILIIQSLSHPNIVKLHEVYETDMEIYLILEYVQGGDLFDAIIESVKFPEPDAALMIMDLCKALVHMHDKSIVHRDLKPENLLVQRNEDKSTTLKLADFGLAKHVVRPIFTVCGTPTYVAPEILSEKGYGLEVDMWAAGVILYILLCGFPPFRSPERDQDELFNIIQLGHFEFLPPYWDNISDAAKDLVSRLLVVDPKKRYTAHQVLQHPWIETAGKTNTVKRQKQVSPSSEGHFRSQHKRVVEQVS\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 10000.0}, {\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 1300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the protein are 10000.0 and 1300.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccccc1-c1ccccc1', 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1cccc2ccccc12'-target protein 'MDLRVGQRPPVEPPPEPTLLALQRPQRLHHHLFLAGLQQQRSVEPMRLSMDTPMPELQVGPQEQELRQLLHKDKSKRSAVASSVVKQKLAEVILKKQQAALERTVHPNSPGIPYRTLEPLETEGATRSMLSSFLPPVPSLPSDPPEHFPLRKTVSEPNLKLRYKPKKSLERRKNPLLRKESAPPSLRRRPAETLGDSSPSSSSTPASGCSSPNDSEHGPNPILGSEALLGQRLRLQETSVAPFALPTVSLLPAITLGLPAPARADSDRRTHPTLGPRGPILGSPHTPLFLPHGLEPEAGGTLPSRLQPILLLDPSGSHAPLLTVPGLGPLPFHFAQSLMTTERLSGSGLHWPLSRTRSEPLPPSATAPPPPGPMQPRLEQLKTHVQVIKRSAKPSEKPRLRQIPSAEDLETDGGGPGQVVDDGLEHRELGHGQPEARGPAPLQQHPQVLLWEQQRLAGRLPRGSTGDTVLLPLAQGGHRPLSRAQSSPAAPASLSAPEPASQARVLSSSETPARTLPFTTGLIYDSVMLKHQCSCGDNSRHPEHAGRIQSIWSRLQERGLRSQCECLRGRKASLEELQSVHSERHVLLYGTNPLSRLKLDNGKLAGLLAQRMFVMLPCGGVGVDTDTIWNELHSSNAARWAAGSVTDLAFKVASRELKNGFAVVRPPGHHADHSTAMGFCFFNSVAIACRQLQQQSKASKILIVDWDVHHGNGTQQTFYQDPSVLYISLHRHDDGNFFPGSGAVDEVGAGSGEGFNVNVAWAGGLDPPMGDPEYLAAFRIVVMPIAREFSPDLVLVSAGFDAAEGHPAPLGGYHVSAKCFGYMTQQLMNLAGGAVVLALEGGHDLTAICDASEACVAALLGNRVDPLSEEGWKQKPNLNAIRSLEAVIRVHSKYWGCMQRLASCPDSWVPRVPGADKEEVEAVTALASLSVGILAEDRPSEQLVEEEEPMNL' pair. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccccc1-c1ccccc1\", \"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1cccc2ccccc12\"], \"target_seq\": \"MDLRVGQRPPVEPPPEPTLLALQRPQRLHHHLFLAGLQQQRSVEPMRLSMDTPMPELQVGPQEQELRQLLHKDKSKRSAVASSVVKQKLAEVILKKQQAALERTVHPNSPGIPYRTLEPLETEGATRSMLSSFLPPVPSLPSDPPEHFPLRKTVSEPNLKLRYKPKKSLERRKNPLLRKESAPPSLRRRPAETLGDSSPSSSSTPASGCSSPNDSEHGPNPILGSEALLGQRLRLQETSVAPFALPTVSLLPAITLGLPAPARADSDRRTHPTLGPRGPILGSPHTPLFLPHGLEPEAGGTLPSRLQPILLLDPSGSHAPLLTVPGLGPLPFHFAQSLMTTERLSGSGLHWPLSRTRSEPLPPSATAPPPPGPMQPRLEQLKTHVQVIKRSAKPSEKPRLRQIPSAEDLETDGGGPGQVVDDGLEHRELGHGQPEARGPAPLQQHPQVLLWEQQRLAGRLPRGSTGDTVLLPLAQGGHRPLSRAQSSPAAPASLSAPEPASQARVLSSSETPARTLPFTTGLIYDSVMLKHQCSCGDNSRHPEHAGRIQSIWSRLQERGLRSQCECLRGRKASLEELQSVHSERHVLLYGTNPLSRLKLDNGKLAGLLAQRMFVMLPCGGVGVDTDTIWNELHSSNAARWAAGSVTDLAFKVASRELKNGFAVVRPPGHHADHSTAMGFCFFNSVAIACRQLQQQSKASKILIVDWDVHHGNGTQQTFYQDPSVLYISLHRHDDGNFFPGSGAVDEVGAGSGEGFNVNVAWAGGLDPPMGDPEYLAAFRIVVMPIAREFSPDLVLVSAGFDAAEGHPAPLGGYHVSAKCFGYMTQQLMNLAGGAVVLALEGGHDLTAICDASEACVAALLGNRVDPLSEEGWKQKPNLNAIRSLEAVIRVHSKYWGCMQRLASCPDSWVPRVPGADKEEVEAVTALASLSVGILAEDRPSEQLVEEEEPMNL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDLRVGQRPPVEPPPEPTLLALQRPQRLHHHLFLAGLQQQRSVEPMRLSMDTPMPELQVGPQEQELRQLLHKDKSKRSAVASSVVKQKLAEVILKKQQAALERTVHPNSPGIPYRTLEPLETEGATRSMLSSFLPPVPSLPSDPPEHFPLRKTVSEPNLKLRYKPKKSLERRKNPLLRKESAPPSLRRRPAETLGDSSPSSSSTPASGCSSPNDSEHGPNPILGSEALLGQRLRLQETSVAPFALPTVSLLPAITLGLPAPARADSDRRTHPTLGPRGPILGSPHTPLFLPHGLEPEAGGTLPSRLQPILLLDPSGSHAPLLTVPGLGPLPFHFAQSLMTTERLSGSGLHWPLSRTRSEPLPPSATAPPPPGPMQPRLEQLKTHVQVIKRSAKPSEKPRLRQIPSAEDLETDGGGPGQVVDDGLEHRELGHGQPEARGPAPLQQHPQVLLWEQQRLAGRLPRGSTGDTVLLPLAQGGHRPLSRAQSSPAAPASLSAPEPASQARVLSSSETPARTLPFTTGLIYDSVMLKHQCSCGDNSRHPEHAGRIQSIWSRLQERGLRSQCECLRGRKASLEELQSVHSERHVLLYGTNPLSRLKLDNGKLAGLLAQRMFVMLPCGGVGVDTDTIWNELHSSNAARWAAGSVTDLAFKVASRELKNGFAVVRPPGHHADHSTAMGFCFFNSVAIACRQLQQQSKASKILIVDWDVHHGNGTQQTFYQDPSVLYISLHRHDDGNFFPGSGAVDEVGAGSGEGFNVNVAWAGGLDPPMGDPEYLAAFRIVVMPIAREFSPDLVLVSAGFDAAEGHPAPLGGYHVSAKCFGYMTQQLMNLAGGAVVLALEGGHDLTAICDASEACVAALLGNRVDPLSEEGWKQKPNLNAIRSLEAVIRVHSKYWGCMQRLASCPDSWVPRVPGADKEEVEAVTALASLSVGILAEDRPSEQLVEEEEPMNL\", \"result\": [{\"smile\": \"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccccc1-c1ccccc1\", \"value\": 14000.0}, {\"smile\": \"O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1cccc2ccccc12\", \"value\": 20000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1ccccc1-c1ccccc1', 'O=C(NO)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(=O)Nc1cccc2ccccc12' and the protein are 14000.0 and 20000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4', 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1' and the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1\"], \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 1100.0}, {\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1\", \"value\": 450.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CNC1CC2OC(C)(C1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4', 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1' and the protein are 1100.0 and 450.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21', 'Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1' and the target protein 'MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1\"], \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\", \"result\": [{\"smile\": \"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"value\": 10000.0}, {\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1\", \"value\": 1500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21', 'Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1' and the protein are 10000.0 and 1500.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the predicted binding value of the drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F' for the target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\"], \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 160.0}, {\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 4500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F' and the protein are 160.0 and 4500.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1', 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O''s affinity with the target protein 'MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\", \"result\": [{\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 62.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1', 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein are 10000.0 and 62.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of the binding affinity between the drug 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the target protein 'MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP\", \"result\": [{\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug 'CC(C)Oc1ccc(/C=C2\\CCn3c2nc2cc(NCCCN(C)C)c(F)cc2c3=O)cc1', 'CCN(CC)CCCNc1cc2nc3n(c(=O)c2cc1F)CC/C3=C\\c1ccc(F)cc1'-target protein 'MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)Oc1ccc(/C=C2\\CCn3c2nc2cc(NCCCN(C)C)c(F)cc2c3=O)cc1\", \"CCN(CC)CCCNc1cc2nc3n(c(=O)c2cc1F)CC/C3=C\\c1ccc(F)cc1\"], \"target_seq\": \"MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE\", \"result\": [{\"smile\": \"CC(C)Oc1ccc(/C=C2\\CCn3c2nc2cc(NCCCN(C)C)c(F)cc2c3=O)cc1\", \"value\": 9600.0}, {\"smile\": \"CCN(CC)CCCNc1cc2nc3n(c(=O)c2cc1F)CC/C3=C\\c1ccc(F)cc1\", \"value\": 61600.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(C)Oc1ccc(/C=C2\\CCn3c2nc2cc(NCCCN(C)C)c(F)cc2c3=O)cc1', 'CCN(CC)CCCNc1cc2nc3n(c(=O)c2cc1F)CC/C3=C\\c1ccc(F)cc1' and the protein are 9600.0 and 61600.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted interaction value for the drug 'N[C@@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O', 'N[C@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O' and the target protein 'MERSTVLIQPGLWTRDTSWTLLYFLCYILPQTSPQVLRIGGIFETVENEPVNVEELAFKFAVTSINRNRTLMPNTTLTYDIQRINLFDSFEASRRACDQLALGVAALFGPSHSSSVSAVQSICNALEVPHIQTRWKHPSVDSRDLFYINLYPDYAAISRAVLDLVLYYNWKTVTVVYEDSTGLIRLQELIKAPSRYNIKIKIRQLPPANKDAKPLLKEMKKSKEFYVIFDCSHETAAEILKQILFMGMMTEYYHYFFTTLDLFALDLELYRYSGVNMTGFRKLNIDNPHVSSIIEKWSMERLQAPPRPETGLLDGMMTTEAALMYDAVYMVAIASHRASQLTVSSLQCHRHKPCALGPRFMNLIKEARWDGLTGRITFNKTDGLRKDFDLDIISLKEEGTEKASGEVSKHLYKVWKKIGIWNSNSGLNMTDGNRDRSNNITDSLANRTLIVTTILEEPYVMYRKSDKPLYGNDRFEAYCLDLLKELSNILGFLYDVKLVPDGKYGAQNDKGEWNGMVKELIDHRADLAVAPLTITYVREKVIDFSKPFMTLGISILYRKPNGTNPGVFSFLNPLSPDIWMYVLLACLGVSCVLFVIARFTPYEWYNPHPCNPDSDVVENNFTLLNSFWFGVGALMQQGSELMPKALSTRIVGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVRDGSTMTFFKKSKISTYEKMWAFMSSRQQSALVKNSDEGIQRVLTTDYALLMESTSIEYVTQRNCNLTQIGGLIDSKGYGVGTPIGSPYRDKITIAILQLQEEGKLHMMKEKWWRGNGCPEEDSKEASALGVENIGGIFIVLAAGLVLSVFVAIGEFLYKSRKNNDVEQKGKSSRLRFYFRNKVRFHGSKKESLGVEKCLSFNAIMEELGISLKNQKKLKKKSRTKGKSSFTSILTCHQRRTQRKETVA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N[C@@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O\", \"N[C@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O\"], \"target_seq\": \"MERSTVLIQPGLWTRDTSWTLLYFLCYILPQTSPQVLRIGGIFETVENEPVNVEELAFKFAVTSINRNRTLMPNTTLTYDIQRINLFDSFEASRRACDQLALGVAALFGPSHSSSVSAVQSICNALEVPHIQTRWKHPSVDSRDLFYINLYPDYAAISRAVLDLVLYYNWKTVTVVYEDSTGLIRLQELIKAPSRYNIKIKIRQLPPANKDAKPLLKEMKKSKEFYVIFDCSHETAAEILKQILFMGMMTEYYHYFFTTLDLFALDLELYRYSGVNMTGFRKLNIDNPHVSSIIEKWSMERLQAPPRPETGLLDGMMTTEAALMYDAVYMVAIASHRASQLTVSSLQCHRHKPCALGPRFMNLIKEARWDGLTGRITFNKTDGLRKDFDLDIISLKEEGTEKASGEVSKHLYKVWKKIGIWNSNSGLNMTDGNRDRSNNITDSLANRTLIVTTILEEPYVMYRKSDKPLYGNDRFEAYCLDLLKELSNILGFLYDVKLVPDGKYGAQNDKGEWNGMVKELIDHRADLAVAPLTITYVREKVIDFSKPFMTLGISILYRKPNGTNPGVFSFLNPLSPDIWMYVLLACLGVSCVLFVIARFTPYEWYNPHPCNPDSDVVENNFTLLNSFWFGVGALMQQGSELMPKALSTRIVGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVRDGSTMTFFKKSKISTYEKMWAFMSSRQQSALVKNSDEGIQRVLTTDYALLMESTSIEYVTQRNCNLTQIGGLIDSKGYGVGTPIGSPYRDKITIAILQLQEEGKLHMMKEKWWRGNGCPEEDSKEASALGVENIGGIFIVLAAGLVLSVFVAIGEFLYKSRKNNDVEQKGKSSRLRFYFRNKVRFHGSKKESLGVEKCLSFNAIMEELGISLKNQKKLKKKSRTKGKSSFTSILTCHQRRTQRKETVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERSTVLIQPGLWTRDTSWTLLYFLCYILPQTSPQVLRIGGIFETVENEPVNVEELAFKFAVTSINRNRTLMPNTTLTYDIQRINLFDSFEASRRACDQLALGVAALFGPSHSSSVSAVQSICNALEVPHIQTRWKHPSVDSRDLFYINLYPDYAAISRAVLDLVLYYNWKTVTVVYEDSTGLIRLQELIKAPSRYNIKIKIRQLPPANKDAKPLLKEMKKSKEFYVIFDCSHETAAEILKQILFMGMMTEYYHYFFTTLDLFALDLELYRYSGVNMTGFRKLNIDNPHVSSIIEKWSMERLQAPPRPETGLLDGMMTTEAALMYDAVYMVAIASHRASQLTVSSLQCHRHKPCALGPRFMNLIKEARWDGLTGRITFNKTDGLRKDFDLDIISLKEEGTEKASGEVSKHLYKVWKKIGIWNSNSGLNMTDGNRDRSNNITDSLANRTLIVTTILEEPYVMYRKSDKPLYGNDRFEAYCLDLLKELSNILGFLYDVKLVPDGKYGAQNDKGEWNGMVKELIDHRADLAVAPLTITYVREKVIDFSKPFMTLGISILYRKPNGTNPGVFSFLNPLSPDIWMYVLLACLGVSCVLFVIARFTPYEWYNPHPCNPDSDVVENNFTLLNSFWFGVGALMQQGSELMPKALSTRIVGGIWWFFTLIIISSYTANLAAFLTVERMESPIDSADDLAKQTKIEYGAVRDGSTMTFFKKSKISTYEKMWAFMSSRQQSALVKNSDEGIQRVLTTDYALLMESTSIEYVTQRNCNLTQIGGLIDSKGYGVGTPIGSPYRDKITIAILQLQEEGKLHMMKEKWWRGNGCPEEDSKEASALGVENIGGIFIVLAAGLVLSVFVAIGEFLYKSRKNNDVEQKGKSSRLRFYFRNKVRFHGSKKESLGVEKCLSFNAIMEELGISLKNQKKLKKKSRTKGKSSFTSILTCHQRRTQRKETVA\", \"result\": [{\"smile\": \"N[C@@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O\", \"value\": 402.0}, {\"smile\": \"N[C@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O\", \"value\": 100000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'N[C@@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O', 'N[C@H](Cn1ccc(=O)n(Cc2ccccc2C(=O)O)c1=O)C(=O)O' and the protein are 402.0 and 100000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug 'Clc1ccc2c(NCCc3ccc(OCCCN4CCCCC4)cc3)ccnc2c1', 'O=C(NCCCC1CCCCC1)OCCCc1cnc[nH]1' and the target protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Clc1ccc2c(NCCc3ccc(OCCCN4CCCCC4)cc3)ccnc2c1\", \"O=C(NCCCC1CCCCC1)OCCCc1cnc[nH]1\"], \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"Clc1ccc2c(NCCc3ccc(OCCCN4CCCCC4)cc3)ccnc2c1\", \"value\": 794.33}, {\"smile\": \"O=C(NCCCC1CCCCC1)OCCCc1cnc[nH]1\", \"value\": 10715.19}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Clc1ccc2c(NCCc3ccc(OCCCN4CCCCC4)cc3)ccnc2c1', 'O=C(NCCCC1CCCCC1)OCCCc1cnc[nH]1' and the protein are 794.33 and 10715.19 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you anticipate the affinity to manifest between the drug 'C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O', 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21' and the target protein 'MSSTQGNGEHWKSLESVGISRKELAMAEALQMEYDALSRLRHDKEENRAKQNADPSLISWDEPGVDFYSKPAGRRTDLKLLRGLSGSDPTLNYNSLSPQEGPPNHSTSQGPQPGSDPWPKGSLSGDYLYIFDGSDGGVSSSPGPGDIEGSCKKLSPPPLPPRASIWDTPPLPPRKGSPSSSKISQPSDINTFSLVEQLPGKLLEHRILEEEEVLGGGGQGRLLGSVDYDGINDAITRLNLKSTYDAEMLRDATRGWKEGRGPLDFSKDTSGKPVARSKTMPPQVPPRTYASRYGNRKNATPGKNRRISAAPVGSRPHTVANGHELFEVSEERDEEVAAFCHMLDILRSGSDIQDYFLTGYVWSAVTPSPEHLGDEVNLKVTVLCDRLQEALTFTCNCSSTVDLLIYQTLCYTHDDLRNVDVGDFVLKPCGLEEFLQNKHALGSHEYIQYCRKFDIDIRLQLMEQKVVRSDLARTVNDDQSPSTLNYLVHLQERPVKQTISRQALSLLFDTYHNEVDAFLLADGDFPLKADRVVQSVKAICNALAAVETPEITSALNQLPPCPSRMQPKIQKDPSVLAVRENREKVVEALTAAILDLVELYCNTFNADFQTAVPGSRKHDLVQEACHFARSLAFTVYATHRIPIIWATSYEDFYLSCSLSHGGKELCSPLQTRRAHFSKYLFHLIVWDQQICFPVQVNRLPRETLLCATLYALPIPPPGSSSEANKQRRVPEALGWVTTPLFNFRQVLTCGRKLLGLWPATQENPSARWSAPNFHQPDSVILQIDFPTSAFDIKFTSPPGDKFSPRYEFGSLREEDQRKLKDIMQKESLYWLTDADKKRLWEKRYYCHSEVSSLPLVLASAPSWEWACLPDIYVLLKQWTHMNHQDALGLLHATFPDQEVRRMAVQWIGSLSDAELLDYLPQLVQALKYECYLDSPLVRFLLKRAVSDLRVTHYFFWLLKDGLKDSQFSIRYQYLLAALLCCCGKGLREEFNRQCWLVNALAKLAQQVREAAPSARQGILRTGLEEVKQFFALNGSCRLPLSPSLLVKGIVPRDCSYFNSNAVPLKLSFQNVDPLGENIRVIFKCGDDLRQDMLTLQMIRIMSKIWVQEGLDMRMVIFRCFSTGRGRGMVEMIPNAETLRKIQVEHGVTGSFKDRPLADWLQKHNPGEDEYEKAVENFIYSCAGCCVATYVLGICDRHNDNIMLKTTGHMFHIDFGRFLGHAQMFGNIKRDRAPFVFTSDMAYVINGGDKPSSRFHDFVDLCCQAYNLIRKHTHLFLNLLGLMLSCGIPELSDLEDLKYVYDALRPQDTEANATTYFTRLIESSLGSVATKLNFFIHNLAQMKFTGSDDRLTLSFASRTHTLKSSGRISDVFLCRHEKIFHPNKGYIYVVKVMRENTHEATYIQRTFEEFQELHNKLRLLFPSSHLPSFPSRFVIGRSRGEAVAERRREELNGYIWHLIHAPPEVAECDLVYTFFHPLPRDEKAMGTSPAPKSSDGTWARPVGKVGGEVKLSISYKNNKLFIMVMHIRGLQLLQDGNDPDPYVKIYLLPDPQKTTKRKTKVARKTCNPTYNEMLVYDGIPKGDLQQRELQLSVLSEQGFWENVLLGEVNIRLRELDLAQEKTGWFALGSRSHGTL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21\"], \"target_seq\": \"MSSTQGNGEHWKSLESVGISRKELAMAEALQMEYDALSRLRHDKEENRAKQNADPSLISWDEPGVDFYSKPAGRRTDLKLLRGLSGSDPTLNYNSLSPQEGPPNHSTSQGPQPGSDPWPKGSLSGDYLYIFDGSDGGVSSSPGPGDIEGSCKKLSPPPLPPRASIWDTPPLPPRKGSPSSSKISQPSDINTFSLVEQLPGKLLEHRILEEEEVLGGGGQGRLLGSVDYDGINDAITRLNLKSTYDAEMLRDATRGWKEGRGPLDFSKDTSGKPVARSKTMPPQVPPRTYASRYGNRKNATPGKNRRISAAPVGSRPHTVANGHELFEVSEERDEEVAAFCHMLDILRSGSDIQDYFLTGYVWSAVTPSPEHLGDEVNLKVTVLCDRLQEALTFTCNCSSTVDLLIYQTLCYTHDDLRNVDVGDFVLKPCGLEEFLQNKHALGSHEYIQYCRKFDIDIRLQLMEQKVVRSDLARTVNDDQSPSTLNYLVHLQERPVKQTISRQALSLLFDTYHNEVDAFLLADGDFPLKADRVVQSVKAICNALAAVETPEITSALNQLPPCPSRMQPKIQKDPSVLAVRENREKVVEALTAAILDLVELYCNTFNADFQTAVPGSRKHDLVQEACHFARSLAFTVYATHRIPIIWATSYEDFYLSCSLSHGGKELCSPLQTRRAHFSKYLFHLIVWDQQICFPVQVNRLPRETLLCATLYALPIPPPGSSSEANKQRRVPEALGWVTTPLFNFRQVLTCGRKLLGLWPATQENPSARWSAPNFHQPDSVILQIDFPTSAFDIKFTSPPGDKFSPRYEFGSLREEDQRKLKDIMQKESLYWLTDADKKRLWEKRYYCHSEVSSLPLVLASAPSWEWACLPDIYVLLKQWTHMNHQDALGLLHATFPDQEVRRMAVQWIGSLSDAELLDYLPQLVQALKYECYLDSPLVRFLLKRAVSDLRVTHYFFWLLKDGLKDSQFSIRYQYLLAALLCCCGKGLREEFNRQCWLVNALAKLAQQVREAAPSARQGILRTGLEEVKQFFALNGSCRLPLSPSLLVKGIVPRDCSYFNSNAVPLKLSFQNVDPLGENIRVIFKCGDDLRQDMLTLQMIRIMSKIWVQEGLDMRMVIFRCFSTGRGRGMVEMIPNAETLRKIQVEHGVTGSFKDRPLADWLQKHNPGEDEYEKAVENFIYSCAGCCVATYVLGICDRHNDNIMLKTTGHMFHIDFGRFLGHAQMFGNIKRDRAPFVFTSDMAYVINGGDKPSSRFHDFVDLCCQAYNLIRKHTHLFLNLLGLMLSCGIPELSDLEDLKYVYDALRPQDTEANATTYFTRLIESSLGSVATKLNFFIHNLAQMKFTGSDDRLTLSFASRTHTLKSSGRISDVFLCRHEKIFHPNKGYIYVVKVMRENTHEATYIQRTFEEFQELHNKLRLLFPSSHLPSFPSRFVIGRSRGEAVAERRREELNGYIWHLIHAPPEVAECDLVYTFFHPLPRDEKAMGTSPAPKSSDGTWARPVGKVGGEVKLSISYKNNKLFIMVMHIRGLQLLQDGNDPDPYVKIYLLPDPQKTTKRKTKVARKTCNPTYNEMLVYDGIPKGDLQQRELQLSVLSEQGFWENVLLGEVNIRLRELDLAQEKTGWFALGSRSHGTL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSTQGNGEHWKSLESVGISRKELAMAEALQMEYDALSRLRHDKEENRAKQNADPSLISWDEPGVDFYSKPAGRRTDLKLLRGLSGSDPTLNYNSLSPQEGPPNHSTSQGPQPGSDPWPKGSLSGDYLYIFDGSDGGVSSSPGPGDIEGSCKKLSPPPLPPRASIWDTPPLPPRKGSPSSSKISQPSDINTFSLVEQLPGKLLEHRILEEEEVLGGGGQGRLLGSVDYDGINDAITRLNLKSTYDAEMLRDATRGWKEGRGPLDFSKDTSGKPVARSKTMPPQVPPRTYASRYGNRKNATPGKNRRISAAPVGSRPHTVANGHELFEVSEERDEEVAAFCHMLDILRSGSDIQDYFLTGYVWSAVTPSPEHLGDEVNLKVTVLCDRLQEALTFTCNCSSTVDLLIYQTLCYTHDDLRNVDVGDFVLKPCGLEEFLQNKHALGSHEYIQYCRKFDIDIRLQLMEQKVVRSDLARTVNDDQSPSTLNYLVHLQERPVKQTISRQALSLLFDTYHNEVDAFLLADGDFPLKADRVVQSVKAICNALAAVETPEITSALNQLPPCPSRMQPKIQKDPSVLAVRENREKVVEALTAAILDLVELYCNTFNADFQTAVPGSRKHDLVQEACHFARSLAFTVYATHRIPIIWATSYEDFYLSCSLSHGGKELCSPLQTRRAHFSKYLFHLIVWDQQICFPVQVNRLPRETLLCATLYALPIPPPGSSSEANKQRRVPEALGWVTTPLFNFRQVLTCGRKLLGLWPATQENPSARWSAPNFHQPDSVILQIDFPTSAFDIKFTSPPGDKFSPRYEFGSLREEDQRKLKDIMQKESLYWLTDADKKRLWEKRYYCHSEVSSLPLVLASAPSWEWACLPDIYVLLKQWTHMNHQDALGLLHATFPDQEVRRMAVQWIGSLSDAELLDYLPQLVQALKYECYLDSPLVRFLLKRAVSDLRVTHYFFWLLKDGLKDSQFSIRYQYLLAALLCCCGKGLREEFNRQCWLVNALAKLAQQVREAAPSARQGILRTGLEEVKQFFALNGSCRLPLSPSLLVKGIVPRDCSYFNSNAVPLKLSFQNVDPLGENIRVIFKCGDDLRQDMLTLQMIRIMSKIWVQEGLDMRMVIFRCFSTGRGRGMVEMIPNAETLRKIQVEHGVTGSFKDRPLADWLQKHNPGEDEYEKAVENFIYSCAGCCVATYVLGICDRHNDNIMLKTTGHMFHIDFGRFLGHAQMFGNIKRDRAPFVFTSDMAYVINGGDKPSSRFHDFVDLCCQAYNLIRKHTHLFLNLLGLMLSCGIPELSDLEDLKYVYDALRPQDTEANATTYFTRLIESSLGSVATKLNFFIHNLAQMKFTGSDDRLTLSFASRTHTLKSSGRISDVFLCRHEKIFHPNKGYIYVVKVMRENTHEATYIQRTFEEFQELHNKLRLLFPSSHLPSFPSRFVIGRSRGEAVAERRREELNGYIWHLIHAPPEVAECDLVYTFFHPLPRDEKAMGTSPAPKSSDGTWARPVGKVGGEVKLSISYKNNKLFIMVMHIRGLQLLQDGNDPDPYVKIYLLPDPQKTTKRKTKVARKTCNPTYNEMLVYDGIPKGDLQQRELQLSVLSEQGFWENVLLGEVNIRLRELDLAQEKTGWFALGSRSHGTL\", \"result\": [{\"smile\": \"C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 5200.0}, {\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O', 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21' and the protein are 5200.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the projected strength of affinity between the drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the target protein 'MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVSYWDTGVLLCALLSCLLLTGSSSGSKLKDPELSLKGTQHIMQAGQTLHLQCRGEAAHKWSLPEMVSKESERLSITKSACGRNGKQFCSTLTLNTAQANHTGFYSCKYLAVPTSKKKETESAIYIFISDTGRPFVEMYSEIPEIIHMTEGRELVIPCRVTSPNITVTLKKFPLDTLIPDGKRIIWDSRKGFIISNATYKEIGLLTCEATVNGHLYKTNYLTHRQTNTIIDVQISTPRPVKLLRGHTLVLNCTATTPLNTRVQMTWSYPDEKNKRASVRRRIDQSNSHANIFYSVLTIDKMQNKDKGLYTCRVRSGPSFKSVNTSVHIYDKAFITVKHRKQQVLETVAGKRSYRLSMKVKAFPSPEVVWLKDGLPATEKSARYLTRGYSLIIKDVTEEDAGNYTILLSIKQSNVFKNLTATLIVNVKPQIYEKAVSSFPDPALYPLGSRQILTCTAYGIPQPTIKWFWHPCNHNHSEARCDFCSNNEESFILDADSNMGNRIESITQRMAIIEGKNKMASTLVVADSRISGIYICIASNKVGTVGRNISFYITDVPNGFHVNLEKMPTEGEDLKLSCTVNKFLYRDVTWILLRTVNNRTMHYSISKQKMAITKEHSITLNLTIMNVSLQDSGTYACRARNVYTGEEILQKKEITIRDQEAPYLLRNLSDHTVAISSSTTLDCHANGVPEPQITWFKNNHKIQQEPGIILGPGSSTLFIERVTEEDEGVYHCKATNQKGSVESSAYLTVQGTSDKSNLELITLTCTCVAATLFWLLLTLFIRKMKRSSSEIKTDYLSIIMDPDEVPLDEQCERLPYDASKWEFARERLKLGKSLGRGAFGKVVQASAFGIKKSPTCRTVAVKMLKEGATASEYKALMTELKILTHIGHHLNVVNLLGACTKQGGPLMVIVEYCKYGNLSNYLKSKRDLFFLNKDAALHMEPKKEKMEPGLEQGKKPRLDSVTSSESFASSGFQEDKSLSDVEEEEDSDGFYKEPITMEDLISYSFQVARGMEFLSSRKCIHRDLAARNILLSENNVVKICDFGLARDIYKNPDYVRKGDTRLPLKWMAPESIFDKIYSTKSDVWSYGVLLWEIFSLGGSPYPGVQMDEDFCSRLREGMRMRAPEYSTPEIYQIMLDCWHRDPKERPRFAELVEKLGDLLQANVQQDGKDYIPINAILTGNSGFTYSTPAFSEDFFKESISAPKFNSGSSDDVRYVNAFKFMSLERIKTFEELLPNATSMFDDYQGDSSTLLASPMLKRFTWTDSKPKASLKIDLRVTSKSKESGLSDVSRPSFCHSSCGHVSEGKRRFTYDHAELERKIACCSPPPDYNSVVLYSTPPI\", \"result\": [{\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 9900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein are 10000.0 and 9900.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1' and the target protein 'MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\"], \"target_seq\": \"MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 1.0}, {\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\", \"value\": 770.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1' and the protein are 1.0 and 770.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the target protein 'MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\"], \"target_seq\": \"MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS\", \"result\": [{\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 2300.0}, {\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the protein are 2300.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding score between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl', 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the target protein 'MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGRGLLRGLWPLHIVLWTRIASTIPPHVQKSVNNDMIVTDNNGAVKFPQLCKFCDVRFSTCDNQKSCMSNCSITSICEKPQEVCVAVWRKNDENITLETVCHDPKLPYHDFILEDAASPKCIMKEKKKPGETFFMCSCSSDECNDNIIFSEEYNTSNPDLLLVIFQVTGISLLPPLGVAISVIIIFYCYRVNRQQKLSSTWETGKTRKLMEFSEHCAIILEDDRSDISSTCANNINHNTELLPIELDTLVGKGRFAEVYKAKLKQNTSEQFETVAVKIFPYEEYASWKTEKDIFSDINLKHENILQFLTAEERKTELGKQYWLITAFHAKGNLQEYLTRHVISWEDLRKLGSSLARGIAHLHSDHTPCGRPKMPIVHRDLKSSNILVKNDLTCCLCDFGLSLRLDPTLSVDDLANSGQVGTARYMAPEVLESRMNLENVESFKQTDVYSMALVLWEMTSRCNAVGEVKDYEPPFGSKVREHPCVESMKDNVLRDRGRPEIPSFWLNHQGIQMVCETLTECWDHDPEARLTAQCVAERFSELEHLDRLSGRSCSEEKIPEDGSLNTTK\", \"result\": [{\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 3000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl', 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein are 10000.0 and 3000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the predicted affinity level for the drug 'Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12', 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the target protein 'MAYSWQTDPNPNESHEKQYEHQEFLFVNQPHSSSQVSLGFDQIVDEISGKIPHYESEIDENTFFVPTAPKWDSTGHSLNEAHQISLNEFTSKSRELSWHQVSKAPAIGFSPSVLPKPQNTNKECSWGSPIGKHHGADDSRFSILAPSFTSLDKINLEKELENENHNYHIGFESSIPPTNSSFSSDFMPKEENKRSGHVNIVEPSLMLLKGSLQPGMWESTWQKNIESIGCSIQLVEVPQSSNTSLASFCNKVKKIRERYHAADVNFNSGKIWSTTTAFPYQLFSKTKFNIHIFIDNSTQPLHFMPCANYLVKDLIAEILHFCTNDQLLPKDHILSVCGSEEFLQNDHCLGSHKMFQKDKSVIQLHLQKSREAPGKLSRKHEEDHSQFYLNQLLEFMHIWKVSRQCLLTLIRKYDFHLKYLLKTQENVYNIIEEVKKICSVLGCVETKQITDAVNELSLILQRKGENFYQSSETSAKGLIEKVTTELSTSIYQLINVYCNSFYADFQPVNVPRCTSYLNPGLPSHLSFTVYAAHNIPETWVHRINFPLEIKSLPRESMLTVKLFGIACATNNANLLAWTCLPLFPKEKSILGSMLFSMTLQSEPPVEMITPGVWDVSQPSPVTLQIDFPATGWEYMKPDSEENRSNLEEPLKECIKHIARLSQKQTPLLLSEEKKRYLWFYRFYCNNENCSLPLVLGSAPGWDERTVSEMHTILRRWTFSQPLEALGLLTSSFPDQEIRKVAVQQLDNLLNDELLEYLPQLVQAVKFEWNLESPLVQLLLHRSLQSIQVAHRLYWLLKNAENEAYFKSWYQKLLAALQFCAGKALNDEFSKEQKLIKILGDIGERVKSASDHQRQEVLKKEIGRLEEFFQDVNTCHLPLNPALCIKGIDHDACSYFTSNALPLKITFINANPMGKNISIIFKAGDDLRQDMLVLQLIQVMDNIWLQEGLDMQMIIYRCLSTGKDQGLVQMVPDAVTLAKIHRHSGLIGPLKENTIKKWFSQHNHLKADYEKALRNFFYSCAGWCVVTFILGVCDRHNDNIMLTKSGHMFHIDFGKFLGHAQTFGGIKRDRAPFIFTSEMEYFITEGGKNPQHFQDFVELCCRAYNIIRKHSQLLLNLLEMMLYAGLPELSGIQDLKYVYNNLRPQDTDLEATSHFTKKIKESLECFPVKLNNLIHTLAQMSAISPAKSTSQTFPQESCLLSTTRSIERATILGFSKKSSNLYLIQVTHSNNETSLTEKSFEQFSKLHSQLQKQFASLTLPEFPHWWHLPFTNSDHRRFRDLNHYMEQILNVSHEVTNSDCVLSFFLSEAVQQTVEESSPVYLGEKFPDKKPKVQLVISYEDVKLTILVKHMKNIHLPDGSAPSAHVEFYLLPYPSEVRRRKTKSVPKCTDPTYNEIVVYDEVTELQGHVLMLIVKSKTVFVGAINIRLCSVPLDKEKWYPLGNSII'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12\", \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"MAYSWQTDPNPNESHEKQYEHQEFLFVNQPHSSSQVSLGFDQIVDEISGKIPHYESEIDENTFFVPTAPKWDSTGHSLNEAHQISLNEFTSKSRELSWHQVSKAPAIGFSPSVLPKPQNTNKECSWGSPIGKHHGADDSRFSILAPSFTSLDKINLEKELENENHNYHIGFESSIPPTNSSFSSDFMPKEENKRSGHVNIVEPSLMLLKGSLQPGMWESTWQKNIESIGCSIQLVEVPQSSNTSLASFCNKVKKIRERYHAADVNFNSGKIWSTTTAFPYQLFSKTKFNIHIFIDNSTQPLHFMPCANYLVKDLIAEILHFCTNDQLLPKDHILSVCGSEEFLQNDHCLGSHKMFQKDKSVIQLHLQKSREAPGKLSRKHEEDHSQFYLNQLLEFMHIWKVSRQCLLTLIRKYDFHLKYLLKTQENVYNIIEEVKKICSVLGCVETKQITDAVNELSLILQRKGENFYQSSETSAKGLIEKVTTELSTSIYQLINVYCNSFYADFQPVNVPRCTSYLNPGLPSHLSFTVYAAHNIPETWVHRINFPLEIKSLPRESMLTVKLFGIACATNNANLLAWTCLPLFPKEKSILGSMLFSMTLQSEPPVEMITPGVWDVSQPSPVTLQIDFPATGWEYMKPDSEENRSNLEEPLKECIKHIARLSQKQTPLLLSEEKKRYLWFYRFYCNNENCSLPLVLGSAPGWDERTVSEMHTILRRWTFSQPLEALGLLTSSFPDQEIRKVAVQQLDNLLNDELLEYLPQLVQAVKFEWNLESPLVQLLLHRSLQSIQVAHRLYWLLKNAENEAYFKSWYQKLLAALQFCAGKALNDEFSKEQKLIKILGDIGERVKSASDHQRQEVLKKEIGRLEEFFQDVNTCHLPLNPALCIKGIDHDACSYFTSNALPLKITFINANPMGKNISIIFKAGDDLRQDMLVLQLIQVMDNIWLQEGLDMQMIIYRCLSTGKDQGLVQMVPDAVTLAKIHRHSGLIGPLKENTIKKWFSQHNHLKADYEKALRNFFYSCAGWCVVTFILGVCDRHNDNIMLTKSGHMFHIDFGKFLGHAQTFGGIKRDRAPFIFTSEMEYFITEGGKNPQHFQDFVELCCRAYNIIRKHSQLLLNLLEMMLYAGLPELSGIQDLKYVYNNLRPQDTDLEATSHFTKKIKESLECFPVKLNNLIHTLAQMSAISPAKSTSQTFPQESCLLSTTRSIERATILGFSKKSSNLYLIQVTHSNNETSLTEKSFEQFSKLHSQLQKQFASLTLPEFPHWWHLPFTNSDHRRFRDLNHYMEQILNVSHEVTNSDCVLSFFLSEAVQQTVEESSPVYLGEKFPDKKPKVQLVISYEDVKLTILVKHMKNIHLPDGSAPSAHVEFYLLPYPSEVRRRKTKSVPKCTDPTYNEIVVYDEVTELQGHVLMLIVKSKTVFVGAINIRLCSVPLDKEKWYPLGNSII\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAYSWQTDPNPNESHEKQYEHQEFLFVNQPHSSSQVSLGFDQIVDEISGKIPHYESEIDENTFFVPTAPKWDSTGHSLNEAHQISLNEFTSKSRELSWHQVSKAPAIGFSPSVLPKPQNTNKECSWGSPIGKHHGADDSRFSILAPSFTSLDKINLEKELENENHNYHIGFESSIPPTNSSFSSDFMPKEENKRSGHVNIVEPSLMLLKGSLQPGMWESTWQKNIESIGCSIQLVEVPQSSNTSLASFCNKVKKIRERYHAADVNFNSGKIWSTTTAFPYQLFSKTKFNIHIFIDNSTQPLHFMPCANYLVKDLIAEILHFCTNDQLLPKDHILSVCGSEEFLQNDHCLGSHKMFQKDKSVIQLHLQKSREAPGKLSRKHEEDHSQFYLNQLLEFMHIWKVSRQCLLTLIRKYDFHLKYLLKTQENVYNIIEEVKKICSVLGCVETKQITDAVNELSLILQRKGENFYQSSETSAKGLIEKVTTELSTSIYQLINVYCNSFYADFQPVNVPRCTSYLNPGLPSHLSFTVYAAHNIPETWVHRINFPLEIKSLPRESMLTVKLFGIACATNNANLLAWTCLPLFPKEKSILGSMLFSMTLQSEPPVEMITPGVWDVSQPSPVTLQIDFPATGWEYMKPDSEENRSNLEEPLKECIKHIARLSQKQTPLLLSEEKKRYLWFYRFYCNNENCSLPLVLGSAPGWDERTVSEMHTILRRWTFSQPLEALGLLTSSFPDQEIRKVAVQQLDNLLNDELLEYLPQLVQAVKFEWNLESPLVQLLLHRSLQSIQVAHRLYWLLKNAENEAYFKSWYQKLLAALQFCAGKALNDEFSKEQKLIKILGDIGERVKSASDHQRQEVLKKEIGRLEEFFQDVNTCHLPLNPALCIKGIDHDACSYFTSNALPLKITFINANPMGKNISIIFKAGDDLRQDMLVLQLIQVMDNIWLQEGLDMQMIIYRCLSTGKDQGLVQMVPDAVTLAKIHRHSGLIGPLKENTIKKWFSQHNHLKADYEKALRNFFYSCAGWCVVTFILGVCDRHNDNIMLTKSGHMFHIDFGKFLGHAQTFGGIKRDRAPFIFTSEMEYFITEGGKNPQHFQDFVELCCRAYNIIRKHSQLLLNLLEMMLYAGLPELSGIQDLKYVYNNLRPQDTDLEATSHFTKKIKESLECFPVKLNNLIHTLAQMSAISPAKSTSQTFPQESCLLSTTRSIERATILGFSKKSSNLYLIQVTHSNNETSLTEKSFEQFSKLHSQLQKQFASLTLPEFPHWWHLPFTNSDHRRFRDLNHYMEQILNVSHEVTNSDCVLSFFLSEAVQQTVEESSPVYLGEKFPDKKPKVQLVISYEDVKLTILVKHMKNIHLPDGSAPSAHVEFYLLPYPSEVRRRKTKSVPKCTDPTYNEIVVYDEVTELQGHVLMLIVKSKTVFVGAINIRLCSVPLDKEKWYPLGNSII\", \"result\": [{\"smile\": \"Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12', 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your calculation for the binding affinity between the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the target protein 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\", \"result\": [{\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 84.0}, {\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein are 84.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value between the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1', 'C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the target protein 'MTTPSHLSDRYELGEILGFGGMSEVHLARDLRLHRDVAVKVLRADLARDPSFYLRFRREAQNAAALNHPAIVAVYDTGEAETPAGPLPYIVMEYVDGVTLRDIVHTEGPMTPKRAIEVIADACQALNFSHQNGIIHRDVKPANIMISATNAVKVMDFGIARAIADSGNSVTQTAAVIGTAQYLSPEQARGDSVDARSDVYSLGCVLYEVLTGEPPFTGDSPVSVAYQHVREDPIPPSARHEGLSADLDAVVLKALAKNPENRYQTAAEMRADLVRVHNGEPPEAPKVLTDAERTSLLSSAAGNLSGPRTDPLPRQDLDDTDRDRSIGSVGRWVAVVAVLAVLTVVVTIAINTFGGITRDVQVPDVRGQSSADAIATLQNRGFKIRTLQKPDSTIPPDHVIGTDPAANTSVSAGDEITVNVSTGPEQREIPDVSTLTYAEAVKKLTAAGFGRFKQANSPSTPELVGKVIGTNPPANQTSAITNVVIIIVGSGPATKDIPDVAGQTVDVAQKNLNVYGFTKFSQASVDSPRPAGEVTGTNPPAGTTVPVDSVIELQVSKGNQFVMPDLSGMFWVDAEPRLRALGWTGMLDKGADVDAGGSQHNRVVYQNPPAGTGVNRDGIITLRFGQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\"], \"target_seq\": \"MTTPSHLSDRYELGEILGFGGMSEVHLARDLRLHRDVAVKVLRADLARDPSFYLRFRREAQNAAALNHPAIVAVYDTGEAETPAGPLPYIVMEYVDGVTLRDIVHTEGPMTPKRAIEVIADACQALNFSHQNGIIHRDVKPANIMISATNAVKVMDFGIARAIADSGNSVTQTAAVIGTAQYLSPEQARGDSVDARSDVYSLGCVLYEVLTGEPPFTGDSPVSVAYQHVREDPIPPSARHEGLSADLDAVVLKALAKNPENRYQTAAEMRADLVRVHNGEPPEAPKVLTDAERTSLLSSAAGNLSGPRTDPLPRQDLDDTDRDRSIGSVGRWVAVVAVLAVLTVVVTIAINTFGGITRDVQVPDVRGQSSADAIATLQNRGFKIRTLQKPDSTIPPDHVIGTDPAANTSVSAGDEITVNVSTGPEQREIPDVSTLTYAEAVKKLTAAGFGRFKQANSPSTPELVGKVIGTNPPANQTSAITNVVIIIVGSGPATKDIPDVAGQTVDVAQKNLNVYGFTKFSQASVDSPRPAGEVTGTNPPAGTTVPVDSVIELQVSKGNQFVMPDLSGMFWVDAEPRLRALGWTGMLDKGADVDAGGSQHNRVVYQNPPAGTGVNRDGIITLRFGQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTTPSHLSDRYELGEILGFGGMSEVHLARDLRLHRDVAVKVLRADLARDPSFYLRFRREAQNAAALNHPAIVAVYDTGEAETPAGPLPYIVMEYVDGVTLRDIVHTEGPMTPKRAIEVIADACQALNFSHQNGIIHRDVKPANIMISATNAVKVMDFGIARAIADSGNSVTQTAAVIGTAQYLSPEQARGDSVDARSDVYSLGCVLYEVLTGEPPFTGDSPVSVAYQHVREDPIPPSARHEGLSADLDAVVLKALAKNPENRYQTAAEMRADLVRVHNGEPPEAPKVLTDAERTSLLSSAAGNLSGPRTDPLPRQDLDDTDRDRSIGSVGRWVAVVAVLAVLTVVVTIAINTFGGITRDVQVPDVRGQSSADAIATLQNRGFKIRTLQKPDSTIPPDHVIGTDPAANTSVSAGDEITVNVSTGPEQREIPDVSTLTYAEAVKKLTAAGFGRFKQANSPSTPELVGKVIGTNPPANQTSAITNVVIIIVGSGPATKDIPDVAGQTVDVAQKNLNVYGFTKFSQASVDSPRPAGEVTGTNPPAGTTVPVDSVIELQVSKGNQFVMPDLSGMFWVDAEPRLRALGWTGMLDKGADVDAGGSQHNRVVYQNPPAGTGVNRDGIITLRFGQ\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 10000.0}, {\"smile\": \"C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 55.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1', 'C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the protein are 10000.0 and 55.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21', 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' to the target protein 'MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\", \"result\": [{\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 10000.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21', 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the target protein 'MAADPTELRLGSLPVFTRDDFEGDWRLVASGGFSQVFQARHRRWRTEYAIKCAPCLPPDAASSDVNYLIEEAAKMKKIKFQHIVSIYGVCKQPLGIVMEFMANGSLEKVLSTHSLCWKLRFRIIHETSLAMNFLHSIKPPLLHLDLKPGNILLDSNMHVKISDFGLSKWMEQSTRMQYIERSALRGMLSYIPPEMFLESNKAPGPKYDVYSFAIVIWELLTQKKPYSGFNMMMIIIRVAAGMRPSLQPVSDQWPSEAQQMVDLMKRCWDQDPKKRPCFLDITIETDILLSLLQSRVAVPESKALARKVSCKLSLRQPGEVNEDISQELMDSDSGNYLKRALQLSDRKNLVPRDEELCIYENKVTPLHFLVAQGSVEQVRLLLAHEVDVDCQTASGYTPLLIAAQDQQPDLCALLLAHGADANRVDEDGWAPLHFAAQNGDDGTARLLLDHGACVDAQEREGWTPLHLAAQNNFENVARLLVSRQADPNLHEAEGKTPLHVAAYFGHVSLVKLLTSQGAELDAQQRNLRTPLHLAVERGKVRAIQHLLKSGAVPDALDQSGYGPLHTAAARGKYLICKMLLRYGASLELPTHQGWTPLHLAAYKGHLEIIHLLAESHANMGALGAVNWTPLHLAARHGEEAVVSALLQCGADPNAAEQSGWTPLHLAVQRSTFLSVINLLEHHANVHARNKVGWTPAHLAALKGNTAILKVLVEAGAQLDVQDGVSCTPLQLALRSRKQGIMSFLEGKEPSVATLGGSKPGAEMEI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\"], \"target_seq\": \"MAADPTELRLGSLPVFTRDDFEGDWRLVASGGFSQVFQARHRRWRTEYAIKCAPCLPPDAASSDVNYLIEEAAKMKKIKFQHIVSIYGVCKQPLGIVMEFMANGSLEKVLSTHSLCWKLRFRIIHETSLAMNFLHSIKPPLLHLDLKPGNILLDSNMHVKISDFGLSKWMEQSTRMQYIERSALRGMLSYIPPEMFLESNKAPGPKYDVYSFAIVIWELLTQKKPYSGFNMMMIIIRVAAGMRPSLQPVSDQWPSEAQQMVDLMKRCWDQDPKKRPCFLDITIETDILLSLLQSRVAVPESKALARKVSCKLSLRQPGEVNEDISQELMDSDSGNYLKRALQLSDRKNLVPRDEELCIYENKVTPLHFLVAQGSVEQVRLLLAHEVDVDCQTASGYTPLLIAAQDQQPDLCALLLAHGADANRVDEDGWAPLHFAAQNGDDGTARLLLDHGACVDAQEREGWTPLHLAAQNNFENVARLLVSRQADPNLHEAEGKTPLHVAAYFGHVSLVKLLTSQGAELDAQQRNLRTPLHLAVERGKVRAIQHLLKSGAVPDALDQSGYGPLHTAAARGKYLICKMLLRYGASLELPTHQGWTPLHLAAYKGHLEIIHLLAESHANMGALGAVNWTPLHLAARHGEEAVVSALLQCGADPNAAEQSGWTPLHLAVQRSTFLSVINLLEHHANVHARNKVGWTPAHLAALKGNTAILKVLVEAGAQLDVQDGVSCTPLQLALRSRKQGIMSFLEGKEPSVATLGGSKPGAEMEI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAADPTELRLGSLPVFTRDDFEGDWRLVASGGFSQVFQARHRRWRTEYAIKCAPCLPPDAASSDVNYLIEEAAKMKKIKFQHIVSIYGVCKQPLGIVMEFMANGSLEKVLSTHSLCWKLRFRIIHETSLAMNFLHSIKPPLLHLDLKPGNILLDSNMHVKISDFGLSKWMEQSTRMQYIERSALRGMLSYIPPEMFLESNKAPGPKYDVYSFAIVIWELLTQKKPYSGFNMMMIIIRVAAGMRPSLQPVSDQWPSEAQQMVDLMKRCWDQDPKKRPCFLDITIETDILLSLLQSRVAVPESKALARKVSCKLSLRQPGEVNEDISQELMDSDSGNYLKRALQLSDRKNLVPRDEELCIYENKVTPLHFLVAQGSVEQVRLLLAHEVDVDCQTASGYTPLLIAAQDQQPDLCALLLAHGADANRVDEDGWAPLHFAAQNGDDGTARLLLDHGACVDAQEREGWTPLHLAAQNNFENVARLLVSRQADPNLHEAEGKTPLHVAAYFGHVSLVKLLTSQGAELDAQQRNLRTPLHLAVERGKVRAIQHLLKSGAVPDALDQSGYGPLHTAAARGKYLICKMLLRYGASLELPTHQGWTPLHLAAYKGHLEIIHLLAESHANMGALGAVNWTPLHLAARHGEEAVVSALLQCGADPNAAEQSGWTPLHLAVQRSTFLSVINLLEHHANVHARNKVGWTPAHLAALKGNTAILKVLVEAGAQLDVQDGVSCTPLQLALRSRKQGIMSFLEGKEPSVATLGGSKPGAEMEI\", \"result\": [{\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12', 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely interaction affinity for the drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the target protein 'MEKYENLGLVGEGSYGMVMKCRNKDTGRIVAIKKFLESDDDKMVKKIAMREIKLLKQLRHENLVNLLEVCKKKKRWYLVFEFVDHTILDDLELFPNGLDYQVVQKYLFQIINGIGFCHSHNIIHRDIKPENILVSQSGVVKLCDFGFARTLAAPGEVYTDYVATRWYRAPELLVGDVKYGKAVDVWAIGCLVTEMFMGEPLFPGDSDIDQLYHIMMCLGNLIPRHQELFNKNPVFAGVRLPEIKEREPLERRYPKLSEVVIDLAKKCLHIDPDKRPFCAELLHHDFFQMDGFAERFSQELQLKVQKDARNVSLSKKSQNRKKEKEKDDSLVEERKTLVVQDTNADPKIKDYKLFKIKGSKIDGEKAEKGNRASNASCLHDSRTSHNKIVPSTSLKDCSNVSVDHTRNPSVAIPPLTHNLSAVAPSINSGMGTETIPIQGYRVDEKTKKCSIPFVKPNRHSPSGIYNINVTTLVSGPPLSDDSGADLPQMEHQH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\"], \"target_seq\": \"MEKYENLGLVGEGSYGMVMKCRNKDTGRIVAIKKFLESDDDKMVKKIAMREIKLLKQLRHENLVNLLEVCKKKKRWYLVFEFVDHTILDDLELFPNGLDYQVVQKYLFQIINGIGFCHSHNIIHRDIKPENILVSQSGVVKLCDFGFARTLAAPGEVYTDYVATRWYRAPELLVGDVKYGKAVDVWAIGCLVTEMFMGEPLFPGDSDIDQLYHIMMCLGNLIPRHQELFNKNPVFAGVRLPEIKEREPLERRYPKLSEVVIDLAKKCLHIDPDKRPFCAELLHHDFFQMDGFAERFSQELQLKVQKDARNVSLSKKSQNRKKEKEKDDSLVEERKTLVVQDTNADPKIKDYKLFKIKGSKIDGEKAEKGNRASNASCLHDSRTSHNKIVPSTSLKDCSNVSVDHTRNPSVAIPPLTHNLSAVAPSINSGMGTETIPIQGYRVDEKTKKCSIPFVKPNRHSPSGIYNINVTTLVSGPPLSDDSGADLPQMEHQH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEKYENLGLVGEGSYGMVMKCRNKDTGRIVAIKKFLESDDDKMVKKIAMREIKLLKQLRHENLVNLLEVCKKKKRWYLVFEFVDHTILDDLELFPNGLDYQVVQKYLFQIINGIGFCHSHNIIHRDIKPENILVSQSGVVKLCDFGFARTLAAPGEVYTDYVATRWYRAPELLVGDVKYGKAVDVWAIGCLVTEMFMGEPLFPGDSDIDQLYHIMMCLGNLIPRHQELFNKNPVFAGVRLPEIKEREPLERRYPKLSEVVIDLAKKCLHIDPDKRPFCAELLHHDFFQMDGFAERFSQELQLKVQKDARNVSLSKKSQNRKKEKEKDDSLVEERKTLVVQDTNADPKIKDYKLFKIKGSKIDGEKAEKGNRASNASCLHDSRTSHNKIVPSTSLKDCSNVSVDHTRNPSVAIPPLTHNLSAVAPSINSGMGTETIPIQGYRVDEKTKKCSIPFVKPNRHSPSGIYNINVTTLVSGPPLSDDSGADLPQMEHQH\", \"result\": [{\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 10000.0}, {\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1', 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the binding affinity of the drug 'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1', 'Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12' with the target protein 'MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\", \"Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAAAAQGGGGGEPRRTEGVGPGVPGEVEMVKGQPFDVGPRYTQLQYIGEGAYGMVSSAYDHVRKTRVAIKKISPFEHQTYCQRTLREIQILLRFRHENVIGIRDILRASTLEAMRDVYIVQDLMETDLYKLLKSQQLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLINTTCDLKICDFGLARIADPEHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINMKARNYLQSLPSKTKVAWAKLFPKSDSKALDLLDRMLTFNPNKRITVEEALAHPYLEQYYDPTDEPVAEEPFTFAMELDDLPKERLKELIFQETARFQPGVLEAP\", \"result\": [{\"smile\": \"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\", \"value\": 10000.0}, {\"smile\": \"Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1', 'Cc1[nH]nc2ccc(-c3cncc(OCC(N)Cc4ccccc4)c3)cc12' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drug 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1''s interaction with the target protein 'MVMADGPRHLQRGPVRVGFYDIEGTLGKGNFAVVKLGRHRITKTEVAIKIIDKSQLDAVNLEKIYREVQIMKMLDHPHIIKLYQVMETKSMLYLVTEYAKNGEIFDYLANHGRLNESEARRKFWQILSAVDYCHGRKIVHRDLKAENLLLDNNMNIKIADFGFGNFFKSGELLATWCGSPPYAAPEVFEGQQYEGPQLDIWSMGVVLYVLVCGALPFDGPTLPILRQRVLEGRFRIPYFMSEDCEHLIRRMLVLDPSKRLTIAQIKEHKWMLIEVPVQRPVLYPQEQENEPSIGEFNEQVLRLMHSLGIDQQKTIESLQNKSYNHFAAIYFLLVERLKSHRSSFPVEQRLDGRQRRPSTIAEQTVAKAQTVGLPVTMHSPNMRLLRSALLPQASNVEAFSFPASGCQAEAAFMEEECVDTPKVNGCLLDPVPPVLVRKGCQSLPSNMMETSIDEGLETEGEAEEDPAHAFEAFQSTRSGQRRHTLSEVTNQLVVMPGAGKIFSMNDSPSLDSVDSEYDMGSVQRDLNFLEDNPSLKDIMLANQPSPRMTSPFISLRPTNPAMQALSSQKREVHNRSPVSFREGRRASDTSLTQGIVAFRQHLQNLARTKGILELNKVQLLYEQIGPEADPNLAPAAPQLQDLASSCPQEEVSQQQESVSTLPASVHPQLSPRQSLETQYLQHRLQKPSLLSKAQNTCQLYCKEPPRSLEQQLQEHRLQQKRLFLQKQSQLQAYFNQMQIAESSYPQPSQQLPLPRQETPPPSQQAPPFSLTQPLSPVLEPSSEQMQYSPFLSQYQEMQLQPLPSTSGPRAAPPLPTQLQQQQPPPPPPPPPPRQPGAAPAPLQFSYQTCELPSAASPAPDYPTPCQYPVDGAQQSDLTGPDCPRSPGLQEAPSSYDPLALSELPGLFDCEMLDAVDPQHNGYVLVN' to be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\", \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"MVMADGPRHLQRGPVRVGFYDIEGTLGKGNFAVVKLGRHRITKTEVAIKIIDKSQLDAVNLEKIYREVQIMKMLDHPHIIKLYQVMETKSMLYLVTEYAKNGEIFDYLANHGRLNESEARRKFWQILSAVDYCHGRKIVHRDLKAENLLLDNNMNIKIADFGFGNFFKSGELLATWCGSPPYAAPEVFEGQQYEGPQLDIWSMGVVLYVLVCGALPFDGPTLPILRQRVLEGRFRIPYFMSEDCEHLIRRMLVLDPSKRLTIAQIKEHKWMLIEVPVQRPVLYPQEQENEPSIGEFNEQVLRLMHSLGIDQQKTIESLQNKSYNHFAAIYFLLVERLKSHRSSFPVEQRLDGRQRRPSTIAEQTVAKAQTVGLPVTMHSPNMRLLRSALLPQASNVEAFSFPASGCQAEAAFMEEECVDTPKVNGCLLDPVPPVLVRKGCQSLPSNMMETSIDEGLETEGEAEEDPAHAFEAFQSTRSGQRRHTLSEVTNQLVVMPGAGKIFSMNDSPSLDSVDSEYDMGSVQRDLNFLEDNPSLKDIMLANQPSPRMTSPFISLRPTNPAMQALSSQKREVHNRSPVSFREGRRASDTSLTQGIVAFRQHLQNLARTKGILELNKVQLLYEQIGPEADPNLAPAAPQLQDLASSCPQEEVSQQQESVSTLPASVHPQLSPRQSLETQYLQHRLQKPSLLSKAQNTCQLYCKEPPRSLEQQLQEHRLQQKRLFLQKQSQLQAYFNQMQIAESSYPQPSQQLPLPRQETPPPSQQAPPFSLTQPLSPVLEPSSEQMQYSPFLSQYQEMQLQPLPSTSGPRAAPPLPTQLQQQQPPPPPPPPPPRQPGAAPAPLQFSYQTCELPSAASPAPDYPTPCQYPVDGAQQSDLTGPDCPRSPGLQEAPSSYDPLALSELPGLFDCEMLDAVDPQHNGYVLVN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVMADGPRHLQRGPVRVGFYDIEGTLGKGNFAVVKLGRHRITKTEVAIKIIDKSQLDAVNLEKIYREVQIMKMLDHPHIIKLYQVMETKSMLYLVTEYAKNGEIFDYLANHGRLNESEARRKFWQILSAVDYCHGRKIVHRDLKAENLLLDNNMNIKIADFGFGNFFKSGELLATWCGSPPYAAPEVFEGQQYEGPQLDIWSMGVVLYVLVCGALPFDGPTLPILRQRVLEGRFRIPYFMSEDCEHLIRRMLVLDPSKRLTIAQIKEHKWMLIEVPVQRPVLYPQEQENEPSIGEFNEQVLRLMHSLGIDQQKTIESLQNKSYNHFAAIYFLLVERLKSHRSSFPVEQRLDGRQRRPSTIAEQTVAKAQTVGLPVTMHSPNMRLLRSALLPQASNVEAFSFPASGCQAEAAFMEEECVDTPKVNGCLLDPVPPVLVRKGCQSLPSNMMETSIDEGLETEGEAEEDPAHAFEAFQSTRSGQRRHTLSEVTNQLVVMPGAGKIFSMNDSPSLDSVDSEYDMGSVQRDLNFLEDNPSLKDIMLANQPSPRMTSPFISLRPTNPAMQALSSQKREVHNRSPVSFREGRRASDTSLTQGIVAFRQHLQNLARTKGILELNKVQLLYEQIGPEADPNLAPAAPQLQDLASSCPQEEVSQQQESVSTLPASVHPQLSPRQSLETQYLQHRLQKPSLLSKAQNTCQLYCKEPPRSLEQQLQEHRLQQKRLFLQKQSQLQAYFNQMQIAESSYPQPSQQLPLPRQETPPPSQQAPPFSLTQPLSPVLEPSSEQMQYSPFLSQYQEMQLQPLPSTSGPRAAPPLPTQLQQQQPPPPPPPPPPRQPGAAPAPLQFSYQTCELPSAASPAPDYPTPCQYPVDGAQQSDLTGPDCPRSPGLQEAPSSYDPLALSELPGLFDCEMLDAVDPQHNGYVLVN\", \"result\": [{\"smile\": \"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\", \"value\": 2600.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein are 2600.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the predicted affinity between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1', 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21' and the target protein 'MDYDFKVKLSSERERVEDLFEYEGCKVGRGTYGHVYKAKRKDGKDDKDYALKQIEGTGISMSACREIALLRELKHPNVISLQKVFLSHADRKVWLLFDYAEHDLWHIIKFHRASKANKKPVQLPRGMVKSLLYQILDGIHYLHANWVLHRDLKPANILVMGEGPERGRVKIADMGFARLFNSPLKPLADLDPVVVTFWYRAPELLLGARHYTKAIDIWAIGCIFAELLTSEPIFHCRQEDIKTSNPYHHDQLDRIFNVMGFPADKDWEDIKKMPEHSTLMKDFRRNTYTNCSLIKYMEKHKVKPDSKAFHLLQKLLTMDPIKRITSEQAMQDPYFLEDPLPTSDVFAGCQIPYPKREFLTEEEPDDKGDKKNQQQQQGNNHTNGTGHPGNQDSSHTQGPPLKKVRVVPPTTTSGGLIMTSDYQRSNPHAAYPNPGPSTSQPQSSMGYSATSQQPPQYSHQTHRY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21\"], \"target_seq\": \"MDYDFKVKLSSERERVEDLFEYEGCKVGRGTYGHVYKAKRKDGKDDKDYALKQIEGTGISMSACREIALLRELKHPNVISLQKVFLSHADRKVWLLFDYAEHDLWHIIKFHRASKANKKPVQLPRGMVKSLLYQILDGIHYLHANWVLHRDLKPANILVMGEGPERGRVKIADMGFARLFNSPLKPLADLDPVVVTFWYRAPELLLGARHYTKAIDIWAIGCIFAELLTSEPIFHCRQEDIKTSNPYHHDQLDRIFNVMGFPADKDWEDIKKMPEHSTLMKDFRRNTYTNCSLIKYMEKHKVKPDSKAFHLLQKLLTMDPIKRITSEQAMQDPYFLEDPLPTSDVFAGCQIPYPKREFLTEEEPDDKGDKKNQQQQQGNNHTNGTGHPGNQDSSHTQGPPLKKVRVVPPTTTSGGLIMTSDYQRSNPHAAYPNPGPSTSQPQSSMGYSATSQQPPQYSHQTHRY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDYDFKVKLSSERERVEDLFEYEGCKVGRGTYGHVYKAKRKDGKDDKDYALKQIEGTGISMSACREIALLRELKHPNVISLQKVFLSHADRKVWLLFDYAEHDLWHIIKFHRASKANKKPVQLPRGMVKSLLYQILDGIHYLHANWVLHRDLKPANILVMGEGPERGRVKIADMGFARLFNSPLKPLADLDPVVVTFWYRAPELLLGARHYTKAIDIWAIGCIFAELLTSEPIFHCRQEDIKTSNPYHHDQLDRIFNVMGFPADKDWEDIKKMPEHSTLMKDFRRNTYTNCSLIKYMEKHKVKPDSKAFHLLQKLLTMDPIKRITSEQAMQDPYFLEDPLPTSDVFAGCQIPYPKREFLTEEEPDDKGDKKNQQQQQGNNHTNGTGHPGNQDSSHTQGPPLKKVRVVPPTTTSGGLIMTSDYQRSNPHAAYPNPGPSTSQPQSSMGYSATSQQPPQYSHQTHRY\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}, {\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1', 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OCC3CCCNC3)c21' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an affinity estimate for the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1', 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21' with the target protein 'MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\"], \"target_seq\": \"MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 2900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1', 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21' and the protein are 10000.0 and 2900.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected binding affinity for the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1'-target protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF' complex? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\", \"result\": [{\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 1.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein are 1.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you determine the affinity value of the drug 'CCCCc1nc2cc(OC)cc(C(=O)O)c2n1Cc1ccc(N(CC)C(C(=O)O)c2ccccc2)cc1', 'CCCCN(Cc1ccc(-c2ccccc2-c2nnn[nH]2)cc1)c1ncnc2cc(C(=O)O)ccc12' for the target protein 'MMLNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLAVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWVPHQIFTFLDVLIQLGVIHDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKKYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSSKKPVPCFEVE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCc1nc2cc(OC)cc(C(=O)O)c2n1Cc1ccc(N(CC)C(C(=O)O)c2ccccc2)cc1\", \"CCCCN(Cc1ccc(-c2ccccc2-c2nnn[nH]2)cc1)c1ncnc2cc(C(=O)O)ccc12\"], \"target_seq\": \"MMLNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLAVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWVPHQIFTFLDVLIQLGVIHDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKKYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSSKKPVPCFEVE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MMLNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLAVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWVPHQIFTFLDVLIQLGVIHDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKKYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSSKKPVPCFEVE\", \"result\": [{\"smile\": \"CCCCc1nc2cc(OC)cc(C(=O)O)c2n1Cc1ccc(N(CC)C(C(=O)O)c2ccccc2)cc1\", \"value\": 5.01}, {\"smile\": \"CCCCN(Cc1ccc(-c2ccccc2-c2nnn[nH]2)cc1)c1ncnc2cc(C(=O)O)ccc12\", \"value\": 35.48}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CCCCc1nc2cc(OC)cc(C(=O)O)c2n1Cc1ccc(N(CC)C(C(=O)O)c2ccccc2)cc1', 'CCCCN(Cc1ccc(-c2ccccc2-c2nnn[nH]2)cc1)c1ncnc2cc(C(=O)O)ccc12' and the protein are 5.01 and 35.48 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'O=C1COc2ccc(CN3CCC(c4ccccc4)CC3)cc2N1', 'NCCOc1c(-c2ccc(Br)cc2)[nH]c(=O)c2c(F)c(F)ccc12' and the target protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1COc2ccc(CN3CCC(c4ccccc4)CC3)cc2N1\", \"NCCOc1c(-c2ccc(Br)cc2)[nH]c(=O)c2c(F)c(F)ccc12\"], \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\", \"result\": [{\"smile\": \"O=C1COc2ccc(CN3CCC(c4ccccc4)CC3)cc2N1\", \"value\": 296.0}, {\"smile\": \"NCCOc1c(-c2ccc(Br)cc2)[nH]c(=O)c2c(F)c(F)ccc12\", \"value\": 10.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'O=C1COc2ccc(CN3CCC(c4ccccc4)CC3)cc2N1', 'NCCOc1c(-c2ccc(Br)cc2)[nH]c(=O)c2c(F)c(F)ccc12' and the protein are 296.0 and 10.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21', 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1'-target protein 'MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\"], \"target_seq\": \"MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI\", \"result\": [{\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 64.0}, {\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21', 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1' and the protein are 64.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected level of affinity between the drug 'CC(C)=NOCC(O)CNC(C)C', 'CC(C)NCC(O)CON=C1CCCC1' and the target protein 'MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)=NOCC(O)CNC(C)C\", \"CC(C)NCC(O)CON=C1CCCC1\"], \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\", \"result\": [{\"smile\": \"CC(C)=NOCC(O)CNC(C)C\", \"value\": 194.98}, {\"smile\": \"CC(C)NCC(O)CON=C1CCCC1\", \"value\": 154.88}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CC(C)=NOCC(O)CNC(C)C', 'CC(C)NCC(O)CON=C1CCCC1' and the protein are 194.98 and 154.88 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction affinity for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'Nc1n[nH]c2nnc(-c3c(-c4ccccc4)nn4ccccc34)cc12' and the target protein 'MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"Nc1n[nH]c2nnc(-c3c(-c4ccccc4)nn4ccccc34)cc12\"], \"target_seq\": \"MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAAAAAGAGPEMVRGQVFDVGPRYTNLSYIGEGAYGMVCSAYDNVNKVRVAIKKISPFEHQTYCQRTLREIKILLRFRHENIIGINDIIRAPTIEQMKDVYIVQDLMETDLYKLLKTQHLSNDHICYFLYQILRGLKYIHSANVLHRDLKPSNLLLNTTCDLKICDFGLARVADPDHDHTGFLTEYVATRWYRAPEIMLNSKGYTKSIDIWSVGCILAEMLSNRPIFPGKHYLDQLNHILGILGSPSQEDLNCIINLKARNYLLSLPHKNKVPWNRLFPNADSKALDLLDKMLTFNPHKRIEVEQALAHPYLEQYYDPSDEPIAEAPFKFDMELDDLPKEKLKELIFEETARFQPGYRS\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}, {\"smile\": \"Nc1n[nH]c2nnc(-c3c(-c4ccccc4)nn4ccccc34)cc12\", \"value\": 850.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'Nc1n[nH]c2nnc(-c3c(-c4ccccc4)nn4ccccc34)cc12' and the protein are 10000.0 and 850.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the binding affinity of the drug 'COc1nc2ccc([C@](O)(c3ccccc3)C3CCN(C(C)=O)CC3)cc2c(Cl)c1C1CCOCC1', 'COc1nc2ccc(C(O)(c3cnc(C)n3C)c3cnc(C)n3C)cc2c(Cl)c1OC1CCOCC1' with the target protein 'MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1nc2ccc([C@](O)(c3ccccc3)C3CCN(C(C)=O)CC3)cc2c(Cl)c1C1CCOCC1\", \"COc1nc2ccc(C(O)(c3cnc(C)n3C)c3cnc(C)n3C)cc2c(Cl)c1OC1CCOCC1\"], \"target_seq\": \"MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK\", \"result\": [{\"smile\": \"COc1nc2ccc([C@](O)(c3ccccc3)C3CCN(C(C)=O)CC3)cc2c(Cl)c1C1CCOCC1\", \"value\": 490.0}, {\"smile\": \"COc1nc2ccc(C(O)(c3cnc(C)n3C)c3cnc(C)n3C)cc2c(Cl)c1OC1CCOCC1\", \"value\": 71.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'COc1nc2ccc([C@](O)(c3ccccc3)C3CCN(C(C)=O)CC3)cc2c(Cl)c1C1CCOCC1', 'COc1nc2ccc(C(O)(c3cnc(C)n3C)c3cnc(C)n3C)cc2c(Cl)c1OC1CCOCC1' and the protein are 490.0 and 71.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl''s affinity with the target protein 'MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\"], \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPLRHWGMARGSKPVGDGAQPMAAMGGLKVLLHWAGPGGGEPWVTFSESSLTAEEVCIHIAHKVGITPPCFNLFALFDAQAQVWLPPNHILEIPRDASLMLYFRIRFYFRNWHGMNPREPAVYRCGPPGTEASSDQTAQGMQLLDPASFEYLFEQGKHEFVNDVASLWELSTEEEIHHFKNESLGMAFLHLCHLALRHGIPLEEVAKKTSFKDCIPRSFRRHIRQHSALTRLRLRNVFRRFLRDFQPGRLSQQMVMVKYLATLERLAPRFGTERVPVCHLRLLAQAEGEPCYIRDSGVAPTDPGPESAAGPPTHEVLVTGTGGIQWWPVEEEVNKEEGSSGSSGRNPQASLFGKKAKAHKAVGQPADRPREPLWAYFCDFRDITHVVLKEHCVSIHRQDNKCLELSLPSRAAALSFVSLVDGYFRLTADSSHYLCHEVAPPRLVMSIRDGIHGPLLEPFVQAKLRPEDGLYLIHWSTSHPYRLILTVAQRSQAPDGMQSLRLRKFPIEQQDGAFVLEGWGRSFPSVRELGAALQGCLLRAGDDCFSLRRCCLPQPGETSNLIIMRGARASPRTLNLSQLSFHRVDQKEITQLSHLGQGTRTNVYEGRLRVEGSGDPEEGKMDDEDPLVPGRDRGQELRVVLKVLDPSHHDIALAFYETASLMSQVSHTHLAFVHGVCVRGPENIMVTEYVEHGPLDVWLRRERGHVPMAWKMVVAQQLASALSYLENKNLVHGNVCGRNILLARLGLAEGTSPFIKLSDPGVGLGALSREERVERIPWLAPECLPGGANSLSTAMDKWGFGATLLEICFDGEAPLQSRSPSEKEHFYQRQHRLPEPSCPQLATLTSQCLTYEPTQRPSFRTILRDLTRLQPHNLADVLTVNPDSPASDPTVFHKRYLKKIRDLGEGHFGKVSLYCYDPTNDGTGEMVAVKALKADCGPQHRSGWKQEIDILRTLYHEHIIKYKGCCEDQGEKSLQLVMEYVPLGSLRDYLPRHSIGLAQLLLFAQQICEGMAYLHAQHYIHRDLAARNVLLDNDRLVKIGDFGLAKAVPEGHEYYRVREDGDSPVFWYAPECLKEYKFYYASDVWSFGVTLYELLTHCDSSQSPPTKFLELIGIAQGQMTVLRLTELLERGERLPRPDKCPCEVYHLMKNCWETEASFRPTFENLIPILKTVHEKYQGQAPSVFSVC\", \"result\": [{\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 0.9}, {\"smile\": \"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 2000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the protein are 0.9 and 2000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the expected binding strength of the drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1', 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' toward the target protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\", \"result\": [{\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 270.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1', 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein are 10000.0 and 270.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1', 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl' and the target protein 'MWLKVGGLLRGTGGQLGQTVGWPCGALGPGPHRWGPCGGSWAQKFYQDGPGRGLGEEDIRRAREARPRKTPRPQLSDRSRERKVPASRISRLANFGGLAVGLGLGVLAEMAKKSMPGGRLQSEGGSGLDSSPFLSEANAERIVQTLCTVRGAALKVGQMLSIQDNSFISPQLQHIFERVRQSADFMPRWQMLRVLEEELGRDWQAKVASLEEVPFAAASIGQVHQGLLRDGTEVAVKIQYPGIAQSIQSDVQNLLAVLKMSAALPAGLFAEQSLQALQQELAWECDYRREAACAQNFRQLLANDPFFRVPAVVKELCTTRVLGMELAGGVPLDQCQGLSQDLRNQICFQLLTLCLRELFEFRFMQTDPNWANFLYDASSHQVTLLDFGASREFGTEFTDHYIEVVKAAADGDRDCVLQKSRDLKFLTGFETKAFSDAHVEAVMILGEPFATQGPYDFGSGETARRIQDLIPVLLRHRLCPPPEETYALHRKLAGAFLACAHLRAHIACRDLFQDTYHRYWASRQPDAATAGSLPTKGDSWVDPS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\", \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\"], \"target_seq\": \"MWLKVGGLLRGTGGQLGQTVGWPCGALGPGPHRWGPCGGSWAQKFYQDGPGRGLGEEDIRRAREARPRKTPRPQLSDRSRERKVPASRISRLANFGGLAVGLGLGVLAEMAKKSMPGGRLQSEGGSGLDSSPFLSEANAERIVQTLCTVRGAALKVGQMLSIQDNSFISPQLQHIFERVRQSADFMPRWQMLRVLEEELGRDWQAKVASLEEVPFAAASIGQVHQGLLRDGTEVAVKIQYPGIAQSIQSDVQNLLAVLKMSAALPAGLFAEQSLQALQQELAWECDYRREAACAQNFRQLLANDPFFRVPAVVKELCTTRVLGMELAGGVPLDQCQGLSQDLRNQICFQLLTLCLRELFEFRFMQTDPNWANFLYDASSHQVTLLDFGASREFGTEFTDHYIEVVKAAADGDRDCVLQKSRDLKFLTGFETKAFSDAHVEAVMILGEPFATQGPYDFGSGETARRIQDLIPVLLRHRLCPPPEETYALHRKLAGAFLACAHLRAHIACRDLFQDTYHRYWASRQPDAATAGSLPTKGDSWVDPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWLKVGGLLRGTGGQLGQTVGWPCGALGPGPHRWGPCGGSWAQKFYQDGPGRGLGEEDIRRAREARPRKTPRPQLSDRSRERKVPASRISRLANFGGLAVGLGLGVLAEMAKKSMPGGRLQSEGGSGLDSSPFLSEANAERIVQTLCTVRGAALKVGQMLSIQDNSFISPQLQHIFERVRQSADFMPRWQMLRVLEEELGRDWQAKVASLEEVPFAAASIGQVHQGLLRDGTEVAVKIQYPGIAQSIQSDVQNLLAVLKMSAALPAGLFAEQSLQALQQELAWECDYRREAACAQNFRQLLANDPFFRVPAVVKELCTTRVLGMELAGGVPLDQCQGLSQDLRNQICFQLLTLCLRELFEFRFMQTDPNWANFLYDASSHQVTLLDFGASREFGTEFTDHYIEVVKAAADGDRDCVLQKSRDLKFLTGFETKAFSDAHVEAVMILGEPFATQGPYDFGSGETARRIQDLIPVLLRHRLCPPPEETYALHRKLAGAFLACAHLRAHIACRDLFQDTYHRYWASRQPDAATAGSLPTKGDSWVDPS\", \"result\": [{\"smile\": \"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1', 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1', 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the target protein 'MRELVNIPLVHILTLVAFSGTEKLPKAPVITTPLETVDALVEEVATFMCAVESYPQPEISWTRNKILIKLFDTRYSIRENGQLLTILSVEDSDDGIYCCTANNGVGGAVESCGALQVKMKPKITRPPINVKIIEGLKAVLPCTTMGNPKPSVSWIKGDSPLRENSRIAVLESGSLRIHNVQKEDAGQYRCVAKNSLGTAYSKVVKLEVEVFARILRAPESHNVTFGSFVTLHCTATGIPVPTITWIENGNAVSSGSIQESVKDRVIDSRLQLFITKPGLYTCIATNKHGEKFSTAKAAATISIAEWSKPQKDNKGYCAQYRGEVCNAVLAKDALVFLNTSYADPEEAQELLVHTAWNELKVVSPVCRPAAEALLCNHIFQECSPGVVPTPIPICREYCLAVKELFCAKEWLVMEEKTHRGLYRSEMHLLSVPECSKLPSMHWDPTACARLPHLDYNKENLKTFPPMTSSKPSVDIPNLPSSSSSSFSVSPTYSMTVIISIMSSFAIFVLLTITTLYCCRRRKQWKNKKRESAAVTLTTLPSELLLDRLHPNPMYQRMPLLLNPKLLSLEYPRNNIEYVRDIGEGAFGRVFQARAPGLLPYEPFTMVAVKMLKEEASADMQADFQREAALMAEFDNPNIVKLLGVCAVGKPMCLLFEYMAYGDLNEFLRSMSPHTVCSLSHSDLSMRAQVSSPGPPPLSCAEQLCIARQVAAGMAYLSERKFVHRDLATRNCLVGENMVVKIADFGLSRNIYSADYYKANENDAIPIRWMPPESIFYNRYTTESDVWAYGVVLWEIFSYGLQPYYGMAHEEVIYYVRDGNILSCPENCPVELYNLMRLCWSKLPADRPSFTSIHRILERMCERAEGTVSV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\"], \"target_seq\": \"MRELVNIPLVHILTLVAFSGTEKLPKAPVITTPLETVDALVEEVATFMCAVESYPQPEISWTRNKILIKLFDTRYSIRENGQLLTILSVEDSDDGIYCCTANNGVGGAVESCGALQVKMKPKITRPPINVKIIEGLKAVLPCTTMGNPKPSVSWIKGDSPLRENSRIAVLESGSLRIHNVQKEDAGQYRCVAKNSLGTAYSKVVKLEVEVFARILRAPESHNVTFGSFVTLHCTATGIPVPTITWIENGNAVSSGSIQESVKDRVIDSRLQLFITKPGLYTCIATNKHGEKFSTAKAAATISIAEWSKPQKDNKGYCAQYRGEVCNAVLAKDALVFLNTSYADPEEAQELLVHTAWNELKVVSPVCRPAAEALLCNHIFQECSPGVVPTPIPICREYCLAVKELFCAKEWLVMEEKTHRGLYRSEMHLLSVPECSKLPSMHWDPTACARLPHLDYNKENLKTFPPMTSSKPSVDIPNLPSSSSSSFSVSPTYSMTVIISIMSSFAIFVLLTITTLYCCRRRKQWKNKKRESAAVTLTTLPSELLLDRLHPNPMYQRMPLLLNPKLLSLEYPRNNIEYVRDIGEGAFGRVFQARAPGLLPYEPFTMVAVKMLKEEASADMQADFQREAALMAEFDNPNIVKLLGVCAVGKPMCLLFEYMAYGDLNEFLRSMSPHTVCSLSHSDLSMRAQVSSPGPPPLSCAEQLCIARQVAAGMAYLSERKFVHRDLATRNCLVGENMVVKIADFGLSRNIYSADYYKANENDAIPIRWMPPESIFYNRYTTESDVWAYGVVLWEIFSYGLQPYYGMAHEEVIYYVRDGNILSCPENCPVELYNLMRLCWSKLPADRPSFTSIHRILERMCERAEGTVSV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRELVNIPLVHILTLVAFSGTEKLPKAPVITTPLETVDALVEEVATFMCAVESYPQPEISWTRNKILIKLFDTRYSIRENGQLLTILSVEDSDDGIYCCTANNGVGGAVESCGALQVKMKPKITRPPINVKIIEGLKAVLPCTTMGNPKPSVSWIKGDSPLRENSRIAVLESGSLRIHNVQKEDAGQYRCVAKNSLGTAYSKVVKLEVEVFARILRAPESHNVTFGSFVTLHCTATGIPVPTITWIENGNAVSSGSIQESVKDRVIDSRLQLFITKPGLYTCIATNKHGEKFSTAKAAATISIAEWSKPQKDNKGYCAQYRGEVCNAVLAKDALVFLNTSYADPEEAQELLVHTAWNELKVVSPVCRPAAEALLCNHIFQECSPGVVPTPIPICREYCLAVKELFCAKEWLVMEEKTHRGLYRSEMHLLSVPECSKLPSMHWDPTACARLPHLDYNKENLKTFPPMTSSKPSVDIPNLPSSSSSSFSVSPTYSMTVIISIMSSFAIFVLLTITTLYCCRRRKQWKNKKRESAAVTLTTLPSELLLDRLHPNPMYQRMPLLLNPKLLSLEYPRNNIEYVRDIGEGAFGRVFQARAPGLLPYEPFTMVAVKMLKEEASADMQADFQREAALMAEFDNPNIVKLLGVCAVGKPMCLLFEYMAYGDLNEFLRSMSPHTVCSLSHSDLSMRAQVSSPGPPPLSCAEQLCIARQVAAGMAYLSERKFVHRDLATRNCLVGENMVVKIADFGLSRNIYSADYYKANENDAIPIRWMPPESIFYNRYTTESDVWAYGVVLWEIFSYGLQPYYGMAHEEVIYYVRDGNILSCPENCPVELYNLMRLCWSKLPADRPSFTSIHRILERMCERAEGTVSV\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 100.0}, {\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1', 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the protein are 100.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'NS(=O)(=O)c1cc(C(=O)CSc2nc3ccccc3[nH]2)ccc1Cl', 'NS(=O)(=O)c1c(F)c(F)c(Sc2nc3ccccc3s2)c(F)c1F''s affinity for the target protein 'MTGHHGWGYGQDDGPSHWHKLYPIAQGDRQSPINIISSQAVYSPSLQPLELSYEACMSLSITNNGHSVQVDFNDSDDRTVVTGGPLEGPYRLKQFHFHWGKKHDVGSEHTVDGKSFPSELHLVHWNAKKYSTFGEAASAPDGLAVVGVFLETGDEHPSMNRLTDALYMVRFKGTKAQFSCFNPKCLLPASRHYWTYPGSLTTPPLSESVTWIVLREPICISERQMGKFRSLLFTSEDDERIHMVNNFRPPQPLKGRVVKASFRA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NS(=O)(=O)c1cc(C(=O)CSc2nc3ccccc3[nH]2)ccc1Cl\", \"NS(=O)(=O)c1c(F)c(F)c(Sc2nc3ccccc3s2)c(F)c1F\"], \"target_seq\": \"MTGHHGWGYGQDDGPSHWHKLYPIAQGDRQSPINIISSQAVYSPSLQPLELSYEACMSLSITNNGHSVQVDFNDSDDRTVVTGGPLEGPYRLKQFHFHWGKKHDVGSEHTVDGKSFPSELHLVHWNAKKYSTFGEAASAPDGLAVVGVFLETGDEHPSMNRLTDALYMVRFKGTKAQFSCFNPKCLLPASRHYWTYPGSLTTPPLSESVTWIVLREPICISERQMGKFRSLLFTSEDDERIHMVNNFRPPQPLKGRVVKASFRA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTGHHGWGYGQDDGPSHWHKLYPIAQGDRQSPINIISSQAVYSPSLQPLELSYEACMSLSITNNGHSVQVDFNDSDDRTVVTGGPLEGPYRLKQFHFHWGKKHDVGSEHTVDGKSFPSELHLVHWNAKKYSTFGEAASAPDGLAVVGVFLETGDEHPSMNRLTDALYMVRFKGTKAQFSCFNPKCLLPASRHYWTYPGSLTTPPLSESVTWIVLREPICISERQMGKFRSLLFTSEDDERIHMVNNFRPPQPLKGRVVKASFRA\", \"result\": [{\"smile\": \"NS(=O)(=O)c1cc(C(=O)CSc2nc3ccccc3[nH]2)ccc1Cl\", \"value\": 30.0}, {\"smile\": \"NS(=O)(=O)c1c(F)c(F)c(Sc2nc3ccccc3s2)c(F)c1F\", \"value\": 0.22}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'NS(=O)(=O)c1cc(C(=O)CSc2nc3ccccc3[nH]2)ccc1Cl', 'NS(=O)(=O)c1c(F)c(F)c(Sc2nc3ccccc3s2)c(F)c1F' and the protein are 30.0 and 0.22 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted value of the affinity between the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the target protein 'MSAAVTAGKLARAPADPGKAGVPGVAAPGAPAAAPPAKEIPEVLVDPRSRRRYVRGRFLGKGGFAKCFEISDADTKEVFAGKIVPKSLLLKPHQREKMSMEISIHRSLAHQHVVGFHGFFEDNDFVFVVLELCRRRSLLELHKRRKALTEPEARYYLRQIVLGCQYLHRNRVIHRDLKLGNLFLNEDLEVKIGDFGLATKVEYDGERKKTLCGTPNYIAPEVLSKKGHSFEVDVWSIGCIMYTLLVGKPPFETSCLKETYLRIKKNEYSIPKHINPVAASLIQKMLQTDPTARPTINELLNDEFFTSGYIPARLPITCLTIPPRFSIAPSSLDPSNRKPLTVLNKGLENPLPERPREKEEPVVRETGEVVDCHLSDMLQQLHSVNASKPSERGLVRQEEAEDPACIPIFWVSKWVDYSDKYGLGYQLCDNSVGVLFNDSTRLILYNDGDSLQYIERDGTESYLTVSSHPNSLMKKITLLKYFRNYMSEHLLKAGANITPREGDELARLPYLRTWFRTRSAIILHLSNGSVQINFFQDHTKLILCPLMAAVTYIDEKRDFRTYRLSLLEEYGCCKELASRLRYARTMVDKLLSSRSASNRLKAS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"MSAAVTAGKLARAPADPGKAGVPGVAAPGAPAAAPPAKEIPEVLVDPRSRRRYVRGRFLGKGGFAKCFEISDADTKEVFAGKIVPKSLLLKPHQREKMSMEISIHRSLAHQHVVGFHGFFEDNDFVFVVLELCRRRSLLELHKRRKALTEPEARYYLRQIVLGCQYLHRNRVIHRDLKLGNLFLNEDLEVKIGDFGLATKVEYDGERKKTLCGTPNYIAPEVLSKKGHSFEVDVWSIGCIMYTLLVGKPPFETSCLKETYLRIKKNEYSIPKHINPVAASLIQKMLQTDPTARPTINELLNDEFFTSGYIPARLPITCLTIPPRFSIAPSSLDPSNRKPLTVLNKGLENPLPERPREKEEPVVRETGEVVDCHLSDMLQQLHSVNASKPSERGLVRQEEAEDPACIPIFWVSKWVDYSDKYGLGYQLCDNSVGVLFNDSTRLILYNDGDSLQYIERDGTESYLTVSSHPNSLMKKITLLKYFRNYMSEHLLKAGANITPREGDELARLPYLRTWFRTRSAIILHLSNGSVQINFFQDHTKLILCPLMAAVTYIDEKRDFRTYRLSLLEEYGCCKELASRLRYARTMVDKLLSSRSASNRLKAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSAAVTAGKLARAPADPGKAGVPGVAAPGAPAAAPPAKEIPEVLVDPRSRRRYVRGRFLGKGGFAKCFEISDADTKEVFAGKIVPKSLLLKPHQREKMSMEISIHRSLAHQHVVGFHGFFEDNDFVFVVLELCRRRSLLELHKRRKALTEPEARYYLRQIVLGCQYLHRNRVIHRDLKLGNLFLNEDLEVKIGDFGLATKVEYDGERKKTLCGTPNYIAPEVLSKKGHSFEVDVWSIGCIMYTLLVGKPPFETSCLKETYLRIKKNEYSIPKHINPVAASLIQKMLQTDPTARPTINELLNDEFFTSGYIPARLPITCLTIPPRFSIAPSSLDPSNRKPLTVLNKGLENPLPERPREKEEPVVRETGEVVDCHLSDMLQQLHSVNASKPSERGLVRQEEAEDPACIPIFWVSKWVDYSDKYGLGYQLCDNSVGVLFNDSTRLILYNDGDSLQYIERDGTESYLTVSSHPNSLMKKITLLKYFRNYMSEHLLKAGANITPREGDELARLPYLRTWFRTRSAIILHLSNGSVQINFFQDHTKLILCPLMAAVTYIDEKRDFRTYRLSLLEEYGCCKELASRLRYARTMVDKLLSSRSASNRLKAS\", \"result\": [{\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein are 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction value for the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' with the target protein 'MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\"], \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}, {\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 84.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the two drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the protein are 10000.0 and 84.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF' is 10000.0."
            },
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug above and the target protein 'MATSRYEPVAEIGVGAYGTVYKARDPHSGHFVALKSVRVPNGGGGGGGLPISTVREVALLRRLEAFEHPNVVRLMDVCATSRTDREIKVTLVFEHVDQDLRTYLDKAPPPGLPAETIKDLMRQFLRGLDFLHANCIVHRDLKPENILVTSGGTVKLADFGLARIYSYQMALTPVVVTLWYRAPEVLLQSTYATPVDMWSVGCIFAEMFRRKPLFCGNSEADQLGKIFDLIGLPPEDDWPRDVSLPRGAFPPRGPRPVQSVVPEMEESGAQLLLEMLTFNPHKRISAFRALQHSYLHKDEGNPE'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MATSRYEPVAEIGVGAYGTVYKARDPHSGHFVALKSVRVPNGGGGGGGLPISTVREVALLRRLEAFEHPNVVRLMDVCATSRTDREIKVTLVFEHVDQDLRTYLDKAPPPGLPAETIKDLMRQFLRGLDFLHANCIVHRDLKPENILVTSGGTVKLADFGLARIYSYQMALTPVVVTLWYRAPEVLLQSTYATPVDMWSVGCIFAEMFRRKPLFCGNSEADQLGKIFDLIGLPPEDDWPRDVSLPRGAFPPRGPRPVQSVVPEMEESGAQLLLEMLTFNPHKRISAFRALQHSYLHKDEGNPE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATSRYEPVAEIGVGAYGTVYKARDPHSGHFVALKSVRVPNGGGGGGGLPISTVREVALLRRLEAFEHPNVVRLMDVCATSRTDREIKVTLVFEHVDQDLRTYLDKAPPPGLPAETIKDLMRQFLRGLDFLHANCIVHRDLKPENILVTSGGTVKLADFGLARIYSYQMALTPVVVTLWYRAPEVLLQSTYATPVDMWSVGCIFAEMFRRKPLFCGNSEADQLGKIFDLIGLPPEDDWPRDVSLPRGAFPPRGPRPVQSVVPEMEESGAQLLLEMLTFNPHKRISAFRALQHSYLHKDEGNPE\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 1900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MATSRYEPVAEIGVGAYGTVYKARDPHSGHFVALKSVRVPNGGGGGGGLPISTVREVALLRRLEAFEHPNVVRLMDVCATSRTDREIKVTLVFEHVDQDLRTYLDKAPPPGLPAETIKDLMRQFLRGLDFLHANCIVHRDLKPENILVTSGGTVKLADFGLARIYSYQMALTPVVVTLWYRAPEVLLQSTYATPVDMWSVGCIFAEMFRRKPLFCGNSEADQLGKIFDLIGLPPEDDWPRDVSLPRGAFPPRGPRPVQSVVPEMEESGAQLLLEMLTFNPHKRISAFRALQHSYLHKDEGNPE' is 1900.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drug 'c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1' for the target protein 'MSFANTSSTFEDKMCEGNRTAMASPQLLPLVVVLSSISLVTVGLNLLVLYAVHSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLIMTKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWFFSFLWVIPILGWHHFMPPAPELREDKCETDFYNVTWFKIMTAIINFYLPTLLMLWFYVKIYKAVRRHCQHRQLTNGSLPSFSELKLRSDDTKEGAKKPGRESPWGVLKRPSRDPSVGLDQKSTSEDPKMTSPTVFSQEGERETRPCFRLDIMQKQSVAEGDVRGSKANDQALSQPKMDEQSLNTCRRISETSEDQTLVDQQSFSRTTDSDTSIEPGPGRVKSRSGSNSGLDYIKITWKRLRSHSRQYVSGLHLNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKSCCSEPMHMFTIWLGYINSTLNPLIYPLCNENFKKTFKKILHIRS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1\"], \"target_seq\": \"MSFANTSSTFEDKMCEGNRTAMASPQLLPLVVVLSSISLVTVGLNLLVLYAVHSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLIMTKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWFFSFLWVIPILGWHHFMPPAPELREDKCETDFYNVTWFKIMTAIINFYLPTLLMLWFYVKIYKAVRRHCQHRQLTNGSLPSFSELKLRSDDTKEGAKKPGRESPWGVLKRPSRDPSVGLDQKSTSEDPKMTSPTVFSQEGERETRPCFRLDIMQKQSVAEGDVRGSKANDQALSQPKMDEQSLNTCRRISETSEDQTLVDQQSFSRTTDSDTSIEPGPGRVKSRSGSNSGLDYIKITWKRLRSHSRQYVSGLHLNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKSCCSEPMHMFTIWLGYINSTLNPLIYPLCNENFKKTFKKILHIRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFANTSSTFEDKMCEGNRTAMASPQLLPLVVVLSSISLVTVGLNLLVLYAVHSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLIMTKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWFFSFLWVIPILGWHHFMPPAPELREDKCETDFYNVTWFKIMTAIINFYLPTLLMLWFYVKIYKAVRRHCQHRQLTNGSLPSFSELKLRSDDTKEGAKKPGRESPWGVLKRPSRDPSVGLDQKSTSEDPKMTSPTVFSQEGERETRPCFRLDIMQKQSVAEGDVRGSKANDQALSQPKMDEQSLNTCRRISETSEDQTLVDQQSFSRTTDSDTSIEPGPGRVKSRSGSNSGLDYIKITWKRLRSHSRQYVSGLHLNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKSCCSEPMHMFTIWLGYINSTLNPLIYPLCNENFKKTFKKILHIRS\", \"result\": [{\"smile\": \"c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1\", \"value\": 9.55}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1' and the protein 'MSFANTSSTFEDKMCEGNRTAMASPQLLPLVVVLSSISLVTVGLNLLVLYAVHSERKLHTVGNLYIVSLSVADLIVGAVVMPMNILYLIMTKWSLGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWFFSFLWVIPILGWHHFMPPAPELREDKCETDFYNVTWFKIMTAIINFYLPTLLMLWFYVKIYKAVRRHCQHRQLTNGSLPSFSELKLRSDDTKEGAKKPGRESPWGVLKRPSRDPSVGLDQKSTSEDPKMTSPTVFSQEGERETRPCFRLDIMQKQSVAEGDVRGSKANDQALSQPKMDEQSLNTCRRISETSEDQTLVDQQSFSRTTDSDTSIEPGPGRVKSRSGSNSGLDYIKITWKRLRSHSRQYVSGLHLNRERKAAKQLGFIMAAFILCWIPYFIFFMVIAFCKSCCSEPMHMFTIWLGYINSTLNPLIYPLCNENFKKTFKKILHIRS' is 9.55."
            },
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the drug above-target protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\": \"c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1\", \"value\": 1000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'c1ccc(C(CCc2ncc(CCNCCc3cnc[nH]3)[nH]2)c2ccccc2)cc1' and the protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' is 1000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' and the target protein 'MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\", \"result\": [{\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' and the protein 'MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY' is 1200.0."
            },
            {
                "from": "human",
                "value": "What is the expected affinity between the drug above and the target protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' and the protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the affinity value of the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' toward the target protein 'MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\"], \"target_seq\": \"MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN\", \"result\": [{\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN' is 10000.0."
            },
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug above and the target protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\", \"result\": [{\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the target protein 'MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\"], \"target_seq\": \"MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the protein 'MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI' is 10000.0."
            },
            {
                "from": "human",
                "value": "What is the anticipated binding score between the drug above and the target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 9000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 9000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely interaction affinity for the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein 'MAASSLEQKLSRLEAKLKQENREARRRIDLNLDISPQRPRPTLQLPLANDGGSRSPSSESSPQHPTPPARPRHMLGLPSTLFTPRSMESIEIDQKLQEIMKQTGYLTIGGQRYQAEINDLENLGEMGSGTCGQVWKMRFRKTGHVIAVKQMRRSGNKEENKRILMDLDVVLKSHDCPYIVQCFGTFITNTDVFIAMELMGTCAEKLKKRMQGPIPERILGKMTVAIVKALYYLKEKHGVIHRDVKPSNILLDERGQIKLCDFGISGRLVDSKAKTRSAGCAAYMAPERIDPPDPTKPDYDIRADVWSLGISLVELATGQFPYKNCKTDFEVLTKVLQEEPPLLPGHMGFSGDFQSFVKDCLTKDHRKRPKYNKLLEHSFIKRYETLEVDVASWFKDVMAKTESPRTSGVLSQPHLPFFR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"MAASSLEQKLSRLEAKLKQENREARRRIDLNLDISPQRPRPTLQLPLANDGGSRSPSSESSPQHPTPPARPRHMLGLPSTLFTPRSMESIEIDQKLQEIMKQTGYLTIGGQRYQAEINDLENLGEMGSGTCGQVWKMRFRKTGHVIAVKQMRRSGNKEENKRILMDLDVVLKSHDCPYIVQCFGTFITNTDVFIAMELMGTCAEKLKKRMQGPIPERILGKMTVAIVKALYYLKEKHGVIHRDVKPSNILLDERGQIKLCDFGISGRLVDSKAKTRSAGCAAYMAPERIDPPDPTKPDYDIRADVWSLGISLVELATGQFPYKNCKTDFEVLTKVLQEEPPLLPGHMGFSGDFQSFVKDCLTKDHRKRPKYNKLLEHSFIKRYETLEVDVASWFKDVMAKTESPRTSGVLSQPHLPFFR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAASSLEQKLSRLEAKLKQENREARRRIDLNLDISPQRPRPTLQLPLANDGGSRSPSSESSPQHPTPPARPRHMLGLPSTLFTPRSMESIEIDQKLQEIMKQTGYLTIGGQRYQAEINDLENLGEMGSGTCGQVWKMRFRKTGHVIAVKQMRRSGNKEENKRILMDLDVVLKSHDCPYIVQCFGTFITNTDVFIAMELMGTCAEKLKKRMQGPIPERILGKMTVAIVKALYYLKEKHGVIHRDVKPSNILLDERGQIKLCDFGISGRLVDSKAKTRSAGCAAYMAPERIDPPDPTKPDYDIRADVWSLGISLVELATGQFPYKNCKTDFEVLTKVLQEEPPLLPGHMGFSGDFQSFVKDCLTKDHRKRPKYNKLLEHSFIKRYETLEVDVASWFKDVMAKTESPRTSGVLSQPHLPFFR\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MAASSLEQKLSRLEAKLKQENREARRRIDLNLDISPQRPRPTLQLPLANDGGSRSPSSESSPQHPTPPARPRHMLGLPSTLFTPRSMESIEIDQKLQEIMKQTGYLTIGGQRYQAEINDLENLGEMGSGTCGQVWKMRFRKTGHVIAVKQMRRSGNKEENKRILMDLDVVLKSHDCPYIVQCFGTFITNTDVFIAMELMGTCAEKLKKRMQGPIPERILGKMTVAIVKALYYLKEKHGVIHRDVKPSNILLDERGQIKLCDFGISGRLVDSKAKTRSAGCAAYMAPERIDPPDPTKPDYDIRADVWSLGISLVELATGQFPYKNCKTDFEVLTKVLQEEPPLLPGHMGFSGDFQSFVKDCLTKDHRKRPKYNKLLEHSFIKRYETLEVDVASWFKDVMAKTESPRTSGVLSQPHLPFFR' is 10000.0."
            },
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug above-target protein 'MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK' interaction? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MELRVGNKYRLGRKIGSGSFGDIYLGANIASGEEVAIKLECVKTKHPQLHIESKFYKMMQGGVGIPSIKWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFSTYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGAARNPEDVDRERREHEREERMGQLRGSATRALPPGPPTGATANRLRSAAEPVASTPASRIQPAGNTSPRAISRVDRERKVSMRLHRGAPANVSSSDLTGRQEVSRIPASQTSVPFDHLGK' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the expected affinity of the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' towards the target protein 'MSAPPVLRPPSPLLPVAAAAAAAAAALVPGSGPGPAPFLAPVAAPVGGISFHLQIGLSREPVLLLQDSSGDYSLAHVREMACSIVDQKFPECGFYGMYDKILLFRHDPTSENILQLVKAASDIQEGDLIEVVLSASATFEDFQIRPHALFVHSYRAPAFCDHCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRRRRLSNVSLTGVSTIRTSSAELSTSAPDEPLLQKSPSESFIGREKRSNSQSYIGRPIHLDKILMSKVKVPHTFVIHSYTRPTVCQYCKKLLKGLFRQGLQCKDCRFNCHKRCAPKVPNNCLGEVTINGDLLSPGAESDVVMEEGSDDNDSERNSGLMDDMEEAMVQDAEMAMAECQNDSGEMQDPDPDHEDANRTISPSTSNNIPLMRVVQSVKHTKRKSSTVMKEGWMVHYTSKDTLRKRHYWRLDSKCITLFQNDTGSRYYKEIPLSEILSLEPVKTSALIPNGANPHCFEITTANVVYYVGENVVNPSSPSPNNSVLTSGVGADVARMWEIAIQHALMPVIPKGSSVGTGTNLHRDISVSISVSNCQIQENVDISTVYQIFPDEVLGSGQFGIVYGGKHRKTGRDVAIKIIDKLRFPTKQESQLRNEVAILQNLHHPGVVNLECMFETPERVFVVMEKLHGDMLEMILSSEKGRLPEHITKFLITQILVALRHLHFKNIVHCDLKPENVLLASADPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRNKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDIHDQIQNAAFMYPPNPWKEISHEAIDLINNLLQVKMRKRYSVDKTLSHPWLQDYQTWLDLRELECKIGERYITHESDDLRWEKYAGEQGLQYPTHLINPSASHSDTPETEETEMKALGERVSIL'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"MSAPPVLRPPSPLLPVAAAAAAAAAALVPGSGPGPAPFLAPVAAPVGGISFHLQIGLSREPVLLLQDSSGDYSLAHVREMACSIVDQKFPECGFYGMYDKILLFRHDPTSENILQLVKAASDIQEGDLIEVVLSASATFEDFQIRPHALFVHSYRAPAFCDHCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRRRRLSNVSLTGVSTIRTSSAELSTSAPDEPLLQKSPSESFIGREKRSNSQSYIGRPIHLDKILMSKVKVPHTFVIHSYTRPTVCQYCKKLLKGLFRQGLQCKDCRFNCHKRCAPKVPNNCLGEVTINGDLLSPGAESDVVMEEGSDDNDSERNSGLMDDMEEAMVQDAEMAMAECQNDSGEMQDPDPDHEDANRTISPSTSNNIPLMRVVQSVKHTKRKSSTVMKEGWMVHYTSKDTLRKRHYWRLDSKCITLFQNDTGSRYYKEIPLSEILSLEPVKTSALIPNGANPHCFEITTANVVYYVGENVVNPSSPSPNNSVLTSGVGADVARMWEIAIQHALMPVIPKGSSVGTGTNLHRDISVSISVSNCQIQENVDISTVYQIFPDEVLGSGQFGIVYGGKHRKTGRDVAIKIIDKLRFPTKQESQLRNEVAILQNLHHPGVVNLECMFETPERVFVVMEKLHGDMLEMILSSEKGRLPEHITKFLITQILVALRHLHFKNIVHCDLKPENVLLASADPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRNKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDIHDQIQNAAFMYPPNPWKEISHEAIDLINNLLQVKMRKRYSVDKTLSHPWLQDYQTWLDLRELECKIGERYITHESDDLRWEKYAGEQGLQYPTHLINPSASHSDTPETEETEMKALGERVSIL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSAPPVLRPPSPLLPVAAAAAAAAAALVPGSGPGPAPFLAPVAAPVGGISFHLQIGLSREPVLLLQDSSGDYSLAHVREMACSIVDQKFPECGFYGMYDKILLFRHDPTSENILQLVKAASDIQEGDLIEVVLSASATFEDFQIRPHALFVHSYRAPAFCDHCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRRRRLSNVSLTGVSTIRTSSAELSTSAPDEPLLQKSPSESFIGREKRSNSQSYIGRPIHLDKILMSKVKVPHTFVIHSYTRPTVCQYCKKLLKGLFRQGLQCKDCRFNCHKRCAPKVPNNCLGEVTINGDLLSPGAESDVVMEEGSDDNDSERNSGLMDDMEEAMVQDAEMAMAECQNDSGEMQDPDPDHEDANRTISPSTSNNIPLMRVVQSVKHTKRKSSTVMKEGWMVHYTSKDTLRKRHYWRLDSKCITLFQNDTGSRYYKEIPLSEILSLEPVKTSALIPNGANPHCFEITTANVVYYVGENVVNPSSPSPNNSVLTSGVGADVARMWEIAIQHALMPVIPKGSSVGTGTNLHRDISVSISVSNCQIQENVDISTVYQIFPDEVLGSGQFGIVYGGKHRKTGRDVAIKIIDKLRFPTKQESQLRNEVAILQNLHHPGVVNLECMFETPERVFVVMEKLHGDMLEMILSSEKGRLPEHITKFLITQILVALRHLHFKNIVHCDLKPENVLLASADPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRNKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDIHDQIQNAAFMYPPNPWKEISHEAIDLINNLLQVKMRKRYSVDKTLSHPWLQDYQTWLDLRELECKIGERYITHESDDLRWEKYAGEQGLQYPTHLINPSASHSDTPETEETEMKALGERVSIL\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein 'MSAPPVLRPPSPLLPVAAAAAAAAAALVPGSGPGPAPFLAPVAAPVGGISFHLQIGLSREPVLLLQDSSGDYSLAHVREMACSIVDQKFPECGFYGMYDKILLFRHDPTSENILQLVKAASDIQEGDLIEVVLSASATFEDFQIRPHALFVHSYRAPAFCDHCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRRRRLSNVSLTGVSTIRTSSAELSTSAPDEPLLQKSPSESFIGREKRSNSQSYIGRPIHLDKILMSKVKVPHTFVIHSYTRPTVCQYCKKLLKGLFRQGLQCKDCRFNCHKRCAPKVPNNCLGEVTINGDLLSPGAESDVVMEEGSDDNDSERNSGLMDDMEEAMVQDAEMAMAECQNDSGEMQDPDPDHEDANRTISPSTSNNIPLMRVVQSVKHTKRKSSTVMKEGWMVHYTSKDTLRKRHYWRLDSKCITLFQNDTGSRYYKEIPLSEILSLEPVKTSALIPNGANPHCFEITTANVVYYVGENVVNPSSPSPNNSVLTSGVGADVARMWEIAIQHALMPVIPKGSSVGTGTNLHRDISVSISVSNCQIQENVDISTVYQIFPDEVLGSGQFGIVYGGKHRKTGRDVAIKIIDKLRFPTKQESQLRNEVAILQNLHHPGVVNLECMFETPERVFVVMEKLHGDMLEMILSSEKGRLPEHITKFLITQILVALRHLHFKNIVHCDLKPENVLLASADPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRNKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDIHDQIQNAAFMYPPNPWKEISHEAIDLINNLLQVKMRKRYSVDKTLSHPWLQDYQTWLDLRELECKIGERYITHESDDLRWEKYAGEQGLQYPTHLINPSASHSDTPETEETEMKALGERVSIL' is 10000.0."
            },
            {
                "from": "human",
                "value": "Can you outline the predicted binding value of the drug above for the target protein 'MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLECRWREMEEHRQAERLQRQLQQEQAYLLSLQHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQTQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLECRWREMEEHRQAERLQRQLQQEQAYLLSLQHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQTQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLECRWREMEEHRQAERLQRQLQQEQAYLLSLQHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQTQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 5800.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein 'MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLECRWREMEEHRQAERLQRQLQQEQAYLLSLQHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQTQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW' is 5800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4''s affinity for the target protein 'MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ\", \"result\": [{\"smile\": \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 2.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ' is 2.2."
            },
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug above and the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 0.35}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' is 0.35."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1''s affinity with the target protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\", \"result\": [{\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL' is 10000.0."
            },
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug above and the target protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\", \"result\": [{\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely interaction affinity for the drug 'CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O' and the target protein 'MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O\"], \"target_seq\": \"MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD\", \"result\": [{\"smile\": \"CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O\", \"value\": 36000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O' and the protein 'MPSIRLADLAQQLDAELHGDGDIVITGVASMQSAQTGHITFMVNPKYREHLGLCQASAVVMTQDDLPFAKSAALVVKNPYLTYARMAQILDTTPQPAQNIAPSAVIDATAKLGNNVSIGANAVIESGVELGDNVIIGAGCFVGKNSKIGAGSRLWANVTIYHEIQIGQNCLIQSGTVVGADGFGYANDRGNWVKIPQIGRVIIGDRVEIGACTTIDRGALDDTIIGNGVIIDNQCQIAHNVVIGDNTAVAGGVIMAGSLKIGRYCMIGGASVINGHMEICDKVTVTGMGMVMRPITEPGVYSSGIPLQPNKVWRKTAALVMNIDDMSKRLKSLERKVNQQD' is 36000.0."
            },
            {
                "from": "human",
                "value": "Can you measure the affinity between the drug above and the target protein 'MIDKSAFVHPTAIVEEGASIGANAHIGPFCIVGPHVEIGEGTVLKSHVVVNGHTKIGRDNEIYQFASIGEVNQDLKYAGEPTRVEIGDRNRIRESVTIHRGTVQGGGLTKVGSDNLLMINAHIAHDCTVGNRCILANNATLAGHVSVDDFAIIGGMTAVHQFCIIGAHVMVGGCSGVAQDVPPYVIAQGNHATPFGVNIEGLKRRGFSREAITAIRNAYKLIYRSGKTLDEVKPEIAELAETYPEVKAFTDFFARSTRGLIR'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MIDKSAFVHPTAIVEEGASIGANAHIGPFCIVGPHVEIGEGTVLKSHVVVNGHTKIGRDNEIYQFASIGEVNQDLKYAGEPTRVEIGDRNRIRESVTIHRGTVQGGGLTKVGSDNLLMINAHIAHDCTVGNRCILANNATLAGHVSVDDFAIIGGMTAVHQFCIIGAHVMVGGCSGVAQDVPPYVIAQGNHATPFGVNIEGLKRRGFSREAITAIRNAYKLIYRSGKTLDEVKPEIAELAETYPEVKAFTDFFARSTRGLIR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MIDKSAFVHPTAIVEEGASIGANAHIGPFCIVGPHVEIGEGTVLKSHVVVNGHTKIGRDNEIYQFASIGEVNQDLKYAGEPTRVEIGDRNRIRESVTIHRGTVQGGGLTKVGSDNLLMINAHIAHDCTVGNRCILANNATLAGHVSVDDFAIIGGMTAVHQFCIIGAHVMVGGCSGVAQDVPPYVIAQGNHATPFGVNIEGLKRRGFSREAITAIRNAYKLIYRSGKTLDEVKPEIAELAETYPEVKAFTDFFARSTRGLIR\", \"result\": [{\"smile\": \"CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O\", \"value\": 4400.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(N)=O)NC(=O)[C@@H](NC(=O)CCNC(=S)Nc1ccc(-c2c3ccc(=O)cc-3oc3cc(O)ccc23)c(C(=O)O)c1)[C@@H](C)O)C(=O)N[C@@H](CC(C)C)C(=O)O' and the protein 'MIDKSAFVHPTAIVEEGASIGANAHIGPFCIVGPHVEIGEGTVLKSHVVVNGHTKIGRDNEIYQFASIGEVNQDLKYAGEPTRVEIGDRNRIRESVTIHRGTVQGGGLTKVGSDNLLMINAHIAHDCTVGNRCILANNATLAGHVSVDDFAIIGGMTAVHQFCIIGAHVMVGGCSGVAQDVPPYVIAQGNHATPFGVNIEGLKRRGFSREAITAIRNAYKLIYRSGKTLDEVKPEIAELAETYPEVKAFTDFFARSTRGLIR' is 4400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted binding affinity of the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' with the target protein 'MSAEVRLRRLQQLVLDPGFLGLEPLLDLLLGVHQELGASELAQDKYVADFLQWAEPIVVRLKEVRLQRDDFEILKVIGRGAFSEVAVVKMKQTGQVYAMKIMNKWDMLKRGEVSCFREERDVLVNGDRRWITQLHFAFQDENYLYLVMEYYVGGDLLTLLSKFGERIPAEMARFYLAEIVMAIDSVHRLGYVHRDIKPDNILLDRCGHIRLADFGSCLKLRADGTVRSLVAVGTPDYLSPEILQAVGGGPGTGSYGPECDWWALGVFAYEMFYGQTPFYADSTAETYGKIVHYKEHLSLPLVDEGVPEEARDFIQRLLCPPETRLGRGGAGDFRTHPFFFGLDWDGLRDSVPPFTPDFEGATDTCNFDLVEDGLTAMVSGGGETLSDIREGAPLGVHLPFVGYSYSCMALRDSEVPGPTPMELEAEQLLEPHVQAPSLEPSVSPQDETAEVAVPAAVPAAEAEAEVTLRELQEALEEEVLTRQSLSREMEAIRTDNQNFASQLREAEARNRDLEAHVRQLQERMELLQAEGATAVTGVPSPRATDPPSHLDGPPAVAVGQCPLVGPGPMHRRHLLLPARVPRPGLSEALSLLLFAVVLSRAAALGCIGLVAHAGQLTAVWRRPGAARAP'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\"], \"target_seq\": \"MSAEVRLRRLQQLVLDPGFLGLEPLLDLLLGVHQELGASELAQDKYVADFLQWAEPIVVRLKEVRLQRDDFEILKVIGRGAFSEVAVVKMKQTGQVYAMKIMNKWDMLKRGEVSCFREERDVLVNGDRRWITQLHFAFQDENYLYLVMEYYVGGDLLTLLSKFGERIPAEMARFYLAEIVMAIDSVHRLGYVHRDIKPDNILLDRCGHIRLADFGSCLKLRADGTVRSLVAVGTPDYLSPEILQAVGGGPGTGSYGPECDWWALGVFAYEMFYGQTPFYADSTAETYGKIVHYKEHLSLPLVDEGVPEEARDFIQRLLCPPETRLGRGGAGDFRTHPFFFGLDWDGLRDSVPPFTPDFEGATDTCNFDLVEDGLTAMVSGGGETLSDIREGAPLGVHLPFVGYSYSCMALRDSEVPGPTPMELEAEQLLEPHVQAPSLEPSVSPQDETAEVAVPAAVPAAEAEAEVTLRELQEALEEEVLTRQSLSREMEAIRTDNQNFASQLREAEARNRDLEAHVRQLQERMELLQAEGATAVTGVPSPRATDPPSHLDGPPAVAVGQCPLVGPGPMHRRHLLLPARVPRPGLSEALSLLLFAVVLSRAAALGCIGLVAHAGQLTAVWRRPGAARAP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSAEVRLRRLQQLVLDPGFLGLEPLLDLLLGVHQELGASELAQDKYVADFLQWAEPIVVRLKEVRLQRDDFEILKVIGRGAFSEVAVVKMKQTGQVYAMKIMNKWDMLKRGEVSCFREERDVLVNGDRRWITQLHFAFQDENYLYLVMEYYVGGDLLTLLSKFGERIPAEMARFYLAEIVMAIDSVHRLGYVHRDIKPDNILLDRCGHIRLADFGSCLKLRADGTVRSLVAVGTPDYLSPEILQAVGGGPGTGSYGPECDWWALGVFAYEMFYGQTPFYADSTAETYGKIVHYKEHLSLPLVDEGVPEEARDFIQRLLCPPETRLGRGGAGDFRTHPFFFGLDWDGLRDSVPPFTPDFEGATDTCNFDLVEDGLTAMVSGGGETLSDIREGAPLGVHLPFVGYSYSCMALRDSEVPGPTPMELEAEQLLEPHVQAPSLEPSVSPQDETAEVAVPAAVPAAEAEAEVTLRELQEALEEEVLTRQSLSREMEAIRTDNQNFASQLREAEARNRDLEAHVRQLQERMELLQAEGATAVTGVPSPRATDPPSHLDGPPAVAVGQCPLVGPGPMHRRHLLLPARVPRPGLSEALSLLLFAVVLSRAAALGCIGLVAHAGQLTAVWRRPGAARAP\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the protein 'MSAEVRLRRLQQLVLDPGFLGLEPLLDLLLGVHQELGASELAQDKYVADFLQWAEPIVVRLKEVRLQRDDFEILKVIGRGAFSEVAVVKMKQTGQVYAMKIMNKWDMLKRGEVSCFREERDVLVNGDRRWITQLHFAFQDENYLYLVMEYYVGGDLLTLLSKFGERIPAEMARFYLAEIVMAIDSVHRLGYVHRDIKPDNILLDRCGHIRLADFGSCLKLRADGTVRSLVAVGTPDYLSPEILQAVGGGPGTGSYGPECDWWALGVFAYEMFYGQTPFYADSTAETYGKIVHYKEHLSLPLVDEGVPEEARDFIQRLLCPPETRLGRGGAGDFRTHPFFFGLDWDGLRDSVPPFTPDFEGATDTCNFDLVEDGLTAMVSGGGETLSDIREGAPLGVHLPFVGYSYSCMALRDSEVPGPTPMELEAEQLLEPHVQAPSLEPSVSPQDETAEVAVPAAVPAAEAEAEVTLRELQEALEEEVLTRQSLSREMEAIRTDNQNFASQLREAEARNRDLEAHVRQLQERMELLQAEGATAVTGVPSPRATDPPSHLDGPPAVAVGQCPLVGPGPMHRRHLLLPARVPRPGLSEALSLLLFAVVLSRAAALGCIGLVAHAGQLTAVWRRPGAARAP' is 1.0."
            },
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug above and the target protein 'MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 8.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the protein 'MAKATSGAAGLRLLLLLLLPLLGKVALGLYFSRDAYWEKLYVDQAAGTPLLYVHALRDAPEEVPSFRLGQHLYGTYRTRLHENNWICIQEDTGLLYLNRSLDHSSWEKLSVRNRGFPLLTVYLKVFLSPTSLREGECQWPGCARVYFSFFNTSFPACSSLKPRELCFPETRPSFRIRENRPPGTFHQFRLLPVQFLCPNISVAYRLLEGEGLPFRCAPDSLEVSTRWALDREQREKYELVAVCTVHAGAREEVVMVPFPVTVYDEDDSAPTFPAGVDTASAVVEFKRKEDTVVATLRVFDADVVPASGELVRRYTSTLLPGDTWAQQTFRVEHWPNETSVQANGSFVRATVHDYRLVLNRNLSISENRTMQLAVLVNDSDFQGPGAGVLLLHFNVSVLPVSLHLPSTYSLSVSRRARRFAQIGKVCVENCQAFSGINVQYKLHSSGANCSTLGVVTSAEDTSGILFVNDTKALRRPKCAELHYMVVATDQQTSRQAQAQLLVTVEGSYVAEEAGCPLSCAVSKRRLECEECGGLGSPTGRCEWRQGDGKGITRNFSTCSPSTKTCPDGHCDVVETQDINICPQDCLRGSIVGGHEPGEPRGIKAGYGTCNCFPEEEKCFCEPEDIQDPLCDELCRTVIAAAVLFSFIVSVLLSAFCIHCYHKFAHKPPISSAEMTFRRPAQAFPVSYSSSGARRPSLDSMENQVSVDAFKILEDPKWEFPRKNLVLGKTLGEGEFGKVVKATAFHLKGRAGYTTVAVKMLKENASPSELRDLLSEFNVLKQVNHPHVIKLYGACSQDGPLLLIVEYAKYGSLRGFLRESRKVGPGYLGSGGSRNSSSLDHPDERALTMGDLISFAWQISQGMQYLAEMKLVHRDLAARNILVAEGRKMKISDFGLSRDVYEEDSYVKRSQGRIPVKWMAIESLFDHIYTTQSDVWSFGVLLWEIVTLGGNPYPGIPPERLFNLLKTGHRMERPDNCSEEMYRLMLQCWKQEPDKRPVFADISKDLEKMMVKRRDYLDLAASTPSDSLIYDDGLSEEETPLVDCNNAPLPRALPSTWIENKLYGMSDPNWPGESPVPLTRADGTNTGFPRYPNDSVYANWMLSPSAAKLMDTFDS' is 8.6."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1''s affinity for the target protein 'MSRESDVEAQQSHGSSACSQPHGSVTQSQGSSSQSQGISSSSTSTMPNSSQSSHSSSGTLSSLETVSTQELYSIPEDQEPEDQEPEEPTPAPWARLWALQDGFANLECVNDNYWFGRDKSCEYCFDEPLLKRTDKYRTYSKKHFRIFREVGPKNSYIAYIEDHSGNGTFVNTELVGKGKRRPLNNNSEIALSLSRNKVFVFFDLTVDDQSVYPKALRDEYIMSKTLGSGACGEVKLAFERKTCKKVAIKIISKRKFAIGSAREADPALNVETEIEILKKLNHPCIIKIKNFFDAEDYYIVLELMEGGELFDKVVGNKRLKEATCKLYFYQMLLAVQYLHENGIIHRDLKPENVLLSSQEEDCLIKITDFGHSKILGETSLMRTLCGTPTYLAPEVLVSVGTAGYNRAVDCWSLGVILFICLSGYPPFSEHRTQVSLKDQITSGKYNFIPEVWAEVSEKALDLVKKLLVVDPKARFTTEEALRHPWLQDEDMKRKFQDLLSEENESTALPQVLAQPSTSRKRPREGEAEGAETTKRPAVCAAVL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"MSRESDVEAQQSHGSSACSQPHGSVTQSQGSSSQSQGISSSSTSTMPNSSQSSHSSSGTLSSLETVSTQELYSIPEDQEPEDQEPEEPTPAPWARLWALQDGFANLECVNDNYWFGRDKSCEYCFDEPLLKRTDKYRTYSKKHFRIFREVGPKNSYIAYIEDHSGNGTFVNTELVGKGKRRPLNNNSEIALSLSRNKVFVFFDLTVDDQSVYPKALRDEYIMSKTLGSGACGEVKLAFERKTCKKVAIKIISKRKFAIGSAREADPALNVETEIEILKKLNHPCIIKIKNFFDAEDYYIVLELMEGGELFDKVVGNKRLKEATCKLYFYQMLLAVQYLHENGIIHRDLKPENVLLSSQEEDCLIKITDFGHSKILGETSLMRTLCGTPTYLAPEVLVSVGTAGYNRAVDCWSLGVILFICLSGYPPFSEHRTQVSLKDQITSGKYNFIPEVWAEVSEKALDLVKKLLVVDPKARFTTEEALRHPWLQDEDMKRKFQDLLSEENESTALPQVLAQPSTSRKRPREGEAEGAETTKRPAVCAAVL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSRESDVEAQQSHGSSACSQPHGSVTQSQGSSSQSQGISSSSTSTMPNSSQSSHSSSGTLSSLETVSTQELYSIPEDQEPEDQEPEEPTPAPWARLWALQDGFANLECVNDNYWFGRDKSCEYCFDEPLLKRTDKYRTYSKKHFRIFREVGPKNSYIAYIEDHSGNGTFVNTELVGKGKRRPLNNNSEIALSLSRNKVFVFFDLTVDDQSVYPKALRDEYIMSKTLGSGACGEVKLAFERKTCKKVAIKIISKRKFAIGSAREADPALNVETEIEILKKLNHPCIIKIKNFFDAEDYYIVLELMEGGELFDKVVGNKRLKEATCKLYFYQMLLAVQYLHENGIIHRDLKPENVLLSSQEEDCLIKITDFGHSKILGETSLMRTLCGTPTYLAPEVLVSVGTAGYNRAVDCWSLGVILFICLSGYPPFSEHRTQVSLKDQITSGKYNFIPEVWAEVSEKALDLVKKLLVVDPKARFTTEEALRHPWLQDEDMKRKFQDLLSEENESTALPQVLAQPSTSRKRPREGEAEGAETTKRPAVCAAVL\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the protein 'MSRESDVEAQQSHGSSACSQPHGSVTQSQGSSSQSQGISSSSTSTMPNSSQSSHSSSGTLSSLETVSTQELYSIPEDQEPEDQEPEEPTPAPWARLWALQDGFANLECVNDNYWFGRDKSCEYCFDEPLLKRTDKYRTYSKKHFRIFREVGPKNSYIAYIEDHSGNGTFVNTELVGKGKRRPLNNNSEIALSLSRNKVFVFFDLTVDDQSVYPKALRDEYIMSKTLGSGACGEVKLAFERKTCKKVAIKIISKRKFAIGSAREADPALNVETEIEILKKLNHPCIIKIKNFFDAEDYYIVLELMEGGELFDKVVGNKRLKEATCKLYFYQMLLAVQYLHENGIIHRDLKPENVLLSSQEEDCLIKITDFGHSKILGETSLMRTLCGTPTYLAPEVLVSVGTAGYNRAVDCWSLGVILFICLSGYPPFSEHRTQVSLKDQITSGKYNFIPEVWAEVSEKALDLVKKLLVVDPKARFTTEEALRHPWLQDEDMKRKFQDLLSEENESTALPQVLAQPSTSRKRPREGEAEGAETTKRPAVCAAVL' is 10000.0."
            },
            {
                "from": "human",
                "value": "What is the predicted interaction value for the drug above and the target protein 'MLTRKPSAAAPAAYPTGRGGDSAVRQLQASPGLGAGATRSGVGTGPPSPIALPPLRASNAAAAAHTIGGSKHTMNDHLHVGSHAHGQIQVQQLFEDNSNKRTVLTTQPNGLTTVGKTGLPVVPERQLDSIHRRQGSSTSLKSMEGMGKVKATPMTPEQAMKQYMQKLTAFEHHEIFSYPEIYFLGLNAKKRQGMTGGPNNGGYDDDQGSYVQVPHDHVAYRYEVLKVIGKGSFGQVVKAYDHKVHQHVALKMVRNEKRFHRQAAEEIRILEHLRKQDKDNTMNVIHMLENFTFRNHICMTFELLSMNLYELIKKNKFQGFSLPLVRKFAHSILQCLDALHKNRIIHCDLKPENILLKQQGRSGIKVIDFGSSCYEHQRVYTYIQSRFYRAPEVILGARYGMPIDMWSLGCILAELLTGYPLLPGEDEGDQLACMIELLGMPSQKLLDASKRAKNFVSSKGYPRYCTVTTLSDGSVVLNGGRSRRGKLRGPPESREWGNALKGCDDPLFLDFLKQCLEWDPAVRMTPGQALRHPWLRRRLPKPPTGEKTSVKRITESTGAITSISKLPPPSSSASKLRTNLAQMTDANGNIQQRTVLPKLVS'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLTRKPSAAAPAAYPTGRGGDSAVRQLQASPGLGAGATRSGVGTGPPSPIALPPLRASNAAAAAHTIGGSKHTMNDHLHVGSHAHGQIQVQQLFEDNSNKRTVLTTQPNGLTTVGKTGLPVVPERQLDSIHRRQGSSTSLKSMEGMGKVKATPMTPEQAMKQYMQKLTAFEHHEIFSYPEIYFLGLNAKKRQGMTGGPNNGGYDDDQGSYVQVPHDHVAYRYEVLKVIGKGSFGQVVKAYDHKVHQHVALKMVRNEKRFHRQAAEEIRILEHLRKQDKDNTMNVIHMLENFTFRNHICMTFELLSMNLYELIKKNKFQGFSLPLVRKFAHSILQCLDALHKNRIIHCDLKPENILLKQQGRSGIKVIDFGSSCYEHQRVYTYIQSRFYRAPEVILGARYGMPIDMWSLGCILAELLTGYPLLPGEDEGDQLACMIELLGMPSQKLLDASKRAKNFVSSKGYPRYCTVTTLSDGSVVLNGGRSRRGKLRGPPESREWGNALKGCDDPLFLDFLKQCLEWDPAVRMTPGQALRHPWLRRRLPKPPTGEKTSVKRITESTGAITSISKLPPPSSSASKLRTNLAQMTDANGNIQQRTVLPKLVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLTRKPSAAAPAAYPTGRGGDSAVRQLQASPGLGAGATRSGVGTGPPSPIALPPLRASNAAAAAHTIGGSKHTMNDHLHVGSHAHGQIQVQQLFEDNSNKRTVLTTQPNGLTTVGKTGLPVVPERQLDSIHRRQGSSTSLKSMEGMGKVKATPMTPEQAMKQYMQKLTAFEHHEIFSYPEIYFLGLNAKKRQGMTGGPNNGGYDDDQGSYVQVPHDHVAYRYEVLKVIGKGSFGQVVKAYDHKVHQHVALKMVRNEKRFHRQAAEEIRILEHLRKQDKDNTMNVIHMLENFTFRNHICMTFELLSMNLYELIKKNKFQGFSLPLVRKFAHSILQCLDALHKNRIIHCDLKPENILLKQQGRSGIKVIDFGSSCYEHQRVYTYIQSRFYRAPEVILGARYGMPIDMWSLGCILAELLTGYPLLPGEDEGDQLACMIELLGMPSQKLLDASKRAKNFVSSKGYPRYCTVTTLSDGSVVLNGGRSRRGKLRGPPESREWGNALKGCDDPLFLDFLKQCLEWDPAVRMTPGQALRHPWLRRRLPKPPTGEKTSVKRITESTGAITSISKLPPPSSSASKLRTNLAQMTDANGNIQQRTVLPKLVS\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the protein 'MLTRKPSAAAPAAYPTGRGGDSAVRQLQASPGLGAGATRSGVGTGPPSPIALPPLRASNAAAAAHTIGGSKHTMNDHLHVGSHAHGQIQVQQLFEDNSNKRTVLTTQPNGLTTVGKTGLPVVPERQLDSIHRRQGSSTSLKSMEGMGKVKATPMTPEQAMKQYMQKLTAFEHHEIFSYPEIYFLGLNAKKRQGMTGGPNNGGYDDDQGSYVQVPHDHVAYRYEVLKVIGKGSFGQVVKAYDHKVHQHVALKMVRNEKRFHRQAAEEIRILEHLRKQDKDNTMNVIHMLENFTFRNHICMTFELLSMNLYELIKKNKFQGFSLPLVRKFAHSILQCLDALHKNRIIHCDLKPENILLKQQGRSGIKVIDFGSSCYEHQRVYTYIQSRFYRAPEVILGARYGMPIDMWSLGCILAELLTGYPLLPGEDEGDQLACMIELLGMPSQKLLDASKRAKNFVSSKGYPRYCTVTTLSDGSVVLNGGRSRRGKLRGPPESREWGNALKGCDDPLFLDFLKQCLEWDPAVRMTPGQALRHPWLRRRLPKPPTGEKTSVKRITESTGAITSISKLPPPSSSASKLRTNLAQMTDANGNIQQRTVLPKLVS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the drug 'CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1' and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1\", \"value\": 300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 300.0."
            },
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug above and the target protein 'MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP\", \"result\": [{\"smile\": \"CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1\", \"value\": 12000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1C/C=C/C(O)C(O)CC/C=C/c2cc(O)cc(O)c2C(=O)O1' and the protein 'MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP' is 12000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected value for the affinity between the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1' and the target protein 'MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 15.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1' and the protein 'MGGRSSCEDPGCPRDEERAPRMGCMKSKFLQVGGNTFSKTETSASPHCPVYVPDPTSTIKPGPNSHNSNTPGIREAGSEDIIVVALYDYEAIHHEDLSFQKGDQMVVLEESGEWWKARSLATRKEGYIPSNYVARVDSLETEEWFFKGISRKDAERQLLAPGNMLGSFMIRDSETTKGSYSLSVRDYDPRQGDTVKHYKIRTLDNGGFYISPRSTFSTLQELVDHYKKGNDGLCQKLSVPCMSSKPQKPWEKDAWEIPRESLKLEKKLGAGQFGEVWMATYNKHTKVAVKTMKPGSMSVEAFLAEANVMKTLQHDKLVKLHAVVTKEPIYIITEFMAKGSLLDFLKSDEGSKQPLPKLIDFSAQIAEGMAFIEQRNYIHRDLRAANILVSASLVCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGSFTIKSDVWSFGILLMEIVTYGRIPYPGMSNPEVIRALERGYRMPRPENCPEELYNIMMRCWKNRPEERPTFEYIQSVLDDFYTATESQYQQQP' is 15.0."
            },
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug above and the target protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 40.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1' and the protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT' is 40.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drug 'CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' for the target protein 'MDRSKENCISGPVKATAPVGGPKRVLVTQQFPCQNPLPVNSGQAQRVLCPSNSSQRVPLQAQKLVSSHKPVQNQKQKQLQATSVPHPVSRPLNNTQKSKQPLPSAPENNPEEELASKQKNEESKKRQWALEDFEIGRPLGKGKFGNVYLAREKQSKFILALKVLFKAQLEKAGVEHQLRREVEIQSHLRHPNILRLYGYFHDATRVYLILEYAPLGTVYRELQKLSKFDEQRTATYITELANALSYCHSKRVIHRDIKPENLLLGSAGELKIADFGWSVHAPSSRRTTLCGTLDYLPPEMIEGRMHDEKVDLWSLGVLCYEFLVGKPPFEANTYQETYKRISRVEFTFPDFVTEGARDLISRLLKHNPSQRPMLREVLEHPWITANSSKPSNCQNKESASKQS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\"], \"target_seq\": \"MDRSKENCISGPVKATAPVGGPKRVLVTQQFPCQNPLPVNSGQAQRVLCPSNSSQRVPLQAQKLVSSHKPVQNQKQKQLQATSVPHPVSRPLNNTQKSKQPLPSAPENNPEEELASKQKNEESKKRQWALEDFEIGRPLGKGKFGNVYLAREKQSKFILALKVLFKAQLEKAGVEHQLRREVEIQSHLRHPNILRLYGYFHDATRVYLILEYAPLGTVYRELQKLSKFDEQRTATYITELANALSYCHSKRVIHRDIKPENLLLGSAGELKIADFGWSVHAPSSRRTTLCGTLDYLPPEMIEGRMHDEKVDLWSLGVLCYEFLVGKPPFEANTYQETYKRISRVEFTFPDFVTEGARDLISRLLKHNPSQRPMLREVLEHPWITANSSKPSNCQNKESASKQS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDRSKENCISGPVKATAPVGGPKRVLVTQQFPCQNPLPVNSGQAQRVLCPSNSSQRVPLQAQKLVSSHKPVQNQKQKQLQATSVPHPVSRPLNNTQKSKQPLPSAPENNPEEELASKQKNEESKKRQWALEDFEIGRPLGKGKFGNVYLAREKQSKFILALKVLFKAQLEKAGVEHQLRREVEIQSHLRHPNILRLYGYFHDATRVYLILEYAPLGTVYRELQKLSKFDEQRTATYITELANALSYCHSKRVIHRDIKPENLLLGSAGELKIADFGWSVHAPSSRRTTLCGTLDYLPPEMIEGRMHDEKVDLWSLGVLCYEFLVGKPPFEANTYQETYKRISRVEFTFPDFVTEGARDLISRLLKHNPSQRPMLREVLEHPWITANSSKPSNCQNKESASKQS\", \"result\": [{\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MDRSKENCISGPVKATAPVGGPKRVLVTQQFPCQNPLPVNSGQAQRVLCPSNSSQRVPLQAQKLVSSHKPVQNQKQKQLQATSVPHPVSRPLNNTQKSKQPLPSAPENNPEEELASKQKNEESKKRQWALEDFEIGRPLGKGKFGNVYLAREKQSKFILALKVLFKAQLEKAGVEHQLRREVEIQSHLRHPNILRLYGYFHDATRVYLILEYAPLGTVYRELQKLSKFDEQRTATYITELANALSYCHSKRVIHRDIKPENLLLGSAGELKIADFGWSVHAPSSRRTTLCGTLDYLPPEMIEGRMHDEKVDLWSLGVLCYEFLVGKPPFEANTYQETYKRISRVEFTFPDFVTEGARDLISRLLKHNPSQRPMLREVLEHPWITANSSKPSNCQNKESASKQS' is 10000.0."
            },
            {
                "from": "human",
                "value": "Infer the affinity value for the drug above-target protein 'MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS' pair. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\", \"result\": [{\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS' is 1200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value is expected between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the target protein 'MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\"], \"target_seq\": \"MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 310.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein 'MASQVLVYPPYVYQTQSSAFCSVKKLKVEPSSCVFQERNYPRTYVNGRNFGNSHPPTKGSAFQTKIPFNRPRGHNFSLQTSAVVLKNTAGATKVIAAQAQQAHVQAPQIGAWRNRLHFLEGPQRCGLKRKSEELDNHSSAMQIVDELSILPAMLQTNMGNPVTVVTATTGSKQNCTTGEGDYQLVQHEVLCSMKNTYEVLDFLGRGTFGQVVKCWKRGTNEIVAIKILKNHPSYARQGQIEVSILARLSTENADEYNFVRAYECFQHRNHTCLVFEMLEQNLYDFLKQNKFSPLPLKVIRPILQQVATALKKLKSLGLIHADLKPENIMLVDPVRQPYRVKVIDFGSASHVSKTVCSTYLQSRYYRAPEIILGLPFCEAIDMWSLGCVIAELFLGWPLYPGALEYDQIRYISQTQGLPGEQLLNVGTKSTRFFCKETDMSHSGWRLKTLEEHEAETGMKSKEARKYIFNSLDDVAHVNTVMDLEGSDLLAEKADRREFVSLLKKMLLIDADLRITPAETLNHPFVNMKHLLDFPHSNHVKSCFHIMDICKSHLNSCDTNNHNKTSLLRPVASSSTATLTANFTKIGTLRSQALTTSAHSVVHHGIPLQAGTAQFGCGDAFQQTLIICPPAIQGIPATHGKPTSYSIRVDNTVPLVTQAPAVQPLQIRPGVLSQTWSGRTQQMLVPAWQQVTPLAPATTTLTSESVAGSHRLGDWGKMISCSNHYNSVMPQPLLTNQITLSAPQPVSVGIAHVVWPQPATTKKNKQCQNRGILVKLMEWEPGREEINAFSWSNSLQNTNIPHSAFISPKIINGKDVEEVSCIETQDNQNSEGEARNCCETSIRQDSDSSVSDKQRQTIIIADSPSPAVSVITISSDTDEEETSQRHSLRECKGSLDCEACQSTLNIDRMCSLSSPDSTLSTSSSGQSSPSPCKRPNSMSDEEQESSCDTVDGSPTSDSSGHDSPFAESTFVEDTHENTELVSSADTETKPAVCSVVVPPVELENGLNADEHMANTDSICQPLIKGRSAPGRLNQPSAVGTRQQKLTSAFQQQHLNFSQVQHFGSGHQEWNGNFGHRRQQAYIPTSVTSNPFTLSHGSPNHTAVHAHLAGNTHLGGQPTLLPYPSSATLSSAAPVAHLLASPCTSRPMLQHPTYNISHPSGIVHQVPVGLNPRLLPSPTIHQTQYKPIFPPHSYIAASPAYTGFPLSPTKLSQYPYM' is 310.0."
            },
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug above and the target protein 'MGHQESPLARAPAGGAAYVKRLCKGLSWREHVESHGSLGAQASPASAAAAEGSATRRARAATSRAARSRRQPGPGADHPQAGAPGGKRAARKWRCAGQVTIQGPAPPRPRAGRRDEAGGARAAPLLLPPPPAAMETGKDGARRGTQSPERKRRSPVPRAPSTKLRPAAAARAMDPVAAEAPGEAFLARRRPEGGGGSARPRYSLLAEIGRGSYGVVYEAVAGRSGARVAVKKIRCDAPENVELALAEFWALTSLKRRHQNVVQFEECVLQRNGLAQRMSHGNKSSQLYLRLVETSLKGERILGYAEEPCYLWFVMEFCEGGDLNQYVLSRRPDPATNKSFMLQLTSAIAFLHKNHIVHRDLKPDNILITERSGTPILKVADFGLSKVCAGLAPRGKEGNQDNKNVNVNKYWLSSACGSDFYMAPEVWEGHYTAKADIFALGIIIWAMIERITFIDSETKKELLGTYIKQGTEIVPVGEALLENPKMELHIPQKRRTSMSEGIKQLLKDMLAANPQDRPDAFELETRMDQVTCAA'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGHQESPLARAPAGGAAYVKRLCKGLSWREHVESHGSLGAQASPASAAAAEGSATRRARAATSRAARSRRQPGPGADHPQAGAPGGKRAARKWRCAGQVTIQGPAPPRPRAGRRDEAGGARAAPLLLPPPPAAMETGKDGARRGTQSPERKRRSPVPRAPSTKLRPAAAARAMDPVAAEAPGEAFLARRRPEGGGGSARPRYSLLAEIGRGSYGVVYEAVAGRSGARVAVKKIRCDAPENVELALAEFWALTSLKRRHQNVVQFEECVLQRNGLAQRMSHGNKSSQLYLRLVETSLKGERILGYAEEPCYLWFVMEFCEGGDLNQYVLSRRPDPATNKSFMLQLTSAIAFLHKNHIVHRDLKPDNILITERSGTPILKVADFGLSKVCAGLAPRGKEGNQDNKNVNVNKYWLSSACGSDFYMAPEVWEGHYTAKADIFALGIIIWAMIERITFIDSETKKELLGTYIKQGTEIVPVGEALLENPKMELHIPQKRRTSMSEGIKQLLKDMLAANPQDRPDAFELETRMDQVTCAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHQESPLARAPAGGAAYVKRLCKGLSWREHVESHGSLGAQASPASAAAAEGSATRRARAATSRAARSRRQPGPGADHPQAGAPGGKRAARKWRCAGQVTIQGPAPPRPRAGRRDEAGGARAAPLLLPPPPAAMETGKDGARRGTQSPERKRRSPVPRAPSTKLRPAAAARAMDPVAAEAPGEAFLARRRPEGGGGSARPRYSLLAEIGRGSYGVVYEAVAGRSGARVAVKKIRCDAPENVELALAEFWALTSLKRRHQNVVQFEECVLQRNGLAQRMSHGNKSSQLYLRLVETSLKGERILGYAEEPCYLWFVMEFCEGGDLNQYVLSRRPDPATNKSFMLQLTSAIAFLHKNHIVHRDLKPDNILITERSGTPILKVADFGLSKVCAGLAPRGKEGNQDNKNVNVNKYWLSSACGSDFYMAPEVWEGHYTAKADIFALGIIIWAMIERITFIDSETKKELLGTYIKQGTEIVPVGEALLENPKMELHIPQKRRTSMSEGIKQLLKDMLAANPQDRPDAFELETRMDQVTCAA\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein 'MGHQESPLARAPAGGAAYVKRLCKGLSWREHVESHGSLGAQASPASAAAAEGSATRRARAATSRAARSRRQPGPGADHPQAGAPGGKRAARKWRCAGQVTIQGPAPPRPRAGRRDEAGGARAAPLLLPPPPAAMETGKDGARRGTQSPERKRRSPVPRAPSTKLRPAAAARAMDPVAAEAPGEAFLARRRPEGGGGSARPRYSLLAEIGRGSYGVVYEAVAGRSGARVAVKKIRCDAPENVELALAEFWALTSLKRRHQNVVQFEECVLQRNGLAQRMSHGNKSSQLYLRLVETSLKGERILGYAEEPCYLWFVMEFCEGGDLNQYVLSRRPDPATNKSFMLQLTSAIAFLHKNHIVHRDLKPDNILITERSGTPILKVADFGLSKVCAGLAPRGKEGNQDNKNVNVNKYWLSSACGSDFYMAPEVWEGHYTAKADIFALGIIIWAMIERITFIDSETKKELLGTYIKQGTEIVPVGEALLENPKMELHIPQKRRTSMSEGIKQLLKDMLAANPQDRPDAFELETRMDQVTCAA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an affinity estimate for the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' with the target protein 'EESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALLPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTQWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGNSEIDQINKVFKELGTPSEKIWPGYSELPVVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"EESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALLPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTQWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGNSEIDQINKVFKELGTPSEKIWPGYSELPVVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"EESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALLPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTQWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGNSEIDQINKVFKELGTPSEKIWPGYSELPVVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 1000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'EESEEEEEEEEEEEEETGSNSEEASEQSAEEVSEEEMSEDEERENENHLLVVPESRFDRDSGESEEAEEEVGEGTPQSSALTEGDYVPDSPALLPIELKQELPKYLPALQGCRSVEEFQCLNRIEEGTYGVVYRAKDKKTDEIVALKRLKMEKEKEGFPITSLREINTILKAQHPNIVTVREIVVGSNMDKIYIVMNYVEHDLKSLMETMKQPFLPGEVKTLMIQLLRGVKHLHDNWILHRDLKTSNLLLSHAGILKVGDFGLAREYGSPLKAYTPVVVTQWYRAPELLLGAKEYSTAVDMWSVGCIFGELLTQKPLFPGNSEIDQINKVFKELGTPSEKIWPGYSELPVVKKMTFSEHPYNNLRKRFGALLSDQGFDLMNKFLTYFPGRRISAEDGLKHEYFRETPLPIDPSMFPTWPAKSEQQRVKRGTSPRPPEGGLGYSQLGDDDLKETGFHLTTTNQGASAAGPGFSLKF' is 1000.0."
            },
            {
                "from": "human",
                "value": "Assess the predicted value of the affinity between the drug above and the target protein 'MLKFQEAAKCVSGSTAISTYPKTLIARRYVLQQKLGSGSFGTVYLVSDKKAKRGEELKVLKEISVGELNPNETVQANLEAQLLSKLDHPAIVKFHASFVEQDNFCIITEYCEGRDLDDKIQEYKQAGKIFPENQIIEWFIQLLLGVDYMHERRILHRDLKSKNVFLKNNLLKIGDFGVSRLLMGSCDLATTLTGTPHYMSPEALKHQGYDTKSDIWSLACILYEMCCMNHAFAGSNFLSIVLKIVEGDTPSLPERYPKELNAIMESMLNKNPSLRPSAIEILKIPYLDEQLQNLMCRYSEMTLEDKNLDCQKEAAHIINAMQKRIHLQTLRALSEVQKMTPRERMRLRKLQAADEKARKLKKIVEEKYEENSKRMQELRSRNFQQLSVDVLHEKTHLKGMEEKEEQPEGRLSCSPQDEDEERWQGREEESDEPTLENLPESQPIPSMDLHELESIVEDATSDLGYHEIPEDPLVAEEYYADAFDSYCEESDEEEEEIALERPEKEIRNEGSQPAYRTNQQDSDIEALARCLENVLGCTSLDTKTITTMAEDMSPGPPIFNSVMARTKMKRMRESAMQKLGTEVFEEVYNYLKRARHQNASEAEIRECLEKVVPQASDCFEVDQLLYFEEQLLITMGKEPTLQNHL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLKFQEAAKCVSGSTAISTYPKTLIARRYVLQQKLGSGSFGTVYLVSDKKAKRGEELKVLKEISVGELNPNETVQANLEAQLLSKLDHPAIVKFHASFVEQDNFCIITEYCEGRDLDDKIQEYKQAGKIFPENQIIEWFIQLLLGVDYMHERRILHRDLKSKNVFLKNNLLKIGDFGVSRLLMGSCDLATTLTGTPHYMSPEALKHQGYDTKSDIWSLACILYEMCCMNHAFAGSNFLSIVLKIVEGDTPSLPERYPKELNAIMESMLNKNPSLRPSAIEILKIPYLDEQLQNLMCRYSEMTLEDKNLDCQKEAAHIINAMQKRIHLQTLRALSEVQKMTPRERMRLRKLQAADEKARKLKKIVEEKYEENSKRMQELRSRNFQQLSVDVLHEKTHLKGMEEKEEQPEGRLSCSPQDEDEERWQGREEESDEPTLENLPESQPIPSMDLHELESIVEDATSDLGYHEIPEDPLVAEEYYADAFDSYCEESDEEEEEIALERPEKEIRNEGSQPAYRTNQQDSDIEALARCLENVLGCTSLDTKTITTMAEDMSPGPPIFNSVMARTKMKRMRESAMQKLGTEVFEEVYNYLKRARHQNASEAEIRECLEKVVPQASDCFEVDQLLYFEEQLLITMGKEPTLQNHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLKFQEAAKCVSGSTAISTYPKTLIARRYVLQQKLGSGSFGTVYLVSDKKAKRGEELKVLKEISVGELNPNETVQANLEAQLLSKLDHPAIVKFHASFVEQDNFCIITEYCEGRDLDDKIQEYKQAGKIFPENQIIEWFIQLLLGVDYMHERRILHRDLKSKNVFLKNNLLKIGDFGVSRLLMGSCDLATTLTGTPHYMSPEALKHQGYDTKSDIWSLACILYEMCCMNHAFAGSNFLSIVLKIVEGDTPSLPERYPKELNAIMESMLNKNPSLRPSAIEILKIPYLDEQLQNLMCRYSEMTLEDKNLDCQKEAAHIINAMQKRIHLQTLRALSEVQKMTPRERMRLRKLQAADEKARKLKKIVEEKYEENSKRMQELRSRNFQQLSVDVLHEKTHLKGMEEKEEQPEGRLSCSPQDEDEERWQGREEESDEPTLENLPESQPIPSMDLHELESIVEDATSDLGYHEIPEDPLVAEEYYADAFDSYCEESDEEEEEIALERPEKEIRNEGSQPAYRTNQQDSDIEALARCLENVLGCTSLDTKTITTMAEDMSPGPPIFNSVMARTKMKRMRESAMQKLGTEVFEEVYNYLKRARHQNASEAEIRECLEKVVPQASDCFEVDQLLYFEEQLLITMGKEPTLQNHL\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MLKFQEAAKCVSGSTAISTYPKTLIARRYVLQQKLGSGSFGTVYLVSDKKAKRGEELKVLKEISVGELNPNETVQANLEAQLLSKLDHPAIVKFHASFVEQDNFCIITEYCEGRDLDDKIQEYKQAGKIFPENQIIEWFIQLLLGVDYMHERRILHRDLKSKNVFLKNNLLKIGDFGVSRLLMGSCDLATTLTGTPHYMSPEALKHQGYDTKSDIWSLACILYEMCCMNHAFAGSNFLSIVLKIVEGDTPSLPERYPKELNAIMESMLNKNPSLRPSAIEILKIPYLDEQLQNLMCRYSEMTLEDKNLDCQKEAAHIINAMQKRIHLQTLRALSEVQKMTPRERMRLRKLQAADEKARKLKKIVEEKYEENSKRMQELRSRNFQQLSVDVLHEKTHLKGMEEKEEQPEGRLSCSPQDEDEERWQGREEESDEPTLENLPESQPIPSMDLHELESIVEDATSDLGYHEIPEDPLVAEEYYADAFDSYCEESDEEEEEIALERPEKEIRNEGSQPAYRTNQQDSDIEALARCLENVLGCTSLDTKTITTMAEDMSPGPPIFNSVMARTKMKRMRESAMQKLGTEVFEEVYNYLKRARHQNASEAEIRECLEKVVPQASDCFEVDQLLYFEEQLLITMGKEPTLQNHL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value do you project for the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein 'MAHLRGFANQHSRVDPEELFTKLDRIGKGSFGEVYKGIDNHTKEVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYITRYFGSYLKSTKLWIIMEYLGGGSALDLLKPGPLEETYIATILREILKGLDYLHSERKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDFKADIWSLGITAIELAKGEPPNSDLHPMRVLFLIPKNSPPTLEGQHSKPFKEFVEACLNKDPRFRPTAKELLKHKFITRYTKKTSFLTELIDRYKRWKSEGHGEESSSEDSDIDGEAEDGEQGPIWTFPPTIRPSPHSKLHKGTALHSSQKPAEPVKRQPRSQCLSTLVRPVFGELKEKHKQSGGSVGALEELENAFSLAEESCPGISDKLMVHLVERVQRFSHNRNHLTSTR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"MAHLRGFANQHSRVDPEELFTKLDRIGKGSFGEVYKGIDNHTKEVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYITRYFGSYLKSTKLWIIMEYLGGGSALDLLKPGPLEETYIATILREILKGLDYLHSERKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDFKADIWSLGITAIELAKGEPPNSDLHPMRVLFLIPKNSPPTLEGQHSKPFKEFVEACLNKDPRFRPTAKELLKHKFITRYTKKTSFLTELIDRYKRWKSEGHGEESSSEDSDIDGEAEDGEQGPIWTFPPTIRPSPHSKLHKGTALHSSQKPAEPVKRQPRSQCLSTLVRPVFGELKEKHKQSGGSVGALEELENAFSLAEESCPGISDKLMVHLVERVQRFSHNRNHLTSTR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAHLRGFANQHSRVDPEELFTKLDRIGKGSFGEVYKGIDNHTKEVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYITRYFGSYLKSTKLWIIMEYLGGGSALDLLKPGPLEETYIATILREILKGLDYLHSERKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDFKADIWSLGITAIELAKGEPPNSDLHPMRVLFLIPKNSPPTLEGQHSKPFKEFVEACLNKDPRFRPTAKELLKHKFITRYTKKTSFLTELIDRYKRWKSEGHGEESSSEDSDIDGEAEDGEQGPIWTFPPTIRPSPHSKLHKGTALHSSQKPAEPVKRQPRSQCLSTLVRPVFGELKEKHKQSGGSVGALEELENAFSLAEESCPGISDKLMVHLVERVQRFSHNRNHLTSTR\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 18.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MAHLRGFANQHSRVDPEELFTKLDRIGKGSFGEVYKGIDNHTKEVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSPYITRYFGSYLKSTKLWIIMEYLGGGSALDLLKPGPLEETYIATILREILKGLDYLHSERKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIKQSAYDFKADIWSLGITAIELAKGEPPNSDLHPMRVLFLIPKNSPPTLEGQHSKPFKEFVEACLNKDPRFRPTAKELLKHKFITRYTKKTSFLTELIDRYKRWKSEGHGEESSSEDSDIDGEAEDGEQGPIWTFPPTIRPSPHSKLHKGTALHSSQKPAEPVKRQPRSQCLSTLVRPVFGELKEKHKQSGGSVGALEELENAFSLAEESCPGISDKLMVHLVERVQRFSHNRNHLTSTR' is 18.0."
            },
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug above and the target protein 'MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1'-target protein 'MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\"], \"target_seq\": \"MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT\", \"result\": [{\"smile\": \"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the protein 'MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT' is 10000.0."
            },
            {
                "from": "human",
                "value": "Infer the expected affinity of the drug above towards the target protein 'MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL\", \"result\": [{\"smile\": \"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\", \"value\": 7800.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the protein 'MTSSLQRPWRVPWLPWTILLVSTAAASQNQERLCAFKDPYQQDLGIGESRISHENGTILCSKGSTCYGLWEKSKGDINLVKQGCWSHIGDPQECHYEECVVTTTPPSIQNGTYRFCCCSTDLCNVNFTENFPPPDTTPLSPPHSFNRDETIIIALASVSVLAVLIVALCFGYRMLTGDRKQGLHSMNMMEAAASEPSLDLDNLKLLELIGRGRYGAVYKGSLDERPVAVKVFSFANRQNFINEKNIYRVPLMEHDNIARFIVGDERVTADGRMEYLLVMEYYPNGSLCKYLSLHTSDWVSSCRLAHSVTRGLAYLHTELPRGDHYKPAISHRDLNSRNVLVKNDGTCVISDFGLSMRLTGNRLVRPGEEDNAAISEVGTIRYMAPEVLEGAVNLRDCESALKQVDMYALGLIYWEIFMRCTDLFPGESVPEYQMAFQTEVGNHPTFEDMQVLVSREKQRPKFPEAWKENSLAVRSLKETIEDCWDQDAEARLTAQCAEERMAELMMIWERNKSVSPTVNPMSTAMQNERNLSHNRRVPKIGPYPDYSSSSYIEDSIHHTDSIVKNISSEHSMSSTPLTIGEKNRNSINYERQQAQARIPSPETSVTSLSTNTTTTNTTGLTPSTGMTTISEMPYPDETNLHTTNVAQSIGPTPVCLQLTEEDLETNKLDPKEVDKNLKESSDENLMEHSLKQFSGPDPLSSTSSSLLYPLIKLAVEATGQQDFTQTANGQACLIPDVLPTQIYPLPKQQNLPKRPTSLPLNTKNSTKEPRLKFGSKHKSNLKQVETGVAKMNTINAAEPHVVTVTMNGVAGRNHSVNSHAATTQYANGTVLSGQTTNIVTHRAQEMLQNQFIGEDTRLNINSSPDEHEPLLRREQQAGHDEGVLDRLVDRRERPLEGGRTNSNNNNSNPCSEQDVLAQGVPSTAADPGPSKPRRAQRPNSLDLSATNVLDGSSIQIGESTQDGKSGSGEKIKKRVKTPYSLKRWRPSTWVISTESLDCEVNNNGSNRAVHSKSSTAVYLAEGGTATTMVSKDIGMNCL' is 7800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the target protein 'MALRRSMGRPGLPPLPLPPPPRLGLLLAALASLLLPESAAAGLKLMGAPVKLTVSQGQPVKLNCSVEGMEEPDIQWVKDGAVVQNLDQLYIPVSEQHWIGFLSLKSVERSDAGRYWCQVEDGGETEISQPVWLTVEGVPFFTVEPKDLAVPPNAPFQLSCEAVGPPEPVTIVWWRGTTKIGGPAPSPSVLNVTGVTQSTMFSCEAHNLKGLASSRTATVHLQALPAAPFNITVTKLSSSNASVAWMPGADGRALLQSCTVQVTQAPGGWEVLAVVVPVPPFTCLLRDLVPATNYSLRVRCANALGPSPYADWVPFQTKGLAPASAPQNLHAIRTDSGLILEWEEVIPEAPLEGPLGPYKLSWVQDNGTQDELTVEGTRANLTGWDPQKDLIVRVCVSNAVGCGPWSQPLVVSSHDRAGQQGPPHSRTSWVPVVLGVLTALVTAAALALILLRKRRKETRFGQAFDSVMARGEPAVHFRAARSFNRERPERIEATLDSLGISDELKEKLEDVLIPEQQFTLGRMLGKGEFGSVREAQLKQEDGSFVKVAVKMLKADIIASSDIEEFLREAACMKEFDHPHVAKLVGVSLRSRAKGRLPIPMVILPFMKHGDLHAFLLASRIGENPFNLPLQTLIRFMVDIACGMEYLSSRNFIHRDLAARNCMLAEDMTVCVADFGLSRKIYSGDYYRQGCASKLPVKWLALESLADNLYTVQSDVWAFGVTMWEIMTRGQTPYAGIENAEIYNYLIGGNRLKQPPECMEDVYDLMYQCWSADPKQRPSFTCLRMELENILGQLSVLSASQDPLYINIERAEEPTAGGSLELPGRDQPYSGAGDGSGMGAVGGTPSDCRYILTPGGLAEQPGQAEHQPESPLNETQRLLLLQQGLLPHSSC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"MALRRSMGRPGLPPLPLPPPPRLGLLLAALASLLLPESAAAGLKLMGAPVKLTVSQGQPVKLNCSVEGMEEPDIQWVKDGAVVQNLDQLYIPVSEQHWIGFLSLKSVERSDAGRYWCQVEDGGETEISQPVWLTVEGVPFFTVEPKDLAVPPNAPFQLSCEAVGPPEPVTIVWWRGTTKIGGPAPSPSVLNVTGVTQSTMFSCEAHNLKGLASSRTATVHLQALPAAPFNITVTKLSSSNASVAWMPGADGRALLQSCTVQVTQAPGGWEVLAVVVPVPPFTCLLRDLVPATNYSLRVRCANALGPSPYADWVPFQTKGLAPASAPQNLHAIRTDSGLILEWEEVIPEAPLEGPLGPYKLSWVQDNGTQDELTVEGTRANLTGWDPQKDLIVRVCVSNAVGCGPWSQPLVVSSHDRAGQQGPPHSRTSWVPVVLGVLTALVTAAALALILLRKRRKETRFGQAFDSVMARGEPAVHFRAARSFNRERPERIEATLDSLGISDELKEKLEDVLIPEQQFTLGRMLGKGEFGSVREAQLKQEDGSFVKVAVKMLKADIIASSDIEEFLREAACMKEFDHPHVAKLVGVSLRSRAKGRLPIPMVILPFMKHGDLHAFLLASRIGENPFNLPLQTLIRFMVDIACGMEYLSSRNFIHRDLAARNCMLAEDMTVCVADFGLSRKIYSGDYYRQGCASKLPVKWLALESLADNLYTVQSDVWAFGVTMWEIMTRGQTPYAGIENAEIYNYLIGGNRLKQPPECMEDVYDLMYQCWSADPKQRPSFTCLRMELENILGQLSVLSASQDPLYINIERAEEPTAGGSLELPGRDQPYSGAGDGSGMGAVGGTPSDCRYILTPGGLAEQPGQAEHQPESPLNETQRLLLLQQGLLPHSSC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALRRSMGRPGLPPLPLPPPPRLGLLLAALASLLLPESAAAGLKLMGAPVKLTVSQGQPVKLNCSVEGMEEPDIQWVKDGAVVQNLDQLYIPVSEQHWIGFLSLKSVERSDAGRYWCQVEDGGETEISQPVWLTVEGVPFFTVEPKDLAVPPNAPFQLSCEAVGPPEPVTIVWWRGTTKIGGPAPSPSVLNVTGVTQSTMFSCEAHNLKGLASSRTATVHLQALPAAPFNITVTKLSSSNASVAWMPGADGRALLQSCTVQVTQAPGGWEVLAVVVPVPPFTCLLRDLVPATNYSLRVRCANALGPSPYADWVPFQTKGLAPASAPQNLHAIRTDSGLILEWEEVIPEAPLEGPLGPYKLSWVQDNGTQDELTVEGTRANLTGWDPQKDLIVRVCVSNAVGCGPWSQPLVVSSHDRAGQQGPPHSRTSWVPVVLGVLTALVTAAALALILLRKRRKETRFGQAFDSVMARGEPAVHFRAARSFNRERPERIEATLDSLGISDELKEKLEDVLIPEQQFTLGRMLGKGEFGSVREAQLKQEDGSFVKVAVKMLKADIIASSDIEEFLREAACMKEFDHPHVAKLVGVSLRSRAKGRLPIPMVILPFMKHGDLHAFLLASRIGENPFNLPLQTLIRFMVDIACGMEYLSSRNFIHRDLAARNCMLAEDMTVCVADFGLSRKIYSGDYYRQGCASKLPVKWLALESLADNLYTVQSDVWAFGVTMWEIMTRGQTPYAGIENAEIYNYLIGGNRLKQPPECMEDVYDLMYQCWSADPKQRPSFTCLRMELENILGQLSVLSASQDPLYINIERAEEPTAGGSLELPGRDQPYSGAGDGSGMGAVGGTPSDCRYILTPGGLAEQPGQAEHQPESPLNETQRLLLLQQGLLPHSSC\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MALRRSMGRPGLPPLPLPPPPRLGLLLAALASLLLPESAAAGLKLMGAPVKLTVSQGQPVKLNCSVEGMEEPDIQWVKDGAVVQNLDQLYIPVSEQHWIGFLSLKSVERSDAGRYWCQVEDGGETEISQPVWLTVEGVPFFTVEPKDLAVPPNAPFQLSCEAVGPPEPVTIVWWRGTTKIGGPAPSPSVLNVTGVTQSTMFSCEAHNLKGLASSRTATVHLQALPAAPFNITVTKLSSSNASVAWMPGADGRALLQSCTVQVTQAPGGWEVLAVVVPVPPFTCLLRDLVPATNYSLRVRCANALGPSPYADWVPFQTKGLAPASAPQNLHAIRTDSGLILEWEEVIPEAPLEGPLGPYKLSWVQDNGTQDELTVEGTRANLTGWDPQKDLIVRVCVSNAVGCGPWSQPLVVSSHDRAGQQGPPHSRTSWVPVVLGVLTALVTAAALALILLRKRRKETRFGQAFDSVMARGEPAVHFRAARSFNRERPERIEATLDSLGISDELKEKLEDVLIPEQQFTLGRMLGKGEFGSVREAQLKQEDGSFVKVAVKMLKADIIASSDIEEFLREAACMKEFDHPHVAKLVGVSLRSRAKGRLPIPMVILPFMKHGDLHAFLLASRIGENPFNLPLQTLIRFMVDIACGMEYLSSRNFIHRDLAARNCMLAEDMTVCVADFGLSRKIYSGDYYRQGCASKLPVKWLALESLADNLYTVQSDVWAFGVTMWEIMTRGQTPYAGIENAEIYNYLIGGNRLKQPPECMEDVYDLMYQCWSADPKQRPSFTCLRMELENILGQLSVLSASQDPLYINIERAEEPTAGGSLELPGRDQPYSGAGDGSGMGAVGGTPSDCRYILTPGGLAEQPGQAEHQPESPLNETQRLLLLQQGLLPHSSC' is 10000.0."
            },
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug above and the target protein 'MEPGRGGTETVGKFEFSRKDLIGHGAFAVVFKGRHREKHDLEVAVKCINKKNLAKSQTLLGKEIKILKELKHENIVALYDFQEMANSVYLVMEYCNGGDLADYLHAMRTLSEDTIRLFLQQIAGAMRLLHSKGIIHRDLKPQNILLSNPAGRRANPNSIRVKIADFGFARYLQSNMMAATLCGSPMYMAPEVIMSQHYDGKADLWSIGTIVYQCLTGKAPFQASSPQDLRLFYEKNKTLVPTIPRETSAPLRQLLLALLQRNHKDRMDFDEFFHHPFLDASPSVRKSPPVPVPSYPSSGSGSSSSSSSTSHLASPPSLGEMQQLQKTLASPADTAGFLHSSRDSGGSKDSSCDTDDFVMVPAQFPGDLVAEAPSAKPPPDSLMCSGSSLVASAGLESHGRTPSPSPPCSSSPSPSGRAGPFSSSRCGASVPIPVPTQVQNYQRIERNLQSPTQFQTPRSSAIRRSGSTSPLGFARASPSPPAHAEHGGVLARKMSLGGGRPYTPSPQVGTIPERPGWSGTPSPQGAEMRGGRSPRPGSSAPEHSPRTSGLGCRLHSAPNLSDLHVVRPKLPKPPTDPLGAVFSPPQASPPQPSHGLQSCRNLRGSPKLPDFLQRNPLPPILGSPTKAVPSFDFPKTPSSQNLLALLARQGVVMTPPRNRTLPDLSEVGPFHGQPLGPGLRPGEDPKGPFGRSFSTSRLTDLLLKAAFGTQAPDPGSTESLQEKPMEIAPSAGFGGSLHPGARAGGTSSPSPVVFTVGSPPSGSTPPQGPRTRMFSAGPTGSASSSARHLVPGPCSEAPAPELPAPGHGCSFADPITANLEGAVTFEAPDLPEETLMEQEHTEILRGLRFTLLFVQHVLEIAALKGSASEAAGGPEYQLQESVVADQISLLSREWGFAEQLVLYLKVAELLSSGLQSAIDQIRAGKLCLSSTVKQVVRRLNELYKASVVSCQGLSLRLQRFFLDKQRLLDRIHSITAERLIFSHAVQMVQSAALDEMFQHREGCVPRYHKALLLLEGLQHMLSDQADIENVTKCKLCIERRLSALLTGICA'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEPGRGGTETVGKFEFSRKDLIGHGAFAVVFKGRHREKHDLEVAVKCINKKNLAKSQTLLGKEIKILKELKHENIVALYDFQEMANSVYLVMEYCNGGDLADYLHAMRTLSEDTIRLFLQQIAGAMRLLHSKGIIHRDLKPQNILLSNPAGRRANPNSIRVKIADFGFARYLQSNMMAATLCGSPMYMAPEVIMSQHYDGKADLWSIGTIVYQCLTGKAPFQASSPQDLRLFYEKNKTLVPTIPRETSAPLRQLLLALLQRNHKDRMDFDEFFHHPFLDASPSVRKSPPVPVPSYPSSGSGSSSSSSSTSHLASPPSLGEMQQLQKTLASPADTAGFLHSSRDSGGSKDSSCDTDDFVMVPAQFPGDLVAEAPSAKPPPDSLMCSGSSLVASAGLESHGRTPSPSPPCSSSPSPSGRAGPFSSSRCGASVPIPVPTQVQNYQRIERNLQSPTQFQTPRSSAIRRSGSTSPLGFARASPSPPAHAEHGGVLARKMSLGGGRPYTPSPQVGTIPERPGWSGTPSPQGAEMRGGRSPRPGSSAPEHSPRTSGLGCRLHSAPNLSDLHVVRPKLPKPPTDPLGAVFSPPQASPPQPSHGLQSCRNLRGSPKLPDFLQRNPLPPILGSPTKAVPSFDFPKTPSSQNLLALLARQGVVMTPPRNRTLPDLSEVGPFHGQPLGPGLRPGEDPKGPFGRSFSTSRLTDLLLKAAFGTQAPDPGSTESLQEKPMEIAPSAGFGGSLHPGARAGGTSSPSPVVFTVGSPPSGSTPPQGPRTRMFSAGPTGSASSSARHLVPGPCSEAPAPELPAPGHGCSFADPITANLEGAVTFEAPDLPEETLMEQEHTEILRGLRFTLLFVQHVLEIAALKGSASEAAGGPEYQLQESVVADQISLLSREWGFAEQLVLYLKVAELLSSGLQSAIDQIRAGKLCLSSTVKQVVRRLNELYKASVVSCQGLSLRLQRFFLDKQRLLDRIHSITAERLIFSHAVQMVQSAALDEMFQHREGCVPRYHKALLLLEGLQHMLSDQADIENVTKCKLCIERRLSALLTGICA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEPGRGGTETVGKFEFSRKDLIGHGAFAVVFKGRHREKHDLEVAVKCINKKNLAKSQTLLGKEIKILKELKHENIVALYDFQEMANSVYLVMEYCNGGDLADYLHAMRTLSEDTIRLFLQQIAGAMRLLHSKGIIHRDLKPQNILLSNPAGRRANPNSIRVKIADFGFARYLQSNMMAATLCGSPMYMAPEVIMSQHYDGKADLWSIGTIVYQCLTGKAPFQASSPQDLRLFYEKNKTLVPTIPRETSAPLRQLLLALLQRNHKDRMDFDEFFHHPFLDASPSVRKSPPVPVPSYPSSGSGSSSSSSSTSHLASPPSLGEMQQLQKTLASPADTAGFLHSSRDSGGSKDSSCDTDDFVMVPAQFPGDLVAEAPSAKPPPDSLMCSGSSLVASAGLESHGRTPSPSPPCSSSPSPSGRAGPFSSSRCGASVPIPVPTQVQNYQRIERNLQSPTQFQTPRSSAIRRSGSTSPLGFARASPSPPAHAEHGGVLARKMSLGGGRPYTPSPQVGTIPERPGWSGTPSPQGAEMRGGRSPRPGSSAPEHSPRTSGLGCRLHSAPNLSDLHVVRPKLPKPPTDPLGAVFSPPQASPPQPSHGLQSCRNLRGSPKLPDFLQRNPLPPILGSPTKAVPSFDFPKTPSSQNLLALLARQGVVMTPPRNRTLPDLSEVGPFHGQPLGPGLRPGEDPKGPFGRSFSTSRLTDLLLKAAFGTQAPDPGSTESLQEKPMEIAPSAGFGGSLHPGARAGGTSSPSPVVFTVGSPPSGSTPPQGPRTRMFSAGPTGSASSSARHLVPGPCSEAPAPELPAPGHGCSFADPITANLEGAVTFEAPDLPEETLMEQEHTEILRGLRFTLLFVQHVLEIAALKGSASEAAGGPEYQLQESVVADQISLLSREWGFAEQLVLYLKVAELLSSGLQSAIDQIRAGKLCLSSTVKQVVRRLNELYKASVVSCQGLSLRLQRFFLDKQRLLDRIHSITAERLIFSHAVQMVQSAALDEMFQHREGCVPRYHKALLLLEGLQHMLSDQADIENVTKCKLCIERRLSALLTGICA\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 2800.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MEPGRGGTETVGKFEFSRKDLIGHGAFAVVFKGRHREKHDLEVAVKCINKKNLAKSQTLLGKEIKILKELKHENIVALYDFQEMANSVYLVMEYCNGGDLADYLHAMRTLSEDTIRLFLQQIAGAMRLLHSKGIIHRDLKPQNILLSNPAGRRANPNSIRVKIADFGFARYLQSNMMAATLCGSPMYMAPEVIMSQHYDGKADLWSIGTIVYQCLTGKAPFQASSPQDLRLFYEKNKTLVPTIPRETSAPLRQLLLALLQRNHKDRMDFDEFFHHPFLDASPSVRKSPPVPVPSYPSSGSGSSSSSSSTSHLASPPSLGEMQQLQKTLASPADTAGFLHSSRDSGGSKDSSCDTDDFVMVPAQFPGDLVAEAPSAKPPPDSLMCSGSSLVASAGLESHGRTPSPSPPCSSSPSPSGRAGPFSSSRCGASVPIPVPTQVQNYQRIERNLQSPTQFQTPRSSAIRRSGSTSPLGFARASPSPPAHAEHGGVLARKMSLGGGRPYTPSPQVGTIPERPGWSGTPSPQGAEMRGGRSPRPGSSAPEHSPRTSGLGCRLHSAPNLSDLHVVRPKLPKPPTDPLGAVFSPPQASPPQPSHGLQSCRNLRGSPKLPDFLQRNPLPPILGSPTKAVPSFDFPKTPSSQNLLALLARQGVVMTPPRNRTLPDLSEVGPFHGQPLGPGLRPGEDPKGPFGRSFSTSRLTDLLLKAAFGTQAPDPGSTESLQEKPMEIAPSAGFGGSLHPGARAGGTSSPSPVVFTVGSPPSGSTPPQGPRTRMFSAGPTGSASSSARHLVPGPCSEAPAPELPAPGHGCSFADPITANLEGAVTFEAPDLPEETLMEQEHTEILRGLRFTLLFVQHVLEIAALKGSASEAAGGPEYQLQESVVADQISLLSREWGFAEQLVLYLKVAELLSSGLQSAIDQIRAGKLCLSSTVKQVVRRLNELYKASVVSCQGLSLRLQRFFLDKQRLLDRIHSITAERLIFSHAVQMVQSAALDEMFQHREGCVPRYHKALLLLEGLQHMLSDQADIENVTKCKLCIERRLSALLTGICA' is 2800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1''s affinity for the target protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\"], \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 2700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV' is 2700.0."
            },
            {
                "from": "human",
                "value": "How would you define the affinity between the drug above and the target protein 'MSSNCTSTTAVAVAPLSASKTKTKKKHFVCQKVKLFRASEPILSVLMWGVNHTINELSNVPVPVMLMPDDFKAYSKIKVDNHLFNKENLPSRFKFKEYCPMVFRNLRERFGIDDQDYQNSVTRSAPINSDSQGRCGTRFLTTYDRRFVIKTVSSEDVAEMHNILKKYHQFIVECHGNTLLPQFLGMYRLTVDGVETYMVVTRNVFSHRLTVHRKYDLKGSTVAREASDKEKAKDLPTFKDNDFLNEGQKLHVGEESKKNFLEKLKRDVEFLAQLKIMDYSLLVGIHDVDRAEQEEMEVEERAEDEECENDGVGGNLLCSYGTPPDSPGNLLSFPRFFGPGEFDPSVDVYAMKSHESSPKKEVYFMAIIDILTPYDTKKKAAHAAKTVKHGAGAEISTVNPEQYSKRFNEFMSNILT'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSNCTSTTAVAVAPLSASKTKTKKKHFVCQKVKLFRASEPILSVLMWGVNHTINELSNVPVPVMLMPDDFKAYSKIKVDNHLFNKENLPSRFKFKEYCPMVFRNLRERFGIDDQDYQNSVTRSAPINSDSQGRCGTRFLTTYDRRFVIKTVSSEDVAEMHNILKKYHQFIVECHGNTLLPQFLGMYRLTVDGVETYMVVTRNVFSHRLTVHRKYDLKGSTVAREASDKEKAKDLPTFKDNDFLNEGQKLHVGEESKKNFLEKLKRDVEFLAQLKIMDYSLLVGIHDVDRAEQEEMEVEERAEDEECENDGVGGNLLCSYGTPPDSPGNLLSFPRFFGPGEFDPSVDVYAMKSHESSPKKEVYFMAIIDILTPYDTKKKAAHAAKTVKHGAGAEISTVNPEQYSKRFNEFMSNILT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSNCTSTTAVAVAPLSASKTKTKKKHFVCQKVKLFRASEPILSVLMWGVNHTINELSNVPVPVMLMPDDFKAYSKIKVDNHLFNKENLPSRFKFKEYCPMVFRNLRERFGIDDQDYQNSVTRSAPINSDSQGRCGTRFLTTYDRRFVIKTVSSEDVAEMHNILKKYHQFIVECHGNTLLPQFLGMYRLTVDGVETYMVVTRNVFSHRLTVHRKYDLKGSTVAREASDKEKAKDLPTFKDNDFLNEGQKLHVGEESKKNFLEKLKRDVEFLAQLKIMDYSLLVGIHDVDRAEQEEMEVEERAEDEECENDGVGGNLLCSYGTPPDSPGNLLSFPRFFGPGEFDPSVDVYAMKSHESSPKKEVYFMAIIDILTPYDTKKKAAHAAKTVKHGAGAEISTVNPEQYSKRFNEFMSNILT\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'MSSNCTSTTAVAVAPLSASKTKTKKKHFVCQKVKLFRASEPILSVLMWGVNHTINELSNVPVPVMLMPDDFKAYSKIKVDNHLFNKENLPSRFKFKEYCPMVFRNLRERFGIDDQDYQNSVTRSAPINSDSQGRCGTRFLTTYDRRFVIKTVSSEDVAEMHNILKKYHQFIVECHGNTLLPQFLGMYRLTVDGVETYMVVTRNVFSHRLTVHRKYDLKGSTVAREASDKEKAKDLPTFKDNDFLNEGQKLHVGEESKKNFLEKLKRDVEFLAQLKIMDYSLLVGIHDVDRAEQEEMEVEERAEDEECENDGVGGNLLCSYGTPPDSPGNLLSFPRFFGPGEFDPSVDVYAMKSHESSPKKEVYFMAIIDILTPYDTKKKAAHAAKTVKHGAGAEISTVNPEQYSKRFNEFMSNILT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1''s affinity for the target protein 'MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\", \"value\": 160.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1' and the protein 'MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW' is 160.0."
            },
            {
                "from": "human",
                "value": "What's the predicted value for the drug above's affinity with the target protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1' and the protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' to the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\"], \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 2.3}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' is 2.3."
            },
            {
                "from": "human",
                "value": "How would you assess the drug above's affinity for the target protein 'MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKELQLEEEAAVFPEVAVAEGPFITGENIDTSSDLMLAQMLQMEYDREYDAQLRREEKKFNGDSKVSISFENYRKVHPYEDSDSSEDEVDWQDTRDDPYRPAKPVPTPKKGFIGKGKDITTKHDEVVCGRKNTARMENFAPEFQVGDGIGMDLKLSNHVFNALKQHAYSEERRSARLHEKKEHSTAEKAVDPKTRLLMYKMVNSGMLETITGCISTGKESVVFHAYGGSMEDEKEDSKVIPTECAIKVFKTTLNEFKNRDKYIKDDFRFKDRFSKLNPRKIIRMWAEKEMHNLARMQRAGIPCPTVVLLKKHILVMSFIGHDQVPAPKLKEVKLNSEEMKEAYYQTLHLMRQLYHECTLVHADLSEYNMLWHAGKVWLIDVSQSVEPTHPHGLEFLFRDCRNVSQFFQKGGVKEALSERELFNAVSGLNITADNEADFLAEIEALEKMNEDHVQKNGRKAASFLKDDGDPPLLYDE'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKELQLEEEAAVFPEVAVAEGPFITGENIDTSSDLMLAQMLQMEYDREYDAQLRREEKKFNGDSKVSISFENYRKVHPYEDSDSSEDEVDWQDTRDDPYRPAKPVPTPKKGFIGKGKDITTKHDEVVCGRKNTARMENFAPEFQVGDGIGMDLKLSNHVFNALKQHAYSEERRSARLHEKKEHSTAEKAVDPKTRLLMYKMVNSGMLETITGCISTGKESVVFHAYGGSMEDEKEDSKVIPTECAIKVFKTTLNEFKNRDKYIKDDFRFKDRFSKLNPRKIIRMWAEKEMHNLARMQRAGIPCPTVVLLKKHILVMSFIGHDQVPAPKLKEVKLNSEEMKEAYYQTLHLMRQLYHECTLVHADLSEYNMLWHAGKVWLIDVSQSVEPTHPHGLEFLFRDCRNVSQFFQKGGVKEALSERELFNAVSGLNITADNEADFLAEIEALEKMNEDHVQKNGRKAASFLKDDGDPPLLYDE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKELQLEEEAAVFPEVAVAEGPFITGENIDTSSDLMLAQMLQMEYDREYDAQLRREEKKFNGDSKVSISFENYRKVHPYEDSDSSEDEVDWQDTRDDPYRPAKPVPTPKKGFIGKGKDITTKHDEVVCGRKNTARMENFAPEFQVGDGIGMDLKLSNHVFNALKQHAYSEERRSARLHEKKEHSTAEKAVDPKTRLLMYKMVNSGMLETITGCISTGKESVVFHAYGGSMEDEKEDSKVIPTECAIKVFKTTLNEFKNRDKYIKDDFRFKDRFSKLNPRKIIRMWAEKEMHNLARMQRAGIPCPTVVLLKKHILVMSFIGHDQVPAPKLKEVKLNSEEMKEAYYQTLHLMRQLYHECTLVHADLSEYNMLWHAGKVWLIDVSQSVEPTHPHGLEFLFRDCRNVSQFFQKGGVKEALSERELFNAVSGLNITADNEADFLAEIEALEKMNEDHVQKNGRKAASFLKDDGDPPLLYDE\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the protein 'MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKELQLEEEAAVFPEVAVAEGPFITGENIDTSSDLMLAQMLQMEYDREYDAQLRREEKKFNGDSKVSISFENYRKVHPYEDSDSSEDEVDWQDTRDDPYRPAKPVPTPKKGFIGKGKDITTKHDEVVCGRKNTARMENFAPEFQVGDGIGMDLKLSNHVFNALKQHAYSEERRSARLHEKKEHSTAEKAVDPKTRLLMYKMVNSGMLETITGCISTGKESVVFHAYGGSMEDEKEDSKVIPTECAIKVFKTTLNEFKNRDKYIKDDFRFKDRFSKLNPRKIIRMWAEKEMHNLARMQRAGIPCPTVVLLKKHILVMSFIGHDQVPAPKLKEVKLNSEEMKEAYYQTLHLMRQLYHECTLVHADLSEYNMLWHAGKVWLIDVSQSVEPTHPHGLEFLFRDCRNVSQFFQKGGVKEALSERELFNAVSGLNITADNEADFLAEIEALEKMNEDHVQKNGRKAASFLKDDGDPPLLYDE' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you quantify the predicted affinity for the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the target protein 'MEQPPAPKSKLKKLSEDSLTKQPEEVFDVLEKLGEGSYGSVFKAIHKESGQVVAIKQVPVESDLQEIIKEISIMQQCDSPYVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLIEDEIATILKSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITSIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDDFTDFVKKCLVKNPEQRATATQLLQHPFIKNAKPVSILRDLITEAMEIKAKRHEEQQRELEEEEENSDEDELDSHTMVKTSVESVGTMRATSTMSEGAQTMIEHNSTMLESDLGTMVINSEDEEEEDGTMKRNATSPQVQRPSFMDYFDKQDFKNKSHENCNQNMHEPFPMSKNVFPDNWKVPQDGDFDFLKNLSLEELQMRLKALDPMMEREIEELRQRYTAKRQPILDAMDAKKRRQQNF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MEQPPAPKSKLKKLSEDSLTKQPEEVFDVLEKLGEGSYGSVFKAIHKESGQVVAIKQVPVESDLQEIIKEISIMQQCDSPYVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLIEDEIATILKSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITSIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDDFTDFVKKCLVKNPEQRATATQLLQHPFIKNAKPVSILRDLITEAMEIKAKRHEEQQRELEEEEENSDEDELDSHTMVKTSVESVGTMRATSTMSEGAQTMIEHNSTMLESDLGTMVINSEDEEEEDGTMKRNATSPQVQRPSFMDYFDKQDFKNKSHENCNQNMHEPFPMSKNVFPDNWKVPQDGDFDFLKNLSLEELQMRLKALDPMMEREIEELRQRYTAKRQPILDAMDAKKRRQQNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEQPPAPKSKLKKLSEDSLTKQPEEVFDVLEKLGEGSYGSVFKAIHKESGQVVAIKQVPVESDLQEIIKEISIMQQCDSPYVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLIEDEIATILKSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITSIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDDFTDFVKKCLVKNPEQRATATQLLQHPFIKNAKPVSILRDLITEAMEIKAKRHEEQQRELEEEEENSDEDELDSHTMVKTSVESVGTMRATSTMSEGAQTMIEHNSTMLESDLGTMVINSEDEEEEDGTMKRNATSPQVQRPSFMDYFDKQDFKNKSHENCNQNMHEPFPMSKNVFPDNWKVPQDGDFDFLKNLSLEELQMRLKALDPMMEREIEELRQRYTAKRQPILDAMDAKKRRQQNF\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 56.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MEQPPAPKSKLKKLSEDSLTKQPEEVFDVLEKLGEGSYGSVFKAIHKESGQVVAIKQVPVESDLQEIIKEISIMQQCDSPYVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLIEDEIATILKSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITSIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDDFTDFVKKCLVKNPEQRATATQLLQHPFIKNAKPVSILRDLITEAMEIKAKRHEEQQRELEEEEENSDEDELDSHTMVKTSVESVGTMRATSTMSEGAQTMIEHNSTMLESDLGTMVINSEDEEEEDGTMKRNATSPQVQRPSFMDYFDKQDFKNKSHENCNQNMHEPFPMSKNVFPDNWKVPQDGDFDFLKNLSLEELQMRLKALDPMMEREIEELRQRYTAKRQPILDAMDAKKRRQQNF' is 56.0."
            },
            {
                "from": "human",
                "value": "What is the anticipated binding affinity for the drug above and the target protein 'MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1'-target protein 'MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\"], \"target_seq\": \"MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'MEKYHGLEKIGEGTYGVVYKAQNNYGETFALKKIRLEKEDEGIPSTTIREISILKELKHSNIVKLYDVIHTKKRLVLVFEHLDQDLKKLLDVCEGGLESVTAKSFLLQLLNGIAYCHDRRVLHRDLKPQNLLINREGELKIADFGLARAFGIPVRKYTHEVVTLWYRAPDVLMGSKKYSTTIDIWSVGCIFAEMVNGTPLFPGVSEADQLMRIFRILGTPNSKNWPNVTELPKYDPNFTVYEPLPWESFLKGLDESGIDLLSKMLKLDPNQRITAKQALEHAYFKENN' is 10000.0."
            },
            {
                "from": "human",
                "value": "Estimate the binding strength of the drug above to the target protein 'MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'MFQASMRSPNMEPFKQQKVEDFYDIGEELGSGQFAIVKKCREKSTGLEYAAKFIKKRQSRASRRGVSREEIEREVSILRQVLHHNVITLHDVYENRTDVVLILELVSGGELFDFLAQKESLSEEEATSFIKQILDGVNYLHTKKIAHFDLKPENIMLLDKNIPIPHIKLIDFGLAHEIEDGVEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGDTKQETLANITAVSYDFDEEFFSQTSELAKDFIRKLLVKETRKRLTIQEALRHPWITPVDNQQAMVRRESVVNLENFRKQYVRRRWKLSFSIVSLCNHLTRSLMKKVHLRPDEDLRNCESDTEEDIARRKALHPRRRSSTS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug 'CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21' and the target protein 'MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21\"], \"target_seq\": \"MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP\", \"result\": [{\"smile\": \"CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21\", \"value\": 129.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21' and the protein 'MASNSSSCPTPGGGHLNGYPVPPYAFFFPPMLGGLSPPGALTTLQHQLPVSGYSTPSPATIETQSSSSEEIVPSPPSPPPLPRIYKPCFVCQDKSSGYHYGVSACEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKESVRNDRNKKKKEVPKPECSESYTLTPEVGELIEKVRKAHQETFPALCQLGKYTTNNSSEQRVSLDIDLWDKFSELSTKCIIKTVEFAKQLPGFTTLTIADQITLLKAACLDILILRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFANQLLPLEMDDAETGLLSAICLICGDRQDLEQPDRVDMLQEPLLEALKVYVRKRRPSRPHMFPKMLMKITDLRSISAKGAERVITLKMEIPGSMPPLIQEMLENSEGLDTLSGQPGGGGRDGGGLAPPPGSCSPSLSPSSNRSSPATHSP' is 129.0."
            },
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug above to the target protein 'MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA\", \"result\": [{\"smile\": \"CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21\", \"value\": 104.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CC=C(c2cccs2)c2cc(C#Cc3ccc(C(=O)O)cc3)ccc21' and the protein 'MATNKERLFAAGALGPGSGYPGAGFPFAFPGALRGSPPFEMLSPSFRGLGQPDLPKEMASLSVETQSTSSEEMVPSSPSPPPPPRVYKPCFVCNDKSSGYHYGVSSCEGCKGFFRRSIQKNMVYTCHRDKNCIINKVTRNRCQYCRLQKCFEVGMSKEAVRNDRNKKKKEVKEEGSPDSYELSPQLEELITKVSKAHQETFPSLCQLGKYTTNSSADHRVQLDLGLWDKFSELATKCIIKIVEFAKRLPGFTGLSIADQITLLKAACLDILMLRICTRYTPEQDTMTFSDGLTLNRTQMHNAGFGPLTDLVFAFAGQLLPLEMDDTETGLLSAICLICGDRMDLEEPEKVDKLQEPLLEALRLYARRRRPSQPYMFPRMLMKITDLRGISTKGAERAITLKMEIPGPMPPLIREMLENPEMFEDDSSQPGPHPNASSEDEVPGGQGKGGLKSPA' is 104.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug 'Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1' and the target protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1\"], \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\", \"result\": [{\"smile\": \"Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1\", \"value\": 48.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1' and the protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA' is 48.0."
            },
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug above-target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1\", \"value\": 6.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nn(C)cc1-c1nc2ncc(Cl)c(N3CCN(Cc4ccc(Cl)cc4)CC3)c2[nH]1' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 6.2."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug 'CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1' and the target protein 'MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\"], \"target_seq\": \"MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH\", \"result\": [{\"smile\": \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1' and the protein 'MFGKKKKKIEISGPSNFEHRVHTGFDPQEQKFTGLPQQWHSLLADTANRPKPMVDPSCITPIQLAPMKTIVRGNKPCKETSINGLLEDFDNISVTRSNSLRKESPPTPDQGASSHGPGHAEENGFITFSQYSSESDTTADYTTEKYREKSLYGDDLDPYYRGSHAAKQNGHVMKMKHGEAYYSEVKPLKSDFARFSADYHSHLDSLSKPSEYSDLKWEYQRASSSSPLDYSFQFTPSRTAGTSGCSKESLAYSESEWGPSLDDYDRRPKSSYLNQTSPQPTMRQRSRSGSGLQEPMMPFGASAFKTHPQGHSYNSYTYPRLSEPTMCIPKVDYDRAQMVLSPPLSGSDTYPRGPAKLPQSQSKSGYSSSSHQYPSGYHKATLYHHPSLQSSSQYISTASYLSSLSLSSSTYPPPSWGSSSDQQPSRVSHEQFRAALQLVVSPGDPREYLANFIKIGEGSTGIVCIATEKHTGKQVAVKKMDLRKQQRRELLFNEVVIMRDYHHDNVVDMYSSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIATVCLSVLRALSYLHNQGVIHRDIKSDSILLTSDGRIKLSDFGFCAQVSKEVPKRKSLVGTPYWMAPEVISRLPYGTEVDIWSLGIMVIEMIDGEPPYFNEPPLQAMRRIRDSLPPRVKDLHKVSSVLRGFLDLMLVREPSQRATAQELLGHPFLKLAGPPSCIVPLMRQYRHH' is 10000.0."
            },
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug above and the target protein 'MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY\", \"result\": [{\"smile\": \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1' and the protein 'MEAPLRPAADILRRNPQQDYELVQRVGSGTYGDVYKARNVHTGELAAVKIIKLEPGDDFSLIQQEIFMVKECKHCNIVAYFGSYLSREKLWICMEYCGGGSLQDIYHVTGPLSELQIAYVCRETLQGLAYLHTKGKMHRDIKGANILLTDHGDVKLADFGVAAKITATIAKRKSFIGTPYWMAPEVAAVEKNGGYNQLCDIWAVGITAIELGELQPPMFDLHPMRALFLMSKSNFQPPKLKDKTKWSSTFHNFVKIALTKNPKKRPTAERLLTHTFVAQPGLSRALAVELLDKVNNPDNHAHYTEADDDDFEPHAIIRHTIRSTNRNARAERTASEINFDKLQFEPPLRKETEARDEMGLSSDPNFMLQWNPFVDGANTGKSTSKRAIPPPLPPKPRISSYPEDNFPDEEKASTIKHCPDSESRAPQILRRQSSPSCGPVAETSSIGNGDGISKLMSENTEGSAQAPQLPRKNDKRDFPKPAINGLPPTPKVLMGACFSKVFDGCPLKINCATSWIHPDTKDQYIIFGTEDGIYTLNLNELHEATMEQLFPRKCTWLYVINNTLMSLSEGKTFQLYSHNLIALFEHAKKPGLAAHIQTHRFPDRILPRKFALTTKIPDTKGCHKCCIVRNPYTGHKYLCGALQSGIVLLQWYEPMQKFMLIKHFDFPLPSPLNVFEMLVIPEQEYPMVCVAISKGTESNQVVQFETINLNSASSWFTEIGAGSQQLDSIHVTQLERDTVLVCLDKFVKIVNLQGKLKSSKKLASELSFDFRIESVVCLQDSVLAFWKHGMQGKSFKSDEVTQEISDETRVFRLLGSDRVVVLESRPTENPTAHSNLYILAGHENSY' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the target protein 'MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT' is 10000.0."
            },
            {
                "from": "human",
                "value": "What is the predicted affinity value for the drug above and the target protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT' is 300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2' for the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2\", \"value\": 5.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2' and the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' is 5.0."
            },
            {
                "from": "human",
                "value": "What is the expected affinity between the drug above and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2\", \"value\": 5.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)c(F)c3)cnc2cc1OCCN1CC2CC1CO2' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21' and the target protein 'MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21\"], \"target_seq\": \"MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE\", \"result\": [{\"smile\": \"O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21' and the protein 'MGKVNVAKLRYMSRDDFRVLTAVEMGMKNHEIVPGSLIASIASLKHGGCNKVLRELVKHKLIAWERTKTVQGYRLTNAGYDYLALKTLSSRQVVESVGNQMGVGKESDIYIVANEEGQQFALKLHRLGRTSFRNLKNKRDYHKHRHNVSWLYLSRLSAMKEFAYMKALYERKFPVPKPIDYNRHAVVMELINGYPLCQIHHVEDPASVYDEAMELIVKLANHGLIHGDFNEFNLILDESDHITMIDFPQMVSTSHPNAEWYFDRDVKCIKDFFMKRFSYESELFPTFKDIRREDTLDVEVSASGYTKEMQADDELLHPLGPDDKNIETKEGSEFSFSDGEVAEKAEVYGSENESERNCLEESEGCYCRSSGDPEQIKEDSLSEESADARSFEMTEFNQALEEIKGQVVENNSVTEFSEEKNRTENYNRQDGQRVQGGVPAGSDEYEDECPHLIALSSLNREFRPFRDEENVGAMNQYRTRTLSITSSGSAVSCSTIPPELVKQKVKRQLTKQQKSAVRRRLQKGEANIFTKQRRENMQNIKSSLEAASFWGE' is 1200.0."
            },
            {
                "from": "human",
                "value": "Calculate the binding affinity between the drug above and the target protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\", \"result\": [{\"smile\": \"O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21' and the protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected level of affinity between the drug 'COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1' and the target protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\"], \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\", \"result\": [{\"smile\": \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1' and the protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST' is 10000.0."
            },
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug above and the target protein 'MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT\", \"result\": [{\"smile\": \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1' and the protein 'MRLLLALLGVLLSVPGPPVLSLEASEEVELEPCLAPSLEQQEQELTVALGQPVRLCCGRAERGGHWYKEGSRLAPAGRVRGWRGRLEIASFLPEDAGRYLCLARGSMIVLQNLTLITGDSLTSSNDDEDPKSHRDPSNRHSYPQQAPYWTHPQRMEKKLHAVPAGNTVKFRCPAAGNPTPTIRWLKDGQAFHGENRIGGIRLRHQHWSLVMESVVPSDRGTYTCLVENAVGSIRYNYLLDVLERSPHRPILQAGLPANTTAVVGSDVELLCKVYSDAQPHIQWLKHIVINGSSFGADGFPYVQVLKTADINSSEVEVLYLRNVSAEDAGEYTCLAGNSIGLSYQSAWLTVLPEEDPTWTAAAPEARYTDIILYASGSLALAVLLLLAGLYRGQALHGRHPRPPATVQKLSRFPLARQFSLESGSSGKSSSSLVRGVRLSSSGPALLAGLVSLDLPLDPLWEFPRDRLVLGKPLGEGCFGQVVRAEAFGMDPARPDQASTVAVKMLKDNASDKDLADLVSEMEVMKLIGRHKNIINLLGVCTQEGPLYVIVECAAKGNLREFLRARRPPGPDLSPDGPRSSEGPLSFPVLVSCAYQVARGMQYLESRKCIHRDLAARNVLVTEDNVMKIADFGLARGVHHIDYYKKTSNGRLPVKWMAPEALFDRVYTHQSDVWSFGILLWEIFTLGGSPYPGIPVEELFSLLREGHRMDRPPHCPPELYGLMRECWHAAPSQRPTFKQLVEALDKVLLAVSEEYLDLRLTFGPYSPSGGDASSTCSSSDSVFSHDPLPLGSSSFPFGSGVQT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the drug 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1' to the target protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\"], \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\", \"result\": [{\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1' and the protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH' is 10000.0."
            },
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the drug above and the target protein 'MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL\", \"result\": [{\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 520.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1' and the protein 'MKKFFDSRREQGGSGLGSGSSGGGGSTSGLGSGYIGRVFGIGRQQVTVDEVLAEGGFAIVFLVRTSNGMKCALKRMFVNNEHDLQVCKREIQIMRDLSGHKNIVGYIDSSINNVSSGDVWEVLILMDFCRGGQVVNLMNQRLQTGFTENEVLQIFCDTCEAVARLHQCKTPIIHRDLKVENILLHDRGHYVLCDFGSATNKFQNPQTEGVNAVEDEIKKYTTLSYRAPEMVNLYSGKIITTKADIWALGCLLYKLCYFTLPFGESQVAICDGNFTIPDNSRYSQDMHCLIRYMLEPDPDKRPDIYQVSYFSFKLLKKECPIPNVQNSPIPAKLPEPVKASEAAAKKTQPKARLTDPIPTTETSIAPRQRPKAGQTQPNPGILPIQPALTPRKRATVQPPPQAAGSSNQPGLLASVPQPKPQAPPSQPLPQTQAKQPQAPPTPQQTPSTQAQGLPAQAQATPQHQQQLFLKQQQQQQQPPPAQQQPAGTFYQQQQAQTQQFQAVHPATQKPAIAQFPVVSQGGSQQQLMQNFYQQQQQQQQQQQQQQLATALHQQQLMTQQAALQQKPTMAAGQQPQPQPAAAPQPAPAQEPAIQAPVRQQPKVQTTPPPAVQGQKVGSLTPPSSPKTQRAGHRRILSDVTHSAVFGVPASKSTQLLQAAAAEASLNKSKSATTTPSGSPRTSQQNVYNPSEGSTWNPFDDDNFSKLTAEELLNKDFAKLGEGKHPEKLGGSAESLIPGFQSTQGDAFATTSFSAGTAEKRKGGQTVDSGLPLLSVSDPFIPLQVPDAPEKLIEGLKSPDTSLLLPDLLPMTDPFGSTSDAVIEKADVAVESLIPGLEPPVPQRLPSQTESVTSNRTDSLTGEDSLLDCSLLSNPTTDLLEEFAPTAISAPVHKAAEDSNLISGFDVPEGSDKVAEDEFDPIPVLITKNPQGGHSRNSSGSSESSLPNLARSLLLVDQLIDL' is 520.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1''s affinity for the target protein 'MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP' is 1200.0."
            },
            {
                "from": "human",
                "value": "Can you outline the predicted binding value of the drug above for the target protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 130.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL' is 130.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the affinity of the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' toward the target protein 'MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS\", \"result\": [{\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the protein 'MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS' is 10000.0."
            },
            {
                "from": "human",
                "value": "What's your evaluation of the drug above's affinity for the target protein 'MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP\", \"result\": [{\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the protein 'MAEPLKEEDGEDGSAEPPGPVKAEPAHTAASVAAKNLALLKARSFDVTFDVGDEYEIIETIGNGAYGVVSSARRRLTGQQVAIKKIPNAFDVVTNAKRTLRELKILKHFKHDNIIAIKDILRPTVPYGEFKSVYVVLDLMESDLHQIIHSSQPLTLEHVRYFLYQLLRGLKYMHSAQVIHRDLKPSNLLVNENCELKIGDFGMARGLCTSPAEHQYFMTEYVATRWYRAPELMLSLHEYTQAIDLWSVGCIFGEMLARRQLFPGKNYVHQLQLIMMVLGTPSPAVIQAVGAERVRAYIQSLPPRQPVPWETVYPGADRQALSLLGRMLRFEPSARISAAAALRHPFLAKYHDPDDEPDCAPPFDFAFDREALTRERIKEAIVAEIEDFHARREGIRQQIRFQPSLQPVASEPGCPDVEMPSPWAPSGDCAMESPPPAPPPCPGPAPDTIDLTLQPPPPVSEPAPPKKDGAISDNTKAALKAALLKSLRSRLRDGPSAPLEAPEPRKPVTAQERQREREEKRRRRQERAKEREKRRQERERKERGAGASGGPSTDPLAGLVLSDNDRSLLERWTRMARPAAPALTSVPAPAPAPTPTPTPVQPTSPPPGPVAQPTGPQPQSAGSTSGPVPQPACPPPGPAPHPTGPPGPIPVPAPPQIATSTSLLAAQSLVPPPGLPGSSTPGVLPYFPPGLPPPDAGGAPQSSMSESPDVNLVTQQLSKSQVEDPLPPVFSGTPKGSGAGYGVGFDLEEFLNQSFDMGVADGPQDGQADSASLSASLLADWLEGHGMNPADIESLQREIQMDSPMLLADLPDLQDP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding score between the drug 'CCCCCCCCOCCCc1cnc[nH]1' and the target protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCCCCCOCCCc1cnc[nH]1\"], \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\": \"CCCCCCCCOCCCc1cnc[nH]1\", \"value\": 100.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCCCCCOCCCc1cnc[nH]1' and the protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' is 100.0."
            },
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the drug above to the target protein 'MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLNRRLRSLTNCFIVSLAVTDLLLGLLVLPFSAIYQLSCKWSFSKVFCNIYTSLDVMLCTASILNLFMISLDRYCAVTDPLRYPVLITPARVAISLVFIWVISITLSFLSIHLGWNSRNETSKDNDTIVKCKVQVNEVYGLVDGLVTFYLPLLIMCITYFRIFKIAREQARRINHIGSWKAATIREHKATVTLAAVMGAFIICWFPYFTVFVYRGLKGDDAVNEVFEDVVLWLGYANSALNPILYAALNRDFRTAYHQLFCCRLASHNSHETSLRLNNSQLNRSQCQEPRWQEDKPLNLQVWSGTEVTAPQGATNR'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLNRRLRSLTNCFIVSLAVTDLLLGLLVLPFSAIYQLSCKWSFSKVFCNIYTSLDVMLCTASILNLFMISLDRYCAVTDPLRYPVLITPARVAISLVFIWVISITLSFLSIHLGWNSRNETSKDNDTIVKCKVQVNEVYGLVDGLVTFYLPLLIMCITYFRIFKIAREQARRINHIGSWKAATIREHKATVTLAAVMGAFIICWFPYFTVFVYRGLKGDDAVNEVFEDVVLWLGYANSALNPILYAALNRDFRTAYHQLFCCRLASHNSHETSLRLNNSQLNRSQCQEPRWQEDKPLNLQVWSGTEVTAPQGATNR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLNRRLRSLTNCFIVSLAVTDLLLGLLVLPFSAIYQLSCKWSFSKVFCNIYTSLDVMLCTASILNLFMISLDRYCAVTDPLRYPVLITPARVAISLVFIWVISITLSFLSIHLGWNSRNETSKDNDTIVKCKVQVNEVYGLVDGLVTFYLPLLIMCITYFRIFKIAREQARRINHIGSWKAATIREHKATVTLAAVMGAFIICWFPYFTVFVYRGLKGDDAVNEVFEDVVLWLGYANSALNPILYAALNRDFRTAYHQLFCCRLASHNSHETSLRLNNSQLNRSQCQEPRWQEDKPLNLQVWSGTEVTAPQGATNR\", \"result\": [{\"smile\": \"CCCCCCCCOCCCc1cnc[nH]1\", \"value\": 2511.89}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCCCCCOCCCc1cnc[nH]1' and the protein 'MAFNGTVPSFCMDFTVYKVTISVILIILILVTVAGNVVVCLAVGLNRRLRSLTNCFIVSLAVTDLLLGLLVLPFSAIYQLSCKWSFSKVFCNIYTSLDVMLCTASILNLFMISLDRYCAVTDPLRYPVLITPARVAISLVFIWVISITLSFLSIHLGWNSRNETSKDNDTIVKCKVQVNEVYGLVDGLVTFYLPLLIMCITYFRIFKIAREQARRINHIGSWKAATIREHKATVTLAAVMGAFIICWFPYFTVFVYRGLKGDDAVNEVFEDVVLWLGYANSALNPILYAALNRDFRTAYHQLFCCRLASHNSHETSLRLNNSQLNRSQCQEPRWQEDKPLNLQVWSGTEVTAPQGATNR' is 2511.89."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1' and the target protein 'MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\"], \"target_seq\": \"MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1' and the protein 'MNGEAICSALPTIPYHKLADLRYLSRGASGTVSSARHADWRVQVAVKHLHIHTPLLDSERKDVLREAEILHKARFSYILPILGICNEPEFLGIVTEYMPNGSLNELLHRKTEYPDVAWPLRFRILHEIALGVNYLHNMTPPLLHHDLKTQNILLDNEFHVKIADFGLSKWRMMSLSQSRSSKSAPEGGTIIYMPPENYEPGQKSRASIKHDIYSYAVITWEVLSRKQPFEDVTNPLQIMYSVSQGHRPVINEESLPYDIPHRARMISLIESGWAQNPDERPSFLKCLIELEPVLRTFEEITFLEAVIQLKKTKLQSVSSAIHLCDKKKMELSLNIPVNHGPQEESCGSSQLHENSGSPETSRSLPAPQDNDFLSRKAQDCYFMKLHHCPGNHSWDSTISGSQRAAFCDHKTTPCSSAIINPLSTAGNSERLQPGIAQQWIQSKREDIVNQMTEACLNQSLDALLSRDLIMKEDYELVSTKPTRTSKVRQLLDTTDIQGEEFAKVIVQKLKDNKQMGLQPYPEILVVSRSPSLNLLQNKSM' is 10000.0."
            },
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug above and the target protein 'MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1' and the protein 'MAHSPVAVQVPGMQNNIADPEELFTKLERIGKGSFGEVFKGIDNRTQQVVAIKIIDLEEAEDEIEDIQQEITVLSQCDSSYVTKYYGSYLKGSKLWIIMEYLGGGSALDLLRAGPFDEFQIATMLKEILKGLDYLHSEKKIHRDIKAANVLLSEQGDVKLADFGVAGQLTDTQIKRNTFVGTPFWMAPEVIQQSAYDSKADIWSLGITAIELAKGEPPNSDMHPMRVLFLIPKNNPPTLVGDFTKSFKEFIDACLNKDPSFRPTAKELLKHKFIVKNSKKTSYLTELIDRFKRWKAEGHSDDESDSEGSDSESTSRENNTHPEWSFTTVRKKPDPKKVQNGAEQDLVQTLSCLSMIITPAFAELKQQDENNASRNQAIEELEKSIAVAEAACPGITDKMVKKLIEKFQKCSADESP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O''s affinity for the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 11.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' is 11.0."
            },
            {
                "from": "human",
                "value": "How would you define the binding affinity of the drug above with the target protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\", \"result\": [{\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 290.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O' and the protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS' is 290.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F'-target protein 'MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP' pair. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\"], \"target_seq\": \"MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP\", \"result\": [{\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F' and the protein 'MQKYEKLEKIGEGTYGTVFKAKNRETHEIVALKRVRLDDDDEGVPSSALREICLLKELKHKNIVRLHDVLHSDKKLTLVFEFCDQDLKKYFDSCNGDLDPEIVKSFLFQLLKGLGFCHSRNVLHRDLKPQNLLINRNGELKLADFGLARAFGIPVRCYSAEVVTLWYRPPDVLFGAKLYSTSIDMWSAGCIFAELANAGRPLFPGNDVDDQLKRIFRLLGTPTEEQWPSMTKLPDYKPYPMYPATTSLVNVVPKLNATGRDLLQNLLKCNPVQRISAEEALQHPYFSDFCPP' is 10000.0."
            },
            {
                "from": "human",
                "value": "How do you project the affinity value between the drug above and the target protein 'MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD\", \"result\": [{\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F' and the protein 'MSQSKGKKRNPGLKIPKEAFEQPQTSSTPPRDLDSKACISIGNQNFEVKADDLEPIMELGRGAYGVVEKMRHVPSGQIMAVKRIRATVNSQEQKRLLMDLDISMRTVDCPFTVTFYGALFREGDVWICMELMDTSLDKFYKQVIDKGQTIPEDILGKIAVSIVKALEHLHSKLSVIHRDVKPSNVLINALGQVKMCDFGISGYLVDSVAKTIDAGCKPYMAPERINPELNQKGYSVKSDIWSLGITMIELAILRFPYDSWGTPFQQLKQVVEEPSPQLPADKFSAEFVDFTSQCLKKNSKERPTYPELMQHPFFTLHESKGTDVASFVKLILGD' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction value for the drug 'CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1' with the target protein 'MPNFAGTWKMRSSENFDELLKALGVNAMLRKVAVAAASKPHVEIRQDGDQFYIKTSTTVRTTEINFKVGEGFEEETVDGRKCRSLPTWENENKIHCTQTLLEGDGPKTYWTRELANDELILTFGADDVVCTRIYVRE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1\"], \"target_seq\": \"MPNFAGTWKMRSSENFDELLKALGVNAMLRKVAVAAASKPHVEIRQDGDQFYIKTSTTVRTTEINFKVGEGFEEETVDGRKCRSLPTWENENKIHCTQTLLEGDGPKTYWTRELANDELILTFGADDVVCTRIYVRE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPNFAGTWKMRSSENFDELLKALGVNAMLRKVAVAAASKPHVEIRQDGDQFYIKTSTTVRTTEINFKVGEGFEEETVDGRKCRSLPTWENENKIHCTQTLLEGDGPKTYWTRELANDELILTFGADDVVCTRIYVRE\", \"result\": [{\"smile\": \"CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1\", \"value\": 1.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1' and the protein 'MPNFAGTWKMRSSENFDELLKALGVNAMLRKVAVAAASKPHVEIRQDGDQFYIKTSTTVRTTEINFKVGEGFEEETVDGRKCRSLPTWENENKIHCTQTLLEGDGPKTYWTRELANDELILTFGADDVVCTRIYVRE' is 1.2."
            },
            {
                "from": "human",
                "value": "How strong do you expect the drug above's interaction with the target protein 'MDTKHFLPLDFSTQVNSSLTSPTGRGSMAAPSLHPSLGPGIGSPGQLHSPISTLSSPINGMGPPFSVISSPMGPHSMSVPTTPTLGFSTGSPQLSSPMNPVSSSEDIKPPLGLNGVLKVPAHPSGNMASFTKHICAICGDRSSGKHYGVYSCEGCKGFFKRTVRKDLTYTCRDNKDCLIDKRQRNRCQYCRYQKCLAMGMKREAVQEERQRGKDRNENEVESTSSANEDMPVERILEAELAVEPKTETYVEANMGLNPSSPNDPVTNICQAADKQLFTLVEWAKRIPHFSELPLDDQVILLRAGWNELLIASFSHRSIAVKDGILLATGLHVHRNSAHSAGVGAIFDRVLTELVSKMRDMQMDKTELGCLRAIVLFNPDSKGLSNPAEVEALREKVYASLEAYCKHKYPEQPGRFAKLLLRLPALRSIGLKCLEHLFFFKLIGDTPIDTFLMEMLEAPHQMT' to be? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDTKHFLPLDFSTQVNSSLTSPTGRGSMAAPSLHPSLGPGIGSPGQLHSPISTLSSPINGMGPPFSVISSPMGPHSMSVPTTPTLGFSTGSPQLSSPMNPVSSSEDIKPPLGLNGVLKVPAHPSGNMASFTKHICAICGDRSSGKHYGVYSCEGCKGFFKRTVRKDLTYTCRDNKDCLIDKRQRNRCQYCRYQKCLAMGMKREAVQEERQRGKDRNENEVESTSSANEDMPVERILEAELAVEPKTETYVEANMGLNPSSPNDPVTNICQAADKQLFTLVEWAKRIPHFSELPLDDQVILLRAGWNELLIASFSHRSIAVKDGILLATGLHVHRNSAHSAGVGAIFDRVLTELVSKMRDMQMDKTELGCLRAIVLFNPDSKGLSNPAEVEALREKVYASLEAYCKHKYPEQPGRFAKLLLRLPALRSIGLKCLEHLFFFKLIGDTPIDTFLMEMLEAPHQMT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDTKHFLPLDFSTQVNSSLTSPTGRGSMAAPSLHPSLGPGIGSPGQLHSPISTLSSPINGMGPPFSVISSPMGPHSMSVPTTPTLGFSTGSPQLSSPMNPVSSSEDIKPPLGLNGVLKVPAHPSGNMASFTKHICAICGDRSSGKHYGVYSCEGCKGFFKRTVRKDLTYTCRDNKDCLIDKRQRNRCQYCRYQKCLAMGMKREAVQEERQRGKDRNENEVESTSSANEDMPVERILEAELAVEPKTETYVEANMGLNPSSPNDPVTNICQAADKQLFTLVEWAKRIPHFSELPLDDQVILLRAGWNELLIASFSHRSIAVKDGILLATGLHVHRNSAHSAGVGAIFDRVLTELVSKMRDMQMDKTELGCLRAIVLFNPDSKGLSNPAEVEALREKVYASLEAYCKHKYPEQPGRFAKLLLRLPALRSIGLKCLEHLFFFKLIGDTPIDTFLMEMLEAPHQMT\", \"result\": [{\"smile\": \"CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1\", \"value\": 200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1=C(C)/C(=C/C(C)=C/C=C/C(C)=C/C(=O)O)CCC1' and the protein 'MDTKHFLPLDFSTQVNSSLTSPTGRGSMAAPSLHPSLGPGIGSPGQLHSPISTLSSPINGMGPPFSVISSPMGPHSMSVPTTPTLGFSTGSPQLSSPMNPVSSSEDIKPPLGLNGVLKVPAHPSGNMASFTKHICAICGDRSSGKHYGVYSCEGCKGFFKRTVRKDLTYTCRDNKDCLIDKRQRNRCQYCRYQKCLAMGMKREAVQEERQRGKDRNENEVESTSSANEDMPVERILEAELAVEPKTETYVEANMGLNPSSPNDPVTNICQAADKQLFTLVEWAKRIPHFSELPLDDQVILLRAGWNELLIASFSHRSIAVKDGILLATGLHVHRNSAHSAGVGAIFDRVLTELVSKMRDMQMDKTELGCLRAIVLFNPDSKGLSNPAEVEALREKVYASLEAYCKHKYPEQPGRFAKLLLRLPALRSIGLKCLEHLFFFKLIGDTPIDTFLMEMLEAPHQMT' is 200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' with respect to the target protein 'MSLCGARANAKMMAAYNGGTSAAAAGHHHHHHHHLPHLPPPHLHHHHHPQHHLHPGSAAAVHPVQQHTSSAAAAAAAAAAAAAMLNPGQQQPYFPSPAPGQAPGPAAAAPAQVQAAAAATVKAHHHQHSHHPQQQLDIEPDRPIGYGAFGVVWSVTDPRDGKRVALKKMPNVFQNLVSCKRVFRELKMLCFFKHDNVLSALDILQPPHIDYFEEIYVVTELMQSDLHKIIVSPQPLSSDHVKVFLYQILRGLKYLHSAGILHRDIKPGNLLVNSNCVLKICDFGLARVEELDESRHMTQEVVTQYYRAPEILMGSRHYSNAIDIWSVGCIFAELLGRRILFQAQSPIQQLDLITDLLGTPSLEAMRTACEGAKAHILRGPHKQPSLPVLYTLSSQATHEAVHLLCRMLVFDPSKRISAKDALAHPYLDEGRLRYHTCMCKCCFSTSTGRVYTSDFEPVTNPKFDDTFEKNLSSVRQVKEIIHQFILEQQKGNRVPLCINPQSAAFKSFISSTVAQPSEMPPSPLVWE'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"MSLCGARANAKMMAAYNGGTSAAAAGHHHHHHHHLPHLPPPHLHHHHHPQHHLHPGSAAAVHPVQQHTSSAAAAAAAAAAAAAMLNPGQQQPYFPSPAPGQAPGPAAAAPAQVQAAAAATVKAHHHQHSHHPQQQLDIEPDRPIGYGAFGVVWSVTDPRDGKRVALKKMPNVFQNLVSCKRVFRELKMLCFFKHDNVLSALDILQPPHIDYFEEIYVVTELMQSDLHKIIVSPQPLSSDHVKVFLYQILRGLKYLHSAGILHRDIKPGNLLVNSNCVLKICDFGLARVEELDESRHMTQEVVTQYYRAPEILMGSRHYSNAIDIWSVGCIFAELLGRRILFQAQSPIQQLDLITDLLGTPSLEAMRTACEGAKAHILRGPHKQPSLPVLYTLSSQATHEAVHLLCRMLVFDPSKRISAKDALAHPYLDEGRLRYHTCMCKCCFSTSTGRVYTSDFEPVTNPKFDDTFEKNLSSVRQVKEIIHQFILEQQKGNRVPLCINPQSAAFKSFISSTVAQPSEMPPSPLVWE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSLCGARANAKMMAAYNGGTSAAAAGHHHHHHHHLPHLPPPHLHHHHHPQHHLHPGSAAAVHPVQQHTSSAAAAAAAAAAAAAMLNPGQQQPYFPSPAPGQAPGPAAAAPAQVQAAAAATVKAHHHQHSHHPQQQLDIEPDRPIGYGAFGVVWSVTDPRDGKRVALKKMPNVFQNLVSCKRVFRELKMLCFFKHDNVLSALDILQPPHIDYFEEIYVVTELMQSDLHKIIVSPQPLSSDHVKVFLYQILRGLKYLHSAGILHRDIKPGNLLVNSNCVLKICDFGLARVEELDESRHMTQEVVTQYYRAPEILMGSRHYSNAIDIWSVGCIFAELLGRRILFQAQSPIQQLDLITDLLGTPSLEAMRTACEGAKAHILRGPHKQPSLPVLYTLSSQATHEAVHLLCRMLVFDPSKRISAKDALAHPYLDEGRLRYHTCMCKCCFSTSTGRVYTSDFEPVTNPKFDDTFEKNLSSVRQVKEIIHQFILEQQKGNRVPLCINPQSAAFKSFISSTVAQPSEMPPSPLVWE\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MSLCGARANAKMMAAYNGGTSAAAAGHHHHHHHHLPHLPPPHLHHHHHPQHHLHPGSAAAVHPVQQHTSSAAAAAAAAAAAAAMLNPGQQQPYFPSPAPGQAPGPAAAAPAQVQAAAAATVKAHHHQHSHHPQQQLDIEPDRPIGYGAFGVVWSVTDPRDGKRVALKKMPNVFQNLVSCKRVFRELKMLCFFKHDNVLSALDILQPPHIDYFEEIYVVTELMQSDLHKIIVSPQPLSSDHVKVFLYQILRGLKYLHSAGILHRDIKPGNLLVNSNCVLKICDFGLARVEELDESRHMTQEVVTQYYRAPEILMGSRHYSNAIDIWSVGCIFAELLGRRILFQAQSPIQQLDLITDLLGTPSLEAMRTACEGAKAHILRGPHKQPSLPVLYTLSSQATHEAVHLLCRMLVFDPSKRISAKDALAHPYLDEGRLRYHTCMCKCCFSTSTGRVYTSDFEPVTNPKFDDTFEKNLSSVRQVKEIIHQFILEQQKGNRVPLCINPQSAAFKSFISSTVAQPSEMPPSPLVWE' is 10000.0."
            },
            {
                "from": "human",
                "value": "Assess the binding affinity of the drug above with the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1''s affinity with the target protein 'MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\"], \"target_seq\": \"MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the protein 'MSANNSPPSAQKSVLPTAIPAVLPAASPCSSPKTGLSARLSNGSFSAPSLTNSRGSVHTVSFLLQIGLTRESVTIEAQELSLSAVKDLVCSIVYQKFPECGFFGMYDKILLFRHDMNSENILQLITSADEIHEGDLVEVVLSALATVEDFQIRPHTLYVHSYKAPTFCDYCGEMLWGLVRQGLKCEGCGLNYHKRCAFKIPNNCSGVRKRRLSNVSLPGPGLSVPRPLQPEYVALPSEESHVHQEPSKRIPSWSGRPIWMEKMVMCRVKVPHTFAVHSYTRPTICQYCKRLLKGLFRQGMQCKDCKFNCHKRCASKVPRDCLGEVTFNGEPSSLGTDTDIPMDIDNNDINSDSSRGLDDTEEPSPPEDKMFFLDPSDLDVERDEEAVKTISPSTSNNIPLMRVVQSIKHTKRKSSTMVKEGWMVHYTSRDNLRKRHYWRLDSKCLTLFQNESGSKYYKEIPLSEILRISSPRDFTNISQGSNPHCFEIITDTMVYFVGENNGDSSHNPVLAATGVGLDVAQSWEKAIRQALMPVTPQASVCTSPGQGKDHKDLSTSISVSNCQIQENVDISTVYQIFADEVLGSGQFGIVYGGKHRKTGRDVAIKVIDKMRFPTKQESQLRNEVAILQNLHHPGIVNLECMFETPERVFVVMEKLHGDMLEMILSSEKSRLPERITKFMVTQILVALRNLHFKNIVHCDLKPENVLLASAEPFPQVKLCDFGFARIIGEKSFRRSVVGTPAYLAPEVLRSKGYNRSLDMWSVGVIIYVSLSGTFPFNEDEDINDQIQNAAFMYPPNPWREISGEAIDLINNLLQVKMRKRYSVDKSLSHPWLQDYQTWLDLREFETRIGERYITHESDDARWEIHAYTHNLVYPKHFIMAPNPDDMEEDP' is 10000.0."
            },
            {
                "from": "human",
                "value": "What's the expected level of affinity between the drug above and the target protein 'MESLVFARRSGPTPSAAELARPLAEGLIKSPKPLMKKQAVKRHHHKHNLRHRYEFLETLGKGTYGKVKKARESSGRLVAIKSIRKDKIKDEQDLMHIRREIEIMSSLNHPHIIAIHEVFENSSKIVIVMEYASRGDLYDYISERQQLSEREARHFFRQIVSAVHYCHQNRVVHRDLKLENILLDANGNIKIADFGLSNLYHQGKFLQTFCGSPLYASPEIVNGKPYTGPEVDSWSLGVLLYILVHGTMPFDGHDHKILVKQISNGAYREPPKPSDACGLIRWLLMVNPTRRATLEDVASHWWVNWGYATRVGEQEAPHEGGHPGSDSARASMADWLRRSSRPLLENGAKVCSFFKQHAPGGGSTTPGLERQHSLKKSRKENDMAQSLHSDTADDTAHRPGKSNLKLPKGILKKKVSASAEGVQEDPPELSPIPASPGQAAPLLPKKGILKKPRQRESGYYSSPEPSESGELLDAGDVFVSGDPKEQKPPQASGLLLHRKGILKLNGKFSQTALELAAPTTFGSLDELAPPRPLARASRPSGAVSEDSILSSESFDQLDLPERLPEPPLRGCVSVDNLTGLEEPPSEGPGSCLRRWRQDPLGDSCFSLTDCQEVTATYRQALRVCSKLT'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MESLVFARRSGPTPSAAELARPLAEGLIKSPKPLMKKQAVKRHHHKHNLRHRYEFLETLGKGTYGKVKKARESSGRLVAIKSIRKDKIKDEQDLMHIRREIEIMSSLNHPHIIAIHEVFENSSKIVIVMEYASRGDLYDYISERQQLSEREARHFFRQIVSAVHYCHQNRVVHRDLKLENILLDANGNIKIADFGLSNLYHQGKFLQTFCGSPLYASPEIVNGKPYTGPEVDSWSLGVLLYILVHGTMPFDGHDHKILVKQISNGAYREPPKPSDACGLIRWLLMVNPTRRATLEDVASHWWVNWGYATRVGEQEAPHEGGHPGSDSARASMADWLRRSSRPLLENGAKVCSFFKQHAPGGGSTTPGLERQHSLKKSRKENDMAQSLHSDTADDTAHRPGKSNLKLPKGILKKKVSASAEGVQEDPPELSPIPASPGQAAPLLPKKGILKKPRQRESGYYSSPEPSESGELLDAGDVFVSGDPKEQKPPQASGLLLHRKGILKLNGKFSQTALELAAPTTFGSLDELAPPRPLARASRPSGAVSEDSILSSESFDQLDLPERLPEPPLRGCVSVDNLTGLEEPPSEGPGSCLRRWRQDPLGDSCFSLTDCQEVTATYRQALRVCSKLT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESLVFARRSGPTPSAAELARPLAEGLIKSPKPLMKKQAVKRHHHKHNLRHRYEFLETLGKGTYGKVKKARESSGRLVAIKSIRKDKIKDEQDLMHIRREIEIMSSLNHPHIIAIHEVFENSSKIVIVMEYASRGDLYDYISERQQLSEREARHFFRQIVSAVHYCHQNRVVHRDLKLENILLDANGNIKIADFGLSNLYHQGKFLQTFCGSPLYASPEIVNGKPYTGPEVDSWSLGVLLYILVHGTMPFDGHDHKILVKQISNGAYREPPKPSDACGLIRWLLMVNPTRRATLEDVASHWWVNWGYATRVGEQEAPHEGGHPGSDSARASMADWLRRSSRPLLENGAKVCSFFKQHAPGGGSTTPGLERQHSLKKSRKENDMAQSLHSDTADDTAHRPGKSNLKLPKGILKKKVSASAEGVQEDPPELSPIPASPGQAAPLLPKKGILKKPRQRESGYYSSPEPSESGELLDAGDVFVSGDPKEQKPPQASGLLLHRKGILKLNGKFSQTALELAAPTTFGSLDELAPPRPLARASRPSGAVSEDSILSSESFDQLDLPERLPEPPLRGCVSVDNLTGLEEPPSEGPGSCLRRWRQDPLGDSCFSLTDCQEVTATYRQALRVCSKLT\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 320.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the protein 'MESLVFARRSGPTPSAAELARPLAEGLIKSPKPLMKKQAVKRHHHKHNLRHRYEFLETLGKGTYGKVKKARESSGRLVAIKSIRKDKIKDEQDLMHIRREIEIMSSLNHPHIIAIHEVFENSSKIVIVMEYASRGDLYDYISERQQLSEREARHFFRQIVSAVHYCHQNRVVHRDLKLENILLDANGNIKIADFGLSNLYHQGKFLQTFCGSPLYASPEIVNGKPYTGPEVDSWSLGVLLYILVHGTMPFDGHDHKILVKQISNGAYREPPKPSDACGLIRWLLMVNPTRRATLEDVASHWWVNWGYATRVGEQEAPHEGGHPGSDSARASMADWLRRSSRPLLENGAKVCSFFKQHAPGGGSTTPGLERQHSLKKSRKENDMAQSLHSDTADDTAHRPGKSNLKLPKGILKKKVSASAEGVQEDPPELSPIPASPGQAAPLLPKKGILKKPRQRESGYYSSPEPSESGELLDAGDVFVSGDPKEQKPPQASGLLLHRKGILKLNGKFSQTALELAAPTTFGSLDELAPPRPLARASRPSGAVSEDSILSSESFDQLDLPERLPEPPLRGCVSVDNLTGLEEPPSEGPGSCLRRWRQDPLGDSCFSLTDCQEVTATYRQALRVCSKLT' is 320.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1''s affinity with the target protein 'MATTATCTRFTDDYQLFEELGKGAFSVVRRCVKKTSTQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIHQILESVNHIHQHDIVHRDLKPENLLLASKCKGAAVKLADFGLAIEVQGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDIWACGVILYILLVGYPPFWDEDQHKLYQQIKAGAYDFPSPEWDTVTPEAKNLINQMLTINPAKRITADQALKHPWVCQRSTVASMMHRQETVECLRKFNARRKLKGAILTTMLVSRNFSAAKSLLNKKSDGGVKKRKSSSSVHLMPQSNNKNSLVSPAQEPAPLQTAMEPQTTVVHNATDGIKGSTESCNTTTEDEDLKARSPEGRSSRDRTAPSAGMQPQPSLCSSAMRKQEIIKITEQLIEAINNGDFEAYTKICDPGLTSFEPEALGNLVEGMDFHKFYFENLLSKNSKPIHTTILNPHVHVIGEDAACIAYIRLTQYIDGQGRPRTSQSEETRVWHRRDGKWLNVHYHCSGAPAAPLQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"MATTATCTRFTDDYQLFEELGKGAFSVVRRCVKKTSTQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIHQILESVNHIHQHDIVHRDLKPENLLLASKCKGAAVKLADFGLAIEVQGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDIWACGVILYILLVGYPPFWDEDQHKLYQQIKAGAYDFPSPEWDTVTPEAKNLINQMLTINPAKRITADQALKHPWVCQRSTVASMMHRQETVECLRKFNARRKLKGAILTTMLVSRNFSAAKSLLNKKSDGGVKKRKSSSSVHLMPQSNNKNSLVSPAQEPAPLQTAMEPQTTVVHNATDGIKGSTESCNTTTEDEDLKARSPEGRSSRDRTAPSAGMQPQPSLCSSAMRKQEIIKITEQLIEAINNGDFEAYTKICDPGLTSFEPEALGNLVEGMDFHKFYFENLLSKNSKPIHTTILNPHVHVIGEDAACIAYIRLTQYIDGQGRPRTSQSEETRVWHRRDGKWLNVHYHCSGAPAAPLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATTATCTRFTDDYQLFEELGKGAFSVVRRCVKKTSTQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIHQILESVNHIHQHDIVHRDLKPENLLLASKCKGAAVKLADFGLAIEVQGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDIWACGVILYILLVGYPPFWDEDQHKLYQQIKAGAYDFPSPEWDTVTPEAKNLINQMLTINPAKRITADQALKHPWVCQRSTVASMMHRQETVECLRKFNARRKLKGAILTTMLVSRNFSAAKSLLNKKSDGGVKKRKSSSSVHLMPQSNNKNSLVSPAQEPAPLQTAMEPQTTVVHNATDGIKGSTESCNTTTEDEDLKARSPEGRSSRDRTAPSAGMQPQPSLCSSAMRKQEIIKITEQLIEAINNGDFEAYTKICDPGLTSFEPEALGNLVEGMDFHKFYFENLLSKNSKPIHTTILNPHVHVIGEDAACIAYIRLTQYIDGQGRPRTSQSEETRVWHRRDGKWLNVHYHCSGAPAAPLQ\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MATTATCTRFTDDYQLFEELGKGAFSVVRRCVKKTSTQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIHQILESVNHIHQHDIVHRDLKPENLLLASKCKGAAVKLADFGLAIEVQGEQQAWFGFAGTPGYLSPEVLRKDPYGKPVDIWACGVILYILLVGYPPFWDEDQHKLYQQIKAGAYDFPSPEWDTVTPEAKNLINQMLTINPAKRITADQALKHPWVCQRSTVASMMHRQETVECLRKFNARRKLKGAILTTMLVSRNFSAAKSLLNKKSDGGVKKRKSSSSVHLMPQSNNKNSLVSPAQEPAPLQTAMEPQTTVVHNATDGIKGSTESCNTTTEDEDLKARSPEGRSSRDRTAPSAGMQPQPSLCSSAMRKQEIIKITEQLIEAINNGDFEAYTKICDPGLTSFEPEALGNLVEGMDFHKFYFENLLSKNSKPIHTTILNPHVHVIGEDAACIAYIRLTQYIDGQGRPRTSQSEETRVWHRRDGKWLNVHYHCSGAPAAPLQ' is 10000.0."
            },
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug above to the target protein 'MLKFKYGARNPLDAGAAEPIASRASRLNLFFQGKPPFMTQQQMSPLSREGILDALFVLFEECSQPALMKIKHVSNFVRKYSDTIAELQELQPSAKDFEVRSLVGCGHFAEVQVVREKATGDIYAMKVMKKKALLAQEQVSFFEEERNILSRSTSPWIPQLQYAFQDKNHLYLVMEYQPGGDLLSLLNRYEDQLDENLIQFYLAELILAVHSVHLMGYVHRDIKPENILVDRTGHIKLVDFGSAAKMNSNKMVNAKLPIGTPDYMAPEVLTVMNGDGKGTYGLDCDWWSVGVIAYEMIYGRSPFAEGTSARTFNNIMNFQRFLKFPDDPKVSSDFLDLIQSLLCGQKERLKFEGLCCHPFFSKIDWNNIRNSPPPFVPTLKSDDDTSNFDEPEKNSWVSSSPCQLSPSGFSGEELPFVGFSYSKALGILGRSESVVSGLDSPAKTSSMEKKLLIKSKELQDSQDKCHKMEQEMTRLHRRVSEVEAVLSQKEVELKASETQRSLLEQDLATYITECSSLKRSLEQARMEVSQEDDKALQLLHDIREQSRKLQEIKEQEYQAQVEEMRLMMNQLEEDLVSARRRSDLYESELRESRLAAEEFKRKATECQHKLLKAKDQGKPEVGEYAKLEKINAEQQLKIQELQEKLEKAVKASTEATELLQNIRQAKERAERELEKLQNREDSSEGIRKKLVEAEELEEKHREAQVSAQHLEVHLKQKEQHYEEKIKVLDNQIKKDLADKETLENMMQRHEEEAHEKGKILSEQKAMINAMDSKIRSLEQRIVELSEANKLAANSSLFTQRNMKAQEEMISELRQQKFYLETQAGKLEAQNRKLEEQLEKISHQDHSDKNRLLELETRLREVSLEHEEQKLELKRQLTELQLSLQERESQLTALQAARAALESQLRQAKTELEETTAEAEEEIQALTAHRDEIQRKFDALRNSCTVITDLEEQLNQLTEDNAELNNQNFYLSKQLDEASGANDEIVQLRSEVDHLRREITEREMQLTSQKQTMEALKTTCTMLEEQVMDLEALNDELLEKERQWEAWRSVLGDEKSQFECRVRELQRMLDTEKQSRARADQRITESRQVVELAVKEHKAEILALQQALKEQKLKAESLSDKLNDLEKKHAMLEMNARSLQQKLETERELKQRLLEEQAKLQQQMDLQKNHIFRLTQGLQEALDRADLLKTERSDLEYQLENIQVLYSHEKVKMEGTISQQTKLIDFLQAKMDQPAKKKKGLFSRRKEDPALPTQVPLQYNELKLALEKEKARCAELEEALQKTRIELRSAREEAAHRKATDHPHPSTPATARQQIAMSAIVRSPEHQPSAMSLLAPPSSRRKESSTPEEFSRRLKERMHHNIPHRFNVGLNMRATKCAVCLDTVHFGRQASKCLECQVMCHPKCSTCLPATCGLPAEYATHFTEAFCRDKMNSPGLQTKEPSSSLHLEGWMKVPRNNKRGQQGWDRKYIVLEGSKVLIYDNEAREAGQRPVEEFELCLPDGDVSIHGAVGASELANTAKADVPYILKMESHPHTTCWPGRTLYLLAPSFPDKQRWVTALESVVAGGRVSREKAEADAKLLGNSLLKLEGDDRLDMNCTLPFSDQVVLVGTEEGLYALNVLKNSLTHVPGIGAVFQIYIIKDLEKLLMIAGEERALCLVDVKKVKQSLAQSHLPAQPDISPNIFEAVKGCHLFGAGKIENGLCICAAMPSKVVILRYNENLSKYCIRKEIETSEPCSCIHFTNYSILIGTNKFYEIDMKQYTLEEFLDKNDHSLAPAVFAASSNSFPVSIVQVNSAGQREEYLLCFHEFGVFVDSYGRRSRTDDLKWSRLPLAFAYREPYLFVTHFNSLEVIEIQARSSAGTPARAYLDIPNPRYLGPAISSGAIYLASSYQDKLRVICCKGNLVKESGTEHHRGPSTSRSSPNKRGPPTYNEHITKRVASSPAPPEGPSHPREPSTPHRYREGRTELRRDKSPGRPLEREKSPGRMLSTRRERSPGRLFEDSSRGRLPAGAVRTPLSQVNKVWDQSSV'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLKFKYGARNPLDAGAAEPIASRASRLNLFFQGKPPFMTQQQMSPLSREGILDALFVLFEECSQPALMKIKHVSNFVRKYSDTIAELQELQPSAKDFEVRSLVGCGHFAEVQVVREKATGDIYAMKVMKKKALLAQEQVSFFEEERNILSRSTSPWIPQLQYAFQDKNHLYLVMEYQPGGDLLSLLNRYEDQLDENLIQFYLAELILAVHSVHLMGYVHRDIKPENILVDRTGHIKLVDFGSAAKMNSNKMVNAKLPIGTPDYMAPEVLTVMNGDGKGTYGLDCDWWSVGVIAYEMIYGRSPFAEGTSARTFNNIMNFQRFLKFPDDPKVSSDFLDLIQSLLCGQKERLKFEGLCCHPFFSKIDWNNIRNSPPPFVPTLKSDDDTSNFDEPEKNSWVSSSPCQLSPSGFSGEELPFVGFSYSKALGILGRSESVVSGLDSPAKTSSMEKKLLIKSKELQDSQDKCHKMEQEMTRLHRRVSEVEAVLSQKEVELKASETQRSLLEQDLATYITECSSLKRSLEQARMEVSQEDDKALQLLHDIREQSRKLQEIKEQEYQAQVEEMRLMMNQLEEDLVSARRRSDLYESELRESRLAAEEFKRKATECQHKLLKAKDQGKPEVGEYAKLEKINAEQQLKIQELQEKLEKAVKASTEATELLQNIRQAKERAERELEKLQNREDSSEGIRKKLVEAEELEEKHREAQVSAQHLEVHLKQKEQHYEEKIKVLDNQIKKDLADKETLENMMQRHEEEAHEKGKILSEQKAMINAMDSKIRSLEQRIVELSEANKLAANSSLFTQRNMKAQEEMISELRQQKFYLETQAGKLEAQNRKLEEQLEKISHQDHSDKNRLLELETRLREVSLEHEEQKLELKRQLTELQLSLQERESQLTALQAARAALESQLRQAKTELEETTAEAEEEIQALTAHRDEIQRKFDALRNSCTVITDLEEQLNQLTEDNAELNNQNFYLSKQLDEASGANDEIVQLRSEVDHLRREITEREMQLTSQKQTMEALKTTCTMLEEQVMDLEALNDELLEKERQWEAWRSVLGDEKSQFECRVRELQRMLDTEKQSRARADQRITESRQVVELAVKEHKAEILALQQALKEQKLKAESLSDKLNDLEKKHAMLEMNARSLQQKLETERELKQRLLEEQAKLQQQMDLQKNHIFRLTQGLQEALDRADLLKTERSDLEYQLENIQVLYSHEKVKMEGTISQQTKLIDFLQAKMDQPAKKKKGLFSRRKEDPALPTQVPLQYNELKLALEKEKARCAELEEALQKTRIELRSAREEAAHRKATDHPHPSTPATARQQIAMSAIVRSPEHQPSAMSLLAPPSSRRKESSTPEEFSRRLKERMHHNIPHRFNVGLNMRATKCAVCLDTVHFGRQASKCLECQVMCHPKCSTCLPATCGLPAEYATHFTEAFCRDKMNSPGLQTKEPSSSLHLEGWMKVPRNNKRGQQGWDRKYIVLEGSKVLIYDNEAREAGQRPVEEFELCLPDGDVSIHGAVGASELANTAKADVPYILKMESHPHTTCWPGRTLYLLAPSFPDKQRWVTALESVVAGGRVSREKAEADAKLLGNSLLKLEGDDRLDMNCTLPFSDQVVLVGTEEGLYALNVLKNSLTHVPGIGAVFQIYIIKDLEKLLMIAGEERALCLVDVKKVKQSLAQSHLPAQPDISPNIFEAVKGCHLFGAGKIENGLCICAAMPSKVVILRYNENLSKYCIRKEIETSEPCSCIHFTNYSILIGTNKFYEIDMKQYTLEEFLDKNDHSLAPAVFAASSNSFPVSIVQVNSAGQREEYLLCFHEFGVFVDSYGRRSRTDDLKWSRLPLAFAYREPYLFVTHFNSLEVIEIQARSSAGTPARAYLDIPNPRYLGPAISSGAIYLASSYQDKLRVICCKGNLVKESGTEHHRGPSTSRSSPNKRGPPTYNEHITKRVASSPAPPEGPSHPREPSTPHRYREGRTELRRDKSPGRPLEREKSPGRMLSTRRERSPGRLFEDSSRGRLPAGAVRTPLSQVNKVWDQSSV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLKFKYGARNPLDAGAAEPIASRASRLNLFFQGKPPFMTQQQMSPLSREGILDALFVLFEECSQPALMKIKHVSNFVRKYSDTIAELQELQPSAKDFEVRSLVGCGHFAEVQVVREKATGDIYAMKVMKKKALLAQEQVSFFEEERNILSRSTSPWIPQLQYAFQDKNHLYLVMEYQPGGDLLSLLNRYEDQLDENLIQFYLAELILAVHSVHLMGYVHRDIKPENILVDRTGHIKLVDFGSAAKMNSNKMVNAKLPIGTPDYMAPEVLTVMNGDGKGTYGLDCDWWSVGVIAYEMIYGRSPFAEGTSARTFNNIMNFQRFLKFPDDPKVSSDFLDLIQSLLCGQKERLKFEGLCCHPFFSKIDWNNIRNSPPPFVPTLKSDDDTSNFDEPEKNSWVSSSPCQLSPSGFSGEELPFVGFSYSKALGILGRSESVVSGLDSPAKTSSMEKKLLIKSKELQDSQDKCHKMEQEMTRLHRRVSEVEAVLSQKEVELKASETQRSLLEQDLATYITECSSLKRSLEQARMEVSQEDDKALQLLHDIREQSRKLQEIKEQEYQAQVEEMRLMMNQLEEDLVSARRRSDLYESELRESRLAAEEFKRKATECQHKLLKAKDQGKPEVGEYAKLEKINAEQQLKIQELQEKLEKAVKASTEATELLQNIRQAKERAERELEKLQNREDSSEGIRKKLVEAEELEEKHREAQVSAQHLEVHLKQKEQHYEEKIKVLDNQIKKDLADKETLENMMQRHEEEAHEKGKILSEQKAMINAMDSKIRSLEQRIVELSEANKLAANSSLFTQRNMKAQEEMISELRQQKFYLETQAGKLEAQNRKLEEQLEKISHQDHSDKNRLLELETRLREVSLEHEEQKLELKRQLTELQLSLQERESQLTALQAARAALESQLRQAKTELEETTAEAEEEIQALTAHRDEIQRKFDALRNSCTVITDLEEQLNQLTEDNAELNNQNFYLSKQLDEASGANDEIVQLRSEVDHLRREITEREMQLTSQKQTMEALKTTCTMLEEQVMDLEALNDELLEKERQWEAWRSVLGDEKSQFECRVRELQRMLDTEKQSRARADQRITESRQVVELAVKEHKAEILALQQALKEQKLKAESLSDKLNDLEKKHAMLEMNARSLQQKLETERELKQRLLEEQAKLQQQMDLQKNHIFRLTQGLQEALDRADLLKTERSDLEYQLENIQVLYSHEKVKMEGTISQQTKLIDFLQAKMDQPAKKKKGLFSRRKEDPALPTQVPLQYNELKLALEKEKARCAELEEALQKTRIELRSAREEAAHRKATDHPHPSTPATARQQIAMSAIVRSPEHQPSAMSLLAPPSSRRKESSTPEEFSRRLKERMHHNIPHRFNVGLNMRATKCAVCLDTVHFGRQASKCLECQVMCHPKCSTCLPATCGLPAEYATHFTEAFCRDKMNSPGLQTKEPSSSLHLEGWMKVPRNNKRGQQGWDRKYIVLEGSKVLIYDNEAREAGQRPVEEFELCLPDGDVSIHGAVGASELANTAKADVPYILKMESHPHTTCWPGRTLYLLAPSFPDKQRWVTALESVVAGGRVSREKAEADAKLLGNSLLKLEGDDRLDMNCTLPFSDQVVLVGTEEGLYALNVLKNSLTHVPGIGAVFQIYIIKDLEKLLMIAGEERALCLVDVKKVKQSLAQSHLPAQPDISPNIFEAVKGCHLFGAGKIENGLCICAAMPSKVVILRYNENLSKYCIRKEIETSEPCSCIHFTNYSILIGTNKFYEIDMKQYTLEEFLDKNDHSLAPAVFAASSNSFPVSIVQVNSAGQREEYLLCFHEFGVFVDSYGRRSRTDDLKWSRLPLAFAYREPYLFVTHFNSLEVIEIQARSSAGTPARAYLDIPNPRYLGPAISSGAIYLASSYQDKLRVICCKGNLVKESGTEHHRGPSTSRSSPNKRGPPTYNEHITKRVASSPAPPEGPSHPREPSTPHRYREGRTELRRDKSPGRPLEREKSPGRMLSTRRERSPGRLFEDSSRGRLPAGAVRTPLSQVNKVWDQSSV\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 420.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MLKFKYGARNPLDAGAAEPIASRASRLNLFFQGKPPFMTQQQMSPLSREGILDALFVLFEECSQPALMKIKHVSNFVRKYSDTIAELQELQPSAKDFEVRSLVGCGHFAEVQVVREKATGDIYAMKVMKKKALLAQEQVSFFEEERNILSRSTSPWIPQLQYAFQDKNHLYLVMEYQPGGDLLSLLNRYEDQLDENLIQFYLAELILAVHSVHLMGYVHRDIKPENILVDRTGHIKLVDFGSAAKMNSNKMVNAKLPIGTPDYMAPEVLTVMNGDGKGTYGLDCDWWSVGVIAYEMIYGRSPFAEGTSARTFNNIMNFQRFLKFPDDPKVSSDFLDLIQSLLCGQKERLKFEGLCCHPFFSKIDWNNIRNSPPPFVPTLKSDDDTSNFDEPEKNSWVSSSPCQLSPSGFSGEELPFVGFSYSKALGILGRSESVVSGLDSPAKTSSMEKKLLIKSKELQDSQDKCHKMEQEMTRLHRRVSEVEAVLSQKEVELKASETQRSLLEQDLATYITECSSLKRSLEQARMEVSQEDDKALQLLHDIREQSRKLQEIKEQEYQAQVEEMRLMMNQLEEDLVSARRRSDLYESELRESRLAAEEFKRKATECQHKLLKAKDQGKPEVGEYAKLEKINAEQQLKIQELQEKLEKAVKASTEATELLQNIRQAKERAERELEKLQNREDSSEGIRKKLVEAEELEEKHREAQVSAQHLEVHLKQKEQHYEEKIKVLDNQIKKDLADKETLENMMQRHEEEAHEKGKILSEQKAMINAMDSKIRSLEQRIVELSEANKLAANSSLFTQRNMKAQEEMISELRQQKFYLETQAGKLEAQNRKLEEQLEKISHQDHSDKNRLLELETRLREVSLEHEEQKLELKRQLTELQLSLQERESQLTALQAARAALESQLRQAKTELEETTAEAEEEIQALTAHRDEIQRKFDALRNSCTVITDLEEQLNQLTEDNAELNNQNFYLSKQLDEASGANDEIVQLRSEVDHLRREITEREMQLTSQKQTMEALKTTCTMLEEQVMDLEALNDELLEKERQWEAWRSVLGDEKSQFECRVRELQRMLDTEKQSRARADQRITESRQVVELAVKEHKAEILALQQALKEQKLKAESLSDKLNDLEKKHAMLEMNARSLQQKLETERELKQRLLEEQAKLQQQMDLQKNHIFRLTQGLQEALDRADLLKTERSDLEYQLENIQVLYSHEKVKMEGTISQQTKLIDFLQAKMDQPAKKKKGLFSRRKEDPALPTQVPLQYNELKLALEKEKARCAELEEALQKTRIELRSAREEAAHRKATDHPHPSTPATARQQIAMSAIVRSPEHQPSAMSLLAPPSSRRKESSTPEEFSRRLKERMHHNIPHRFNVGLNMRATKCAVCLDTVHFGRQASKCLECQVMCHPKCSTCLPATCGLPAEYATHFTEAFCRDKMNSPGLQTKEPSSSLHLEGWMKVPRNNKRGQQGWDRKYIVLEGSKVLIYDNEAREAGQRPVEEFELCLPDGDVSIHGAVGASELANTAKADVPYILKMESHPHTTCWPGRTLYLLAPSFPDKQRWVTALESVVAGGRVSREKAEADAKLLGNSLLKLEGDDRLDMNCTLPFSDQVVLVGTEEGLYALNVLKNSLTHVPGIGAVFQIYIIKDLEKLLMIAGEERALCLVDVKKVKQSLAQSHLPAQPDISPNIFEAVKGCHLFGAGKIENGLCICAAMPSKVVILRYNENLSKYCIRKEIETSEPCSCIHFTNYSILIGTNKFYEIDMKQYTLEEFLDKNDHSLAPAVFAASSNSFPVSIVQVNSAGQREEYLLCFHEFGVFVDSYGRRSRTDDLKWSRLPLAFAYREPYLFVTHFNSLEVIEIQARSSAGTPARAYLDIPNPRYLGPAISSGAIYLASSYQDKLRVICCKGNLVKESGTEHHRGPSTSRSSPNKRGPPTYNEHITKRVASSPAPPEGPSHPREPSTPHRYREGRTELRRDKSPGRPLEREKSPGRMLSTRRERSPGRLFEDSSRGRLPAGAVRTPLSQVNKVWDQSSV' is 420.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you quantify the predicted affinity for the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the target protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\"], \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 5.9}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL' is 5.9."
            },
            {
                "from": "human",
                "value": "How strong do you expect the drug above's interaction with the target protein 'MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV' to be? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 7.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the protein 'MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV' is 7.6."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the affinity between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the target protein 'MDVSLCPAKCSFWRIFLLGSVWLDYVGSVLACPANCVCSKTEINCRRPDDGNLFPLLEGQDSGNSNGNASINITDISRNITSIHIENWRSLHTLNAVDMELYTGLQKLTIKNSGLRSIQPRAFAKNPHLRYINLSSNRLTTLSWQLFQTLSLRELQLEQNFFNCSCDIRWMQLWQEQGEAKLNSQNLYCINADGSQLPLFRMNISQCDLPEISVSHVNLTVREGDNAVITCNGSGSPLPDVDWIVTGLQSINTHQTNLNWTNVHAINLTLVNVTSEDNGFTLTCIAENVVGMSNASVALTVYYPPRVVSLEEPELRLEHCIEFVVRGNPPPTLHWLHNGQPLRESKIIHVEYYQEGEISEGCLLFNKPTHYNNGNYTLIAKNPLGTANQTINGHFLKEPFPESTDNFILFDEVSPTPPITVTHKPEEDTFGVSIAVGLAAFACVLLVVLFVMINKYGRRSKFGMKGPVAVISGEEDSASPLHHINHGITTPSSLDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQHEHIVKFYGVCGDGDPLIMVFEYMKHGDLNKFLRAHGPDAMILVDGQPRQAKGELGLSQMLHIASQIASGMVYLASQHFVHRDLATRNCLVGANLLVKIGDFGMSRDVYSTDYYRLFNPSGNDFCIWCEVGGHTMLPIRWMPPESIMYRKFTTESDVWSFGVILWEIFTYGKQPWFQLSNTEVIECITQGRVLERPRVCPKEVYDVMLGCWQREPQQRLNIKEIYKILHALGKATPIYLDILG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\"], \"target_seq\": \"MDVSLCPAKCSFWRIFLLGSVWLDYVGSVLACPANCVCSKTEINCRRPDDGNLFPLLEGQDSGNSNGNASINITDISRNITSIHIENWRSLHTLNAVDMELYTGLQKLTIKNSGLRSIQPRAFAKNPHLRYINLSSNRLTTLSWQLFQTLSLRELQLEQNFFNCSCDIRWMQLWQEQGEAKLNSQNLYCINADGSQLPLFRMNISQCDLPEISVSHVNLTVREGDNAVITCNGSGSPLPDVDWIVTGLQSINTHQTNLNWTNVHAINLTLVNVTSEDNGFTLTCIAENVVGMSNASVALTVYYPPRVVSLEEPELRLEHCIEFVVRGNPPPTLHWLHNGQPLRESKIIHVEYYQEGEISEGCLLFNKPTHYNNGNYTLIAKNPLGTANQTINGHFLKEPFPESTDNFILFDEVSPTPPITVTHKPEEDTFGVSIAVGLAAFACVLLVVLFVMINKYGRRSKFGMKGPVAVISGEEDSASPLHHINHGITTPSSLDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQHEHIVKFYGVCGDGDPLIMVFEYMKHGDLNKFLRAHGPDAMILVDGQPRQAKGELGLSQMLHIASQIASGMVYLASQHFVHRDLATRNCLVGANLLVKIGDFGMSRDVYSTDYYRLFNPSGNDFCIWCEVGGHTMLPIRWMPPESIMYRKFTTESDVWSFGVILWEIFTYGKQPWFQLSNTEVIECITQGRVLERPRVCPKEVYDVMLGCWQREPQQRLNIKEIYKILHALGKATPIYLDILG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDVSLCPAKCSFWRIFLLGSVWLDYVGSVLACPANCVCSKTEINCRRPDDGNLFPLLEGQDSGNSNGNASINITDISRNITSIHIENWRSLHTLNAVDMELYTGLQKLTIKNSGLRSIQPRAFAKNPHLRYINLSSNRLTTLSWQLFQTLSLRELQLEQNFFNCSCDIRWMQLWQEQGEAKLNSQNLYCINADGSQLPLFRMNISQCDLPEISVSHVNLTVREGDNAVITCNGSGSPLPDVDWIVTGLQSINTHQTNLNWTNVHAINLTLVNVTSEDNGFTLTCIAENVVGMSNASVALTVYYPPRVVSLEEPELRLEHCIEFVVRGNPPPTLHWLHNGQPLRESKIIHVEYYQEGEISEGCLLFNKPTHYNNGNYTLIAKNPLGTANQTINGHFLKEPFPESTDNFILFDEVSPTPPITVTHKPEEDTFGVSIAVGLAAFACVLLVVLFVMINKYGRRSKFGMKGPVAVISGEEDSASPLHHINHGITTPSSLDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQHEHIVKFYGVCGDGDPLIMVFEYMKHGDLNKFLRAHGPDAMILVDGQPRQAKGELGLSQMLHIASQIASGMVYLASQHFVHRDLATRNCLVGANLLVKIGDFGMSRDVYSTDYYRLFNPSGNDFCIWCEVGGHTMLPIRWMPPESIMYRKFTTESDVWSFGVILWEIFTYGKQPWFQLSNTEVIECITQGRVLERPRVCPKEVYDVMLGCWQREPQQRLNIKEIYKILHALGKATPIYLDILG\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the protein 'MDVSLCPAKCSFWRIFLLGSVWLDYVGSVLACPANCVCSKTEINCRRPDDGNLFPLLEGQDSGNSNGNASINITDISRNITSIHIENWRSLHTLNAVDMELYTGLQKLTIKNSGLRSIQPRAFAKNPHLRYINLSSNRLTTLSWQLFQTLSLRELQLEQNFFNCSCDIRWMQLWQEQGEAKLNSQNLYCINADGSQLPLFRMNISQCDLPEISVSHVNLTVREGDNAVITCNGSGSPLPDVDWIVTGLQSINTHQTNLNWTNVHAINLTLVNVTSEDNGFTLTCIAENVVGMSNASVALTVYYPPRVVSLEEPELRLEHCIEFVVRGNPPPTLHWLHNGQPLRESKIIHVEYYQEGEISEGCLLFNKPTHYNNGNYTLIAKNPLGTANQTINGHFLKEPFPESTDNFILFDEVSPTPPITVTHKPEEDTFGVSIAVGLAAFACVLLVVLFVMINKYGRRSKFGMKGPVAVISGEEDSASPLHHINHGITTPSSLDAGPDTVVIGMTRIPVIENPQYFRQGHNCHKPDTYVQHIKRRDIVLKRELGEGAFGKVFLAECYNLSPTKDKMLVAVKALKDPTLAARKDFQREAELLTNLQHEHIVKFYGVCGDGDPLIMVFEYMKHGDLNKFLRAHGPDAMILVDGQPRQAKGELGLSQMLHIASQIASGMVYLASQHFVHRDLATRNCLVGANLLVKIGDFGMSRDVYSTDYYRLFNPSGNDFCIWCEVGGHTMLPIRWMPPESIMYRKFTTESDVWSFGVILWEIFTYGKQPWFQLSNTEVIECITQGRVLERPRVCPKEVYDVMLGCWQREPQQRLNIKEIYKILHALGKATPIYLDILG' is 10000.0."
            },
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drug above for the target protein 'MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1' and the protein 'MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21''s affinity with the target protein 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\"], \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\", \"result\": [{\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21' and the protein 'MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL' is 10000.0."
            },
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug above and the target protein 'MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH\", \"result\": [{\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21' and the protein 'MATCIGEKIEDFKVGNLLGKGSFAGVYRAESIHTGLEVAIKMIDKKAMYKAGMVQRVQNEVKIHCQLKHPSILELYNYFEDSNYVYLVLEMCHNGEMNRYLKNRVKPFSENEARHFMHQIITGMLYLHSHGILHRDLTLSNLLLTRNMNIKIADFGLATQLKMPHEKHYTLCGTPNYISPEIATRSAHGLESDVWSLGCMFYTLLIGRPPFDTDTVKNTLNKVVLADYEMPSFLSIEAKDLIHQLLRRNPADRLSLSSVLDHPFMSRNSSTKSKDLGTVEDSIDSGHATISTAITASSSTSISGSLFDKRRLLIGQPLPNKMTVFPKNKSSTDFSSSGDGNSFYTQWGNQETSNSGRGRVIQDAEERPHSRYLRRAYSSDRSGTSNSQSQAKTYTMERCHSAEMLSVSKRSGGGENEERYSPTDNNANIFNFFKEKTSSSSGSFERPDNNQALSNHLCPGKTPFPFADPTPQTETVQQWFGNLQINAHLRKTTEYDSISPNRDFQGHPDLQKDTSKNAWTDTKVKKNSDASDNAHSVKQQNTMKYMTALHSKPEIIQQECVFGSDPLSEQSKTRGMEPPWGYQNRTLRSITSPLVAHRLKPIRQKTKKAVVSILDSEEVCVELVKEYASQEYVKEVLQISSDGNTITIYYPNGGRGFPLADRPPSPTDNISRYSFDNLPEKYWRKYQYASRFVQLVRSKSPKITYFTRYAKCILMENSPGADFEVWFYDGVKIHKTEDFIQVIEKTGKSYTLKSESEVNSLKEEIKMYMDHANEGHRICLALESIISEEERKTRSAPFFPIIIGRKPGSTSSPKALSPPPSVDSNYPTRERASFNRMVMHSAASPTQAPILNPSMVTNEGLGLTTTASGTDISSNSLKDCLPKSAQLLKSVFVKNVGWATQLTSGAVWVQFNDGSQLVVQAGVSSISYTSPNGQTTRYGENEKLPDYIKQKLQCLSSILLMFSNPTPNFH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the target protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 35.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR' is 35.0."
            },
            {
                "from": "human",
                "value": "Can you provide a prediction for the affinity between the drug above and the target protein 'MSGEVRLRQLEQFILDGPAQTNGQCFSVETLLDILICLYDECNNSPLRREKNILEYLEWAKPFTSKVKQMRLHREDFEILKVIGRGAFGEVAVVKLKNADKVFAMKILNKWEMLKRAETACFREERDVLVNGDNKWITTLHYAFQDDNNLYLVMDYYVGGDLLTLLSKFEDRLPEDMARFYLAEMVIAIDSVHQLHYVHRDIKPDNILMDMNGHIRLADFGSCLKLMEDGTVQSSVAVGTPDYISPEILQAMEDGKGRYGPECDWWSLGVCMYEMLYGETPFYAESLVETYGKIMNHKERFQFPAQVTDVSENAKDLIRRLICSREHRLGQNGIEDFKKHPFFSGIDWDNIRNCEAPYIPEVSSPTDTSNFDVDDDCLKNSETMPPPTHTAFSGHHLPFVGFTYTSSCVLSDRSCLRVTAGPTSLDLDVNVQRTLDNNLATEAYERRIKRLEQEKLELSRKLQESTQTVQALQYSTVDGPLTASKDLEIKNLKEEIEKLRKQVTESSHLEQQLEEANAVRQELDDAFRQIKAYEKQIKTLQQEREDLNKELVQASERLKNQSKELKDAHCQRKLAMQEFMEINERLTELHTQKQKLARHVRDKEEEVDLVMQKVESLRQELRRTERAKKELEVHTEALAAEASKDRKLREQSEHYSKQLENELEGLKQKQISYSPGVCSIEHQQEITKLKTDLEKKSIFYEEELSKREGIHANEIKNLKKELHDSEGQQLALNKEIMILKDKLEKTRRESQSEREEFESEFKQQYEREKVLLTEENKKLTSELDKLTTLYENLSIHNQQLEEEVKDLADKKESVAHWEAQITEIIQWVSDEKDARGYLQALASKMTEELEALRNSSLGTRATDMPWKMRRFAKLDMSARLELQSALDAEIRAKQAIQEELNKVKASNIITECKLKDSEKKNLELLSEIEQLIKDTEELRSEKGIEHQDSQHSFLAFLNTPTDALDQFERSPSCTPASKGRRTVDSTPLSVHTPTLRKKGCPGSTGFPPKRKTHQFFVKSFTTPTKCHQCTSLMVGLIRQGCSCEVCGFSCHITCVNKAPTTCPVPPEQTKGPLGIDPQKGIGTAYEGHVRIPKPAGVKKGWQRALAIVCDFKLFLYDIAEGKASQPSVVISQVIDMRDEEFSVSSVLASDVIHASRKDIPCIFRVTASQLSASNNKCSILMLADTENEKNKWVGVLSELHKILKKNKFRDRSVYVPKEAYDSTLPLIKTTQAAAIIDHERIALGNEEGLFVVHVTKDEIIRVGDNKKIHQIELIPNDQLVAVISGRNRHVRLFPMSALDGRETDFYKLSETKGCQTVTSGKVRHGALTCLCVAMKRQVLCYELFQSKTRHRKFKEIQVPYNVQWMAIFSEQLCVGFQSGFLRYPLNGEGNPYSMLHSNDHTLSFIAHQPMDAICAVEISSKEYLLCFNSIGIYTDCQGRRSRQQELMWPANPSSCCYNAPYLSVYSENAVDIFDVNSMEWIQTLPLKKVRPLNNEGSLNLLGLETIRLIYFKNKMAEGDELVVPETSDNSRKQMVRNINNKRRYSFRVPEEERMQQRREMLRDPEMRNKLISNPTNFNHIAHMGPGDGIQILKDLPMNPRPQESRTVFSGSVSIPSITKSRPEPGRSMSASSGLSARSSAQNGSALKREFSGGSYSAKRQPMPSPSEGSLSSGGMDQGSDAPARDFDGEDSDSPRHSTASNSSNLSSPPSPASPRKTKSLSLESTDRGSWDP'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSGEVRLRQLEQFILDGPAQTNGQCFSVETLLDILICLYDECNNSPLRREKNILEYLEWAKPFTSKVKQMRLHREDFEILKVIGRGAFGEVAVVKLKNADKVFAMKILNKWEMLKRAETACFREERDVLVNGDNKWITTLHYAFQDDNNLYLVMDYYVGGDLLTLLSKFEDRLPEDMARFYLAEMVIAIDSVHQLHYVHRDIKPDNILMDMNGHIRLADFGSCLKLMEDGTVQSSVAVGTPDYISPEILQAMEDGKGRYGPECDWWSLGVCMYEMLYGETPFYAESLVETYGKIMNHKERFQFPAQVTDVSENAKDLIRRLICSREHRLGQNGIEDFKKHPFFSGIDWDNIRNCEAPYIPEVSSPTDTSNFDVDDDCLKNSETMPPPTHTAFSGHHLPFVGFTYTSSCVLSDRSCLRVTAGPTSLDLDVNVQRTLDNNLATEAYERRIKRLEQEKLELSRKLQESTQTVQALQYSTVDGPLTASKDLEIKNLKEEIEKLRKQVTESSHLEQQLEEANAVRQELDDAFRQIKAYEKQIKTLQQEREDLNKELVQASERLKNQSKELKDAHCQRKLAMQEFMEINERLTELHTQKQKLARHVRDKEEEVDLVMQKVESLRQELRRTERAKKELEVHTEALAAEASKDRKLREQSEHYSKQLENELEGLKQKQISYSPGVCSIEHQQEITKLKTDLEKKSIFYEEELSKREGIHANEIKNLKKELHDSEGQQLALNKEIMILKDKLEKTRRESQSEREEFESEFKQQYEREKVLLTEENKKLTSELDKLTTLYENLSIHNQQLEEEVKDLADKKESVAHWEAQITEIIQWVSDEKDARGYLQALASKMTEELEALRNSSLGTRATDMPWKMRRFAKLDMSARLELQSALDAEIRAKQAIQEELNKVKASNIITECKLKDSEKKNLELLSEIEQLIKDTEELRSEKGIEHQDSQHSFLAFLNTPTDALDQFERSPSCTPASKGRRTVDSTPLSVHTPTLRKKGCPGSTGFPPKRKTHQFFVKSFTTPTKCHQCTSLMVGLIRQGCSCEVCGFSCHITCVNKAPTTCPVPPEQTKGPLGIDPQKGIGTAYEGHVRIPKPAGVKKGWQRALAIVCDFKLFLYDIAEGKASQPSVVISQVIDMRDEEFSVSSVLASDVIHASRKDIPCIFRVTASQLSASNNKCSILMLADTENEKNKWVGVLSELHKILKKNKFRDRSVYVPKEAYDSTLPLIKTTQAAAIIDHERIALGNEEGLFVVHVTKDEIIRVGDNKKIHQIELIPNDQLVAVISGRNRHVRLFPMSALDGRETDFYKLSETKGCQTVTSGKVRHGALTCLCVAMKRQVLCYELFQSKTRHRKFKEIQVPYNVQWMAIFSEQLCVGFQSGFLRYPLNGEGNPYSMLHSNDHTLSFIAHQPMDAICAVEISSKEYLLCFNSIGIYTDCQGRRSRQQELMWPANPSSCCYNAPYLSVYSENAVDIFDVNSMEWIQTLPLKKVRPLNNEGSLNLLGLETIRLIYFKNKMAEGDELVVPETSDNSRKQMVRNINNKRRYSFRVPEEERMQQRREMLRDPEMRNKLISNPTNFNHIAHMGPGDGIQILKDLPMNPRPQESRTVFSGSVSIPSITKSRPEPGRSMSASSGLSARSSAQNGSALKREFSGGSYSAKRQPMPSPSEGSLSSGGMDQGSDAPARDFDGEDSDSPRHSTASNSSNLSSPPSPASPRKTKSLSLESTDRGSWDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGEVRLRQLEQFILDGPAQTNGQCFSVETLLDILICLYDECNNSPLRREKNILEYLEWAKPFTSKVKQMRLHREDFEILKVIGRGAFGEVAVVKLKNADKVFAMKILNKWEMLKRAETACFREERDVLVNGDNKWITTLHYAFQDDNNLYLVMDYYVGGDLLTLLSKFEDRLPEDMARFYLAEMVIAIDSVHQLHYVHRDIKPDNILMDMNGHIRLADFGSCLKLMEDGTVQSSVAVGTPDYISPEILQAMEDGKGRYGPECDWWSLGVCMYEMLYGETPFYAESLVETYGKIMNHKERFQFPAQVTDVSENAKDLIRRLICSREHRLGQNGIEDFKKHPFFSGIDWDNIRNCEAPYIPEVSSPTDTSNFDVDDDCLKNSETMPPPTHTAFSGHHLPFVGFTYTSSCVLSDRSCLRVTAGPTSLDLDVNVQRTLDNNLATEAYERRIKRLEQEKLELSRKLQESTQTVQALQYSTVDGPLTASKDLEIKNLKEEIEKLRKQVTESSHLEQQLEEANAVRQELDDAFRQIKAYEKQIKTLQQEREDLNKELVQASERLKNQSKELKDAHCQRKLAMQEFMEINERLTELHTQKQKLARHVRDKEEEVDLVMQKVESLRQELRRTERAKKELEVHTEALAAEASKDRKLREQSEHYSKQLENELEGLKQKQISYSPGVCSIEHQQEITKLKTDLEKKSIFYEEELSKREGIHANEIKNLKKELHDSEGQQLALNKEIMILKDKLEKTRRESQSEREEFESEFKQQYEREKVLLTEENKKLTSELDKLTTLYENLSIHNQQLEEEVKDLADKKESVAHWEAQITEIIQWVSDEKDARGYLQALASKMTEELEALRNSSLGTRATDMPWKMRRFAKLDMSARLELQSALDAEIRAKQAIQEELNKVKASNIITECKLKDSEKKNLELLSEIEQLIKDTEELRSEKGIEHQDSQHSFLAFLNTPTDALDQFERSPSCTPASKGRRTVDSTPLSVHTPTLRKKGCPGSTGFPPKRKTHQFFVKSFTTPTKCHQCTSLMVGLIRQGCSCEVCGFSCHITCVNKAPTTCPVPPEQTKGPLGIDPQKGIGTAYEGHVRIPKPAGVKKGWQRALAIVCDFKLFLYDIAEGKASQPSVVISQVIDMRDEEFSVSSVLASDVIHASRKDIPCIFRVTASQLSASNNKCSILMLADTENEKNKWVGVLSELHKILKKNKFRDRSVYVPKEAYDSTLPLIKTTQAAAIIDHERIALGNEEGLFVVHVTKDEIIRVGDNKKIHQIELIPNDQLVAVISGRNRHVRLFPMSALDGRETDFYKLSETKGCQTVTSGKVRHGALTCLCVAMKRQVLCYELFQSKTRHRKFKEIQVPYNVQWMAIFSEQLCVGFQSGFLRYPLNGEGNPYSMLHSNDHTLSFIAHQPMDAICAVEISSKEYLLCFNSIGIYTDCQGRRSRQQELMWPANPSSCCYNAPYLSVYSENAVDIFDVNSMEWIQTLPLKKVRPLNNEGSLNLLGLETIRLIYFKNKMAEGDELVVPETSDNSRKQMVRNINNKRRYSFRVPEEERMQQRREMLRDPEMRNKLISNPTNFNHIAHMGPGDGIQILKDLPMNPRPQESRTVFSGSVSIPSITKSRPEPGRSMSASSGLSARSSAQNGSALKREFSGGSYSAKRQPMPSPSEGSLSSGGMDQGSDAPARDFDGEDSDSPRHSTASNSSNLSSPPSPASPRKTKSLSLESTDRGSWDP\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 6200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MSGEVRLRQLEQFILDGPAQTNGQCFSVETLLDILICLYDECNNSPLRREKNILEYLEWAKPFTSKVKQMRLHREDFEILKVIGRGAFGEVAVVKLKNADKVFAMKILNKWEMLKRAETACFREERDVLVNGDNKWITTLHYAFQDDNNLYLVMDYYVGGDLLTLLSKFEDRLPEDMARFYLAEMVIAIDSVHQLHYVHRDIKPDNILMDMNGHIRLADFGSCLKLMEDGTVQSSVAVGTPDYISPEILQAMEDGKGRYGPECDWWSLGVCMYEMLYGETPFYAESLVETYGKIMNHKERFQFPAQVTDVSENAKDLIRRLICSREHRLGQNGIEDFKKHPFFSGIDWDNIRNCEAPYIPEVSSPTDTSNFDVDDDCLKNSETMPPPTHTAFSGHHLPFVGFTYTSSCVLSDRSCLRVTAGPTSLDLDVNVQRTLDNNLATEAYERRIKRLEQEKLELSRKLQESTQTVQALQYSTVDGPLTASKDLEIKNLKEEIEKLRKQVTESSHLEQQLEEANAVRQELDDAFRQIKAYEKQIKTLQQEREDLNKELVQASERLKNQSKELKDAHCQRKLAMQEFMEINERLTELHTQKQKLARHVRDKEEEVDLVMQKVESLRQELRRTERAKKELEVHTEALAAEASKDRKLREQSEHYSKQLENELEGLKQKQISYSPGVCSIEHQQEITKLKTDLEKKSIFYEEELSKREGIHANEIKNLKKELHDSEGQQLALNKEIMILKDKLEKTRRESQSEREEFESEFKQQYEREKVLLTEENKKLTSELDKLTTLYENLSIHNQQLEEEVKDLADKKESVAHWEAQITEIIQWVSDEKDARGYLQALASKMTEELEALRNSSLGTRATDMPWKMRRFAKLDMSARLELQSALDAEIRAKQAIQEELNKVKASNIITECKLKDSEKKNLELLSEIEQLIKDTEELRSEKGIEHQDSQHSFLAFLNTPTDALDQFERSPSCTPASKGRRTVDSTPLSVHTPTLRKKGCPGSTGFPPKRKTHQFFVKSFTTPTKCHQCTSLMVGLIRQGCSCEVCGFSCHITCVNKAPTTCPVPPEQTKGPLGIDPQKGIGTAYEGHVRIPKPAGVKKGWQRALAIVCDFKLFLYDIAEGKASQPSVVISQVIDMRDEEFSVSSVLASDVIHASRKDIPCIFRVTASQLSASNNKCSILMLADTENEKNKWVGVLSELHKILKKNKFRDRSVYVPKEAYDSTLPLIKTTQAAAIIDHERIALGNEEGLFVVHVTKDEIIRVGDNKKIHQIELIPNDQLVAVISGRNRHVRLFPMSALDGRETDFYKLSETKGCQTVTSGKVRHGALTCLCVAMKRQVLCYELFQSKTRHRKFKEIQVPYNVQWMAIFSEQLCVGFQSGFLRYPLNGEGNPYSMLHSNDHTLSFIAHQPMDAICAVEISSKEYLLCFNSIGIYTDCQGRRSRQQELMWPANPSSCCYNAPYLSVYSENAVDIFDVNSMEWIQTLPLKKVRPLNNEGSLNLLGLETIRLIYFKNKMAEGDELVVPETSDNSRKQMVRNINNKRRYSFRVPEEERMQQRREMLRDPEMRNKLISNPTNFNHIAHMGPGDGIQILKDLPMNPRPQESRTVFSGSVSIPSITKSRPEPGRSMSASSGLSARSSAQNGSALKREFSGGSYSAKRQPMPSPSEGSLSSGGMDQGSDAPARDFDGEDSDSPRHSTASNSSNLSSPPSPASPRKTKSLSLESTDRGSWDP' is 6200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the target protein 'MSNICQRLWEYLEPYLPCLSTEADKSTVIENPGALCSPQSQRHGHYFVALFDYQARTAEDLSFRAGDKLQVLDTLHEGWWFARHLEKRRDGSSQQLQGYIPSNYVAEDRSLQAEPWFFGAIGRSDAEKQLLYSENKTGSFLIRESESQKGEFSLSVLDGAVVKHYRIKRLDEGGFFLTRRRIFSTLNEFVSHYTKTSDGLCVKLGKPCLKIQVPAPFDLSYKTVDQWEIDRNSIQLLKRLGSGQFGEVWEGLWNNTTPVAVKTLKPGSMDPNDFLREAQIMKNLRHPKLIQLYAVCTLEDPIYIITELMRHGSLQEYLQNDTGSKIHLTQQVDMAAQVASGMAYLESRNYIHRDLAARNVLVGEHNIYKVADFGLARVFKVDNEDIYESRHEIKLPVKWTAPEAIRSNKFSIKSDVWSFGILLYEIITYGKMPYSGMTGAQVIQMLAQNYRLPQPSNCPQQFYNIMLECWNAEPKERPTFETLRWKLEDYFETDSSYSDANNFIR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"MSNICQRLWEYLEPYLPCLSTEADKSTVIENPGALCSPQSQRHGHYFVALFDYQARTAEDLSFRAGDKLQVLDTLHEGWWFARHLEKRRDGSSQQLQGYIPSNYVAEDRSLQAEPWFFGAIGRSDAEKQLLYSENKTGSFLIRESESQKGEFSLSVLDGAVVKHYRIKRLDEGGFFLTRRRIFSTLNEFVSHYTKTSDGLCVKLGKPCLKIQVPAPFDLSYKTVDQWEIDRNSIQLLKRLGSGQFGEVWEGLWNNTTPVAVKTLKPGSMDPNDFLREAQIMKNLRHPKLIQLYAVCTLEDPIYIITELMRHGSLQEYLQNDTGSKIHLTQQVDMAAQVASGMAYLESRNYIHRDLAARNVLVGEHNIYKVADFGLARVFKVDNEDIYESRHEIKLPVKWTAPEAIRSNKFSIKSDVWSFGILLYEIITYGKMPYSGMTGAQVIQMLAQNYRLPQPSNCPQQFYNIMLECWNAEPKERPTFETLRWKLEDYFETDSSYSDANNFIR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSNICQRLWEYLEPYLPCLSTEADKSTVIENPGALCSPQSQRHGHYFVALFDYQARTAEDLSFRAGDKLQVLDTLHEGWWFARHLEKRRDGSSQQLQGYIPSNYVAEDRSLQAEPWFFGAIGRSDAEKQLLYSENKTGSFLIRESESQKGEFSLSVLDGAVVKHYRIKRLDEGGFFLTRRRIFSTLNEFVSHYTKTSDGLCVKLGKPCLKIQVPAPFDLSYKTVDQWEIDRNSIQLLKRLGSGQFGEVWEGLWNNTTPVAVKTLKPGSMDPNDFLREAQIMKNLRHPKLIQLYAVCTLEDPIYIITELMRHGSLQEYLQNDTGSKIHLTQQVDMAAQVASGMAYLESRNYIHRDLAARNVLVGEHNIYKVADFGLARVFKVDNEDIYESRHEIKLPVKWTAPEAIRSNKFSIKSDVWSFGILLYEIITYGKMPYSGMTGAQVIQMLAQNYRLPQPSNCPQQFYNIMLECWNAEPKERPTFETLRWKLEDYFETDSSYSDANNFIR\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 2700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MSNICQRLWEYLEPYLPCLSTEADKSTVIENPGALCSPQSQRHGHYFVALFDYQARTAEDLSFRAGDKLQVLDTLHEGWWFARHLEKRRDGSSQQLQGYIPSNYVAEDRSLQAEPWFFGAIGRSDAEKQLLYSENKTGSFLIRESESQKGEFSLSVLDGAVVKHYRIKRLDEGGFFLTRRRIFSTLNEFVSHYTKTSDGLCVKLGKPCLKIQVPAPFDLSYKTVDQWEIDRNSIQLLKRLGSGQFGEVWEGLWNNTTPVAVKTLKPGSMDPNDFLREAQIMKNLRHPKLIQLYAVCTLEDPIYIITELMRHGSLQEYLQNDTGSKIHLTQQVDMAAQVASGMAYLESRNYIHRDLAARNVLVGEHNIYKVADFGLARVFKVDNEDIYESRHEIKLPVKWTAPEAIRSNKFSIKSDVWSFGILLYEIITYGKMPYSGMTGAQVIQMLAQNYRLPQPSNCPQQFYNIMLECWNAEPKERPTFETLRWKLEDYFETDSSYSDANNFIR' is 2700.0."
            },
            {
                "from": "human",
                "value": "What's your estimation of the binding affinity between the drug above and the target protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the binding affinity between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the target protein 'MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 2500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein 'MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV' is 2500.0."
            },
            {
                "from": "human",
                "value": "What's your forecast for the drug above's affinity towards the target protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the target protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS' is 10000.0."
            },
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the drug above and the target protein 'MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF'? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF\", \"result\": [{\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the protein 'MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the expected binding affinity of the drug 'Nc1nc2c(ncn2COC(CO)CO)c(=O)[nH]1' for the target protein 'MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc2c(ncn2COC(CO)CO)c(=O)[nH]1\"], \"target_seq\": \"MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF\", \"result\": [{\"smile\": \"Nc1nc2c(ncn2COC(CO)CO)c(=O)[nH]1\", \"value\": 15849.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1nc2c(ncn2COC(CO)CO)c(=O)[nH]1' and the protein 'MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF' is 15849.0."
            },
            {
                "from": "human",
                "value": "And what is the expected affinity value when relating the drug 'O=c1[nH]cnc2ccccc12' to the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1[nH]cnc2ccccc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF\", \"result\": [{\"smile\": \"O=c1[nH]cnc2ccccc12\", \"value\": 316228.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=c1[nH]cnc2ccccc12' and the protein 'MADPRPDPDELARRAAQVIADRTGIGEHDVAVVLGSGWLPAVAALGSPTTVLPQAELPGFVPPTAAGHAGELLSVPIGAHRVLVLAGRIHAYEGHDLRYVVHPVRAARAAGAQIMVLTNAAGGLRADLQVGQPVLISDHLNLTARSPLVGGEFVDLTDAYSPRLRELARQSDPQLAEGVYAGLPGPHYETPAEIRMLQTLGADLVGMSTVHETIAARAAGAEVLGVSLVTNLAAGITGEPLSHAEVLAAGAASATRMGALLADVIARF' is 316228.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity value of the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' for the target protein 'MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS' is 500.0."
            },
            {
                "from": "human",
                "value": "And assess the binding affinity of the drug 'O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl' with the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS\", \"result\": [{\"smile\": \"O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl' and the protein 'MATGGRRGAAAAPLLVAVAALLLGAAGHLYPGEVCPGMDIRNNLTRLHELENCSVIEGHLQILLMFKTRPEDFRDLSFPKLIMITDYLLLFRVYGLESLKDLFPNLTVIRGSRLFFNYALVIFEMVHLKELGLYNLMNITRGSVRIEKNNELCYLATIDWSRILDSVEDNYIVLNKDDNEECGDICPGTAKGKTNCPATVINGQFVERCWTHSHCQKVCPTICKSHGCTAEGLCCHSECLGNCSQPDDPTKCVACRNFYLDGRCVETCPPPYYHFQDWRCVNFSFCQDLHHKCKNSRRQGCHQYVIHNNKCIPECPSGYTMNSSNLLCTPCLGPCPKVCHLLEGEKTIDSVTSAQELRGCTVINGSLIINIRGGNNLAAELEANLGLIEEISGYLKIRRSYALVSLSFFRKLRLIRGETLEIGNYSFYALDNQNLRQLWDWSKHNLTITQGKLFFHYNPKLCLSEIHKMEEVSGTKGRQERNDIALKTNGDQASCENELLKFSYIRTSFDKILLRWEPYWPPDFRDLLGFMLFYKEAPYQNVTEFDGQDACGSNSWTVVDIDPPLRSNDPKSQNHPGWLMRGLKPWTQYAIFVKTLVTFSDERRTYGAKSDIIYVQTDATNPSVPLDPISVSNSSSQIILKWKPPSDPNGNITHYLVFWERQAEDSELFELDYCLKGLKLPSRTWSPPFESEDSQKHNQSEYEDSAGECCSCPKTDSQILKELEESSFRKTFEDYLHNVVFVPRKTSSGTGAEDPRPSRKRRSLGDVGNVTVAVPTVAAFPNTSSTSVPTSPEEHRPFEKVVNKESLVISGLRHFTGYRIELQACNQDTPEERCSVAAYVSARTMPEAKADDIVGPVTHEIFENNVVHLMWQEPKEPNGLIVLYEVSYRRYGDEELHLCVSRKHFALERGCRLRGLSPGNYSVRIRATSLAGNGSWTEPTYFYVTDYLDVPSNIAKIIIGPLIFVFLFSVVIGSIYLFLRKRQPDGPLGPLYASSNPEYLSASDVFPCSVYVPDEWEVSREKITLLRELGQGSFGMVYEGNARDIIKGEAETRVAVKTVNESASLRERIEFLNEASVMKGFTCHHVVRLLGVVSKGQPTLVVMELMAHGDLKSYLRSLRPEAENNPGRPPPTLQEMIQMAAEIADGMAYLNAKKFVHRDLAARNCMVAHDFTVKIGDFGMTRDIYETDYYRKGGKGLLPVRWMAPESLKDGVFTTSSDMWSFGVVLWEITSLAEQPYQGLSNEQVLKFVMDGGYLDQPDNCPERVTDLMRMCWQFNPKMRPTFLEIVNLLKDDLHPSFPEVSFFHSEENKAPESEELEMEFEDMENVPLDRSSHCQREEAGGRDGGSSLGFKRSYEEHIPYTHMNGGKKNGRILTLPRSNPS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the drug 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1''s affinity for the target protein 'MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\"], \"target_seq\": \"MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL\", \"result\": [{\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1' and the protein 'MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL' is 10000.0."
            },
            {
                "from": "human",
                "value": "And infer the expected affinity of the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' towards the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL\", \"result\": [{\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' and the protein 'MGANTSRKPPVFDENEDVNFDHFEILRAIGKGSFGKVCIVQKNDTKKMYAMKYMNKQKCVERNEVRNVFKELQIMQGLEHPFLVNLWYSFQDEEDMFMVVDLLLGGDLRYHLQQNVHFKEETVKLFICELVMALDYLQNQRIIHRDMKPDNILLDEHGHVHITDFNIAAMLPRETQITTMAGTKPYMAPEMFSSRKGAGYSFAVDWWSLGVTAYELLRGRRPYHIRSSTSSKEIVHTFETTVVTYPSAWSQEMVSLLKKLLEPNPDQRFSQLSDVQNFPYMNDINWDAVFQKRLIPGFIPNKGRLNCDPTFELEEMILESKPLHKKKKRLAKKEKDMRKCDSSQTCLLQEHLDSVQKEFIIFNREKVNRDFNKRQPNLALEQTKDPQGEDGQNNNL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the target protein 'MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT' is 10000.0."
            },
            {
                "from": "human",
                "value": "And how would you assess the drug 'Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21''s affinity for the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT\", \"result\": [{\"smile\": \"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21' and the protein 'MELLPPLPQSFLLLLLLPAKPAAGEDWQCPRTPYAASRDFDVKYVVPSFSAGGLVQAMVTYEGDRNESAVFVAIRNRLHVLGPDLKSVQSLATGPAGDPGCQTCAACGPGPHGPPGDTDTKVLVLDPALPALVSCGSSLQGRCFLHDLEPQGTAVHLAAPACLFSAHHNRPDDCPDCVASPLGTRVTVVEQGQASYFYVASSLDAAVAASFSPRSVSIRRLKADASGFAPGFVALSVLPKHLVSYSIEYVHSFHTGAFVYFLTVQPASVTDDPSALHTRLARLSATEPELGDYRELVLDCRFAPKRRRRGAPEGGQPYPVLRVAHSAPVGAQLATELSIAEGQEVLFGVFVTGKDGGPGVGPNSVVCAFPIDLLDTLIDEGVERCCESPVHPGLRRGLDFFQSPSFCPNPPGLEALSPNTSCRHFPLLVSSSFSRVDLFNGLLGPVQVTALYVTRLDNVTVAHMGTMDGRILQVELVRSLNYLLYVSNFSLGDSGQPVQRDVSRLGDHLLFASGDQVFQVPIQGPGCRHFLTCGRCLRAWHFMGCGWCGNMCGQQKECPGSWQQDHCPPKLTEFHPHSGPLRGSTRLTLCGSNFYLHPSGLVPEGTHQVTVGQSPCRPLPKDSSKLRPVPRKDFVEEFECELEPLGTQAVGPTNVSLTVTNMPPGKHFRVDGTSVLRGFSFMEPVLIAVQPLFGPRAGGTCLTLEGQSLSVGTSRAVLVNGTECLLARVSEGQLLCATPPGATVASVPLSLQVGGAQVPGSWTFQYREDPVVLSISPNCGYINSHITICGQHLTSAWHLVLSFHDGLRAVESRCERQLPEQQLCRLPEYVVRDPQGWVAGNLSARGDGAAGFTLPGFRFLPPPHPPSANLVPLKPEEHAIKFEYIGLGAVADCVGINVTVGGESCQHEFRGDMVVCPLPPSLQLGQDGAPLQVCVDGECHILGRVVRPGPDGVPQSTLLGILLPLLLLVAALATALVFSYWWRRKQLVLPPNLNDLASLDQTAGATPLPILYSGSDYRSGLALPAIDGLDSTTCVHGASFSDSEDESCVPLLRKESIQLRDLDSALLAEVKDVLIPHERVVTHSDRVIGKGHFGVVYHGEYIDQAQNRIQCAIKSLSRITEMQQVEAFLREGLLMRGLNHPNVLALIGIMLPPEGLPHVLLPYMCHGDLLQFIRSPQRNPTVKDLISFGLQVARSMEYLAEQKFVHRDLAARNCMLDESFTVKVADFGLARDILDREYYSVQQHRHARLPVKWMALESLQTYRFTTKSDVWSFGVLLWELLTRGAPPYRHIDPFDLTHFLAQGRRLPQPEYCPDSLYQVMQQCWEADPAVRPTFRVLVGEVEQIVSALLGDHYVQLPATYMNLGPSTSHEMNVRPEQPQFSPMPGNVRRPRPLSEPPRPT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the target protein 'MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL' is 10000.0."
            },
            {
                "from": "human",
                "value": "And how would you quantify the predicted affinity for the drug 'C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl'-target protein mentioned above relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the protein 'MADSGLDKKSTKCPDCSSASQKDVLCVCSSKTRVPPVLVVEMSQTSSIGSAESLISLERKKEKNINRDITSRKDLPSRTSNVERKASQQQWGRGNFTEGKVPHIRIENGAAIEEIYTFGRILGKGSFGIVIEATDKETETKWAIKKVNKEKAGSSAVKLLEREVNILKSVKHEHIIHLEQVFETPKKMYLVMELCEDGELKEILDRKGHFSENETRWIIQSLASAIAYLHNNDIVHRDLKLENIMVKSSLIDDNNEINLNIKVTDFGLAVKKQSRSEAMLQATCGTPIYMAPEVISAHDYSQQCDIWSIGVVMYMLLRGEPPFLASSEEKLFELIRKGELHFENAVWNSISDCAKSVLKQLMKVDPAHRITAKELLDNQWLTGNKLSSVRPTNVLEMMKEWKNNPESVEENTTEEKNKPSTEEKLKSYQPWGNVPDANYTSDEEEEKQSTAYEKQFPATSKDNFDMCSSSFTSSKLLPAEIKGEMEKTPVTPSQGTATKYPAKSGALSRTKKKL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction for the affinity between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the target protein 'MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL\", \"result\": [{\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 2700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein 'MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL' is 2700.0."
            },
            {
                "from": "human",
                "value": "And what is the expected affinity level for the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 1300.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the protein 'MGCVQCKDKEATKLTEERDGSLNQSSGYRYGTDPTPQHYPSFGVTSIPNYNNFHAAGGQGLTVFGGVNSSSHTGTLRTRGGTGVTLFVALYDYEARTEDDLSFHKGEKFQILNSSEGDWWEARSLTTGETGYIPSNYVAPVDSIQAEEWYFGKLGRKDAERQLLSFGNPRGTFLIRESETTKGAYSLSIRDWDDMKGDHVKHYKIRKLDNGGYYITTRAQFETLQQLVQHYSERAAGLCCRLVVPCHKGMPRLTDLSVKTKDVWEIPRESLQLIKRLGNGQFGEVWMGTWNGNTKVAIKTLKPGTMSPESFLEEAQIMKKLKHDKLVQLYAVVSEEPIYIVTEYMNKGSLLDFLKDGEGRALKLPNLVDMAAQVAAGMAYIERMNYIHRDLRSANILVGNGLICKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELVTKGRVPYPGMNNREVLEQVERGYRMPCPQDCPISLHELMIHCWKKDPEERPTFEYLQSFLEDYFTATEPQYQPGENL' is 1300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction affinity for the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the target protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 1300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS' is 1300.0."
            },
            {
                "from": "human",
                "value": "And assess the projected affinity value between the drug 'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\", \"result\": [{\"smile\": \"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\", \"value\": 2300.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1' and the protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS' is 2300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1''s affinity for the target protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\"], \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\", \"result\": [{\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA' is 10000.0."
            },
            {
                "from": "human",
                "value": "And what is the anticipated interaction strength between the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\", \"result\": [{\"smile\": \"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' and the target protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS' is 1200.0."
            },
            {
                "from": "human",
                "value": "And how do you determine the affinity value of the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' for the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 69.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MKAPAVLAPGILVLLFTLVQRSNGECKEALAKSEMNVNMKYQLPNFTAETPIQNVILHEHHIFLGATNYIYVLNEEDLQKVAEYKTGPVLEHPDCFPCQDCSSKANLSGGVWKDNINMALVVDTYYDDQLISCGSVNRGTCQRHVFPHNHTADIQSEVHCIFSPQIEEPSQCPDCVVSALGAKVLSSVKDRFINFFVGNTINSSYFPDHPLHSISVRRLKETKDGFMFLTDQSYIDVLPEFRDSYPIKYVHAFESNNFIYFLTVQRETLDAQTFHTRIIRFCSINSGLHSYMEMPLECILTEKRKKRSTKKEVFNILQAAYVSKPGAQLARQIGASLNDDILFGVFAQSKPDSAEPMDRSAMCAFPIKYVNDFFNKIVNKNNVRCLQHFYGPNHEHCFNRTLLRNSSGCEARRDEYRTEFTTALQRVDLFMGQFSEVLLTSISTFIKGDLTIANLGTSEGRFMQVVVSRSGPSTPHVNFLLDSHPVSPEVIVEHTLNQNGYTLVITGKKITKIPLNGLGCRHFQSCSQCLSAPPFVQCGWCHDKCVRSEECLSGTWTQQICLPAIYKVFPNSAPLEGGTRLTICGWDFGFRRNNKFDLKKTRVLLGNESCTLTLSESTMNTLKCTVGPAMNKHFNMSIIISNGHGTTQYSTFSYVDPVITSISPKYGPMAGGTLLTLTGNYLNSGNSRHISIGGKTCTLKSVSNSILECYTPAQTISTEFAVKLKIDLANRETSIFSYREDPIVYEIHPTKSFISGGSTITGVGKNLNSVSVPRMVINVHEAGRNFTVACQHRSNSEIICCTTPSLQQLNLQLPLKTKAFFMLDGILSKYFDLIYVHNPVFKPFEKPVMISMGNENVLEIKGNDIDPEAVKGEVLKVGNKSCENIHLHSEAVLCTVPNDLLKLNSELNIEWKQAISSTVLGKVIVQPDQNFTGLIAGVVSISTALLLLLGFFLWLKKRKQIKDLGSELVRYDARVHTPHLDRLVSARSVSPTTEMVSNESVDYRATFPEDQFPNSSQNGSCRQVQYPLTDMSPILTSGDSDISSPLLQNTVHIDLSALNPELVQAVQHVVIGPSSLIVHFNEVIGRGHFGCVYHGTLLDNDGKKIHCAVKSLNRITDIGEVSQFLTEGIIMKDFSHPNVLSLLGICLRSEGSPLVVLPYMKHGDLRNFIRNETHNPTVKDLIGFGLQVAKGMKYLASKKFVHRDLAARNCMLDEKFTVKVADFGLARDMYDKEYYSVHNKTGAKLPVKWMALESLQTQKFTTKSDVWSFGVLLWELMTRGAPPYPDVNTFDITVYLLQGRRLLQPEYCPDPLYEVMLKCWHPKAEMRPSFSELVSRISAIFSTFIGEHYVHVNATYVNVKCVAPYPSLLSSEDNADDEVDTRPASFWETS' is 69.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the binding affinity between the drug 'C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the target protein 'MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\"], \"target_seq\": \"MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the protein 'MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV' is 10000.0."
            },
            {
                "from": "human",
                "value": "And how do you foresee the affinity between the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV\", \"result\": [{\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1' and the target protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\"], \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\", \"value\": 2000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1' and the protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA' is 2000.0."
            },
            {
                "from": "human",
                "value": "And what's your forecast for the affinity between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\", \"result\": [{\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 1.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'CN(C)C1CCN(C(=O)c2ccc(NC(=O)Nc3ccc(-c4nc(N5CCOCC5)nc(N5CCOCC5)n4)cc3)cc2)CC1' and the target protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C1CCN(C(=O)c2ccc(NC(=O)Nc3ccc(-c4nc(N5CCOCC5)nc(N5CCOCC5)n4)cc3)cc2)CC1\"], \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"CN(C)C1CCN(C(=O)c2ccc(NC(=O)Nc3ccc(-c4nc(N5CCOCC5)nc(N5CCOCC5)n4)cc3)cc2)CC1\", \"value\": 76.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C1CCN(C(=O)c2ccc(NC(=O)Nc3ccc(-c4nc(N5CCOCC5)nc(N5CCOCC5)n4)cc3)cc2)CC1' and the protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ' is 76.0."
            },
            {
                "from": "human",
                "value": "And assess the predicted value of the affinity between the drug 'Cc1nc2c(N)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CCOCC1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc2c(N)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CCOCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"Cc1nc2c(N)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CCOCC1\", \"value\": 15.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nc2c(N)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CCOCC1' and the protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ' is 15.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' and the target protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH' is 1.0."
            },
            {
                "from": "human",
                "value": "And what is the expected affinity level for the drug 'CCOc1cc2c(Oc3cccc(NC(=O)Nc4cc(C(C)(CF)CF)on4)c3)ncnc2cc1OC' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2c(Oc3cccc(NC(=O)Nc4cc(C(C)(CF)CF)on4)c3)ncnc2cc1OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\": \"CCOc1cc2c(Oc3cccc(NC(=O)Nc4cc(C(C)(CF)CF)on4)c3)ncnc2cc1OC\", \"value\": 250.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2c(Oc3cccc(NC(=O)Nc4cc(C(C)(CF)CF)on4)c3)ncnc2cc1OC' and the protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH' is 250.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding strength of the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' to the target protein 'MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\"], \"target_seq\": \"MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS\", \"result\": [{\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 1500.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the protein 'MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS' is 1500.0."
            },
            {
                "from": "human",
                "value": "And determine the affinity value of the drug 'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1' for the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1' and the protein 'MEIDMTYQLKVIQQSLEQEEAEHKATKARLADKNKIYESIEEAKSEAMKEMEKKLLEERTLKQKVENLLLEAEKRCSLLDCDLKQSQQKINELLKQKDVLNEDVRNLTLKIEQETQKRCLTQNDLKMQTQQVNTLKMSEKQLKQENNHLMEMKMNLEKQNAELRKERQDADGQMKELQDQLEAEQYFSTLYKTQVRELKEECEEKTKLGKELQQKKQELQDERDSLAAQLEITLTKADSEQLARSIAEEQYSDLEKEKIMKELEIKEMMARHKQELTEKDATIASLEETNRTLTSDVANLANEKEELNNKLKDVQEQLSRLKDEEISAAAIKAQFEKQLLTERTLKTQAVNKLAEIMNRKEPVKRGNDTDVRRKEKENRKLHMELKSEREKLTQQMIKYQKELNEMQAQIAEESQIRIELQMTLDSKDSDIEQLRSQLQALHIGLDSSSIGSGPGDAEADDGFPESRLEGWLSLPVRNNTKKFGWVKKYVIVSSKKILFYDSEQDKEQSNPYMVLDIDKLFHVRPVTQTDVYRADAKEIPRIFQILYANEGESKKEQEFPVEPVGEKSNYICHKGHEFIPTLYHFPTNCEACMKPLWHMFKPPPALECRRCHIKCHKDHMDKKEEIIAPCKVYYDISTAKNLLLLANSTEEQQKWVSRLVKKIPKKPPAPDPFARSSPRTSMKIQQNQSIRRPSRQLAPNKPS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug 'CN(C)c1ccc2c(c1)OC1=CC(N(C)C)C=CC1=C2c1ccc(C(=O)O)cc1C(=O)N[C@H](CCCCN)C(=O)NC(N)=NCCC[C@@H](NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)CCCCCNCCNS(=O)(=O)c1cccc2cnccc12)C(N)=O' and the target protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)c1ccc2c(c1)OC1=CC(N(C)C)C=CC1=C2c1ccc(C(=O)O)cc1C(=O)N[C@H](CCCCN)C(=O)NC(N)=NCCC[C@@H](NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)CCCCCNCCNS(=O)(=O)c1cccc2cnccc12)C(N)=O\"], \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\", \"result\": [{\"smile\": \"CN(C)c1ccc2c(c1)OC1=CC(N(C)C)C=CC1=C2c1ccc(C(=O)O)cc1C(=O)N[C@H](CCCCN)C(=O)NC(N)=NCCC[C@@H](NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)CCCCCNCCNS(=O)(=O)c1cccc2cnccc12)C(N)=O\", \"value\": 3.2}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)c1ccc2c(c1)OC1=CC(N(C)C)C=CC1=C2c1ccc(C(=O)O)cc1C(=O)N[C@H](CCCCN)C(=O)NC(N)=NCCC[C@@H](NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)[C@@H](CCCN=C(N)N)NC(=O)CCCCCNCCNS(=O)(=O)c1cccc2cnccc12)C(N)=O' and the protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF' is 3.2."
            },
            {
                "from": "human",
                "value": "And estimate the binding strength of the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' to the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 1.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the protein 'MSELEEDFAKILMLKEERIKELEKRLSEKEEEIQELKRKLHKCQSVLPVPSTHIGPRTTRAQGISAEPQTYRSFHDLRQAFRKFTKSERSKDLIKEAILDNDFMKNLELSQIQEIVDCMYPVEYGKDSCIIKEGDVGSLVYVMEDGKVEVTKEGVKLCTMGPGKVFGELAILYNCTRTATVKTLVNVKLWAIDRQCFQTIMMRTGLIKHTEYMEFLKSVPTFQSLPEEILSKLADVLEETHYENGEYIIRQGARGDTFFIISKGTVNVTREDSPSEDPVFLRTLGKGDWFGEKALQGEDVRTANVIAAEAVTCLVIDRDSFKHLIGGLDDVSNKAYEDAEAKAKYEAEAAFFANLKLSDFNIIDTLGVGGFGRVELVQLKSEESKTFAMKILKKRHIVDTRQQEHIRSEKQIMQGAHSDFIVRLYRTFKDSKYLYMLMEACLGGELWTILRDRGSFEDSTTRFYTACVVEAFAYLHSKGIIYRDLKPENLILDHRGYAKLVDFGFAKKIGFGKKTWTFCGTPEYVAPEIILNKGHDISADYWSLGILMYELLTGSPPFSGPDPMKTYNIILRGIDMIEFPKKIAKNAANLIKKLCRDNPSERLGNLKNGVKDIQKHKWFEGFNWEGLRKGTLTPPIIPSVASPTDTSNFDSFPEDNDEPPPDDNSGWDIDF' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the expected affinity of the drug 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' towards the target protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\"], \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' and the protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH' is 10000.0."
            },
            {
                "from": "human",
                "value": "And can you calculate the expected binding strength of the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' toward the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MLSNSQGQSPPVPFPAPAPPPQPPTPALPHPPAQPPPPPPQQFPQFHVKSGLQIKKNAIIDDYKVTSQVLGLGINGKVLQIFNKRTQEKFALKMLQDCPKARREVELHWRASQCPHIVRIVDVYENLYAGRKCLLIVMECLDGGELFSRIQDRGDQAFTEREASEIMKSIGEAIQYLHSINIAHRDVKPENLLYTSKRPNAILKLTDFGFAKETTSHNSLTTPCYTPYYVAPEVLGPEKYDKSCDMWSLGVIMYILLCGYPPFYSNHGLAISPGMKTRIRMGQYEFPNPEWSEVSEEVKMLIRNLLKTEPTQRMTITEFMNHPWIMQSTKVPQTPLHTSRVLKEDKERWEDVKEEMTSALATMRVDYEQIKIKKIEDASNPLLLKRRKKARALEAAALAH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug 'O=C(NCCNCC(O)COc1ccc(OCCOCCc2ccc(F)cc2)cc1)Nc1ccc(O)cc1' with the target protein 'MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NCCNCC(O)COc1ccc(OCCOCCc2ccc(F)cc2)cc1)Nc1ccc(O)cc1\"], \"target_seq\": \"MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV\", \"result\": [{\"smile\": \"O=C(NCCNCC(O)COc1ccc(OCCOCCc2ccc(F)cc2)cc1)Nc1ccc(O)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NCCNCC(O)COc1ccc(OCCOCCc2ccc(F)cc2)cc1)Nc1ccc(O)cc1' and the protein 'MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV' is 1.0."
            },
            {
                "from": "human",
                "value": "And how would you quantify the predicted affinity for the drug 'O=C(CCCCCNC(=O)COc1ccc(/C=C/c2ccc3n2[B-](F)(F)[N+]2=C(c4cccs4)C=CC2=C3)cc1)NCCCCNC[C@H](O)COc1cccc2ccccc12'-target protein mentioned above relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(CCCCCNC(=O)COc1ccc(/C=C/c2ccc3n2[B-](F)(F)[N+]2=C(c4cccs4)C=CC2=C3)cc1)NCCCCNC[C@H](O)COc1cccc2ccccc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV\", \"result\": [{\"smile\": \"O=C(CCCCCNC(=O)COc1ccc(/C=C/c2ccc3n2[B-](F)(F)[N+]2=C(c4cccs4)C=CC2=C3)cc1)NCCCCNC[C@H](O)COc1cccc2ccccc12\", \"value\": 13.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(CCCCCNC(=O)COc1ccc(/C=C/c2ccc3n2[B-](F)(F)[N+]2=C(c4cccs4)C=CC2=C3)cc1)NCCCCNC[C@H](O)COc1cccc2ccccc12' and the protein 'MGAGVLVLGASEPGNLSSAAPLPDGAATAARLLVPASPPASLLPPASESPEPLSQQWTAGMGLLMALIVLLIVAGNVLVIVAIAKTPRLQTLTNLFIMSLASADLVMGLLVVPFGATIVVWGRWEYGSFFCELWTSVDVLCVTASIETLCVIALDRYLAITSPFRYQSLLTRARARGLVCTVWAISALVSFLPILMHWWRAESDEARRCYNDPKCCDFVTNRAYAIASSVVSFYVPLCIMAFVYLRVFREAQKQVKKIDSCERRFLGGPARPPSPSPSPVPAPAPPPGPPRPAAAAATAPLANGRAGKRRPSRLVALREQKALKTLGIIMGVFTLCWLPFFLANVVKAFHRELVPDRLFVFFNWLGYANSAFNPIIYCRSPDFRKAFQRLLCCARRAARRRHATHGDRPRASGCLARPGPPPSPGAASDDDDDDVVGATPPARLLEPWAGCNGGAAADSDSSLDEPCRPGFASESKV' is 13.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1'-target protein 'MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 190.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS' is 190.0."
            },
            {
                "from": "human",
                "value": "And what's the predicted value for the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl''s affinity with the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\", \"result\": [{\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl' and the protein 'MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'O=C(O)CC/C=C\\CO[C@H]1[C@@H](OCc2ccc(-c3ccc(CO)cc3)cc2)CC[C@@H]1N1CCCCCC1'-target protein 'MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)CC/C=C\\CO[C@H]1[C@@H](OCc2ccc(-c3ccc(CO)cc3)cc2)CC[C@@H]1N1CCCCCC1\"], \"target_seq\": \"MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP\", \"result\": [{\"smile\": \"O=C(O)CC/C=C\\CO[C@H]1[C@@H](OCc2ccc(-c3ccc(CO)cc3)cc2)CC[C@@H]1N1CCCCCC1\", \"value\": 19.95}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)CC/C=C\\CO[C@H]1[C@@H](OCc2ccc(-c3ccc(CO)cc3)cc2)CC[C@@H]1N1CCCCCC1' and the protein 'MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP' is 19.95."
            },
            {
                "from": "human",
                "value": "And what is the expected affinity level for the drug 'CCCCc1ccncc1-c1ccc(CO[C@@H]2CC[C@H](N3CCCCCC3)[C@H]2OC/C=C\\CCC(=O)O)cc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCc1ccncc1-c1ccc(CO[C@@H]2CC[C@H](N3CCCCCC3)[C@H]2OC/C=C\\CCC(=O)O)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP\", \"result\": [{\"smile\": \"CCCCc1ccncc1-c1ccc(CO[C@@H]2CC[C@H](N3CCCCCC3)[C@H]2OC/C=C\\CCC(=O)O)cc1\", \"value\": 10.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCc1ccncc1-c1ccc(CO[C@@H]2CC[C@H](N3CCCCCC3)[C@H]2OC/C=C\\CCC(=O)O)cc1' and the protein 'MWLNSTSLGACFRPVNITLQERRAIASPWFAASFCALGLGSNLLALSVLAGARPGAGPRSSFLALLCGLVLTDFLGLLVTGAVVASQHAALLDWRATDPGCRLCHFMGAAMVFFGLCPLLLGAAMAAERFVGITRPFSRPAATSRRAWATVGLVWVGAGTLGLLPLLGLGRYSVQYPGSWCFLTLGAERGDVAFGLMFALLGSVSVGLSLLLNTVSVATLCRVYHAREATQRPRDCEVEMMVQLVGIMVVATVCWMPLLVFILQTLLQTLPVMSPSGQLLRTTERQLLIYLRVATWNQILDPWVYILFRRSVLRRLHPRFTSQLQAVSLHSPPTQAMLSGP' is 10.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'COc1ccc(NC(=O)c2cnc3ccccc3n2)cc1' and the target protein 'MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(NC(=O)c2cnc3ccccc3n2)cc1\"], \"target_seq\": \"MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA\", \"result\": [{\"smile\": \"COc1ccc(NC(=O)c2cnc3ccccc3n2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc(NC(=O)c2cnc3ccccc3n2)cc1' and the protein 'MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA' is 10000.0."
            },
            {
                "from": "human",
                "value": "And calculate the binding affinity between the drug 'CCOc1nc2ccccc2nc1C(=O)Nc1ccc(N(CC)CC)cc1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1nc2ccccc2nc1C(=O)Nc1ccc(N(CC)CC)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA\", \"result\": [{\"smile\": \"CCOc1nc2ccccc2nc1C(=O)Nc1ccc(N(CC)CC)cc1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1nc2ccccc2nc1C(=O)Nc1ccc(N(CC)CC)cc1' and the protein 'MVLWLQLALLALLLPTSLAQGEVRGKGTAQAHNSTRPALQRLSDHLLADYRKSVRPVRDWRKPTTVSIDAIVYAILSVDEKNQVLTTYIWYRQFWTDEFLQWNPEDFDNITKLSIPTDSIWVPDILINEFVDVGKSPNIPYVYVRHQGEVQNYKPLQVVTACSLDIYNFPFDVQNCSLTFTSWLHTIQDINISLWRLPEKVKSDKSVFMNQGEWELLGVLTEFLEFSDRESRGSFAEMKFYVVIRRRPLFYAVTLLLPSIFLMIVDIVGFYLPPDSGERVSFKITLLLGYSVFLIIVSDTLPATAIGTPLISVYFVVCMALLVISLAETILIVRLVHKQDLQQPVPLWLRHLVLERIAGLLCLGEQLTSHRGPATLQATKTDDFSGSTLLPAMGNHCGPLGGPQDLEKTSRGRGSPPPPPREASLAMCGLLQELASIRHFLEKREETREVARDWLRVGSVLDKLLFRVYLLAVLAYSITLVTLWSVWHYA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding affinity for the drug 'C#C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4CC(=O)CC[C@@H]4[C@H]3C(=C)C[C@@]21CC' and the target protein 'MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C#C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4CC(=O)CC[C@@H]4[C@H]3C(=C)C[C@@]21CC\"], \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\", \"result\": [{\"smile\": \"C#C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4CC(=O)CC[C@@H]4[C@H]3C(=C)C[C@@]21CC\", \"value\": 1.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C#C[C@]1(O)CC[C@H]2[C@@H]3CC[C@H]4CC(=O)CC[C@@H]4[C@H]3C(=C)C[C@@]21CC' and the protein 'MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH' is 1.6."
            },
            {
                "from": "human",
                "value": "And how do you estimate the affinity level for the drug 'CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\", \"result\": [{\"smile\": \"CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C\", \"value\": 115.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(=O)[C@H]1CC[C@H]2[C@@H]3CCC4=CC(=O)CC[C@]4(C)[C@H]3CC[C@]12C' and the protein 'MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH' is 115.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely affinity value for the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C'-target protein 'MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP' is 10000.0."
            },
            {
                "from": "human",
                "value": "And what's your forecast for the affinity between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP\", \"result\": [{\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein 'MVVFNGLLKIKICEAVSLKPTAWSLRHAVGPRPQTFLLDPYIALNVDDSRIGQTATKQKTNSPAWHDEFVTDVCNGRKIELAVFHDAPIGYDDFVANCTIQFEELLQNGSRHFEDWIDLEPEGRVYVIIDLSGSSGEAPKDNEERVFRERMRPRKRQGAVRRRVHQVNGHKFMATYLRQPTYCSHCRDFIWGVIGKQGYQCQVCTCVVHKRCHELIITKCAGLKKQETPDQVGSQRFSVNMPHKFGIHNYKVPTFCDHCGSLLWGLLRQGLQCKVCKMNVHRRCETNVAPNCGVDARGIAKVLADLGVTPDKITNSGQRRKKLIAGAESPQPASGSSPSEEDRSKSAPTSPCDQEIKELENNIRKALSFDNRGEEHRAASSPDGQLMSPGENGEVRQGQAKRLGLDEFNFIKVLGKGSFGKVMLAELKGKDEVYAVKVLKKDVILQDDDVDCTMTEKRILALARKHPYLTQLYCCFQTKDRLFFVMEYVNGGDLMFQIQRSRKFDEPRSRFYAAEVTSALMFLHQHGVIYRDLKLDNILLDAEGHCKLADFGMCKEGILNGVTTTTFCGTPDYIAPEILQELEYGPSVDWWALGVLMYEMMAGQPPFEADNEDDLFESILHDDVLYPVWLSKEAVSILKAFMTKNPHKRLGCVASQNGEDAIKQHPFFKEIDWVLLEQKKIKPPFKPRIKTKRDVNNFDQDFTREEPVLTLVDEAIVKQINQEEFKGFSYFGEDLMP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug 'O=C(O)c1cccc(-c2ccc3c(c2)C(=NNc2nc4ccccc4s2)CCC3)n1' and the target protein 'MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1cccc(-c2ccc3c(c2)C(=NNc2nc4ccccc4s2)CCC3)n1\"], \"target_seq\": \"MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK\", \"result\": [{\"smile\": \"O=C(O)c1cccc(-c2ccc3c(c2)C(=NNc2nc4ccccc4s2)CCC3)n1\", \"value\": 4.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1cccc(-c2ccc3c(c2)C(=NNc2nc4ccccc4s2)CCC3)n1' and the protein 'MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK' is 4.1."
            },
            {
                "from": "human",
                "value": "And calculate the binding affinity between the drug 'Cc1cc2c(C(C)C)c(O)c(O)c(C=O)c2c(O)c1-c1c(C)cc2c(C(C)C)c(O)c(O)c(C=O)c2c1O' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(C(C)C)c(O)c(O)c(C=O)c2c(O)c1-c1c(C)cc2c(C(C)C)c(O)c(O)c(C=O)c2c1O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK\", \"result\": [{\"smile\": \"Cc1cc2c(C(C)C)c(O)c(O)c(C=O)c2c(O)c1-c1c(C)cc2c(C(C)C)c(O)c(O)c(C=O)c2c1O\", \"value\": 1710.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(C(C)C)c(O)c(O)c(C=O)c2c(O)c1-c1c(C)cc2c(C(C)C)c(O)c(O)c(C=O)c2c1O' and the protein 'MSQSNRELVVDFLSYKLSQKGYSWSQFSDVEENRTEAPEGTESEMETPSAINGNPSWHLADSPAVNGATGHSSSLDAREVIPMAAVKQALREAGDEFELRYRRAFSDLTSQLHITPGTAYQSFEQVVNELFRDGVNWGRIVAFFSFGGALCVESVDKEMQVLVSRIAAWMATYLNDHLEPWIQENGGWDTFVELYGNNAAAESRKGQERFNRWFLTGMTVAGVVLLGSLFSRK' is 1710.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected binding affinity for the drug 'CCCCN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(Br)c(c34)C[C@H]21'-target protein 'MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL' complex? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCCN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(Br)c(c34)C[C@H]21\"], \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\", \"result\": [{\"smile\": \"CCCCN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(Br)c(c34)C[C@H]21\", \"value\": 1148.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCCCN1C[C@H](C(=O)N2CCN(c3ccccn3)CC2)C[C@@H]2c3cccc4[nH]c(Br)c(c34)C[C@H]21' and the protein 'MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL' is 1148.0."
            },
            {
                "from": "human",
                "value": "And estimate the interaction affinity for the drug 'CN1C[C@@H](C(=O)N2CCN(c3cccc(=O)n3C)CC2)C[C@H]2c3cccc4[nH]c(Br)c(c34)C[C@@H]21' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1C[C@@H](C(=O)N2CCN(c3cccc(=O)n3C)CC2)C[C@H]2c3cccc4[nH]c(Br)c(c34)C[C@@H]21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\", \"result\": [{\"smile\": \"CN1C[C@@H](C(=O)N2CCN(c3cccc(=O)n3C)CC2)C[C@H]2c3cccc4[nH]c(Br)c(c34)C[C@@H]21\", \"value\": 417.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1C[C@@H](C(=O)N2CCN(c3cccc(=O)n3C)CC2)C[C@H]2c3cccc4[nH]c(Br)c(c34)C[C@@H]21' and the protein 'MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL' is 417.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding score between the drug 'C[C@H]1CNC(=O)c2cnn3ccc(nc23)N[C@H](C)c2cc(F)ccc2O1' and the target protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@H]1CNC(=O)c2cnn3ccc(nc23)N[C@H](C)c2cc(F)ccc2O1\"], \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": [{\"smile\": \"C[C@H]1CNC(=O)c2cnn3ccc(nc23)N[C@H](C)c2cc(F)ccc2O1\", \"value\": 0.082}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@H]1CNC(=O)c2cnn3ccc(nc23)N[C@H](C)c2cc(F)ccc2O1' and the protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG' is 0.082."
            },
            {
                "from": "human",
                "value": "And what is the expected affinity value when relating the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21' to the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG\", \"result\": [{\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 11.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21' and the protein 'MGMACLTMTEMEGTSTSSIYQNGDISGNANSMKQIDPVLQVYLYHSLGKSEADYLTFPSGEYVAEEICIAASKACGITPVYHNMFALMSETERIWYPPNHVFHIDESTRHNVLYRIRFYFPRWYCSGSNRAYRHGISRGAEAPLLDDFVMSYLFAQWRHDFVHGWIKVPVTHETQEECLGMAVLDMMRIAKENDQTPLAIYNSISYKTFLPKCIRAKIQDYHILTRKRIRYRFRRFIQQFSQCKATARNLKLKYLINLETLQSAFYTEKFEVKEPGSGPSGEEIFATIIITGNGGIQWSRGKHKESETLTEQDLQLYCDFPNIIDVSIKQANQEGSNESRVVTIHKQDGKNLEIELSSLREALSFVSLIDGYYRLTADAHHYLCKEVAPPAVLENIQSNCHGPISMDFAISKLKKAGNQTGLYVLRCSPKDFNKYFLTFAVERENVIEYKHCLITKNENEEYNLSGTKKNFSSLKDLLNCYQMETVRSDNIIFQFTKCCPPKPKDKSNLLVFRTNGVSDVPTSPTLQRPTHMNQMVFHKIRNEDLIFNESLGQGTFTKIFKGVRREVGDYGQLHETEVLLKVLDKAHRNYSESFFEAASMMSKLSHKHLVLNYGVCVCGDENILVQEFVKFGSLDTYLKKNKNCINILWKLEVAKQLAWAMHFLEENTLIHGNVCAKNILLIREEDRKTGNPPFIKLSDPGISITVLPKDILQERIPWVPPECIENPKNLNLATDKWSFGTTLWEICSGGDKPLSALDSQRKLQFYEDRHQLPAPKWAELANLINNCMDYEPDFRPSFRAIIRDLNSLFTPDYELLTENDMLPNMRIGALGFSGAFEDRDPTQFEERHLKFLQQLGKGNFGSVEMCRYDPLQDNTGEVVAVKKLQHSTEEHLRDFEREIEILKSLQHDNIVKYKGVCYSAGRRNLKLIMEYLPYGSLRDYLQKHKERIDHIKLLQYTSQICKGMEYLGTKRYIHRDLATRNILVENENRVKIGDFGLTKVLPQDKEYYKVKEPGESPIFWYAPESLTESKFSVASDVWSFGVVLYELFTYIEKSKSPPAEFMRMIGNDKQGQMIVFHLIELLKNNGRLPRPDGCPDEIYMIMTECWNNNVNQRPSFRDLALRVDQIRDNMAG' is 11.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O' and the target protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\"], \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\", \"value\": 6700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O' and the protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS' is 6700.0."
            },
            {
                "from": "human",
                "value": "And how strong is the predicted affinity of the drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' for the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS\", \"result\": [{\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 100.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MHTGGETSACKPSSVRLAPSFSFHAAGLQMAGQMPHSHQYSDRRQPNISDQQVSALSYSDQIQQPLTNQVMPDIVMLQRRMPQTFRDPATAPLRKLSVDLIKTYKHINEVYYAKKKRRHQQGQGDDSSHKKERKVYNDGYDDDNYDYIVKNGEKWMDRYEIDSLIGKGSFGQVVKAYDRVEQEWVAIKIIKNKKAFLNQAQIEVRLLELMNKHDTEMKYYIVHLKRHFMFRNHLCLVFEMLSYNLYDLLRNTNFRGVSLNLTRKFAQQMCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGMPYDLAIDMWSLGCILVEMHTGEPLFSGANEVDQMNKIVEVLGIPPAHILDQAPKARKFFEKLPDGTWNLKKTKDGKREYKPPGTRKLHNILGVETGGPGGRRAGESGHTVADYLKFKDLILRMLDYDPKTRIQPYYALQHSFFKKTADEGTNTSNSVSTSPAMEQSQSSGTTSSTSSSSGGSSGTSNSGRARSDPTHQHRHSGGHFTAAVQAMDCETHSPQVRQQFPAPLGWSGTEAPTQVTVETHPVQETTFHVAPQQNALHHHHGNSSHHHHHHHHHHHHHGQQALGNRTRPRVYNSPTNSSSTQDSMEVGHSHHSMTSLSSSTTSSSTSSSSTGNQGNQAYQNRPVAANTLDFGQNGAMDVNLTVYSNPRQETGIAGHPTYQFSANTGPAHYMTEGHLTMRQGADREESPMTGVCVQQSPVASS' is 100.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(N)=O)[C@@H](C)O)[C@@H](C)O' and the target protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(N)=O)[C@@H](C)O)[C@@H](C)O\"], \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\", \"result\": [{\"smile\": \"CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(N)=O)[C@@H](C)O)[C@@H](C)O\", \"value\": 85000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](Cc1ccccc1)C(=O)N[C@H](C(=O)N[C@@H](C)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CCC(=O)O)C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C(=O)N[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@@H](CO)C(N)=O)[C@@H](C)O)[C@@H](C)O' and the protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM' is 85000.0."
            },
            {
                "from": "human",
                "value": "And infer the affinity value for the drug 'O=C(O)c1cc(-c2ccccc2)n(-c2ccccc2)n1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1cc(-c2ccccc2)n(-c2ccccc2)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\", \"result\": [{\"smile\": \"O=C(O)c1cc(-c2ccccc2)n(-c2ccccc2)n1\", \"value\": 1150.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1cc(-c2ccccc2)n(-c2ccccc2)n1' and the protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM' is 1150.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the affinity of the drug 'O=C(O)c1cc2ccc(-n3c(-c4ccc(Cl)c(Cl)c4)n[nH]c3=S)cc2s1' toward the target protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1cc2ccc(-n3c(-c4ccc(Cl)c(Cl)c4)n[nH]c3=S)cc2s1\"], \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\", \"result\": [{\"smile\": \"O=C(O)c1cc2ccc(-n3c(-c4ccc(Cl)c(Cl)c4)n[nH]c3=S)cc2s1\", \"value\": 22000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1cc2ccc(-n3c(-c4ccc(Cl)c(Cl)c4)n[nH]c3=S)cc2s1' and the protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM' is 22000.0."
            },
            {
                "from": "human",
                "value": "And what's the likely affinity value for the drug 'Cc1cccc(-c2n[nH]c(=S)n2-c2ccc3cc(C(=O)O)sc3c2)c1'-target protein mentioned above interaction? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc(-c2n[nH]c(=S)n2-c2ccc3cc(C(=O)O)sc3c2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM\", \"result\": [{\"smile\": \"Cc1cccc(-c2n[nH]c(=S)n2-c2ccc3cc(C(=O)O)sc3c2)c1\", \"value\": 250000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cccc(-c2n[nH]c(=S)n2-c2ccc3cc(C(=O)O)sc3c2)c1' and the protein 'MVGQLSEGAIAAIMQKGDTNIKPILQVINIRPITTGNSPPRYRLLMSDGLNTLSSFMLATQLNPLVEEEQLSSNCVCQIHRFIVNTLKDGRRVVILMELEVLKSAEAVGVKIGNPVPYNEGLGQPQVAPPAPAASPAASSRPQPQNGSSGMGSTVSKAYGASKTFGKAAGPSLSHTSGGTQSKVVPIASLTPYQSKWTICARVTNKSQIRTWSNSRGEGKLFSLELVDESGEIRATAFNEQVDKFFPLIEVNKVYYFSKGTLKIANKQFTAVKNDYEMTFNNETSVMPCEDDHHLPTVQFDFTGIDDLENKSKDSLVDIIGICKSYEDATKITVRSNNREVAKRNIYLMDTSGKVVTATLWGEDADKFDGSRQPVLAIKGARVSDFGGRSLSVLSSSTIIANPDIPEAYKLRGWFDAEGQALDGVSISDLKSGGVGGSNTNWKTLYEVKSENLGQGDKPDYFSSVATVVYLRKENCMYQACPTQDCNKKVIDQQNGLYRCEKCDTEFPNFKYRMILSVNIADFQENQWVTCFQESAEAILGQNAAYLGELKDKNEQAFEEVFQNANFRSFIFRVRVKVETYNDESRIKATVMDVKPVDYREYGRRLVMSIRRSALM' is 250000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the binding affinity of the drug 'Cn1cc(CN2CCCC3(CCN(c4cnc5ccccc5n4)CC3)C2=O)c2ccccc21' with the target protein 'MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(CN2CCCC3(CCN(c4cnc5ccccc5n4)CC3)C2=O)c2ccccc21\"], \"target_seq\": \"MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW\", \"result\": [{\"smile\": \"Cn1cc(CN2CCCC3(CCN(c4cnc5ccccc5n4)CC3)C2=O)c2ccccc21\", \"value\": 134.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(CN2CCCC3(CCN(c4cnc5ccccc5n4)CC3)C2=O)c2ccccc21' and the protein 'MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW' is 134.0."
            },
            {
                "from": "human",
                "value": "And what affinity value is expected between the drug 'O=C1N(Cc2c[nH]c3ccccc23)CCCC12CCN(c1nccc3occc13)CC2' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C1N(Cc2c[nH]c3ccccc23)CCCC12CCN(c1nccc3occc13)CC2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW\", \"result\": [{\"smile\": \"O=C1N(Cc2c[nH]c3ccccc23)CCCC12CCN(c1nccc3occc13)CC2\", \"value\": 139.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C1N(Cc2c[nH]c3ccccc23)CCCC12CCN(c1nccc3occc13)CC2' and the protein 'MSGTKLEDSPPCRNWSSASELNETQEPFLNPTDYDDEEFLRYLWREYLHPKEYEWVLIAGYIIVFVVALIGNVLVCVAVWKNHHMRTVTNYFIVNLSLADVLVTITCLPATLVVDITETWFFGQSLCKVIPYLQTVSVSVSVLTLSCIALDRWYAICHPLMFKSTAKRARNSIVIIWIVSCIIMIPQAIVMECSTVFPGLANKTTLFTVCDERWGGEIYPKMYHICFFLVTYMAPLCLMVLAYLQIFRKLWCRQIPGTSSVVQRKWKPLQPVSQPRGPGQPTKSRMSAVAAEIKQIRARRKTARMLMIVLLVFAICYLPISILNVLKRVFGMFAHTEDRETVYAWFTFSHWLVYANSAANPIIYNFLSGKFREEFKAAFSCCCLGVHHRQEDRLTRGRTSTESRKSLTTQISNFDNISKLSEQVVLTSISTLPAANGAGPLQNW' is 139.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1'-target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\"], \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 20.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 20.0."
            },
            {
                "from": "human",
                "value": "And what's the likely interaction affinity for the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 6000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 6000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drug 'COc1ccc([C@@H]2C(=O)N([C@H]3CC[C@H](N)CC3)[C@H]2c2ccc(C#N)cc2)cc1'-target protein 'MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM' interaction? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc([C@@H]2C(=O)N([C@H]3CC[C@H](N)CC3)[C@H]2c2ccc(C#N)cc2)cc1\"], \"target_seq\": \"MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM\", \"result\": [{\"smile\": \"COc1ccc([C@@H]2C(=O)N([C@H]3CC[C@H](N)CC3)[C@H]2c2ccc(C#N)cc2)cc1\", \"value\": 53000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc([C@@H]2C(=O)N([C@H]3CC[C@H](N)CC3)[C@H]2c2ccc(C#N)cc2)cc1' and the protein 'MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM' is 53000.0."
            },
            {
                "from": "human",
                "value": "And can you estimate the interaction value between the drug 'N#Cc1ccc(-n2nc(C(=O)N3CCC[C@@H](N)C3)cc2OCC(=O)N2CCOCC2)cc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#Cc1ccc(-n2nc(C(=O)N3CCC[C@@H](N)C3)cc2OCC(=O)N2CCOCC2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM\", \"result\": [{\"smile\": \"N#Cc1ccc(-n2nc(C(=O)N3CCC[C@@H](N)C3)cc2OCC(=O)N2CCOCC2)cc1\", \"value\": 210.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N#Cc1ccc(-n2nc(C(=O)N3CCC[C@@H](N)C3)cc2OCC(=O)N2CCOCC2)cc1' and the protein 'MLSGKKAAAAAAAAAAAATGTEAGPGTAGGSENGSEVAAQPAGLSGPAEVGPGAVGERTPRKKEPPRASPPGGLAEPPGSAGPQAGPTVVPGSATPMETGIAETPEGRRTSRRKRAKVEYREMDESLANLSEDEYYSEEERNAKAEKEKKLPPPPPQAPPEEENESEPEEPSGVEGAAFQSRLPHDRMTSQEAACFPDIISGPQQTQKVFLFIRNRTLQLWLDNPKIQLTFEATLQQLEAPYNSDTVLVHRVHSYLERHGLINFGIYKRIKPLPTKKTGKVIIIGSGVSGLAAARQLQSFGMDVTLLEARDRVGGRVATFRKGNYVADLGAMVVTGLGGNPMAVVSKQVNMELAKIKQKCPLYEANGQAVPKEKDEMVEQEFNRLLEATSYLSHQLDFNVLNNKPVSLGQALEVVIQLQEKHVKDEQIEHWKKIVKTQEELKELLNKMVNLKEKIKELHQQYKEASEVKPPRDITAEFLVKSKHRDLTALCKEYDELAETQGKLEEKLQELEANPPSDVYLSSRDRQILDWHFANLEFANATPLSTLSLKHWDQDDDFEFTGSHLTVRNGYSCVPVALAEGLDIKLNTAVRQVRYTASGCEVIAVNTRSTSQTFIYKCDAVLCTLPLGVLKQQPPAVQFVPPLPEWKTSAVQRMGFGNLNKVVLCFDRVFWDPSVNLFGHVGSTTASRGELFLFWNLYKAPILLALVAGEAAGIMENISDDVIVGRCLAILKGIFGSSAVPQPKETVVSRWRADPWARGSYSYVAAGSSGNDYDLMAQPITPGPSIPGAPQPIPRLFFAGEHTIRNYPATVHGALLSGLREAGRIADQFLGAMYTLPRQATPGVPAQQSPSM' is 210.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the binding affinity you would expect for the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the target protein 'MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\"], \"target_seq\": \"MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 62.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the protein 'MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP' is 62.0."
            },
            {
                "from": "human",
                "value": "And estimate the binding affinity that exists between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP\", \"result\": [{\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein 'MEPFLRRRLAFLSFFWDKIWPAGGEPDHGTPGSLDPNTDPVPTLPAEPCSPFPQLFLALYDFTARCGGELSVRRGDRLCALEEGGGYIFARRLSGQPSAGLVPITHVAKASPETLSDQPWYFSGVSRTQAQQLLLSPPNEPGAFLIRPSESSLGGYSLSVRAQAKVCHYRVSMAADGSLYLQKGRLFPGLEELLTYYKANWKLIQNPLLQPCMPQKAPRQDVWERPHSEFALGRKLGEGYFGEVWEGLWLGSLPVAIKVIKSANMKLTDLAKEIQTLKGLRHERLIRLHAVCSGGEPVYIVTELMRKGNLQAFLGTPEGRALRLPPLLGFACQVAEGMSYLEEQRVVHRDLAARNVLVDDGLACKVADFGLARLLKDDIYSPSSSSKIPVKWTAPEAANYRVFSQKSDVWSFGVLLHEVFTYGQCPYEGMTNHETLQQIMRGYRLPRPAACPAEVYVLMLECWRSSPEERPSFATLREKLHAIHRCHP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug 'CC(C)n1nc(-c2cc3cc(O)ccc3[nH]2)c2c(N)ncnc21' and the target protein 'MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)n1nc(-c2cc3cc(O)ccc3[nH]2)c2c(N)ncnc21\"], \"target_seq\": \"MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA\", \"result\": [{\"smile\": \"CC(C)n1nc(-c2cc3cc(O)ccc3[nH]2)c2c(N)ncnc21\", \"value\": 780.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)n1nc(-c2cc3cc(O)ccc3[nH]2)c2c(N)ncnc21' and the protein 'MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA' is 780.0."
            },
            {
                "from": "human",
                "value": "And what's your forecast for the affinity between the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1' and the protein 'MASSSVPPATVSAATAGPGPGFGFASKTKKKHFVQQKVKVFRAADPLVGVFLWGVAHSINELSQVPPPVMLLPDDFKASSKIKVNNHLFHRENLPSHFKFKEYCPQVFRNLRDRFGIDDQDYLVSLTRNPPSESEGSDGRFLISYDRTLVIKEVSSEDIADMHSNLSNYHQYIVKCHGNTLLPQFLGMYRVSVDNEDSYMLVMRNMFSHRLPVHRKYDLKGSLVSREASDKEKVKELPTLKDMDFLNKNQKVYIGEEEKKIFLEKLKRDVEFLVQLKIMDYSLLLGIHDIIRGSEPEEEAPVREDESEVDGDCSLTGPPALVGSYGTSPEGIGGYIHSHRPLGPGEFESFIDVYAIRSAEGAPQKEVYFMGLIDILTQYDAKKKAAHAAKTVKHGAGAEISTVHPEQYAKRFLDFITNIFA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely interaction affinity for the drug 'COCC(=O)NC/C=C/c1ccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c2c1' and the target protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COCC(=O)NC/C=C/c1ccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c2c1\"], \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"COCC(=O)NC/C=C/c1ccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COCC(=O)NC/C=C/c1ccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c2c1' and the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS' is 10000.0."
            },
            {
                "from": "human",
                "value": "And can you gauge the expected affinity value for the drug 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C'-target protein mentioned above relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS\", \"result\": [{\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C' and the protein 'MEGGPAVCCQDPRAELVERVAAIDVTHLEEADGGPEPTRNGVDPPPRARAASVIPGSTSRLLPARPSLSARKLSLQERPAGSYLEAQAGPYATGPASHISPRAWRRPTIESHHVAISDAEDCVQLNQYKLQSEIGKGAYGVVRLAYNESEDRHYAMKVLSKKKLLKQYGFPRRPPPRGSQAAQGGPAKQLLPLERVYQEIAILKKLDHVNVVKLIEVLDDPAEDNLYLVFDLLRKGPVMEVPCDKPFSEEQARLYLRDVILGLEYLHCQKIVHRDIKPSNLLLGDDGHVKIADFGVSNQFEGNDAQLSSTAGTPAFMAPEAISDSGQSFSGKALDVWATGVTLYCFVYGKCPFIDDFILALHRKIKNEPVVFPEEPEISEELKDLILKMLDKNPETRIGVPDIKLHPWVTKNGEEPLPSEEEHCSVVEVTEEEVKNSVRLIPSWTTVILVKSMLRKRSFGNPFEPQARREERSMSAPGNLLVKEGFGEGGKSPELPGVQEDEAAS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the drug 'CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1' and the target protein 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1\"], \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\", \"result\": [{\"smile\": \"CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1\", \"value\": 2800.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1' and the protein 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS' is 2800.0."
            },
            {
                "from": "human",
                "value": "And assess the strength of the binding affinity between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the protein 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an estimate of the affinity between the drug 'Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1' and the target protein 'MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1\"], \"target_seq\": \"MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC\", \"result\": [{\"smile\": \"Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1\", \"value\": 45.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1' and the protein 'MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC' is 45.0."
            },
            {
                "from": "human",
                "value": "And can you estimate the strength of binding between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein 'MSRRRFDCRSISGLLTTTPQIPIKMENFNNFYILTSKELGRGKFAVVRQCISKSTGQEYAAKFLKKRRRGQDCRAEILHEIAVLELAKSCPRVINLHEVYENTSEIILILEYAAGGEIFSLCLPELAEMVSENDVIRLIKQILEGVYYLHQNNIVHLDLKPQNILLSSIYPLGDIKIVDFGMSRKIGHACELREIMGTPEYLAPEILNYDPITTATDMWNIGIIAYMLLTHTSPFVGEDNQETYLNISQVNVDYSEETFSSVSQLATDFIQSLLVKNPEKRPTAEICLSHSWLQQWDFENLFHPEETSSSSQTQDHSVRSSEDKTSKSSCNGTCGDREDKENIPEDSSMVSKRFRFDDSLPNPHELVSDLLC' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you anticipate the affinity to manifest between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the target protein 'MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\"], \"target_seq\": \"MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the protein 'MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ' is 10000.0."
            },
            {
                "from": "human",
                "value": "And how would you quantify the predicted affinity for the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1'-target protein mentioned above relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 1200.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MSGPVPSRARVYTDVNTHRPREYWDYESHVVEWGNQDDYQLVRKLGRGKYSEVFEAINITNNEKVVVKILKPVKKKKIKREIKILENLRGGPNIITLADIVKDPVSRTPALVFEHVNNTDFKQLYQTLTDYDIRFYMYEILKALDYCHSMGIMHRDVKPHNVMIDHEHRKLRLIDWGLAEFYHPGQEYNVRVASRYFKGPELLVDYQMYDYSLDMWSLGCMLASMIFRKEPFFHGHDNYDQLVRIAKVLGTEDLYDYIDKYNIELDPRFNDILGRHSRKRWERFVHSENQHLVSPEALDFLDKLLRYDHQSRLTAREAMEHPYFYTVVKDQARMGSSSMPGGSTPVSSANMMSGISSVPTPSPLGPLAGSPVIAAANPLGMPVPAAAGAQQ' is 1200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1' with respect to the target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1\"], \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 1200.0."
            },
            {
                "from": "human",
                "value": "And infer the expected affinity of the drug 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1' towards the target protein mentioned above. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 520.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 520.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1' and the target protein 'MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\"], \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1' and the protein 'MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA' is 10000.0."
            },
            {
                "from": "human",
                "value": "And can you gauge the expected affinity value for the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21'-target protein mentioned above relationship? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA\", \"result\": [{\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21' and the protein 'MEEEGGSSGGAAGTSADGGDGGEQLLTVKHELRTANLTGHAEKVGIENFELLKVLGTGAYGKVFLVRKISGHDTGKLYAMKVLKKATIVQKAKTTEHTRTERQVLEHIRQSPFLVTLHYAFQTETKLHLILDYINGGELFTHLSQRERFTEHEVQIYVGEIVLALEHLHKLGIIYRDIKLENILLDSNGHVVLTDFGLSKEFVADETERAYSFCGTIEYMAPDIVRGGDSGHDKAVDWWSLGVLMYELLTGASPFTVDGEKNSQAEISRRILKSEPPYPQEMSALAKDLIQRLLMKDPKKRLGCGPRDADEIKEHLFFQKINWDDLAAKKVPAPFKPVIRDELDVSNFAEEFTEMDPTYSPAALPQSSEKLFQGYSFVAPSILFKRNAAVIDPLQFHMGVERPGVTNVARSAMMKDSPFYQHYDLDLKDKPLGEGSFSICRKCVHKKSNQAFAVKIISKRMEANTQKEITALKLCEGHPNIVKLHEVFHDQLHTFLVMELLNGGELFERIKKKKHFSETEASYIMRKLVSAVSHMHDVGVVHRDLKPENLLFTDENDNLEIKIIDFGFARLKPPDNQPLKTPCFTLHYAAPELLNQNGYDESCDLWSLGVILYTMLSGQVPFQSHDRSLTCTSAVEIMKKIKKGDFSFEGEAWKNVSQEAKDLIQGLLTVDPNKRLKMSGLRYNEWLQDGSQLSSNPLMTPDILGSSGAAVHTCVKATFHAFNKYKREGFCLQNVDKAPLAKRRKMKKTSTSTETRSSSSESSHSSSSHSHGKTTPTKTLQPSNPADSNNPETLFQFSDSVA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'NS(=O)(=O)c1ccc(N/N=C2\\C(=O)Nc3ccc(C(=O)O)cc32)cc1' and the target protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NS(=O)(=O)c1ccc(N/N=C2\\C(=O)Nc3ccc(C(=O)O)cc32)cc1\"], \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\": \"NS(=O)(=O)c1ccc(N/N=C2\\C(=O)Nc3ccc(C(=O)O)cc32)cc1\", \"value\": 8.43}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'NS(=O)(=O)c1ccc(N/N=C2\\C(=O)Nc3ccc(C(=O)O)cc32)cc1' and the protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL' is 8.43."
            },
            {
                "from": "human",
                "value": "And what is the predicted affinity value for the drug 'O=C(Cc1ccc(-c2cccs2)cc1)Nc1cc(C2CC2)[nH]n1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(Cc1ccc(-c2cccs2)cc1)Nc1cc(C2CC2)[nH]n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\": \"O=C(Cc1ccc(-c2cccs2)cc1)Nc1cc(C2CC2)[nH]n1\", \"value\": 79.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(Cc1ccc(-c2cccs2)cc1)Nc1cc(C2CC2)[nH]n1' and the protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL' is 79.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the target protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\"], \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF' is 10000.0."
            },
            {
                "from": "human",
                "value": "And can you give a forecast of the affinity value for the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\", \"result\": [{\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' and the target protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\"], \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\", \"result\": [{\"smile\": \"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\", \"value\": 210.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' and the protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN' is 210.0."
            },
            {
                "from": "human",
                "value": "And can you measure the affinity between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the target protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\"], \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV' is 10000.0."
            },
            {
                "from": "human",
                "value": "And can you outline the affinity between the drug 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV\", \"result\": [{\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1' and the protein 'MQSTANYLWHTDDLLGQGATASVYKARNKKSGELVAVKVFNTTSYLRPREVQVREFEVLRKLNHQNIVKLFAVEETGGSRQKVLVMEYCSSGSLLSVLESPENAFGLPEDEFLVVLRCVVAGMNHLRENGIVHRDIKPGNIMRLVGEEGQSIYKLTDFGAARELDDDEKFVSVYGTEEYLHPDMYERAVLRKPQQKAFGVTVDLWSIGVTLYHAATGSLPFIPFGGPRRNKEIMYRITTEKPAGAIAGAQRRENGPLEWSYTLPITCQLSLGLQSQLVPILANILEVEQAKCWGFDQFFAETSDILQRVVVHVFSLSQAVLHHIYIHAHNTIAIFQEAVHKQTSVAPRHQEYLFEGHLCVLEPSVSAQHIAHTTASSPLTLFSTAIPKGLAFRDPALDVPKFVPKVDLQADYNTAKGVLGAGYQALRLARALLDGQELMFRGLHWVMEVLQATCRRTLEVARTSLLYLSSSLGTERFSSVAGTPEIQELKAAAELRSRLRTLAEVLSRCSQNITETQESLSSLNRELVKSRDQVHEDRSIQQIQCCLDKMNFIYKQFKKSRMRPGLGYNEEQIHKLDKVNFSHLAKRLLQVFQEECVQKYQASLVTHGKRMRVVHETRNHLRLVGCSVAACNTEAQGVQESLSKLLEELSHQLLQDRAKGAQASPPPIAPYPSPTRKDLLLHMQELCEGMKLLASDLLDNNRIIERLNRVPAPPDV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the drug 'COc1ccc2c(c1)c(CC(=O)NC(C)c1ccncc1)c(C)n2C(=O)c1ccc(Cl)cc1'-target protein 'MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc2c(c1)c(CC(=O)NC(C)c1ccncc1)c(C)n2C(=O)c1ccc(Cl)cc1\"], \"target_seq\": \"MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS\", \"result\": [{\"smile\": \"COc1ccc2c(c1)c(CC(=O)NC(C)c1ccncc1)c(C)n2C(=O)c1ccc(Cl)cc1\", \"value\": 370.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc2c(c1)c(CC(=O)NC(C)c1ccncc1)c(C)n2C(=O)c1ccc(Cl)cc1' and the protein 'MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS' is 370.0."
            },
            {
                "from": "human",
                "value": "And what's the expected level of affinity between the drug 'CCC(C(=O)Nc1ccncc1)c1ccccc1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC(C(=O)Nc1ccncc1)c1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS\", \"result\": [{\"smile\": \"CCC(C(=O)Nc1ccncc1)c1ccccc1\", \"value\": 440.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC(C(=O)Nc1ccncc1)c1ccccc1' and the protein 'MFIEAIVLALTALILYSVYSVKSFNTTRPTDPPVYPVTVPFLGHIVQFGKNPLEFMQRCKRDLKSGVFTISIGGQRVTIVGDPHEHSRFFSPRNEILSPREVYTIMTPVFGEGVAYAAPYPRMREQLNFLAEELTIAKFQNFVPAIQHEVRKFMAENWKEDEGVINLLEDCGAMIINTACQCLFGEDLRKRLNARHFAQLLSKMESSLIPAAVFMPWLLRLPLPQSARCREARAELQKILGEIIVAREKEEASKDNNTSDLLGGLLKAVYRDGTRMSLHEVCGMIVAAMFAGQHTSTITTSWSMLHLMHPKNKKWLDKLHKEIDEFPAQLNYDNVMDEMPFAERCVRESIRRDPPLLMVMRMVKAEVKVGSYVVPKGDIIACSPLLSHHDEEAFPNPRLWDPERDEKVDGAFIGFGAGVHKCIGQKFALLQVKTILATAFREYDFQLLRDEVPDPDYHTMVVGPTLNQCLVKYTRKKKLPS' is 440.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the target protein 'MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF\", \"result\": [{\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein 'MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF' is 10000.0."
            },
            {
                "from": "human",
                "value": "And how do you project the affinity value between the drug 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF\", \"result\": [{\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 5300.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21' and the protein 'MDRMKKIKRQLSMTLSSAPEIVHEDLKMGSDGESDQASATSSDEVQSPVRVRMRNHPPRKISTEDINKRLSLPADIRLPEGYLEKLTLNSPIFDKPLSRRLRRVSLSEIGFGKLETYIKLDKLGEGTYATVYKGKSKLTDNLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIIHTEKSLTLVFEYLDKDLKQYLDDCGNIINMHNVKLFLFQLLRGLAYCHRQKVLHRDLKPQNLLINERGELKLADFGLARAKSIPTKTYSNEVVTLWYRPPDILLGSTDYSTQIDMWGVGCIFYEMATGRPLFPGSTVEEQLHFIFRILGTPTEETWPGILSNEEFKTYNYPKYRAEALLSHAPRLDSDGADLLTKLLQFEGRNRISAEDAMKHPFFLSLGERIHKLPDTTSIFALKEIQLQKEASLRSSSMPDSGRPAFRVVDTEF' is 5300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value is expected between the drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1' and the target protein 'MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\"], \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\", \"result\": [{\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 2200.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1' and the protein 'MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR' is 2200.0."
            },
            {
                "from": "human",
                "value": "And can you outline the affinity between the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\", \"result\": [{\"smile\": \"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\", \"value\": 32.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' and the protein 'MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR' is 32.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC''s affinity for the target protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\"], \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\", \"result\": [{\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\", \"value\": 3000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC' and the protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI' is 3000.0."
            },
            {
                "from": "human",
                "value": "And can you estimate the strength of binding between the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\", \"result\": [{\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected binding affinity for the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1'-target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' complex? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\"], \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1\", \"value\": 3.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(C=Cc3ccccn3)n[nH]c2c1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 3.6."
            },
            {
                "from": "human",
                "value": "And can you outline the affinity between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 6.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 6.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the target protein 'MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ' is 10000.0."
            },
            {
                "from": "human",
                "value": "And what is the anticipated affinity strength between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 670.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MVIMSEFSADPAGQGQGQQKPLRVGFYDIERTLGKGNFAVVKLARHRVTKTQVAIKIIDKTRLDSSNLEKIYREVQLMKLLNHPHIIKLYQVMETKDMLYIVTEFAKNGEMFDYLTSNGHLSENEARKKFWQILSAVEYCHDHHIVHRDLKTENLLLDGNMDIKLADFGFGNFYKSGEPLSTWCGSPPYAAPEVFEGKEYEGPQLDIWSLGVVLYVLVCGSLPFDGPNLPTLRQRVLEGRFRIPFFMSQDCESLIRRMLVVDPARRITIAQIRQHRWMRAEPCLPGPACPAFSAHSYTSNLGDYDEQALGIMQTLGVDRQRTVESLQNSSYNHFAAIYYLLLERLKEYRNAQCARPGPARQPRPRSSDLSGLEVPQEGLSTDPFRPALLCPQPQTLVQSVLQAEMDCELQSSLQWPLFFPVDASCSGVFRPRPVSPSSLLDTAISEEARQGPGLEEEQDTQESLPSSTGRRHTLAEVSTRLSPLTAPCIVVSPSTTASPAEGTSSDSCLTFSASKSPAGLSGTPATQGLLGACSPVRLASPFLGSQSATPVLQAQGGLGGAVLLPVSFQEGRRASDTSLTQGLKAFRQQLRKTTRTKGFLGLNKIKGLARQVCQAPASRASRGGLSPFHAPAQSPGLHGGAAGSREGWSLLEEVLEQQRLLQLQHHPAAAPGCSQAPQPAPAPFVIAPCDGPGAAPLPSTLLTSGLPLLPPPLLQTGASPVASAAQLLDTHLHIGTGPTALPAVPPPRLARLAPGCEPLGLLQGDCEMEDLMPCSLGTFVLVQ' is 670.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug 'Cc1cccc(C(=O)Nc2cc([N+](=O)[O-])ccc2N2CCN(C)CC2)c1Cl' and the target protein 'MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cccc(C(=O)Nc2cc([N+](=O)[O-])ccc2N2CCN(C)CC2)c1Cl\"], \"target_seq\": \"MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC\", \"result\": [{\"smile\": \"Cc1cccc(C(=O)Nc2cc([N+](=O)[O-])ccc2N2CCN(C)CC2)c1Cl\", \"value\": 1100.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cccc(C(=O)Nc2cc([N+](=O)[O-])ccc2N2CCN(C)CC2)c1Cl' and the protein 'MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC' is 1100.0."
            },
            {
                "from": "human",
                "value": "And can you outline the affinity between the drug 'CN1CCN(c2ccc(Br)cc2NC(=O)c2ccccc2Cl)CC1' and the target protein mentioned above? MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCN(c2ccc(Br)cc2NC(=O)c2ccccc2Cl)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC\", \"result\": [{\"smile\": \"CN1CCN(c2ccc(Br)cc2NC(=O)c2ccccc2Cl)CC1\", \"value\": 10000.0}]}} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCN(c2ccc(Br)cc2NC(=O)c2ccccc2Cl)CC1' and the protein 'MATEEKKPETEAARAQPTPSSSATQSKPTPVKPNYALKFTLAGHTKAVSSVKFSPNGEWLASSSADKLIKIWGAYDGKFEKTISGHKLGISDVAWSSDSNLLVSASDDKTLKIWDVSSGKCLKTLKGHSNYVFCCNFNPQSNLIVSGSFDESVRIWDVKTGKCLKTLPAHSDPVSAVHFNRDGSLIVSSSYDGLCRIWDTASGQCLKTLIDDDNPPVSFVKFSPNGKYILAATLDNTLKLWDYSKGKCLKTYTGHKNEKYCIFANFSVTGGKWIVSGSEDNLVYIWNLQTKEIVQKLQGHTDVVISTACHPTENIIASAALENDKTIKLWKSDC' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the predicted affinity between the drug 'cnNCC(cCCccOCc2)c(-(C=c(CN(c3Ccc2c2n13cc()2cO1c)3NcO)cc)CC3)CcCA' and the target protein 'MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cnNCC(cCCccOCc2)c(-(C=c(CN(c3Ccc2c2n13cc()2cO1c)3NcO)cc)CC3)CcCA\"], \"target_seq\": \"MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI\", \"result\": [{\"smile\":\"cnNCC(cCCccOCc2)c(-(C=c(CN(c3Ccc2c2n13cc()2cO1c)3NcO)cc)CC3)CcCA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that’s right for this drug is 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI\", \"result\": [{\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1' and the protein 'MAESAGASSFFPLVVLLLAGSGGSGPRGVQALLCACTSCLQANYTCETDGACMVSIFNLDGMEHHVRTCIPKVELVPAGKPFYCLSSEDLRNTHCCYTDYCNRIDLRVPSGHLKEPEHPSMWGPVELVGIIAGPVFLLFLIIIIVFLVINYHQRVYHNRQRLDMEDPSCEMCLSKDKTLQDLVYDLSTSGSGSGLPLFVQRTVARTIVLQEIIGKGRFGEVWRGRWRGGDVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTIEGMIKLALSAASGLAHLHMEIVGTQGKPGIAHRDLKSKNILVKKNGMCAIADLGLAVRHDAVTDTIDIAPNQRVGTKRYMAPEVLDETINMKHFDSFKCADIYALGLVYWEIARRCNSGGVHEEYQLPYYDLVPSDPSIEEMRKVVCDQKLRPNIPNWWQSYEALRVMGKMMRECWYANGAARLTALRIKKTLSQLSVQEDVKI' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the affinity between the drug 'cCNO1=1)22ccc)1c(CcNcc)Cc1c=C(Oc(cA' and the target protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cCNO1=1)22ccc)1c(CcNcc)Cc1c=C(Oc(cA\"], \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\", \"result\": [{\"smile\":\"cCNO1=1)22ccc)1c(CcNcc)Cc1c=C(Oc(cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'NC(=O)c1cccc2c1C(=O)N(Cc1ccccc1)C2'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1cccc2c1C(=O)N(Cc1ccccc1)C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW\", \"result\": [{\"smile\": \"NC(=O)c1cccc2c1C(=O)N(Cc1ccccc1)C2\", \"value\": 320.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'NC(=O)c1cccc2c1C(=O)N(Cc1ccccc1)C2' and the protein 'MAESSDKLYRVEYAKSGRASCKKCSESIPKDSLRMAIMVQSPMFDGKVPHWYHFSCFWKVGHSIRHPDVEVDGFSELRWDDQQKVKKTAEAGGVTGKGQDGIGSKAEKTLGDFAAEYAKSNRSTCKGCMEKIEKGQVRLSKKMVDPEKPQLGMIDRWYHPGCFVKNREELGFRPEYSASQLKGFSLLATEDKEALKKQLPGVKSEGKRKGDEVDGVDEVAKKKSKKEKDKDSKLEKALKAQNDLIWNIKDELKKVCSTNDLKELLIFNKQQVPSGESAILDRVADGMVFGALLPCEECSGQLVFKSDAYYCTGDVTAWTKCMVKTQTPNRKEWVTPKEFREISYLKKLKVKKQDRIFPPETSASVAATPPPSTASAPAAVNSSASADKPLSNMKILTLGKLSRNKDEVKAMIEKLGGKLTGTANKASLCISTKKEVEKMNKKMEEVKEANIRVVSEDFLQDVSASTKSLQELFLAHILSPWGAEVKAEPVEVVAPRGKSGAALSKKSKGQVKEEGINKSEKRMKLTLKGGAAVDPDSGLEHSAHVLEKGGKVFSATLGLVDIVKGTNSYYKLQLLEDDKENRYWIFRSWGRVGTVIGSNKLEQMPSKEDAIEHFMKLYEEKTGNAWHSKNFTKYPKKFYPLEIDYGQDEEAVKKLTVNPGTKSKLPKPVQDLIKMIFDVESMKKAMVEYEIDLQKMPLGKLSKRQIQAAYSILSEVQQAVSQGSSDSQILDLSNRFYTLIPHDFGMKKPPLLNNADSVQAKVEMLDNLLDIEVAYSLLRGGSDDSSKDPIDVNYEKLKTDIKVVDRDSEEAEIIRKYVKNTHATTHNAYDLEVIDIFKIEREGECQRYKPFKQLHNRRLLWHGSRTTNFAGILSQGLRIAPPEAPVTGYMFGKGIYFADMVSKSANYCHTSQGDPIGLILLGEVALGNMYELKHASHISKLPKGKHSVKGLGKTTPDPSANISLDGVDVPLGTGISSGVNDTSLLYNEYIVYDIAQVNLKYLLKLKFNFKTSLW' is 320.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'nc-(cc-3)cc=-3)ccccccS]c)cc1(c((2)n3cc)1nH2ccc3OFC([A' and the target protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"nc-(cc-3)cc=-3)ccccccS]c)cc1(c((2)n3cc)1nH2ccc3OFC([A\"], \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\", \"result\": [{\"smile\":\"nc-(cc-3)cc=-3)ccccccS]c)cc1(c((2)n3cc)1nH2ccc3OFC([A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The correct expression for the drug is 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1' and the protein 'MAQKENSYPWPYGRQTAPSGLSTLPQRVLRKEPVTPSALVLMSRSNVQPTAAPGQKVMENSSGTPDILTRHFTIDDFEIGRPLGKGKFGNVYLAREKKSHFIVALKVLFKSQIEKEGVEHQLRREIEIQAHLHHPNILRLYNYFYDRRRIYLILEYAPRGELYKELQKSCTFDEQRTATIMEELADALMYCHGKKVIHRDIKPENLLLGLKGELKIADFGWSVHAPSLRRKTMCGTLDYLPPEMIEGRMHNEKVDLWCIGVLCYELLVGNPPFESASHNETYRRIVKVDLKFPASVPMGAQDLISKLLRHNPSERLPLAQVSAHPWVRANSRRVLPPSALQSVA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of the binding affinity between the drug '(1)CcO]C@CC)O@NH3Cc[c4cC@[(1c=CCF)]c]3NcC1)2Cc([cC4@HHcCN2(@1cCc(C)A' and the target protein 'MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(1)CcO]C@CC)O@NH3Cc[c4cC@[(1c=CCF)]c]3NcC1)2Cc([cC4@HHcCN2(@1cCc(C)A\"], \"target_seq\": \"MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI\", \"result\": [{\"smile\":\"(1)CcO]C@CC)O@NH3Cc[c4cC@[(1c=CCF)]c]3NcC1)2Cc([cC4@HHcCN2(@1cCc(C)A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper expression for the drug is 'COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(F)cc4)CC3)CN(C)[C@@H]1C2'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(F)cc4)CC3)CN(C)[C@@H]1C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI\", \"result\": [{\"smile\": \"COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(F)cc4)CC3)CN(C)[C@@H]1C2\", \"value\": 26303.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(F)cc4)CC3)CN(C)[C@@H]1C2' and the protein 'MELTSEQFNGSQVWIPSPFDLNGSLGPSNGSNQTEPYYDMTSNAVLTFIYFVVCVVGLCGNTLVIYVILRYAKMKTITNIYILNLAIADELFMLGLPFLAMQVALVHWPFGKAICRVVMTVDGINQFTSIFCLTVMSIDRYLAVVHPIKSAKWRRPRTAKMINVAVWGVSLLVILPIMIYAGLRSNQWGRSSCTINWPGESGAWYTGFIIYAFILGFLVPLTIICLCYLFIIIKVKSSGIRVGSSKRKKSEKKVTRMVSIVVAVFIFCWLPFYIFNVSSVSVAISPTPALKGMFDFVVILTYANSCANPILYAFLSDNFKKSFQNVLCLVKVSGAEDGERSDSKQDKSRLNETTETQRTLLNGDLQTSI' is 26303.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug 'OlNO2N(cc4CC2cC31C1)))NO(cClCcC(C))(Ccc)CcCccC(NcC3(4c#cnccA' with the target protein 'MAAPSPSGGGGSGGGSGSGTPGPVGSPAPGHPAVSSMQGKRKALKLNFANPPFKSTARFTLNPNPTGVQNPHIERLRTHSIESSGKLKISPEQHWDFTAEDLKDLGEIGRGAYGSVNKMVHKPSGQIMAVKRIRSTVDEKEQKQLLMDLDVVMRSSDCPYIVQFYGALFREGDCWICMELMSTSFDKFYKYVYSVLDDVIPEEILGKITLATVKALNHLKENLKIIHRDIKPSNILLDRSGNIKLCDFGISGQLVDSIAKTRDAGCRPYMAPERIDPSASRQGYDVRSDVWSLGITLYELATGRFPYPKWNSVFDQLTQVVKGDPPQLSNSEEREFSPSFINFVNLCLTKDESKRPKYKELLKHPFILMYEERAVEVACYVCKILDQMPATPSSPMYVD'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"OlNO2N(cc4CC2cC31C1)))NO(cClCcC(C))(Ccc)CcCccC(NcC3(4c#cnccA\"], \"target_seq\": \"MAAPSPSGGGGSGGGSGSGTPGPVGSPAPGHPAVSSMQGKRKALKLNFANPPFKSTARFTLNPNPTGVQNPHIERLRTHSIESSGKLKISPEQHWDFTAEDLKDLGEIGRGAYGSVNKMVHKPSGQIMAVKRIRSTVDEKEQKQLLMDLDVVMRSSDCPYIVQFYGALFREGDCWICMELMSTSFDKFYKYVYSVLDDVIPEEILGKITLATVKALNHLKENLKIIHRDIKPSNILLDRSGNIKLCDFGISGQLVDSIAKTRDAGCRPYMAPERIDPSASRQGYDVRSDVWSLGITLYELATGRFPYPKWNSVFDQLTQVVKGDPPQLSNSEEREFSPSFINFVNLCLTKDESKRPKYKELLKHPFILMYEERAVEVACYVCKILDQMPATPSSPMYVD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAPSPSGGGGSGGGSGSGTPGPVGSPAPGHPAVSSMQGKRKALKLNFANPPFKSTARFTLNPNPTGVQNPHIERLRTHSIESSGKLKISPEQHWDFTAEDLKDLGEIGRGAYGSVNKMVHKPSGQIMAVKRIRSTVDEKEQKQLLMDLDVVMRSSDCPYIVQFYGALFREGDCWICMELMSTSFDKFYKYVYSVLDDVIPEEILGKITLATVKALNHLKENLKIIHRDIKPSNILLDRSGNIKLCDFGISGQLVDSIAKTRDAGCRPYMAPERIDPSASRQGYDVRSDVWSLGITLYELATGRFPYPKWNSVFDQLTQVVKGDPPQLSNSEEREFSPSFINFVNLCLTKDESKRPKYKELLKHPFILMYEERAVEVACYVCKILDQMPATPSSPMYVD\", \"result\": [{\"smile\":\"OlNO2N(cc4CC2cC31C1)))NO(cClCcC(C))(Ccc)CcCccC(NcC3(4c#cnccA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid smile for the drug is 'COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAPSPSGGGGSGGGSGSGTPGPVGSPAPGHPAVSSMQGKRKALKLNFANPPFKSTARFTLNPNPTGVQNPHIERLRTHSIESSGKLKISPEQHWDFTAEDLKDLGEIGRGAYGSVNKMVHKPSGQIMAVKRIRSTVDEKEQKQLLMDLDVVMRSSDCPYIVQFYGALFREGDCWICMELMSTSFDKFYKYVYSVLDDVIPEEILGKITLATVKALNHLKENLKIIHRDIKPSNILLDRSGNIKLCDFGISGQLVDSIAKTRDAGCRPYMAPERIDPSASRQGYDVRSDVWSLGITLYELATGRFPYPKWNSVFDQLTQVVKGDPPQLSNSEEREFSPSFINFVNLCLTKDESKRPKYKELLKHPFILMYEERAVEVACYVCKILDQMPATPSSPMYVD\", \"result\": [{\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 2600.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl' and the protein 'MAAPSPSGGGGSGGGSGSGTPGPVGSPAPGHPAVSSMQGKRKALKLNFANPPFKSTARFTLNPNPTGVQNPHIERLRTHSIESSGKLKISPEQHWDFTAEDLKDLGEIGRGAYGSVNKMVHKPSGQIMAVKRIRSTVDEKEQKQLLMDLDVVMRSSDCPYIVQFYGALFREGDCWICMELMSTSFDKFYKYVYSVLDDVIPEEILGKITLATVKALNHLKENLKIIHRDIKPSNILLDRSGNIKLCDFGISGQLVDSIAKTRDAGCRPYMAPERIDPSASRQGYDVRSDVWSLGITLYELATGRFPYPKWNSVFDQLTQVVKGDPPQLSNSEEREFSPSFINFVNLCLTKDESKRPKYKELLKHPFILMYEERAVEVACYVCKILDQMPATPSSPMYVD' is 2600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug 'cC43cc1()O((cCccc3(C2=c))c(2nc))4nC)occN((F-F1cF()cccCC)NcFcA''s affinity with the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cC43cc1()O((cCccc3(C2=c))c(2nc))4nC)occN((F-F1cF()cccCC)NcFcA\"], \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\":\"cC43cc1()O((cCccc3(C2=c))c(2nc))4nC)occN((F-F1cF()cccCC)NcFcA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CC(C)(c1cc(NC(=O)Cc2ccc(-c3cnc4c(N)cccc4c3)cc2F)no1)C(F)(F)F'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(c1cc(NC(=O)Cc2ccc(-c3cnc4c(N)cccc4c3)cc2F)no1)C(F)(F)F\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"CC(C)(c1cc(NC(=O)Cc2ccc(-c3cnc4c(N)cccc4c3)cc2F)no1)C(F)(F)F\", \"value\": 5.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(c1cc(NC(=O)Cc2ccc(-c3cnc4c(N)cccc4c3)cc2F)no1)C(F)(F)F' and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' is 5.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the drug 'O(H1CC)CCCcSCcN=c(CcC=)NcN)c(1)4Oc1nCcC=(1=232ccCOC(l31COC]c)4cC((c=Cc()l)c[cC)C1A' and the target protein 'DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O(H1CC)CCCcSCcN=c(CcC=)NcN)c(1)4Oc1nCcC=(1=232ccCOC(l31COC]c)4cC((c=Cc()l)c[cC)C1A\"], \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\", \"result\": [{\"smile\":\"O(H1CC)CCCcSCcN=c(CcC=)NcN)c(1)4Oc1nCcC=(1=232ccCOC(l31COC]c)4cC((c=Cc()l)c[cC)C1A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' is the valid smile corresponding to the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 6700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the protein 'DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA' is 6700.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'ccNc[c)C)CccCcC1OH1(Cc@1(c-c1@]CCcO()cA'-target protein 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL' pair. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"ccNc[c)C)CccCcC1OH1(Cc@1(c-c1@]CCcO()cA\"], \"target_seq\": \"MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL\", \"result\": [{\"smile\":\"ccNc[c)C)CccCcC1OH1(Cc@1(c-c1@]CCcO()cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CC(C)(C)NC[C@@H](O)COc1ccccc1-c1ccccc1' is the smile that’s right for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)NC[C@@H](O)COc1ccccc1-c1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL\", \"result\": [{\"smile\": \"CC(C)(C)NC[C@@H](O)COc1ccccc1-c1ccccc1\", \"value\": 0.018197}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)NC[C@@H](O)COc1ccccc1-c1ccccc1' and the protein 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL' is 0.018197."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drug '4o3cOccc=NC)CO-4C(cC)22Cc(((c3ccCcc1Oc(c)l)c)Cc1)(OA''s interaction with the target protein 'MVDMGALDNLIANTAYLQARKPSDCDSKELQRRRRSLALPGLQGCAELRQKLSLNFHSLCEQQPIGRRLFRDFLATVPTFRKAATFLEDVQNWELAEEGPTKDSALQGLVATCASAPAPGNPQPFLSQAVATKCQAATTEEERVAAVTLAKAEAMAFLQEQPFKDFVTSAFYDKFLQWKLFEMQPVSDKYFTEFRVLGKGGFGEVCAVQVKNTGKMYACKKLDKKRLKKKGGEKMALLEKEILEKVSSPFIVSLAYAFESKTHLCLVMSLMNGGDLKFHIYNVGTRGLDMSRVIFYSAQIACGMLHLHELGIVYRDMKPENVLLDDLGNCRLSDLGLAVEMKGGKPITQRAGTNGYMAPEILMEKVSYSYPVDWFAMGCSIYEMVAGRTPFKDYKEKVSKEDLKQRTLQDEVKFQHDNFTEEAKDICRLFLAKKPEQRLGSREKSDDPRKHHFFKTINFPRLEAGLIEPPFVPDPSVVYAKDIAEIDDFSEVRGVEFDDKDKQFFKNFATGAVPIAWQEEIIETGLFEELNDPNRPTGCEEGNSSKSGVCLLL' to be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"4o3cOccc=NC)CO-4C(cC)22Cc(((c3ccCcc1Oc(c)l)c)Cc1)(OA\"], \"target_seq\": \"MVDMGALDNLIANTAYLQARKPSDCDSKELQRRRRSLALPGLQGCAELRQKLSLNFHSLCEQQPIGRRLFRDFLATVPTFRKAATFLEDVQNWELAEEGPTKDSALQGLVATCASAPAPGNPQPFLSQAVATKCQAATTEEERVAAVTLAKAEAMAFLQEQPFKDFVTSAFYDKFLQWKLFEMQPVSDKYFTEFRVLGKGGFGEVCAVQVKNTGKMYACKKLDKKRLKKKGGEKMALLEKEILEKVSSPFIVSLAYAFESKTHLCLVMSLMNGGDLKFHIYNVGTRGLDMSRVIFYSAQIACGMLHLHELGIVYRDMKPENVLLDDLGNCRLSDLGLAVEMKGGKPITQRAGTNGYMAPEILMEKVSYSYPVDWFAMGCSIYEMVAGRTPFKDYKEKVSKEDLKQRTLQDEVKFQHDNFTEEAKDICRLFLAKKPEQRLGSREKSDDPRKHHFFKTINFPRLEAGLIEPPFVPDPSVVYAKDIAEIDDFSEVRGVEFDDKDKQFFKNFATGAVPIAWQEEIIETGLFEELNDPNRPTGCEEGNSSKSGVCLLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVDMGALDNLIANTAYLQARKPSDCDSKELQRRRRSLALPGLQGCAELRQKLSLNFHSLCEQQPIGRRLFRDFLATVPTFRKAATFLEDVQNWELAEEGPTKDSALQGLVATCASAPAPGNPQPFLSQAVATKCQAATTEEERVAAVTLAKAEAMAFLQEQPFKDFVTSAFYDKFLQWKLFEMQPVSDKYFTEFRVLGKGGFGEVCAVQVKNTGKMYACKKLDKKRLKKKGGEKMALLEKEILEKVSSPFIVSLAYAFESKTHLCLVMSLMNGGDLKFHIYNVGTRGLDMSRVIFYSAQIACGMLHLHELGIVYRDMKPENVLLDDLGNCRLSDLGLAVEMKGGKPITQRAGTNGYMAPEILMEKVSYSYPVDWFAMGCSIYEMVAGRTPFKDYKEKVSKEDLKQRTLQDEVKFQHDNFTEEAKDICRLFLAKKPEQRLGSREKSDDPRKHHFFKTINFPRLEAGLIEPPFVPDPSVVYAKDIAEIDDFSEVRGVEFDDKDKQFFKNFATGAVPIAWQEEIIETGLFEELNDPNRPTGCEEGNSSKSGVCLLL\", \"result\": [{\"smile\":\"4o3cOccc=NC)CO-4C(cC)22Cc(((c3ccCcc1Oc(c)l)c)Cc1)(OA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' represents the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVDMGALDNLIANTAYLQARKPSDCDSKELQRRRRSLALPGLQGCAELRQKLSLNFHSLCEQQPIGRRLFRDFLATVPTFRKAATFLEDVQNWELAEEGPTKDSALQGLVATCASAPAPGNPQPFLSQAVATKCQAATTEEERVAAVTLAKAEAMAFLQEQPFKDFVTSAFYDKFLQWKLFEMQPVSDKYFTEFRVLGKGGFGEVCAVQVKNTGKMYACKKLDKKRLKKKGGEKMALLEKEILEKVSSPFIVSLAYAFESKTHLCLVMSLMNGGDLKFHIYNVGTRGLDMSRVIFYSAQIACGMLHLHELGIVYRDMKPENVLLDDLGNCRLSDLGLAVEMKGGKPITQRAGTNGYMAPEILMEKVSYSYPVDWFAMGCSIYEMVAGRTPFKDYKEKVSKEDLKQRTLQDEVKFQHDNFTEEAKDICRLFLAKKPEQRLGSREKSDDPRKHHFFKTINFPRLEAGLIEPPFVPDPSVVYAKDIAEIDDFSEVRGVEFDDKDKQFFKNFATGAVPIAWQEEIIETGLFEELNDPNRPTGCEEGNSSKSGVCLLL\", \"result\": [{\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 980.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the protein 'MVDMGALDNLIANTAYLQARKPSDCDSKELQRRRRSLALPGLQGCAELRQKLSLNFHSLCEQQPIGRRLFRDFLATVPTFRKAATFLEDVQNWELAEEGPTKDSALQGLVATCASAPAPGNPQPFLSQAVATKCQAATTEEERVAAVTLAKAEAMAFLQEQPFKDFVTSAFYDKFLQWKLFEMQPVSDKYFTEFRVLGKGGFGEVCAVQVKNTGKMYACKKLDKKRLKKKGGEKMALLEKEILEKVSSPFIVSLAYAFESKTHLCLVMSLMNGGDLKFHIYNVGTRGLDMSRVIFYSAQIACGMLHLHELGIVYRDMKPENVLLDDLGNCRLSDLGLAVEMKGGKPITQRAGTNGYMAPEILMEKVSYSYPVDWFAMGCSIYEMVAGRTPFKDYKEKVSKEDLKQRTLQDEVKFQHDNFTEEAKDICRLFLAKKPEQRLGSREKSDDPRKHHFFKTINFPRLEAGLIEPPFVPDPSVVYAKDIAEIDDFSEVRGVEFDDKDKQFFKNFATGAVPIAWQEEIIETGLFEELNDPNRPTGCEEGNSSKSGVCLLL' is 980.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you determine the affinity value of the drug '=occ)(c))((cc12c(Cc13(N-)O(OccC)COcC2c4CcC3Cc4cOcl)A' for the target protein 'MDDAAVLKRRGYLLGINLGEGSYAKVKSAYSERLKFNVAIKIIDRKKAPADFLEKFLPREIEILAMLNHCSIIKTYEIFETSHGKVYIVMELAVQGDLLELIKTRGALHEDEARKKFHQLSLAIKYCHDLDVVHRDLKCDNLLLDKDFNIKLSDFSFSKRCLRDDSGRMALSKTFCGSPAYAAPEVLQGIPYQPKVYDIWSLGVILYIMVCGSMPYDDSNIKKMLRIQKEHRVNFPRSKHLTGECKDLIYHMLQPDVNRRLHIDEILSHCWMQPKARGSPSVAINKEGESSRGTEPLWTPEPGSDKKSATKLEPEGEAQPQAQPETKPEGTAMQMSRQSEILGFPSKPSTMETEEGPPQQPPETRAQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"=occ)(c))((cc12c(Cc13(N-)O(OccC)COcC2c4CcC3Cc4cOcl)A\"], \"target_seq\": \"MDDAAVLKRRGYLLGINLGEGSYAKVKSAYSERLKFNVAIKIIDRKKAPADFLEKFLPREIEILAMLNHCSIIKTYEIFETSHGKVYIVMELAVQGDLLELIKTRGALHEDEARKKFHQLSLAIKYCHDLDVVHRDLKCDNLLLDKDFNIKLSDFSFSKRCLRDDSGRMALSKTFCGSPAYAAPEVLQGIPYQPKVYDIWSLGVILYIMVCGSMPYDDSNIKKMLRIQKEHRVNFPRSKHLTGECKDLIYHMLQPDVNRRLHIDEILSHCWMQPKARGSPSVAINKEGESSRGTEPLWTPEPGSDKKSATKLEPEGEAQPQAQPETKPEGTAMQMSRQSEILGFPSKPSTMETEEGPPQQPPETRAQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDDAAVLKRRGYLLGINLGEGSYAKVKSAYSERLKFNVAIKIIDRKKAPADFLEKFLPREIEILAMLNHCSIIKTYEIFETSHGKVYIVMELAVQGDLLELIKTRGALHEDEARKKFHQLSLAIKYCHDLDVVHRDLKCDNLLLDKDFNIKLSDFSFSKRCLRDDSGRMALSKTFCGSPAYAAPEVLQGIPYQPKVYDIWSLGVILYIMVCGSMPYDDSNIKKMLRIQKEHRVNFPRSKHLTGECKDLIYHMLQPDVNRRLHIDEILSHCWMQPKARGSPSVAINKEGESSRGTEPLWTPEPGSDKKSATKLEPEGEAQPQAQPETKPEGTAMQMSRQSEILGFPSKPSTMETEEGPPQQPPETRAQ\", \"result\": [{\"smile\":\"=occ)(c))((cc12c(Cc13(N-)O(OccC)COcC2c4CcC3Cc4cOcl)A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right smile for the drug is 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDDAAVLKRRGYLLGINLGEGSYAKVKSAYSERLKFNVAIKIIDRKKAPADFLEKFLPREIEILAMLNHCSIIKTYEIFETSHGKVYIVMELAVQGDLLELIKTRGALHEDEARKKFHQLSLAIKYCHDLDVVHRDLKCDNLLLDKDFNIKLSDFSFSKRCLRDDSGRMALSKTFCGSPAYAAPEVLQGIPYQPKVYDIWSLGVILYIMVCGSMPYDDSNIKKMLRIQKEHRVNFPRSKHLTGECKDLIYHMLQPDVNRRLHIDEILSHCWMQPKARGSPSVAINKEGESSRGTEPLWTPEPGSDKKSATKLEPEGEAQPQAQPETKPEGTAMQMSRQSEILGFPSKPSTMETEEGPPQQPPETRAQ\", \"result\": [{\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1' and the protein 'MDDAAVLKRRGYLLGINLGEGSYAKVKSAYSERLKFNVAIKIIDRKKAPADFLEKFLPREIEILAMLNHCSIIKTYEIFETSHGKVYIVMELAVQGDLLELIKTRGALHEDEARKKFHQLSLAIKYCHDLDVVHRDLKCDNLLLDKDFNIKLSDFSFSKRCLRDDSGRMALSKTFCGSPAYAAPEVLQGIPYQPKVYDIWSLGVILYIMVCGSMPYDDSNIKKMLRIQKEHRVNFPRSKHLTGECKDLIYHMLQPDVNRRLHIDEILSHCWMQPKARGSPSVAINKEGESSRGTEPLWTPEPGSDKKSATKLEPEGEAQPQAQPETKPEGTAMQMSRQSEILGFPSKPSTMETEEGPPQQPPETRAQ' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'C3=CO(1CCc))=1(COc)2cCCcNCC3cCNcF)C1(1cnOccn2cccc(NlcA'-target protein 'MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP' pair. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C3=CO(1CCc))=1(COc)2cCCcNCC3cCNcF)C1(1cnOccn2cccc(NlcA\"], \"target_seq\": \"MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP\", \"result\": [{\"smile\":\"C3=CO(1CCc))=1(COc)2cCCcNCC3cCNcF)C1(1cnOccn2cccc(NlcA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right smile for the drug is 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP\", \"result\": [{\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the protein 'MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drug 'c31c3)21Oc(nnc3-c)c3ccc(Nccccn2c-c(cO(Ncnc)())A' for the target protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c31c3)21Oc(nnc3-c)c3ccc(Nccccn2c-c(cO(Ncnc)())A\"], \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\", \"result\": [{\"smile\":\"c31c3)21Oc(nnc3-c)c3ccc(Nccccn2c-c(cO(Ncnc)())A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA\", \"result\": [{\"smile\": \"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1' and the protein 'MSDVAIVKEGWLHKRGEYIKTWRPRYFLLKNDGTFIGYKERPQDVDQREAPLNNFSVAQCQLMKTERPRPNTFIIRCLQWTTVIERTFHVETPEEREEWTTAIQTVADGLKKQEEEEMDFRSGSPSDNSGAEEMEVSLAKPKHRVTMNEFEYLKLLGKGTFGKVILVKEKATGRYYAMKILKKEVIVAKDEVAHTLTENRVLQNSRHPFLTALKYSFQTHDRLCFVMEYANGGELFFHLSRERVFSEDRARFYGAEIVSALDYLHSEKNVVYRDLKLENLMLDKDGHIKITDFGLCKEGIKDGATMKTFCGTPEYLAPEVLEDNDYGRAVDWWGLGVVMYEMMCGRLPFYNQDHEKLFELILMEEIRFPRTLGPEAKSLLSGLLKKDPKQRLGGGSEDAKEIMQHRFFAGIVWQHVYEKKLSPPFKPQVTSETDTRYFDEEFTAQMITITPPDQDDSMECVDSERRPHFPQFSYSASGTA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the affinity value between the drug 'O()c2C4FCFn)c(CCN((3)ccCc)(cc21)c(c)CcNcc1F4nC)3cc(CNNNOcCCc=A' and the target protein 'MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGKVFLVRKAGGHDAGKLYAMKVLRKAALVQRAKTQEHTRTERSVLELVRQAPFLVTLHYAFQTDAKLHLILDYVSGGEMFTHLYQRQYFKEAEVRVYGGEIVLALEHLHKLGIIYRDLKLENVLLDSEGHIVLTDFGLSKEFLTEEKERTFSFCGTIEYMAPEIIRSKTGHGKAVDWWSLGILLFELLTGASPFTLEGERNTQAEVSRRILKCSPPFPPRIGPVAQDLLQRLLCKDPKKRLGAGPQGAQEVRNHPFFQGLDWVALAARKIPAPFRPQIRSELDVGNFAEEFTRLEPVYSPPGSPPPGDPRIFQGYSFVAPSILFDHNNAVMTDGLEAPGAGDRPGRAAVARSAMMQDSPFFQQYELDLREPALGQGSFSVCRRCRQRQSGQEFAVKILSRRLEANTQREVAALRLCQSHPNVVNLHEVHHDQLHTYLVLELLRGGELLEHIRKKRHFSESEASQILRSLVSAVSFMHEEAGVVHRDLKPENILYADDTPGAPVKIIDFGFARLRPQSPGVPMQTPCFTLQYAAPELLAQQGYDESCDLWSLGVILYMMLSGQVPFQGASGQGGQSQAAEIMCKIREGRFSLDGEAWQGVSEEAKELVRGLLTVDPAKRLKLEGLRGSSWLQDGSARSSPPLRTPDVLESSGPAVRSGLNATFMAFNRGKREGFFLKSVENAPLAKRRKQKLRSATASRRGSPAPANPGRAPVASKGAPRRANGPLPPS'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O()c2C4FCFn)c(CCN((3)ccCc)(cc21)c(c)CcNcc1F4nC)3cc(CNNNOcCCc=A\"], \"target_seq\": \"MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGKVFLVRKAGGHDAGKLYAMKVLRKAALVQRAKTQEHTRTERSVLELVRQAPFLVTLHYAFQTDAKLHLILDYVSGGEMFTHLYQRQYFKEAEVRVYGGEIVLALEHLHKLGIIYRDLKLENVLLDSEGHIVLTDFGLSKEFLTEEKERTFSFCGTIEYMAPEIIRSKTGHGKAVDWWSLGILLFELLTGASPFTLEGERNTQAEVSRRILKCSPPFPPRIGPVAQDLLQRLLCKDPKKRLGAGPQGAQEVRNHPFFQGLDWVALAARKIPAPFRPQIRSELDVGNFAEEFTRLEPVYSPPGSPPPGDPRIFQGYSFVAPSILFDHNNAVMTDGLEAPGAGDRPGRAAVARSAMMQDSPFFQQYELDLREPALGQGSFSVCRRCRQRQSGQEFAVKILSRRLEANTQREVAALRLCQSHPNVVNLHEVHHDQLHTYLVLELLRGGELLEHIRKKRHFSESEASQILRSLVSAVSFMHEEAGVVHRDLKPENILYADDTPGAPVKIIDFGFARLRPQSPGVPMQTPCFTLQYAAPELLAQQGYDESCDLWSLGVILYMMLSGQVPFQGASGQGGQSQAAEIMCKIREGRFSLDGEAWQGVSEEAKELVRGLLTVDPAKRLKLEGLRGSSWLQDGSARSSPPLRTPDVLESSGPAVRSGLNATFMAFNRGKREGFFLKSVENAPLAKRRKQKLRSATASRRGSPAPANPGRAPVASKGAPRRANGPLPPS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGKVFLVRKAGGHDAGKLYAMKVLRKAALVQRAKTQEHTRTERSVLELVRQAPFLVTLHYAFQTDAKLHLILDYVSGGEMFTHLYQRQYFKEAEVRVYGGEIVLALEHLHKLGIIYRDLKLENVLLDSEGHIVLTDFGLSKEFLTEEKERTFSFCGTIEYMAPEIIRSKTGHGKAVDWWSLGILLFELLTGASPFTLEGERNTQAEVSRRILKCSPPFPPRIGPVAQDLLQRLLCKDPKKRLGAGPQGAQEVRNHPFFQGLDWVALAARKIPAPFRPQIRSELDVGNFAEEFTRLEPVYSPPGSPPPGDPRIFQGYSFVAPSILFDHNNAVMTDGLEAPGAGDRPGRAAVARSAMMQDSPFFQQYELDLREPALGQGSFSVCRRCRQRQSGQEFAVKILSRRLEANTQREVAALRLCQSHPNVVNLHEVHHDQLHTYLVLELLRGGELLEHIRKKRHFSESEASQILRSLVSAVSFMHEEAGVVHRDLKPENILYADDTPGAPVKIIDFGFARLRPQSPGVPMQTPCFTLQYAAPELLAQQGYDESCDLWSLGVILYMMLSGQVPFQGASGQGGQSQAAEIMCKIREGRFSLDGEAWQGVSEEAKELVRGLLTVDPAKRLKLEGLRGSSWLQDGSARSSPPLRTPDVLESSGPAVRSGLNATFMAFNRGKREGFFLKSVENAPLAKRRKQKLRSATASRRGSPAPANPGRAPVASKGAPRRANGPLPPS\", \"result\": [{\"smile\":\"O()c2C4FCFn)c(CCN((3)ccCc)(cc21)c(c)CcNcc1F4nC)3cc(CNNNOcCCc=A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile fitting for the drug is 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGKVFLVRKAGGHDAGKLYAMKVLRKAALVQRAKTQEHTRTERSVLELVRQAPFLVTLHYAFQTDAKLHLILDYVSGGEMFTHLYQRQYFKEAEVRVYGGEIVLALEHLHKLGIIYRDLKLENVLLDSEGHIVLTDFGLSKEFLTEEKERTFSFCGTIEYMAPEIIRSKTGHGKAVDWWSLGILLFELLTGASPFTLEGERNTQAEVSRRILKCSPPFPPRIGPVAQDLLQRLLCKDPKKRLGAGPQGAQEVRNHPFFQGLDWVALAARKIPAPFRPQIRSELDVGNFAEEFTRLEPVYSPPGSPPPGDPRIFQGYSFVAPSILFDHNNAVMTDGLEAPGAGDRPGRAAVARSAMMQDSPFFQQYELDLREPALGQGSFSVCRRCRQRQSGQEFAVKILSRRLEANTQREVAALRLCQSHPNVVNLHEVHHDQLHTYLVLELLRGGELLEHIRKKRHFSESEASQILRSLVSAVSFMHEEAGVVHRDLKPENILYADDTPGAPVKIIDFGFARLRPQSPGVPMQTPCFTLQYAAPELLAQQGYDESCDLWSLGVILYMMLSGQVPFQGASGQGGQSQAAEIMCKIREGRFSLDGEAWQGVSEEAKELVRGLLTVDPAKRLKLEGLRGSSWLQDGSARSSPPLRTPDVLESSGPAVRSGLNATFMAFNRGKREGFFLKSVENAPLAKRRKQKLRSATASRRGSPAPANPGRAPVASKGAPRRANGPLPPS\", \"result\": [{\"smile\": \"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1' and the protein 'MGDEDDDESCAVELRITEANLTGHEEKVSVENFELLKVLGTGAYGKVFLVRKAGGHDAGKLYAMKVLRKAALVQRAKTQEHTRTERSVLELVRQAPFLVTLHYAFQTDAKLHLILDYVSGGEMFTHLYQRQYFKEAEVRVYGGEIVLALEHLHKLGIIYRDLKLENVLLDSEGHIVLTDFGLSKEFLTEEKERTFSFCGTIEYMAPEIIRSKTGHGKAVDWWSLGILLFELLTGASPFTLEGERNTQAEVSRRILKCSPPFPPRIGPVAQDLLQRLLCKDPKKRLGAGPQGAQEVRNHPFFQGLDWVALAARKIPAPFRPQIRSELDVGNFAEEFTRLEPVYSPPGSPPPGDPRIFQGYSFVAPSILFDHNNAVMTDGLEAPGAGDRPGRAAVARSAMMQDSPFFQQYELDLREPALGQGSFSVCRRCRQRQSGQEFAVKILSRRLEANTQREVAALRLCQSHPNVVNLHEVHHDQLHTYLVLELLRGGELLEHIRKKRHFSESEASQILRSLVSAVSFMHEEAGVVHRDLKPENILYADDTPGAPVKIIDFGFARLRPQSPGVPMQTPCFTLQYAAPELLAQQGYDESCDLWSLGVILYMMLSGQVPFQGASGQGGQSQAAEIMCKIREGRFSLDGEAWQGVSEEAKELVRGLLTVDPAKRLKLEGLRGSSWLQDGSARSSPPLRTPDVLESSGPAVRSGLNATFMAFNRGKREGFFLKSVENAPLAKRRKQKLRSATASRRGSPAPANPGRAPVASKGAPRRANGPLPPS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug ')(cn)F)=O(Fcccc(CC(cC(3ccCFN))21cOcclN=C)ON12())c3(ccccc(A' and the target protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\")(cn)F)=O(Fcccc(CC(cC(3ccCFN))21cOcclN=C)ON12())c3(ccccc(A\"], \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\", \"result\": [{\"smile\":\")(cn)F)=O(Fcccc(CC(cC(3ccCFN))21cOcclN=C)ON12())c3(ccccc(A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 62.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1' and the protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL' is 62.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drug 'c)NcC2cc1Cc(lCccCc2cCn(n)33nc.1NA' to the target protein 'MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c)NcC2cc1Cc(lCccCc2cCn(n)33nc.1NA\"], \"target_seq\": \"MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM\", \"result\": [{\"smile\":\"c)NcC2cc1Cc(lCccCc2cCn(n)33nc.1NA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl' and the protein 'MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the interaction value between the drug '4ccNNCC2@C3c]C()cH@==(O))1[4([CC([1]-cO@CcCCCO1]H)c([2)cOcCC(c1)c@H@]CC3cNCA' and the target protein 'MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"4ccNNCC2@C3c]C()cH@==(O))1[4([CC([1]-cO@CcCCCO1]H)c([2)cOcCC(c1)c@H@]CC3cNCA\"], \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\", \"result\": [{\"smile\":\"4ccNNCC2@C3c]C()cH@==(O))1[4([CC([1]-cO@CcCCCO1]H)c([2)cOcCC(c1)c@H@]CC3cNCA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The recommended facial expression for the drug is 'COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(C(=O)[O-])cc4)CC3)CN(C)[C@@H]1C2'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(C(=O)[O-])cc4)CC3)CN(C)[C@@H]1C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL\", \"result\": [{\"smile\": \"COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(C(=O)[O-])cc4)CC3)CN(C)[C@@H]1C2\", \"value\": 81283.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cccc2c1C[C@H]1C[C@@H](C(=O)N3CCN(c4ccc(C(=O)[O-])cc4)CC3)CN(C)[C@@H]1C2' and the protein 'MFPNGTAPSPTSSPSSSPGGCGEGVCSRGPGSGAADGMEEPGRNSSQNGTLSEGQGSAILISFIYSVVCLVGLCGNSMVIYVILRYAKMKTATNIYILNLAIADELLMLSVPFLVTSTLLRHWPFGALLCRLVLSVDAVNMFTSIYCLTVLSVDRYVAVVHPIKAARYRRPTVAKVVNLGVWVLSLLVILPIVVFSRTAANSDGTVACNMLMPEPAQRWLVGFVLYTFLMGFLLPVGAICLCYVLIIAKMRMVALKAGWQQRKRSERKITLMVMMVVMVFVICWMPFYVVQLVNVFAEQDDATVSQLSVILGYANSCANPILYGFLSDNFKRSFQRILCLSWMDNAAEEPVDYYATALKSRAYSVEDFQPENLESGGVFRNGTCASRISTL' is 81283.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drug '(1c)C(33cNcNcO3n2C=C2NccC)ccccc1ccCcnc(Cc)3A' for the target protein 'MKHLYGLFHYNPMMLGLESLPDPTDTWEIIETIGKGTYGKVYKVTNKRDGSLAAVKILDPVSDMDEEIEAEYNILQFLPNHPNVVKFYGMFYKADHCVGGQLWLVLELCNGGSVTELVKGLLRCGQRLDEAMISYILYGALLGLQHLHNNRIIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRLRRNTSVGTPFWMAPEVIACEQQYDSSYDARCDVWSLGITAIELGDGDPPLFDMHPVKTLFKIPRNPPPTLLHPEKWCEEFNHFISQCLIKDFERRPSVTHLLDHPFIKGVHGKVLFLQKQLAKVLQDQKHQNPVAKTRHERMHTRRPYHVEDAEKYCLEDDLVNLEVLDEDTIIHQLQKRYADLLIYTYVGDILIALNPFQNLSIYSPQFSRLYHGVKRASNPPHIFASADAAYQCMVTLSKDQCIVISGESGSGKTESAHLIVQHLTFLGKANNQTLREKILQVNSLVEAFGNSCTAINDNSSRFGKYLEMMFTPTGVVMGARISEYLLEKSRVIKQAAREKNFHIFYYIYAGLHHQKKLSDFRLPEEKPPRYIADETGRVMHDITSKESYRRQFEAIQHCFRIIGFTDKEVHSVYRILAGILNIGNIEFAAISSQHQTDKSEVPNAEALQNAASVLCISPEELQEALTSHCVVTRGETIIRANTVDRAADVRDAMSKALYGRLFSWIVNRINTLLQPDENICSAGGGMNVGILDIFGFENFQRNSFEQLCINIANEQIQYYFNQHVFALEQMEYQNEGIDAVPVEYEDNRPLLDMFLQKPLGLLALLDEESRFPQATDQTLVDKFEDNLRCKYFWRPKGVELCFGIQHYAGKVLYDASGVLEKNRDTLPADVVVVLRTSENKLLQQLFSIPLTKTGNLAQTRARITVASSSLPPHFSAGKAKVDTLEVIRHPEETTNMKRQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNDDREALQFSRERVLAQLRSTGILETVSIRRQGYSHRILFEEFVKRYYYLAFTAHQTPLASKESCVAILEKSRLDHWVLGKTKVFLKYYHVEQLNLLLREVIGRVVVLQAYTKGWLGARRYKRVREKREKGAIAIQSAWRGYDARRKFKKISNRRNESAAHNQAGDTSNQSSGPHSPVAAGTRGSAEVQDCSEPGDHKVLRGSVHRRSHSQAESNNGRTQTSSNSPAVTEKNGHSQAQSSPKGCDIFAGHANKHSVSGTDLLSSRICHPAPDQQGLSLWGAPQKPGSENGLAQKHRTPRRRCQQPKMLSSPEDTMYYNQLNGTLEYQGSKRKPRKLGQIKVLDGEDEYYKSLSPVDCIPEENNSAHPSFFSSSSKGDSFAQH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(1c)C(33cNcNcO3n2C=C2NccC)ccccc1ccCcnc(Cc)3A\"], \"target_seq\": \"MKHLYGLFHYNPMMLGLESLPDPTDTWEIIETIGKGTYGKVYKVTNKRDGSLAAVKILDPVSDMDEEIEAEYNILQFLPNHPNVVKFYGMFYKADHCVGGQLWLVLELCNGGSVTELVKGLLRCGQRLDEAMISYILYGALLGLQHLHNNRIIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRLRRNTSVGTPFWMAPEVIACEQQYDSSYDARCDVWSLGITAIELGDGDPPLFDMHPVKTLFKIPRNPPPTLLHPEKWCEEFNHFISQCLIKDFERRPSVTHLLDHPFIKGVHGKVLFLQKQLAKVLQDQKHQNPVAKTRHERMHTRRPYHVEDAEKYCLEDDLVNLEVLDEDTIIHQLQKRYADLLIYTYVGDILIALNPFQNLSIYSPQFSRLYHGVKRASNPPHIFASADAAYQCMVTLSKDQCIVISGESGSGKTESAHLIVQHLTFLGKANNQTLREKILQVNSLVEAFGNSCTAINDNSSRFGKYLEMMFTPTGVVMGARISEYLLEKSRVIKQAAREKNFHIFYYIYAGLHHQKKLSDFRLPEEKPPRYIADETGRVMHDITSKESYRRQFEAIQHCFRIIGFTDKEVHSVYRILAGILNIGNIEFAAISSQHQTDKSEVPNAEALQNAASVLCISPEELQEALTSHCVVTRGETIIRANTVDRAADVRDAMSKALYGRLFSWIVNRINTLLQPDENICSAGGGMNVGILDIFGFENFQRNSFEQLCINIANEQIQYYFNQHVFALEQMEYQNEGIDAVPVEYEDNRPLLDMFLQKPLGLLALLDEESRFPQATDQTLVDKFEDNLRCKYFWRPKGVELCFGIQHYAGKVLYDASGVLEKNRDTLPADVVVVLRTSENKLLQQLFSIPLTKTGNLAQTRARITVASSSLPPHFSAGKAKVDTLEVIRHPEETTNMKRQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNDDREALQFSRERVLAQLRSTGILETVSIRRQGYSHRILFEEFVKRYYYLAFTAHQTPLASKESCVAILEKSRLDHWVLGKTKVFLKYYHVEQLNLLLREVIGRVVVLQAYTKGWLGARRYKRVREKREKGAIAIQSAWRGYDARRKFKKISNRRNESAAHNQAGDTSNQSSGPHSPVAAGTRGSAEVQDCSEPGDHKVLRGSVHRRSHSQAESNNGRTQTSSNSPAVTEKNGHSQAQSSPKGCDIFAGHANKHSVSGTDLLSSRICHPAPDQQGLSLWGAPQKPGSENGLAQKHRTPRRRCQQPKMLSSPEDTMYYNQLNGTLEYQGSKRKPRKLGQIKVLDGEDEYYKSLSPVDCIPEENNSAHPSFFSSSSKGDSFAQH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKHLYGLFHYNPMMLGLESLPDPTDTWEIIETIGKGTYGKVYKVTNKRDGSLAAVKILDPVSDMDEEIEAEYNILQFLPNHPNVVKFYGMFYKADHCVGGQLWLVLELCNGGSVTELVKGLLRCGQRLDEAMISYILYGALLGLQHLHNNRIIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRLRRNTSVGTPFWMAPEVIACEQQYDSSYDARCDVWSLGITAIELGDGDPPLFDMHPVKTLFKIPRNPPPTLLHPEKWCEEFNHFISQCLIKDFERRPSVTHLLDHPFIKGVHGKVLFLQKQLAKVLQDQKHQNPVAKTRHERMHTRRPYHVEDAEKYCLEDDLVNLEVLDEDTIIHQLQKRYADLLIYTYVGDILIALNPFQNLSIYSPQFSRLYHGVKRASNPPHIFASADAAYQCMVTLSKDQCIVISGESGSGKTESAHLIVQHLTFLGKANNQTLREKILQVNSLVEAFGNSCTAINDNSSRFGKYLEMMFTPTGVVMGARISEYLLEKSRVIKQAAREKNFHIFYYIYAGLHHQKKLSDFRLPEEKPPRYIADETGRVMHDITSKESYRRQFEAIQHCFRIIGFTDKEVHSVYRILAGILNIGNIEFAAISSQHQTDKSEVPNAEALQNAASVLCISPEELQEALTSHCVVTRGETIIRANTVDRAADVRDAMSKALYGRLFSWIVNRINTLLQPDENICSAGGGMNVGILDIFGFENFQRNSFEQLCINIANEQIQYYFNQHVFALEQMEYQNEGIDAVPVEYEDNRPLLDMFLQKPLGLLALLDEESRFPQATDQTLVDKFEDNLRCKYFWRPKGVELCFGIQHYAGKVLYDASGVLEKNRDTLPADVVVVLRTSENKLLQQLFSIPLTKTGNLAQTRARITVASSSLPPHFSAGKAKVDTLEVIRHPEETTNMKRQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNDDREALQFSRERVLAQLRSTGILETVSIRRQGYSHRILFEEFVKRYYYLAFTAHQTPLASKESCVAILEKSRLDHWVLGKTKVFLKYYHVEQLNLLLREVIGRVVVLQAYTKGWLGARRYKRVREKREKGAIAIQSAWRGYDARRKFKKISNRRNESAAHNQAGDTSNQSSGPHSPVAAGTRGSAEVQDCSEPGDHKVLRGSVHRRSHSQAESNNGRTQTSSNSPAVTEKNGHSQAQSSPKGCDIFAGHANKHSVSGTDLLSSRICHPAPDQQGLSLWGAPQKPGSENGLAQKHRTPRRRCQQPKMLSSPEDTMYYNQLNGTLEYQGSKRKPRKLGQIKVLDGEDEYYKSLSPVDCIPEENNSAHPSFFSSSSKGDSFAQH\", \"result\": [{\"smile\":\"(1c)C(33cNcNcO3n2C=C2NccC)ccccc1ccCcnc(Cc)3A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The true smile for the drug is 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKHLYGLFHYNPMMLGLESLPDPTDTWEIIETIGKGTYGKVYKVTNKRDGSLAAVKILDPVSDMDEEIEAEYNILQFLPNHPNVVKFYGMFYKADHCVGGQLWLVLELCNGGSVTELVKGLLRCGQRLDEAMISYILYGALLGLQHLHNNRIIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRLRRNTSVGTPFWMAPEVIACEQQYDSSYDARCDVWSLGITAIELGDGDPPLFDMHPVKTLFKIPRNPPPTLLHPEKWCEEFNHFISQCLIKDFERRPSVTHLLDHPFIKGVHGKVLFLQKQLAKVLQDQKHQNPVAKTRHERMHTRRPYHVEDAEKYCLEDDLVNLEVLDEDTIIHQLQKRYADLLIYTYVGDILIALNPFQNLSIYSPQFSRLYHGVKRASNPPHIFASADAAYQCMVTLSKDQCIVISGESGSGKTESAHLIVQHLTFLGKANNQTLREKILQVNSLVEAFGNSCTAINDNSSRFGKYLEMMFTPTGVVMGARISEYLLEKSRVIKQAAREKNFHIFYYIYAGLHHQKKLSDFRLPEEKPPRYIADETGRVMHDITSKESYRRQFEAIQHCFRIIGFTDKEVHSVYRILAGILNIGNIEFAAISSQHQTDKSEVPNAEALQNAASVLCISPEELQEALTSHCVVTRGETIIRANTVDRAADVRDAMSKALYGRLFSWIVNRINTLLQPDENICSAGGGMNVGILDIFGFENFQRNSFEQLCINIANEQIQYYFNQHVFALEQMEYQNEGIDAVPVEYEDNRPLLDMFLQKPLGLLALLDEESRFPQATDQTLVDKFEDNLRCKYFWRPKGVELCFGIQHYAGKVLYDASGVLEKNRDTLPADVVVVLRTSENKLLQQLFSIPLTKTGNLAQTRARITVASSSLPPHFSAGKAKVDTLEVIRHPEETTNMKRQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNDDREALQFSRERVLAQLRSTGILETVSIRRQGYSHRILFEEFVKRYYYLAFTAHQTPLASKESCVAILEKSRLDHWVLGKTKVFLKYYHVEQLNLLLREVIGRVVVLQAYTKGWLGARRYKRVREKREKGAIAIQSAWRGYDARRKFKKISNRRNESAAHNQAGDTSNQSSGPHSPVAAGTRGSAEVQDCSEPGDHKVLRGSVHRRSHSQAESNNGRTQTSSNSPAVTEKNGHSQAQSSPKGCDIFAGHANKHSVSGTDLLSSRICHPAPDQQGLSLWGAPQKPGSENGLAQKHRTPRRRCQQPKMLSSPEDTMYYNQLNGTLEYQGSKRKPRKLGQIKVLDGEDEYYKSLSPVDCIPEENNSAHPSFFSSSSKGDSFAQH\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein 'MKHLYGLFHYNPMMLGLESLPDPTDTWEIIETIGKGTYGKVYKVTNKRDGSLAAVKILDPVSDMDEEIEAEYNILQFLPNHPNVVKFYGMFYKADHCVGGQLWLVLELCNGGSVTELVKGLLRCGQRLDEAMISYILYGALLGLQHLHNNRIIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRLRRNTSVGTPFWMAPEVIACEQQYDSSYDARCDVWSLGITAIELGDGDPPLFDMHPVKTLFKIPRNPPPTLLHPEKWCEEFNHFISQCLIKDFERRPSVTHLLDHPFIKGVHGKVLFLQKQLAKVLQDQKHQNPVAKTRHERMHTRRPYHVEDAEKYCLEDDLVNLEVLDEDTIIHQLQKRYADLLIYTYVGDILIALNPFQNLSIYSPQFSRLYHGVKRASNPPHIFASADAAYQCMVTLSKDQCIVISGESGSGKTESAHLIVQHLTFLGKANNQTLREKILQVNSLVEAFGNSCTAINDNSSRFGKYLEMMFTPTGVVMGARISEYLLEKSRVIKQAAREKNFHIFYYIYAGLHHQKKLSDFRLPEEKPPRYIADETGRVMHDITSKESYRRQFEAIQHCFRIIGFTDKEVHSVYRILAGILNIGNIEFAAISSQHQTDKSEVPNAEALQNAASVLCISPEELQEALTSHCVVTRGETIIRANTVDRAADVRDAMSKALYGRLFSWIVNRINTLLQPDENICSAGGGMNVGILDIFGFENFQRNSFEQLCINIANEQIQYYFNQHVFALEQMEYQNEGIDAVPVEYEDNRPLLDMFLQKPLGLLALLDEESRFPQATDQTLVDKFEDNLRCKYFWRPKGVELCFGIQHYAGKVLYDASGVLEKNRDTLPADVVVVLRTSENKLLQQLFSIPLTKTGNLAQTRARITVASSSLPPHFSAGKAKVDTLEVIRHPEETTNMKRQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNDDREALQFSRERVLAQLRSTGILETVSIRRQGYSHRILFEEFVKRYYYLAFTAHQTPLASKESCVAILEKSRLDHWVLGKTKVFLKYYHVEQLNLLLREVIGRVVVLQAYTKGWLGARRYKRVREKREKGAIAIQSAWRGYDARRKFKKISNRRNESAAHNQAGDTSNQSSGPHSPVAAGTRGSAEVQDCSEPGDHKVLRGSVHRRSHSQAESNNGRTQTSSNSPAVTEKNGHSQAQSSPKGCDIFAGHANKHSVSGTDLLSSRICHPAPDQQGLSLWGAPQKPGSENGLAQKHRTPRRRCQQPKMLSSPEDTMYYNQLNGTLEYQGSKRKPRKLGQIKVLDGEDEYYKSLSPVDCIPEENNSAHPSFFSSSSKGDSFAQH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug ')n1NCc2=nCNCc2(c1)CC))lC)COOcc(cc3nNs2Cc(3C2c((c(ccc)CNA' and the target protein 'MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\")n1NCc2=nCNCc2(c1)CC))lC)COOcc(cc3nNs2Cc(3C2c((c(ccc)CNA\"], \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\", \"result\": [{\"smile\":\")n1NCc2=nCNCc2(c1)CC))lC)COOcc(cc3nNs2Cc(3C2c((c(ccc)CNA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right smile for the drug is 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\", \"result\": [{\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1' and the protein 'MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the affinity between the drug 'ncS(c4(cncC2)((c3)2nc-)c1ccc1COClc)c4NccCc(clcc3=c)A' and the target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"ncS(c4(cncC2)((c3)2nc-)c1ccc1COClc)c4NccCc(clcc3=c)A\"], \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\":\"ncS(c4(cncC2)((c3)2nc-)c1ccc1COClc)c4NccCc(clcc3=c)A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' is the designated smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 180.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' is 180.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding affinity for the drug '3c[11ccc3(c/]CCNcc)(cn=C=)OcCcH2C1cCN1Ccc/n2A' and the target protein 'MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"3c[11ccc3(c/]CCNcc)(cn=C=)OcCcH2C1cCN1Ccc/n2A\"], \"target_seq\": \"MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA\", \"result\": [{\"smile\":\"3c[11ccc3(c/]CCNcc)(cn=C=)OcCcH2C1cCN1Ccc/n2A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1' represents the valid smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA\", \"result\": [{\"smile\": \"O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MVWRVPPFLLPILFLASHVGAAVDLTLLANLRLTDPQRFFLTCVSGEAGAGRGSDAWGPPLLLEKDDRIVRTPPGPPLRLARNGSHQVTLRGFSKPSDLVGVFSCVGGAGARRTRVIYVHNSPGAHLLPDKVTHTVNKGDTAVLSARVHKEKQTDVIWKSNGSYFYTLDWHEAQDGRFLLQLPNVQPPSSGIYSATYLEASPLGSAFFRLIVRGCGAGRWGPGCTKECPGCLHGGVCHDHDGECVCPPGFTGTRCEQACREGRFGQSCQEQCPGISGCRGLTFCLPDPYGCSCGSGWRGSQCQEACAPGHFGADCRLQCQCQNGGTCDRFSGCVCPSGWHGVHCEKSDRIPQILNMASELEFNLETMPRINCAAAGNPFPVRGSIELRKPDGTVLLSTKAIVEPEKTTAEFEVPRLVLADSGFWECRVSTSGGQDSRRFKVNVKVPPVPLAAPRLLTKQSRQLVVSPLVSFSGDGPISTVRLHYRPQDSTMDWSTIVVDPSENVTLMNLRPKTGYSVRVQLSRPGEGGEGAWGPPTLMTTDCPEPLLQPWLEGWHVEGTDRLRVSWSLPLVPGPLVGDGFLLRLWDGTRGQERRENVSSPQARTALLTGLTPGTHYQLDVQLYHCTLLGPASPPAHVLLPPSGPPAPRHLHAQALSDSEIQLTWKHPEALPGPISKYVVEVQVAGGAGDPLWIDVDRPEETSTIIRGLNASTRYLFRMRASIQGLGDWSNTVEESTLGNGLQAEGPVQESRAAEEGLDQQLILAVVGSVSATCLTILAALLTLVCIRRSCLHRRRTFTYQSGSGEETILQFSSGTLTLTRRPKLQPEPLSYPVLEWEDITFEDLIGEGNFGQVIRAMIKKDGLKMNAAIKMLKEYASENDHRDFAGELEVLCKLGHHPNIINLLGACKNRGYLYIAIEYAPYGNLLDFLRKSRVLETDPAFAREHGTASTLSSRQLLRFASDAANGMQYLSEKQFIHRDLAARNVLVGENLASKIADFGLSRGEEVYVKKTMGRLPVRWMAIESLNYSVYTTKSDVWSFGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRMEQPRNCDDEVYELMRQCWRDRPYERPPFAQIALQLGRMLEARKAYVNMSLFENFTYAGIDATAEEA' is 300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the drug 'Cc6)Cc2(C@H1C)4)(NcC@)[]15c)(=cc1cn[][=]ccc([c(Cc2@HCC34OcNcO2cc36C5Oc2n1c]CcccOc3@C(c@)3cHA' to the target protein 'MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc6)Cc2(C@H1C)4)(NcC@)[]15c)(=cc1cn[][=]ccc([c(Cc2@HCC34OcNcO2cc36C5Oc2n1c]CcccOc3@C(c@)3cHA\"], \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\", \"result\": [{\"smile\":\"Cc6)Cc2(C@H1C)4)(NcC@)[]15c)(=cc1cn[][=]ccc([c(Cc2@HCC34OcNcO2cc36C5Oc2n1c]CcccOc3@C(c@)3cHA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 17.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MSSPRAVVQLGKAQPAGEELATANQTAQQPSSPAMRRLTVDDFEIGRPLGKGKFGNVYLARLKESHFIVALKVLFKSQIEKEGLEHQLRREIEIQAHLQHPNILRLYNYFHDARRVYLILEYAPRGELYKELQKSEKLDEQRTATIIEELADALTYCHDKKVIHRDIKPENLLLGFRGEVKIADFGWSVHTPSLRRKTMCGTLDYLPPEMIEGRTYDEKVDLWCIGVLCYELLVGYPPFESASHSETYRRILKVDVRFPLSMPLGARDLISRLLRYQPLERLPLAQILKHPWVQAHSRRVLPPCAQMAS' is 17.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'cNCncc(1(NF3()(F1c)c)cc4cCn4=c-c3cC())2ncc)cc-2c(F(cc(c)C)c5c(5ncccOn)A' and the target protein 'MAGRGSLVSWRAFHGCDSAEELPRVSPRFLRAWHPPPVSARMPTRRWAPGTQCITKCEHTRPKPGELAFRKGDVVTILEACENKSWYRVKHHTSGQEGLLAAGALREREALSADPKLSLMPWFHGKISGQEAVQQLQPPEDGLFLVRESARHPGDYVLCVSFGRDVIHYRVLHRDGHLTIDEAVFFCNLMDMVEHYSKDKGAICTKLVRPKRKHGTKSAEEELARAGWLLNLQHLTLGAQIGEGEFGAVLQGEYLGQKVAVKNIKCDVTAQAFLDETAVMTKMQHENLVRLLGVILHQGLYIVMEHVSKGNLVNFLRTRGRALVNTAQLLQFSLHVAEGMEYLESKKLVHRDLAARNILVSEDLVAKVSDFGLAKAERKGLDSSRLPVKWTAPEALKHGKFTSKSDVWSFGVLLWEVFSYGRAPYPKMSLKEVSEAVEKGYRMEPPEGCPGPVHVLMSSCWEAEPARRPPFRKLAEKLARELRSAGAPASVSGQDADGSTSPRSQEP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cNCncc(1(NF3()(F1c)c)cc4cCn4=c-c3cC())2ncc)cc-2c(F(cc(c)C)c5c(5ncccOn)A\"], \"target_seq\": \"MAGRGSLVSWRAFHGCDSAEELPRVSPRFLRAWHPPPVSARMPTRRWAPGTQCITKCEHTRPKPGELAFRKGDVVTILEACENKSWYRVKHHTSGQEGLLAAGALREREALSADPKLSLMPWFHGKISGQEAVQQLQPPEDGLFLVRESARHPGDYVLCVSFGRDVIHYRVLHRDGHLTIDEAVFFCNLMDMVEHYSKDKGAICTKLVRPKRKHGTKSAEEELARAGWLLNLQHLTLGAQIGEGEFGAVLQGEYLGQKVAVKNIKCDVTAQAFLDETAVMTKMQHENLVRLLGVILHQGLYIVMEHVSKGNLVNFLRTRGRALVNTAQLLQFSLHVAEGMEYLESKKLVHRDLAARNILVSEDLVAKVSDFGLAKAERKGLDSSRLPVKWTAPEALKHGKFTSKSDVWSFGVLLWEVFSYGRAPYPKMSLKEVSEAVEKGYRMEPPEGCPGPVHVLMSSCWEAEPARRPPFRKLAEKLARELRSAGAPASVSGQDADGSTSPRSQEP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGRGSLVSWRAFHGCDSAEELPRVSPRFLRAWHPPPVSARMPTRRWAPGTQCITKCEHTRPKPGELAFRKGDVVTILEACENKSWYRVKHHTSGQEGLLAAGALREREALSADPKLSLMPWFHGKISGQEAVQQLQPPEDGLFLVRESARHPGDYVLCVSFGRDVIHYRVLHRDGHLTIDEAVFFCNLMDMVEHYSKDKGAICTKLVRPKRKHGTKSAEEELARAGWLLNLQHLTLGAQIGEGEFGAVLQGEYLGQKVAVKNIKCDVTAQAFLDETAVMTKMQHENLVRLLGVILHQGLYIVMEHVSKGNLVNFLRTRGRALVNTAQLLQFSLHVAEGMEYLESKKLVHRDLAARNILVSEDLVAKVSDFGLAKAERKGLDSSRLPVKWTAPEALKHGKFTSKSDVWSFGVLLWEVFSYGRAPYPKMSLKEVSEAVEKGYRMEPPEGCPGPVHVLMSSCWEAEPARRPPFRKLAEKLARELRSAGAPASVSGQDADGSTSPRSQEP\", \"result\": [{\"smile\":\"cNCncc(1(NF3()(F1c)c)cc4cCn4=c-c3cC())2ncc)cc-2c(F(cc(c)C)c5c(5ncccOn)A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' is the smile to use for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGRGSLVSWRAFHGCDSAEELPRVSPRFLRAWHPPPVSARMPTRRWAPGTQCITKCEHTRPKPGELAFRKGDVVTILEACENKSWYRVKHHTSGQEGLLAAGALREREALSADPKLSLMPWFHGKISGQEAVQQLQPPEDGLFLVRESARHPGDYVLCVSFGRDVIHYRVLHRDGHLTIDEAVFFCNLMDMVEHYSKDKGAICTKLVRPKRKHGTKSAEEELARAGWLLNLQHLTLGAQIGEGEFGAVLQGEYLGQKVAVKNIKCDVTAQAFLDETAVMTKMQHENLVRLLGVILHQGLYIVMEHVSKGNLVNFLRTRGRALVNTAQLLQFSLHVAEGMEYLESKKLVHRDLAARNILVSEDLVAKVSDFGLAKAERKGLDSSRLPVKWTAPEALKHGKFTSKSDVWSFGVLLWEVFSYGRAPYPKMSLKEVSEAVEKGYRMEPPEGCPGPVHVLMSSCWEAEPARRPPFRKLAEKLARELRSAGAPASVSGQDADGSTSPRSQEP\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the protein 'MAGRGSLVSWRAFHGCDSAEELPRVSPRFLRAWHPPPVSARMPTRRWAPGTQCITKCEHTRPKPGELAFRKGDVVTILEACENKSWYRVKHHTSGQEGLLAAGALREREALSADPKLSLMPWFHGKISGQEAVQQLQPPEDGLFLVRESARHPGDYVLCVSFGRDVIHYRVLHRDGHLTIDEAVFFCNLMDMVEHYSKDKGAICTKLVRPKRKHGTKSAEEELARAGWLLNLQHLTLGAQIGEGEFGAVLQGEYLGQKVAVKNIKCDVTAQAFLDETAVMTKMQHENLVRLLGVILHQGLYIVMEHVSKGNLVNFLRTRGRALVNTAQLLQFSLHVAEGMEYLESKKLVHRDLAARNILVSEDLVAKVSDFGLAKAERKGLDSSRLPVKWTAPEALKHGKFTSKSDVWSFGVLLWEVFSYGRAPYPKMSLKEVSEAVEKGYRMEPPEGCPGPVHVLMSSCWEAEPARRPPFRKLAEKLARELRSAGAPASVSGQDADGSTSPRSQEP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction strength between the drug '1Cc=N=FcCcCC3)1ccc(2c)C)c)()C2cN(cCN/(n#Oclc(Cc)(3cc/OCNCA' and the target protein 'MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"1Cc=N=FcCcCC3)1ccc(2c)C)c)()C2cN(cCN/(n#Oclc(Cc)(3cc/OCNCA\"], \"target_seq\": \"MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\":\"1Cc=N=FcCcCC3)1ccc(2c)C)c)()C2cN(cCN/(n#Oclc(Cc)(3cc/OCNCA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' is the smile that’s right for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 16.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the protein 'MLEICLKLVGCKSKKGLSSSSSCYLEEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' is 16.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug '1)OC)(ccOn1Ccc(cccc(N1C=O1)CC()OnccA' and the target protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"1)OC)(ccOn1Ccc(cccc(N1C=O1)CC()OnccA\"], \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\":\"1)OC)(ccOn1Ccc(cccc(N1C=O1)CC()OnccA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CCOc1ccc(C(C)=O)cc1Cc1c(O)nc(N)nc1O' is the smile that’s right for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1ccc(C(C)=O)cc1Cc1c(O)nc(N)nc1O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL\", \"result\": [{\"smile\": \"CCOc1ccc(C(C)=O)cc1Cc1c(O)nc(N)nc1O\", \"value\": 100000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1ccc(C(C)=O)cc1Cc1c(O)nc(N)nc1O' and the protein 'MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL' is 100000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug 'C1(c(1Oc)()3c/c(Occ=CC3)(cN/#2C)=CCCFCCcnNCclc2(cNc))cccNA''s affinity with the target protein 'MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C1(c(1Oc)()3c/c(Occ=CC3)(cN/#2C)=CCCFCCcnNCclc2(cNc))cccNA\"], \"target_seq\": \"MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL\", \"result\": [{\"smile\":\"C1(c(1Oc)()3c/c(Occ=CC3)(cN/#2C)=CCCFCCcnNCclc2(cNc))cccNA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' is the smile intended for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the protein 'MSSPPPARSGFYRQEVTKTAWEVRAVYRDLQPVGSGAYGAVCSAVDGRTGAKVAIKKLYRPFQSELFAKRAYRELRLLKHMRHENVIGLLDVFTPDETLDDFTDFYLVMPFMGTDLGKLMKHEKLGEDRIQFLVYQMLKGLRYIHAAGIIHRDLKPGNLAVNEDCELKILDFGLARQADSEMTGYVVTRWYRAPEVILNWMRYTQTVDIWSVGCIMAEMITGKTLFKGSDHLDQLKEIMKVTGTPPAEFVQRLQSDEAKNYMKGLPELEKKDFASILTNASPLAVNLLEKMLVLDAEQRVTAGEALAHPYFESLHDTEDEPQVQKYDDSFDDVDRTLDEWKRVTYKEVLSFKPPRQLGARVSKETPL' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the affinity between the drug '=CcnccC225==cccC(Cc321)5)Ccc(cO31OcC()nc(cNcNcccCCCc44c)cC2(4cCn)4A' and the target protein 'MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"=CcnccC225==cccC(Cc321)5)Ccc(cO31OcC()nc(cNcNcccCCCc44c)cC2(4cCn)4A\"], \"target_seq\": \"MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM\", \"result\": [{\"smile\":\"=CcnccC225==cccC(Cc321)5)Ccc(cO31OcC()nc(cNcNcccCCCc44c)cC2(4cCn)4A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that aligns with the drug is 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM\", \"result\": [{\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21' and the protein 'MEAAVAAPRPRLLLLVLAAAAAAAAALLPGATALQCFCHLCTKDNFTCVTDGLCFVSVTETTDKVIHNSMCIAEIDLIPRDRPFVCAPSSKTGSVTTTYCCNQDHCNKIELPTTVKSSPGLGPVELAAVIAGPVCFVCISLMLMVYICHNRTVIHHRVPNEEDPSLDRPFISEGTTLKDLIYDMTTSGSGSGLPLLVQRTIARTIVLQESIGKGRFGEVWRGKWRGEEVAVKIFSSREERSWFREAEIYQTVMLRHENILGFIAADNKDNGTWTQLWLVSDYHEHGSLFDYLNRYTVTVEGMIKLALSTASGLAHLHMEIVGTQGKPAIAHRDLKSKNILVKKNGTCCIADLGLAVRHDSATDTIDIAPNHRVGTKRYMAPEVLDDSINMKHFESFKRADIYAMGLVFWEIARRCSIGGIHEDYQLPYYDLVPSDPSVEEMRKVVCEQKLRPNIPNRWQSCEALRVMAKIMRECWYANGAARLTALRIKKTLSQLSQQEGIKM' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the projected strength of affinity between the drug 'Fcc3OcNC(crcCnB2cc2n)cC(cO11CccC)NcCc1CC31)c(A' and the target protein 'MGNYKSRPTQTCTDEWKKKVSESYVITIERLEDDLQIKEKELTELRNIFGSDEAFSKVNLNYRTENGLSLLHLCCICGGKKSHIRTLMLKGLRPSRLTRNGFTALHLAVYKDNAELITSLLHSGADIQQVGYGGLTALHIATIAGHLEAADVLLQHGANVNIQDAVFFTPLHIAAYYGHEQVTRLLLKFGADVNVSGEVGDRPLHLASAKGFLNIAKLLMEEGSKADVNAQDNEDHVPLHFCSRFGHHDIVKYLLQSDLEVQPHVVNIYGDTPLHLACYNGKFEVAKEIIQISGTESLTKENIFSETAFHSACTYGKSIDLVKFLLDQNVININHQGRDGHTGLHSACYHGHIRLVQFLLDNGADMNLVACDPSRSSGEKDEQTCLMWAYEKGHDAIVTLLKHYKRPQDELPCNEYSQPGGDGSYVSVPSPLGKIKSMTKEKADILLLRAGLPSHFHLQLSEIEFHEIIGSGSFGKVYKGRCRNKIVAIKRYRANTYCSKSDVDMFCREVSILCQLNHPCVIQFVGACLNDPSQFAIVTQYISGGSLFSLLHEQKRILDLQSKLIIAVDVAKGMEYLHNLTQPIIHRDLNSHNILLYEDGHAVVADFGESRFLQSLDEDNMTKQPGNLRWMAPEVFTQCTRYTIKADVFSYALCLWEILTGEIPFAHLKPAAAAADMAYHHIRPPIGYSIPKPISSLLIRGWNACPEGRPEFSEVVMKLEECLCNIELMSPASSNSSGSLSPSSSSDCLVNRGGPGRSHVAALRSRFELEYALNARSYAALSQSAGQYSSQGLSLEEMKRSLQYTPIDKYGYVSDPMSSMHFHSCRNSSSFEDSS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Fcc3OcNC(crcCnB2cc2n)cC(cO11CccC)NcCc1CC31)c(A\"], \"target_seq\": \"MGNYKSRPTQTCTDEWKKKVSESYVITIERLEDDLQIKEKELTELRNIFGSDEAFSKVNLNYRTENGLSLLHLCCICGGKKSHIRTLMLKGLRPSRLTRNGFTALHLAVYKDNAELITSLLHSGADIQQVGYGGLTALHIATIAGHLEAADVLLQHGANVNIQDAVFFTPLHIAAYYGHEQVTRLLLKFGADVNVSGEVGDRPLHLASAKGFLNIAKLLMEEGSKADVNAQDNEDHVPLHFCSRFGHHDIVKYLLQSDLEVQPHVVNIYGDTPLHLACYNGKFEVAKEIIQISGTESLTKENIFSETAFHSACTYGKSIDLVKFLLDQNVININHQGRDGHTGLHSACYHGHIRLVQFLLDNGADMNLVACDPSRSSGEKDEQTCLMWAYEKGHDAIVTLLKHYKRPQDELPCNEYSQPGGDGSYVSVPSPLGKIKSMTKEKADILLLRAGLPSHFHLQLSEIEFHEIIGSGSFGKVYKGRCRNKIVAIKRYRANTYCSKSDVDMFCREVSILCQLNHPCVIQFVGACLNDPSQFAIVTQYISGGSLFSLLHEQKRILDLQSKLIIAVDVAKGMEYLHNLTQPIIHRDLNSHNILLYEDGHAVVADFGESRFLQSLDEDNMTKQPGNLRWMAPEVFTQCTRYTIKADVFSYALCLWEILTGEIPFAHLKPAAAAADMAYHHIRPPIGYSIPKPISSLLIRGWNACPEGRPEFSEVVMKLEECLCNIELMSPASSNSSGSLSPSSSSDCLVNRGGPGRSHVAALRSRFELEYALNARSYAALSQSAGQYSSQGLSLEEMKRSLQYTPIDKYGYVSDPMSSMHFHSCRNSSSFEDSS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGNYKSRPTQTCTDEWKKKVSESYVITIERLEDDLQIKEKELTELRNIFGSDEAFSKVNLNYRTENGLSLLHLCCICGGKKSHIRTLMLKGLRPSRLTRNGFTALHLAVYKDNAELITSLLHSGADIQQVGYGGLTALHIATIAGHLEAADVLLQHGANVNIQDAVFFTPLHIAAYYGHEQVTRLLLKFGADVNVSGEVGDRPLHLASAKGFLNIAKLLMEEGSKADVNAQDNEDHVPLHFCSRFGHHDIVKYLLQSDLEVQPHVVNIYGDTPLHLACYNGKFEVAKEIIQISGTESLTKENIFSETAFHSACTYGKSIDLVKFLLDQNVININHQGRDGHTGLHSACYHGHIRLVQFLLDNGADMNLVACDPSRSSGEKDEQTCLMWAYEKGHDAIVTLLKHYKRPQDELPCNEYSQPGGDGSYVSVPSPLGKIKSMTKEKADILLLRAGLPSHFHLQLSEIEFHEIIGSGSFGKVYKGRCRNKIVAIKRYRANTYCSKSDVDMFCREVSILCQLNHPCVIQFVGACLNDPSQFAIVTQYISGGSLFSLLHEQKRILDLQSKLIIAVDVAKGMEYLHNLTQPIIHRDLNSHNILLYEDGHAVVADFGESRFLQSLDEDNMTKQPGNLRWMAPEVFTQCTRYTIKADVFSYALCLWEILTGEIPFAHLKPAAAAADMAYHHIRPPIGYSIPKPISSLLIRGWNACPEGRPEFSEVVMKLEECLCNIELMSPASSNSSGSLSPSSSSDCLVNRGGPGRSHVAALRSRFELEYALNARSYAALSQSAGQYSSQGLSLEEMKRSLQYTPIDKYGYVSDPMSSMHFHSCRNSSSFEDSS\", \"result\": [{\"smile\":\"Fcc3OcNC(crcCnB2cc2n)cC(cO11CccC)NcCc1CC31)c(A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The drug's correct smile is 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGNYKSRPTQTCTDEWKKKVSESYVITIERLEDDLQIKEKELTELRNIFGSDEAFSKVNLNYRTENGLSLLHLCCICGGKKSHIRTLMLKGLRPSRLTRNGFTALHLAVYKDNAELITSLLHSGADIQQVGYGGLTALHIATIAGHLEAADVLLQHGANVNIQDAVFFTPLHIAAYYGHEQVTRLLLKFGADVNVSGEVGDRPLHLASAKGFLNIAKLLMEEGSKADVNAQDNEDHVPLHFCSRFGHHDIVKYLLQSDLEVQPHVVNIYGDTPLHLACYNGKFEVAKEIIQISGTESLTKENIFSETAFHSACTYGKSIDLVKFLLDQNVININHQGRDGHTGLHSACYHGHIRLVQFLLDNGADMNLVACDPSRSSGEKDEQTCLMWAYEKGHDAIVTLLKHYKRPQDELPCNEYSQPGGDGSYVSVPSPLGKIKSMTKEKADILLLRAGLPSHFHLQLSEIEFHEIIGSGSFGKVYKGRCRNKIVAIKRYRANTYCSKSDVDMFCREVSILCQLNHPCVIQFVGACLNDPSQFAIVTQYISGGSLFSLLHEQKRILDLQSKLIIAVDVAKGMEYLHNLTQPIIHRDLNSHNILLYEDGHAVVADFGESRFLQSLDEDNMTKQPGNLRWMAPEVFTQCTRYTIKADVFSYALCLWEILTGEIPFAHLKPAAAAADMAYHHIRPPIGYSIPKPISSLLIRGWNACPEGRPEFSEVVMKLEECLCNIELMSPASSNSSGSLSPSSSSDCLVNRGGPGRSHVAALRSRFELEYALNARSYAALSQSAGQYSSQGLSLEEMKRSLQYTPIDKYGYVSDPMSSMHFHSCRNSSSFEDSS\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 2800.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the protein 'MGNYKSRPTQTCTDEWKKKVSESYVITIERLEDDLQIKEKELTELRNIFGSDEAFSKVNLNYRTENGLSLLHLCCICGGKKSHIRTLMLKGLRPSRLTRNGFTALHLAVYKDNAELITSLLHSGADIQQVGYGGLTALHIATIAGHLEAADVLLQHGANVNIQDAVFFTPLHIAAYYGHEQVTRLLLKFGADVNVSGEVGDRPLHLASAKGFLNIAKLLMEEGSKADVNAQDNEDHVPLHFCSRFGHHDIVKYLLQSDLEVQPHVVNIYGDTPLHLACYNGKFEVAKEIIQISGTESLTKENIFSETAFHSACTYGKSIDLVKFLLDQNVININHQGRDGHTGLHSACYHGHIRLVQFLLDNGADMNLVACDPSRSSGEKDEQTCLMWAYEKGHDAIVTLLKHYKRPQDELPCNEYSQPGGDGSYVSVPSPLGKIKSMTKEKADILLLRAGLPSHFHLQLSEIEFHEIIGSGSFGKVYKGRCRNKIVAIKRYRANTYCSKSDVDMFCREVSILCQLNHPCVIQFVGACLNDPSQFAIVTQYISGGSLFSLLHEQKRILDLQSKLIIAVDVAKGMEYLHNLTQPIIHRDLNSHNILLYEDGHAVVADFGESRFLQSLDEDNMTKQPGNLRWMAPEVFTQCTRYTIKADVFSYALCLWEILTGEIPFAHLKPAAAAADMAYHHIRPPIGYSIPKPISSLLIRGWNACPEGRPEFSEVVMKLEECLCNIELMSPASSNSSGSLSPSSSSDCLVNRGGPGRSHVAALRSRFELEYALNARSYAALSQSAGQYSSQGLSLEEMKRSLQYTPIDKYGYVSDPMSSMHFHSCRNSSSFEDSS' is 2800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the affinity of the drug ')3(1[(cOCN(CcNCN)cc)C3=(n)HCCc]cN411CcCC)1c4)2nC(OcC@C(C2C=CC@CcCONA' toward the target protein 'MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\")3(1[(cOCN(CcNCN)cc)C3=(n)HCCc]cN411CcCC)1c4)2nC(OcC@C(C2C=CC@CcCONA\"], \"target_seq\": \"MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG\", \"result\": [{\"smile\":\")3(1[(cOCN(CcNCN)cc)C3=(n)HCCc]cN411CcCC)1c4)2nC(OcC@C(C2C=CC@CcCONA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' is the smile intended for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG\", \"result\": [{\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MQGGNSGVRKREEEGDGAGAVAAPPAIDFPAEGPDPEYDESDVPAEIQVLKEPLQQPTFPFAVANQLLLVSLLEHLSHVHEPNPLRSRQVFKLLCQTFIKMGLLSSFTCSDEFSSLRLHHNRAITHLMRSAKERVRQDPCEDISRIQKIRSREVALEAQTSRYLNEFEELAILGKGGYGRVYKVRNKLDGQYYAIKKILIKGATKTVCMKVLREVKVLAGLQHPNIVGYHTAWIEHVHVIQPRADRAAIELPSLEVLSDQEEDREQCGVKNDESSSSSIIFAEPTPEKEKRFGESDTENQNNKSVKYTTNLVIRESGELESTLELQENGLAGLSASSIVEQQLPLRRNSHLEESFTSTEESSEENVNFLGQTEAQYHLMLHIQMQLCELSLWDWIVERNKRGREYVDESACPYVMANVATKIFQELVEGVFYIHNMGIVHRDLKPRNIFLHGPDQQVKIGDFGLACTDILQKNTDWTNRNGKRTPTHTSRVGTCLYASPEQLEGSEYDAKSDMYSLGVVLLELFQPFGTEMERAEVLTGLRTGQLPESLRKRCPVQAKYIQHLTRRNSSQRPSAIQLLQSELFQNSGNVNLTLQMKIIEQEKEIAELKKQLNLLSQDKGVRDDGKDGGVG' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity between the drug '(2C2/(cCC1c=CNC\\=c)cCc3nCc)cC)N3cFO2Cc(C2cc)c)(Cc1CcN(nA' and the target protein 'MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(2C2/(cCC1c=CNC\\=c)cCc3nCc)cC)N3cFO2Cc(C2cc)c)(Cc1CcN(nA\"], \"target_seq\": \"MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE\", \"result\": [{\"smile\":\"(2C2/(cCC1c=CNC\\=c)cCc3nCc)cC)N3cFO2Cc(C2cc)c)(Cc1CcN(nA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that's valid for the drug is 'CCN(CC)c1ccc(/C=C2\\CCn3c2nc2cc(NCCN(C)C)c(F)cc2c3=O)cc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)c1ccc(/C=C2\\CCn3c2nc2cc(NCCN(C)C)c(F)cc2c3=O)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE\", \"result\": [{\"smile\": \"CCN(CC)c1ccc(/C=C2\\CCn3c2nc2cc(NCCN(C)C)c(F)cc2c3=O)cc1\", \"value\": 45700.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)c1ccc(/C=C2\\CCn3c2nc2cc(NCCN(C)C)c(F)cc2c3=O)cc1' and the protein 'MANLERTFIAIKPDGVQRGLVGEIIKRFEQKGFRLVAMKFLRASEEHLKQHYIDLKDRPFFPGLVKYMNSGPVVAMVWEGLNVVKTGRVMLGETNPADSKPGTIRGDFCIQVGRNIIHGSDSVKSAEKEISLWFKPEELVDYKSCAHDWVYE' is 45700.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted interaction value for the drug 'CC3@(1H2[CC4C[H=)C4@(C@C][CO3CH[)C(C2][C@]C)@]1CCCOC]@C@CA' and the target protein 'MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC3@(1H2[CC4C[H=)C4@(C@C][CO3CH[)C(C2][C@]C)@]1CCCOC]@C@CA\"], \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\", \"result\": [{\"smile\":\"CC3@(1H2[CC4C[H=)C4@(C@C][CO3CH[)C(C2][C@]C)@]1CCCOC]@C@CA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that fits the drug is 'CC1(O)CC[C@H]2[C@@H]3CC=C4CC(O)CC[C@]4(C)[C@H]3CC[C@@]21C'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(O)CC[C@H]2[C@@H]3CC=C4CC(O)CC[C@]4(C)[C@H]3CC[C@@]21C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH\", \"result\": [{\"smile\": \"CC1(O)CC[C@H]2[C@@H]3CC=C4CC(O)CC[C@]4(C)[C@H]3CC[C@@]21C\", \"value\": 4.4}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(O)CC[C@H]2[C@@H]3CC=C4CC(O)CC[C@]4(C)[C@H]3CC[C@@]21C' and the protein 'MESRGPLATSRLLLLLLLLLLRHTRQGWALRPVLPTQSAHDPPAVHLSNGPGQEPIAVMTFDLTKITKTSSSFEVRTWDPEGVIFYGDTNPKDDWFMLGLRDGRPEIQLHNHWAQLTVGAGPRLDDGRWHQVEVKMEGDSVLLEVDGEEVLRLRQVSGPLTSKRHPIMRIALGGLLFPASNLRLPLVPALDGCLRRDSWLDKQAEISASAPTSLRSCDVESNPGIFLPPGTQAEFNLRDIPQPHAEPWAFSLDLGLKQAAGSGHLLALGTPENPSWLSLHLQDQKVVLSSGSGPGLDLPLVLGLPLQLKLSMSRVVLSQGSKMKALALPPLGLAPLLNLWAKPQGRLFLGALPGEDSSTSFCLNGLWAQGQRLDVDQALNRSHEIWTHSCPQSPGNGTDASH' is 4.4."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug '3)1CO(OFc(cc)=2cnc)(C)ncBNl)OcC(c1Crcccc3N(C2cA' with the target protein 'MALLRDVSLQDPRDRFELLQRVGAGTYGDVYKARDTVTSELAAVKIVKLDPGDDISSLQQEITILRECRHPNVVAYIGSYLRNDRLWICMEFCGGGSLQEIYHATGPLEERQIAYVCREALKGLHHLHSQGKIHRDIKGANLLLTLQGDVKLADFGVSGELTASVAKRRSFIGTPYWMAPEVAAVERKGGYNELCDVWALGITAIELGELQPPLFHLHPMRALMLMSKSSFQPPKLRDKTRWTQNFHHFLKLALTKNPKKRPTAEKLLQHPFTTQQLPRALLTQLLDKASDPHLGTPSPEDCELETYDMFPDTIHSRGQHGPAERTPSEIQFHQVKFGAPRRKETDPLNEPWEEEWTLLGKEELSGSLLQSVQEALEERSLTIRSASEFQELDSPDDTMGTIKRAPFLGPLPTDPPAEEPLSSPPGTLPPPPSGPNSSPLLPTAWATMKQREDPERSSCHGLPPTPKVHMGACFSKVFNGCPLRIHAAVTWIHPVTRDQFLVVGAEEGIYTLNLHELHEDTLEKLISHRCSWLYCVNNVLLSLSGKSTHIWAHDLPGLFEQRRLQQQVPLSIPTNRLTQRIIPRRFALSTKIPDTKGCLQCRVVRNPYTGATFLLAALPTSLLLLQWYEPLQKFLLLKNFSSPLPSPAGMLEPLVLDGKELPQVCVGAEGPEGPGCRVLFHVLPLEAGLTPDILIPPEGIPGSAQQVIQVDRDTILVSFERCVRIVNMQGEPTATLAPELTFDFPIETVVCLQDSVLAFWSHGMQGRSLDTNEVTQEITDETRIFRVLGAHRDIILESIPTDNPEAHSNLYILTGHQSTY'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"3)1CO(OFc(cc)=2cnc)(C)ncBNl)OcC(c1Crcccc3N(C2cA\"], \"target_seq\": \"MALLRDVSLQDPRDRFELLQRVGAGTYGDVYKARDTVTSELAAVKIVKLDPGDDISSLQQEITILRECRHPNVVAYIGSYLRNDRLWICMEFCGGGSLQEIYHATGPLEERQIAYVCREALKGLHHLHSQGKIHRDIKGANLLLTLQGDVKLADFGVSGELTASVAKRRSFIGTPYWMAPEVAAVERKGGYNELCDVWALGITAIELGELQPPLFHLHPMRALMLMSKSSFQPPKLRDKTRWTQNFHHFLKLALTKNPKKRPTAEKLLQHPFTTQQLPRALLTQLLDKASDPHLGTPSPEDCELETYDMFPDTIHSRGQHGPAERTPSEIQFHQVKFGAPRRKETDPLNEPWEEEWTLLGKEELSGSLLQSVQEALEERSLTIRSASEFQELDSPDDTMGTIKRAPFLGPLPTDPPAEEPLSSPPGTLPPPPSGPNSSPLLPTAWATMKQREDPERSSCHGLPPTPKVHMGACFSKVFNGCPLRIHAAVTWIHPVTRDQFLVVGAEEGIYTLNLHELHEDTLEKLISHRCSWLYCVNNVLLSLSGKSTHIWAHDLPGLFEQRRLQQQVPLSIPTNRLTQRIIPRRFALSTKIPDTKGCLQCRVVRNPYTGATFLLAALPTSLLLLQWYEPLQKFLLLKNFSSPLPSPAGMLEPLVLDGKELPQVCVGAEGPEGPGCRVLFHVLPLEAGLTPDILIPPEGIPGSAQQVIQVDRDTILVSFERCVRIVNMQGEPTATLAPELTFDFPIETVVCLQDSVLAFWSHGMQGRSLDTNEVTQEITDETRIFRVLGAHRDIILESIPTDNPEAHSNLYILTGHQSTY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALLRDVSLQDPRDRFELLQRVGAGTYGDVYKARDTVTSELAAVKIVKLDPGDDISSLQQEITILRECRHPNVVAYIGSYLRNDRLWICMEFCGGGSLQEIYHATGPLEERQIAYVCREALKGLHHLHSQGKIHRDIKGANLLLTLQGDVKLADFGVSGELTASVAKRRSFIGTPYWMAPEVAAVERKGGYNELCDVWALGITAIELGELQPPLFHLHPMRALMLMSKSSFQPPKLRDKTRWTQNFHHFLKLALTKNPKKRPTAEKLLQHPFTTQQLPRALLTQLLDKASDPHLGTPSPEDCELETYDMFPDTIHSRGQHGPAERTPSEIQFHQVKFGAPRRKETDPLNEPWEEEWTLLGKEELSGSLLQSVQEALEERSLTIRSASEFQELDSPDDTMGTIKRAPFLGPLPTDPPAEEPLSSPPGTLPPPPSGPNSSPLLPTAWATMKQREDPERSSCHGLPPTPKVHMGACFSKVFNGCPLRIHAAVTWIHPVTRDQFLVVGAEEGIYTLNLHELHEDTLEKLISHRCSWLYCVNNVLLSLSGKSTHIWAHDLPGLFEQRRLQQQVPLSIPTNRLTQRIIPRRFALSTKIPDTKGCLQCRVVRNPYTGATFLLAALPTSLLLLQWYEPLQKFLLLKNFSSPLPSPAGMLEPLVLDGKELPQVCVGAEGPEGPGCRVLFHVLPLEAGLTPDILIPPEGIPGSAQQVIQVDRDTILVSFERCVRIVNMQGEPTATLAPELTFDFPIETVVCLQDSVLAFWSHGMQGRSLDTNEVTQEITDETRIFRVLGAHRDIILESIPTDNPEAHSNLYILTGHQSTY\", \"result\": [{\"smile\":\"3)1CO(OFc(cc)=2cnc)(C)ncBNl)OcC(c1Crcccc3N(C2cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid expression for the drug is 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALLRDVSLQDPRDRFELLQRVGAGTYGDVYKARDTVTSELAAVKIVKLDPGDDISSLQQEITILRECRHPNVVAYIGSYLRNDRLWICMEFCGGGSLQEIYHATGPLEERQIAYVCREALKGLHHLHSQGKIHRDIKGANLLLTLQGDVKLADFGVSGELTASVAKRRSFIGTPYWMAPEVAAVERKGGYNELCDVWALGITAIELGELQPPLFHLHPMRALMLMSKSSFQPPKLRDKTRWTQNFHHFLKLALTKNPKKRPTAEKLLQHPFTTQQLPRALLTQLLDKASDPHLGTPSPEDCELETYDMFPDTIHSRGQHGPAERTPSEIQFHQVKFGAPRRKETDPLNEPWEEEWTLLGKEELSGSLLQSVQEALEERSLTIRSASEFQELDSPDDTMGTIKRAPFLGPLPTDPPAEEPLSSPPGTLPPPPSGPNSSPLLPTAWATMKQREDPERSSCHGLPPTPKVHMGACFSKVFNGCPLRIHAAVTWIHPVTRDQFLVVGAEEGIYTLNLHELHEDTLEKLISHRCSWLYCVNNVLLSLSGKSTHIWAHDLPGLFEQRRLQQQVPLSIPTNRLTQRIIPRRFALSTKIPDTKGCLQCRVVRNPYTGATFLLAALPTSLLLLQWYEPLQKFLLLKNFSSPLPSPAGMLEPLVLDGKELPQVCVGAEGPEGPGCRVLFHVLPLEAGLTPDILIPPEGIPGSAQQVIQVDRDTILVSFERCVRIVNMQGEPTATLAPELTFDFPIETVVCLQDSVLAFWSHGMQGRSLDTNEVTQEITDETRIFRVLGAHRDIILESIPTDNPEAHSNLYILTGHQSTY\", \"result\": [{\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' and the protein 'MALLRDVSLQDPRDRFELLQRVGAGTYGDVYKARDTVTSELAAVKIVKLDPGDDISSLQQEITILRECRHPNVVAYIGSYLRNDRLWICMEFCGGGSLQEIYHATGPLEERQIAYVCREALKGLHHLHSQGKIHRDIKGANLLLTLQGDVKLADFGVSGELTASVAKRRSFIGTPYWMAPEVAAVERKGGYNELCDVWALGITAIELGELQPPLFHLHPMRALMLMSKSSFQPPKLRDKTRWTQNFHHFLKLALTKNPKKRPTAEKLLQHPFTTQQLPRALLTQLLDKASDPHLGTPSPEDCELETYDMFPDTIHSRGQHGPAERTPSEIQFHQVKFGAPRRKETDPLNEPWEEEWTLLGKEELSGSLLQSVQEALEERSLTIRSASEFQELDSPDDTMGTIKRAPFLGPLPTDPPAEEPLSSPPGTLPPPPSGPNSSPLLPTAWATMKQREDPERSSCHGLPPTPKVHMGACFSKVFNGCPLRIHAAVTWIHPVTRDQFLVVGAEEGIYTLNLHELHEDTLEKLISHRCSWLYCVNNVLLSLSGKSTHIWAHDLPGLFEQRRLQQQVPLSIPTNRLTQRIIPRRFALSTKIPDTKGCLQCRVVRNPYTGATFLLAALPTSLLLLQWYEPLQKFLLLKNFSSPLPSPAGMLEPLVLDGKELPQVCVGAEGPEGPGCRVLFHVLPLEAGLTPDILIPPEGIPGSAQQVIQVDRDTILVSFERCVRIVNMQGEPTATLAPELTFDFPIETVVCLQDSVLAFWSHGMQGRSLDTNEVTQEITDETRIFRVLGAHRDIILESIPTDNPEAHSNLYILTGHQSTY' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'C1cc4ccC(cccNC3cCO2cCCC2CON1CCCCn1C(cCOCC(ON()3ncCc)=Nc)))(41cA' and the target protein 'MERRASETPEDGDPEEDTATALQRLVELTTSRVTPVRSLRDQYHLIRKLGSGSYGRVLLAQPHQGGPAVALKLLRRDLVLRSTFLREFCVGRCVSAHPGLLQTLAGPLQTPRYFAFAQEYAPCGDLSGMLQERGLPELLVKRVVAQLAGALDFLHSRGLVHADVKPDNVLVFDPVCSRVALGDLGLTRPEGSPTPAPPVPLPTAPPELCLLLPPDTLPLRPAVDSWGLGVLLFCAATACFPWDVALAPNPEFEAFAGWVTTKPQPPQPPPPWDQFAPPALALLQGLLDLDPETRSPPLAVLDFLGDDWGLQGNREGPGVLGSAVSYEDREEGGSSLEEWTDEGDDSKSGGRTGTDGGAP'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C1cc4ccC(cccNC3cCO2cCCC2CON1CCCCn1C(cCOCC(ON()3ncCc)=Nc)))(41cA\"], \"target_seq\": \"MERRASETPEDGDPEEDTATALQRLVELTTSRVTPVRSLRDQYHLIRKLGSGSYGRVLLAQPHQGGPAVALKLLRRDLVLRSTFLREFCVGRCVSAHPGLLQTLAGPLQTPRYFAFAQEYAPCGDLSGMLQERGLPELLVKRVVAQLAGALDFLHSRGLVHADVKPDNVLVFDPVCSRVALGDLGLTRPEGSPTPAPPVPLPTAPPELCLLLPPDTLPLRPAVDSWGLGVLLFCAATACFPWDVALAPNPEFEAFAGWVTTKPQPPQPPPPWDQFAPPALALLQGLLDLDPETRSPPLAVLDFLGDDWGLQGNREGPGVLGSAVSYEDREEGGSSLEEWTDEGDDSKSGGRTGTDGGAP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERRASETPEDGDPEEDTATALQRLVELTTSRVTPVRSLRDQYHLIRKLGSGSYGRVLLAQPHQGGPAVALKLLRRDLVLRSTFLREFCVGRCVSAHPGLLQTLAGPLQTPRYFAFAQEYAPCGDLSGMLQERGLPELLVKRVVAQLAGALDFLHSRGLVHADVKPDNVLVFDPVCSRVALGDLGLTRPEGSPTPAPPVPLPTAPPELCLLLPPDTLPLRPAVDSWGLGVLLFCAATACFPWDVALAPNPEFEAFAGWVTTKPQPPQPPPPWDQFAPPALALLQGLLDLDPETRSPPLAVLDFLGDDWGLQGNREGPGVLGSAVSYEDREEGGSSLEEWTDEGDDSKSGGRTGTDGGAP\", \"result\": [{\"smile\":\"C1cc4ccC(cccNC3cCO2cCCC2CON1CCCCn1C(cCOCC(ON()3ncCc)=Nc)))(41cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid smile for the drug is 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERRASETPEDGDPEEDTATALQRLVELTTSRVTPVRSLRDQYHLIRKLGSGSYGRVLLAQPHQGGPAVALKLLRRDLVLRSTFLREFCVGRCVSAHPGLLQTLAGPLQTPRYFAFAQEYAPCGDLSGMLQERGLPELLVKRVVAQLAGALDFLHSRGLVHADVKPDNVLVFDPVCSRVALGDLGLTRPEGSPTPAPPVPLPTAPPELCLLLPPDTLPLRPAVDSWGLGVLLFCAATACFPWDVALAPNPEFEAFAGWVTTKPQPPQPPPPWDQFAPPALALLQGLLDLDPETRSPPLAVLDFLGDDWGLQGNREGPGVLGSAVSYEDREEGGSSLEEWTDEGDDSKSGGRTGTDGGAP\", \"result\": [{\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein 'MERRASETPEDGDPEEDTATALQRLVELTTSRVTPVRSLRDQYHLIRKLGSGSYGRVLLAQPHQGGPAVALKLLRRDLVLRSTFLREFCVGRCVSAHPGLLQTLAGPLQTPRYFAFAQEYAPCGDLSGMLQERGLPELLVKRVVAQLAGALDFLHSRGLVHADVKPDNVLVFDPVCSRVALGDLGLTRPEGSPTPAPPVPLPTAPPELCLLLPPDTLPLRPAVDSWGLGVLLFCAATACFPWDVALAPNPEFEAFAGWVTTKPQPPQPPPPWDQFAPPALALLQGLLDLDPETRSPPLAVLDFLGDDWGLQGNREGPGVLGSAVSYEDREEGGSSLEEWTDEGDDSKSGGRTGTDGGAP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug '2C4)Nnc())CC2(c)cC1cc1CcOcc=()C(cO3N(cO3(CnccccS)4ccncCnA' and the target protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"2C4)Nnc())CC2(c)cC1cc1CcOcc=()C(cO3N(cO3(CnccccS)4ccncCnA\"], \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\":\"2C4)Nnc())CC2(c)cC1cc1CcOcc=()C(cO3N(cO3(CnccccS)4ccncCnA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that's valid for the drug is 'COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)nn4C)c3)c2cc1OC'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)nn4C)c3)c2cc1OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\": \"COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)nn4C)c3)c2cc1OC\", \"value\": 750.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2ncnc(Sc3cccc(NC(=O)Nc4cc(C(C)(C)C)nn4C)c3)c2cc1OC' and the protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH' is 750.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the affinity value of the drug 'ccc11CcSnN[CccC)H1]cCcN=1(cnA' toward the target protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"ccc11CcSnN[CccC)H1]cCcN=1(cnA\"], \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\":\"ccc11CcSnN[CccC)H1]cCcN=1(cnA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper expression for the drug is 'S=C(NCCc1cnc[nH]1)NCc1ccccc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"S=C(NCCc1cnc[nH]1)NCc1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\": \"S=C(NCCc1cnc[nH]1)NCc1ccccc1\", \"value\": 1584.89}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'S=C(NCCc1cnc[nH]1)NCc1ccccc1' and the protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' is 1584.89."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drug 'ccn42O3c-3occ)4cc3Nnc1(CcncCcOc(2cc3C)c1CA' with the target protein 'MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"ccn42O3c-3occ)4cc3Nnc1(CcncCcOc(2cc3C)c1CA\"], \"target_seq\": \"MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED\", \"result\": [{\"smile\":\"ccn42O3c-3occ)4cc3Nnc1(CcncCcOc(2cc3C)c1CA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein 'MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected level of affinity between the drug 'C)cl)(C]OC43Ccccc3N))c(cCc(COc1(NC)O=CcNC14cCC=H=C=c1(1cc(S(2CCcc(C[1OC)l1)C)=C2nA' and the target protein 'MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C)cl)(C]OC43Ccccc3N))c(cCc(COc1(NC)O=CcNC14cCC=H=C=c1(1cc(S(2CCcc(C[1OC)l1)C)=C2nA\"], \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\", \"result\": [{\"smile\":\"C)cl)(C]OC43Ccccc3N))c(cCc(COc1(NC)O=CcNC14cCC=H=C=c1(1cc(S(2CCcc(C[1OC)l1)C)=C2nA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' is the designated smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1' and the protein 'MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'CCCn]3ccC)([cC4@1cc25=(]6C2(ccNH13(HcC[[]=CCc3cH@6c(cc2c)@c@ccO1OCc1)5Nc)(c])nc@[Oc2CO)c43cA' and the target protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCn]3ccC)([cC4@1cc25=(]6C2(ccNH13(HcC[[]=CCc3cH@6c(cc2c)@c@ccO1OCc1)5Nc)(c])nc@[Oc2CO)c43cA\"], \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\":\"CCCn]3ccC)([cC4@1cc25=(]6C2(ccNH13(HcC[[]=CCc3cH@6c(cc2c)@c@ccO1OCc1)5Nc)(c])nc@[Oc2CO)c43cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that corresponds to the drug is 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the affinity between the drug 'C))C3c4c1=c)1HC]cCncFnnCcccNc([2OC)()C(2Occc3c(c4cn(CCccNNOA' and the target protein 'MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C))C3c4c1=c)1HC]cCncFnnCcccNc([2OC)()C(2Occc3c(c4cn(CCccNNOA\"], \"target_seq\": \"MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS\", \"result\": [{\"smile\":\"C))C3c4c1=c)1HC]cCncFnnCcccNc([2OC)()C(2Occc3c(c4cn(CCccNNOA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that's valid for the drug is 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1' and the protein 'MFPLIGKTIIFDNFPDPSDTWEITETIGKGTYGKVFKVLNKKNGQKAAVKILDPIHDIDEEIEAEYNILKALSDHPNVVRFYGIYFKKDKVNGDKLWLVLELCSGGSVTDLVKGFLKRGERMSEPLIAYILHEALMGLQHLHNNKTIHRDVKGNNILLTTEGGVKLVDFGVSAQLTSTRHRRNTSVGTPFWMAPEVIACEQQLDTTYDARCDTWSLGITAIELGDGDPPLADLHPMRALFKIPRNPPPKLRQPELWSAEFNDFISKCLTKDYEKRPTVSELLQHKFITQIEGKDVMLQKQLTEFIGIHQCMGGTEKARRERIHTKKGNFNRPLISNLKDVDDLATLEILDENTVSEQLEKCYSRDQIYVYVGDILIALNPFQSLGLYSTKHSKLYIGSKRTASPPHIFAMADLGYQSMITYNSDQCIVISGESGAGKTENAHLLVQQLTVLGKANNRTLQEKILQVNNLVEAFGNACTIINDNSSRFGKYLEMKFTSSGAVVGAQISEYLLEKSRVIHQAIGEKNFHIFYYIYAGLAEKKKLAHYKLPENKPPRYLQNDHLRTVQDIMNNSFYKSQYELIEQCFKVIGFTMEQLGSIYSILAAILNVGNIEFSSVATEHQIDKSHISNHTALENCASLLCIRADELQEALTSHCVVTRGETIIRPNTVEKATDVRDAMAKTLYGRLFSWIVNCINSLLKHDSSPSGNGDELSIGILDIFGFENFKKNSFEQLCINIANEQIQYYYNQHVFAWEQNEYLNEDVDARVIEYEDNWPLLDMFLQKPMGLLSLLDEESRFPKATDQTLVEKFEGNLKSQYFWRPKRMELSFGIHHYAGKVLYNASGFLAKNRDTLPTDIVLLLRSSDNSVIRQLVNHPLTKTGNLPHSKTKNVINYQMRTSEKLINLAKGDTGEATRHARETTNMKTQTVASYFRYSLMDLLSKMVVGQPHFVRCIKPNSERQARKYDKEKVLLQLRYTGILETARIRRLGFSHRILFANFIKRYYLLCYKSSEEPRMSPDTCATILEKAGLDNWALGKTKVFLKYYHVEQLNLMRKEAIDKLILIQACVRAFLCSRRYQKIQEKRKESAIIIQSAARGHLVRKQRKEIVDMKNTAVTTIQTSDQEFDYKKNFENTRESFVKKQAENAISANERFISAPNNKGSVSVVKTSTFKPEEETTNAVESNNRVYQTPKKMNNVYEEEVKQEFYLVGPEVSPKQKSVKDLEENSNLRKVEKEEAMIQSYYQRYTEERNCEESKAAYLERKAISERPSYPVPWLAENETSFKKTLEPTLSQRSIYQNANSMEKEKKTSVVTQRAPICSQEEGRGRLRHETVKERQVEPVTQAQEEEDKAAVFIQSKYRGYKRRQQLRKDKMSSFKHQRIVTTPTEVARNTHNLYSYPTKHEEINNIKKKDNKDSKATSEREACGLAIFSKQISKLSEEYFILQKKLNEMILSQQLKSLYLGVSHHKPINRRVSSQQCLSGVCKGEEPKILRPPRRPRKPKTLNNPEDSTYYYLLHKSIQEEKRRPRKDSQGKLLDLEDFYYKEFLPSRSGPKEHSPSLRERRPQQELQNQCIKANERCWAAESPEKEEEREPAANPYDFRRLLRKTSQRRRLVQQS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug '(Fcc3H)c1c3Occ(cc-n3c)cn1c2c2cc(cccc-()c)3][cn-A'-target protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(Fcc3H)c1c3Occ(cc-n3c)cn1c2c2cc(cccc-()c)3][cn-A\"], \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\", \"result\": [{\"smile\":\"(Fcc3H)c1c3Occ(cc-n3c)cn1c2c2cc(cccc-()c)3][cn-A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1' is the smile that’s right for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\", \"result\": [{\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1' and the protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE' is 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your calculation for the binding affinity between the drug 'CcC)ccNccCNncc)1)Nc(OFccc(=n4HC2n)c(ccC)ccc(C4CO]CC1O3n([32A' and the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CcC)ccNccCNncc)1)Nc(OFccc(=n4HC2n)c(ccC)ccc(C4CO]CC1O3n([32A\"], \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\":\"CcC)ccNccCNncc)1)Nc(OFccc(=n4HC2n)c(ccC)ccc(C4CO]CC1O3n([32A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' serves as the correct smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 600.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' is 600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug 'cccnc3cOc(c))[c3O)11c3Cn]cn2c(c)-2c43(Occ-=c(H4-ccNc(Cc)A'-target protein 'METVQLRNPPRRQLKKLDEDSLTKQPEEVFDVLEKLGEGSYGSVYKAIHKETGQIVAIKQVPVESDLQEIIKEISIMQQCDSPHVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLTEDEIATILQSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITAIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDNFTDFVKQCLVKSPEQRATATQLLQHPFVRSAKGVSILRDLINEAMDVKLKRQESQQREVDQDDEENSEEDEMDSGTMVRAVGDEMGTVRVASTMTDGANTMIEHDDTLPSQLGTMVINAEDEEEEGTMKRRDETMQPAKPSFLEYFEQKEKENQINSFGKSVPGPLKNSSDWKIPQDGDYEFLKSWTVEDLQKRLLALDPMMEQEIEEIRQKYQSKRQPILDAIEAKKRRQQNF' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cccnc3cOc(c))[c3O)11c3Cn]cn2c(c)-2c43(Occ-=c(H4-ccNc(Cc)A\"], \"target_seq\": \"METVQLRNPPRRQLKKLDEDSLTKQPEEVFDVLEKLGEGSYGSVYKAIHKETGQIVAIKQVPVESDLQEIIKEISIMQQCDSPHVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLTEDEIATILQSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITAIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDNFTDFVKQCLVKSPEQRATATQLLQHPFVRSAKGVSILRDLINEAMDVKLKRQESQQREVDQDDEENSEEDEMDSGTMVRAVGDEMGTVRVASTMTDGANTMIEHDDTLPSQLGTMVINAEDEEEEGTMKRRDETMQPAKPSFLEYFEQKEKENQINSFGKSVPGPLKNSSDWKIPQDGDYEFLKSWTVEDLQKRLLALDPMMEQEIEEIRQKYQSKRQPILDAIEAKKRRQQNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"METVQLRNPPRRQLKKLDEDSLTKQPEEVFDVLEKLGEGSYGSVYKAIHKETGQIVAIKQVPVESDLQEIIKEISIMQQCDSPHVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLTEDEIATILQSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITAIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDNFTDFVKQCLVKSPEQRATATQLLQHPFVRSAKGVSILRDLINEAMDVKLKRQESQQREVDQDDEENSEEDEMDSGTMVRAVGDEMGTVRVASTMTDGANTMIEHDDTLPSQLGTMVINAEDEEEEGTMKRRDETMQPAKPSFLEYFEQKEKENQINSFGKSVPGPLKNSSDWKIPQDGDYEFLKSWTVEDLQKRLLALDPMMEQEIEEIRQKYQSKRQPILDAIEAKKRRQQNF\", \"result\": [{\"smile\":\"cccnc3cOc(c))[c3O)11c3Cn]cn2c(c)-2c43(Occ-=c(H4-ccNc(Cc)A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The smile that valid fits the drug is 'NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"METVQLRNPPRRQLKKLDEDSLTKQPEEVFDVLEKLGEGSYGSVYKAIHKETGQIVAIKQVPVESDLQEIIKEISIMQQCDSPHVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLTEDEIATILQSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITAIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDNFTDFVKQCLVKSPEQRATATQLLQHPFVRSAKGVSILRDLINEAMDVKLKRQESQQREVDQDDEENSEEDEMDSGTMVRAVGDEMGTVRVASTMTDGANTMIEHDDTLPSQLGTMVINAEDEEEEGTMKRRDETMQPAKPSFLEYFEQKEKENQINSFGKSVPGPLKNSSDWKIPQDGDYEFLKSWTVEDLQKRLLALDPMMEQEIEEIRQKYQSKRQPILDAIEAKKRRQQNF\", \"result\": [{\"smile\": \"NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1' and the protein 'METVQLRNPPRRQLKKLDEDSLTKQPEEVFDVLEKLGEGSYGSVYKAIHKETGQIVAIKQVPVESDLQEIIKEISIMQQCDSPHVVKYYGSYFKNTDLWIVMEYCGAGSVSDIIRLRNKTLTEDEIATILQSTLKGLEYLHFMRKIHRDIKAGNILLNTEGHAKLADFGVAGQLTDTMAKRNTVIGTPFWMAPEVIQEIGYNCVADIWSLGITAIEMAEGKPPYADIHPMRAIFMIPTNPPPTFRKPELWSDNFTDFVKQCLVKSPEQRATATQLLQHPFVRSAKGVSILRDLINEAMDVKLKRQESQQREVDQDDEENSEEDEMDSGTMVRAVGDEMGTVRVASTMTDGANTMIEHDDTLPSQLGTMVINAEDEEEEGTMKRRDETMQPAKPSFLEYFEQKEKENQINSFGKSVPGPLKNSSDWKIPQDGDYEFLKSWTVEDLQKRLLALDPMMEQEIEEIRQKYQSKRQPILDAIEAKKRRQQNF' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'c)cON()cCc)1ccC1(NnC2cc(2OccccCccnc33OcA' and the target protein 'MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c)cON()cCc)1ccC1(NnC2cc(2OccccCccnc33OcA\"], \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\", \"result\": [{\"smile\":\"c)cON()cCc)1ccC1(NnC2cc(2OccccCccnc33OcA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1' works as the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR\", \"result\": [{\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1' and the protein 'MSTFRQEDVEDHYEMGEELGSGQFAIVRKCRQKGTGKEYAAKFIKKRRLSSSRRGVSREEIEREVNILREIRHPNIITLHDIFENKTDVVLILELVSGGELFDFLAEKESLTEDEATQFLKQILDGVHYLHSKRIAHFDLKPENIMLLDKNVPNPRIKLIDFGIAHKIEAGNEFKNIFGTPEFVAPEIVNYEPLGLEADMWSIGVITYILLSGASPFLGETKQETLTNISAVNYDFDEEYFSNTSELAKDFIRRLLVKDPKRRMTIAQSLEHSWIKAIRRRNVRGEDSGRKPERRRLKTTRLKEYTIKSHSSLPPNNSYADFERFSKVLEEAAAAEEGLRELQRSRRLCHEDVEALAAIYEEKEAWYREESDSLGQDLRRLRQELLKTEALKRQAQEEAKGALLGTSGLKRRFSRLENRYEALAKQVASEMRFVQDLVRALEQEKLQGVECGLR' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug '@cOHC))H]21[CC2cCNcc(c@cC1CccO@c1@Oc3)C3c]52c([@4@)66NcC]C(nc3(c=[c)([Cnccc]H4[cc(]CHOc352cc=)1OA' and the target protein 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"@cOHC))H]21[CC2cCNcc(c@cC1CccO@c1@Oc3)C3c]52c([@4@)66NcC]C(nc3(c=[c)([Cnccc]H4[cc(]CHOc352cc=)1OA\"], \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\", \"result\": [{\"smile\":\"@cOHC))H]21[CC2cCNcc(c@cC1CccO@c1@Oc3)C3c]52c([@4@)66NcC]C(nc3(c=[c)([Cnccc]H4[cc(]CHOc352cc=)1OA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O' corresponds to the right smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O' and the protein 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the affinity between the drug 'O2c(2C(N4nC(NcCNcNC]1)CCC@CnCOc)3CN1Cc(41cC)C1O=))c=cC[cCC3)(CC@cH(A' and the target protein 'MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O2c(2C(N4nC(NcCNcNC]1)CCC@CnCOc)3CN1Cc(41cC)C1O=))c=cC[cCC3)(CC@cH(A\"], \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\", \"result\": [{\"smile\":\"O2c(2C(N4nC(NcCNcNC]1)CCC@CnCOc)3CN1Cc(41cC)C1O=))c=cC[cCC3)(CC@cH(A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' is the accurate smile for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK\", \"result\": [{\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 5400.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MARENGESSSSWKKQAEDIKKIFEFKETLGTGAFSEVVLAEEKATGKLFAVKCIPKKALKGKESSIENEIAVLRKIKHENIVALEDIYESPNHLYLVMQLVSGGELFDRIVEKGFYTEKDASTLIRQVLDAVYYLHRMGIVHRDLKPENLLYYSQDEESKIMISDFGLSKMEGKGDVMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVIAYILLCGYPPFYDENDSKLFEQILKAEYEFDSPYWDDISDSAKDFIRNLMEKDPNKRYTCEQAARHPWIAGDTALNKNIHESVSAQIRKNFAKSKWRQAFNATAVVRHMRKLHLGSSLDSSNASVSSSLSLASQKDCLAPSTLCSFISSSSGVSGVGAERRPRPTTVTAVHSGSK' is 5400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug ')Ocnc))F()n)cc)3((C(n=)Cc4c)c3(c1Cccc4ccn5cc(nC(2c1cNNFc2cc--)(5ccccF(A''s affinity for the target protein 'MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSNDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\")Ocnc))F()n)cc)3((C(n=)Cc4c)c3(c1Cccc4ccn5cc(nC(2c1cNNFc2cc--)(5ccccF(A\"], \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSNDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSNDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\", \"result\": [{\"smile\":\")Ocnc))F()n)cc)3((C(n=)Cc4c)c3(c1Cccc4ccn5cc(nC(2c1cNNFc2cc--)(5ccccF(A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' is the smile meant for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSNDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1' and the protein 'MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSNDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an affinity estimate for the drug '(Oc(=c+2O1])cc[-O)cccC)O(c)[c)cN]=-(c2C(c1lA' with the target protein 'METLCQRLNVCQDKILTHYENDSTDLRDHIDYWKHMRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLQDVSLEVYLTAPTGCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICEEASVTVVEGQVDYYGLYYVHEGIRTYFVQFKDDAEKYSKNKVWEVHAGGQVILCPTSVFSSNEVSSPEIIRQHLANHPAATHTKAVALGTEETQTTIQRPRSEPDTGNPCHTTKLLHRDSVDSAPILTAFNSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRFKKHCTLYTAVSSTWHWTGHNVKHKSAIVTLTYDSEWQRDQFLSQVKIPKTITVSTGFMSI'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(Oc(=c+2O1])cc[-O)cccC)O(c)[c)cN]=-(c2C(c1lA\"], \"target_seq\": \"METLCQRLNVCQDKILTHYENDSTDLRDHIDYWKHMRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLQDVSLEVYLTAPTGCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICEEASVTVVEGQVDYYGLYYVHEGIRTYFVQFKDDAEKYSKNKVWEVHAGGQVILCPTSVFSSNEVSSPEIIRQHLANHPAATHTKAVALGTEETQTTIQRPRSEPDTGNPCHTTKLLHRDSVDSAPILTAFNSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRFKKHCTLYTAVSSTWHWTGHNVKHKSAIVTLTYDSEWQRDQFLSQVKIPKTITVSTGFMSI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"METLCQRLNVCQDKILTHYENDSTDLRDHIDYWKHMRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLQDVSLEVYLTAPTGCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICEEASVTVVEGQVDYYGLYYVHEGIRTYFVQFKDDAEKYSKNKVWEVHAGGQVILCPTSVFSSNEVSSPEIIRQHLANHPAATHTKAVALGTEETQTTIQRPRSEPDTGNPCHTTKLLHRDSVDSAPILTAFNSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRFKKHCTLYTAVSSTWHWTGHNVKHKSAIVTLTYDSEWQRDQFLSQVKIPKTITVSTGFMSI\", \"result\": [{\"smile\":\"(Oc(=c+2O1])cc[-O)cccC)O(c)[c)cN]=-(c2C(c1lA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid-suited smile for the drug is 'O=C(O)c1ccc(-c2ccc([N+](=O)[O-])c(Cl)c2)cc1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1ccc(-c2ccc([N+](=O)[O-])c(Cl)c2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"METLCQRLNVCQDKILTHYENDSTDLRDHIDYWKHMRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLQDVSLEVYLTAPTGCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICEEASVTVVEGQVDYYGLYYVHEGIRTYFVQFKDDAEKYSKNKVWEVHAGGQVILCPTSVFSSNEVSSPEIIRQHLANHPAATHTKAVALGTEETQTTIQRPRSEPDTGNPCHTTKLLHRDSVDSAPILTAFNSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRFKKHCTLYTAVSSTWHWTGHNVKHKSAIVTLTYDSEWQRDQFLSQVKIPKTITVSTGFMSI\", \"result\": [{\"smile\": \"O=C(O)c1ccc(-c2ccc([N+](=O)[O-])c(Cl)c2)cc1\", \"value\": 10000000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1ccc(-c2ccc([N+](=O)[O-])c(Cl)c2)cc1' and the protein 'METLCQRLNVCQDKILTHYENDSTDLRDHIDYWKHMRLECAIYYKAREMGFKHINHQVVPTLAVSKNKALQAIELQLTLETIYNSQYSNEKWTLQDVSLEVYLTAPTGCIKKHGYTVEVQFDGDICNTMHYTNWTHIYICEEASVTVVEGQVDYYGLYYVHEGIRTYFVQFKDDAEKYSKNKVWEVHAGGQVILCPTSVFSSNEVSSPEIIRQHLANHPAATHTKAVALGTEETQTTIQRPRSEPDTGNPCHTTKLLHRDSVDSAPILTAFNSSHKGRINCNSNTTPIVHLKGDANTLKCLRYRFKKHCTLYTAVSSTWHWTGHNVKHKSAIVTLTYDSEWQRDQFLSQVKIPKTITVSTGFMSI' is 10000000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of the binding affinity between the drug ']@23cOc(cFCNCC1c/1c)=)C(O1CCnC=)NcN3C1[CC\\c2=(c][OcCO)((C(HC)H)cA' and the target protein 'MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"]@23cOc(cFCNCC1c/1c)=)C(O1CCnC=)NcN3C1[CC\\c2=(c][OcCO)((C(HC)H)cA\"], \"target_seq\": \"MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF\", \"result\": [{\"smile\":\"]@23cOc(cFCNCC1c/1c)=)C(O1CCnC=)NcN3C1[CC\\c2=(c][OcCO)((C(HC)H)cA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 550.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the protein 'MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF' is 550.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drug 'C]NccOncl3c)(C3c2Nn3cccc(Ccc-CC1()13c(n)21nC)c1cOHcCn[A' and the target protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C]NccOncl3c)(C3c2Nn3cccc(Ccc-CC1()13c(n)21nC)c1cOHcCn[A\"], \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\":\"C]NccOncl3c)(C3c2Nn3cccc(Ccc-CC1()13c(n)21nC)c1cOHcCn[A\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid-suited smile for the drug is 'Cc1c(Cl)cccc1Nc1c(CO)nc2c(-c3c[nH]cn3)cc(N3CCOCC3)nn12'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1c(Cl)cccc1Nc1c(CO)nc2c(-c3c[nH]cn3)cc(N3CCOCC3)nn12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ\", \"result\": [{\"smile\": \"Cc1c(Cl)cccc1Nc1c(CO)nc2c(-c3c[nH]cn3)cc(N3CCOCC3)nn12\", \"value\": 17.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1c(Cl)cccc1Nc1c(CO)nc2c(-c3c[nH]cn3)cc(N3CCOCC3)nn12' and the protein 'MPPGVDCPMEFWTKEENQSVVVDFLLPTGVYLNFPVSRNANLSTIKQLLWHRAQYEPLFHMLSGPEAYVFTCINQTAEQQELEDEQRRLCDVQPFLPVLRLVAREGDRVKKLINSQISLLIGKGLHEFDSLCDPEVNDFRAKMCQFCEEAAARRQQLGWEAWLQYSFPLQLEPSAQTWGPGTLRLPNRALLVNVKFEGSEESFTFQVSTKDVPLALMACALRKKATVFRQPLVEQPEDYTLQVNGRHEYLYGSYPLCQFQYICSCLHSGLTPHLTMVHSSSILAMRDEQSNPAPQVQKPRAKPPPIPAKKPSSVSLWSLEQPFRIELIQGSKVNADERMKLVVQAGLFHGNEMLCKTVSSSEVSVCSEPVWKQRLEFDINICDLPRMARLCFALYAVIEKAKKARSTKKKSKKADCPIAWANLMLFDYKDQLKTGERCLYMWPSVPDEKGELLNPTGTVRSNPNTDSAAALLICLPEVAPHPVYYPALEKILELGRHSECVHVTEEEQLQLREILERRGSGELYEHEKDLVWKLRHEVQEHFPEALARLLLVTKWNKHEDVAQMLYLLCSWPELPVLSALELLDFSFPDCHVGSFAIKSLRKLTDDELFQYLLQLVQVLKYESYLDCELTKFLLDRALANRKIGHFLFWHLRSEMHVPSVALRFGLILEAYCRGSTHHMKVLMKQGEALSKLKALNDFVKLSSQKTPKPQTKELMHLCMRQEAYLEALSHLQSPLDPSTLLAEVCVEQCTFMDSKMKPLWIMYSNEEAGSGGSVGIIFKNGDDLRQDMLTLQMIQLMDVLWKQEGLDLRMTPYGCLPTGDRTGLIEVVLRSDTIANIQLNKSNMAATAAFNKDALLNWLKSKNPGEALDRAIEEFTLSCAGYCVATYVLGIGDRHSDNIMIRESGQLFHIDFGHFLGNFKTKFGINRERVPFILTYDFVHVIQQGKTNNSEKFERFRGYCERAYTILRRHGLLFLHLFALMRAAGLPELSCSKDIQYLKDSLALGKTEEEALKHFRVKFNEALRESWKTKVNWLAHNVSKDNRQ' is 17.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'NcccCcCCC)3312c)nnc3Ccccc(ccCc)O1=3((cccN2NA' and the target protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NcccCcCCC)3312c)nnc3Ccccc(ccCc)O1=3((cccN2NA\"], \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\", \"result\": [{\"smile\":\"NcccCcCCC)3312c)nnc3Ccccc(ccCc)O1=3((cccN2NA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' is the smile that’s right for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein 'MEGDGVPWGSEPVSGPGPGGGGMIRELCRGFGRYRRYLGRLRQNLRETQKFFRDIKCSHNHTCLSSLTGGGGAERGPAGDVAETGLQAGQLSCISFPPKEEKYLQQIVDCLPCILILGQDCNVKCQLLNLLLGVQVLPTTKLGSEESCKLRRLRFTYGTQTRVSLALPGQYELVHTLVAHQGNWETIPEEDLEVQENNEDAAHVLAELEVTMHHALLQEVDVVVAPCQGLRPTVDVLGDLVNDFLPVITYALHKDELSERDEQELQEIRKYFSFPVFFFKVPKLGSEIIDSSTRRMESERSPLYRQLIDLGYLSSSHWNCGAPGQDTKAQSMLVEQSEKLRHLSTFSHQVLQTRLVDAAKALNLVHCHCLDIFINQAFDMQRDLQITPKRLEYTRKKENELYESLMNIANRKQEEMKDMIVETLNTMKEELLDDATNMEFKDVIVPENGEPVGTREIKCCIRQIQELIISRLNQAVANKLISSVDYLRESFVGTLERCLQSLEKSQDVSVHITSNYLKQILNAAYHVEVTFHSGSSVTRMLWEQIKQIIQRITWVSPPAITLEWKRKVAQEAIESLSASKLAKSICSQFRTRLNSSHEAFAASLRQLEAGHSGRLEKTEDLWLRVRKDHAPRLARLSLESCSLQDVLLHRKPKLGQELGRGQYGVVYLCDNWGGHFPCALKSVVPPDEKHWNDLALEFHYMRSLPKHERLVDLHGSVIDYNYGGGSSIAVLLIMERLHRDLYTGLKAGLTLETRLQIALDVVEGIRFLHSQGLVHRDIKLKNVLLDKQNRAKITDLGFCKPEAMMSGSIVGTPIHMAPELFTGKYDNSVDVYAFGILFWYICSGSVKLPEAFERCASKDHLWNNVRRGARPERLPVFDEECWQLMEACWDGDPLKRPLLGIVQPMLQGIMNRLCKSNSEQPNRGLDDST' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drug 'cccC)C@cn2-cH3HCC(Oc]2sCccO([c]3OC)c(C1c)cc1[-NnA''s affinity for the target protein 'MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"cccC)C@cn2-cH3HCC(Oc]2sCccO([c]3OC)c(C1c)cc1[-NnA\"], \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\", \"result\": [{\"smile\":\"cccC)C@cn2-cH3HCC(Oc]2sCccO([c]3OC)c(C1c)cc1[-NnA\", \"value\":\"invalid smile\"}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your drug smile, please give me the right one."
            },
            {
                "from": "human",
                "value": "'CCOCCNC[C@H](O)COc1ccc(-c2nc(-c3cccs3)c[nH]2)cc1' is the smile intended for the drug. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOCCNC[C@H](O)COc1ccc(-c2nc(-c3cccs3)c[nH]2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL\", \"result\": [{\"smile\": \"CCOCCNC[C@H](O)COc1ccc(-c2nc(-c3cccs3)c[nH]2)cc1\", \"value\": 1071.52}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOCCNC[C@H](O)COc1ccc(-c2nc(-c3cccs3)c[nH]2)cc1' and the protein 'MGHLGNGSDFLLAPNASHAPDHNVTRERDEAWVVGMAIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGASHILMNMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVVILMVWVVSGLTSFLPIQMHWYRATHKDAINCYAEETCCDFFTNQAYAIASSIVSFYLPLVVMVFVYSRVFQVAKKQLQKIDRSEGRFHTQNLSQVEQDGRSGHGLRRSSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIPKEVYILLNWVGYVNSAFNPLIYCRSPDFRIAFQELLCLRRSALKAYGNDCSSNSNGKTDYTGEPNVCHQGQEKERELLCEDPPGTEDLVSCPGTVPSDSIDSQGRNYSTNDSLL' is 1071.52."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the target protein 'DSLLRPNNGFFGSNGTGRRHVSDVASTADQPRKWRAWSFRRSEVYQADLNSPLKQKIKDIAALVFDHCSLPKTALTSKQRQPQPVHHVSSRKTYQHAKFKESRPSTDVDLQTDLKQPNDPPGLVSAEEIGPMGRASYRPLNFVSEPFEEGTDHVTDVASQRTRRKHKLNIQEKQVRFEERPYYMKVVKDLEHESVKCTFENSRTAPPWPILSHLRRAGGNLVVTGQDSGKSISTEPNPFTFSSNTYLGTLNSKKEKGKEASLSANIEEEELAVVEMRAEETSPQIWCPRDQKSRPGRVDYYLDDNTDSYSRVEEVLMCGQDTPDVLSQQRLPGTFSTSEDEQHSLIISGRDNGREQFKNGAVRHLKSRGQTGSKYQILQDELAVPLVEAEVRPVVEAPEIAIAKKVGSRGSPSVQLGCNRRTFFHKEMRVEINEPFDHLQRRTFSNASRQFLETKEPVVKNEIELSALKTLIEHKQNQTDLRPPELGKSVRSQQLSLSEMRPADSHAQSQVARFQPQSLLENPPTMSVQESHDRVRGEYTSQDDMKELPESGLVAKLTLQMDLSQRIHSRVRSIHRAQRSIGLSGSYFGFQPRESVEEMSGPLESLQLNHYDKKLGLSQTRLHWPEFVRSACMLMSLTRQSSSYLDLIIMLAERKIPQPTSLFDKSKVYGGIESQVREGREAQDSKEVLGLTLLELGGNGQMFRRDPWHLRIRQSSLCVQVSVSVKERPQTLEPSVNTLSDGQVHEFLVRLKMAMAWTKEGRSKREEVCVEPSTKQNEKYSSERDVNVEPDKEKQRSEISWVSHKQRGTDLETFAAIENHVPDVESGQSRTSLKSILSRAILEKGARFYLEEGEGHIYRQLIRTSGVPPVEPEEDMILDIETAIMTDSQEGRESRRVYPFAYSDLEPENRLRYPTQRQPKYPQSDAPGSGREQNKSNDERESRLEGEQAVWRLELRKHRQKQESEYVKIPGLHKSVGGNRVGDEGWRECKKQEYEPLTDTGISAIRMPPESREVRAVGPEQNVTHARSAVRLPSFSVILDHAHQTKWFHSYIQLTKESVRRGLKNLIYERGPVLSQERLGSYKEHPEEGTATQCSGKRRLTFDEDIEAPAHSGGKVRKVAEYEFTPDISIYTMLWESILNPPTSDSYAPRGYAPKVNGKEEDPLKTNHKSGEAMRIARPILRKSEEGSRECQELAVTEEKSLPCSVDPAIQHGQGEKIQMVPRNEGITDQHRSAKKVEWETLQTLHRDLVYGVLLVIEQIKQHDDTERNPVRIAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\"], \"target_seq\": \"DSLLRPNNGFFGSNGTGRRHVSDVASTADQPRKWRAWSFRRSEVYQADLNSPLKQKIKDIAALVFDHCSLPKTALTSKQRQPQPVHHVSSRKTYQHAKFKESRPSTDVDLQTDLKQPNDPPGLVSAEEIGPMGRASYRPLNFVSEPFEEGTDHVTDVASQRTRRKHKLNIQEKQVRFEERPYYMKVVKDLEHESVKCTFENSRTAPPWPILSHLRRAGGNLVVTGQDSGKSISTEPNPFTFSSNTYLGTLNSKKEKGKEASLSANIEEEELAVVEMRAEETSPQIWCPRDQKSRPGRVDYYLDDNTDSYSRVEEVLMCGQDTPDVLSQQRLPGTFSTSEDEQHSLIISGRDNGREQFKNGAVRHLKSRGQTGSKYQILQDELAVPLVEAEVRPVVEAPEIAIAKKVGSRGSPSVQLGCNRRTFFHKEMRVEINEPFDHLQRRTFSNASRQFLETKEPVVKNEIELSALKTLIEHKQNQTDLRPPELGKSVRSQQLSLSEMRPADSHAQSQVARFQPQSLLENPPTMSVQESHDRVRGEYTSQDDMKELPESGLVAKLTLQMDLSQRIHSRVRSIHRAQRSIGLSGSYFGFQPRESVEEMSGPLESLQLNHYDKKLGLSQTRLHWPEFVRSACMLMSLTRQSSSYLDLIIMLAERKIPQPTSLFDKSKVYGGIESQVREGREAQDSKEVLGLTLLELGGNGQMFRRDPWHLRIRQSSLCVQVSVSVKERPQTLEPSVNTLSDGQVHEFLVRLKMAMAWTKEGRSKREEVCVEPSTKQNEKYSSERDVNVEPDKEKQRSEISWVSHKQRGTDLETFAAIENHVPDVESGQSRTSLKSILSRAILEKGARFYLEEGEGHIYRQLIRTSGVPPVEPEEDMILDIETAIMTDSQEGRESRRVYPFAYSDLEPENRLRYPTQRQPKYPQSDAPGSGREQNKSNDERESRLEGEQAVWRLELRKHRQKQESEYVKIPGLHKSVGGNRVGDEGWRECKKQEYEPLTDTGISAIRMPPESREVRAVGPEQNVTHARSAVRLPSFSVILDHAHQTKWFHSYIQLTKESVRRGLKNLIYERGPVLSQERLGSYKEHPEEGTATQCSGKRRLTFDEDIEAPAHSGGKVRKVAEYEFTPDISIYTMLWESILNPPTSDSYAPRGYAPKVNGKEEDPLKTNHKSGEAMRIARPILRKSEEGSRECQELAVTEEKSLPCSVDPAIQHGQGEKIQMVPRNEGITDQHRSAKKVEWETLQTLHRDLVYGVLLVIEQIKQHDDTERNPVRIAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQSTVDQKRASHHESNGFAGRIHLLPDLLQQSHSSSTSSTSSSPSSSQPTPTMSPQTPQDKLTANETQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW' is the true sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQSTVDQKRASHHESNGFAGRIHLLPDLLQQSHSSSTSSTSSSPSSSQPTPTMSPQTPQDKLTANETQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQSTVDQKRASHHESNGFAGRIHLLPDLLQQSHSSSTSSTSSSPSSSQPTPTMSPQTPQDKLTANETQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 1400.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the protein 'MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQSTVDQKRASHHESNGFAGRIHLLPDLLQQSHSSSTSSTSSSPSSSQPTPTMSPQTPQDKLTANETQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW' is 1400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the target protein 'DMKVAIKASQHEGKYQFYVPLPNYGPRGLCQDKMKANLLLVNHFYLIVAMLSVAENRMCLNLYLDMHINKYIIIKKRESGIQRLNPLVKKKGEPMTPEVILFQNLVPGYWSPNQSKELQAAIPPVCSRIDDNCTYDADFLVALRDDSRYGAKISSCFRPGGLRTYMTYINRDKALALRVCLQAPMFLILMFLLGVGQDHASDKSNESKMFKRMTETRYDGESHGASRQIKFCHRAADLPGCSDKSEKFDKTVKLYHPEYEAVDEYTEPFVDGLPAEQLAVRQQEQAMVDIPHIPSVSFHTWKAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\"], \"target_seq\": \"DMKVAIKASQHEGKYQFYVPLPNYGPRGLCQDKMKANLLLVNHFYLIVAMLSVAENRMCLNLYLDMHINKYIIIKKRESGIQRLNPLVKKKGEPMTPEVILFQNLVPGYWSPNQSKELQAAIPPVCSRIDDNCTYDADFLVALRDDSRYGAKISSCFRPGGLRTYMTYINRDKALALRVCLQAPMFLILMFLLGVGQDHASDKSNESKMFKRMTETRYDGESHGASRQIKFCHRAADLPGCSDKSEKFDKTVKLYHPEYEAVDEYTEPFVDGLPAEQLAVRQQEQAMVDIPHIPSVSFHTWKAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C' and the protein 'MDEQSQGMQGPPVPQFQPQKALRPDMGYNTLANFRIEKKIGRGQFSEVYRAACLLDGVPVALKKVQIFDLMDAKARADCIKEIDLLKQLNHPNVIKYYASFIEDNELNIVLELADAGDLSRMIKHFKKQKRLIPERTVWKYFVQLCSALEHMHSRRVMHRDIKPANVFITATGVVKLGDLGLGRFFSSKTTAAHSLVGTPYYMSPERIHENGYNFKSDIWSLGCLLYEMAALQSPFYGDKMNLYSLCKKIEQCDYPPLPSDHYSEELRQLVNMCINPDPEKRPDVTYVYDVAKRMHACTASS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CC(C)(C)CCCCNC(=O)c1coc(C2C3CCC(O3)C2Cc2ccccc2CCC(=O)O)n1''s affinity with the target protein 'SGEFPGAADLQVRVRFLETAQLLVSSYRLLTDLWLPWGGPVVCELNPGVCPFRFTCTTVGLNTLQVTTSGWSVEASAFPILFRAPTMTRLASNCENWSVPSQMACLRYRLAFYFGFAGMAPYGFLRLCEPLGIQRPQVAILSAALGLVQLLLALVRGSSCIEVLWLSSFREQWLARGGAAVLTGMVPLRLHGFAASCFLLVLRPLQTQPAVLFRAVSLLINLLTIQSHLYVSWGFGLEKGLMITHRATSPLASWMAIRPTHQSYRLQTSIGVAPVDGRRFTVVVSLMSSLRDVRQLPGGDLARVVLLQELIWALMLLFGLAPVGANMALSSTQGLSGHGARARAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)CCCCNC(=O)c1coc(C2C3CCC(O3)C2Cc2ccccc2CCC(=O)O)n1\"], \"target_seq\": \"SGEFPGAADLQVRVRFLETAQLLVSSYRLLTDLWLPWGGPVVCELNPGVCPFRFTCTTVGLNTLQVTTSGWSVEASAFPILFRAPTMTRLASNCENWSVPSQMACLRYRLAFYFGFAGMAPYGFLRLCEPLGIQRPQVAILSAALGLVQLLLALVRGSSCIEVLWLSSFREQWLARGGAAVLTGMVPLRLHGFAASCFLLVLRPLQTQPAVLFRAVSLLINLLTIQSHLYVSWGFGLEKGLMITHRATSPLASWMAIRPTHQSYRLQTSIGVAPVDGRRFTVVVSLMSSLRDVRQLPGGDLARVVLLQELIWALMLLFGLAPVGANMALSSTQGLSGHGARARAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence that corresponds to the protein is 'MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ\", \"result\": [{\"smile\": \"CC(C)(C)CCCCNC(=O)c1coc(C2C3CCC(O3)C2Cc2ccccc2CCC(=O)O)n1\", \"value\": 0.6}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)CCCCNC(=O)c1coc(C2C3CCC(O3)C2Cc2ccccc2CCC(=O)O)n1' and the protein 'MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ' is 0.6."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the target protein 'ACLAEFDELIEQTHAIGLTSDVEKGVPKDDFPIVLINFGGRWFLKDSDQSDKQFVTWRQEFFTHILLASLHYNKLHLEAAKPGEYFRLGIFKYAHTVGESLHPEWGTSQALVKPVEIPVMLIVQKFPVDHLDPLVQLILRVRLLGESIGEKTMGLELPKREVTALQGYSGENRDPGGGLAEYQTCLLAFSLVLHAETKPPPSVRNRIRYMVDLDIKLLESFKDIAMSFPLDTKATIPESTRTPSWLKNFMLSERISRKLVASATLRTGYNGRVKIVQLHLLLYKEKYPRMQRRPQMQLYIPRYYFAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\"], \"target_seq\": \"ACLAEFDELIEQTHAIGLTSDVEKGVPKDDFPIVLINFGGRWFLKDSDQSDKQFVTWRQEFFTHILLASLHYNKLHLEAAKPGEYFRLGIFKYAHTVGESLHPEWGTSQALVKPVEIPVMLIVQKFPVDHLDPLVQLILRVRLLGESIGEKTMGLELPKREVTALQGYSGENRDPGGGLAEYQTCLLAFSLVLHAETKPPPSVRNRIRYMVDLDIKLLESFKDIAMSFPLDTKATIPESTRTPSWLKNFMLSERISRKLVASATLRTGYNGRVKIVQLHLLLYKEKYPRMQRRPQMQLYIPRYYFAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence that’s right for this protein is 'MDMFQKVEKIGEGTYGVVYKAKNRETGQLVALKKIRLDLEMEGVPSTAIREISLLKELKHPNIVRLLDVVHNERKLYLVFEFLSQDLKKYMDSTPGSELPLHLIKSYLFQLLQGVSFCHSHRVIHRDLKPQNLLINELGAIKLADFGLARAFGVPLRTYTHEVVTLWYRAPEILLGSKFYTTAVDIWSIGCIFAEMVTRKALFPGDSEIDQLFRIFRMLGTPSEDTWPGVTQLPDYKGSFPKWTRKGLEEIVPNLEPEGRDLLMQLLQYDPSQRITAKTALAHPYFSSPEPSPAARQYVLQRFRH'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDMFQKVEKIGEGTYGVVYKAKNRETGQLVALKKIRLDLEMEGVPSTAIREISLLKELKHPNIVRLLDVVHNERKLYLVFEFLSQDLKKYMDSTPGSELPLHLIKSYLFQLLQGVSFCHSHRVIHRDLKPQNLLINELGAIKLADFGLARAFGVPLRTYTHEVVTLWYRAPEILLGSKFYTTAVDIWSIGCIFAEMVTRKALFPGDSEIDQLFRIFRMLGTPSEDTWPGVTQLPDYKGSFPKWTRKGLEEIVPNLEPEGRDLLMQLLQYDPSQRITAKTALAHPYFSSPEPSPAARQYVLQRFRH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDMFQKVEKIGEGTYGVVYKAKNRETGQLVALKKIRLDLEMEGVPSTAIREISLLKELKHPNIVRLLDVVHNERKLYLVFEFLSQDLKKYMDSTPGSELPLHLIKSYLFQLLQGVSFCHSHRVIHRDLKPQNLLINELGAIKLADFGLARAFGVPLRTYTHEVVTLWYRAPEILLGSKFYTTAVDIWSIGCIFAEMVTRKALFPGDSEIDQLFRIFRMLGTPSEDTWPGVTQLPDYKGSFPKWTRKGLEEIVPNLEPEGRDLLMQLLQYDPSQRITAKTALAHPYFSSPEPSPAARQYVLQRFRH\", \"result\": [{\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1' and the protein 'MDMFQKVEKIGEGTYGVVYKAKNRETGQLVALKKIRLDLEMEGVPSTAIREISLLKELKHPNIVRLLDVVHNERKLYLVFEFLSQDLKKYMDSTPGSELPLHLIKSYLFQLLQGVSFCHSHRVIHRDLKPQNLLINELGAIKLADFGLARAFGVPLRTYTHEVVTLWYRAPEILLGSKFYTTAVDIWSIGCIFAEMVTRKALFPGDSEIDQLFRIFRMLGTPSEDTWPGVTQLPDYKGSFPKWTRKGLEEIVPNLEPEGRDLLMQLLQYDPSQRITAKTALAHPYFSSPEPSPAARQYVLQRFRH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug 'CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the target protein 'KCGINETDVDTCFAILHKTPALSQPGIVLTPGEGIIILTCMLNPLTDIWHTCALDFACNLFLVVALAGIPNNSHNAEGSGEPAMKYEHINRAWIGLRCLKDRNLLLHSKCTAWGDRPDGAIDWLKLEYPAQCDVEAAKNKGADGGIQDEIEARDEASPKKYTHGEIVIYEKQQRMWNSGNRSKVTVNALENLTGNVCPPQVTEDVNRRLEAHAAITVFNRCGKLSGAHRLAAAELRAEKFLCTDWKSQMFLSISVICATWEFMSDPGGGKGISVIVIENIDFEEGFYNDGEGPEYLFFKVKVFISMNPATEQMKSGIQGVVGIQTYAELLDVGRLVVKIKVRIQREASKWVGMLWNPVALIIIRENITVPLHHELLVRNSFSGTVCVDRVYNSLYNQVQLLGPTFVRQPPDKNIVQTELSTTDYVGHPAFIRWSTCESNIPTKQTQEWVPALGELRGKRPRFLWVYLSHYNNEANLKYTEHKNTGLDLELTELDLSVITYCTTGEDLHHLHPSHCCKAANHKISIQRVGRGAIYAGAVETALVSPTSEPRLIWHSESGDFFRCLDSESSHYSEAVGLEPSISTRPMDVYNSNRHEAVIDNLTPKNAVKIPEYIFLPVVRGQKSDKKNEEMTRVIEPLHPTDKRGWALVGSPVDMLLVFLFQRGACFDARKKLLAEMAAGDIPSDQGYCIAPLDNMFGCRLLLEYGPTTQCYLRETHEQTWKKTGPLTGYGNDLEMPNRNHLRPRNVSSSCDQCCGAIVKPHIILESILCDAELAEKKVKGNCQVFWHFFCTYIKYQDYSRTLGVIRVSKGSKWDIVLGRITQAKVSWGPCDKLEPFAPRQVYAQGSFPSPLGKVIQRNLNDNLTSISLDKTYAELPNGSKNFLCHTHPVGSEPASVVGMVQFQPCLLYHSLKIFVQGGKDMCSGSPGKEQVPLEMTYECAMLPLLISPDRYSGYSDVLASSFPQGGAFNPRKPVIKNRQVERTEQKDEELLQLMETTIILNMGKARALGIHMNLEHENVYQKGFTLFHEFGGRRIQLPITYTRGCQRPICNASDKKCAAVDLLNWLPARAEREGSPFLIMALYPNPQLRGTLDGVKLNENDVAQNQAEPPLCRLYASTGTDAAAFVSSYWSSQVNRCDPKNGETDYMVDVTQGKDGIAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\"], \"target_seq\": \"KCGINETDVDTCFAILHKTPALSQPGIVLTPGEGIIILTCMLNPLTDIWHTCALDFACNLFLVVALAGIPNNSHNAEGSGEPAMKYEHINRAWIGLRCLKDRNLLLHSKCTAWGDRPDGAIDWLKLEYPAQCDVEAAKNKGADGGIQDEIEARDEASPKKYTHGEIVIYEKQQRMWNSGNRSKVTVNALENLTGNVCPPQVTEDVNRRLEAHAAITVFNRCGKLSGAHRLAAAELRAEKFLCTDWKSQMFLSISVICATWEFMSDPGGGKGISVIVIENIDFEEGFYNDGEGPEYLFFKVKVFISMNPATEQMKSGIQGVVGIQTYAELLDVGRLVVKIKVRIQREASKWVGMLWNPVALIIIRENITVPLHHELLVRNSFSGTVCVDRVYNSLYNQVQLLGPTFVRQPPDKNIVQTELSTTDYVGHPAFIRWSTCESNIPTKQTQEWVPALGELRGKRPRFLWVYLSHYNNEANLKYTEHKNTGLDLELTELDLSVITYCTTGEDLHHLHPSHCCKAANHKISIQRVGRGAIYAGAVETALVSPTSEPRLIWHSESGDFFRCLDSESSHYSEAVGLEPSISTRPMDVYNSNRHEAVIDNLTPKNAVKIPEYIFLPVVRGQKSDKKNEEMTRVIEPLHPTDKRGWALVGSPVDMLLVFLFQRGACFDARKKLLAEMAAGDIPSDQGYCIAPLDNMFGCRLLLEYGPTTQCYLRETHEQTWKKTGPLTGYGNDLEMPNRNHLRPRNVSSSCDQCCGAIVKPHIILESILCDAELAEKKVKGNCQVFWHFFCTYIKYQDYSRTLGVIRVSKGSKWDIVLGRITQAKVSWGPCDKLEPFAPRQVYAQGSFPSPLGKVIQRNLNDNLTSISLDKTYAELPNGSKNFLCHTHPVGSEPASVVGMVQFQPCLLYHSLKIFVQGGKDMCSGSPGKEQVPLEMTYECAMLPLLISPDRYSGYSDVLASSFPQGGAFNPRKPVIKNRQVERTEQKDEELLQLMETTIILNMGKARALGIHMNLEHENVYQKGFTLFHEFGGRRIQLPITYTRGCQRPICNASDKKCAAVDLLNWLPARAEREGSPFLIMALYPNPQLRGTLDGVKLNENDVAQNQAEPPLCRLYASTGTDAAAFVSSYWSSQVNRCDPKNGETDYMVDVTQGKDGIAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA' fits the protein's sequence requirement. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA\", \"result\": [{\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1' and the protein 'DDANGKSQTAGFLKGSLGPHAHLWKLDGEIWGSMDSLASLVLCGVSLLLSGTVEGAMDLILINSLPLVSDAETSLTCIASGWRPHEPITIGRDFEALMNQHQDPLEVTQDVTREWAKKVVWKREKASKINGAYFCEGRVRGEAIRIRTMKMRQQASFLPATLTMTVDKGDNVNISFKKVLIKEEDAVIYKNGSFIHSVPRHEVPDILEVHLPHAQPQDAGVYSARYIGGNLFTSAFTRLIVRRCEAQKWGPECNHLCTACMNNGVCHEDTGECICPPGFMGRTCEKACELHTFGRTCKERCSGQEGCKSYVFCLPDPYGCSCATGWKGLQCNEACHPGFYGPDCKLRCSCNNGEMCDRFQGCLCSPGWQGLQCEREGIPRMTPKIVDLPDHIEVNSGKFNPICKASGWPLPTNEEMTLVKPDGTVLHPKDFNHTDHFSVAIFTIHRILPPDSGVWVCSVNTVAGMVEKPFNISVKVLPKPLNAPNVIDTGHNFAVINISSEPYFGDGPIKSKKLLYKPVNHYEAWQHIQVTNEIVTLNYLEPRTEYELCVQLVRRGEGGEGHPGPVRRFTTASIGLPPPRGLNLLPKSQTTLNLTWQPIFPSSEDDFYVEVERRSVQKSDQQNIKVPGNLTSVLLNNLHPREQYVVRARVNTKAQGEWSEDLTAWTLSDILPPQPENIKISNITHSSAVISWTILDGYSISSITIRYKVQGKNEDQHVDVKIKNATITQYQLKGLEPETAYQVDIFAENNIGSSNPAFSHELVTLPESQAPADLGGGKMLLIAILGSAGMTCLTVLLAFLIILQLKRANVQRRMAQAFQNVREEPAVQFNSGTLALNRKVKNNPDPTIYPVLDWNDIKFQDVIGEGNFGQVLKARIKKDGLRMDAAIKRMKEYASKDDHRDFAGELEVLCKLGHHPNIINLLGACEHRGYLYLAIEYAPHGNLLDFLRKSRVLETDPAFAIANSTASTLSSQQLLHFAADVARGMDYLSQKQFIHRDLAARNILVGENYVAKIADFGLSRGQEVYVKKTMGRLPVRWMAIESLNYSVYTTNSDVWSYGVLLWEIVSLGGTPYCGMTCAELYEKLPQGYRLEKPLNCDDEVYDLMRQCWREKPYERPSFAQILVSLNRMLEERKTYVNTTLYEKFTYAGIDCSAEEAA' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein 'GEDNPIWYALFRAVTRSSCELKLDENPAEKFTCEQQYGYQIVRVLSVAFRTWHLAMTAVQSWGDDCGIQNLIEPEAAFAKVRGGTFTALGFIRPETLDECIMASKYFAYRQAEPDSKGNRPRQVSQQGYTYGNVGPFDYSKDSAYDLIFKGGQADTPGFLVTPAPSHDSYASPIPFKLSNYYSAPWDNVHLTWQTSGHLLSPEGMLKRSEARLGKLVQERLKAIEVKEVWEPCPEHTGKIDSLKSLDVEVKDCSLASGCIEIYFAEYGSLLTGIAMVPFAMQDIFLGLESPLLGLLIKMFSDSAIGTDTELWQIVATWLEKQDGDLKCMWLLGEKNPDSMSYDGFTVTGGSELAKDPFGHYNTASIVILDEVGTAGLRKDMKGAAGLEQDLFQADGVERTYPFKPIMLEGYPGRKIMPQVQMYSIRQTFEDRTKHCAWYKMHPRAPGLHPLNDLEGAIRNGRCERDHRIVVQPFECRLAKSEFYKQKGEPAITEINNRINFNTELEGYRTGNTLVYRLTLIATPAETRMAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"GEDNPIWYALFRAVTRSSCELKLDENPAEKFTCEQQYGYQIVRVLSVAFRTWHLAMTAVQSWGDDCGIQNLIEPEAAFAKVRGGTFTALGFIRPETLDECIMASKYFAYRQAEPDSKGNRPRQVSQQGYTYGNVGPFDYSKDSAYDLIFKGGQADTPGFLVTPAPSHDSYASPIPFKLSNYYSAPWDNVHLTWQTSGHLLSPEGMLKRSEARLGKLVQERLKAIEVKEVWEPCPEHTGKIDSLKSLDVEVKDCSLASGCIEIYFAEYGSLLTGIAMVPFAMQDIFLGLESPLLGLLIKMFSDSAIGTDTELWQIVATWLEKQDGDLKCMWLLGEKNPDSMSYDGFTVTGGSELAKDPFGHYNTASIVILDEVGTAGLRKDMKGAAGLEQDLFQADGVERTYPFKPIMLEGYPGRKIMPQVQMYSIRQTFEDRTKHCAWYKMHPRAPGLHPLNDLEGAIRNGRCERDHRIVVQPFECRLAKSEFYKQKGEPAITEINNRINFNTELEGYRTGNTLVYRLTLIATPAETRMAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT' is the sequence to use for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MGCVFCKKLEPVATAKEDAGLEGDFRSYGAADHYGPDPTKARPASSFAHIPNYSNFSSQAINPGFLDSGTIRGVSGIGVTLFIALYDYEARTEDDLTFTKGEKFHILNNTEGDWWEARSLSSGKTGCIPSNYVAPVDSIQAEEWYFGKIGRKDAERQLLSPGNPQGAFLIRESETTKGAYSLSIRDWDQTRGDHVKHYKIRKLDMGGYYITTRVQFNSVQELVQHYMEVNDGLCNLLIAPCTIMKPQTLGLAKDAWEISRSSITLERRLGTGCFGDVWLGTWNGSTKVAVKTLKPGTMSPKAFLEEAQVMKLLRHDKLVQLYAVVSEEPIYIVTEFMCHGSLLDFLKNPEGQDLRLPQLVDMAAQVAEGMAYMERMNYIHRDLRAANILVGERLACKIADFGLARLIKDDEYNPCQGSKFPIKWTAPEAALFGRFTIKSDVWSFGILLTELITKGRIPYPGMNKREVLEQVEQGYHMPCPPGCPASLYEAMEQTWRLDPEERPTFEYLQSFLEDYFTSAEPQYQPGDQT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value is expected between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the target protein 'FGKKKVYRKSKDCRDINPDHFGECKIESSQSGKAWTLVSVYGHKSKPANLIRGSGSLVASDFGEESNKVEAQDKTSIKAKHKLNLHGFKVGQVESMSAFQYFHREKGIDFLDSRNSSQSSQGDKLSIKFNGILKANEDRLLAVCFVLLMTDRCLRLNKNSGSEYTSKRDNLKTKKLKTGAVMKHTGRDLEIDLSRIGNLWDLVKKSDQSQQLDNKGLDSYAEVKKSQVEVATKDKTDFQISLYGYRNYNKKDKLTTETRRVLGYDLFEAPAKGPTKRFDIKFEEAFHLDGGSEESSELKIIIDAGEGLTTTTYVEDESAELMPLQSRFTKTGRLPSVRPDAMYAETNSVRGPEEDTLANFDYGTLFETGINFSGDTLMNSLGLIHAIELKKIIDGLSEPVKCSIKDISISSVNKARLRLEIYPNAEANVAGECMYLNYSRSLEKEQEPAQLKGSTFEKDKQKSSFVMLGQYQHIKGCSGKFPYTITASSDLIGRKQSKFNLAQYDFLMEEETVAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\"], \"target_seq\": \"FGKKKVYRKSKDCRDINPDHFGECKIESSQSGKAWTLVSVYGHKSKPANLIRGSGSLVASDFGEESNKVEAQDKTSIKAKHKLNLHGFKVGQVESMSAFQYFHREKGIDFLDSRNSSQSSQGDKLSIKFNGILKANEDRLLAVCFVLLMTDRCLRLNKNSGSEYTSKRDNLKTKKLKTGAVMKHTGRDLEIDLSRIGNLWDLVKKSDQSQQLDNKGLDSYAEVKKSQVEVATKDKTDFQISLYGYRNYNKKDKLTTETRRVLGYDLFEAPAKGPTKRFDIKFEEAFHLDGGSEESSELKIIIDAGEGLTTTTYVEDESAELMPLQSRFTKTGRLPSVRPDAMYAETNSVRGPEEDTLANFDYGTLFETGINFSGDTLMNSLGLIHAIELKKIIDGLSEPVKCSIKDISISSVNKARLRLEIYPNAEANVAGECMYLNYSRSLEKEQEPAQLKGSTFEKDKQKSSFVMLGQYQHIKGCSGKFPYTITASSDLIGRKQSKFNLAQYDFLMEEETVAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVIIDGREFPEGEGRSKKEAKNAAAKLAVEILNKEKKAVSPLLLTTTNSSEGLSMGNYIGLINRIAQKKRLTVNYEQCASGVHGPEGFHYKCKMGQKEYSIGTGSTKQEAKQLAAKLAYLQILSEETSVKSDYLSSGSFATTCESQSNSLVTSTLASESSSEGDFSADTSEINSNSDSLNSSSLLMNGLRNNQRKAKRSLAPRFDLPDMKETKYTVDKRFGMDFKEIELIGSGGFGQVFKAKHRIDGKTYVIKRVKYNNEKAEREVKALAKLDHVNIVHYNGCWDGFDYDPETSDDSLESSDYDPENSKNSSRSKTKCLFIQMEFCDKGTLEQWIEKRRGEKLDKVLALELFEQITKGVDYIHSKKLIHRDLKPSNIFLVDTKQVKIGDFGLVTSLKNDGKRTRSKGTLRYMSPEQISSQDYGKEVDLYALGLILAELLHVCDTAFETSKFFTDLRDGIISDIFDKKKKK' fits the protein's sequence requirement. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVIIDGREFPEGEGRSKKEAKNAAAKLAVEILNKEKKAVSPLLLTTTNSSEGLSMGNYIGLINRIAQKKRLTVNYEQCASGVHGPEGFHYKCKMGQKEYSIGTGSTKQEAKQLAAKLAYLQILSEETSVKSDYLSSGSFATTCESQSNSLVTSTLASESSSEGDFSADTSEINSNSDSLNSSSLLMNGLRNNQRKAKRSLAPRFDLPDMKETKYTVDKRFGMDFKEIELIGSGGFGQVFKAKHRIDGKTYVIKRVKYNNEKAEREVKALAKLDHVNIVHYNGCWDGFDYDPETSDDSLESSDYDPENSKNSSRSKTKCLFIQMEFCDKGTLEQWIEKRRGEKLDKVLALELFEQITKGVDYIHSKKLIHRDLKPSNIFLVDTKQVKIGDFGLVTSLKNDGKRTRSKGTLRYMSPEQISSQDYGKEVDLYALGLILAELLHVCDTAFETSKFFTDLRDGIISDIFDKKKKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVIIDGREFPEGEGRSKKEAKNAAAKLAVEILNKEKKAVSPLLLTTTNSSEGLSMGNYIGLINRIAQKKRLTVNYEQCASGVHGPEGFHYKCKMGQKEYSIGTGSTKQEAKQLAAKLAYLQILSEETSVKSDYLSSGSFATTCESQSNSLVTSTLASESSSEGDFSADTSEINSNSDSLNSSSLLMNGLRNNQRKAKRSLAPRFDLPDMKETKYTVDKRFGMDFKEIELIGSGGFGQVFKAKHRIDGKTYVIKRVKYNNEKAEREVKALAKLDHVNIVHYNGCWDGFDYDPETSDDSLESSDYDPENSKNSSRSKTKCLFIQMEFCDKGTLEQWIEKRRGEKLDKVLALELFEQITKGVDYIHSKKLIHRDLKPSNIFLVDTKQVKIGDFGLVTSLKNDGKRTRSKGTLRYMSPEQISSQDYGKEVDLYALGLILAELLHVCDTAFETSKFFTDLRDGIISDIFDKKKKK\", \"result\": [{\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 1900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O' and the protein 'MAGDLSAGFFMEELNTYRQKQGVVLKYQELPNSGPPHDRRFTFQVIIDGREFPEGEGRSKKEAKNAAAKLAVEILNKEKKAVSPLLLTTTNSSEGLSMGNYIGLINRIAQKKRLTVNYEQCASGVHGPEGFHYKCKMGQKEYSIGTGSTKQEAKQLAAKLAYLQILSEETSVKSDYLSSGSFATTCESQSNSLVTSTLASESSSEGDFSADTSEINSNSDSLNSSSLLMNGLRNNQRKAKRSLAPRFDLPDMKETKYTVDKRFGMDFKEIELIGSGGFGQVFKAKHRIDGKTYVIKRVKYNNEKAEREVKALAKLDHVNIVHYNGCWDGFDYDPETSDDSLESSDYDPENSKNSSRSKTKCLFIQMEFCDKGTLEQWIEKRRGEKLDKVLALELFEQITKGVDYIHSKKLIHRDLKPSNIFLVDTKQVKIGDFGLVTSLKNDGKRTRSKGTLRYMSPEQISSQDYGKEVDLYALGLILAELLHVCDTAFETSKFFTDLRDGIISDIFDKKKKK' is 1900.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the expected binding strength of the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' toward the target protein 'TFLAWQVILCPCALKGMGLQGEVLMIVPYIERPYMGMWDLGSGLSVFPVFLANTIDYSSICNPAMTQRKGERASDHVLNPPREKLPAFPTSGGGPANIGSVGKMKSALRYTAMSNQSERKLPSVAFGFQDLWGVVSGLETFPMNVFRPEYTLEPTLYGTREKSRDKWLRKDRLNQIVGDKGVSKLKPQLGATESVKCTCRSALREAPGVRETGRIPKAGQPDCCLADQKMRVASAVVTNSIVSIGRDVSIGVSLQDAISMNFLVSPYQAERKSHPSIHEEGYYGRIKNIVRPVLGQVTYTFAVSSYVEPRGEFVWNTKPLKYTLNEQANLHKKIELLAAFTEENEKMQPHDRTLSFVGCRIDTCSNYQALICEQAVEGGTSQLPFWDVEYPQTMETGFGECATRAPAPKKAEVVKQKKVGQEAIVKQRPLGKTYEVRVFCIYTFCESTTPVKIRTYPADYIQVLELMRIGEVTRRGGRTVLLGAAHWEHCVTVCEGTRKSERQTGSCSWFKWDSGYRATWYGSVSHTYFDGDISAKLVLKYYTPEVQLTEESSVCIIQFCTAWVGDETPGTYPKLHAAAKSVQCHEPLSQGVSSIDPNESDIQVCYYLAIMSCMGPLSGEDTDPYSLFEAARGPPDVTFLDGNCRFEAASLVCALKAPFKAESGNGERFLWGFLMNRDHPIMCRSVGYSCIEVLFEWGFGGRASTAIVFDDEIPSLYVALKKQHRPDKAMMDLDAKVKESLVIKTAAMGPDAGALHHLEFHFGYNEIPVDSYILMFEGAPLGGRFEEAMLIIMLSSVNEEWLYQVAPSTRQVALRRPVKQDVVSPDLANENTVVLVRHRIDPAMDAGPIVVHQATGADKTLILQNVDYENTEYYRFLGPTDTSNSNPRCGQGGGGNRPKWFSVHEADAPTFGARSYTLRFTEEAQLMGTVISHSETASSGEVPDQEMDDARPLAEELSADFVLGLSASWKITVEMQAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"TFLAWQVILCPCALKGMGLQGEVLMIVPYIERPYMGMWDLGSGLSVFPVFLANTIDYSSICNPAMTQRKGERASDHVLNPPREKLPAFPTSGGGPANIGSVGKMKSALRYTAMSNQSERKLPSVAFGFQDLWGVVSGLETFPMNVFRPEYTLEPTLYGTREKSRDKWLRKDRLNQIVGDKGVSKLKPQLGATESVKCTCRSALREAPGVRETGRIPKAGQPDCCLADQKMRVASAVVTNSIVSIGRDVSIGVSLQDAISMNFLVSPYQAERKSHPSIHEEGYYGRIKNIVRPVLGQVTYTFAVSSYVEPRGEFVWNTKPLKYTLNEQANLHKKIELLAAFTEENEKMQPHDRTLSFVGCRIDTCSNYQALICEQAVEGGTSQLPFWDVEYPQTMETGFGECATRAPAPKKAEVVKQKKVGQEAIVKQRPLGKTYEVRVFCIYTFCESTTPVKIRTYPADYIQVLELMRIGEVTRRGGRTVLLGAAHWEHCVTVCEGTRKSERQTGSCSWFKWDSGYRATWYGSVSHTYFDGDISAKLVLKYYTPEVQLTEESSVCIIQFCTAWVGDETPGTYPKLHAAAKSVQCHEPLSQGVSSIDPNESDIQVCYYLAIMSCMGPLSGEDTDPYSLFEAARGPPDVTFLDGNCRFEAASLVCALKAPFKAESGNGERFLWGFLMNRDHPIMCRSVGYSCIEVLFEWGFGGRASTAIVFDDEIPSLYVALKKQHRPDKAMMDLDAKVKESLVIKTAAMGPDAGALHHLEFHFGYNEIPVDSYILMFEGAPLGGRFEEAMLIIMLSSVNEEWLYQVAPSTRQVALRRPVKQDVVSPDLANENTVVLVRHRIDPAMDAGPIVVHQATGADKTLILQNVDYENTEYYRFLGPTDTSNSNPRCGQGGGGNRPKWFSVHEADAPTFGARSYTLRFTEEAQLMGTVISHSETASSGEVPDQEMDDARPLAEELSADFVLGLSASWKITVEMQAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you determine the affinity value of the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' for the target protein 'FRSDMTNLDMVQHRQRKLRVAVEAACSFFVSENGLSKSSKLGLMAEEVQLQLDSWDGQASGRESTINRNDSPHIVGQKWEYTIYEKLTLSSVEMAPPGQNRKVCRSFLGPHFGIWSTFTPNEPLILLVINRHQRLKGSLTSKSSVAYEILYFATKFVIDLNIVPKTAAQDERVLVSIIVLIGCSSQIVWDKLKQTSILLGHGGVVAVRTQISGGTPYREEYFGLEDFMLPYDLVSNAPLHSSENTAAGPEENFGSHDEVPIGHCYSKCMMAMPLKDSETRLSGMEDLIQRSNRVPGCQLDRTPKVEIATALEIIQLAPLQQETKRPMLLNSLFLKQQGFLQLEDPIMDILRHTVNEYGQSNTESIHRNGQPADTEETLQAAFLSQGALNNDHALSDREDDQNAPKFPMLPLGYKVALHTLLAEPPVYAHSIWSSSLRVEFHSKIGYDDCTRTVDFSGDEERSVSSRNSREAPLFSPPTATATITSELLISGISDEKRETECKDFAEVKNEAVQKRELVVDELLGSLNFKIPNNFAGYYHGHNSGTGIAISSSKVHMPGLRNKHMVSQESEDRPCVTELGPYASPRLREKIAYSAVDQPGINKQCLRYVVFVLKLIFEHRRNKEDCDVKFTSRKSMRDEQSEDTQAWVQPRFISFKLGYKVVQHLRDLSEEDVDETHRLSLIIPIKISLAVGDTELEVNQGDRRSGLPSVSRVLGAQVPWRLRDLEEISYEFCKKLLLLVSQAIKGLQLMLNSSLLPNGASNAEYILMIDQLSMQVPHDTVVAAPICISDSMKPHPQVIARLGSVSPDGTKERAQDRAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"FRSDMTNLDMVQHRQRKLRVAVEAACSFFVSENGLSKSSKLGLMAEEVQLQLDSWDGQASGRESTINRNDSPHIVGQKWEYTIYEKLTLSSVEMAPPGQNRKVCRSFLGPHFGIWSTFTPNEPLILLVINRHQRLKGSLTSKSSVAYEILYFATKFVIDLNIVPKTAAQDERVLVSIIVLIGCSSQIVWDKLKQTSILLGHGGVVAVRTQISGGTPYREEYFGLEDFMLPYDLVSNAPLHSSENTAAGPEENFGSHDEVPIGHCYSKCMMAMPLKDSETRLSGMEDLIQRSNRVPGCQLDRTPKVEIATALEIIQLAPLQQETKRPMLLNSLFLKQQGFLQLEDPIMDILRHTVNEYGQSNTESIHRNGQPADTEETLQAAFLSQGALNNDHALSDREDDQNAPKFPMLPLGYKVALHTLLAEPPVYAHSIWSSSLRVEFHSKIGYDDCTRTVDFSGDEERSVSSRNSREAPLFSPPTATATITSELLISGISDEKRETECKDFAEVKNEAVQKRELVVDELLGSLNFKIPNNFAGYYHGHNSGTGIAISSSKVHMPGLRNKHMVSQESEDRPCVTELGPYASPRLREKIAYSAVDQPGINKQCLRYVVFVLKLIFEHRRNKEDCDVKFTSRKSMRDEQSEDTQAWVQPRFISFKLGYKVVQHLRDLSEEDVDETHRLSLIIPIKISLAVGDTELEVNQGDRRSGLPSVSRVLGAQVPWRLRDLEEISYEFCKKLLLLVSQAIKGLQLMLNSSLLPNGASNAEYILMIDQLSMQVPHDTVVAAPICISDSMKPHPQVIARLGSVSPDGTKERAQDRAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein 'MGDTVVEPAPLKPTSEPTSGPPGNNGGSLLSVITEGVGELSVIDPEVAQKACQEVLEKVKLLHGGVAVSSRGTPLELVNGDGVDSEIRCLDDPPAQIREEEDEMGAAVASGTAKGARRRRQNNSAKQSWLLRLFESKLFDISMAISYLYNSKEPGVQAYIGNRLFCFRNEDVDFYLPQLLNMYIHMDEDVGDAIKPYIVHRCRQSINFSLQCALLLGAYSSDMHISTQRHSRGTKLRKLILSDELKPAHRKRELPSLSPAPDTGLSPSKRTHQRSKSDATASISLSSNLKRTASNPKVENEDEELSSSTESIDNSFSSPVRLAPEREFIKSLMAIGKRLATLPTKEQKTQRLISELSLLNHKLPARVWLPTAGFDHHVVRVPHTQAVVLNSKDKAPYLIYVEVLECENFDTTSVPARIPENRIRSTRSVENLPECGITHEQRAGSFSTVPNYDNDDEAWSVDDIGELQVELPEVHTNSCDNISQFSVDSITSQESKEPVFIAAGDIRRRLSEQLAHTPTAFKRDPEDPSAVALKEPWQEKVRRIREGSPYGHLPNWRLLSVIVKCGDDLRQELLAFQVLKQLQSIWEQERVPLWIKPYKILVISADSGMIEPVVNAVSIHQVKKQSQLSLLDYFLQEHGSYTTEAFLSAQRNFVQSCAGYCLVCYLLQVKDRHNGNILLDAEGHIIHIDFGFILSSSPRNLGFETSAFKLTTEFVDVMGGLDGDMFNYYKMLMLQGLIAARKHMDKVVQIVEIMQQGSQLPCFHGSSTIRNLKERFHMSMTEEQLQLLVEQMVDGSMRSITTKLYDGFQYLTNGIM' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you quantify the predicted affinity for the drug 'COc1cc2ncnc(Oc3cccc(NC(=O)Nc4cc(C(F)(F)F)ccn4)c3)c2cc1OC' and the target protein 'KPLMPGIMNSLPQLAQIGSLADDESVADRAVKDIVQPKAMRENGGGIHPKRFDQPSACFHGPDAAAKIGLKRPPLFEFVIVARLCFLLGALMLVVDSMADSMISQSTTLTPKLPDTVLNPDLATTVILTQLMLGKEFNRPDQIFGGRSYGREMCLSTPFKQKLDIASTLADAFRGVEFVDCVGVSLSTNVSPQPEGIKGAILQENMHKKKGIHAIPGSRSPEPSLTNKLSRILLKTWKPSEIDFYRNFSSRVEMPNAVQRQSVAEDPGIDSAGEISSNGPNSFIDSAFASHIAHLQKYISGAHISKVSYIDRTAPSLESNRRYVQICKKLTLHEVSYKPNLTRMQPTISENPKVWLIGPPGGYEGHEPLGDQLYFEDERIRFGSKLTRADMTRSTWFWAAYCQQRNDTQPIVNVGESGGAESVLGAQIPFMLRSEVQPFGFGLVVTYDGCEDIIHAPQSSLREKNTEHVTGEEVVSVGTEKVLQCARFTLPWPKPGSADSCQDESESVLAPLEHDAVYNKEPQHRKGSSQAFIFTDMSEEHFTPKDEKWTNLVPNFDAKSETSLHGISLRQSLLMMRTAYMFQESQPQISQNIHISGHGNSGLSNRAGDAGFSHVAYYTLERRILRHQQKAFDLPGPKTSLRPCAAQGSLLQSSTSECSRWSRYFGLKSIARICPEKSQLTVYFTPRKPGIPSQTNDISGWCLSDALLHQLDKPGGNAAQSSLTVILLPKTQGRSFDDQVAMVGEKALDLSGGFALTNSINRFYKSAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Oc3cccc(NC(=O)Nc4cc(C(F)(F)F)ccn4)c3)c2cc1OC\"], \"target_seq\": \"KPLMPGIMNSLPQLAQIGSLADDESVADRAVKDIVQPKAMRENGGGIHPKRFDQPSACFHGPDAAAKIGLKRPPLFEFVIVARLCFLLGALMLVVDSMADSMISQSTTLTPKLPDTVLNPDLATTVILTQLMLGKEFNRPDQIFGGRSYGREMCLSTPFKQKLDIASTLADAFRGVEFVDCVGVSLSTNVSPQPEGIKGAILQENMHKKKGIHAIPGSRSPEPSLTNKLSRILLKTWKPSEIDFYRNFSSRVEMPNAVQRQSVAEDPGIDSAGEISSNGPNSFIDSAFASHIAHLQKYISGAHISKVSYIDRTAPSLESNRRYVQICKKLTLHEVSYKPNLTRMQPTISENPKVWLIGPPGGYEGHEPLGDQLYFEDERIRFGSKLTRADMTRSTWFWAAYCQQRNDTQPIVNVGESGGAESVLGAQIPFMLRSEVQPFGFGLVVTYDGCEDIIHAPQSSLREKNTEHVTGEEVVSVGTEKVLQCARFTLPWPKPGSADSCQDESESVLAPLEHDAVYNKEPQHRKGSSQAFIFTDMSEEHFTPKDEKWTNLVPNFDAKSETSLHGISLRQSLLMMRTAYMFQESQPQISQNIHISGHGNSGLSNRAGDAGFSHVAYYTLERRILRHQQKAFDLPGPKTSLRPCAAQGSLLQSSTSECSRWSRYFGLKSIARICPEKSQLTVYFTPRKPGIPSQTNDISGWCLSDALLHQLDKPGGNAAQSSLTVILLPKTQGRSFDDQVAMVGEKALDLSGGFALTNSINRFYKSAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH\", \"result\": [{\"smile\": \"COc1cc2ncnc(Oc3cccc(NC(=O)Nc4cc(C(F)(F)F)ccn4)c3)c2cc1OC\", \"value\": 250.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2ncnc(Oc3cccc(NC(=O)Nc4cc(C(F)(F)F)ccn4)c3)c2cc1OC' and the protein 'MAALSGGGGGGAEPGQALFNGDMEPEAGAGAGAAASSAADPAIPEEVWNIKQMIKLTQEHIEALLDKFGGEHNPPSIYLEAYEEYTSKLDALQQREQQLLESLGNGTDFSVSSSASMDTVTSSSSSSLSVLPSSLSVFQNPTDVARSNPKSPQKPIVRVFLPNKQRTVVPARCGVTVRDSLKKALMMRGLIPECCAVYRIQDGEKKPIGWDTDISWLTGEELHVEVLENVPLTTHNFVRKTFFTLAFCDFCRKLLFQGFRCQTCGYKFHQRCSTEVPLMCVNYDQLDLLFVSKFFEHHPIPQEEASLAETALTSGSSPSAPASDSIGPQILTSPSPSKSIPIPQPFRPADEDHRNQFGQRDRSSSAPNVHINTIEPVNIDDLIRDQGFRGDGGSTTGLSATPPASLPGSLTNVKALQKSPGPQRERKSSSSSEDRNRMKTLGRRDSSDDWEIPDGQITVGQRIGSGSFGTVYKGKWHGDVAVKMLNVTAPTPQQLQAFKNEVGVLRKTRHVNILLFMGYSTKPQLAIVTQWCEGSSLYHHLHIIETKFEMIKLIDIARQTAQGMDYLHAKSIIHRDLKSNNIFLHEDLTVKIGDFGLATVKSRWSGSHQFEQLSGSILWMAPEVIRMQDKNPYSFQSDVYAFGIVLYELMTGQLPYSNINNRDQIIFMVGRGYLSPDLSKVRSNCPKAMKRLMAECLKKKRDERPLFPQILASIELLARSLPKIHRSASEPSLNRAGFQTEDFSLYACASPKTPIQAGGYGAFPVH' is 250.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the affinity between the drug 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' and the target protein 'VVQDRTFDGITFPQGYSRFVKLKLVNTDQHDVRGHAATFTADLRLRTPSPSVAALRRDNRFASKGDYKKAFVQSSNILPPYFDLQGPGPHDSRFTYPFLETNEPEGSGTDLQYTLVKESYAKVAGTAPGKYRDQVVSTQSARNKKPTTLTSTSSAPIDYNIAISRPIMAKKLGPIAGSQPERPQRNPVGLSSTIKQAPKFTVINGLLKGAAIGSRLYKNVSVLKFFKEPSQCPVFVASFKSMTVDWESLVTRREQAYSVRLCIKREIVHAASRPIVFIENGAPTIESDQENGGTMEHLPTNCRQANCELLELDTSPQAMYALDNTLDTDLLRYLPDCCVNYIIYPEANRALAGWARQIKLFHLGCSRSGVYQVEFPVTKLVAPLRKACELDILTYAPRFYPLSEITLCDDVAVNPTHYSMAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\"], \"target_seq\": \"VVQDRTFDGITFPQGYSRFVKLKLVNTDQHDVRGHAATFTADLRLRTPSPSVAALRRDNRFASKGDYKKAFVQSSNILPPYFDLQGPGPHDSRFTYPFLETNEPEGSGTDLQYTLVKESYAKVAGTAPGKYRDQVVSTQSARNKKPTTLTSTSSAPIDYNIAISRPIMAKKLGPIAGSQPERPQRNPVGLSSTIKQAPKFTVINGLLKGAAIGSRLYKNVSVLKFFKEPSQCPVFVASFKSMTVDWESLVTRREQAYSVRLCIKREIVHAASRPIVFIENGAPTIESDQENGGTMEHLPTNCRQANCELLELDTSPQAMYALDNTLDTDLLRYLPDCCVNYIIYPEANRALAGWARQIKLFHLGCSRSGVYQVEFPVTKLVAPLRKACELDILTYAPRFYPLSEITLCDDVAVNPTHYSMAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPDRPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYSRAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDELRDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASTPTNATAASDANTGDRGQTNNAASASASNST' is the sequence meant for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPDRPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYSRAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDELRDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASTPTNATAASDANTGDRGQTNNAASASASNST\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPDRPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYSRAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDELRDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASTPTNATAASDANTGDRGQTNNAASASASNST\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F' and the protein 'MSGRPRTTSFAESCKPVQQPSAFGSMKVSRDKDGSKVTTVVATPGQGPDRPQEVSYTDTKVIGNGSFGVVYQAKLCDSGELVAIKKVLQDKRFKNRELQIMRKLDHCNIVRLRYFFYSSGEKKDEVYLNLVLDYVPETVYRVARHYSRAKQTLPVIYVKLYMYQLFRSLAYIHSFGICHRDIKPQNLLLDPDTAVLKLCDFGSAKQLVRGEPNVSYICSRYYRAPELIFGATDYTSSIDVWSAGCVLAELLLGQPIFPGDSGVDQLVEIIKVLGTPTREQIREMNPNYTEFKFPQIKAHPWTKVFRPRTPPEAIALCSRLLEYTPTARLTPLEACAHSFFDELRDPNVKLPNGRDTPALFNFTTQELSSNPPLATILIPPHARIQAAASTPTNATAASDANTGDRGQTNNAASASASNST' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity value of the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' for the target protein 'SVTGGSDSPNSHSLESSMPGERMPEPSVACYKDERVVNLIADKEDPAAEKVLAVRSQTKMFRYPEILCTSLKGVLVKTEDDVAHKESDEKAKALMQLETTSRNPVIKLVQILDSGWVKEYAQHRSSKILMLGYKDLDALGDMKGEDSNLVQGENTLHHTLLSNRKCDKSSEIPNTKISKVDRYLTPVINAELRYESSVTPVFNERYGKQQNLDDSSKYFSANFSGHVENGVKLTKFVCQQYKLYLVSEEQFVSFPMQSWGESAINTECKDKKGSMFVLSWYLKKRSEMNIEYYSIQTKLKRPLCVKKVNFQLEERFGKSYAAHGPCRKYKWGAFVNSCRPRDLLIRIIPITVVECQNRWSRAHLGMRLWDSEIHFGLVVSLQKPSRWEEPAHHKSGVEKLEINCTQDYHKVRQHKSFKQTMQSGHNLRFLVEVYLKAGRCSKFFSYLDSSLASMYEDPVKSWSSRGLPRSWYANIYESTDVFNFKPDLHCYESDNVSLSTLQYLEMEVQDSPTKAHSLSLEQIGSLKSLQWIQEGKYKDGQLIFKSKIPRQRHNDPNVKMLEKLQKDSNSSEKQSINQSLPYPETQFYHAVKYLGGPEQDYEPYMNTQRWFENVYKLVYSNVGYAFAFCFDGGKHYYQAKVSINEWFMLSTGAEKSTHLKGYHIQYQLPVVFPMYAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\"], \"target_seq\": \"SVTGGSDSPNSHSLESSMPGERMPEPSVACYKDERVVNLIADKEDPAAEKVLAVRSQTKMFRYPEILCTSLKGVLVKTEDDVAHKESDEKAKALMQLETTSRNPVIKLVQILDSGWVKEYAQHRSSKILMLGYKDLDALGDMKGEDSNLVQGENTLHHTLLSNRKCDKSSEIPNTKISKVDRYLTPVINAELRYESSVTPVFNERYGKQQNLDDSSKYFSANFSGHVENGVKLTKFVCQQYKLYLVSEEQFVSFPMQSWGESAINTECKDKKGSMFVLSWYLKKRSEMNIEYYSIQTKLKRPLCVKKVNFQLEERFGKSYAAHGPCRKYKWGAFVNSCRPRDLLIRIIPITVVECQNRWSRAHLGMRLWDSEIHFGLVVSLQKPSRWEEPAHHKSGVEKLEINCTQDYHKVRQHKSFKQTMQSGHNLRFLVEVYLKAGRCSKFFSYLDSSLASMYEDPVKSWSSRGLPRSWYANIYESTDVFNFKPDLHCYESDNVSLSTLQYLEMEVQDSPTKAHSLSLEQIGSLKSLQWIQEGKYKDGQLIFKSKIPRQRHNDPNVKMLEKLQKDSNSSEKQSINQSLPYPETQFYHAVKYLGGPEQDYEPYMNTQRWFENVYKLVYSNVGYAFAFCFDGGKHYYQAKVSINEWFMLSTGAEKSTHLKGYHIQYQLPVVFPMYAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right sequence for the protein is 'MDTKSILEELLLKRSQQKKKMSPNNYKERLFVLTKTNLSYYEYDKMKRGSRKGSIEIKKIRCVEKVNLEEQTPVERQYPFQIVYKDGLLYVYASNEESRSQWLKALQKEIRGNPHLLVKYHSGFFVDGKFLCCQQSCKAAPGCTLWEAYANLHTAVNEEKHRVPTFPDRVLKIPRAVPVLKMDAPSSSTTLAQYDNESKKNYGSQPPSSSTSLAQYDSNSKKIYGSQPNFNMQYIPREDFPDWWQVRKLKSSSSSEDVASSNQKERNVNHTTSKISWEFPESSSSEEEENLDDYDWFAGNISRSQSEQLLRQKGKEGAFMVRNSSQVGMYTVSLFSKAVNDKKGTVKHYHVHTNAENKLYLAENYCFDSIPKLIHYHQHNSAGMITRLRHPVSTKANKVPDSVSLGNGIWELKREEITLLKELGSGQFGVVQLGKWKGQYDVAVKMIKEGSMSEDEFFQEAQTMMKLSHPKLVKFYGVCSKEYPIYIVTEYISNGCLLNYLRSHGKGLEPSQLLEMCYDVCEGMAFLESHQFIHRDLAARNCLVDRDLCVKVSDFGMTRYVLDDQYVSSVGTKFPVKWSAPEVFHYFKYSSKSDVWAFGILMWEVFSLGKQPYDLYDNSQVVLKVSQGHRLYRPHLASDTIYQIMYSCWHELPEKRPTFQQLLSSIEPLREKDKH'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDTKSILEELLLKRSQQKKKMSPNNYKERLFVLTKTNLSYYEYDKMKRGSRKGSIEIKKIRCVEKVNLEEQTPVERQYPFQIVYKDGLLYVYASNEESRSQWLKALQKEIRGNPHLLVKYHSGFFVDGKFLCCQQSCKAAPGCTLWEAYANLHTAVNEEKHRVPTFPDRVLKIPRAVPVLKMDAPSSSTTLAQYDNESKKNYGSQPPSSSTSLAQYDSNSKKIYGSQPNFNMQYIPREDFPDWWQVRKLKSSSSSEDVASSNQKERNVNHTTSKISWEFPESSSSEEEENLDDYDWFAGNISRSQSEQLLRQKGKEGAFMVRNSSQVGMYTVSLFSKAVNDKKGTVKHYHVHTNAENKLYLAENYCFDSIPKLIHYHQHNSAGMITRLRHPVSTKANKVPDSVSLGNGIWELKREEITLLKELGSGQFGVVQLGKWKGQYDVAVKMIKEGSMSEDEFFQEAQTMMKLSHPKLVKFYGVCSKEYPIYIVTEYISNGCLLNYLRSHGKGLEPSQLLEMCYDVCEGMAFLESHQFIHRDLAARNCLVDRDLCVKVSDFGMTRYVLDDQYVSSVGTKFPVKWSAPEVFHYFKYSSKSDVWAFGILMWEVFSLGKQPYDLYDNSQVVLKVSQGHRLYRPHLASDTIYQIMYSCWHELPEKRPTFQQLLSSIEPLREKDKH\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDTKSILEELLLKRSQQKKKMSPNNYKERLFVLTKTNLSYYEYDKMKRGSRKGSIEIKKIRCVEKVNLEEQTPVERQYPFQIVYKDGLLYVYASNEESRSQWLKALQKEIRGNPHLLVKYHSGFFVDGKFLCCQQSCKAAPGCTLWEAYANLHTAVNEEKHRVPTFPDRVLKIPRAVPVLKMDAPSSSTTLAQYDNESKKNYGSQPPSSSTSLAQYDSNSKKIYGSQPNFNMQYIPREDFPDWWQVRKLKSSSSSEDVASSNQKERNVNHTTSKISWEFPESSSSEEEENLDDYDWFAGNISRSQSEQLLRQKGKEGAFMVRNSSQVGMYTVSLFSKAVNDKKGTVKHYHVHTNAENKLYLAENYCFDSIPKLIHYHQHNSAGMITRLRHPVSTKANKVPDSVSLGNGIWELKREEITLLKELGSGQFGVVQLGKWKGQYDVAVKMIKEGSMSEDEFFQEAQTMMKLSHPKLVKFYGVCSKEYPIYIVTEYISNGCLLNYLRSHGKGLEPSQLLEMCYDVCEGMAFLESHQFIHRDLAARNCLVDRDLCVKVSDFGMTRYVLDDQYVSSVGTKFPVKWSAPEVFHYFKYSSKSDVWAFGILMWEVFSLGKQPYDLYDNSQVVLKVSQGHRLYRPHLASDTIYQIMYSCWHELPEKRPTFQQLLSSIEPLREKDKH\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl' and the protein 'MDTKSILEELLLKRSQQKKKMSPNNYKERLFVLTKTNLSYYEYDKMKRGSRKGSIEIKKIRCVEKVNLEEQTPVERQYPFQIVYKDGLLYVYASNEESRSQWLKALQKEIRGNPHLLVKYHSGFFVDGKFLCCQQSCKAAPGCTLWEAYANLHTAVNEEKHRVPTFPDRVLKIPRAVPVLKMDAPSSSTTLAQYDNESKKNYGSQPPSSSTSLAQYDSNSKKIYGSQPNFNMQYIPREDFPDWWQVRKLKSSSSSEDVASSNQKERNVNHTTSKISWEFPESSSSEEEENLDDYDWFAGNISRSQSEQLLRQKGKEGAFMVRNSSQVGMYTVSLFSKAVNDKKGTVKHYHVHTNAENKLYLAENYCFDSIPKLIHYHQHNSAGMITRLRHPVSTKANKVPDSVSLGNGIWELKREEITLLKELGSGQFGVVQLGKWKGQYDVAVKMIKEGSMSEDEFFQEAQTMMKLSHPKLVKFYGVCSKEYPIYIVTEYISNGCLLNYLRSHGKGLEPSQLLEMCYDVCEGMAFLESHQFIHRDLAARNCLVDRDLCVKVSDFGMTRYVLDDQYVSSVGTKFPVKWSAPEVFHYFKYSSKSDVWAFGILMWEVFSLGKQPYDLYDNSQVVLKVSQGHRLYRPHLASDTIYQIMYSCWHELPEKRPTFQQLLSSIEPLREKDKH' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity relationship between the drug 'CC(C)NCC(O)c1cccc(O)c1' and the target protein 'HQLYRLVSPFLVFVRREVAIAHQVIGINQALVIKMTYGNAYFPSSSMLSTDGAHCVSKLLGTVRKEWCGQCNCAIMLRGIFQDYGSAQMRRSQRCGGAILVANPFQNYCLLVMMIYVITIANVVLRNRVGKLDDTGPMALFVIVVTPNDLAIGKDFFCGKNVNANWCQIVSRSQIQEWVFCISLSELCENVELNHGFLTDAVTSNYPACKETGLHKWLMAFKPIVCSSLVRQALSEIFTAKNIRKLNATYTIPFNTEADYFITTHQSILWQDNIMRDYSSLVKLSPLLWHLTESSRLVSQILNVKCDILLLDSEGTQCCPLLVGFVWNWSEAFDEGSYSYSLVYSVKFLLIIFKQGIEGIPPLNTRVETEAEAGGAFDLDFFQSLPPRYKHYVRDKIFTMGTKAAIETFRDTFSSVGNAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)NCC(O)c1cccc(O)c1\"], \"target_seq\": \"HQLYRLVSPFLVFVRREVAIAHQVIGINQALVIKMTYGNAYFPSSSMLSTDGAHCVSKLLGTVRKEWCGQCNCAIMLRGIFQDYGSAQMRRSQRCGGAILVANPFQNYCLLVMMIYVITIANVVLRNRVGKLDDTGPMALFVIVVTPNDLAIGKDFFCGKNVNANWCQIVSRSQIQEWVFCISLSELCENVELNHGFLTDAVTSNYPACKETGLHKWLMAFKPIVCSSLVRQALSEIFTAKNIRKLNATYTIPFNTEADYFITTHQSILWQDNIMRDYSSLVKLSPLLWHLTESSRLVSQILNVKCDILLLDSEGTQCCPLLVGFVWNWSEAFDEGSYSYSLVYSVKFLLIIFKQGIEGIPPLNTRVETEAEAGGAFDLDFFQSLPPRYKHYVRDKIFTMGTKAAIETFRDTFSSVGNAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MGQPGNRSVFLLAPNASHAPDQNVTLERDEAWVVGMGILMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGACHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYLAITSPFKYQCLLTKNKARVVILMVWIVSGLTSFLPIQMHWYRASHKEAINCYAKETCCDFFTNQPYAIASSIVSFYLPLVVMVFVYSRVFQVAKRQLQKIDKSEGRFHAQNVSQVEQDGRSGLGQRRTSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIKDNLIRKEIYILLNWLGYINSAFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGCSSNSNDRTDYTGEQSGYHLGEEKDSELLCEDPPGTENFVNQQGTVPSDSIDSQGRNCSTNDSLL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGQPGNRSVFLLAPNASHAPDQNVTLERDEAWVVGMGILMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGACHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYLAITSPFKYQCLLTKNKARVVILMVWIVSGLTSFLPIQMHWYRASHKEAINCYAKETCCDFFTNQPYAIASSIVSFYLPLVVMVFVYSRVFQVAKRQLQKIDKSEGRFHAQNVSQVEQDGRSGLGQRRTSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIKDNLIRKEIYILLNWLGYINSAFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGCSSNSNDRTDYTGEQSGYHLGEEKDSELLCEDPPGTENFVNQQGTVPSDSIDSQGRNCSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGQPGNRSVFLLAPNASHAPDQNVTLERDEAWVVGMGILMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGACHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYLAITSPFKYQCLLTKNKARVVILMVWIVSGLTSFLPIQMHWYRASHKEAINCYAKETCCDFFTNQPYAIASSIVSFYLPLVVMVFVYSRVFQVAKRQLQKIDKSEGRFHAQNVSQVEQDGRSGLGQRRTSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIKDNLIRKEIYILLNWLGYINSAFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGCSSNSNDRTDYTGEQSGYHLGEEKDSELLCEDPPGTENFVNQQGTVPSDSIDSQGRNCSTNDSLL\", \"result\": [{\"smile\": \"CC(C)NCC(O)c1cccc(O)c1\", \"value\": 457.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)NCC(O)c1cccc(O)c1' and the protein 'MGQPGNRSVFLLAPNASHAPDQNVTLERDEAWVVGMGILMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGACHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYLAITSPFKYQCLLTKNKARVVILMVWIVSGLTSFLPIQMHWYRASHKEAINCYAKETCCDFFTNQPYAIASSIVSFYLPLVVMVFVYSRVFQVAKRQLQKIDKSEGRFHAQNVSQVEQDGRSGLGQRRTSKFYLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIKDNLIRKEIYILLNWLGYINSAFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGCSSNSNDRTDYTGEQSGYHLGEEKDSELLCEDPPGTENFVNQQGTVPSDSIDSQGRNCSTNDSLL' is 457.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21''s interaction with the target protein 'RRPQSVDETVLGPGLKRILCQMLLYLSSLSSVRKNLTSDIKLFCNRPSCARGYRLPQKSEPFGKVQLIDNLMIITELPHKEQKLLEYSCDLVHGGMTLELDASNKNLLAIKYPKVCSSADVAEQGVSKESLDDWSPSGHPSTERLRITPCPRATVISFYSKQNIMESIYQSAAQPEGEEAENQEYTAGSLGNKKVPIGGEQFNIEVQQIRSEEYIKKELHLFVKVIPSKQVGTQIQLAMGASERFEPIEKNIVGGLKVLSGTNAKNTSHGDSAGYEDLNPQTSGEEAEADKTAPRLRTLPKAKSLALKQHTKLKLMPDYLIVKDVRSKVESYDYKSWKYKLWYHEGPCSNNYIRLGTRGMFNDKGKQHVLGAQNPEAMYPRYSVSEQWIQNRARSNNYLFSPDLKIIMKLHNGVSTMLKPAMNEMDRTAEVARHVKLVIVETQLIPILFCEAAAAAAAAAAAAAAAAAA' to be? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"RRPQSVDETVLGPGLKRILCQMLLYLSSLSSVRKNLTSDIKLFCNRPSCARGYRLPQKSEPFGKVQLIDNLMIITELPHKEQKLLEYSCDLVHGGMTLELDASNKNLLAIKYPKVCSSADVAEQGVSKESLDDWSPSGHPSTERLRITPCPRATVISFYSKQNIMESIYQSAAQPEGEEAENQEYTAGSLGNKKVPIGGEQFNIEVQQIRSEEYIKKELHLFVKVIPSKQVGTQIQLAMGASERFEPIEKNIVGGLKVLSGTNAKNTSHGDSAGYEDLNPQTSGEEAEADKTAPRLRTLPKAKSLALKQHTKLKLMPDYLIVKDVRSKVESYDYKSWKYKLWYHEGPCSNNYIRLGTRGMFNDKGKQHVLGAQNPEAMYPRYSVSEQWIQNRARSNNYLFSPDLKIIMKLHNGVSTMLKPAMNEMDRTAEVARHVKLVIVETQLIPILFCEAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid sequence for the protein is 'MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\", \"result\": [{\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' and the protein 'MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the predicted affinity level for the drug 'C[C@@H](Cc1ccccc1)NC[C@@H](O)c1cc(O)cc(O)c1' and the target protein 'IVLITNYLCRLCGESNYWGRIVFDLYKQQVMEPNSVTTGELTQTISSVQFRSFDTLALMVSGGSVASGASLDNFFDLLLNLTPLVGVTVGLFLCMQQVIVGPTINQGERTTSCVFIVQAHFWDVCLLLITNSISHRNKLLMVMNRSIERVFEAEGKQVKIVKGLIWENKVAAANSPEITYYMLNAKIEIGFVRDQLWSECGVKADDNGRAECVSIVQLQFMNIFRLGLCTYVSTVLNSCAPHQKESYELPGLQFFNLYVIRHIYFSSNMSYGRSSANVFRGMHIHHARALAVLIATSVYQFAAPWTPQCTKHADLGALVQYIFGKGNCNFQESLLWKMRGVEPIKIWEAVPYFCILQSDFFITEPHATRHEDVGSFVPAFKTITSNHDIITIGFGWGYVCDKAIDLKGNDDLRAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Cc1ccccc1)NC[C@@H](O)c1cc(O)cc(O)c1\"], \"target_seq\": \"IVLITNYLCRLCGESNYWGRIVFDLYKQQVMEPNSVTTGELTQTISSVQFRSFDTLALMVSGGSVASGASLDNFFDLLLNLTPLVGVTVGLFLCMQQVIVGPTINQGERTTSCVFIVQAHFWDVCLLLITNSISHRNKLLMVMNRSIERVFEAEGKQVKIVKGLIWENKVAAANSPEITYYMLNAKIEIGFVRDQLWSECGVKADDNGRAECVSIVQLQFMNIFRLGLCTYVSTVLNSCAPHQKESYELPGLQFFNLYVIRHIYFSSNMSYGRSSANVFRGMHIHHARALAVLIATSVYQFAAPWTPQCTKHADLGALVQYIFGKGNCNFQESLLWKMRGVEPIKIWEAVPYFCILQSDFFITEPHATRHEDVGSFVPAFKTITSNHDIITIGFGWGYVCDKAIDLKGNDDLRAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL\", \"result\": [{\"smile\": \"C[C@@H](Cc1ccccc1)NC[C@@H](O)c1cc(O)cc(O)c1\", \"value\": 28840.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'C[C@@H](Cc1ccccc1)NC[C@@H](O)c1cc(O)cc(O)c1' and the protein 'MGQPGNGSAFLLAPNGSHAPDHDVTQERDEVWVVGMGIVMSLIVLAIVFGNVLVITAIAKFERLQTVTNYFITSLACADLVMGLAVVPFGAAHILMKMWTFGNFWCEFWTSIDVLCVTASIETLCVIAVDRYFAITSPFKYQSLLTKNKARVIILMVWIVSGLTSFLPIQMHWYRATHQEAINCYANETCCDFFTNQAYAIASSIVSFYVPLVIMVFVYSRVFQEAKRQLQKIDKSEGRFHVQNLSQVEQDGRTGHGLRRSSKFCLKEHKALKTLGIIMGTFTLCWLPFFIVNIVHVIQDNLIRKEVYILLNWIGYVNSGFNPLIYCRSPDFRIAFQELLCLRRSSLKAYGNGYSSNGNTGEQSGYHVEQEKENKLLCEDLPGTEDFVGHQGTVPSDNIDSQGRNCSTNDSLL' is 28840.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein 'VHQDKEKSKMPPSKQRSPRCDMIMFDACNMEKERAVATPTDEDNIKPKILMVFTRSLIKIKISPPEGQWMHTQRRDLDANKTKGLWFYKAALERVMGFAQSPTFMKTELLARPQQFHDDVKHDRLKQTHVLIFDISLTVPYLPLDEQHVCTFALIPAGIHSRIKNVCYLVMDRLVDPAAIARGMETSKAFRLIESSGTVEAANSTMKYPQFLNSSMIVILLNDEEEFETMIEVSGFMSIGGGMMIRGLNARFVSAEGSPPLCEKEFSRAGEGVVICYVSSVPKSWSTRDPLIPSVLRKLERDDYEDEKDGYITTGKVLTKGNSPYLVKRFQVSNPKYADNPELNDVVAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"VHQDKEKSKMPPSKQRSPRCDMIMFDACNMEKERAVATPTDEDNIKPKILMVFTRSLIKIKISPPEGQWMHTQRRDLDANKTKGLWFYKAALERVMGFAQSPTFMKTELLARPQQFHDDVKHDRLKQTHVLIFDISLTVPYLPLDEQHVCTFALIPAGIHSRIKNVCYLVMDRLVDPAAIARGMETSKAFRLIESSGTVEAANSTMKYPQFLNSSMIVILLNDEEEFETMIEVSGFMSIGGGMMIRGLNARFVSAEGSPPLCEKEFSRAGEGVVICYVSSVPKSWSTRDPLIPSVLRKLERDDYEDEKDGYITTGKVLTKGNSPYLVKRFQVSNPKYADNPELNDVVAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MESPASSQPASMPQSKGKSKRKKDLRISCMSKPPAPNPTPPRNLDSRTFITIGDRNFEVEADDLVTISELGRGAYGVVEKVRHAQSGTIMAVKRIRATVNSQEQKRLLMDLDINMRTVDCFYTVTFYGALFREGDVWICMELMDTSLDKFYRKVLDKNMTIPEDILGEIAVSIVRALEHLHSKLSVIHRDVKPSNVLINKEGHVKMCDFGISGYLVDSVAKTMDAGCKPYMAPERINPELNQKGYNVKSDVWSLGITMIEMAILRFPYESWGTPFQQLKQVVEEPSPQLPADRFSPEFVDFTAQCLRKNPAERMSYLELMEHPFFTLHKTKKTDIAAFVKEILGEDS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the target protein 'STLANSHGNETVARHIKRQYRFAQSGRQPMMCPVNGHYRELLVAKDEPHMEEEEEARRLLFSGNGQHAPSKSEMAPASAAASPLQALHGGFPLQMREKARDHPISPSFRAYFDPRLNNVFVKNEYPEAVDDAFPREARPLIAIIIMIFFRGADPTLDVENPEANKGKEGDEEHGERSFLFQKFAMSLTMELSLQATSFDMKHRDLSILPIQEFQNMIQGCPPGPGEKGFVKMFAASADRSRIQNTAQQGGACADIKLIVQYDRSGSKHCCEFVKGQLEFSGSFYLLCKGLRVGVGRKKAAQFNRTMENIRHLFLLAKYHDLRQTLADKEIGFSAGKEKLAKRAGNPMEMSKSTLLLGERRNAVGTAGGEEMDLDEEDWEAIQLPKKLHAGETNEDIGMEDLRAFVAFAPTYHSFFACVSTNDLLDPDGINVLVKSALPGFLFEVCAFCSLMFPSFSEELGFKSLALEYTRVNQLGSLIVLEQAEVRQQYLTHGKMTLLTGQFHQPAVALAALYFPRKDPAFRESAVWGYIARKDLNVDVPLSREAAQLLAHPFAYFDRLSTGAAGKEYEDPVLFQTMITDAGVQKIDIHRVRGGSEPLSVPLRPVVVIIASVRLQRAFGQKEQTLNQGAAIIMAHHFIHGAPAARSSAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\"], \"target_seq\": \"STLANSHGNETVARHIKRQYRFAQSGRQPMMCPVNGHYRELLVAKDEPHMEEEEEARRLLFSGNGQHAPSKSEMAPASAAASPLQALHGGFPLQMREKARDHPISPSFRAYFDPRLNNVFVKNEYPEAVDDAFPREARPLIAIIIMIFFRGADPTLDVENPEANKGKEGDEEHGERSFLFQKFAMSLTMELSLQATSFDMKHRDLSILPIQEFQNMIQGCPPGPGEKGFVKMFAASADRSRIQNTAQQGGACADIKLIVQYDRSGSKHCCEFVKGQLEFSGSFYLLCKGLRVGVGRKKAAQFNRTMENIRHLFLLAKYHDLRQTLADKEIGFSAGKEKLAKRAGNPMEMSKSTLLLGERRNAVGTAGGEEMDLDEEDWEAIQLPKKLHAGETNEDIGMEDLRAFVAFAPTYHSFFACVSTNDLLDPDGINVLVKSALPGFLFEVCAFCSLMFPSFSEELGFKSLALEYTRVNQLGSLIVLEQAEVRQQYLTHGKMTLLTGQFHQPAVALAALYFPRKDPAFRESAVWGYIARKDLNVDVPLSREAAQLLAHPFAYFDRLSTGAAGKEYEDPVLFQTMITDAGVQKIDIHRVRGGSEPLSVPLRPVVVIIASVRLQRAFGQKEQTLNQGAAIIMAHHFIHGAPAARSSAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the protein 'MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein 'DEPKHYRFELTIKFNYLLSHLFQRLYFDASKKLCDKDIKGEKAGSEQLIVKRWSQTEKRRLLMEESREIPEYLNPTILLSTEGGNFELRVFTLPPIASLLVKLLHNMNEQDCCRLRGKLDEIYYMIEDELKTAFCIPHSPLMEKTTLLKHDEAAIREDKVSKLKTSLPAAVTLKEYDDKGVGPECFIKQANAERDRDTKNIWPNKKGIKMGKLMKDYIDLEYRLRGLGSVQVRPDLPGLIKQHFDRRLRVGGDIDDGEHCCSEGKVPINIIKLENKFPGEIVLEHIEFGANSKAVVAIMYTEAINWGDEKGDSDEIKQLKQGKQAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"DEPKHYRFELTIKFNYLLSHLFQRLYFDASKKLCDKDIKGEKAGSEQLIVKRWSQTEKRRLLMEESREIPEYLNPTILLSTEGGNFELRVFTLPPIASLLVKLLHNMNEQDCCRLRGKLDEIYYMIEDELKTAFCIPHSPLMEKTTLLKHDEAAIREDKVSKLKTSLPAAVTLKEYDDKGVGPECFIKQANAERDRDTKNIWPNKKGIKMGKLMKDYIDLEYRLRGLGSVQVRPDLPGLIKQHFDRRLRVGGDIDDGEHCCSEGKVPINIIKLENKFPGEIVLEHIEFGANSKAVVAIMYTEAINWGDEKGDSDEIKQLKQGKQAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence proper for the protein is 'MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 1100.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein 'MLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELIRHEENEVSDKQI' is 1100.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'Cc1ccc(NC(=O)c2cccc(C(F)(F)F)c2)cc1Nc1ncccc1-c1ncnc(Nc2ccc(OCCNC(=O)CCCCNC(=O)CCC3=[N+]4B(F)n5c(C)cc(C)c5C=C4C=C3)cc2)n1' and the target protein 'VVYKAVCRMYGGRWIHYGKGEVKREVRQSDIADPCATYSWGDESLHGPFGDELLALLSTKPVLANLEKQNAIQIFPKPFKLEKHEFRHQPTKKLLKYTCGNLPNHLKWMTATCSLSGDKEHATDRFVDREAWSGDMIKELYTDYEFCIEMHALAGVRPLVVAAKNKLTLKASAKEVYSDVMGQLELLIKLNFTTHVGLGQAEEGVDLKPDREQMIDRVRTAGFGSVNDDKCNSILTEPTWSVTQGLGSVNPNYAFQGIASSYLVDLIEEDGLGFVACVIGTQIVYMWEGESEPVVQNFLKSFIVWPNEYYLLPDAALRMLGEVLRDKLSDTIKGYYMHKLCRIYSMAPLDKVARLSHETGKEFETTVVFSPPRKTLSAVGPERVAVGFYLLYVAKHRLMKQAGRTLGNKGCDEALRKAVPRLSYMVKEMDVDKEAQAGECWYAVNENISKAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(NC(=O)c2cccc(C(F)(F)F)c2)cc1Nc1ncccc1-c1ncnc(Nc2ccc(OCCNC(=O)CCCCNC(=O)CCC3=[N+]4B(F)n5c(C)cc(C)c5C=C4C=C3)cc2)n1\"], \"target_seq\": \"VVYKAVCRMYGGRWIHYGKGEVKREVRQSDIADPCATYSWGDESLHGPFGDELLALLSTKPVLANLEKQNAIQIFPKPFKLEKHEFRHQPTKKLLKYTCGNLPNHLKWMTATCSLSGDKEHATDRFVDREAWSGDMIKELYTDYEFCIEMHALAGVRPLVVAAKNKLTLKASAKEVYSDVMGQLELLIKLNFTTHVGLGQAEEGVDLKPDREQMIDRVRTAGFGSVNDDKCNSILTEPTWSVTQGLGSVNPNYAFQGIASSYLVDLIEEDGLGFVACVIGTQIVYMWEGESEPVVQNFLKSFIVWPNEYYLLPDAALRMLGEVLRDKLSDTIKGYYMHKLCRIYSMAPLDKVARLSHETGKEFETTVVFSPPRKTLSAVGPERVAVGFYLLYVAKHRLMKQAGRTLGNKGCDEALRKAVPRLSYMVKEMDVDKEAQAGECWYAVNENISKAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence fitting for the protein is 'MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2cccc(C(F)(F)F)c2)cc1Nc1ncccc1-c1ncnc(Nc2ccc(OCCNC(=O)CCCCNC(=O)CCC3=[N+]4B(F)n5c(C)cc(C)c5C=C4C=C3)cc2)n1\", \"value\": 17.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(NC(=O)c2cccc(C(F)(F)F)c2)cc1Nc1ncccc1-c1ncnc(Nc2ccc(OCCNC(=O)CCCCNC(=O)CCC3=[N+]4B(F)n5c(C)cc(C)c5C=C4C=C3)cc2)n1' and the protein 'MSAIQAAWPSGTECIAKYNFHGTAEQDLPFCKGDVLTIVAVTKDPNWYKAKNKVGREGIIPANYVQKREGVKAGTKLSLMPWFHGKITREQAERLLYPPETGLFLVRESTNYPGDYTLCVSCDGKVEHYRIMYHASKLSIDEEVYFENLMQLVEHYTSDADGLCTRLIKPKVMEGTVAAQDEFYRSGWALNMKELKLLQTIGKGEFGDVMLGDYRGNKVAVKCIKNDATAQAFLAEASVMTQLRHSNLVQLLGVIVEEKGGLYIVTEYMAKGSLVDYLRSRGRSVLGGDCLLKFSLDVCEAMEYLEGNNFVHRDLAARNVLVSEDNVAKVSDFGLTKEASSTQDTGKLPVKWTAPEALREKKFSTKSDVWSFGILLWEIYSFGRVPYPRIPLKDVVPRVEKGYKMDAPDGCPPAVYEVMKNCWHLDAAMRPSFLQLREQLEHIKTHELHL' is 17.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1''s affinity with the target protein 'MDDVKPPGPPLVPKEPRPPFQLHRRRIDPRRHDRKESGPDQDKRRKYSFKRKSGKAAEMMIQTGPGNLGGVSPKPVATSNIDLHGLDPDVPLGEENTPAVTVGLPSLRRYNPLASKHVESPLSAQPFSHEGGPQAFSASGQESLSEVPDMLLGLSISAPERLPLGVPGDVRPEYGAIQIPFIQDRSPSLPQFDVGEKSGQIFRSHLPADPPHFEQPPRPSPPPNVVPREGLIAQKAHHLSSGTLRSYSTQTEDSSEKVVSHHLSRSEGGVKRFLILSGFLKSNLFSESELVQVYGARLAVSGGVYGEPKEAPKRKSEQDELVQPVSLLPRKPRGDGSSRIGEGKPIPFSSATVELSVGPLSQDPLHVASLPSSEGKKRMPQSRYLSGFLVEPASTHDPHHCKVDGPTGELRTPDSVVDRYRPSYDMGLGLLVSYFGLGSGLLKFVAKPWAGRIPHAGKPVFRSGMTEELDLDPILVKRGRRPSDKGGHACQPHDEIKQVPSGARNLLIDESSSDPNRCESKISGQSGKLDSQMRAGSIKTAGYGCWITEELSRRRCFKEGLHEQAGQVRPLPELLHLDKLTILKRKILNGGRWPLECSESLTPKFEIRILEYLGVGRSRIHATTGRRKPMAPSPAGTGLMPGRPIPGAGHKSPCFRSEDYYQHQKGTFPSTLAHPFSRMPAAGPPPLSGISMCPSETRNNQKEPAPCLYEWFRSEGSSLNHLPEFTRNLRRLAKTDMLKMEYVTKVASGEAPVNLTTLTHSRALRSADSLGESRCILSAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\"], \"target_seq\": \"MDDVKPPGPPLVPKEPRPPFQLHRRRIDPRRHDRKESGPDQDKRRKYSFKRKSGKAAEMMIQTGPGNLGGVSPKPVATSNIDLHGLDPDVPLGEENTPAVTVGLPSLRRYNPLASKHVESPLSAQPFSHEGGPQAFSASGQESLSEVPDMLLGLSISAPERLPLGVPGDVRPEYGAIQIPFIQDRSPSLPQFDVGEKSGQIFRSHLPADPPHFEQPPRPSPPPNVVPREGLIAQKAHHLSSGTLRSYSTQTEDSSEKVVSHHLSRSEGGVKRFLILSGFLKSNLFSESELVQVYGARLAVSGGVYGEPKEAPKRKSEQDELVQPVSLLPRKPRGDGSSRIGEGKPIPFSSATVELSVGPLSQDPLHVASLPSSEGKKRMPQSRYLSGFLVEPASTHDPHHCKVDGPTGELRTPDSVVDRYRPSYDMGLGLLVSYFGLGSGLLKFVAKPWAGRIPHAGKPVFRSGMTEELDLDPILVKRGRRPSDKGGHACQPHDEIKQVPSGARNLLIDESSSDPNRCESKISGQSGKLDSQMRAGSIKTAGYGCWITEELSRRRCFKEGLHEQAGQVRPLPELLHLDKLTILKRKILNGGRWPLECSESLTPKFEIRILEYLGVGRSRIHATTGRRKPMAPSPAGTGLMPGRPIPGAGHKSPCFRSEDYYQHQKGTFPSTLAHPFSRMPAAGPPPLSGISMCPSETRNNQKEPAPCLYEWFRSEGSSLNHLPEFTRNLRRLAKTDMLKMEYVTKVASGEAPVNLTTLTHSRALRSADSLGESRCILSAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP\", \"result\": [{\"smile\": \"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1' and the protein 'MSSGAKEGGGGSPAYHLPHPHPHPPQHAQYVGPYRLEKTLGKGQTGLVKLGVHCITGQKVAIKIVNREKLSESVLMKVEREIAILKLIEHPHVLKLHDVYENKKYLYLVLEHVSGGELFDYLVKKGRLTPKEARKFFRQIVSALDFCHSYSICHRDLKPENLLLDEKNNIRIADFGMASLQVGDSLLETSCGSPHYACPEVIKGEKYDGRRADMWSCGVILFALLVGALPFDDDNLRQLLEKVKRGVFHMPHFIPPDCQSLLRGMIEVEPEKRLSLEQIQKHPWYLGGKHEPDPCLEPAPGRRVAMRSLPSNGELDPDVLESMASLGCFRDRERLHRELRSEEENQEKMIYYLLLDRKERYPSCEDQDLPPRNDVDPPRKRVDSPMLSRHGKRRPERKSMEVLSITDAGGGGSPVPTRRALEMAQHSQRSRSVSGASTGLSSSPLSSPRSPVFSFSPEPGAGDEARGGGSPTSKTQTLPSRGPRGGGAGEQPPPPSARSTPLPGPPGSPRSSGGTPLHSPLHTPRASPTGTPGTTPPPSPGGGVGGAAWRSRLNSIRNSFLGSPRFHRRKMQVPTAEEMSSLTPESSPELAKRSWFGNFISLDKEEQIFLVLKDKPLSSIKADIVHAFLSIPSLSHSVLSQTSFRAEYKASGGPSVFQKPVRFQVDISSSEGPEPSPRRDGSGGGGIYSVTFTLISGPSRRFKRVVETIQAQLLSTHDQPSVQALADEKNGAQTRPAGAPPRSLQPPPGRPDPELSSSPRRGPPKDKKLLATNGTPLP' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity value of the drug 'Cc1nc2c(-c3cnc[nH]3)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CC(O)C1' for the target protein 'HLYLVARPEVNKSIVVTLQHVFESLMDLIFRNVVMYDNEDAKDLEKDGEKYEKPKGITQFQGADKLHVDSKVSTKSVVFDVLGGVVAEEEIFCFLYSEVMVRFFNPNIFAQLKNNPVNDKNSVQRGMYTDIQGFEEAKWARRSITVKMVGKDCVNLFMAWPSTLSCLQEFLKTHQPLVILLDMYVLCTPDFAKLPLAIKYLQLKSILFPFRKKIFKVTQRNRLCNGLMSFSVSEHNLSTNNPLNGPFIRLEAACLLHVLVATELLSHYGKKKLLLGKLNHKFILSVETLWLDMFEVHKDERIEPGNKCDSIFLLKREWHDKDSNIEYNKMCSFNESDYQFMGRSPRVLKGLEAHIYSIMIELSDHRLLSQLLTKGYLRSEIIQVKKEILRPFKRELAKALMYALMLSYWFKSPGANWDDFNMSDEPLTIICQAQILFRALDELIYKNFCMEILLEFEPVKQDQNANPTLLDQMPTRSDALEDQFQIGGKSPTGWTASLSKQKRAYDWNNSIKVFEIQKSPKEDSEGVIHVILGELRPCTGKMLIANRDGIVKDIGDEVRQKATGQCVPFVIGPTTGEYHPCFRSQANNTKALEISPEVDTAHTVHGELQLVISVAYAQFIQYMLKSLERSRSDDSHPQLKAKKTTVLELFRIQALMRTMELKLMLKMVFLPLANSNELNLNEIGLDEIIKRVDCRTKNAETPLLLKDGPPGVPYQFWDHKSKYGRLWRKHTLWCEEPKVEFRYHFIDEIFEILCGLPDILLAVARFYQLLTVDLPAGCNIPSKVKVTDAVDDSDVSIDLVDDPGWLVWSAQSTSDIHYIQVMGNADHKASMKDCRDELQCREIRENISYVDCNYTESKRGVFPLLEHAVYLQNALGEAAVDSETIRWILSCMVAFYYKLGTLQAVIPGVNELRQEAPVDRENASRRLGKMPLFASPKGYSLLAGNTIKVVLNDSSLLLVQKPAPPLDGAVLDRREPQKSIFAKEGEMRNNILGLEVGKLTWIYVMAFLVVLFLNNESIGYGEYIYPINRKEPQMQPFHLKWKYQPSEQEAPLKKRSWCLCALNTLYICIYAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1nc2c(-c3cnc[nH]3)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CC(O)C1\"], \"target_seq\": \"HLYLVARPEVNKSIVVTLQHVFESLMDLIFRNVVMYDNEDAKDLEKDGEKYEKPKGITQFQGADKLHVDSKVSTKSVVFDVLGGVVAEEEIFCFLYSEVMVRFFNPNIFAQLKNNPVNDKNSVQRGMYTDIQGFEEAKWARRSITVKMVGKDCVNLFMAWPSTLSCLQEFLKTHQPLVILLDMYVLCTPDFAKLPLAIKYLQLKSILFPFRKKIFKVTQRNRLCNGLMSFSVSEHNLSTNNPLNGPFIRLEAACLLHVLVATELLSHYGKKKLLLGKLNHKFILSVETLWLDMFEVHKDERIEPGNKCDSIFLLKREWHDKDSNIEYNKMCSFNESDYQFMGRSPRVLKGLEAHIYSIMIELSDHRLLSQLLTKGYLRSEIIQVKKEILRPFKRELAKALMYALMLSYWFKSPGANWDDFNMSDEPLTIICQAQILFRALDELIYKNFCMEILLEFEPVKQDQNANPTLLDQMPTRSDALEDQFQIGGKSPTGWTASLSKQKRAYDWNNSIKVFEIQKSPKEDSEGVIHVILGELRPCTGKMLIANRDGIVKDIGDEVRQKATGQCVPFVIGPTTGEYHPCFRSQANNTKALEISPEVDTAHTVHGELQLVISVAYAQFIQYMLKSLERSRSDDSHPQLKAKKTTVLELFRIQALMRTMELKLMLKMVFLPLANSNELNLNEIGLDEIIKRVDCRTKNAETPLLLKDGPPGVPYQFWDHKSKYGRLWRKHTLWCEEPKVEFRYHFIDEIFEILCGLPDILLAVARFYQLLTVDLPAGCNIPSKVKVTDAVDDSDVSIDLVDDPGWLVWSAQSTSDIHYIQVMGNADHKASMKDCRDELQCREIRENISYVDCNYTESKRGVFPLLEHAVYLQNALGEAAVDSETIRWILSCMVAFYYKLGTLQAVIPGVNELRQEAPVDRENASRRLGKMPLFASPKGYSLLAGNTIKVVLNDSSLLLVQKPAPPLDGAVLDRREPQKSIFAKEGEMRNNILGLEVGKLTWIYVMAFLVVLFLNNESIGYGEYIYPINRKEPQMQPFHLKWKYQPSEQEAPLKKRSWCLCALNTLYICIYAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS\", \"result\": [{\"smile\": \"Cc1nc2c(-c3cnc[nH]3)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CC(O)C1\", \"value\": 3.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1nc2c(-c3cnc[nH]3)cc(N3CCOCC3)nn2c1Nc1cccc(C(F)(F)F)c1CN1CC(O)C1' and the protein 'MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS' is 3.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' with respect to the target protein 'CSAQDAEENDSSASWPYIDEGVTQPVCLSNSASCRALSEKMQKLYVAARSYFFVVSAMTMVAIDGTGGSICVTRPKEPPSYKEHPQYPQVNSVEPQDMNQESSSFDLSDKKMLKKLDDIVNCKKPKIPTSPITESLSEKKSVLTSPTDQKRYMGKHKKGEHAPSEKGSGAPWGHQAIRSTPLLGGEGRAEAQQTLAHHVRTQNDGQCLIEAMIVMSVSGKQEDDPLLMEIFTAARPCVIYESVIDTNGSLDSNRLSSAKEQKKLSETSHLYLQQAMQGIPAGPTILDESGEVEPEPQNVPARYSPQGPRLFAVAQRLPVFRETRNLCGLAALRGISEQILHSVERSSLLIIFGKGDESPPINMKRSHAGQRSIFRNETGQSKYTRLHAFVSEQYAEANICAQSPVSKLQEDTKYMIRAAAQIDKLLYNRTSVSEVEENIMASRTAPQNRGNVPWPADQSMPLRDKQWTPGQQPVAFSASNKKINRSIFCKNNSSTTHLSESEKREKALWTLITHCIADHSSKSLITSQCMETTLEGTILVSDQPGGDTMVLASVCTKLERMIYMWSYKEGRTLLPMESVQFMMEDNTSNWGELGKWYLVGTAVNRTAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\"], \"target_seq\": \"CSAQDAEENDSSASWPYIDEGVTQPVCLSNSASCRALSEKMQKLYVAARSYFFVVSAMTMVAIDGTGGSICVTRPKEPPSYKEHPQYPQVNSVEPQDMNQESSSFDLSDKKMLKKLDDIVNCKKPKIPTSPITESLSEKKSVLTSPTDQKRYMGKHKKGEHAPSEKGSGAPWGHQAIRSTPLLGGEGRAEAQQTLAHHVRTQNDGQCLIEAMIVMSVSGKQEDDPLLMEIFTAARPCVIYESVIDTNGSLDSNRLSSAKEQKKLSETSHLYLQQAMQGIPAGPTILDESGEVEPEPQNVPARYSPQGPRLFAVAQRLPVFRETRNLCGLAALRGISEQILHSVERSSLLIIFGKGDESPPINMKRSHAGQRSIFRNETGQSKYTRLHAFVSEQYAEANICAQSPVSKLQEDTKYMIRAAAQIDKLLYNRTSVSEVEENIMASRTAPQNRGNVPWPADQSMPLRDKQWTPGQQPVAFSASNKKINRSIFCKNNSSTTHLSESEKREKALWTLITHCIADHSSKSLITSQCMETTLEGTILVSDQPGGDTMVLASVCTKLERMIYMWSYKEGRTLLPMESVQFMMEDNTSNWGELGKWYLVGTAVNRTAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS' is the valid sequence for this protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\", \"result\": [{\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 630.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS' is 630.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the predicted affinity level for the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1' and the target protein 'QLEAVVSMEKLLKELVWMRLQALAKKQKLNDAQHNTEEGTQLIKLQPKENEVSLLHGIIIHQMPHKDMREFSFDSGLSLKIPKESLEDKDEILQNRVKLLSIIEKLEESMEGEHPTDKNRKQPKLSAVMKSKRPTECLKYLPTNQTKLAMTYHVLQLIERQALVDDLEEETKQDTNELIGNLQKWGWHTHRNRIVSLGEKEYIWALAGGKQTEAMTLQAKEFHSHEPNTSQLNQEPITSSSKYFVKGTDKLTQSKAALANDIVFQKALQDTNSQGGLELWTLGIGEIKTKNEGAIAVIFLGYRKLEQMTKDRCAKQFLCDMKKLSEALIVVISHESYMEQPLLKTHDPRYFSCQRVASLELKEERMFKMDLDKWHSSNEAEHLYNELNYQVCLSKKNHAIQMNLTRAGMQKDSIQYRPGIPLRKSKSTKKDKVRGQVSKKVSDYGEDESNMRGRQTGHQEEQVLYYPKSFKCESIMQAILENILYLIAFIMDITKAKKEWRMPEKQVGRQQKTKQQQGLKKEVFLIYQLKEEMIPEAAVKLLQEEEEVYGQIVSPTCFAYEDIYASSSRVITSSTRIVEPKAYEDQDDTNKEIKDQIESMTLSLKDEIINQFELWLADVPTITRNLEVLNAKLNLLKCKTVVLVAGSMEKVLQMSSSSYLKKDSSKGLVEIEVEMKEISVFNEYLFVYAIAQSLDLGLVIDYPKLSWFIVRGISFLCDISADIKEGLSYSATMGCSMVEGIFEHMPTLKSLHKERSDVSLYLVKYPEGKKRQQGVKQGQILRGDVRQLNNAEPETNARMEHPVKEENLYFLINLRYCGLSQEAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\"], \"target_seq\": \"QLEAVVSMEKLLKELVWMRLQALAKKQKLNDAQHNTEEGTQLIKLQPKENEVSLLHGIIIHQMPHKDMREFSFDSGLSLKIPKESLEDKDEILQNRVKLLSIIEKLEESMEGEHPTDKNRKQPKLSAVMKSKRPTECLKYLPTNQTKLAMTYHVLQLIERQALVDDLEEETKQDTNELIGNLQKWGWHTHRNRIVSLGEKEYIWALAGGKQTEAMTLQAKEFHSHEPNTSQLNQEPITSSSKYFVKGTDKLTQSKAALANDIVFQKALQDTNSQGGLELWTLGIGEIKTKNEGAIAVIFLGYRKLEQMTKDRCAKQFLCDMKKLSEALIVVISHESYMEQPLLKTHDPRYFSCQRVASLELKEERMFKMDLDKWHSSNEAEHLYNELNYQVCLSKKNHAIQMNLTRAGMQKDSIQYRPGIPLRKSKSTKKDKVRGQVSKKVSDYGEDESNMRGRQTGHQEEQVLYYPKSFKCESIMQAILENILYLIAFIMDITKAKKEWRMPEKQVGRQQKTKQQQGLKKEVFLIYQLKEEMIPEAAVKLLQEEEEVYGQIVSPTCFAYEDIYASSSRVITSSTRIVEPKAYEDQDDTNKEIKDQIESMTLSLKDEIINQFELWLADVPTITRNLEVLNAKLNLLKCKTVVLVAGSMEKVLQMSSSSYLKKDSSKGLVEIEVEMKEISVFNEYLFVYAIAQSLDLGLVIDYPKLSWFIVRGISFLCDISADIKEGLSYSATMGCSMVEGIFEHMPTLKSLHKERSDVSLYLVKYPEGKKRQQGVKQGQILRGDVRQLNNAEPETNARMEHPVKEENLYFLINLRYCGLSQEAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid expression for the protein is 'MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\", \"result\": [{\"smile\": \"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1' and the protein 'MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted binding strength of the drug 'CC(C)C[C@H](NC(=O)N1CCOCC1)C(=O)N[C@H](C#N)Cc1ccc(Cl)c(Cl)c1' with the target protein 'AEAHHDAGIQYQSLVRQGLKDKDDIRGSDVEKWNCYQPTDTSDIQLVTSWAPGMEAGCNKMDKTMIQVSLEMGHSYAVGDSVNRIKQCLVRKYCKLEAKQYYPAMVKHFPKPPLWLCNVSHSLMEESEVLSEGQFARTMLYNADPDESVGRKHYEAEMGNDPGYMAGSGCHECVLYYSKHRLWGKWLILGNNSPGNYGPPTCRQSEVYLGLSYEWKLNRSVGIASVSKVYVTLGEFNRCYVRHCFSKFSWSSTMFIAVKKTILGFGDQGLGHRVDLKKTEGAKLTLSLYCNAVHKRNKNYGSVNLSTKHKVKWALVFEGYNMNEDAANNYVAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)C[C@H](NC(=O)N1CCOCC1)C(=O)N[C@H](C#N)Cc1ccc(Cl)c(Cl)c1\"], \"target_seq\": \"AEAHHDAGIQYQSLVRQGLKDKDDIRGSDVEKWNCYQPTDTSDIQLVTSWAPGMEAGCNKMDKTMIQVSLEMGHSYAVGDSVNRIKQCLVRKYCKLEAKQYYPAMVKHFPKPPLWLCNVSHSLMEESEVLSEGQFARTMLYNADPDESVGRKHYEAEMGNDPGYMAGSGCHECVLYYSKHRLWGKWLILGNNSPGNYGPPTCRQSEVYLGLSYEWKLNRSVGIASVSKVYVTLGEFNRCYVRHCFSKFSWSSTMFIAVKKTILGFGDQGLGHRVDLKKTEGAKLTLSLYCNAVHKRNKNYGSVNLSTKHKVKWALVFEGYNMNEDAANNYVAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper sequence to use with the protein is 'MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI\", \"result\": [{\"smile\": \"CC(C)C[C@H](NC(=O)N1CCOCC1)C(=O)N[C@H](C#N)Cc1ccc(Cl)c(Cl)c1\", \"value\": 7.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)C[C@H](NC(=O)N1CCOCC1)C(=O)N[C@H](C#N)Cc1ccc(Cl)c(Cl)c1' and the protein 'MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI' is 7.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the target protein 'LLEAPNLGANKGVSAAGLNIRGKRPSEADIGRFISLVLWLHKESEIERPEPLIGLQQLYKRVFILPWLSDAETNICFPLQSATDEKRSSEAHSPKVYLMSVALNRSIDLQLGVLPSWLASLIECQLQSPVDKEGSDRPMSKAREKLTLDHLHSDLIVAHNYAARSDRSDDVRNEKGDQSFRVHGEPFQFSPEYDITLQDAGVPHQVGLDTSVPLRVERLETLAGPGTCYKVGIEDTEQVRTAVPDNADLLGGDFESHQCAVERFALLLGLLLPFQPDKFQLLCCARNEEFCPSKLAYAGLKWGFEVVSALQLLLLPMIRPAAQGERCVCALTIDSHEDLEASETLRKCKYRVHPFRDSGGGWPFVIGMLDHQNLMLDCFCGHVLLKRYLLVRYHFAYCVLLPGCRTASRMLAPGAGASTSQCLLYLDLDHSSGESLGHCVHILMGYCLRLVLMTAQRSVQIEARKGSPVRSKTSSSRDARYCSYYIEYLSLRKYIFYRGVHLGMDIYPGSLSMPEQLIELFAFCAALCHLSSHHEALCAIAGPCKGSPPFMRLPPSPPRRPDSGFELFVIVPALIADLGWGSQPALFQNVDELQDLLVALKHSVSKDADERRAWSKYVDPVAGFPLGSQHSLLLQSDVSLPVKLAGAILPKLAPHSNLLLKTIDWDISGEDAQNEYFGCLATGMHTQQRDGILRDGDNFSEAHVQFLLVDELHRQRLEGERASIGVVVLEFRLAITFALGSPHQAGYVTLQLPKLAQFAGASERLGTQPRGVGLETALGIVEGRLEQKQQLVLMAADKTPWLCSCAMEHSREAIVCEYALSVFAPQVDRGLGCPWIRSLSWAPPSQPLFLRRVGTMYPWDEVGPFEIKMGETRRKRQGYVKSPLTPAQDRLSWVMLPGPRIHGPAPSHHKERLDSEDPTDFAEVKLLSFTHRDFFSRLSHDQLAGPLTGAVFMRPGAHEFTGLLNLRLTYASRVVAQYGGCRCFQQRGPYDLKPVRTPREVEMEAGEHDTLQILRPPLPVQVSPLLSPLPSGDGTLAKYTEWGPCFYRDEVFDPRVRLSPLKSIVKLQEPALAVPWLAGATKVADLRFKCRVAQHSSSEFMTRVVRLFSFSLTACKRSAHSQQQAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"LLEAPNLGANKGVSAAGLNIRGKRPSEADIGRFISLVLWLHKESEIERPEPLIGLQQLYKRVFILPWLSDAETNICFPLQSATDEKRSSEAHSPKVYLMSVALNRSIDLQLGVLPSWLASLIECQLQSPVDKEGSDRPMSKAREKLTLDHLHSDLIVAHNYAARSDRSDDVRNEKGDQSFRVHGEPFQFSPEYDITLQDAGVPHQVGLDTSVPLRVERLETLAGPGTCYKVGIEDTEQVRTAVPDNADLLGGDFESHQCAVERFALLLGLLLPFQPDKFQLLCCARNEEFCPSKLAYAGLKWGFEVVSALQLLLLPMIRPAAQGERCVCALTIDSHEDLEASETLRKCKYRVHPFRDSGGGWPFVIGMLDHQNLMLDCFCGHVLLKRYLLVRYHFAYCVLLPGCRTASRMLAPGAGASTSQCLLYLDLDHSSGESLGHCVHILMGYCLRLVLMTAQRSVQIEARKGSPVRSKTSSSRDARYCSYYIEYLSLRKYIFYRGVHLGMDIYPGSLSMPEQLIELFAFCAALCHLSSHHEALCAIAGPCKGSPPFMRLPPSPPRRPDSGFELFVIVPALIADLGWGSQPALFQNVDELQDLLVALKHSVSKDADERRAWSKYVDPVAGFPLGSQHSLLLQSDVSLPVKLAGAILPKLAPHSNLLLKTIDWDISGEDAQNEYFGCLATGMHTQQRDGILRDGDNFSEAHVQFLLVDELHRQRLEGERASIGVVVLEFRLAITFALGSPHQAGYVTLQLPKLAQFAGASERLGTQPRGVGLETALGIVEGRLEQKQQLVLMAADKTPWLCSCAMEHSREAIVCEYALSVFAPQVDRGLGCPWIRSLSWAPPSQPLFLRRVGTMYPWDEVGPFEIKMGETRRKRQGYVKSPLTPAQDRLSWVMLPGPRIHGPAPSHHKERLDSEDPTDFAEVKLLSFTHRDFFSRLSHDQLAGPLTGAVFMRPGAHEFTGLLNLRLTYASRVVAQYGGCRCFQQRGPYDLKPVRTPREVEMEAGEHDTLQILRPPLPVQVSPLLSPLPSGDGTLAKYTEWGPCFYRDEVFDPRVRLSPLKSIVKLQEPALAVPWLAGATKVADLRFKCRVAQHSSSEFMTRVVRLFSFSLTACKRSAHSQQQAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS' is the sequence to use for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS\", \"result\": [{\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein 'MAPPSEETPLIPQRSCSLLSTEAGALHVLLPARGPGPPQRLSFSFGDHLAEDLCVQAAKASGILPVYHSLFALATEDLSCWFPPSHIFSVEDASTQVLLYRIRFYFPNWFGLEKCHRFGLRKDLASAILDLPVLEHLFAQHRSDLVSGRLPVGLSLKEQGECLSLAVLDLARMAREQAQRPGELLKTVSYKACLPPSLRDLIQGLSFVTRRRIRRTVRRALRRVAACQADRHSLMAKYIMDLERLDPAGAAETFHVGLPGALGGHDGLGLLRVAGDGGIAWTQGEQEVLQPFCDFPEIVDISIKQAPRVGPAGEHRLVTVTRTDNQILEAEFPGLPEALSFVALVDGYFRLTTDSQHFFCKEVAPPRLLEEVAEQCHGPITLDFAINKLKTGGSRPGSYVLRRSPQDFDSFLLTVCVQNPLGPDYKGCLIRRSPTGTFLLVGLSRPHSSLRELLATCWDGGLHVDGVAVTLTSCCIPRPKEKSNLIVVQRGHSPPTSSLVQPQSQYQLSQMTFHKIPADSLEWHENLGHGSFTKIYRGCRHEVVDGEARKTEVLLKVMDAKHKNCMESFLEAASLMSQVSYRHLVLLHGVCMAGDSTMVQEFVHLGAIDMYLRKRGHLVPASWKLQVVKQLAYALNYLEDKGLPHGNVSARKVLLAREGADGSPPFIKLSDPGVSPAVLSLEMLTDRIPWVAPECLREAQTLSLEADKWGFGATVWEVFSGVTMPISALDPAKKLQFYEDRQQLPAPKWTELALLIQQCMAYEPVQRPSFRAVIRDLNSLISSDYELLSDPTPGALAPRDGLWNGAQLYACQDPTIFEERHLKYISQLGKGNFGSVELCRYDPLGDNTGALVAVKQLQHSGPDQQRDFQREIQILKALHSDFIVKYRGVSYGPGRQSLRLVMEYLPSGCLRDFLQRHRARLDASRLLLYSSQICKGMEYLGSRRCVHRDLAARNILVESEAHVKIADFGLAKLLPLDKDYYVVREPGQSPIFWYAPESLSDNIFSRQSDVWSFGVVLYELFTYCDKSCSPSAEFLRMMGCERDVPALCRLLELLEEGQRLPAPPACPAEVHELMKLCWAPSPQDRPSFSALGPQLDMLWSGSRGCETHAFTAHPEGKHHSLSFS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the target protein 'TAQPSHHNPYRAKTMKDSEGAKSGEAYGYPIASQPPPTQCQVSTPYSPNKSFDRSLPIDAASQKFNELRPVGLHPDNIQLGPRGPVMEASQACLPMVHLLKGFGDEGPTFSWLKTRKCRKGSGRQMDGYTAEHLHDSTDSSFRREAIAYTPSNKKPALCITYLPYNGEPLPVMISSALESVLIVGWRPPLLGGRVQGDGNPPVEHNEGLPQTLELPTLHYELDIMGHLFSPIRAQPPDEGNSRLATLALLLSTEYNHVGQAYYLQSYLEDMTVRRAVLFTPTAHDEEASDGLQELKGPAKYPFEHRAMQCRPVTIDLPQDPLLASGNHARFEQSQSTRAPDGARCLVSPSVSYGSCKIITDGRSGAPPLWRCPVLSLIAENNKQPGDPHSYAALYTMIDNTPRLDFAGSRKSVLGHPQKHDGIRLLYWPPEVQPGFTRSRSSSSPILAGFATKPEFLAPPQIRTRRKRSIASPGNQLSPQTHCDCARGGRSRSKYLRASPDLIQYITLVKLLGLFQGRLHQNLRRDDAGKSVLGKNLAGMSRIRMPGKSGPQVGRPPADVYYGPHLGLKLDALDPLGTFKAMRLRKPHLNPVLLEPAIDTPPRRYGLQIENVLVPQPVSIEAFVSGQTFAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\"], \"target_seq\": \"TAQPSHHNPYRAKTMKDSEGAKSGEAYGYPIASQPPPTQCQVSTPYSPNKSFDRSLPIDAASQKFNELRPVGLHPDNIQLGPRGPVMEASQACLPMVHLLKGFGDEGPTFSWLKTRKCRKGSGRQMDGYTAEHLHDSTDSSFRREAIAYTPSNKKPALCITYLPYNGEPLPVMISSALESVLIVGWRPPLLGGRVQGDGNPPVEHNEGLPQTLELPTLHYELDIMGHLFSPIRAQPPDEGNSRLATLALLLSTEYNHVGQAYYLQSYLEDMTVRRAVLFTPTAHDEEASDGLQELKGPAKYPFEHRAMQCRPVTIDLPQDPLLASGNHARFEQSQSTRAPDGARCLVSPSVSYGSCKIITDGRSGAPPLWRCPVLSLIAENNKQPGDPHSYAALYTMIDNTPRLDFAGSRKSVLGHPQKHDGIRLLYWPPEVQPGFTRSRSSSSPILAGFATKPEFLAPPQIRTRRKRSIASPGNQLSPQTHCDCARGGRSRSKYLRASPDLIQYITLVKLLGLFQGRLHQNLRRDDAGKSVLGKNLAGMSRIRMPGKSGPQVGRPPADVYYGPHLGLKLDALDPLGTFKAMRLRKPHLNPVLLEPAIDTPPRRYGLQIENVLVPQPVSIEAFVSGQTFAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence that matches the protein is 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS\", \"result\": [{\"smile\": \"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1' and the protein 'MAVPPGHGPFSGFPGPQEHTQVLPDVRLLPRRLPLAFRDATSAPLRKLSVDLIKTYKHINEVYYAKKKRRAQQAPPQDSSNKKEKKVLNHGYDDDNHDYIVRSGERWLERYEIDSLIGKGSFGQVVKAYDHQTQELVAIKIIKNKKAFLNQAQIELRLLELMNQHDTEMKYYIVHLKRHFMFRNHLCLVFELLSYNLYDLLRNTHFRGVSLNLTRKLAQQLCTALLFLATPELSIIHCDLKPENILLCNPKRSAIKIVDFGSSCQLGQRIYQYIQSRFYRSPEVLLGTPYDLAIDMWSLGCILVEMHTGEPLFSGSNEVDQMNRIVEVLGIPPAAMLDQAPKARKYFERLPGGGWTLRRTKELRKDYQGPGTRRLQEVLGVQTGGPGGRRAGEPGHSPADYLRFQDLVLRMLEYEPAARISPLGALQHGFFRRTADEATNTGPAGSSASTSPAPLDTCPSSSTASSISSSGGSSGSSSDNRTYRYSNRYCGGPGPPITDCEMNSPQVPPSQPLRPWAGGDVPHKTHQAPASASSLPGTGAQLPPQPRYLGRPPSPTSPPPPELMDVSLVGGPADCSPPHPAPAPQHPAASALRTRMTGGRPPLPPPDDPATLGPHLGLRGVPQSTAASS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the binding affinity you would expect for the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein 'VELGGLFRVPVPPSLRSTSPYERGDLDDKAIPGSKDVLSIASHSEASPMEPRNPRDLRALSNSTETHSRATIIPVCFVVPKESQVTAPRYESASQLCGRSLSFTASPEEDSSEVLEKPDVRGSLSHLQTSLHRFAYKRNGTQSHDKLRSVMEHTGPLADSDRKHGLMREPRWLSRLLMHASAQLVTYYFPCNRVSSEERPEPSRAAPGVDALPVETKSGPHEALPTPLGGNSTSALSADKNPESQTIMQSRGKAPVLDWIRPENTPSLDVAMSPRPFSLGAEESDRKSTEFRSGGVPLASLHEMGKHGLRGFAEGPLKIWVLPNGLKREPDEPAEATPEPKVYPEESPPGQTGPLHGSTQVDHTSYTGAKAFCEDSTSPSPATEATEELPKHTYFDGLKGLGAPILIQPGSDLLRLEHIAMTLLTGAAREDLILNLLDPTFSPCKTGCSTSMCTDTAEAVERLNRNYDLGKSRVESRVQKTAAHSISLTTESTIKVARALNQRGCPFPYLKNSFQVHHPAAPLLPKMPRPARFEANDPGRVDKGHQMRNLFPIHPPYFPYPSVPETSKFTLGPLKGRAGNLLGGPPKWLPETSQRAEDCYSPITQGLTLPDNPIQSRRVELKDKKDDKEDEDPREYQAAPASSRPQQIKLIECQSYLSSEKLTRSPVETHEADAEYSLASYPGVSIQPKMERMVGGRLRERAWSVDKGRPASYPVQISRQGVAGTAPPPKFDSQPDTVVSSLREGAPEPGSRFLASIQIRSPGNQKYKLEFVRMPPEIPGVKHRSDPDESGAKASPSASAARLQYKLYVPSIDSSDQPTNSARSRVGEDREGSLISTCGPSPGVLDFSKEPLESVASYETEAFRRLTVEPTEPFIKIVHTVPRPLRTLGIEARGSLPSSSRSTPGGTLFPRGQLETSLSSSVTVPARKTTESSASAEGVHLTRADHRLNPRSISFRDILLAKHLVLSEGLRKYLSITAWRSRVEQGPLASCASLGDEQRQSHGSRPVRGLSKATLHSKVASEVDAAPELLSPLFGRLFQPPLKHVISASILIRTMITPKRPEHSQHDLFQSGAKTETSYDLIGPCLDGRKRDTGVAHGSKAQSFGDKFSHSAMHRPVNGILLVQESPDSQLAEYSSCLLFTSDHSGGQPERLDLVAAGKANRSSAITDAEEVSTWEGAEAVGGAVEVIKELDLSTYPGRKQDVLLLSLSERLDAEQSSATNLEIANLSVLTLQVETVVTGRMSPRSRSTPSRGWESKLAAVDESAAAKYEPPGWHYKEGDTPPGLRQVPRSTAAMFFFGGGSAYHREHKPAPTNIYLPHSSGLFLHDLGSQEIKFFKNRGAVGHNDPHGPKDGKFHRPDGVLDIDSDSLRVIRLTSVINEPARPNPPPPVMQLMPHARNTVFPPLSPHGSKPHRRPPGEEESTFVALPSGSLLPLRLVRAAMNTFFERSPLTGLRSEQHFPDKSSRSGRESKDAISSVGGSRLGETKSGGEGALSGVPYTRSNAPLFGVTRSMCRSHESYSKSHARKKMECISVQRSFSAGSLGRTMSKATASEVPACFIGFSRSGPAGSAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"VELGGLFRVPVPPSLRSTSPYERGDLDDKAIPGSKDVLSIASHSEASPMEPRNPRDLRALSNSTETHSRATIIPVCFVVPKESQVTAPRYESASQLCGRSLSFTASPEEDSSEVLEKPDVRGSLSHLQTSLHRFAYKRNGTQSHDKLRSVMEHTGPLADSDRKHGLMREPRWLSRLLMHASAQLVTYYFPCNRVSSEERPEPSRAAPGVDALPVETKSGPHEALPTPLGGNSTSALSADKNPESQTIMQSRGKAPVLDWIRPENTPSLDVAMSPRPFSLGAEESDRKSTEFRSGGVPLASLHEMGKHGLRGFAEGPLKIWVLPNGLKREPDEPAEATPEPKVYPEESPPGQTGPLHGSTQVDHTSYTGAKAFCEDSTSPSPATEATEELPKHTYFDGLKGLGAPILIQPGSDLLRLEHIAMTLLTGAAREDLILNLLDPTFSPCKTGCSTSMCTDTAEAVERLNRNYDLGKSRVESRVQKTAAHSISLTTESTIKVARALNQRGCPFPYLKNSFQVHHPAAPLLPKMPRPARFEANDPGRVDKGHQMRNLFPIHPPYFPYPSVPETSKFTLGPLKGRAGNLLGGPPKWLPETSQRAEDCYSPITQGLTLPDNPIQSRRVELKDKKDDKEDEDPREYQAAPASSRPQQIKLIECQSYLSSEKLTRSPVETHEADAEYSLASYPGVSIQPKMERMVGGRLRERAWSVDKGRPASYPVQISRQGVAGTAPPPKFDSQPDTVVSSLREGAPEPGSRFLASIQIRSPGNQKYKLEFVRMPPEIPGVKHRSDPDESGAKASPSASAARLQYKLYVPSIDSSDQPTNSARSRVGEDREGSLISTCGPSPGVLDFSKEPLESVASYETEAFRRLTVEPTEPFIKIVHTVPRPLRTLGIEARGSLPSSSRSTPGGTLFPRGQLETSLSSSVTVPARKTTESSASAEGVHLTRADHRLNPRSISFRDILLAKHLVLSEGLRKYLSITAWRSRVEQGPLASCASLGDEQRQSHGSRPVRGLSKATLHSKVASEVDAAPELLSPLFGRLFQPPLKHVISASILIRTMITPKRPEHSQHDLFQSGAKTETSYDLIGPCLDGRKRDTGVAHGSKAQSFGDKFSHSAMHRPVNGILLVQESPDSQLAEYSSCLLFTSDHSGGQPERLDLVAAGKANRSSAITDAEEVSTWEGAEAVGGAVEVIKELDLSTYPGRKQDVLLLSLSERLDAEQSSATNLEIANLSVLTLQVETVVTGRMSPRSRSTPSRGWESKLAAVDESAAAKYEPPGWHYKEGDTPPGLRQVPRSTAAMFFFGGGSAYHREHKPAPTNIYLPHSSGLFLHDLGSQEIKFFKNRGAVGHNDPHGPKDGKFHRPDGVLDIDSDSLRVIRLTSVINEPARPNPPPPVMQLMPHARNTVFPPLSPHGSKPHRRPPGEEESTFVALPSGSLLPLRLVRAAMNTFFERSPLTGLRSEQHFPDKSSRSGRESKDAISSVGGSRLGETKSGGEGALSGVPYTRSNAPLFGVTRSMCRSHESYSKSHARKKMECISVQRSFSAGSLGRTMSKATASEVPACFIGFSRSGPAGSAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence fitting for the protein is 'MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP\", \"result\": [{\"smile\": \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 150.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MSDSLWTALSNFSMPSFPGGSMFRRTKSCRTSNRKSLILTSTSPTLPRPHSPLPGHLGSSPLDSPRNFSPNTPAHFSFASSRRADGRRWSLASLPSSGYGTNTPSSTVSSSCSSQERLHQLPYQPTVDELHFLSKHFGSTESITDEDGGRRSPAVRPRSRSLSPGRSPSSYDNEIVMMNHVYKERFPKATAQMEEKLRDFTRAYEPDSVLPLADGVLSFIHHQIIELARDCLTKSRDGLITTVYFYELQENLEKLLQDAYERSESLEVAFVTQLVKKLLIIISRPARLLECLEFNPEEFYHLLEAAEGHAKEGHLVKTDIPRYIIRQLGLTRDPFPDVVHLEEQDSGGSNTPEQDDLSEGRSSKAKKPPGENDFDTIKLISNGAYGAVYLVRHRDTRQRFAMKKINKQNLILRNQIQQAFVERDILTFAENPFVVGMFCSFETRRHLCMVMEYVEGGDCATLLKNIGALPVEMARMYFAETVLALEYLHNYGIVHRDLKPDNLLITSMGHIKLTDFGLSKMGLMSLTTNLYEGHIEKDAREFLDKQVCGTPEYIAPEVILRQGYGKPVDWWAMGIILYEFLVGCVPFFGDTPEELFGQVISDDILWPEGDEALPTEAQLLISSLLQTNPLVRLGAGGAFEVKQHSFFRDLDWTGLLRQKAEFIPHLESEDDTSYFDTRSDRYHHVNSYDEDDTTEEEPVEIRQFSSCSPRFSKVYSSMEQLSQHEPKTPVAAAGSSKREPSTKGPEEKVAGKREGLGGLTLREKTWRGGSPEIKRFSASEASFLEGEASPPLGARRRFSALLEPSRFSAPQEDEDEARLRRPPRPSSDPAGSLDARAPKEETQGEGTSSAGDSEATDRPRPGDLCPPSKDGDASGPRATNDLVLRRARHQQMSGDVAVEKRPSRTGGKVIKSASATALSVMIPAVDPHGSSPLASPMSPRSLSSNPSSRDSSPSRDYSPAVSGLRSPITIQRSGKKYGFTLRAIRVYMGDTDVYSVHHIVWHVEEGGPAQEAGLCAGDLITHVNGEPVHGMVHPEVVELILKSGNKVAVTTTPFENTSIRIGPARRSSYKAKMARRNKRPSAKEGQESKKRSSLFRKITKQSNLLHTSRSLSSLNRSLSSSDSLPGSPTHGLPARSPTHSYRSTPDSAYLGASSQSSSPASSTPNSPASSASHHIRPSTLHGLSPKLHRQYRSARCKSAGNIPLSPLAHTPSPTQASPPPLPGHTVGSSHTTQSFPAKLHSSPPVVRPRPKSAEPPRSPLLKRVQSAEKLGASLSADKKGALRKHSLEVGHPDFRKDFHGELALHSLAESDGETPPVEGLGAPRQVAVRRLGRQESPLSLGADPLLPEGASRPPVSSKEKESPGGAEACTPPRATTPGGRTLERDVGCTRHQSVQTEDGTGGMARAVAKAALSPVQEHETGRRSSSGEAGTPLVPIVVEPARPGAKAVVPQPLGADSKGLQEPAPLAPSVPEAPRGRERWVLEVVEERTTLSGPRSKPASPKLSPEPQTPSLAPAKCSAPSSAVTPVPPASLLGSGTKPQVGLTSRCPAEAVPPAGLTKKGVSSPAPPGP' is 150.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the affinity between the drug 'Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F' and the target protein 'LNAIRYGEGSQEVCSNTGEKSHTLEGGMSSLWEGLAIKRKIHIGYEGVRTPLNKENLSLDKYELCHPIPSFVLLKPVIPVEEFYWTLKKFRPMLNIFCNSEKFSVDSHMRLAPKEMLLSFHVEQALDDNPYIEPCEQSLLCRFRSVLSHCLITYLETVELPQCVLVGCLVEGGVLGECKIGVEGTLSDKAEGSALDSAKHNPGALVSCIGAQIGKIHSAQPKKEPKSFNMHGPQVCTFSITTSRTTAQGHVLMDTFVKAMRKDEGKPEPESLASALPRVTDWRKPREWSPYEYAALVSSIKKQADTVISSSISSENIPYKYGAIYMLHGVEHHEQDNHSIGCSDFRSGTSNKYRPIIASRQGWNAETMKEGANYKFMTTYLRKTGVQHECKLLAPEAAMQSNDYLKVSKAASSGLVLNSFDRNHVQAKLTKSQFVYIPLIKPTRELKACSLVPPPTCKEENKVICSQSLDIDFTIFAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F\"], \"target_seq\": \"LNAIRYGEGSQEVCSNTGEKSHTLEGGMSSLWEGLAIKRKIHIGYEGVRTPLNKENLSLDKYELCHPIPSFVLLKPVIPVEEFYWTLKKFRPMLNIFCNSEKFSVDSHMRLAPKEMLLSFHVEQALDDNPYIEPCEQSLLCRFRSVLSHCLITYLETVELPQCVLVGCLVEGGVLGECKIGVEGTLSDKAEGSALDSAKHNPGALVSCIGAQIGKIHSAQPKKEPKSFNMHGPQVCTFSITTSRTTAQGHVLMDTFVKAMRKDEGKPEPESLASALPRVTDWRKPREWSPYEYAALVSSIKKQADTVISSSISSENIPYKYGAIYMLHGVEHHEQDNHSIGCSDFRSGTSNKYRPIIASRQGWNAETMKEGANYKFMTTYLRKTGVQHECKLLAPEAAMQSNDYLKVSKAASSGLVLNSFDRNHVQAKLTKSQFVYIPLIKPTRELKACSLVPPPTCKEENKVICSQSLDIDFTIFAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGRKEEDDCSSWKKQTTNIRKTFIFMEVLGSGAFSEVFLVKQRLTGKLFALKCIKKSPAFRDSSLENEIAVLKKIKHENIVTLEDIYESTTHYYLVMQLVSGGELFDRILERGVYTEKDASLVIQQVLSAVKYLHENGIVHRDLKPENLLYLTPEENSKIMITDFGLSKMEQNGIMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVITYILLCGYPPFYEETESKLFEKIKEGYYEFESPFWDDISESAKDFICHLLEKDPNERYTCEKALSHPWIDGNTALHRDIYPSVSLQIQKNFAKSKWRQAFNAAAVVHHMRKLHMNLHSPGVRPEVENRPPETQASETSRPSSPEITITEAPVLDHSVALPALTQLPCQHGRRPTAPGGRSLNCLVNGSLHISSSLVPMHQGSLAAGPCGCCSSCLNIGSKGKSSYCSEPTLLKKANKKQNFKSEVMVPVKASGSSHCRAGQTGVCLIM' fits the protein's sequence requirement. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGRKEEDDCSSWKKQTTNIRKTFIFMEVLGSGAFSEVFLVKQRLTGKLFALKCIKKSPAFRDSSLENEIAVLKKIKHENIVTLEDIYESTTHYYLVMQLVSGGELFDRILERGVYTEKDASLVIQQVLSAVKYLHENGIVHRDLKPENLLYLTPEENSKIMITDFGLSKMEQNGIMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVITYILLCGYPPFYEETESKLFEKIKEGYYEFESPFWDDISESAKDFICHLLEKDPNERYTCEKALSHPWIDGNTALHRDIYPSVSLQIQKNFAKSKWRQAFNAAAVVHHMRKLHMNLHSPGVRPEVENRPPETQASETSRPSSPEITITEAPVLDHSVALPALTQLPCQHGRRPTAPGGRSLNCLVNGSLHISSSLVPMHQGSLAAGPCGCCSSCLNIGSKGKSSYCSEPTLLKKANKKQNFKSEVMVPVKASGSSHCRAGQTGVCLIM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGRKEEDDCSSWKKQTTNIRKTFIFMEVLGSGAFSEVFLVKQRLTGKLFALKCIKKSPAFRDSSLENEIAVLKKIKHENIVTLEDIYESTTHYYLVMQLVSGGELFDRILERGVYTEKDASLVIQQVLSAVKYLHENGIVHRDLKPENLLYLTPEENSKIMITDFGLSKMEQNGIMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVITYILLCGYPPFYEETESKLFEKIKEGYYEFESPFWDDISESAKDFICHLLEKDPNERYTCEKALSHPWIDGNTALHRDIYPSVSLQIQKNFAKSKWRQAFNAAAVVHHMRKLHMNLHSPGVRPEVENRPPETQASETSRPSSPEITITEAPVLDHSVALPALTQLPCQHGRRPTAPGGRSLNCLVNGSLHISSSLVPMHQGSLAAGPCGCCSSCLNIGSKGKSSYCSEPTLLKKANKKQNFKSEVMVPVKASGSSHCRAGQTGVCLIM\", \"result\": [{\"smile\": \"Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F' and the protein 'MGRKEEDDCSSWKKQTTNIRKTFIFMEVLGSGAFSEVFLVKQRLTGKLFALKCIKKSPAFRDSSLENEIAVLKKIKHENIVTLEDIYESTTHYYLVMQLVSGGELFDRILERGVYTEKDASLVIQQVLSAVKYLHENGIVHRDLKPENLLYLTPEENSKIMITDFGLSKMEQNGIMSTACGTPGYVAPEVLAQKPYSKAVDCWSIGVITYILLCGYPPFYEETESKLFEKIKEGYYEFESPFWDDISESAKDFICHLLEKDPNERYTCEKALSHPWIDGNTALHRDIYPSVSLQIQKNFAKSKWRQAFNAAAVVHHMRKLHMNLHSPGVRPEVENRPPETQASETSRPSSPEITITEAPVLDHSVALPALTQLPCQHGRRPTAPGGRSLNCLVNGSLHISSSLVPMHQGSLAAGPCGCCSSCLNIGSKGKSSYCSEPTLLKKANKKQNFKSEVMVPVKASGSSHCRAGQTGVCLIM' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity rating expected for the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein 'PDKIDGPSLVECAPDEMLGISLDVELASIVNMARYKKMPFDINFIAVGCRETPYLDVYIRGPKELVKLFSVLPGLLLLLGPVVLYKDSAPYPLKFPEQLSIIHDGMNQGLYDGQLLRAGLRKDEMTVVELTDLGEDKAIPERLMKGHTLDMMKADECDFSHDPCFLLCLGKTIHSKEKEGFDGNLQLQGAVFFFFVEQMQTVLLEFANDNCRQGKEKKQFIPSQEFSRIRTQFEAFQAFWAAAYEAAEKLFSRDLPLWFNEADDRKSGNEDEVLPAMPAKSETYDTWGGEEELNMWKELSEDLQISGKLGQCQKGKWLKFSYRNPPAGWNELKPGQEVSSTELPVILKHEDLKTDNHFAKEVDATQPVAYDSLSMPLGAAFQVSYGAMVGSLAEKEKKYNHFVSSQQRLSNLYRAAIRMGYKELTDSTQFALASRCPPGSIYSLPDIIFLKETQKFLLLDASKSRKKIFQIFLDPCAYGSCQAGSFGCRGFTGDFDVGTGILAKKRSGYEAEQRYQAWQEGKKKFFVLARALKLTIGQDFRHDGEQGRKIVVIPLSGMFLKDKAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"PDKIDGPSLVECAPDEMLGISLDVELASIVNMARYKKMPFDINFIAVGCRETPYLDVYIRGPKELVKLFSVLPGLLLLLGPVVLYKDSAPYPLKFPEQLSIIHDGMNQGLYDGQLLRAGLRKDEMTVVELTDLGEDKAIPERLMKGHTLDMMKADECDFSHDPCFLLCLGKTIHSKEKEGFDGNLQLQGAVFFFFVEQMQTVLLEFANDNCRQGKEKKQFIPSQEFSRIRTQFEAFQAFWAAAYEAAEKLFSRDLPLWFNEADDRKSGNEDEVLPAMPAKSETYDTWGGEEELNMWKELSEDLQISGKLGQCQKGKWLKFSYRNPPAGWNELKPGQEVSSTELPVILKHEDLKTDNHFAKEVDATQPVAYDSLSMPLGAAFQVSYGAMVGSLAEKEKKYNHFVSSQQRLSNLYRAAIRMGYKELTDSTQFALASRCPPGSIYSLPDIIFLKETQKFLLLDASKSRKKIFQIFLDPCAYGSCQAGSFGCRGFTGDFDVGTGILAKKRSGYEAEQRYQAWQEGKKKFFVLARALKLTIGQDFRHDGEQGRKIVVIPLSGMFLKDKAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The proper facial expression for the protein is 'MDFGSLETVVANSAFIAARGSFDGSSSQPSRDKKYLAKLKLPPLSKCESLRDSLSLEFESVCLEQPIGKKLFQQFLQSAEKHLPALELWKDIEDYDTADNDLQPQKAQTILAQYLDPQAKLFCSFLDEGIVAKFKEGPVEIQDGLFQPLLQATLAHLGQAPFQEYLGSLYFLRFLQWKWLEAQPMGEDWFLDFRVLGKGGFGEVSACQMKATGKLYACKKLNKKRLKKRKGYQGAMVEKKILMKVHSRFIVSLAYAFETKADLCLVMTIMNGGDIRYHIYNVNEENPGFPEPRALFYTAQIICGLEHLHQRRIVYRDLKPENVLLDNDGNVRISDLGLAVELLDGQSKTKGYAGTPGFMAPELLQGEEYDFSVDYFALGVTLYEMIAARGPFRARGEKVENKELKHRIISEPVKYPDKFSQASKDFCEALLEKDPEKRLGFRDETCDKLRAHPLFKDLNWRQLEAGMLMPPFIPDSKTVYAKDIQDVGAFSTVKGVAFDKTDTEFFQEFATGNCPIPWQEEMIETGIFGELNVWRSDGQMPDDMKGISGGSSSSSKSGMCLVS'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDFGSLETVVANSAFIAARGSFDGSSSQPSRDKKYLAKLKLPPLSKCESLRDSLSLEFESVCLEQPIGKKLFQQFLQSAEKHLPALELWKDIEDYDTADNDLQPQKAQTILAQYLDPQAKLFCSFLDEGIVAKFKEGPVEIQDGLFQPLLQATLAHLGQAPFQEYLGSLYFLRFLQWKWLEAQPMGEDWFLDFRVLGKGGFGEVSACQMKATGKLYACKKLNKKRLKKRKGYQGAMVEKKILMKVHSRFIVSLAYAFETKADLCLVMTIMNGGDIRYHIYNVNEENPGFPEPRALFYTAQIICGLEHLHQRRIVYRDLKPENVLLDNDGNVRISDLGLAVELLDGQSKTKGYAGTPGFMAPELLQGEEYDFSVDYFALGVTLYEMIAARGPFRARGEKVENKELKHRIISEPVKYPDKFSQASKDFCEALLEKDPEKRLGFRDETCDKLRAHPLFKDLNWRQLEAGMLMPPFIPDSKTVYAKDIQDVGAFSTVKGVAFDKTDTEFFQEFATGNCPIPWQEEMIETGIFGELNVWRSDGQMPDDMKGISGGSSSSSKSGMCLVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDFGSLETVVANSAFIAARGSFDGSSSQPSRDKKYLAKLKLPPLSKCESLRDSLSLEFESVCLEQPIGKKLFQQFLQSAEKHLPALELWKDIEDYDTADNDLQPQKAQTILAQYLDPQAKLFCSFLDEGIVAKFKEGPVEIQDGLFQPLLQATLAHLGQAPFQEYLGSLYFLRFLQWKWLEAQPMGEDWFLDFRVLGKGGFGEVSACQMKATGKLYACKKLNKKRLKKRKGYQGAMVEKKILMKVHSRFIVSLAYAFETKADLCLVMTIMNGGDIRYHIYNVNEENPGFPEPRALFYTAQIICGLEHLHQRRIVYRDLKPENVLLDNDGNVRISDLGLAVELLDGQSKTKGYAGTPGFMAPELLQGEEYDFSVDYFALGVTLYEMIAARGPFRARGEKVENKELKHRIISEPVKYPDKFSQASKDFCEALLEKDPEKRLGFRDETCDKLRAHPLFKDLNWRQLEAGMLMPPFIPDSKTVYAKDIQDVGAFSTVKGVAFDKTDTEFFQEFATGNCPIPWQEEMIETGIFGELNVWRSDGQMPDDMKGISGGSSSSSKSGMCLVS\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 85.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MDFGSLETVVANSAFIAARGSFDGSSSQPSRDKKYLAKLKLPPLSKCESLRDSLSLEFESVCLEQPIGKKLFQQFLQSAEKHLPALELWKDIEDYDTADNDLQPQKAQTILAQYLDPQAKLFCSFLDEGIVAKFKEGPVEIQDGLFQPLLQATLAHLGQAPFQEYLGSLYFLRFLQWKWLEAQPMGEDWFLDFRVLGKGGFGEVSACQMKATGKLYACKKLNKKRLKKRKGYQGAMVEKKILMKVHSRFIVSLAYAFETKADLCLVMTIMNGGDIRYHIYNVNEENPGFPEPRALFYTAQIICGLEHLHQRRIVYRDLKPENVLLDNDGNVRISDLGLAVELLDGQSKTKGYAGTPGFMAPELLQGEEYDFSVDYFALGVTLYEMIAARGPFRARGEKVENKELKHRIISEPVKYPDKFSQASKDFCEALLEKDPEKRLGFRDETCDKLRAHPLFKDLNWRQLEAGMLMPPFIPDSKTVYAKDIQDVGAFSTVKGVAFDKTDTEFFQEFATGNCPIPWQEEMIETGIFGELNVWRSDGQMPDDMKGISGGSSSSSKSGMCLVS' is 85.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the interaction value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the target protein 'SAEPGQEFKQPKGKLEAFQRIQRLDFSQYRPKPKYSPAHAKRFSGLDEDAPLPRSRRSKTRIARAKSSLSQSSFSSTQGRKGPPGSERAITPRENNRSVKELDLRKSESSKESLEAPGGRSDPFPKTRLGSNKGDVGSIHLQRSRVERVPSKSKSYGQSHQKYRADPGSLSPYALLLNPNKPAGPAQNVVPQRRDELSPTRPKAGGLRDPPSATPLDPRQLSALIRLHKKTCDQKNRAPIPLGLAGSKEFIARRFAAPSPLSSHFETYSGRRSNTRKKSPSKRKSLSRVGEKCRKNTPAETSEKKLLPVTYVTWLRLFYNKPFPTIPNTAKYQQPGASGARSLEDVAVFAVPLSLKMPASLWHIYPSGGVPAPHGTGPAHRSNGEGQSLDLEKASESVDVPHSMNRSGSAKDDADAEGSYPGYKLMKRPAPIFAPTQESPDPPAKLSESANRLMWLEGPLTRRVPARRTKLGDLLWRYDLPEGSPSSSFIASIINHLHDVGSTRKLLPVHGRSVESAREQGQSMDCYAPGMYKPCPSRSPQSLIQGEWPPRGQHAFKLYWAAGRFRNVRDVLILGRQEHLLEALMARVVGLDFDSSESTQQEGKHQDEPKGPYLSKGGQLLFTAAISKQAETLAYEDKSSRADVRLESDQLPILLWGESLVEQKQSRSIPSPDRAGSLSQTISISTPHSQEDSLPLGPLKSIRDQLPREEGKSPPLAESTIATVRKPEPSSSRKRRSLPSGGSASKSEINMGRRSLAQPHSLQRGLESGHPLAGDPDKKILLSHGLSLAMTDKPKVNPSDEERLIIARPQGEKNKPLSEVKRNKGTILLMVRDLSLPPLGEPQPQQTARRERDTGKSNWQTILSKASRVSSPQQIKGYFDFSSTYIVSRAFDKLGQLKRIDEETQFVSKLALETPPFQPLARETVPQNTCTQSVKKTAEDKRSPSGCYPEYALTSPVAAAHADEDQPKDVIGAEPKAGRSDGSGPAVQAAGHFHLIDDQKGKIQTSVSFSSARSSYPLAADRDNHNLKHQKERRKAALEGDCESKFTHLEREDGGGVAGVYLNGKASQSGMLLELPTHGPAPEAAEYSSMKVQTGSSEKYGDANAREPNNRSSSEAEDQACEAHPSAPSEVKATARSTRKPSQLTGPPPLGKAMESPSDGYINNPPYTSRTNKVQRPKTDRIEQEGNSAKGLARLSIGVDDKNIVSTKSPDLRDGIKPDILTSHASKTLGVCLSGDSSAAAVGKDQRKAPSTYGSPCAKRAAHSLKKAGTFTSHRRQLSTDIISRNKPSLGLLYFRRESRCQDYGKKLTPPQLNKQEALVCNYEEKSPKLGYLPRETSSANRLRSASPASTLMNQPKTKHQSPDITSVSIMLREHSIAKGRTADSSSSGIYTGKYTSLKTSAFDAMYALAEGIEAVTLPPLKQSKSPGPTGGSMSLGNKGNLHGYMSGGPSGELSGRRTNQQLSRRGEIHIVELDDYEEGKVTSASWGRVHVCVRDSGTQRSPEAGREADDEAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\"], \"target_seq\": \"SAEPGQEFKQPKGKLEAFQRIQRLDFSQYRPKPKYSPAHAKRFSGLDEDAPLPRSRRSKTRIARAKSSLSQSSFSSTQGRKGPPGSERAITPRENNRSVKELDLRKSESSKESLEAPGGRSDPFPKTRLGSNKGDVGSIHLQRSRVERVPSKSKSYGQSHQKYRADPGSLSPYALLLNPNKPAGPAQNVVPQRRDELSPTRPKAGGLRDPPSATPLDPRQLSALIRLHKKTCDQKNRAPIPLGLAGSKEFIARRFAAPSPLSSHFETYSGRRSNTRKKSPSKRKSLSRVGEKCRKNTPAETSEKKLLPVTYVTWLRLFYNKPFPTIPNTAKYQQPGASGARSLEDVAVFAVPLSLKMPASLWHIYPSGGVPAPHGTGPAHRSNGEGQSLDLEKASESVDVPHSMNRSGSAKDDADAEGSYPGYKLMKRPAPIFAPTQESPDPPAKLSESANRLMWLEGPLTRRVPARRTKLGDLLWRYDLPEGSPSSSFIASIINHLHDVGSTRKLLPVHGRSVESAREQGQSMDCYAPGMYKPCPSRSPQSLIQGEWPPRGQHAFKLYWAAGRFRNVRDVLILGRQEHLLEALMARVVGLDFDSSESTQQEGKHQDEPKGPYLSKGGQLLFTAAISKQAETLAYEDKSSRADVRLESDQLPILLWGESLVEQKQSRSIPSPDRAGSLSQTISISTPHSQEDSLPLGPLKSIRDQLPREEGKSPPLAESTIATVRKPEPSSSRKRRSLPSGGSASKSEINMGRRSLAQPHSLQRGLESGHPLAGDPDKKILLSHGLSLAMTDKPKVNPSDEERLIIARPQGEKNKPLSEVKRNKGTILLMVRDLSLPPLGEPQPQQTARRERDTGKSNWQTILSKASRVSSPQQIKGYFDFSSTYIVSRAFDKLGQLKRIDEETQFVSKLALETPPFQPLARETVPQNTCTQSVKKTAEDKRSPSGCYPEYALTSPVAAAHADEDQPKDVIGAEPKAGRSDGSGPAVQAAGHFHLIDDQKGKIQTSVSFSSARSSYPLAADRDNHNLKHQKERRKAALEGDCESKFTHLEREDGGGVAGVYLNGKASQSGMLLELPTHGPAPEAAEYSSMKVQTGSSEKYGDANAREPNNRSSSEAEDQACEAHPSAPSEVKATARSTRKPSQLTGPPPLGKAMESPSDGYINNPPYTSRTNKVQRPKTDRIEQEGNSAKGLARLSIGVDDKNIVSTKSPDLRDGIKPDILTSHASKTLGVCLSGDSSAAAVGKDQRKAPSTYGSPCAKRAAHSLKKAGTFTSHRRQLSTDIISRNKPSLGLLYFRRESRCQDYGKKLTPPQLNKQEALVCNYEEKSPKLGYLPRETSSANRLRSASPASTLMNQPKTKHQSPDITSVSIMLREHSIAKGRTADSSSSGIYTGKYTSLKTSAFDAMYALAEGIEAVTLPPLKQSKSPGPTGGSMSLGNKGNLHGYMSGGPSGELSGRRTNQQLSRRGEIHIVELDDYEEGKVTSASWGRVHVCVRDSGTQRSPEAGREADDEAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MPSSSDTALGGGGGLSWAEKKLEERRKRRRFLSPQQPPLLLPLLQPQLLQPPPPPPPLLFLAAPGTAAAAAAAAAASSSCFSPGPPLEVKRLARGKRRAGGRQKRRRGPRAGQEAEKRRVFSLPQPQQDGGGGASSGGGVTPLVEYEDVSSQSEQGLLLGGASAATAATAAGGTGGSGGSPASSSGTQRRGEGSERRPRRDRRSSSGRSKERHREHRRRDGQRGGSEASKSRSRHSHSGEERAEVAKSGSSSSSGGRRKSASATSSSSSSRKDRDSKAHRSRTKSSKEPPSAYKEPPKAYREDKTEPKAYRRRRSLSPLGGRDDSPVSHRASQSLRSRKSPSPAGGGSSPYSRRLPRSPSPYSRRRSPSYSRHSSYERGGDVSPSPYSSSSWRRSRSPYSPVLRRSGKSRSRSPYSSRHSRSRSRHRLSRSRSRHSSISPSTLTLKSSLAAELNKNKKARAAEAARAAEAAKAAEATKAAEAAAKAAKASNTSTPTKGNTETSASASQTNHVKDVKKIKIEHAPSPSSGGTLKNDKAKTKPPLQVTKVENNLIVDKATKKAVIVGKESKSAATKEESVSLKEKTKPLTPSIGAKEKEQHVALVTSTLPPLPLPPMLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELISRICGSPCPAVWPDVIKLPYFNTMKPKKQYRRKLREEFVFIPAAALDLFDYMLALDPSKRCTAEQALQCEFLRDVEPSKMPPPDLPLWQDCHELWSKKRRRQKQMGMTDDVSTIKAPRKDLSLGLDDSRTNTPQGVLPSSQLKSQGSSNVAPVKTGPGQHLNHSELAILLNLLQSKTSVNMADFVQVLNIKVNSETQQQLNKINLPAGILATGEKQTDPSTPQQESSKPLGGIQPSSQTIQPKVETDAAQAAVQSAFAVLLTQLIKAQQSKQKDVLLEERENGSGHEASLQLRPPPEPSTPVSGQDDLIQHQDMRILELTPEPDRPRILPPDQRPPEPPEPPPVTEEDLDYRTENQHVPTTSSSLTDPHAGVKAALLQLLAQHQPQDDPKREGGIDYQAGDTYVSTSDYKDNFGSSSFSSAPYVSNDGLGSSSAPPLERRSFIGNSDIQSLDNYSTASSHSGGPPQPSAFSESFPSSVAGYGDIYLNAGPMLFSGDKDHRFEYSHGPIAVLANSSDPSTGPESTHPLPAKMHNYNYGGNLQENPSGPSLMHGQTWTSPAQGPGYSQGYRGHISTSTGRGRGRGLPY' represents the valid sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPSSSDTALGGGGGLSWAEKKLEERRKRRRFLSPQQPPLLLPLLQPQLLQPPPPPPPLLFLAAPGTAAAAAAAAAASSSCFSPGPPLEVKRLARGKRRAGGRQKRRRGPRAGQEAEKRRVFSLPQPQQDGGGGASSGGGVTPLVEYEDVSSQSEQGLLLGGASAATAATAAGGTGGSGGSPASSSGTQRRGEGSERRPRRDRRSSSGRSKERHREHRRRDGQRGGSEASKSRSRHSHSGEERAEVAKSGSSSSSGGRRKSASATSSSSSSRKDRDSKAHRSRTKSSKEPPSAYKEPPKAYREDKTEPKAYRRRRSLSPLGGRDDSPVSHRASQSLRSRKSPSPAGGGSSPYSRRLPRSPSPYSRRRSPSYSRHSSYERGGDVSPSPYSSSSWRRSRSPYSPVLRRSGKSRSRSPYSSRHSRSRSRHRLSRSRSRHSSISPSTLTLKSSLAAELNKNKKARAAEAARAAEAAKAAEATKAAEAAAKAAKASNTSTPTKGNTETSASASQTNHVKDVKKIKIEHAPSPSSGGTLKNDKAKTKPPLQVTKVENNLIVDKATKKAVIVGKESKSAATKEESVSLKEKTKPLTPSIGAKEKEQHVALVTSTLPPLPLPPMLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELISRICGSPCPAVWPDVIKLPYFNTMKPKKQYRRKLREEFVFIPAAALDLFDYMLALDPSKRCTAEQALQCEFLRDVEPSKMPPPDLPLWQDCHELWSKKRRRQKQMGMTDDVSTIKAPRKDLSLGLDDSRTNTPQGVLPSSQLKSQGSSNVAPVKTGPGQHLNHSELAILLNLLQSKTSVNMADFVQVLNIKVNSETQQQLNKINLPAGILATGEKQTDPSTPQQESSKPLGGIQPSSQTIQPKVETDAAQAAVQSAFAVLLTQLIKAQQSKQKDVLLEERENGSGHEASLQLRPPPEPSTPVSGQDDLIQHQDMRILELTPEPDRPRILPPDQRPPEPPEPPPVTEEDLDYRTENQHVPTTSSSLTDPHAGVKAALLQLLAQHQPQDDPKREGGIDYQAGDTYVSTSDYKDNFGSSSFSSAPYVSNDGLGSSSAPPLERRSFIGNSDIQSLDNYSTASSHSGGPPQPSAFSESFPSSVAGYGDIYLNAGPMLFSGDKDHRFEYSHGPIAVLANSSDPSTGPESTHPLPAKMHNYNYGGNLQENPSGPSLMHGQTWTSPAQGPGYSQGYRGHISTSTGRGRGRGLPY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPSSSDTALGGGGGLSWAEKKLEERRKRRRFLSPQQPPLLLPLLQPQLLQPPPPPPPLLFLAAPGTAAAAAAAAAASSSCFSPGPPLEVKRLARGKRRAGGRQKRRRGPRAGQEAEKRRVFSLPQPQQDGGGGASSGGGVTPLVEYEDVSSQSEQGLLLGGASAATAATAAGGTGGSGGSPASSSGTQRRGEGSERRPRRDRRSSSGRSKERHREHRRRDGQRGGSEASKSRSRHSHSGEERAEVAKSGSSSSSGGRRKSASATSSSSSSRKDRDSKAHRSRTKSSKEPPSAYKEPPKAYREDKTEPKAYRRRRSLSPLGGRDDSPVSHRASQSLRSRKSPSPAGGGSSPYSRRLPRSPSPYSRRRSPSYSRHSSYERGGDVSPSPYSSSSWRRSRSPYSPVLRRSGKSRSRSPYSSRHSRSRSRHRLSRSRSRHSSISPSTLTLKSSLAAELNKNKKARAAEAARAAEAAKAAEATKAAEAAAKAAKASNTSTPTKGNTETSASASQTNHVKDVKKIKIEHAPSPSSGGTLKNDKAKTKPPLQVTKVENNLIVDKATKKAVIVGKESKSAATKEESVSLKEKTKPLTPSIGAKEKEQHVALVTSTLPPLPLPPMLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELISRICGSPCPAVWPDVIKLPYFNTMKPKKQYRRKLREEFVFIPAAALDLFDYMLALDPSKRCTAEQALQCEFLRDVEPSKMPPPDLPLWQDCHELWSKKRRRQKQMGMTDDVSTIKAPRKDLSLGLDDSRTNTPQGVLPSSQLKSQGSSNVAPVKTGPGQHLNHSELAILLNLLQSKTSVNMADFVQVLNIKVNSETQQQLNKINLPAGILATGEKQTDPSTPQQESSKPLGGIQPSSQTIQPKVETDAAQAAVQSAFAVLLTQLIKAQQSKQKDVLLEERENGSGHEASLQLRPPPEPSTPVSGQDDLIQHQDMRILELTPEPDRPRILPPDQRPPEPPEPPPVTEEDLDYRTENQHVPTTSSSLTDPHAGVKAALLQLLAQHQPQDDPKREGGIDYQAGDTYVSTSDYKDNFGSSSFSSAPYVSNDGLGSSSAPPLERRSFIGNSDIQSLDNYSTASSHSGGPPQPSAFSESFPSSVAGYGDIYLNAGPMLFSGDKDHRFEYSHGPIAVLANSSDPSTGPESTHPLPAKMHNYNYGGNLQENPSGPSLMHGQTWTSPAQGPGYSQGYRGHISTSTGRGRGRGLPY\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the protein 'MPSSSDTALGGGGGLSWAEKKLEERRKRRRFLSPQQPPLLLPLLQPQLLQPPPPPPPLLFLAAPGTAAAAAAAAAASSSCFSPGPPLEVKRLARGKRRAGGRQKRRRGPRAGQEAEKRRVFSLPQPQQDGGGGASSGGGVTPLVEYEDVSSQSEQGLLLGGASAATAATAAGGTGGSGGSPASSSGTQRRGEGSERRPRRDRRSSSGRSKERHREHRRRDGQRGGSEASKSRSRHSHSGEERAEVAKSGSSSSSGGRRKSASATSSSSSSRKDRDSKAHRSRTKSSKEPPSAYKEPPKAYREDKTEPKAYRRRRSLSPLGGRDDSPVSHRASQSLRSRKSPSPAGGGSSPYSRRLPRSPSPYSRRRSPSYSRHSSYERGGDVSPSPYSSSSWRRSRSPYSPVLRRSGKSRSRSPYSSRHSRSRSRHRLSRSRSRHSSISPSTLTLKSSLAAELNKNKKARAAEAARAAEAAKAAEATKAAEAAAKAAKASNTSTPTKGNTETSASASQTNHVKDVKKIKIEHAPSPSSGGTLKNDKAKTKPPLQVTKVENNLIVDKATKKAVIVGKESKSAATKEESVSLKEKTKPLTPSIGAKEKEQHVALVTSTLPPLPLPPMLPEDKEADSLRGNISVKAVKKEVEKKLRCLLADLPLPPELPGGDDLSKSPEEKKTATQLHSKRRPKICGPRYGETKEKDIDWGKRCVDKFDIIGIIGEGTYGQVYKARDKDTGEMVALKKVRLDNEKEGFPITAIREIKILRQLTHQSIINMKEIVTDKEDALDFKKDKGAFYLVFEYMDHDLMGLLESGLVHFNENHIKSFMRQLMEGLDYCHKKNFLHRDIKCSNILLNNRGQIKLADFGLARLYSSEESRPYTNKVITLWYRPPELLLGEERYTPAIDVWSCGCILGELFTKKPIFQANQELAQLELISRICGSPCPAVWPDVIKLPYFNTMKPKKQYRRKLREEFVFIPAAALDLFDYMLALDPSKRCTAEQALQCEFLRDVEPSKMPPPDLPLWQDCHELWSKKRRRQKQMGMTDDVSTIKAPRKDLSLGLDDSRTNTPQGVLPSSQLKSQGSSNVAPVKTGPGQHLNHSELAILLNLLQSKTSVNMADFVQVLNIKVNSETQQQLNKINLPAGILATGEKQTDPSTPQQESSKPLGGIQPSSQTIQPKVETDAAQAAVQSAFAVLLTQLIKAQQSKQKDVLLEERENGSGHEASLQLRPPPEPSTPVSGQDDLIQHQDMRILELTPEPDRPRILPPDQRPPEPPEPPPVTEEDLDYRTENQHVPTTSSSLTDPHAGVKAALLQLLAQHQPQDDPKREGGIDYQAGDTYVSTSDYKDNFGSSSFSSAPYVSNDGLGSSSAPPLERRSFIGNSDIQSLDNYSTASSHSGGPPQPSAFSESFPSSVAGYGDIYLNAGPMLFSGDKDHRFEYSHGPIAVLANSSDPSTGPESTHPLPAKMHNYNYGGNLQENPSGPSLMHGQTWTSPAQGPGYSQGYRGHISTSTGRGRGRGLPY' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21'-target protein 'NRSIVVRSELERIRMSFKLKPQLPKELAAPQGLTEISCSQMNEQPVEKGQIVTDLLNMIMHRMGGYTQGEPKGVRGDLELTESTQFPLKKVGNAPTMGGGICLDREGLYLSSLAAAYQLAVRITILCLEQEQEARGTIDSRGESQRGWLFVELFLAMPYSMQCQTWLGFMQRLCLGERTSNSNSIDESKEVQKNFLFVIKIRVSPLFKSEEMILPFQMQGVLRNEDGGVQCAAETRLERALLEIEQAMTFLQQGQYQLDLILQGQFERDLRGKTTLEEHLSTNTDAVQMSKNSLHSETSLISNRIDVKAYHTECSCDHHSELRYEQDCENPKENEERTNKLNLNPNMLNAELLKLCWILNQQASIISEKVWEDRSVGSQLKEPASGLDLKQTPLAIKTISWPRRKMLPTRDSQSDQWKKPQEAEQRESLLGNVQVVDNLADDQKMASELQDRGTSCVREFCPHVLHTLDEPREERLNNELTEHPDKFDETQQFILQITPDKTRNQGGQLTCEKGLFDFIWYLLSAWTDESCRLLLQLAWGNKSEKWTVPNDKKGAHALQLRIVQQKAHLRLLPEQEIDVYLGEVLYQVERVESCIITAQTTSVHGPRENPRIWLLSASESLMLVVTLATGKPAKTAKKREAYEQLLNERLFVDAKWNEQWGMQPPQFRMVDLPVLLVCISASDSMDINLSRNCILMWKYCTRLWHSYNPTSQPMALVILKDKQSVKAVYELQVLNEGQRLTDLQPKDVLRQAPQDSAAAAAAAAAAAAAAAAAA' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\"], \"target_seq\": \"NRSIVVRSELERIRMSFKLKPQLPKELAAPQGLTEISCSQMNEQPVEKGQIVTDLLNMIMHRMGGYTQGEPKGVRGDLELTESTQFPLKKVGNAPTMGGGICLDREGLYLSSLAAAYQLAVRITILCLEQEQEARGTIDSRGESQRGWLFVELFLAMPYSMQCQTWLGFMQRLCLGERTSNSNSIDESKEVQKNFLFVIKIRVSPLFKSEEMILPFQMQGVLRNEDGGVQCAAETRLERALLEIEQAMTFLQQGQYQLDLILQGQFERDLRGKTTLEEHLSTNTDAVQMSKNSLHSETSLISNRIDVKAYHTECSCDHHSELRYEQDCENPKENEERTNKLNLNPNMLNAELLKLCWILNQQASIISEKVWEDRSVGSQLKEPASGLDLKQTPLAIKTISWPRRKMLPTRDSQSDQWKKPQEAEQRESLLGNVQVVDNLADDQKMASELQDRGTSCVREFCPHVLHTLDEPREERLNNELTEHPDKFDETQQFILQITPDKTRNQGGQLTCEKGLFDFIWYLLSAWTDESCRLLLQLAWGNKSEKWTVPNDKKGAHALQLRIVQQKAHLRLLPEQEIDVYLGEVLYQVERVESCIITAQTTSVHGPRENPRIWLLSASESLMLVVTLATGKPAKTAKKREAYEQLLNERLFVDAKWNEQWGMQPPQFRMVDLPVLLVCISASDSMDINLSRNCILMWKYCTRLWHSYNPTSQPMALVILKDKQSVKAVYELQVLNEGQRLTDLQPKDVLRQAPQDSAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSWSPSLTTQTCGAWEMKERLGTGGFGNVIRWHNQETGEQIAIKQCRQELSPRNRERWCLEIQIMRRLTHPNVVAARDVPEGMQNLAPNDLPLLAMEYCQGGDLRKYLNQFENCCGLREGAILTLLSDIASALRYLHENRIIHRDLKPENIVLQQGEQRLIHKIIDLGYAKELDQGSLCTSFVGTLQYLAPELLEQQKYTVTVDYWSFGTLAFECITGFRPFLPNWQPVQWHSKVRQKSEVDIVVSEDLNGTVKFSSSLPYPNNLNSVLAERLEKWLQLMLMWHPRQRGTDPTYGPNGCFKALDDILNLKLVHILNMVTGTIHTYPVTEDESLQSLKARIQQDTGIPEEDQELLQEAGLALIPDKPATQCISDGKLNEGHTLDMDLVFLFDNSKITYETQISPRPQPESVSCILQEPKRNLAFFQLRKVWGQVWHSIQTLKEDCNRLQQGQRAAMMNLLRNNSCLSKMKNSMASMSQQLKAKLDFFKTSIQIDLEKYSEQTEFGITSDKLLLAWREMEQAVELCGRENEVKLLVERMMALQTDIVDLQRSPMGRKQGGTLDDLEEQARELYRRLREKPRDQRTEGDSQEMVRLLLQAIQSFEKKVRVIYTQLSKTVVCKQKALELLPKVEEVVSLMNEDEKTVVRLQEKRQKELWNLLKIACSKVRGPVSGSPDSMNASRLSQPGQLMSQPSTASNSLPEPAKKSEELVAEAHNLCTLLENAIQDTVREQDQSFTALDWSWLQTEEEEHSCLEQAS' works as the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSWSPSLTTQTCGAWEMKERLGTGGFGNVIRWHNQETGEQIAIKQCRQELSPRNRERWCLEIQIMRRLTHPNVVAARDVPEGMQNLAPNDLPLLAMEYCQGGDLRKYLNQFENCCGLREGAILTLLSDIASALRYLHENRIIHRDLKPENIVLQQGEQRLIHKIIDLGYAKELDQGSLCTSFVGTLQYLAPELLEQQKYTVTVDYWSFGTLAFECITGFRPFLPNWQPVQWHSKVRQKSEVDIVVSEDLNGTVKFSSSLPYPNNLNSVLAERLEKWLQLMLMWHPRQRGTDPTYGPNGCFKALDDILNLKLVHILNMVTGTIHTYPVTEDESLQSLKARIQQDTGIPEEDQELLQEAGLALIPDKPATQCISDGKLNEGHTLDMDLVFLFDNSKITYETQISPRPQPESVSCILQEPKRNLAFFQLRKVWGQVWHSIQTLKEDCNRLQQGQRAAMMNLLRNNSCLSKMKNSMASMSQQLKAKLDFFKTSIQIDLEKYSEQTEFGITSDKLLLAWREMEQAVELCGRENEVKLLVERMMALQTDIVDLQRSPMGRKQGGTLDDLEEQARELYRRLREKPRDQRTEGDSQEMVRLLLQAIQSFEKKVRVIYTQLSKTVVCKQKALELLPKVEEVVSLMNEDEKTVVRLQEKRQKELWNLLKIACSKVRGPVSGSPDSMNASRLSQPGQLMSQPSTASNSLPEPAKKSEELVAEAHNLCTLLENAIQDTVREQDQSFTALDWSWLQTEEEEHSCLEQAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSWSPSLTTQTCGAWEMKERLGTGGFGNVIRWHNQETGEQIAIKQCRQELSPRNRERWCLEIQIMRRLTHPNVVAARDVPEGMQNLAPNDLPLLAMEYCQGGDLRKYLNQFENCCGLREGAILTLLSDIASALRYLHENRIIHRDLKPENIVLQQGEQRLIHKIIDLGYAKELDQGSLCTSFVGTLQYLAPELLEQQKYTVTVDYWSFGTLAFECITGFRPFLPNWQPVQWHSKVRQKSEVDIVVSEDLNGTVKFSSSLPYPNNLNSVLAERLEKWLQLMLMWHPRQRGTDPTYGPNGCFKALDDILNLKLVHILNMVTGTIHTYPVTEDESLQSLKARIQQDTGIPEEDQELLQEAGLALIPDKPATQCISDGKLNEGHTLDMDLVFLFDNSKITYETQISPRPQPESVSCILQEPKRNLAFFQLRKVWGQVWHSIQTLKEDCNRLQQGQRAAMMNLLRNNSCLSKMKNSMASMSQQLKAKLDFFKTSIQIDLEKYSEQTEFGITSDKLLLAWREMEQAVELCGRENEVKLLVERMMALQTDIVDLQRSPMGRKQGGTLDDLEEQARELYRRLREKPRDQRTEGDSQEMVRLLLQAIQSFEKKVRVIYTQLSKTVVCKQKALELLPKVEEVVSLMNEDEKTVVRLQEKRQKELWNLLKIACSKVRGPVSGSPDSMNASRLSQPGQLMSQPSTASNSLPEPAKKSEELVAEAHNLCTLLENAIQDTVREQDQSFTALDWSWLQTEEEEHSCLEQAS\", \"result\": [{\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21' and the protein 'MSWSPSLTTQTCGAWEMKERLGTGGFGNVIRWHNQETGEQIAIKQCRQELSPRNRERWCLEIQIMRRLTHPNVVAARDVPEGMQNLAPNDLPLLAMEYCQGGDLRKYLNQFENCCGLREGAILTLLSDIASALRYLHENRIIHRDLKPENIVLQQGEQRLIHKIIDLGYAKELDQGSLCTSFVGTLQYLAPELLEQQKYTVTVDYWSFGTLAFECITGFRPFLPNWQPVQWHSKVRQKSEVDIVVSEDLNGTVKFSSSLPYPNNLNSVLAERLEKWLQLMLMWHPRQRGTDPTYGPNGCFKALDDILNLKLVHILNMVTGTIHTYPVTEDESLQSLKARIQQDTGIPEEDQELLQEAGLALIPDKPATQCISDGKLNEGHTLDMDLVFLFDNSKITYETQISPRPQPESVSCILQEPKRNLAFFQLRKVWGQVWHSIQTLKEDCNRLQQGQRAAMMNLLRNNSCLSKMKNSMASMSQQLKAKLDFFKTSIQIDLEKYSEQTEFGITSDKLLLAWREMEQAVELCGRENEVKLLVERMMALQTDIVDLQRSPMGRKQGGTLDDLEEQARELYRRLREKPRDQRTEGDSQEMVRLLLQAIQSFEKKVRVIYTQLSKTVVCKQKALELLPKVEEVVSLMNEDEKTVVRLQEKRQKELWNLLKIACSKVRGPVSGSPDSMNASRLSQPGQLMSQPSTASNSLPEPAKKSEELVAEAHNLCTLLENAIQDTVREQDQSFTALDWSWLQTEEEEHSCLEQAS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug 'COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl' and the target protein 'ESRVLNRPLYQLQESKYRMIGPDLGFRDFNQKHPPTQRTHKSKPISQHLYGQQLYRDLTESSKFSKSDNEFALQGTLYEMLVIYFYENDPLLEIVHMEIYKKVIVVYFTLDPRCMYAIEYKLIEHTFRHVTTAAIKERDFPDVLEASGAESKETAGLTEDQDSPSEGIYAPAAVKVFAFDVERFLTMWDTVEAVDRPDVRLIPLMQANSSTRHEASLLLLELIFRGSRDLWTVTSIHRWSTYGKVIERNKEPDELIVFQGLEERMSYKMDLLNGTQFLFCKAQYKEVLNPGVEYGKEENCRVLLINCDMMRHYRGLGDEIGRVCGGVRKRRYKSLEPVNPVTVVRGNQKWRFDSFMVPRYPHELRILPPKILAIADIVAAVLTFVETAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\"], \"target_seq\": \"ESRVLNRPLYQLQESKYRMIGPDLGFRDFNQKHPPTQRTHKSKPISQHLYGQQLYRDLTESSKFSKSDNEFALQGTLYEMLVIYFYENDPLLEIVHMEIYKKVIVVYFTLDPRCMYAIEYKLIEHTFRHVTTAAIKERDFPDVLEASGAESKETAGLTEDQDSPSEGIYAPAAVKVFAFDVERFLTMWDTVEAVDRPDVRLIPLMQANSSTRHEASLLLLELIFRGSRDLWTVTSIHRWSTYGKVIERNKEPDELIVFQGLEERMSYKMDLLNGTQFLFCKAQYKEVLNPGVEYGKEENCRVLLINCDMMRHYRGLGDEIGRVCGGVRKRRYKSLEPVNPVTVVRGNQKWRFDSFMVPRYPHELRILPPKILAIADIVAAVLTFVETAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The right sequence for the protein is 'MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\", \"result\": [{\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 340.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl' and the protein 'MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY' is 340.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the affinity between the drug 'NC(=O)c1ccc(S(N)(=O)=O)cc1' and the target protein 'RLPDTKNAGPLMKPLVGFWDRSHGAKFDWVHPSGIMLKHSDVLEGFSHADVVDPFEADLFKRDKEQKIGKAKLYVIPLGQVDIRQKHYNHFLHIEHAHEYELQVHENDAGDADGPSYRYHGILTDTQKTSGYLLTAVLNIEFGQELDGLKPFEPWFGGEDDKVKGLISFPTPFGSGVANTLGVSTVLELLVPWWQKTVNSFANEGWLQDSPNLTAKCPKKLYSKKRQNKSAKPSIDYNTQWQPSGKTVSLPSVHRSDKGLAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1ccc(S(N)(=O)=O)cc1\"], \"target_seq\": \"RLPDTKNAGPLMKPLVGFWDRSHGAKFDWVHPSGIMLKHSDVLEGFSHADVVDPFEADLFKRDKEQKIGKAKLYVIPLGQVDIRQKHYNHFLHIEHAHEYELQVHENDAGDADGPSYRYHGILTDTQKTSGYLLTAVLNIEFGQELDGLKPFEPWFGGEDDKVKGLISFPTPFGSGVANTLGVSTVLELLVPWWQKTVNSFANEGWLQDSPNLTAKCPKKLYSKKRQNKSAKPSIDYNTQWQPSGKTVSLPSVHRSDKGLAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDDSQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVLGIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEPISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDDSQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVLGIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEPISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDDSQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVLGIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEPISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK\", \"result\": [{\"smile\": \"NC(=O)c1ccc(S(N)(=O)=O)cc1\", \"value\": 120.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'NC(=O)c1ccc(S(N)(=O)=O)cc1' and the protein 'MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTHTAKYDPSLKPLSVSYDQATSLRILNNGHAFNVEFDDSQDKAVLKGGPLDGTYRLIQFHFHWGSLDGQGSEHTVDKKKYAAELHLVHWNTKYGDFGKAVQQPDGLAVLGIFLKVGSAKPGLQKVVDVLDSIKTKGKSADFTNFDPRGLLPESLDYWTYPGSLTTPPLLECVTWIVLKEPISVSSEQVLKFRKLNFNGEGEPEELMVDNWRPAQPLKNRQIKASFK' is 120.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1' and the target protein 'EEANVIPSNILAIIMQLSRSKGLYERLVDGLEFTRVNLESYLKRLKCYGDSGDVKDGRTNTPSYAMACLMNNCFKVGDLARDRCEKILIRAAKPYGLVMQAGVTINALLLGGLLKCGDLIVCNAPGLIACGSMIISDFVQFVDMCSQFHAMSTQGTVQRPGSFGPSTVYAEYIAAGVLQRFGLGIDETFVSKVTKTSIECPGIVVSLPNPILARFGYTFNTRDSQKQIHVILPRNLDADKMNIFMILSIYDDVNGMENMMKKVWGKNATSMDVATVQNGVIDSCMEACFYGEGKGVDRSDNAGFMQKIWNEGAFEGERMQNQRVRVLEPNPFPDSQTRLRHTRDPIFSAPLQWTNFYDRLHQGIDSLNVGIALIQSRDYQIMRTIWSLPVTTSYAQSINRFYGVYQVDFHFVVTDRDEAQYLVWEVPATDRRWTCVGQGFETTLGKWNASSGYPENIPVITSTHSEDDSIIKLTCHSPTGRDMYTSARLVGETVEYDAGNTFGSSGYMSHDDATSVPPTKCSIEAGASPYTTFPDSLIAAAQIIEEYQQSAQISKGPMKSCSCKLLGPGKDVLFCGPLNAEQKFAYLEDRVKRSSMQTRIRKITCKMLEVNEAINQVPVYGPFTCTITEEQVASLAQPAAEDPIMELLSTHQMTEPTEPIMVSQDWSCYGAWWYIRRASVVTYKSNTDTGETGTESADSGLIPRENFREMLAVVIKIIIIIGRSGFRMKDRLGSAKSSVQFQAKKPRGRVPRSIHFGTPSNVVNNCHCGGARMVSPEKSEDYPGESERIYYEVLLTAGFRVEVGCAMSAGKFSGIRLVQKVLWLVANPIHTANDSLDLVAVGADNNQIARNGFNYAAKCKKGQCYKVLSGIAPNHVAPLAKCTRYLSGDVEQSEAGDNGLYMCNMSTGITDRSKTTAVRPGMWQFGEDREDTGTNREYNETETMAACQCQTKLCRYWFSAVEIRTISEEEHMLEYAVYRPLVRIEQSGAPGDRPDPGKHKRESPKEITYFSPQKNARKDTTSTNMQPQELDQVRLNFLDNLLVSTFPIAFWSMFHAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\"], \"target_seq\": \"EEANVIPSNILAIIMQLSRSKGLYERLVDGLEFTRVNLESYLKRLKCYGDSGDVKDGRTNTPSYAMACLMNNCFKVGDLARDRCEKILIRAAKPYGLVMQAGVTINALLLGGLLKCGDLIVCNAPGLIACGSMIISDFVQFVDMCSQFHAMSTQGTVQRPGSFGPSTVYAEYIAAGVLQRFGLGIDETFVSKVTKTSIECPGIVVSLPNPILARFGYTFNTRDSQKQIHVILPRNLDADKMNIFMILSIYDDVNGMENMMKKVWGKNATSMDVATVQNGVIDSCMEACFYGEGKGVDRSDNAGFMQKIWNEGAFEGERMQNQRVRVLEPNPFPDSQTRLRHTRDPIFSAPLQWTNFYDRLHQGIDSLNVGIALIQSRDYQIMRTIWSLPVTTSYAQSINRFYGVYQVDFHFVVTDRDEAQYLVWEVPATDRRWTCVGQGFETTLGKWNASSGYPENIPVITSTHSEDDSIIKLTCHSPTGRDMYTSARLVGETVEYDAGNTFGSSGYMSHDDATSVPPTKCSIEAGASPYTTFPDSLIAAAQIIEEYQQSAQISKGPMKSCSCKLLGPGKDVLFCGPLNAEQKFAYLEDRVKRSSMQTRIRKITCKMLEVNEAINQVPVYGPFTCTITEEQVASLAQPAAEDPIMELLSTHQMTEPTEPIMVSQDWSCYGAWWYIRRASVVTYKSNTDTGETGTESADSGLIPRENFREMLAVVIKIIIIIGRSGFRMKDRLGSAKSSVQFQAKKPRGRVPRSIHFGTPSNVVNNCHCGGARMVSPEKSEDYPGESERIYYEVLLTAGFRVEVGCAMSAGKFSGIRLVQKVLWLVANPIHTANDSLDLVAVGADNNQIARNGFNYAAKCKKGQCYKVLSGIAPNHVAPLAKCTRYLSGDVEQSEAGDNGLYMCNMSTGITDRSKTTAVRPGMWQFGEDREDTGTNREYNETETMAACQCQTKLCRYWFSAVEIRTISEEEHMLEYAVYRPLVRIEQSGAPGDRPDPGKHKRESPKEITYFSPQKNARKDTTSTNMQPQELDQVRLNFLDNLLVSTFPIAFWSMFHAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The sequence that corresponds to the protein is 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG\", \"result\": [{\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1' and the protein 'MALRRLGAALLLLPLLAAVEETLMDSTTATAELGWMVHPPSGWEEVSGYDENMNTIRTYQVCNVFESSQNNWLRTKFIRRRGAHRIHVEMKFSVRDCSSIPSVPGSCKETFNLYYYEADFDSATKTFPNWMENPWVKVDTIAADESFSQVDLGGRVMKINTEVRSFGPVSRSGFYLAFQDYGGCMSLIAVRVFYRKCPRIIQNGAIFQETLSGAESTSLVAARGSCIANAEEVDVPIKLYCNGDGEWLVPIGRCMCKAGFEAVENGTVCRGCPSGTFKANQGDEACTHCPINSRTTSEGATNCVCRNGYYRADLDPLDMPCTTIPSAPQAVISSVNETSLMLEWTPPRDSGGREDLVYNIICKSCGSGRGACTRCGDNVQYAPRQLGLTEPRIYISDLLAHTQYTFEIQAVNGVTDQSPFSPQFASVNITTNQAAPSAVSIMHQVSRTVDSITLSWSQPDQPNGVILDYELQYYEKELSEYNATAIKSPTNTVTVQGLKAGAIYVFQVRARTVAGYGRYSGKMYFQTMTEAEYQTSIQEKLPLIIGSSAAGLVFLIAVVVIAIVCNRRGFERADSEYTDKLQHYTSGHMTPGMKIYIDPFTYEDPNEAVREFAKEIDISCVKIEQVIGAGEFGEVCSGHLKLPGKREIFVAIKTLKSGYTEKQRRDFLSEASIMGQFDHPNVIHLEGVVTKSTPVMIITEFMENGSLDSFLRQNDGQFTVIQLVGMLRGIAAGMKYLADMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDTSDPTYTSALGGKIPIRWTAPEAIQYRKFTSASDVWSYGIVMWEVMSYGERPYWDMTNQDVINAIEQDYRLPPPMDCPSALHQLMLDCWQKDRNHRPKFGQIVNTLDKMIRNPNSLKAMAPLSSGINLPLLDRTIPDYTSFNTVDEWLEAIKMGQYKESFANAGFTSFDVVSQMMMEDILRVGVTLAGHQKKILNSIQVMRAQMNQIQSVEGQPLARRPRATGRTKRCQPRDVTKKTCNSNDGKKKGMGKKKTDPGRGREIQGIFFKEDSHKESNDCSCGG' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drug 'c1ccc2cc(-c3cc(CN4CCCCC4)c4ccccc4n3)ccc2c1' and the target protein 'IEGALNDQFAPCSKQGIFTGMVSMKINPEKFGDTGENFDNTHKCKITLEKKFGIFMLHDRKPMNYLEDIRRIPPVVGSFGTGVHKVGCVNGFKDTGGFATFEEIGGCGNRKSVNSVATMFFLRNLHWYGGAIAEKAPKSTDTGGSQFGTKEGFELAGFVRAEISEAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c1ccc2cc(-c3cc(CN4CCCCC4)c4ccccc4n3)ccc2c1\"], \"target_seq\": \"IEGALNDQFAPCSKQGIFTGMVSMKINPEKFGDTGENFDNTHKCKITLEKKFGIFMLHDRKPMNYLEDIRRIPPVVGSFGTGVHKVGCVNGFKDTGGFATFEEIGGCGNRKSVNSVATMFFLRNLHWYGGAIAEKAPKSTDTGGSQFGTKEGFELAGFVRAEISEAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGFGYKGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTNGSQFFICTAKTEWLDGKHVVFGKVKEGMNIVEAMERFGSRNGKTSKKITIADCGQLE'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGFGYKGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTNGSQFFICTAKTEWLDGKHVVFGKVKEGMNIVEAMERFGSRNGKTSKKITIADCGQLE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGFGYKGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTNGSQFFICTAKTEWLDGKHVVFGKVKEGMNIVEAMERFGSRNGKTSKKITIADCGQLE\", \"result\": [{\"smile\": \"c1ccc2cc(-c3cc(CN4CCCCC4)c4ccccc4n3)ccc2c1\", \"value\": 212.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'c1ccc2cc(-c3cc(CN4CCCCC4)c4ccccc4n3)ccc2c1' and the protein 'MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGFGYKGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTNGSQFFICTAKTEWLDGKHVVFGKVKEGMNIVEAMERFGSRNGKTSKKITIADCGQLE' is 212.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1' and the target protein 'WFHGIELLELNVKGGQDIAMMAYTLMFRAIGFTRANTLLTIWLDMSTGHEIIWKRPKNPFGHPLIHPLFISDGLEDQYLAQLPSIFALAEGTIGGVDLLVEKFDPDSLCPAYTTTFSVKAFPPIVLRFEIPIDDAATTEKIPEDLFSHIPRGFRANLSAEEENEGKGNRWVNTGKVKMPKIVDELHALDPDPPHKGDPYLEDVEQRKPAKLETEGAVSSTHRDLVYRFKGCWDFGPVEDLQLATSDCLNLLKRDVVLMVGVPKLAKFDHAVSVHLKPFNRKIAVTPYSLKYGQSRPRTSVYAAAKKFRELHDQAFQNPVYPFEVSRFLHKWKRRDGEFEWWEIDFSGKAHYFDRISVKAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1\"], \"target_seq\": \"WFHGIELLELNVKGGQDIAMMAYTLMFRAIGFTRANTLLTIWLDMSTGHEIIWKRPKNPFGHPLIHPLFISDGLEDQYLAQLPSIFALAEGTIGGVDLLVEKFDPDSLCPAYTTTFSVKAFPPIVLRFEIPIDDAATTEKIPEDLFSHIPRGFRANLSAEEENEGKGNRWVNTGKVKMPKIVDELHALDPDPPHKGDPYLEDVEQRKPAKLETEGAVSSTHRDLVYRFKGCWDFGPVEDLQLATSDCLNLLKRDVVLMVGVPKLAKFDHAVSVHLKPFNRKIAVTPYSLKYGQSRPRTSVYAAAKKFRELHDQAFQNPVYPFEVSRFLHKWKRRDGEFEWWEIDFSGKAHYFDRISVKAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF' works as the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF\", \"result\": [{\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1' and the protein 'MEAPGLAQAAAAESDSRKVAEETPDGAPALCPSPEALSPEPPVYSLQDFDTLATVGTGTFGRVHLVKEKTAKHFFALKVMSIPDVIRLKQEQHVHNEKSVLKEVSHPFLIRLFWTWHDERFLYMLMEYVPGGELFSYLRNRGRFSSTTGLFYSAEIICAIEYLHSKEIVYRDLKPENILLDRDGHIKLTDFGFAKKLVDRTWTLCGTPEYLAPEVIQSKGHGRAVDWWALGILIFEMLSGFPPFFDDNPFGIYQKILAGKIDFPRHLDFHVKDLIKKLLVVDRTRRLGNMKNGANDVKHHRWFRSVDWEAVPQRKLKPPIVPKIAGDGDTSNFETYPENDWDTAAPVPQKDLEIFKNF' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4''s affinity with the target protein 'LGQVVEMTKRAPPEPADAHYLDELWGVAAYMDYKSNIMERHVQLRMGLDVAKTKFLFSPVDLPWDGYLDDVHVPSATKMCRTSADFVEDSSPSKIETIAATVFPEKTGTVLLLDSPLATKAGTNRIMYVAVMILKYRDNALHFGKKKKFIEEKPEGFNAQSCIPEIEMLSKVLKKIPITKNKSDHGPAEWQLLTRQAVRQGHESYTQKEERESGRTKKLLISEQQGLITTLIFVANTEDCAGQAPSIEDEGTVSQPILTLPEVDEDCHIPFDPIEKIIVKAREVEPKILLPLQSVIDGMTFTTMLHEVYENTALEQKFQSIEQGLMPKRHEVFLDEMQAGFILKSDFKEAAFRPISSPLEPLEGGPSTMKAPKRIGGKSVAGGGKFDTTNKLANNDPYVTTNKVVSKKLKYSQRLSIEDEPTIPLCPSKRSSKISEGLFKISATLNPDAVSDEREEMGVLELPKETKEATYKAVDPNNMVGDPAPMPAVAQKVDPVKTLKNPSNITVSPEPKLNNLGKGNPGEPAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"LGQVVEMTKRAPPEPADAHYLDELWGVAAYMDYKSNIMERHVQLRMGLDVAKTKFLFSPVDLPWDGYLDDVHVPSATKMCRTSADFVEDSSPSKIETIAATVFPEKTGTVLLLDSPLATKAGTNRIMYVAVMILKYRDNALHFGKKKKFIEEKPEGFNAQSCIPEIEMLSKVLKKIPITKNKSDHGPAEWQLLTRQAVRQGHESYTQKEERESGRTKKLLISEQQGLITTLIFVANTEDCAGQAPSIEDEGTVSQPILTLPEVDEDCHIPFDPIEKIIVKAREVEPKILLPLQSVIDGMTFTTMLHEVYENTALEQKFQSIEQGLMPKRHEVFLDEMQAGFILKSDFKEAAFRPISSPLEPLEGGPSTMKAPKRIGGKSVAGGGKFDTTNKLANNDPYVTTNKVVSKKLKYSQRLSIEDEPTIPLCPSKRSSKISEGLFKISATLNPDAVSDEREEMGVLELPKETKEATYKAVDPNNMVGDPAPMPAVAQKVDPVKTLKNPSNITVSPEPKLNNLGKGNPGEPAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSDNGELEDKPPAPPVRMSSTIFSTGGKDPLSANHSLKPLPSVPEEKKPRHKIISIFSGTEKGSKKKEKERPEISPPSDFEHTIHVGFDAVTGEFTGMPEQWARLLQTSNITKLEQKKNPQAVLDVLKFYDSNTVKQKYLSFTPPEKDGFPSGTPALNAKGTEAPAVVTEEEDDDEETAPPVIAPRPDHTKSIYTRSVIDPVPAPVGDSHVDGAAKSLDKQKKKTKMTDEEIMEKLRTIVSIGDPKKKYTRYEKIGQGASGTVFTATDVALGQEVAIKQINLQKQPKKELIINEILVMKELKNPNIVNFLDSYLVGDELFVVMEYLAGGSLTDVVTETCMDEAQIAAVCRECLQALEFLHANQVIHRDIKSDNVLLGMEGSVKLTDFGFCAQITPEQSKRSTMVGTPYWMAPEVVTRKAYGPKVDIWSLGIMAIEMVEGEPPYLNENPLRALYLIATNGTPELQNPEKLSPIFRDFLNRCLEMDVEKRGSAKELLQHPFLKLAKPLSSLTPLIMAAKEAMKSNR' is the accurate sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSDNGELEDKPPAPPVRMSSTIFSTGGKDPLSANHSLKPLPSVPEEKKPRHKIISIFSGTEKGSKKKEKERPEISPPSDFEHTIHVGFDAVTGEFTGMPEQWARLLQTSNITKLEQKKNPQAVLDVLKFYDSNTVKQKYLSFTPPEKDGFPSGTPALNAKGTEAPAVVTEEEDDDEETAPPVIAPRPDHTKSIYTRSVIDPVPAPVGDSHVDGAAKSLDKQKKKTKMTDEEIMEKLRTIVSIGDPKKKYTRYEKIGQGASGTVFTATDVALGQEVAIKQINLQKQPKKELIINEILVMKELKNPNIVNFLDSYLVGDELFVVMEYLAGGSLTDVVTETCMDEAQIAAVCRECLQALEFLHANQVIHRDIKSDNVLLGMEGSVKLTDFGFCAQITPEQSKRSTMVGTPYWMAPEVVTRKAYGPKVDIWSLGIMAIEMVEGEPPYLNENPLRALYLIATNGTPELQNPEKLSPIFRDFLNRCLEMDVEKRGSAKELLQHPFLKLAKPLSSLTPLIMAAKEAMKSNR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSDNGELEDKPPAPPVRMSSTIFSTGGKDPLSANHSLKPLPSVPEEKKPRHKIISIFSGTEKGSKKKEKERPEISPPSDFEHTIHVGFDAVTGEFTGMPEQWARLLQTSNITKLEQKKNPQAVLDVLKFYDSNTVKQKYLSFTPPEKDGFPSGTPALNAKGTEAPAVVTEEEDDDEETAPPVIAPRPDHTKSIYTRSVIDPVPAPVGDSHVDGAAKSLDKQKKKTKMTDEEIMEKLRTIVSIGDPKKKYTRYEKIGQGASGTVFTATDVALGQEVAIKQINLQKQPKKELIINEILVMKELKNPNIVNFLDSYLVGDELFVVMEYLAGGSLTDVVTETCMDEAQIAAVCRECLQALEFLHANQVIHRDIKSDNVLLGMEGSVKLTDFGFCAQITPEQSKRSTMVGTPYWMAPEVVTRKAYGPKVDIWSLGIMAIEMVEGEPPYLNENPLRALYLIATNGTPELQNPEKLSPIFRDFLNRCLEMDVEKRGSAKELLQHPFLKLAKPLSSLTPLIMAAKEAMKSNR\", \"result\": [{\"smile\": \"CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 3.1}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CN[C@@H]1C[C@H]2O[C@@](C)([C@@H]1OC)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MSDNGELEDKPPAPPVRMSSTIFSTGGKDPLSANHSLKPLPSVPEEKKPRHKIISIFSGTEKGSKKKEKERPEISPPSDFEHTIHVGFDAVTGEFTGMPEQWARLLQTSNITKLEQKKNPQAVLDVLKFYDSNTVKQKYLSFTPPEKDGFPSGTPALNAKGTEAPAVVTEEEDDDEETAPPVIAPRPDHTKSIYTRSVIDPVPAPVGDSHVDGAAKSLDKQKKKTKMTDEEIMEKLRTIVSIGDPKKKYTRYEKIGQGASGTVFTATDVALGQEVAIKQINLQKQPKKELIINEILVMKELKNPNIVNFLDSYLVGDELFVVMEYLAGGSLTDVVTETCMDEAQIAAVCRECLQALEFLHANQVIHRDIKSDNVLLGMEGSVKLTDFGFCAQITPEQSKRSTMVGTPYWMAPEVVTRKAYGPKVDIWSLGIMAIEMVEGEPPYLNENPLRALYLIATNGTPELQNPEKLSPIFRDFLNRCLEMDVEKRGSAKELLQHPFLKLAKPLSSLTPLIMAAKEAMKSNR' is 3.1."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein 'SAQERSIEFKNDKRIETLLAKLLLKENAAQVSMRDAIIDIKLPKAFWPFQFQRAKNLGTRLKKPEGGSSIKIERGVSAEEEDVTELQGDQPSEPSSQFSIHDDEQNGSITQNSTRVKLLPLGERTKTITTKLLKDEEQEMTSHENRQIINGKQEDEVSQSEFEELFFFKGFRNSELQREPHLDRNSAKLSKKHELSKRSELPLEDILGREKKDHEVIEIIRDAPNNQEEYEESVEFRRGGDKQVKEIETEVLNVFSEFNKDTLQSSDKIQKQALKVLSVKTWGTTSSVYPIRNIDEQTEEAAELIEVLESKVDLDYETLESVVEAMKLVFNLKADGEGKQKDTEKKIAEELNKGTSQKTLERTDKENHKQIVEPVVEPKKLLEKEIEGITMTNCMEEQAAIVAEKNEPEHKQLPNKVAVMPDSNSFKSRQVEHMKERSKQQIKKERQPFEREPKTLESYQTETGTEAQTNLDLCTDGQKIDDSAVSLEERLEKESEEEMSKTKTLELSLQARQEDSEGSAEPLQSKEDLEAQKIMPRRDVVLKKELYMSQMIEECQLLAEKVSSATVRPDEWETRKSSVISARSSVEILYTKVDVIERIRDSNTELDLIPTANAITVSREKKKEEPSIGLARTTEVIHSLEKHMRSQAHKSQSLQQILPAKQFKQFEDEMTTDAEKIESHESISEIIKKEEVMDKAGRILDLTDMTSNVPRKELKENIQQKCRNHIIEIADKHEEEFNNSACVTNNEYENSQFHPGTALATKKVILNHNIWHNNNLEQINARDKHEKERKEKVIASMKKKEERGNVTTTPELVMVADPKLQDPPNREEIIDFASKETEKPEQVIRQAQIDLELQEQQLEDKKRNVEPDRQAEQQDEEACLQELMAQLDVFFSETGIGITDAQETARNEIRYNREPNKQEWVMKLIQEYQDREDQMVKERKEADGLLEEEETPHSEELRSDIQIPEDKDEVRDITLQEFESKILKIDGLASKLWEAESNKRHQKPGKQRALCYLHKEVNSLEFNSKPENWNSEYTSTVIDQAQKERNVISATKFENLERDLDGNESKTRGDGNQEAKPTFKTKNLLLAQSSQREIQPQMLKTQKVKPFETDRIANKQMLEHYNDDTLDLSSQPVLERESIEHMKLLFMCNLCSLCNLQRQLIKGEQAQLMEQADQLKTKTWMNFDKEIGVGEIKRDAQNKVKQTRDLKESCKDGTAQQTSTEVQQIVYKNGQEILVAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"SAQERSIEFKNDKRIETLLAKLLLKENAAQVSMRDAIIDIKLPKAFWPFQFQRAKNLGTRLKKPEGGSSIKIERGVSAEEEDVTELQGDQPSEPSSQFSIHDDEQNGSITQNSTRVKLLPLGERTKTITTKLLKDEEQEMTSHENRQIINGKQEDEVSQSEFEELFFFKGFRNSELQREPHLDRNSAKLSKKHELSKRSELPLEDILGREKKDHEVIEIIRDAPNNQEEYEESVEFRRGGDKQVKEIETEVLNVFSEFNKDTLQSSDKIQKQALKVLSVKTWGTTSSVYPIRNIDEQTEEAAELIEVLESKVDLDYETLESVVEAMKLVFNLKADGEGKQKDTEKKIAEELNKGTSQKTLERTDKENHKQIVEPVVEPKKLLEKEIEGITMTNCMEEQAAIVAEKNEPEHKQLPNKVAVMPDSNSFKSRQVEHMKERSKQQIKKERQPFEREPKTLESYQTETGTEAQTNLDLCTDGQKIDDSAVSLEERLEKESEEEMSKTKTLELSLQARQEDSEGSAEPLQSKEDLEAQKIMPRRDVVLKKELYMSQMIEECQLLAEKVSSATVRPDEWETRKSSVISARSSVEILYTKVDVIERIRDSNTELDLIPTANAITVSREKKKEEPSIGLARTTEVIHSLEKHMRSQAHKSQSLQQILPAKQFKQFEDEMTTDAEKIESHESISEIIKKEEVMDKAGRILDLTDMTSNVPRKELKENIQQKCRNHIIEIADKHEEEFNNSACVTNNEYENSQFHPGTALATKKVILNHNIWHNNNLEQINARDKHEKERKEKVIASMKKKEERGNVTTTPELVMVADPKLQDPPNREEIIDFASKETEKPEQVIRQAQIDLELQEQQLEDKKRNVEPDRQAEQQDEEACLQELMAQLDVFFSETGIGITDAQETARNEIRYNREPNKQEWVMKLIQEYQDREDQMVKERKEADGLLEEEETPHSEELRSDIQIPEDKDEVRDITLQEFESKILKIDGLASKLWEAESNKRHQKPGKQRALCYLHKEVNSLEFNSKPENWNSEYTSTVIDQAQKERNVISATKFENLERDLDGNESKTRGDGNQEAKPTFKTKNLLLAQSSQREIQPQMLKTQKVKPFETDRIANKQMLEHYNDDTLDLSSQPVLERESIEHMKLLFMCNLCSLCNLQRQLIKGEQAQLMEQADQLKTKTWMNFDKEIGVGEIKRDAQNKVKQTRDLKESCKDGTAQQTSTEVQQIVYKNGQEILVAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSFFNFRKIFKLGSEKKKKQYEHVKRDLNPEDFWEIIGELGDGAFGKVYKAQNKETSVLAAAKVIDTKSEEELEDYMVEIDILASCDHPNIVKLLDAFYYENNLWILIEFCAGGAVDAVMLELERPLTESQIQVVCKQTLDALNYLHDNKIIHRDLKAGNILFTLDGDIKLADFGVSAKNTRTIQRRDSFIGTPYWMAPEVVMCETSKDRPYDYKADVWSLGITLIEMAEIEPPHHELNPMRVLLKIAKSEPPTLAQPSRWSSNFKDFLKKCLEKNVDARWTTSQLLQHPFVTVDSNKPIRELIAEAKAEVTEEVEDGKEEDEEEETENSLPIPASKRASSDLSIASSEEDKLSQNACILESVSEKTERSNSEDKLNSKILNEKPTTDEPEKAVEDINEHITDAQLEAMTELHDRTAVIKENEREKRPKLENLPDTEDQETVDINSVSEGKENNIMITLETNIEHNLKSEEEKDQEKQQMFENKLIKSEEIKDTILQTVDLVSQETGEKEANIQAVDSEVGLTKEDTQEKLGEDDKTQKDVISNTSDVIGTCEAADVAQKVDEDSAEDTQSNDGKEVVEVGQKLINKPMVGPEAGGTKEVPIKEIVEMNEIEEGKNKEQAINSSENIMDINEEPGTTEGEEITESSSTEEMEVRSVVADTDQKALGSEVQDASKVTTQIDKEKKEIPVSIKKEPEVTVVSQPTEPQPVLIPSININSDSGENKEEIGSLSKTETILPPESENPKENDNDSGTGSTADTSSIDLNLSISSFLSKTKDSGSISLQETRRQKKTLKKTRKFIVDGVEVSVTTSKIVTDSDSKTEELRFLRRQELRELRFLQKEEQRAQQQLNSKLQQQREQIFRRFEQEMMSKKRQYDQEIENLEKQQKQTIERLEQEHTNRLRDEAKRIKGEQEKELSKFQNMLKNRKKEVINEVEKAPKELRKELMKRRKEELAQSQHAQEQEFVQKQQQELDGSLKKIIQQQKAELANIERECLNNKQQLMRAREAAIWELEERHLQEKHQLLKQQLKDQYFMQRHQLLKRHEKETEQMQRYNQRLIEELKNRQTQERARLPKIQRSEAKTRMAMFKKSLRINSTATPDQDRDKIKQFAAQEEKRQKNERMAQHQKHENQMRDLQLQCEANVRELHQLQNEKCHLLVEHETQKLKELDEEHSQELKEWREKLRPRKKTLEEEFARKLQEQEVFFKMTGESECLNPSTQSRISKFYPIPSLHSTGS' represents the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSFFNFRKIFKLGSEKKKKQYEHVKRDLNPEDFWEIIGELGDGAFGKVYKAQNKETSVLAAAKVIDTKSEEELEDYMVEIDILASCDHPNIVKLLDAFYYENNLWILIEFCAGGAVDAVMLELERPLTESQIQVVCKQTLDALNYLHDNKIIHRDLKAGNILFTLDGDIKLADFGVSAKNTRTIQRRDSFIGTPYWMAPEVVMCETSKDRPYDYKADVWSLGITLIEMAEIEPPHHELNPMRVLLKIAKSEPPTLAQPSRWSSNFKDFLKKCLEKNVDARWTTSQLLQHPFVTVDSNKPIRELIAEAKAEVTEEVEDGKEEDEEEETENSLPIPASKRASSDLSIASSEEDKLSQNACILESVSEKTERSNSEDKLNSKILNEKPTTDEPEKAVEDINEHITDAQLEAMTELHDRTAVIKENEREKRPKLENLPDTEDQETVDINSVSEGKENNIMITLETNIEHNLKSEEEKDQEKQQMFENKLIKSEEIKDTILQTVDLVSQETGEKEANIQAVDSEVGLTKEDTQEKLGEDDKTQKDVISNTSDVIGTCEAADVAQKVDEDSAEDTQSNDGKEVVEVGQKLINKPMVGPEAGGTKEVPIKEIVEMNEIEEGKNKEQAINSSENIMDINEEPGTTEGEEITESSSTEEMEVRSVVADTDQKALGSEVQDASKVTTQIDKEKKEIPVSIKKEPEVTVVSQPTEPQPVLIPSININSDSGENKEEIGSLSKTETILPPESENPKENDNDSGTGSTADTSSIDLNLSISSFLSKTKDSGSISLQETRRQKKTLKKTRKFIVDGVEVSVTTSKIVTDSDSKTEELRFLRRQELRELRFLQKEEQRAQQQLNSKLQQQREQIFRRFEQEMMSKKRQYDQEIENLEKQQKQTIERLEQEHTNRLRDEAKRIKGEQEKELSKFQNMLKNRKKEVINEVEKAPKELRKELMKRRKEELAQSQHAQEQEFVQKQQQELDGSLKKIIQQQKAELANIERECLNNKQQLMRAREAAIWELEERHLQEKHQLLKQQLKDQYFMQRHQLLKRHEKETEQMQRYNQRLIEELKNRQTQERARLPKIQRSEAKTRMAMFKKSLRINSTATPDQDRDKIKQFAAQEEKRQKNERMAQHQKHENQMRDLQLQCEANVRELHQLQNEKCHLLVEHETQKLKELDEEHSQELKEWREKLRPRKKTLEEEFARKLQEQEVFFKMTGESECLNPSTQSRISKFYPIPSLHSTGS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFFNFRKIFKLGSEKKKKQYEHVKRDLNPEDFWEIIGELGDGAFGKVYKAQNKETSVLAAAKVIDTKSEEELEDYMVEIDILASCDHPNIVKLLDAFYYENNLWILIEFCAGGAVDAVMLELERPLTESQIQVVCKQTLDALNYLHDNKIIHRDLKAGNILFTLDGDIKLADFGVSAKNTRTIQRRDSFIGTPYWMAPEVVMCETSKDRPYDYKADVWSLGITLIEMAEIEPPHHELNPMRVLLKIAKSEPPTLAQPSRWSSNFKDFLKKCLEKNVDARWTTSQLLQHPFVTVDSNKPIRELIAEAKAEVTEEVEDGKEEDEEEETENSLPIPASKRASSDLSIASSEEDKLSQNACILESVSEKTERSNSEDKLNSKILNEKPTTDEPEKAVEDINEHITDAQLEAMTELHDRTAVIKENEREKRPKLENLPDTEDQETVDINSVSEGKENNIMITLETNIEHNLKSEEEKDQEKQQMFENKLIKSEEIKDTILQTVDLVSQETGEKEANIQAVDSEVGLTKEDTQEKLGEDDKTQKDVISNTSDVIGTCEAADVAQKVDEDSAEDTQSNDGKEVVEVGQKLINKPMVGPEAGGTKEVPIKEIVEMNEIEEGKNKEQAINSSENIMDINEEPGTTEGEEITESSSTEEMEVRSVVADTDQKALGSEVQDASKVTTQIDKEKKEIPVSIKKEPEVTVVSQPTEPQPVLIPSININSDSGENKEEIGSLSKTETILPPESENPKENDNDSGTGSTADTSSIDLNLSISSFLSKTKDSGSISLQETRRQKKTLKKTRKFIVDGVEVSVTTSKIVTDSDSKTEELRFLRRQELRELRFLQKEEQRAQQQLNSKLQQQREQIFRRFEQEMMSKKRQYDQEIENLEKQQKQTIERLEQEHTNRLRDEAKRIKGEQEKELSKFQNMLKNRKKEVINEVEKAPKELRKELMKRRKEELAQSQHAQEQEFVQKQQQELDGSLKKIIQQQKAELANIERECLNNKQQLMRAREAAIWELEERHLQEKHQLLKQQLKDQYFMQRHQLLKRHEKETEQMQRYNQRLIEELKNRQTQERARLPKIQRSEAKTRMAMFKKSLRINSTATPDQDRDKIKQFAAQEEKRQKNERMAQHQKHENQMRDLQLQCEANVRELHQLQNEKCHLLVEHETQKLKELDEEHSQELKEWREKLRPRKKTLEEEFARKLQEQEVFFKMTGESECLNPSTQSRISKFYPIPSLHSTGS\", \"result\": [{\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 220.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein 'MSFFNFRKIFKLGSEKKKKQYEHVKRDLNPEDFWEIIGELGDGAFGKVYKAQNKETSVLAAAKVIDTKSEEELEDYMVEIDILASCDHPNIVKLLDAFYYENNLWILIEFCAGGAVDAVMLELERPLTESQIQVVCKQTLDALNYLHDNKIIHRDLKAGNILFTLDGDIKLADFGVSAKNTRTIQRRDSFIGTPYWMAPEVVMCETSKDRPYDYKADVWSLGITLIEMAEIEPPHHELNPMRVLLKIAKSEPPTLAQPSRWSSNFKDFLKKCLEKNVDARWTTSQLLQHPFVTVDSNKPIRELIAEAKAEVTEEVEDGKEEDEEEETENSLPIPASKRASSDLSIASSEEDKLSQNACILESVSEKTERSNSEDKLNSKILNEKPTTDEPEKAVEDINEHITDAQLEAMTELHDRTAVIKENEREKRPKLENLPDTEDQETVDINSVSEGKENNIMITLETNIEHNLKSEEEKDQEKQQMFENKLIKSEEIKDTILQTVDLVSQETGEKEANIQAVDSEVGLTKEDTQEKLGEDDKTQKDVISNTSDVIGTCEAADVAQKVDEDSAEDTQSNDGKEVVEVGQKLINKPMVGPEAGGTKEVPIKEIVEMNEIEEGKNKEQAINSSENIMDINEEPGTTEGEEITESSSTEEMEVRSVVADTDQKALGSEVQDASKVTTQIDKEKKEIPVSIKKEPEVTVVSQPTEPQPVLIPSININSDSGENKEEIGSLSKTETILPPESENPKENDNDSGTGSTADTSSIDLNLSISSFLSKTKDSGSISLQETRRQKKTLKKTRKFIVDGVEVSVTTSKIVTDSDSKTEELRFLRRQELRELRFLQKEEQRAQQQLNSKLQQQREQIFRRFEQEMMSKKRQYDQEIENLEKQQKQTIERLEQEHTNRLRDEAKRIKGEQEKELSKFQNMLKNRKKEVINEVEKAPKELRKELMKRRKEELAQSQHAQEQEFVQKQQQELDGSLKKIIQQQKAELANIERECLNNKQQLMRAREAAIWELEERHLQEKHQLLKQQLKDQYFMQRHQLLKRHEKETEQMQRYNQRLIEELKNRQTQERARLPKIQRSEAKTRMAMFKKSLRINSTATPDQDRDKIKQFAAQEEKRQKNERMAQHQKHENQMRDLQLQCEANVRELHQLQNEKCHLLVEHETQKLKELDEEHSQELKEWREKLRPRKKTLEEEFARKLQEQEVFFKMTGESECLNPSTQSRISKFYPIPSLHSTGS' is 220.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O' and the target protein 'TQRISEPQCSLKPMMGNKADLVFRDDDASVVDERESQSTQHYGIYKNSQVDLRCLREPLQDAQVIAYPFMVEKYVQMPQLNENNPDPSPMYLQARPEVINAYIVYGALLGQIQTPGNEPETVRHQAFRHTPRSAIVSPIAQNQVLSPKYHESLQLDILHMLKVVVIPTPAEPKQANDTIEFSKNLLINHAASEDVLKRAHPSCAQDQQFGNMLGDPKLKLGIHGDEGSPSAQEQQASGSFPTPSGIFQHDSSQPLIFTLTILADFPYIALTSEPASKLDQCELVGCDAEHQADVRYDGGRQPPVVALIQQRAAQDSTAGGSLESLQVKKVTMLKLPQKRMELQHDPAQGRTLPHVDTSHPPTSPLRQAPQHTERGLIPGTSAPRHFAEDEDRAHPSARDGLHSGSLSPRQLNLPPAYSGGLLTPNMALMYEPKSVPRTDEASIVPALRPGVLSHTVLHSMQRTLQDLQGNHKQDQLVDVQAEHERGISKPTSQRYQATSLEQLGASVSTSTTGNNEETNGAKDTIPEDCRDLNRTHYADECMMGPHHSGSPPAETSGHELYEKLTTQQGLIFLGKLEFHSLHDLYTDPTLFLSILWSKPPMSFAGYHRSSVDPPNFLMGFDSEPQSFANPAMLLIMPHRGSPPLLGRHMGQSPSQLSQVPDFPAIRNFQLFSASIFQKAVNAVPAQLPKEAQAAQHLCVQGLQLDSVPDGNPKEKLTPPIRQLKTAQSATVSECSGSQHCDIQNQMVDIHMSRRYHQAQKVDSEHPFAGYGENLVAMQEELNIGFGRLFRELLCYEHSCIDNQISQYLASGLQSADSSHGPKQEENMRSALQGCQERSSLILQPEQGPRPQPLHKLASNVPLLRVYCSLAPMFGQELLLSRLEATIQHMLSSLFQQGYMDGTPAEATHGPEFASTMPQLSEQFNQPEIPQYSQQLNQLSPDPMRAHCRFFISYDWKQPARLPPGQQLCWMPSRMMGLLQTLDHSKMKYQFNVENEASVQSYSQAQQPLHLMKSQRHEKEKLRHINLNQLQIQKLALLPSQEVPYFHKHQANSNMNHGCAGTGNISGVDCVSSRSDVAQTNHQAQETLMELLSGAPQHLKSSVGIQQTTQGIRNGKIERAQVCRRGAGQLPDSHPNLMCDLQQKAEGTHLSCSPHCSDESSGAPAMAYTYLKGGNQIDNHPQHHAPDQANPYMLPSLRSLFREMPALQTSVSLTSGILSPPTGVVQEFPILSVIGKQGRQRNLGLIQRRAPHHMGKFEHPVVDQAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\"], \"target_seq\": \"TQRISEPQCSLKPMMGNKADLVFRDDDASVVDERESQSTQHYGIYKNSQVDLRCLREPLQDAQVIAYPFMVEKYVQMPQLNENNPDPSPMYLQARPEVINAYIVYGALLGQIQTPGNEPETVRHQAFRHTPRSAIVSPIAQNQVLSPKYHESLQLDILHMLKVVVIPTPAEPKQANDTIEFSKNLLINHAASEDVLKRAHPSCAQDQQFGNMLGDPKLKLGIHGDEGSPSAQEQQASGSFPTPSGIFQHDSSQPLIFTLTILADFPYIALTSEPASKLDQCELVGCDAEHQADVRYDGGRQPPVVALIQQRAAQDSTAGGSLESLQVKKVTMLKLPQKRMELQHDPAQGRTLPHVDTSHPPTSPLRQAPQHTERGLIPGTSAPRHFAEDEDRAHPSARDGLHSGSLSPRQLNLPPAYSGGLLTPNMALMYEPKSVPRTDEASIVPALRPGVLSHTVLHSMQRTLQDLQGNHKQDQLVDVQAEHERGISKPTSQRYQATSLEQLGASVSTSTTGNNEETNGAKDTIPEDCRDLNRTHYADECMMGPHHSGSPPAETSGHELYEKLTTQQGLIFLGKLEFHSLHDLYTDPTLFLSILWSKPPMSFAGYHRSSVDPPNFLMGFDSEPQSFANPAMLLIMPHRGSPPLLGRHMGQSPSQLSQVPDFPAIRNFQLFSASIFQKAVNAVPAQLPKEAQAAQHLCVQGLQLDSVPDGNPKEKLTPPIRQLKTAQSATVSECSGSQHCDIQNQMVDIHMSRRYHQAQKVDSEHPFAGYGENLVAMQEELNIGFGRLFRELLCYEHSCIDNQISQYLASGLQSADSSHGPKQEENMRSALQGCQERSSLILQPEQGPRPQPLHKLASNVPLLRVYCSLAPMFGQELLLSRLEATIQHMLSSLFQQGYMDGTPAEATHGPEFASTMPQLSEQFNQPEIPQYSQQLNQLSPDPMRAHCRFFISYDWKQPARLPPGQQLCWMPSRMMGLLQTLDHSKMKYQFNVENEASVQSYSQAQQPLHLMKSQRHEKEKLRHINLNQLQIQKLALLPSQEVPYFHKHQANSNMNHGCAGTGNISGVDCVSSRSDVAQTNHQAQETLMELLSGAPQHLKSSVGIQQTTQGIRNGKIERAQVCRRGAGQLPDSHPNLMCDLQQKAEGTHLSCSPHCSDESSGAPAMAYTYLKGGNQIDNHPQHHAPDQANPYMLPSLRSLFREMPALQTSVSLTSGILSPPTGVVQEFPILSVIGKQGRQRNLGLIQRRAPHHMGKFEHPVVDQAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid expression for the protein is 'MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV\", \"result\": [{\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 1300.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O' and the protein 'MPARIGYYEIDRTIGKGNFAVVKRATHLVTKAKVAIKIIDKTQLDEENLKKIFREVQIMKMLCHPHIIRLYQVMETERMIYLVTEYASGGEIFDHLVAHGRMAEKEARRKFKQIVTAVYFCHCRNIVHRDLKAENLLLDANLNIKIADFGFSNLFTPGQLLKTWCGSPPYAAPELFEGKEYDGPKVDIWSLGVVLYVLVCGALPFDGSTLQNLRARVLSGKFRIPFFMSTECEHLIRHMLVLDPNKRLSMEQICKHKWMKLGDADPNFDRLIAECQQLKEERQVDPLNEDVLLAMEDMGLDKEQTLQSLRSDAYDHYSAIYSLLCDRHKRHKTLRLGALPSMPRALAFQAPVNIQAEQAGTAMNISVPQVQLINPENQIVEPDGTLNLDSDEGEEPSPEALVRYLSMRRHTVGVADPRTEVMEDLQKLLPGFPGVNPQAPFLQVAPNVNFMHNLLPMQNLQPTGQLEYKEQSLLQPPTLQLLNGMGPLGRRASDGGANIQLHAQQLLKRPRGPSPLVTMTPAVPAVTPVDEESSDGEPDQEAVQSSTYKDSNTLHLPTERFSPVRRFSDGAASIQAFKAHLEKMGNNSSIKQLQQECEQLQKMYGGQIDERTLEKTQQQHMLYQQEQHHQILQQQIQDSICPPQPSPPLQAACENQPALLTHQLQRLRIQPSSPPPNHPNNHLFRQPSNSPPPMSSAMIQPHGAASSSQFQGLPSRSAIFQQQPENCSSPPNVALTCLGMQQPAQSQQVTIQVQEPVDMLSNMPGTAAGSSGRGISISPSAGQMQMQHRTNLMATLSYGHRPLSKQLSADSAEAHSLNVNRFSPANYDQAHLHPHLFSDQSRGSPSSYSPSTGVGFSPTQALKVPPLDQFPTFPPSAHQQPPHYTTSALQQALLSPTPPDYTRHQQVPHILQGLLSPRHSLTGHSDIRLPPTEFAQLIKRQQQQRQQQQQQQQQQEYQELFRHMNQGDAGSLAPSLGGQSMTERQALSYQNADSYHHHTSPQHLLQIRAQECVSQASSPTPPHGYAHQPALMHSESMEEDCSCEGAKDGFQDSKSSSTLTKGCHDSPLLLSTGGPGDPESLLGTVSHAQELGIHPYGHQPTAAFSKNKVPSREPVIGNCMDRSSPGQAVELPDHNGLGYPARPSVHEHHRPRALQRHHTIQNSDDAYVQLDNLPGMSLVAGKALSSARMSDAVLSQSSLMGSQQFQDGENEECGASLGGHEHPDLSDGSQHLNSSCYPSTCITDILLSYKHPEVSFSMEQAGV' is 1300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the target protein 'QILKLIQEQAQRVVGKRQRRLAQDAAEHFRGLLDHLSNDYAEQSHVGFSYLMDPVRHLWLMKHNSWLARRSQPNRSCLLLNNDQLGLVPDYAQAIILPVLGIQDFCGLICEIKLPIFSPLREELGVLMVEQPTEGGHDVFLKDRSLHDETCLDWINAGMLAPEAHHVNLAQGGMLPTGTQMPAQDSKDPAQTLLMGHYELAWCRENTIFGTSFTPKTIMLAAGEWAVSRQKLYIKRCGVLVLLEKDIQCSHQSAPMFREGCLIPRRHLTYVIRAYNHRALREHFLQLGELSQGHSCYDDEGFVGIAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\"], \"target_seq\": \"QILKLIQEQAQRVVGKRQRRLAQDAAEHFRGLLDHLSNDYAEQSHVGFSYLMDPVRHLWLMKHNSWLARRSQPNRSCLLLNNDQLGLVPDYAQAIILPVLGIQDFCGLICEIKLPIFSPLREELGVLMVEQPTEGGHDVFLKDRSLHDETCLDWINAGMLAPEAHHVNLAQGGMLPTGTQMPAQDSKDPAQTLLMGHYELAWCRENTIFGTSFTPKTIMLAAGEWAVSRQKLYIKRCGVLVLLEKDIQCSHQSAPMFREGCLIPRRHLTYVIRAYNHRALREHFLQLGELSQGHSCYDDEGFVGIAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGHALCVCSRGTVIIDNKRYLFIQKLGEGGFSYVDLVEGLHDGHFYALKRILCHEQQDREEAQREADMHRLFNHPNILRLVAYCLRERGAKHEAWLLLPFFKRGTLWNEIERLKDKGNFLTEDQILWLLLGICRGLEAIHAKGYAHRDLKPTNILLGDEGQPVLMDLGSMNQACIHVEGSRQALTLQDWAAQRCTISYRAPELFSVQSHCVIDERTDVWSLGCVLYAMMFGEGPYDMVFQKGDSVALAVQNQLSIPQSPRHSSALRQLLNSMMTVDPHQRPHIPLLLSQLEALQPPAPGQHTTQI' is the true sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGHALCVCSRGTVIIDNKRYLFIQKLGEGGFSYVDLVEGLHDGHFYALKRILCHEQQDREEAQREADMHRLFNHPNILRLVAYCLRERGAKHEAWLLLPFFKRGTLWNEIERLKDKGNFLTEDQILWLLLGICRGLEAIHAKGYAHRDLKPTNILLGDEGQPVLMDLGSMNQACIHVEGSRQALTLQDWAAQRCTISYRAPELFSVQSHCVIDERTDVWSLGCVLYAMMFGEGPYDMVFQKGDSVALAVQNQLSIPQSPRHSSALRQLLNSMMTVDPHQRPHIPLLLSQLEALQPPAPGQHTTQI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGHALCVCSRGTVIIDNKRYLFIQKLGEGGFSYVDLVEGLHDGHFYALKRILCHEQQDREEAQREADMHRLFNHPNILRLVAYCLRERGAKHEAWLLLPFFKRGTLWNEIERLKDKGNFLTEDQILWLLLGICRGLEAIHAKGYAHRDLKPTNILLGDEGQPVLMDLGSMNQACIHVEGSRQALTLQDWAAQRCTISYRAPELFSVQSHCVIDERTDVWSLGCVLYAMMFGEGPYDMVFQKGDSVALAVQNQLSIPQSPRHSSALRQLLNSMMTVDPHQRPHIPLLLSQLEALQPPAPGQHTTQI\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 730.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1' and the protein 'MGHALCVCSRGTVIIDNKRYLFIQKLGEGGFSYVDLVEGLHDGHFYALKRILCHEQQDREEAQREADMHRLFNHPNILRLVAYCLRERGAKHEAWLLLPFFKRGTLWNEIERLKDKGNFLTEDQILWLLLGICRGLEAIHAKGYAHRDLKPTNILLGDEGQPVLMDLGSMNQACIHVEGSRQALTLQDWAAQRCTISYRAPELFSVQSHCVIDERTDVWSLGCVLYAMMFGEGPYDMVFQKGDSVALAVQNQLSIPQSPRHSSALRQLLNSMMTVDPHQRPHIPLLLSQLEALQPPAPGQHTTQI' is 730.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl' and the target protein 'VADQHESGRLQAHEHSFTVRNECDDTVDTHQSCPSFRMHLETPMWPPAKIPDSRRRNAENFHRFDYSRSIQKNFQLYSYLYCRLLSPVYNIKKQANPREEFYGESDRVSRLRRRYVSSIRDRTRFRRRYSGRDLERYSKTYSKWHFTIWRFERLMRASLLYAEREHIDGNSRQIYRLWEKERDAGPRGKGEDRNSNFSPPHRQGREAQIYCEGYEAILGYLVTILRRDWSVDKLFYEVDHFRDSIWPYCSIRLHMVDTMNSLTIFTTELECQRDRRYSGSHLSDSFARKHVGKRLSIIEYASEHPKYSRMDEGYHGQKQNARLRLVKVRVRPWHLFSALDDLSPRGRARHMSSESHRKRNYRTRIRGRCEDINETESTHGSARKDHRCIKGDYEGTHRDEVKKMDYKESSYDLSHEKRSYFTKNLRSDQADIYSSVRDDGETEQKDHSHKGDPYLVVLYTSLLFHSVSVHVSNFLARRAVNYVSNLERRFRRLRYRVERAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\"], \"target_seq\": \"VADQHESGRLQAHEHSFTVRNECDDTVDTHQSCPSFRMHLETPMWPPAKIPDSRRRNAENFHRFDYSRSIQKNFQLYSYLYCRLLSPVYNIKKQANPREEFYGESDRVSRLRRRYVSSIRDRTRFRRRYSGRDLERYSKTYSKWHFTIWRFERLMRASLLYAEREHIDGNSRQIYRLWEKERDAGPRGKGEDRNSNFSPPHRQGREAQIYCEGYEAILGYLVTILRRDWSVDKLFYEVDHFRDSIWPYCSIRLHMVDTMNSLTIFTTELECQRDRRYSGSHLSDSFARKHVGKRLSIIEYASEHPKYSRMDEGYHGQKQNARLRLVKVRVRPWHLFSALDDLSPRGRARHMSSESHRKRNYRTRIRGRCEDINETESTHGSARKDHRCIKGDYEGTHRDEVKKMDYKESSYDLSHEKRSYFTKNLRSDQADIYSSVRDDGETEQKDHSHKGDPYLVVLYTSLLFHSVSVHVSNFLARRAVNYVSNLERRFRRLRYRVERAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The correct expression for the protein is 'MPHPRRYHSSERGSRGSYREHYRSRKHKRRRSRSWSSSSDRTRRRRREDSYHVRSRSSYDDRSSDRRVYDRRYCGSYRRNDYSRDRGDAYYDTDYRHSYEYQRENSSYRSQRSSRRKHRRRRRRSRTFSRSSSQHSSRRAKSVEDDAEGHLIYHVGDWLQERYEIVSTLGEGTFGRVVQCVDHRRGGARVALKIIKNVEKYKEAARLEINVLEKINEKDPDNKNLCVQMFDWFDYHGHMCISFELLGLSTFDFLKDNNYLPYPIHQVRHMAFQLCQAVKFLHDNKLTHTDLKPENILFVNSDYELTYNLEKKRDERSVKSTAVRVVDFGSATFDHEHHSTIVSTRHYRAPEVILELGWSQPCDVWSIGCIIFEYYVGFTLFQTHDNREHLAMMERILGPIPSRMIRKTRKQKYFYRGRLDWDENTSAGRYVRENCKPLRRYLTSEAEEHHQLFDLIESMLEYEPAKRLTLGEALQHPFFARLRAEPPNKLWDSSRDISR'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPHPRRYHSSERGSRGSYREHYRSRKHKRRRSRSWSSSSDRTRRRRREDSYHVRSRSSYDDRSSDRRVYDRRYCGSYRRNDYSRDRGDAYYDTDYRHSYEYQRENSSYRSQRSSRRKHRRRRRRSRTFSRSSSQHSSRRAKSVEDDAEGHLIYHVGDWLQERYEIVSTLGEGTFGRVVQCVDHRRGGARVALKIIKNVEKYKEAARLEINVLEKINEKDPDNKNLCVQMFDWFDYHGHMCISFELLGLSTFDFLKDNNYLPYPIHQVRHMAFQLCQAVKFLHDNKLTHTDLKPENILFVNSDYELTYNLEKKRDERSVKSTAVRVVDFGSATFDHEHHSTIVSTRHYRAPEVILELGWSQPCDVWSIGCIIFEYYVGFTLFQTHDNREHLAMMERILGPIPSRMIRKTRKQKYFYRGRLDWDENTSAGRYVRENCKPLRRYLTSEAEEHHQLFDLIESMLEYEPAKRLTLGEALQHPFFARLRAEPPNKLWDSSRDISR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPHPRRYHSSERGSRGSYREHYRSRKHKRRRSRSWSSSSDRTRRRRREDSYHVRSRSSYDDRSSDRRVYDRRYCGSYRRNDYSRDRGDAYYDTDYRHSYEYQRENSSYRSQRSSRRKHRRRRRRSRTFSRSSSQHSSRRAKSVEDDAEGHLIYHVGDWLQERYEIVSTLGEGTFGRVVQCVDHRRGGARVALKIIKNVEKYKEAARLEINVLEKINEKDPDNKNLCVQMFDWFDYHGHMCISFELLGLSTFDFLKDNNYLPYPIHQVRHMAFQLCQAVKFLHDNKLTHTDLKPENILFVNSDYELTYNLEKKRDERSVKSTAVRVVDFGSATFDHEHHSTIVSTRHYRAPEVILELGWSQPCDVWSIGCIIFEYYVGFTLFQTHDNREHLAMMERILGPIPSRMIRKTRKQKYFYRGRLDWDENTSAGRYVRENCKPLRRYLTSEAEEHHQLFDLIESMLEYEPAKRLTLGEALQHPFFARLRAEPPNKLWDSSRDISR\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl' and the protein 'MPHPRRYHSSERGSRGSYREHYRSRKHKRRRSRSWSSSSDRTRRRRREDSYHVRSRSSYDDRSSDRRVYDRRYCGSYRRNDYSRDRGDAYYDTDYRHSYEYQRENSSYRSQRSSRRKHRRRRRRSRTFSRSSSQHSSRRAKSVEDDAEGHLIYHVGDWLQERYEIVSTLGEGTFGRVVQCVDHRRGGARVALKIIKNVEKYKEAARLEINVLEKINEKDPDNKNLCVQMFDWFDYHGHMCISFELLGLSTFDFLKDNNYLPYPIHQVRHMAFQLCQAVKFLHDNKLTHTDLKPENILFVNSDYELTYNLEKKRDERSVKSTAVRVVDFGSATFDHEHHSTIVSTRHYRAPEVILELGWSQPCDVWSIGCIIFEYYVGFTLFQTHDNREHLAMMERILGPIPSRMIRKTRKQKYFYRGRLDWDENTSAGRYVRENCKPLRRYLTSEAEEHHQLFDLIESMLEYEPAKRLTLGEALQHPFFARLRAEPPNKLWDSSRDISR' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'Nc1cc(CN2CCC(C(=O)N3CCC(n4c(=O)n(-c5ncccn5)c5cc(F)ccc54)CC3)CC2)ccn1' and the target protein 'VSPDQEYIDAKLVLPGVVSPFYLMLEWCFAARVGAKHEMCGVTPRSRSLRLWYHILNLLLQMGGTSTDSFITRFASGFFPLVAWTLSFWLDMGRQFLAFLSSMAAPLSVSFAGAPAGNRTRSQGFVSLSLRPLASFSAFSCMCSGSKRGGQYGNVGCLQAKCESWYPSQWAGPWVRSRWYLGLKEELCSAYILILLVQEFYATTALLAAGWLIGLKAYSIPTPAVFIGRRALAVMPGLVSISPSVYTPQKYLGGAFGASPPVEGLLPKQARVNIAVIALGKKLLLHGSVETLEVTVKRAEFSLRTWFRTISHFHAFSCLNRMGLRRPLTEFYGGANALVCQLLPATCKDYEYPASIPSLWDTGARGNAHYDAVVWCGNISLMPRSAEASITAASSYSFWLPANETDAAYVRRNRGSKDLRLAGVCPAGFVNHLPAPSTIKESRDLAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1cc(CN2CCC(C(=O)N3CCC(n4c(=O)n(-c5ncccn5)c5cc(F)ccc54)CC3)CC2)ccn1\"], \"target_seq\": \"VSPDQEYIDAKLVLPGVVSPFYLMLEWCFAARVGAKHEMCGVTPRSRSLRLWYHILNLLLQMGGTSTDSFITRFASGFFPLVAWTLSFWLDMGRQFLAFLSSMAAPLSVSFAGAPAGNRTRSQGFVSLSLRPLASFSAFSCMCSGSKRGGQYGNVGCLQAKCESWYPSQWAGPWVRSRWYLGLKEELCSAYILILLVQEFYATTALLAAGWLIGLKAYSIPTPAVFIGRRALAVMPGLVSISPSVYTPQKYLGGAFGASPPVEGLLPKQARVNIAVIALGKKLLLHGSVETLEVTVKRAEFSLRTWFRTISHFHAFSCLNRMGLRRPLTEFYGGANALVCQLLPATCKDYEYPASIPSLWDTGARGNAHYDAVVWCGNISLMPRSAEASITAASSYSFWLPANETDAAYVRRNRGSKDLRLAGVCPAGFVNHLPAPSTIKESRDLAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' fits the protein's sequence requirement. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\": \"Nc1cc(CN2CCC(C(=O)N3CCC(n4c(=O)n(-c5ncccn5)c5cc(F)ccc54)CC3)CC2)ccn1\", \"value\": 10.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Nc1cc(CN2CCC(C(=O)N3CCC(n4c(=O)n(-c5ncccn5)c5cc(F)ccc54)CC3)CC2)ccn1' and the protein 'MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK' is 10.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1'-target protein 'YKRGLGGARFLIKIIDKDETLSLEEAQATMQPSIMKSLQWVDCLHGFPLDEYARIPGKSIEVKIKRKLFGLKYYGKRDFCEVPRTTRGHPRNCTLLSPVARYPFRSINKITMIRENSELVIGVAAHSTHREKILYIFGSHKVLLYPMYADDLTAMEGNTGRRRASAVRDRSLFHRQPLERHIMDYHADSTNEGLRIKGFQGESLSGMSPFQKRLKDGVLMRREYSMRQDRKFNFSGGDLAVLSGLMHSEDRVTGTGIWEFHREHNNSGPLVKNGAYYAAAAGREITSFLPAWSYFEVLRGKQCFLRNTRTSENMIKSPRLHGPVKTPLVTLYVKGTDQDSRNCSNLDVILGSEALILYPVNESQGLTRSGQARFPDDSLYYISSKNPMGPARDMRSLKTEVSYTSQRTQGRDRNRAAAAAAAAAAAAAAAAAA' relationship? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"YKRGLGGARFLIKIIDKDETLSLEEAQATMQPSIMKSLQWVDCLHGFPLDEYARIPGKSIEVKIKRKLFGLKYYGKRDFCEVPRTTRGHPRNCTLLSPVARYPFRSINKITMIRENSELVIGVAAHSTHREKILYIFGSHKVLLYPMYADDLTAMEGNTGRRRASAVRDRSLFHRQPLERHIMDYHADSTNEGLRIKGFQGESLSGMSPFQKRLKDGVLMRREYSMRQDRKFNFSGGDLAVLSGLMHSEDRVTGTGIWEFHREHNNSGPLVKNGAYYAAAAGREITSFLPAWSYFEVLRGKQCFLRNTRTSENMIKSPRLHGPVKTPLVTLYVKGTDQDSRNCSNLDVILGSEALILYPVNESQGLTRSGQARFPDDSLYYISSKNPMGPARDMRSLKTEVSYTSQRTQGRDRNRAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The correct sequence for the protein is 'MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR\", \"result\": [{\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein 'MELRVGNRYRLGRKIGSGSFGDIYLGTDIAAGEEVAIKLECVKTKHPQLHIESKIYKMMQGGVGIPTIRWCGAEGDYNVMVMELLGPSLEDLFNFCSRKFSLKTVLLLADQMISRIEYIHSKNFIHRDVKPDNFLMGLGKKGNLVYIIDFGLAKKYRDARTHQHIPYRENKNLTGTARYASINTHLGIEQSRRDDLESLGYVLMYFNLGSLPWQGLKAATKRQKYERISEKKMSTPIEVLCKGYPSEFATYLNFCRSLRFDDKPDYSYLRQLFRNLFHRQGFSYDYVFDWNMLKFGASRAADDAERERRDREERLRHSRNPATRGLPSTASGRLRGTQEVAPPTPLTPTSHTANTSPRPVSGMERERKVSMRLHRGAPVNISSSDLTGRQDTSRMSTSQIPGRVASSGLQSVVHR' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the binding affinity of the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' with the target protein 'NRYSDEQINRAVLFQIVQCTSRDCGEPRRDGYPLTSLSLARPPVRPEFVVSTGFFCSEEVVTSPSDHSSLSESVGSSRENRARWQHDIRTASDAIDEISGRLRIHFQARGLGSSGLPSVLWGGVDVLHGLAIPKPKAVRACCDYLNLIHGDVCFSLRERIRDLTWASALLGEKRGQNFIHVIQSGCDEAVFELHPAPPQEENDGDVSSALPERPFLLQDEPSLEHDKPTDRLKVLPLESGSKGRWHHGVARLYGGFPSFGILMQPSSYICDRRHSTSIPPREQHKQLFLPLLMEAIRFAPKDSRHYTKPGSRHLSIVYHSVDFPMFEMIWTKLNLSPHHEKAPDLPVEDVPLMRESSQKPLLAFQIPGALGVEHLHSILDLGRSSRGAWINHHELWSGSNRHPEAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"NRYSDEQINRAVLFQIVQCTSRDCGEPRRDGYPLTSLSLARPPVRPEFVVSTGFFCSEEVVTSPSDHSSLSESVGSSRENRARWQHDIRTASDAIDEISGRLRIHFQARGLGSSGLPSVLWGGVDVLHGLAIPKPKAVRACCDYLNLIHGDVCFSLRERIRDLTWASALLGEKRGQNFIHVIQSGCDEAVFELHPAPPQEENDGDVSSALPERPFLLQDEPSLEHDKPTDRLKVLPLESGSKGRWHHGVARLYGGFPSFGILMQPSSYICDRRHSTSIPPREQHKQLFLPLLMEAIRFAPKDSRHYTKPGSRHLSIVYHSVDFPMFEMIWTKLNLSPHHEKAPDLPVEDVPLMRESSQKPLLAFQIPGALGVEHLHSILDLGRSSRGAWINHHELWSGSNRHPEAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MPHEPHEPLTPPFSALPDPAGAPSRRQSRQRPQLSSDSPSAFRASRSHSRNATRSHSHSHSPRHSLRHSPGSGSCGSSSGHRPCADILEVGMLLSKINSLAHLRAAPCNDLHATKLAPGKEKEPLESQYQVGPLLGSGGFGSVYSGIRVSDNLPVAIKHVEKDRISDWGELPNGTRVPMEVVLLKKVSSGFSGVIRLLDWFERPDSFVLILERPEPVQDLFDFITERGALQEELARSFFWQVLEAVRHCHNCGVLHRDIKDENILIDLNRGELKLIDFGSGALLKDTVYTDFDGTRVYSPPEWIRYHRYHGRSAAVWSLGILLYDMVCGDIPFEHDEEIIRGQVFFRQRVSSECQHLIRWCLALRPSDRPTFEEIQNHPWMQDVLLPQETAEIHLHSLSPGPSK' represents the right sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPHEPHEPLTPPFSALPDPAGAPSRRQSRQRPQLSSDSPSAFRASRSHSRNATRSHSHSHSPRHSLRHSPGSGSCGSSSGHRPCADILEVGMLLSKINSLAHLRAAPCNDLHATKLAPGKEKEPLESQYQVGPLLGSGGFGSVYSGIRVSDNLPVAIKHVEKDRISDWGELPNGTRVPMEVVLLKKVSSGFSGVIRLLDWFERPDSFVLILERPEPVQDLFDFITERGALQEELARSFFWQVLEAVRHCHNCGVLHRDIKDENILIDLNRGELKLIDFGSGALLKDTVYTDFDGTRVYSPPEWIRYHRYHGRSAAVWSLGILLYDMVCGDIPFEHDEEIIRGQVFFRQRVSSECQHLIRWCLALRPSDRPTFEEIQNHPWMQDVLLPQETAEIHLHSLSPGPSK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPHEPHEPLTPPFSALPDPAGAPSRRQSRQRPQLSSDSPSAFRASRSHSRNATRSHSHSHSPRHSLRHSPGSGSCGSSSGHRPCADILEVGMLLSKINSLAHLRAAPCNDLHATKLAPGKEKEPLESQYQVGPLLGSGGFGSVYSGIRVSDNLPVAIKHVEKDRISDWGELPNGTRVPMEVVLLKKVSSGFSGVIRLLDWFERPDSFVLILERPEPVQDLFDFITERGALQEELARSFFWQVLEAVRHCHNCGVLHRDIKDENILIDLNRGELKLIDFGSGALLKDTVYTDFDGTRVYSPPEWIRYHRYHGRSAAVWSLGILLYDMVCGDIPFEHDEEIIRGQVFFRQRVSSECQHLIRWCLALRPSDRPTFEEIQNHPWMQDVLLPQETAEIHLHSLSPGPSK\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein 'MPHEPHEPLTPPFSALPDPAGAPSRRQSRQRPQLSSDSPSAFRASRSHSRNATRSHSHSHSPRHSLRHSPGSGSCGSSSGHRPCADILEVGMLLSKINSLAHLRAAPCNDLHATKLAPGKEKEPLESQYQVGPLLGSGGFGSVYSGIRVSDNLPVAIKHVEKDRISDWGELPNGTRVPMEVVLLKKVSSGFSGVIRLLDWFERPDSFVLILERPEPVQDLFDFITERGALQEELARSFFWQVLEAVRHCHNCGVLHRDIKDENILIDLNRGELKLIDFGSGALLKDTVYTDFDGTRVYSPPEWIRYHRYHGRSAAVWSLGILLYDMVCGDIPFEHDEEIIRGQVFFRQRVSSECQHLIRWCLALRPSDRPTFEEIQNHPWMQDVLLPQETAEIHLHSLSPGPSK' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted interaction value for the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the target protein 'DKANNFPKLNRDLVQSLERRLVGIVEAQCDCADTSDPEMRYCSKLRKGVRNSERDLNVIPLSCPGEKVNPRGEVKLGISNEDAMGVVDRGETGSEDGATQRPQILVPKLDSASQRPFTKMLLSSSGAWLKILGIKHRIHFMSRAYSPVYHIREVPFRFLEIIMTQDENNMLHCGENYCILLPISLNPKFLRTGIRSLVYKGMSGWWCEFSGGVIGSPDAPVKASVGLAFLDCTTEHFDYYENNLSICKAVGASMEILLEPVQRLTQGVNLESLIALVFADIDFVTANDTAKTYFCIQFTKGHAPLCRTAALTVCETGKLLLTEQDARRNTYHAVVMYGDHSCLQEGVGLDESIRLISIYQLCNDDRGIYVSIKAPGPLYTPLVKIELRRYKYGLLCEAEGDMNAHVPCSAPCNKAWQGMNWDVLLLEYVKHFISDAFGISCTMNEPIKGIRPCHEHSDDKVCDPGQTNRSTSKERKTPKNPDNHYSCSKGDTMPVSAQPTLQANKSTTLPCQSTLPGAVLKNTSACQFLYTAQDFRAALYQNNTAEAFNMKIQHLRERLIEPRGQQIGPPKVYQHLDTPTSLCIMYGKVNFIIEKLLCGKVNLDCFYGNDSGMCKALLCRLLNDGKSKHTIEGRDLMNNAVSATSVVTSEVAQKIEPNGSKTSPAERFGKLTREPACSEACDGEPRGRSPLIGALELSCVNVVYECEPCRTGNLLEDPQCIPPRRTGVERPDCVGKKVQQQDQGTNCVAPKKKGLGRTVGKPAAHSGRMWNNGVSGLLIVTRTLGPEKCITKPSPVCVKGCESDHYCNSRLYSVRFKIAGNTYLQTRAETGLPFSIVRVGILLDVYIHPPDSLEDMPNEEIDKDYIQVISHSFWQLETGAEKKNSSLMCNCIAACYGPRVYNVMPIHKNAFKPPIEPPPHKACCLISGGNTHTSAILELNGASIQFYIEKTSPCKGNCLRLREQYDFQLFRNWDCPELQAPLAVCENRVGGPPSLLDLCIFPHIAMIGDHNRTLMPQENRSYWCNGTHNCTCILCEDCLEEDISALDVGMNITQWLNVPGKLESRIVPTLLFRIDAVFKEKCDLRDFTSESNLCQSAVCIGVESPVSAKAHADDLQASWHRNGELQQGSHQALSLGILQVWNNTGGEQKTAPMNDLEETFHATSWCSNPSSYPLIGEDVKESQQFPTSHYKNTEGIGQAEIPIDCEFYFAAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\"], \"target_seq\": \"DKANNFPKLNRDLVQSLERRLVGIVEAQCDCADTSDPEMRYCSKLRKGVRNSERDLNVIPLSCPGEKVNPRGEVKLGISNEDAMGVVDRGETGSEDGATQRPQILVPKLDSASQRPFTKMLLSSSGAWLKILGIKHRIHFMSRAYSPVYHIREVPFRFLEIIMTQDENNMLHCGENYCILLPISLNPKFLRTGIRSLVYKGMSGWWCEFSGGVIGSPDAPVKASVGLAFLDCTTEHFDYYENNLSICKAVGASMEILLEPVQRLTQGVNLESLIALVFADIDFVTANDTAKTYFCIQFTKGHAPLCRTAALTVCETGKLLLTEQDARRNTYHAVVMYGDHSCLQEGVGLDESIRLISIYQLCNDDRGIYVSIKAPGPLYTPLVKIELRRYKYGLLCEAEGDMNAHVPCSAPCNKAWQGMNWDVLLLEYVKHFISDAFGISCTMNEPIKGIRPCHEHSDDKVCDPGQTNRSTSKERKTPKNPDNHYSCSKGDTMPVSAQPTLQANKSTTLPCQSTLPGAVLKNTSACQFLYTAQDFRAALYQNNTAEAFNMKIQHLRERLIEPRGQQIGPPKVYQHLDTPTSLCIMYGKVNFIIEKLLCGKVNLDCFYGNDSGMCKALLCRLLNDGKSKHTIEGRDLMNNAVSATSVVTSEVAQKIEPNGSKTSPAERFGKLTREPACSEACDGEPRGRSPLIGALELSCVNVVYECEPCRTGNLLEDPQCIPPRRTGVERPDCVGKKVQQQDQGTNCVAPKKKGLGRTVGKPAAHSGRMWNNGVSGLLIVTRTLGPEKCITKPSPVCVKGCESDHYCNSRLYSVRFKIAGNTYLQTRAETGLPFSIVRVGILLDVYIHPPDSLEDMPNEEIDKDYIQVISHSFWQLETGAEKKNSSLMCNCIAACYGPRVYNVMPIHKNAFKPPIEPPPHKACCLISGGNTHTSAILELNGASIQFYIEKTSPCKGNCLRLREQYDFQLFRNWDCPELQAPLAVCENRVGGPPSLLDLCIFPHIAMIGDHNRTLMPQENRSYWCNGTHNCTCILCEDCLEEDISALDVGMNITQWLNVPGKLESRIVPTLLFRIDAVFKEKCDLRDFTSESNLCQSAVCIGVESPVSAKAHADDLQASWHRNGELQQGSHQALSLGILQVWNNTGGEQKTAPMNDLEETFHATSWCSNPSSYPLIGEDVKESQQFPTSHYKNTEGIGQAEIPIDCEFYFAAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 530.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1' and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' is 530.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C''s affinity with the target protein 'EIELGPTGGCLDGYLKELTLEMGSTQLNNLTRVPIIAKLINGQTKKKGGNTLTMVASDEFRPRELQGYGTGVDAPDDGQSTNAPNVWEVLTTNYAKIYTLPPYRLAYLPGEKRNNTITQNAAQGQISIGKIQAEVEIDGPSPASYIAVYKEEGGHIIRPCKKAIHTWSEELYMKAMARSTGESSRKNMGMKNSAYGLFKPLLAECEVEGKGMALTVKVAPYMSKIRYLVELENVMQSPFSAGGPEPIHEQFFWLDQEDGMTFVWAELDVSHIDSGVKMDKEPEVQPAATGMPDFIGMHLFEHQDTCTLSARSYEDKGYREPFNWIVILKQKFFRLKSEGKASWRVQEQLDKDELFSVKASPRKKYWFLDAPGGRRSGLVYEVFVIHTPITSFYGIIKNELVPFIANPMCGVGDSRCGGWQIWTDADSAPDFWESILYETYVLNVTKPKTDLCKLTLCYTFAAELPHSVYYSNFRIKTLEAFREKQGTAPSRKERGSSPSFTAQLGRTVLSPYLPTRLGRVAPNVTTEDAAVRGEYYGRKSCDTAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\"], \"target_seq\": \"EIELGPTGGCLDGYLKELTLEMGSTQLNNLTRVPIIAKLINGQTKKKGGNTLTMVASDEFRPRELQGYGTGVDAPDDGQSTNAPNVWEVLTTNYAKIYTLPPYRLAYLPGEKRNNTITQNAAQGQISIGKIQAEVEIDGPSPASYIAVYKEEGGHIIRPCKKAIHTWSEELYMKAMARSTGESSRKNMGMKNSAYGLFKPLLAECEVEGKGMALTVKVAPYMSKIRYLVELENVMQSPFSAGGPEPIHEQFFWLDQEDGMTFVWAELDVSHIDSGVKMDKEPEVQPAATGMPDFIGMHLFEHQDTCTLSARSYEDKGYREPFNWIVILKQKFFRLKSEGKASWRVQEQLDKDELFSVKASPRKKYWFLDAPGGRRSGLVYEVFVIHTPITSFYGIIKNELVPFIANPMCGVGDSRCGGWQIWTDADSAPDFWESILYETYVLNVTKPKTDLCKLTLCYTFAAELPHSVYYSNFRIKTLEAFREKQGTAPSRKERGSSPSFTAQLGRTVLSPYLPTRLGRVAPNVTTEDAAVRGEYYGRKSCDTAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid expression for the protein is 'MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 120.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C' and the protein 'MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL' is 120.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value do you project for the drug 'CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12' and the target protein 'HNHKLEFEGNAFMVYGGVNEAKEFNLMPQDKGQQLLKSLGKNNVKPLQSRLQRKKTIETAAKKNEELEVELTDNKVEKGNLTIQVYKREELTAVMEIHAEFQFKENVREFIKIRSSNKRPEKSDIKNTECRGLLEEPTLVQSIGDEHNVKDQDSNKQQDEDRFSKCNRLRLQEEQKANDDLTLQLEMSEKNLLDNRLQWRELKHESSKTDVSENQLKMFPAMKTSDYDKLESTVECKLLAEECRVKRDLMQDMPVLKEEDEMADRFISCEGRNALYEADKTVDKSEDDYAKTGRLLPNVDLAYLDREYATLDRGNATVMRSTINQNYCGRPLCGGTWSWCQNVGSFRETMDTHEKTLATNKINEQLDRFEKQNVTEETANLDESAVFDEKLALAKKPVLQSMDLKRIFRLAEADTPSLNKCDGKINLKPEERDERRESIADQKYEGRVQQDNLSTLKAEEAALPWVRAVLDELVKQQDLELNARRSLFSDPLEPADFAVDDLEDMDNNSQDAYLLFEQTKRVDEAVLSERLINDQKYVLSEVMKEYALSRQDKSEFNKRSLGEEEFMAYLQKSKFFIMKEVDDDHVSKQLHDCVRAAQYQEESNRQHNVLRDNCKKQNLVHQSKYLEKKKEKESMFPIKKGKRAINLRRVEPRLTYELAMFHETEMQGDNANVKEAEIEDREYMKDGDSKELEYHISLEYEVIAMKYMETRSVLSSIQQENKRKHIVNNMNNLRGMKFREQKPNKKYSVYYSDANNKSEIKEHDRHIAVQDTSDTHEEAAAQEFKNTGWLRLGPIFKSVERIKDLELYQFNAEDYKEQAVQDILAYQEEIQHRIKMQSERLLENQMISMALFGPEHVNTLKEGIKTSLKLIQVEKSGVRLSNHHDNMMDQVKTYEINTQGECQELLKKNKIKMSRDEKKQEDLVNVRVIKTLERLNYALATHPLDALCSFQWVNFGACSLCVALFESHVGLVLDERRETKEISWYSEYRVEEETSYVGRKFVLALEQLISKGEDKKYVDMFTHVSYPLPIQKTGNLEGSAENRASATCVLRTALEMPKLRSKVKHKRDSSMTQELPAVDHLSPKQPMDIDDQKNELSDKLKQQKEIFLLKQEDEKEKFDESGVLWRFVEKELEFTNIDLSTEPPLNQSEELVLKTSEIWTDQRHKELFFQLAVQKLFTLREQGNDIEQIEKETTSSEDKKDSQLRQRKALLTLLLNENFENLKRSKMNAAKDQTLHLLQQLNEISTPRNQMTQLLIEKLDGALDHEARKCTSVGGKNMPSIKQERTNYKKTESERNAVRRWSMMSVPPGLDKETEWAEICATILRRGKLENGKKQPDLPKFESQSYRRKNFYLNAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12\"], \"target_seq\": \"HNHKLEFEGNAFMVYGGVNEAKEFNLMPQDKGQQLLKSLGKNNVKPLQSRLQRKKTIETAAKKNEELEVELTDNKVEKGNLTIQVYKREELTAVMEIHAEFQFKENVREFIKIRSSNKRPEKSDIKNTECRGLLEEPTLVQSIGDEHNVKDQDSNKQQDEDRFSKCNRLRLQEEQKANDDLTLQLEMSEKNLLDNRLQWRELKHESSKTDVSENQLKMFPAMKTSDYDKLESTVECKLLAEECRVKRDLMQDMPVLKEEDEMADRFISCEGRNALYEADKTVDKSEDDYAKTGRLLPNVDLAYLDREYATLDRGNATVMRSTINQNYCGRPLCGGTWSWCQNVGSFRETMDTHEKTLATNKINEQLDRFEKQNVTEETANLDESAVFDEKLALAKKPVLQSMDLKRIFRLAEADTPSLNKCDGKINLKPEERDERRESIADQKYEGRVQQDNLSTLKAEEAALPWVRAVLDELVKQQDLELNARRSLFSDPLEPADFAVDDLEDMDNNSQDAYLLFEQTKRVDEAVLSERLINDQKYVLSEVMKEYALSRQDKSEFNKRSLGEEEFMAYLQKSKFFIMKEVDDDHVSKQLHDCVRAAQYQEESNRQHNVLRDNCKKQNLVHQSKYLEKKKEKESMFPIKKGKRAINLRRVEPRLTYELAMFHETEMQGDNANVKEAEIEDREYMKDGDSKELEYHISLEYEVIAMKYMETRSVLSSIQQENKRKHIVNNMNNLRGMKFREQKPNKKYSVYYSDANNKSEIKEHDRHIAVQDTSDTHEEAAAQEFKNTGWLRLGPIFKSVERIKDLELYQFNAEDYKEQAVQDILAYQEEIQHRIKMQSERLLENQMISMALFGPEHVNTLKEGIKTSLKLIQVEKSGVRLSNHHDNMMDQVKTYEINTQGECQELLKKNKIKMSRDEKKQEDLVNVRVIKTLERLNYALATHPLDALCSFQWVNFGACSLCVALFESHVGLVLDERRETKEISWYSEYRVEEETSYVGRKFVLALEQLISKGEDKKYVDMFTHVSYPLPIQKTGNLEGSAENRASATCVLRTALEMPKLRSKVKHKRDSSMTQELPAVDHLSPKQPMDIDDQKNELSDKLKQQKEIFLLKQEDEKEKFDESGVLWRFVEKELEFTNIDLSTEPPLNQSEELVLKTSEIWTDQRHKELFFQLAVQKLFTLREQGNDIEQIEKETTSSEDKKDSQLRQRKALLTLLLNENFENLKRSKMNAAKDQTLHLLQQLNEISTPRNQMTQLLIEKLDGALDHEARKCTSVGGKNMPSIKQERTNYKKTESERNAVRRWSMMSVPPGLDKETEWAEICATILRRGKLENGKKQPDLPKFESQSYRRKNFYLNAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS' is the proper sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS\", \"result\": [{\"smile\": \"CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12\", \"value\": 470.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC1CCN(C(=O)CC#N)CC1N(C)c1ncnc2[nH]ccc12' and the protein 'MSTGDSFETRFEKMDNLLRDPKSEVNSDCLLDGLDALVYDLDFPALRKNKNIDNFLSRYKDTINKIRDLRMKAEDYEVVKVIGRGAFGEVQLVRHKSTRKVYAMKLLSKFEMIKRSDSAFFWEERDIMAFANSPWVVQLFYAFQDDRYLYMVMEYMPGGDLVNLMSNYDVPEKWARFYTAEVVLALDAIHSMGFIHRDVKPDNMLLDKSGHLKLADFGTCMKMNKEGMVRCDTAVGTPDYISPEVLKSQGGDGYYGRECDWWSVGVFLYEMLVGDTPFYADSLVGTYSKIMNHKNSLTFPDDNDISKEAKNLICAFLTDREVRLGRNGVEEIKRHLFFKNDQWAWETLRDTVAPVVPDLSSDIDTSNFDDLEEDKGEEETFPIPKAFVGNQLPFVGFTYYSNRRYLSSANPNDNRTSSNADKSLQESLQKTIYKLEEQLHNEMQLKDEMEQKCRTSNIKLDKIMKELDEEGNQRRNLESTVSQIEKEKMLLQHRINEYQRKAEQENEKRRNVENEVSTLKDQLEDLKKVSQNSQLANEKLSQLQKQLEEANDLLRTESDTAVRLRKSHTEMSKSISQLESLNRELQERNRILENSKSQTDKDYYQLQAILEAERRDRGHDSEMIGDLQARITSLQEEVKHLKHNLEKVEGERKEAQDMLNHSEKEKNNLEIDLNYKLKSLQQRLEQEVNEHKVTKARLTDKHQSIEEAKSVAMCEMEKKLKEEREAREKAENRVVQIEKQCSMLDVDLKQSQQKLEHLTGNKERMEDEVKNLTLQLEQESNKRLLLQNELKTQAFEADNLKGLEKQMKQEINTLLEAKRLLEFELAQLTKQYRGNEGQMRELQDQLEAEQYFSTLYKTQVKELKEEIEEKNRENLKKIQELQNEKETLATQLDLAETKAESEQLARGLLEEQYFELTQESKKAASRNRQEITDKDHTVSRLEEANSMLTKDIEILRRENEELTEKMKKAEEEYKLEKEEEISNLKAAFEKNINTERTLKTQAVNKLAEIMNRKDFKIDRKKANTQDLRKKEKENRKLQLELNQEREKFNQMVVKHQKELNDMQAQLVEECAHRNELQMQLASKESDIEQLRAKLLDLSDSTSVASFPSADETDGNLPESRIEGWLSVPNRGNIKRYGWKKQYVVVSSKKILFYNDEQDKEQSNPSMVLDIDKLFHVRPVTQGDVYRAETEEIPKIFQILYANEGECRKDVEMEPVQQAEKTNFQNHKGHEFIPTLYHFPANCDACAKPLWHVFKPPPALECRRCHVKCHRDHLDKKEDLICPCKVSYDVTSARDMLLLACSQDEQKKWVTHLVKKIPKNPPSGFVRASPRTLSTRSTANQSFRKVVKNTSGKTS' is 470.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the affinity value of the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' toward the target protein 'RIMAVLVKHEATIVHGTGRMHRHNYKSSGIGFSDQDYPIEDSSGMSMFLCMKSPYEGEKVCSRDFPPSPQTHRGRSRGALHYYLIDYVKRDHFFRDKINAYSKFRDICFESAEQKWSVQLLNSDWCEPIDHRRHYKLMHEKRKWTFEHRQVKQSHMEGRETEGVDRRNRKHTILESRDNDMWIDYSVPNRRDEVLTEESIKKPECYQRQSIHRDPSCVHRGEEDRSLPTHSESIKGSDRGSLQDCKHDFNDIFSRTHNDALLFIYALREEHSKVMKKHQYSKFSLECAHCPYCTYIKILKFETYNGDAPAEVRSTEVYLKECNVGKSRLWHIRPTHRCSSFLRWGHKWFNLLHSNDDAHDHVDDVADFLSSIISNFEDHGARRSRLSKSRTNHQIELIDVDLREGKGYVHTGTKRHKSLIHDYEKRSTLSYDIVKKSESKHRSKQKCALKPGLYYYRLGTHLAHIEYDVVRPLTRLIATQHLLKAAAAAAAAAAAAAAAAAA'. MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\"], \"target_seq\": \"RIMAVLVKHEATIVHGTGRMHRHNYKSSGIGFSDQDYPIEDSSGMSMFLCMKSPYEGEKVCSRDFPPSPQTHRGRSRGALHYYLIDYVKRDHFFRDKINAYSKFRDICFESAEQKWSVQLLNSDWCEPIDHRRHYKLMHEKRKWTFEHRQVKQSHMEGRETEGVDRRNRKHTILESRDNDMWIDYSVPNRRDEVLTEESIKKPECYQRQSIHRDPSCVHRGEEDRSLPTHSESIKGSDRGSLQDCKHDFNDIFSRTHNDALLFIYALREEHSKVMKKHQYSKFSLECAHCPYCTYIKILKFETYNGDAPAEVRSTEVYLKECNVGKSRLWHIRPTHRCSSFLRWGHKWFNLLHSNDDAHDHVDDVADFLSSIISNFEDHGARRSRLSKSRTNHQIELIDVDLREGKGYVHTGTKRHKSLIHDYEKRSTLSYDIVKKSESKHRSKQKCALKPGLYYYRLGTHLAHIEYDVVRPLTRLIATQHLLKAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MRHSKRTYCPDWDDKDWDYGKWRSSSSHKRRKRSHSSAQENKRCKYNHSKMCDSHYLESRSINEKDYHSRRYIDEYRNDYTQGCEPGHRQRDHESRYQNHSSKSSGRSGRSSYKSKHRIHHSTSHRRSHGKSHRRKRTRSVEDDEEGHLICQSGDVLSARYEIVDTLGEGAFGKVVECIDHKAGGRHVAVKIVKNVDRYCEAARSEIQVLEHLNTTDPNSTFRCVQMLEWFEHHGHICIVFELLGLSTYDFIKENGFLPFRLDHIRKMAYQICKSVNFLHSNKLTHTDLKPENILFVQSDYTEAYNPKIKRDERTLINPDIKVVDFGSATYDDEHHSTLVSTRHYRAPEVILALGWSQPCDVWSIGCILIEYYLGFTVFPTHDSKEHLAMMERILGPLPKHMIQKTRKRKYFHHDRLDWDEHSSAGRYVSRRCKPLKEFMLSQDVEHERLFDLIQKMLEYDPAKRITLREALKHPFFDLLKKSI' serves as the correct sequence for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRHSKRTYCPDWDDKDWDYGKWRSSSSHKRRKRSHSSAQENKRCKYNHSKMCDSHYLESRSINEKDYHSRRYIDEYRNDYTQGCEPGHRQRDHESRYQNHSSKSSGRSGRSSYKSKHRIHHSTSHRRSHGKSHRRKRTRSVEDDEEGHLICQSGDVLSARYEIVDTLGEGAFGKVVECIDHKAGGRHVAVKIVKNVDRYCEAARSEIQVLEHLNTTDPNSTFRCVQMLEWFEHHGHICIVFELLGLSTYDFIKENGFLPFRLDHIRKMAYQICKSVNFLHSNKLTHTDLKPENILFVQSDYTEAYNPKIKRDERTLINPDIKVVDFGSATYDDEHHSTLVSTRHYRAPEVILALGWSQPCDVWSIGCILIEYYLGFTVFPTHDSKEHLAMMERILGPLPKHMIQKTRKRKYFHHDRLDWDEHSSAGRYVSRRCKPLKEFMLSQDVEHERLFDLIQKMLEYDPAKRITLREALKHPFFDLLKKSI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRHSKRTYCPDWDDKDWDYGKWRSSSSHKRRKRSHSSAQENKRCKYNHSKMCDSHYLESRSINEKDYHSRRYIDEYRNDYTQGCEPGHRQRDHESRYQNHSSKSSGRSGRSSYKSKHRIHHSTSHRRSHGKSHRRKRTRSVEDDEEGHLICQSGDVLSARYEIVDTLGEGAFGKVVECIDHKAGGRHVAVKIVKNVDRYCEAARSEIQVLEHLNTTDPNSTFRCVQMLEWFEHHGHICIVFELLGLSTYDFIKENGFLPFRLDHIRKMAYQICKSVNFLHSNKLTHTDLKPENILFVQSDYTEAYNPKIKRDERTLINPDIKVVDFGSATYDDEHHSTLVSTRHYRAPEVILALGWSQPCDVWSIGCILIEYYLGFTVFPTHDSKEHLAMMERILGPLPKHMIQKTRKRKYFHHDRLDWDEHSSAGRYVSRRCKPLKEFMLSQDVEHERLFDLIQKMLEYDPAKRITLREALKHPFFDLLKKSI\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 3900.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein 'MRHSKRTYCPDWDDKDWDYGKWRSSSSHKRRKRSHSSAQENKRCKYNHSKMCDSHYLESRSINEKDYHSRRYIDEYRNDYTQGCEPGHRQRDHESRYQNHSSKSSGRSGRSSYKSKHRIHHSTSHRRSHGKSHRRKRTRSVEDDEEGHLICQSGDVLSARYEIVDTLGEGAFGKVVECIDHKAGGRHVAVKIVKNVDRYCEAARSEIQVLEHLNTTDPNSTFRCVQMLEWFEHHGHICIVFELLGLSTYDFIKENGFLPFRLDHIRKMAYQICKSVNFLHSNKLTHTDLKPENILFVQSDYTEAYNPKIKRDERTLINPDIKVVDFGSATYDDEHHSTLVSTRHYRAPEVILALGWSQPCDVWSIGCILIEYYLGFTVFPTHDSKEHLAMMERILGPLPKHMIQKTRKRKYFHHDRLDWDEHSSAGRYVSRRCKPLKEFMLSQDVEHERLFDLIQKMLEYDPAKRITLREALKHPFFDLLKKSI' is 3900.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'COc1ccc(CC(C)N)cc1O' with respect to the target protein 'TVGSFSIENVERGGMFDPFWLVSAKCFKERVMVKSTKGRPVHGADKFSQFRITQRMKALFHNINGSVSTKCVSVEYSHLRFAISTLRGDALGTHFSRKWEIELDILASQLANLQAKGPWKKTCVQCMPPTRINLIYTLFVVPMSLTFPILIMMVSKLLAVDFCIALLSLWQTKYMVQESESILMYLAEAWSLSINSAFTGTIMPASVATLLPAGPDSYSRARHLEFPALTDMCFVLQMILDSMANQNTKFKSVRLMAVFSFAHGPILSTQLNNIVVKNNKCEVVLTTIYYTSQHIIYQVRYTTALFIEKLGLEACQNTAKDLLKNLMYDPARSITKSATLSISISGISDRTIVQTNNEIDSRCGSDFLAGTSLNTTIIGACASIELEGNLSGTPNIATAVFEWQRILVVTIAFLTFYDNSTKNKNLIKIAQLTMVLIPPITALVEYKLSLTFHQLLKFVIADYFRWRSRPPLIFIALVLAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(CC(C)N)cc1O\"], \"target_seq\": \"TVGSFSIENVERGGMFDPFWLVSAKCFKERVMVKSTKGRPVHGADKFSQFRITQRMKALFHNINGSVSTKCVSVEYSHLRFAISTLRGDALGTHFSRKWEIELDILASQLANLQAKGPWKKTCVQCMPPTRINLIYTLFVVPMSLTFPILIMMVSKLLAVDFCIALLSLWQTKYMVQESESILMYLAEAWSLSINSAFTGTIMPASVATLLPAGPDSYSRARHLEFPALTDMCFVLQMILDSMANQNTKFKSVRLMAVFSFAHGPILSTQLNNIVVKNNKCEVVLTTIYYTSQHIIYQVRYTTALFIEKLGLEACQNTAKDLLKNLMYDPARSITKSATLSISISGISDRTIVQTNNEIDSRCGSDFLAGTSLNTTIIGACASIELEGNLSGTPNIATAVFEWQRILVVTIAFLTFYDNSTKNKNLIKIAQLTMVLIPPITALVEYKLSLTFHQLLKFVIADYFRWRSRPPLIFIALVLAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "The valid expression for the protein is 'MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI'. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI\", \"result\": [{\"smile\": \"COc1ccc(CC(C)N)cc1O\", \"value\": 135.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'COc1ccc(CC(C)N)cc1O' and the protein 'MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI' is 135.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the target protein 'SKLDPSLSILGLPGKARRGHCAREECSHMCNRQAQPLQRQNGQVRDREVMVTLPFEANTANSASIQCAGEQPGGGSRGVASFGRRGDPPCQCSGQICPALSLSVIAGLPETAQASRAQGDPSAGTMPGWVSPLACGVKGAGPSWVPTPREGSVLYQSWSRVEFGSPVGLPEVYTPLGTELPTNQLGGVPNDLMPSGIRPGGSEQALTPHGDEGGADREILGPTGWVEACMDPYGAGGSGLHHDETWGVLRTYLLVDPYHGSLIRPAGDPGDAIVEVSVLGKGFLQQGGTKGAESMQEKDIGGCYTMCNLSVCEVLYGRAPEFGGEADWLVLYPQLARGRNGFAHWTLKLTLCRALAKGPYSAGHVWPCSEERFWAACLRSHSEKPLEVCSWEGILGTGQQSEYSAGGERVALNEKVGPSGSAPVYDLTDIAAYEIGPLEGRVGGNLAAWEGKLELWSPAGSGLMQQTQYALTVPAVRVSARGEGHFQLRGGHGRNSSENGLAGLQLPEGVLGTSVGPPPQAQGVLLDLQQGVRPRLLAQPLSVMDVWCEGDLLFTAKWLGSMMPLSADPKSCILPGLHAAGIQWPLWEAMPGNPLERSENNEVPAASIGNELWVRGPFGWLLVRGLPRFYSAFLEALAIPPYHRYERRAFLLPPNSGKFCQTVLVLGAKLQIQRYHTSSRGAITGGSDKGSHVLLGSRCPQELGGFPKERIARSGLAALLPSMIESLVLPILGGARARLGCTKAPRLYRAFQMQELVPGHPGRIGSCTSPLLSRRFLASDSRRSSMALPLKFCTEPSVLMGTPDFPFSEPTLGGSLSELISLGLGVHRGLMLCYEGGGGPDDDLPDGEEQIWGCASGQSGSTLFAAAAAAAAAAAAAAAAAA'? MemoryPool(arguments=dict_keys([]))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\"], \"target_seq\": \"SKLDPSLSILGLPGKARRGHCAREECSHMCNRQAQPLQRQNGQVRDREVMVTLPFEANTANSASIQCAGEQPGGGSRGVASFGRRGDPPCQCSGQICPALSLSVIAGLPETAQASRAQGDPSAGTMPGWVSPLACGVKGAGPSWVPTPREGSVLYQSWSRVEFGSPVGLPEVYTPLGTELPTNQLGGVPNDLMPSGIRPGGSEQALTPHGDEGGADREILGPTGWVEACMDPYGAGGSGLHHDETWGVLRTYLLVDPYHGSLIRPAGDPGDAIVEVSVLGKGFLQQGGTKGAESMQEKDIGGCYTMCNLSVCEVLYGRAPEFGGEADWLVLYPQLARGRNGFAHWTLKLTLCRALAKGPYSAGHVWPCSEERFWAACLRSHSEKPLEVCSWEGILGTGQQSEYSAGGERVALNEKVGPSGSAPVYDLTDIAAYEIGPLEGRVGGNLAAWEGKLELWSPAGSGLMQQTQYALTVPAVRVSARGEGHFQLRGGHGRNSSENGLAGLQLPEGVLGTSVGPPPQAQGVLLDLQQGVRPRLLAQPLSVMDVWCEGDLLFTAKWLGSMMPLSADPKSCILPGLHAAGIQWPLWEAMPGNPLERSENNEVPAASIGNELWVRGPFGWLLVRGLPRFYSAFLEALAIPPYHRYERRAFLLPPNSGKFCQTVLVLGAKLQIQRYHTSSRGAITGGSDKGSHVLLGSRCPQELGGFPKERIARSGLAALLPSMIESLVLPILGGARARLGCTKAPRLYRAFQMQELVPGHPGRIGSCTSPLLSRRFLASDSRRSSMALPLKFCTEPSVLMGTPDFPFSEPTLGGSLSELISLGLGVHRGLMLCYEGGGGPDDDLPDGEEQIWGCASGQSGSTLFAAAAAAAAAAAAAAAAAA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"invalid sequence\", \"result\": []} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "There might exist something wrong with your target protein sequence, please give me the right one."
            },
            {
                "from": "human",
                "value": "'MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS' is the sequence meant for the protein. MemoryPool(arguments=dict_keys(['user_smiles','user_target_sequence' ,'drug_target_affinity_regression_result']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS\", \"result\": [{\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity value between the drug 'CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1' and the protein 'MGCWGQLLVWFGAAGAILCSSPGSQETFLRSSPLPLASPSPRDPKVSAPPSILEPASPLNSPGTEGSWLFSTCGASGRHGPTQTQCDGAYAGTSVVVTVGAAGQLRGVQLWRVPGPGQYLISAYGAAGGKGAKNHLSRAHGVFVSAIFSLGLGESLYILVGQQGEDACPGGSPESQLVCLGESRAVEEHAAMDGSEGVPGSRRWAGGGGGGGGATYVFRVRAGELEPLLVAAGGGGRAYLRPRDRGRTQASPEKLENRSEAPGSGGRGGAAGGGGGWTSRAPSPQAGRSLQEGAEGGQGCSEAWATLGWAAAGGFGGGGGACTAGGGGGGYRGGDASETDNLWADGEDGVSFIHPSSELFLQPLAVTENHGEVEIRRHLNCSHCPLRDCQWQAELQLAECLCPEGMELAVDNVTCMDLHKPPGPLVLMVAVVATSTLSLLMVCGVLILVKQKKWQGLQEMRLPSPELELSKLRTSAIRTAPNPYYCQVGLGPAQSWPLPPGVTEVSPANVTLLRALGHGAFGEVYEGLVIGLPGDSSPLQVAIKTLPELCSPQDELDFLMEALIISKFRHQNIVRCVGLSLRATPRLILLELMSGGDMKSFLRHSRPHLGQPSPLVMRDLLQLAQDIAQGCHYLEENHFIHRDIAARNCLLSCAGPSRVAKIGDFGMARDIYRASYYRRGDRALLPVKWMPPEAFLEGIFTSKTDSWSFGVLLWEIFSLGYMPYPGRTNQEVLDFVVGGGRMDPPRGCPGPVYRIMTQCWQHEPELRPSFASILERLQYCTQDPDVLNSLLPMELGPTPEEEGTSGLGNRSLECLRPPQPQELSPEKLKSWGGSPLGPWLSSGLKPLKSRGLQPQNLWNPTYRS' is 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the generated drugs and the target protein 'MEGDGGTPWALALLRTFDAGEFTGWEKVGSGGFGQVYKVRHVHWKTWLAIKCSPSLHVDDRERMELLEEAKKMEMAKFRYILPVYGICREPVGLVMEYMETGSLEKLLASEPLPWDLRFRIIHETAVGMNFLHCMAPPLLHLDLKPANILLDAHYHVKISDFGLAKCNGLSHSHDLSMDGLFGTIAYLPPERIREKSRLFDTKHDVYSFAIVIWGVLTQKKPFADEKNILHIMVKVVKGHRPELPPVCRARPRACSHLIRLMQRCWQGDPRVRPTFQEITSETEDLCEKPDDEVKETAHDLDVKSPPEPRSEVVPARLKRASAPTFDNDYSLSELLSQLDSGVSQAVEGPEELSRSSSESKLPSSGSGKRLSGVSSVDSAFSSRGSLSLSFEREPSTSDLGTTDVQKKKLVDAIVSGDTSKLMKILQPQDVDLALDSGASLLHLAVEAGQEECAKWLLLNNANPNLSNRRGSTPLHMAVERRVRGVVELLLARKISVNAKDEDQWTALHFAAQNGDESSTRLLLEKNASVNEVDFEGRTPMHVACQHGQENIVRILLRRGVDVSLQGKDAWLPLHYAAWQGHLPIVKLLAKQPGVSVNAQTLDGRTPLHLAAQRGHYRVARILIDLCSDVNVCSLLAQTPLHVAAETGHTSTARLLLHRGAGKKAVTSDGYTALHLAARNGHLATVKLLVEEKADVLARGPLNQTALHLAAAHGHSEVVEELVSADVIDLFDEQGLSALHLAAQGRHAQTVETLLRHGAHINLQSLKFQGGHGPAATLLRRSKT'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEGDGGTPWALALLRTFDAGEFTGWEKVGSGGFGQVYKVRHVHWKTWLAIKCSPSLHVDDRERMELLEEAKKMEMAKFRYILPVYGICREPVGLVMEYMETGSLEKLLASEPLPWDLRFRIIHETAVGMNFLHCMAPPLLHLDLKPANILLDAHYHVKISDFGLAKCNGLSHSHDLSMDGLFGTIAYLPPERIREKSRLFDTKHDVYSFAIVIWGVLTQKKPFADEKNILHIMVKVVKGHRPELPPVCRARPRACSHLIRLMQRCWQGDPRVRPTFQEITSETEDLCEKPDDEVKETAHDLDVKSPPEPRSEVVPARLKRASAPTFDNDYSLSELLSQLDSGVSQAVEGPEELSRSSSESKLPSSGSGKRLSGVSSVDSAFSSRGSLSLSFEREPSTSDLGTTDVQKKKLVDAIVSGDTSKLMKILQPQDVDLALDSGASLLHLAVEAGQEECAKWLLLNNANPNLSNRRGSTPLHMAVERRVRGVVELLLARKISVNAKDEDQWTALHFAAQNGDESSTRLLLEKNASVNEVDFEGRTPMHVACQHGQENIVRILLRRGVDVSLQGKDAWLPLHYAAWQGHLPIVKLLAKQPGVSVNAQTLDGRTPLHLAAQRGHYRVARILIDLCSDVNVCSLLAQTPLHVAAETGHTSTARLLLHRGAGKKAVTSDGYTALHLAARNGHLATVKLLVEEKADVLARGPLNQTALHLAAAHGHSEVVEELVSADVIDLFDEQGLSALHLAAQGRHAQTVETLLRHGAHINLQSLKFQGGHGPAATLLRRSKT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGDGGTPWALALLRTFDAGEFTGWEKVGSGGFGQVYKVRHVHWKTWLAIKCSPSLHVDDRERMELLEEAKKMEMAKFRYILPVYGICREPVGLVMEYMETGSLEKLLASEPLPWDLRFRIIHETAVGMNFLHCMAPPLLHLDLKPANILLDAHYHVKISDFGLAKCNGLSHSHDLSMDGLFGTIAYLPPERIREKSRLFDTKHDVYSFAIVIWGVLTQKKPFADEKNILHIMVKVVKGHRPELPPVCRARPRACSHLIRLMQRCWQGDPRVRPTFQEITSETEDLCEKPDDEVKETAHDLDVKSPPEPRSEVVPARLKRASAPTFDNDYSLSELLSQLDSGVSQAVEGPEELSRSSSESKLPSSGSGKRLSGVSSVDSAFSSRGSLSLSFEREPSTSDLGTTDVQKKKLVDAIVSGDTSKLMKILQPQDVDLALDSGASLLHLAVEAGQEECAKWLLLNNANPNLSNRRGSTPLHMAVERRVRGVVELLLARKISVNAKDEDQWTALHFAAQNGDESSTRLLLEKNASVNEVDFEGRTPMHVACQHGQENIVRILLRRGVDVSLQGKDAWLPLHYAAWQGHLPIVKLLAKQPGVSVNAQTLDGRTPLHLAAQRGHYRVARILIDLCSDVNVCSLLAQTPLHVAAETGHTSTARLLLHRGAGKKAVTSDGYTALHLAARNGHLATVKLLVEEKADVLARGPLNQTALHLAAAHGHSEVVEELVSADVIDLFDEQGLSALHLAAQGRHAQTVETLLRHGAHINLQSLKFQGGHGPAATLLRRSKT\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 900.0}, {\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MEGDGGTPWALALLRTFDAGEFTGWEKVGSGGFGQVYKVRHVHWKTWLAIKCSPSLHVDDRERMELLEEAKKMEMAKFRYILPVYGICREPVGLVMEYMETGSLEKLLASEPLPWDLRFRIIHETAVGMNFLHCMAPPLLHLDLKPANILLDAHYHVKISDFGLAKCNGLSHSHDLSMDGLFGTIAYLPPERIREKSRLFDTKHDVYSFAIVIWGVLTQKKPFADEKNILHIMVKVVKGHRPELPPVCRARPRACSHLIRLMQRCWQGDPRVRPTFQEITSETEDLCEKPDDEVKETAHDLDVKSPPEPRSEVVPARLKRASAPTFDNDYSLSELLSQLDSGVSQAVEGPEELSRSSSESKLPSSGSGKRLSGVSSVDSAFSSRGSLSLSFEREPSTSDLGTTDVQKKKLVDAIVSGDTSKLMKILQPQDVDLALDSGASLLHLAVEAGQEECAKWLLLNNANPNLSNRRGSTPLHMAVERRVRGVVELLLARKISVNAKDEDQWTALHFAAQNGDESSTRLLLEKNASVNEVDFEGRTPMHVACQHGQENIVRILLRRGVDVSLQGKDAWLPLHYAAWQGHLPIVKLLAKQPGVSVNAQTLDGRTPLHLAAQRGHYRVARILIDLCSDVNVCSLLAQTPLHVAAETGHTSTARLLLHRGAGKKAVTSDGYTALHLAARNGHLATVKLLVEEKADVLARGPLNQTALHLAAAHGHSEVVEELVSADVIDLFDEQGLSALHLAAQGRHAQTVETLLRHGAHINLQSLKFQGGHGPAATLLRRSKT' are 10000.0, 900.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the generated drugs with respect to the target protein 'MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\", \"value\": 880.0}, {\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MKKFKRRLSLTLRGSQTIDESLSELAEQMTIEENSSKDNEPIVKNGRPPTSHSMHSFLHQYTGSFKKPPLRRPHSVIGGSLGSFMAMPRNGSRLDIVHENLKMGSDGESDQASGTSSDEVQSPTGVCLRNRIHRRISMEDLNKRLSLPADIRIPDGYLEKLQINSPPFDQPMSRRSRRASLSEIGFGKMETYIKLEKLGEGTYATVYKGRSKLTENLVALKEIRLEHEEGAPCTAIREVSLLKDLKHANIVTLHDIVHTDKSLTLVFEYLDKDLKQYMDDCGNIMSMHNVKLFLYQILRGLAYCHRRKVLHRDLKPQNLLINEKGELKLADFGLARAKSVPTKTYSNEVVTLWYRPPDVLLGSSEYSTQIDMWGVGCIFFEMASGRPLFPGSTVEDELHLIFRLLGTPSQETWPGISSNEEFKNYNFPKYKPQPLINHAPRLDSEGIELITKFLQYESKKRVSAEEAMKHVYFRSLGPRIHALPESVSIFSLKEIQLQKDPGFRNSSYPETGHGKNRRQSMLF' are 880.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the expected value for the affinity between the optimized drugs and the target protein 'MERRLRALEQLARGEAGGCPGLDGLLDLLLALHHELSSGPLRRERSVAQFLSWASPFVSKVKELRLQRDDFEILKVIGRGAFGEVTVVRQRDTGQIFAMKMLHKWEMLKRAETACFREERDVLVKGDSRWVTTLHYAFQDEEYLYLVMDYYAGGDLLTLLSRFEDRLPPELAQFYLAEMVLAIHSLHQLGYVHRDVKPDNVLLDVNGHIRLADFGSCLRLNTNGMVDSSVAVGTPDYISPEILQAMEEGKGHYGPQCDWWSLGVCAYELLFGETPFYAESLVETYGKIMNHEDHLQFPPDVPDVPASAQDLIRQLLCRQEERLGRGGLDDFRNHPFFEGVDWERLASSTAPYIPELRGPMDTSNFDVDDDTLNHPGTLPPPSHGAFSGHHLPFVGFTYTSGSHSPESSSEAWAALERKLQCLEQEKVELSRKHQEALHAPTDHRELEQLRKEVQTLRDRLPEMLRDKASLSQTDGPPAGSPGQDSDLRQELDRLHRELAEGRAGLQAQEQELCRAQGQQEELLQRLQEAQEREAATASQTRALSSQLEEARAAQRELEAQVSSLSRQVTQLQGQWEQRLEESSQAKTIHTASETNGMGPPEGGPQEAQLRKEVAALREQLEQAHSHRPSGKEEALCQLQEENRRLSREQERLEAELAQEQESKQRLEGERRETESNWEAQLADILSWVNDEKVSRGYLQALATKMAEELESLRNVGTQTLPARPLDHQWKARRLQKMEASARLELQSALEAEIRAKQGLQERLTQVQEAQLQAERRLQEAEKQSQALQQELAMLREELRARGPVDTKPSNSLIPFLSFRSSEKDSAKDPGISGEATRHGGEPDLRPEGRRSLRMGAVFPRAPTANTASTEGLPAKPGSHTLRPRSFPSPTKCLRCTSLMLGLGRQGLGCDACGYFCHTTCAPQAPPCPVPPDLLRTALGVHPETGTGTAYEGFLSVPRPSGVRRGWQRVFAALSDSRLLLFDAPDLRLSPPSGALLQVLDLRDPQFSATPVLASDVIHAQSRDLPRIFRVTTSQLAVPPTTCTVLLLAESEGERERWLQVLGELQRLLLDARPRPRPVYTLKEAYDNGLPLLPHTLCAAILDQDRLALGTEEGLFVIHLRSNDIFQVGECRRVQQLTLSPSAGLLVVLCGRGPSVRLFALAELENIEVAGAKIPESRGCQVLAAGSILQARTPVLCVAVKRQVLCYQLGPGPGPWQRRIRELQAPATVQSLGLLGDRLCVGAAGGFALYPLLNEAAPLALGAGLVPEELPPSRGGLGEALGAVELSLSEFLLLFTTAGIYVDGAGRKSRGHELLWPAAPMGWGYAAPYLTVFSENSIDVFDVRRAEWVQTVPLKKVRPLNPEGSLFLYGTEKVRLTYLRNQLAEKDEFDIPDLTDNSRRQLFRTKSKRRFFFRVSEEQQKQQRREMLKDPFVRSKLISPPTNFNHLVHVGPANGRPGARDKSPAPEEKGRVARGSGPQRPHSFSEALRRPASMGSEGLGGDADPMKRKPWTSLSSESVSCPQGSLSPATSLMQVSERPRSLPLSPELESSP'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MERRLRALEQLARGEAGGCPGLDGLLDLLLALHHELSSGPLRRERSVAQFLSWASPFVSKVKELRLQRDDFEILKVIGRGAFGEVTVVRQRDTGQIFAMKMLHKWEMLKRAETACFREERDVLVKGDSRWVTTLHYAFQDEEYLYLVMDYYAGGDLLTLLSRFEDRLPPELAQFYLAEMVLAIHSLHQLGYVHRDVKPDNVLLDVNGHIRLADFGSCLRLNTNGMVDSSVAVGTPDYISPEILQAMEEGKGHYGPQCDWWSLGVCAYELLFGETPFYAESLVETYGKIMNHEDHLQFPPDVPDVPASAQDLIRQLLCRQEERLGRGGLDDFRNHPFFEGVDWERLASSTAPYIPELRGPMDTSNFDVDDDTLNHPGTLPPPSHGAFSGHHLPFVGFTYTSGSHSPESSSEAWAALERKLQCLEQEKVELSRKHQEALHAPTDHRELEQLRKEVQTLRDRLPEMLRDKASLSQTDGPPAGSPGQDSDLRQELDRLHRELAEGRAGLQAQEQELCRAQGQQEELLQRLQEAQEREAATASQTRALSSQLEEARAAQRELEAQVSSLSRQVTQLQGQWEQRLEESSQAKTIHTASETNGMGPPEGGPQEAQLRKEVAALREQLEQAHSHRPSGKEEALCQLQEENRRLSREQERLEAELAQEQESKQRLEGERRETESNWEAQLADILSWVNDEKVSRGYLQALATKMAEELESLRNVGTQTLPARPLDHQWKARRLQKMEASARLELQSALEAEIRAKQGLQERLTQVQEAQLQAERRLQEAEKQSQALQQELAMLREELRARGPVDTKPSNSLIPFLSFRSSEKDSAKDPGISGEATRHGGEPDLRPEGRRSLRMGAVFPRAPTANTASTEGLPAKPGSHTLRPRSFPSPTKCLRCTSLMLGLGRQGLGCDACGYFCHTTCAPQAPPCPVPPDLLRTALGVHPETGTGTAYEGFLSVPRPSGVRRGWQRVFAALSDSRLLLFDAPDLRLSPPSGALLQVLDLRDPQFSATPVLASDVIHAQSRDLPRIFRVTTSQLAVPPTTCTVLLLAESEGERERWLQVLGELQRLLLDARPRPRPVYTLKEAYDNGLPLLPHTLCAAILDQDRLALGTEEGLFVIHLRSNDIFQVGECRRVQQLTLSPSAGLLVVLCGRGPSVRLFALAELENIEVAGAKIPESRGCQVLAAGSILQARTPVLCVAVKRQVLCYQLGPGPGPWQRRIRELQAPATVQSLGLLGDRLCVGAAGGFALYPLLNEAAPLALGAGLVPEELPPSRGGLGEALGAVELSLSEFLLLFTTAGIYVDGAGRKSRGHELLWPAAPMGWGYAAPYLTVFSENSIDVFDVRRAEWVQTVPLKKVRPLNPEGSLFLYGTEKVRLTYLRNQLAEKDEFDIPDLTDNSRRQLFRTKSKRRFFFRVSEEQQKQQRREMLKDPFVRSKLISPPTNFNHLVHVGPANGRPGARDKSPAPEEKGRVARGSGPQRPHSFSEALRRPASMGSEGLGGDADPMKRKPWTSLSSESVSCPQGSLSPATSLMQVSERPRSLPLSPELESSP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERRLRALEQLARGEAGGCPGLDGLLDLLLALHHELSSGPLRRERSVAQFLSWASPFVSKVKELRLQRDDFEILKVIGRGAFGEVTVVRQRDTGQIFAMKMLHKWEMLKRAETACFREERDVLVKGDSRWVTTLHYAFQDEEYLYLVMDYYAGGDLLTLLSRFEDRLPPELAQFYLAEMVLAIHSLHQLGYVHRDVKPDNVLLDVNGHIRLADFGSCLRLNTNGMVDSSVAVGTPDYISPEILQAMEEGKGHYGPQCDWWSLGVCAYELLFGETPFYAESLVETYGKIMNHEDHLQFPPDVPDVPASAQDLIRQLLCRQEERLGRGGLDDFRNHPFFEGVDWERLASSTAPYIPELRGPMDTSNFDVDDDTLNHPGTLPPPSHGAFSGHHLPFVGFTYTSGSHSPESSSEAWAALERKLQCLEQEKVELSRKHQEALHAPTDHRELEQLRKEVQTLRDRLPEMLRDKASLSQTDGPPAGSPGQDSDLRQELDRLHRELAEGRAGLQAQEQELCRAQGQQEELLQRLQEAQEREAATASQTRALSSQLEEARAAQRELEAQVSSLSRQVTQLQGQWEQRLEESSQAKTIHTASETNGMGPPEGGPQEAQLRKEVAALREQLEQAHSHRPSGKEEALCQLQEENRRLSREQERLEAELAQEQESKQRLEGERRETESNWEAQLADILSWVNDEKVSRGYLQALATKMAEELESLRNVGTQTLPARPLDHQWKARRLQKMEASARLELQSALEAEIRAKQGLQERLTQVQEAQLQAERRLQEAEKQSQALQQELAMLREELRARGPVDTKPSNSLIPFLSFRSSEKDSAKDPGISGEATRHGGEPDLRPEGRRSLRMGAVFPRAPTANTASTEGLPAKPGSHTLRPRSFPSPTKCLRCTSLMLGLGRQGLGCDACGYFCHTTCAPQAPPCPVPPDLLRTALGVHPETGTGTAYEGFLSVPRPSGVRRGWQRVFAALSDSRLLLFDAPDLRLSPPSGALLQVLDLRDPQFSATPVLASDVIHAQSRDLPRIFRVTTSQLAVPPTTCTVLLLAESEGERERWLQVLGELQRLLLDARPRPRPVYTLKEAYDNGLPLLPHTLCAAILDQDRLALGTEEGLFVIHLRSNDIFQVGECRRVQQLTLSPSAGLLVVLCGRGPSVRLFALAELENIEVAGAKIPESRGCQVLAAGSILQARTPVLCVAVKRQVLCYQLGPGPGPWQRRIRELQAPATVQSLGLLGDRLCVGAAGGFALYPLLNEAAPLALGAGLVPEELPPSRGGLGEALGAVELSLSEFLLLFTTAGIYVDGAGRKSRGHELLWPAAPMGWGYAAPYLTVFSENSIDVFDVRRAEWVQTVPLKKVRPLNPEGSLFLYGTEKVRLTYLRNQLAEKDEFDIPDLTDNSRRQLFRTKSKRRFFFRVSEEQQKQQRREMLKDPFVRSKLISPPTNFNHLVHVGPANGRPGARDKSPAPEEKGRVARGSGPQRPHSFSEALRRPASMGSEGLGGDADPMKRKPWTSLSSESVSCPQGSLSPATSLMQVSERPRSLPLSPELESSP\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}, {\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 10000.0}, {\"smile\": \"CNC(=O)c1ccccc1Sc1ccc2c(/C=C/c3ccccn3)n[nH]c2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MERRLRALEQLARGEAGGCPGLDGLLDLLLALHHELSSGPLRRERSVAQFLSWASPFVSKVKELRLQRDDFEILKVIGRGAFGEVTVVRQRDTGQIFAMKMLHKWEMLKRAETACFREERDVLVKGDSRWVTTLHYAFQDEEYLYLVMDYYAGGDLLTLLSRFEDRLPPELAQFYLAEMVLAIHSLHQLGYVHRDVKPDNVLLDVNGHIRLADFGSCLRLNTNGMVDSSVAVGTPDYISPEILQAMEEGKGHYGPQCDWWSLGVCAYELLFGETPFYAESLVETYGKIMNHEDHLQFPPDVPDVPASAQDLIRQLLCRQEERLGRGGLDDFRNHPFFEGVDWERLASSTAPYIPELRGPMDTSNFDVDDDTLNHPGTLPPPSHGAFSGHHLPFVGFTYTSGSHSPESSSEAWAALERKLQCLEQEKVELSRKHQEALHAPTDHRELEQLRKEVQTLRDRLPEMLRDKASLSQTDGPPAGSPGQDSDLRQELDRLHRELAEGRAGLQAQEQELCRAQGQQEELLQRLQEAQEREAATASQTRALSSQLEEARAAQRELEAQVSSLSRQVTQLQGQWEQRLEESSQAKTIHTASETNGMGPPEGGPQEAQLRKEVAALREQLEQAHSHRPSGKEEALCQLQEENRRLSREQERLEAELAQEQESKQRLEGERRETESNWEAQLADILSWVNDEKVSRGYLQALATKMAEELESLRNVGTQTLPARPLDHQWKARRLQKMEASARLELQSALEAEIRAKQGLQERLTQVQEAQLQAERRLQEAEKQSQALQQELAMLREELRARGPVDTKPSNSLIPFLSFRSSEKDSAKDPGISGEATRHGGEPDLRPEGRRSLRMGAVFPRAPTANTASTEGLPAKPGSHTLRPRSFPSPTKCLRCTSLMLGLGRQGLGCDACGYFCHTTCAPQAPPCPVPPDLLRTALGVHPETGTGTAYEGFLSVPRPSGVRRGWQRVFAALSDSRLLLFDAPDLRLSPPSGALLQVLDLRDPQFSATPVLASDVIHAQSRDLPRIFRVTTSQLAVPPTTCTVLLLAESEGERERWLQVLGELQRLLLDARPRPRPVYTLKEAYDNGLPLLPHTLCAAILDQDRLALGTEEGLFVIHLRSNDIFQVGECRRVQQLTLSPSAGLLVVLCGRGPSVRLFALAELENIEVAGAKIPESRGCQVLAAGSILQARTPVLCVAVKRQVLCYQLGPGPGPWQRRIRELQAPATVQSLGLLGDRLCVGAAGGFALYPLLNEAAPLALGAGLVPEELPPSRGGLGEALGAVELSLSEFLLLFTTAGIYVDGAGRKSRGHELLWPAAPMGWGYAAPYLTVFSENSIDVFDVRRAEWVQTVPLKKVRPLNPEGSLFLYGTEKVRLTYLRNQLAEKDEFDIPDLTDNSRRQLFRTKSKRRFFFRVSEEQQKQQRREMLKDPFVRSKLISPPTNFNHLVHVGPANGRPGARDKSPAPEEKGRVARGSGPQRPHSFSEALRRPASMGSEGLGGDADPMKRKPWTSLSSESVSCPQGSLSPATSLMQVSERPRSLPLSPELESSP' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding strength of the generated drugs to the target protein 'MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 3200.0}, {\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\", \"value\": 3100.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MASNPERGEILLTELQGDSRSLPFSENVSAVQKLDFSDTMVQQKLDDIKDRIKREIRKELKIKEGAENLRKVTTDKKSLAYVDNILKKSNKKLEELHHKLQELNAHIVVSDPEDITDCPRTPDTPNNDPRCSTSNNRLKALQKQLDIELKVKQGAENMIQMYSNGSSKDRKLHGTAQQLLQDSKTKIEVIRMQILQAVQTNELAFDNAKPVISPLELRMEELRHHFRIEFAVAEGAKNVMKLLGSGKVTDRKALSEAQARFNESSQKLDLLKYSLEQRLNEVPKNHPKSRIIIEELSLVAASPTLSPRQSMISTQNQYSTLSKPAALTGTLEVRLMGCQDILENVPGRSKATSVALPGWSPSETRSSFMSRTSKSKSGSSRNLLKTDDLSNDVCAVLKLDNTVVGQTSWKPISNQSWDQKFTLELDRSRELEISVYWRDWRSLCAVKFLRLEDFLDNQRHGMCLYLEPQGTLFAEVTFFNPVIERRPKLQRQKKIFSKQQGKTFLRAPQMNINIATWGRLVRRAIPTVNHSGTFSPQAPVPTTVPVVDVRIPQLAPPASDSTVTKLDFDLEPEPPPAPPRASSLGEIDESSELRVLDIPGQDSETVFDIQNDRNSILPKSQSEYKPDTPQSGLEYSGIQELEDRRSQQRFQFNLQDFRCCAVLGRGHFGKVLLAEYKNTNEMFAIKALKKGDIVARDEVDSLMCEKRIFETVNSVRHPFLVNLFACFQTKEHVCFVMEYAAGGDLMMHIHTDVFSEPRAVFYAACVVLGLQYLHEHKIVYRDLKLDNLLLDTEGFVKIADFGLCKEGMGYGDRTSTFCGTPEFLAPEVLTETSYTRAVDWWGLGVLIYEMLVGESPFPGDDEEEVFDSIVNDEVRYPRFLSTEAISIMRRLLRRNPERRLGASEKDAEDVKKHPFFRLIDWSALMDKKVKPPFIPTIRGREDVSNFDDEFTSEAPILTPPREPRILSEEEQEMFRDFDYIADWC' are 10000.0, 3200.0 and 3100.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the generated drugs and the target protein 'MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 1.0}, {\"smile\": \"Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1\", \"value\": 190.0}, {\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MSFGRDMELEHFDERDKAQRYSRGSRVNGLPSPTHSAHCSFYRTRTLQTLSSEKKAKKVRFYRNGDRYFKGIVYAISPDRFRSFEALLADLTRTLSDNVNLPQGVRTIYTIDGLKKISSLDQLVEGESYVCGSIEPFKKLEYTKNVNPNWSVNVKTTSASRAVSSLATAKGSPSEVRENKDFIRPKLVTIIRSGVKPRKAVRILLNKKTAHSFEQVLTDITDAIKLDSGVVKRLYTLDGKQVMCLQDFFGDDDIFIACGPEKFRYQDDFLLDESECRVVKSTSYTKIASSSRRSTTKSPGPSRRSKSPASTSSVNGTPGSQLSTPRSGKSPSPSPTSPGSLRKQRSSQHGGSSTSLASTKVCSSMDENDGPGEEVSEEGFQIPATITERYKVGRTIGDGNFAVVKECVERSTAREYALKIIKKSKCRGKEHMIQNEVSILRRVKHPNIVLLIEEMDVPTELYLVMELVKGGDLFDAITSTNKYTERDASGMLYNLASAIKYLHSLNIVHRDIKPENLLVYEHQDGSKSLKLGDFGLATIVDGPLYTVCGTPTYVAPEIIAETGYGLKVDIWAAGVITYILLCGFPPFRGSGDDQEVLFDQILMGQVDFPSPYWDNVSDSAKELITMMLLVDVDQRFSAVQVLEHPWVNDDGLPENEHQLSVAGKIKKHFNTGPKPNSTAAGVSVIATTALDKERQVFRRRRNQDVRSRYKAQPAPPELNSESEDYSPSSSETVRSPNSPF' are 1.0, 190.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likely binding affinity for the generated drugs and the target protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 9000.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 240.0}, {\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR' are 9000.0, 240.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the predicted affinity value between the generated drugs and the target protein 'MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV\", \"result\": [{\"smile\": \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\", \"value\": 2800.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}, {\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV' are 2800.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your forecast for the optimized drugs' affinity towards the target protein 'MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN\", \"result\": [{\"smile\": \"O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 10000.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 1500.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MELLRTITYQPAASTKMCEQALGKGCGADSKKKRPPQPPEESQPPQSQAQVPPAAPHHHHHHSHSGPEISRIIVDPTTGKRYCRGKVLGKGGFAKCYEMTDLTNNKVYAAKIIPHSRVAKPHQREKIDKEIELHRILHHKHVVQFYHYFEDKENIYILLEYCSRRSMAHILKARKVLTEPEVRYYLRQIVSGLKYLHEQEILHRDLKLGNFFINEAMELKVGDFGLAARLEPLEHRRRTICGTPNYLSPEVLNKQGHGCESDIWALGCVMYTMLLGRPPFETTNLKETYRCIREARYTMPSSLLAPAKHLIASMLSKNPEDRPSLDDIIRHDFFLQGFTPDRLSSSCCHTVPDFHLSSPAKNFFKKAAAALFGGKKDKARYIDTHNRVSKEDEDIYKLRHDLKKTSITQQPSKHRTDEELQPPTTTVARSGTPAVENKQQIGDAIRMIVRGTLGSCSSSSECLEDSTMGSVADTVARVLRGCLENMPEADCIPKEQLSTSFQWVTKWVDYSNKYGFGYQLSDHTVGVLFNNGAHMSLLPDKKTVHYYAELGQCSVFPATDAPEQFISQVTVLKYFSHYMEENLMDGGDLPSVTDIRRPRLYLLQWLKSDKALMMLFNDGTFQVNFYHDHTKIIICSQNEEYLLTYINEDRISTTFRLTTLLMSGCSSELKNRMEYALNMLLQRCN' are 10000.0, 10000.0 and 1500.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the projected affinity value between the generated drugs and the target protein 'MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS\", \"result\": [{\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 10000.0}, {\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 10000.0}, {\"smile\": \"CN1CCN(c2ccc3nc(-c4c(N)c5c(F)cccc5[nH]c4=O)[nH]c3c2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MCFSFIMPPAMADILDIWAVDSQIASDGSIPVDFLLPTGIYIQLEVPREATISYIKQMLWKQVHNYPMFNLLMDIDSYMFACVNQTAVYEELEDETRRLCDVRPFLPVLKLVTRSCDPGEKLDSKIGVLIGKGLHEFDSLKDPEVNEFRRKMRKFSEEKILSLVGLSWMDWLKQTYPPEHEPSIPENLEDKLYGGKLIVAVHFENCQDVFSFQVSPNMNPIKVNELAIQKRLTIHGKEDEVSPYDYVLQVSGRVEYVFGDHPLIQFQYIRNCVMNRALPHFILVECCKIKKMYEQEMIAIEAAINRNSSNLPLPLPPKKTRIISHVWENNNPFQIVLVKGNKLNTEETVKVHVRAGLFHGTELLCKTIVSSEVSGKNDHIWNEPLEFDINICDLPRMARLCFAVYAVLDKVKTKKSTKTINPSKYQTIRKAGKVHYPVAWVNTMVFDFKGQLRTGDIILHSWSSFPDELEEMLNPMGTVQTNPYTENATALHVKFPENKKQPYYYPPFDKIIEKAAEIASSDSANVSSRGGKKFLPVLKEILDRDPLSQLCENEMDLIWTLRQDCREIFPQSLPKLLLSIKWNKLEDVAQLQALLQIWPKLPPREALELLDFNYPDQYVREYAVGCLRQMSDEELSQYLLQLVQVLKYEPFLDCALSRFLLERALGNRRIGQFLFWHLRSEVHIPAVSVQFGVILEAYCRGSVGHMKVLSKQVEALNKLKTLNSLIKLNAVKLNRAKGKEAMHTCLKQSAYREALSDLQSPLNPCVILSELYVEKCKYMDSKMKPLWLVYNNKVFGEDSVGVIFKNGDDLRQDMLTLQMLRLMDLLWKEAGLDLRMLPYGCLATGDRSGLIEVVSTSETIADIQLNSSNVAAAAAFNKDALLNWLKEYNSGDDLDRAIEEFTLSCAGYCVASYVLGIGDRHSDNIMVKKTGQLFHIDFGHILGNFKSKFGIKRERVPFILTYDFIHVIQQGKTGNTEKFGRFRQCCEDAYLILRRHGNLFITLFALMLTAGLPELTSVKDIQYLKDSLALGKSEEEALKQFKQKFDEALRESWTTKVNWMAHTVRKDYRS' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the interaction value between the optimized drugs and the target protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)n[nH]1\", \"value\": 10000.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 1.0}, {\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 160.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MAGNCGARGALSAHTLLFDLPPALLGELCAVLDSCDGALGWRGLAERLSSSWLDVRHIEKYVDQGKSGTRELLWSWAQKNKTIGDLLQVLQEMGHRRAIHLITNYGAVLSPSEKSYQEGGFPNILFKETANVTVDNVLIPEHNEKGILLKSSISFQNIIEGTRNFHKDFLIGEGEIFEVYRVEIQNLTYAVKLFKQEKKMQCKKHWKRFLSELEVLLLFHHPNILELAAYFTETEKFCLIYPYMRNGTLFDRLQCVGDTAPLPWHIRIGILIGISKAIHYLHNVQPCSVICGSISSANILLDDQFQPKLTDFAMAHFRSHLEHQSCTINMTSSSSKHLWYMPEEYIRQGKLSIKTDVYSFGIVIMEVLTGCRVVLDDPKHIQLRDLLRELMEKRGLDSCLSFLDKKVPPCPRNFSAKLFCLAGRCAATRAKLRPSMDEVLNTLESTQASLYFAEDPPTSLKSFRCPSPLFLENVPSIPVEDDESQNNNLLPSDEGLRIDRMTQKTPFECSQSEVMFLSLDKKPESKRNEEACNMPSSSCEESWFPKYIVPSQDLRPYKVNIDPSSEAPGHSCRSRPVESSCSSKFSWDEYEQYKKE' are 10000.0, 1.0 and 160.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the binding affinity you would expect for the generated drugs and the target protein 'MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 10000.0}, {\"smile\": \"CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1\", \"value\": 10000.0}, {\"smile\": \"CCCC(=O)O[C@@H]1[C@H](C)[C@]2(O)C(CC(=O)C[C@]3(O)C(=O)C(C)=CC32)C2C(C)(C)[C@]21OC(=O)CCC\", \"value\": 0.177}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MAPFLRIAFNSYELGSLQAEDEANQPFCAVKMKEALSTERGKTLVQKKPTMYPEWKSTFDAHIYEGRVIQIVLMRAAEEPVSEVTVGVSVLAERCKKNNGKAEFWLDLQPQAKVLMSVQYFLEDVDCKQSMRSEDEAKFPTMNRRGAIKQAKIHYIKNHEFIATFFGQPTFCSVCKDFVWGLNKQGYKCRQCNAAIHKKCIDKIIGRCTGTAANSRDTIFQKERFNIDMPHRFKVHNYMSPTFCDHCGSLLWGLVKQGLKCEDCGMNVHHKCREKVANLCGINQKLLAEALNQVTQRASRRSDSASSEPVGIYQGFEKKTGVAGEDMQDNSGTYGKIWEGSSKCNINNFIFHKVLGKGSFGKVLLGELKGRGEYFAIKALKKDVVLIDDDVECTMVEKRVLTLAAENPFLTHLICTFQTKDHLFFVMEFLNGGDLMYHIQDKGRFELYRATFYAAEIMCGLQFLHSKGIIYRDLKLDNVLLDRDGHIKIADFGMCKENIFGESRASTFCGTPDYIAPEILQGLKYTFSVDWWSFGVLLYEMLIGQSPFHGDDEDELFESIRVDTPHYPRWITKESKDILEKLFEREPTKRLGVTGNIKIHPFFKTINWTLLEKRRLEPPFRPKVKSPRDYSNFDQEFLNEKARLSYSDKNLIDSMDQSAFAGFSFVNPKFEHLLED' are 10000.0, 10000.0 and 0.177 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the generated drugs-target protein 'MLRGGRRGQLGWHSWAAGPGSLLAWLILASAGAAPCPDACCPHGSSGLRCTRDGALDSLHHLPGAENLTELYIENQQHLQHLELRDLRGLGELRNLTIVKSGLRFVAPDAFHFTPRLSRLNLSFNALESLSWKTVQGLSLQELVLSGNPLHCSCALRWLQRWEEEGLGGVPEQKLQCHGQGPLAHMPNASCGVPTLKVQVPNASVDVGDDVLLRCQVEGRGLEQAGWILTELEQSATVMKSGGLPSLGLTLANVTSDLNRKNVTCWAENDVGRAEVSVQVNVSFPASVQLHTAVEMHHWCIPFSVDGQPAPSLRWLFNGSVLNETSFIFTEFLEPAANETVRHGCLRLNQPTHVNNGNYTLLAANPFGQASASIMAAFMDNPFEFNPEDPIPDTNSTSGDPVEKKDETPFGVSVAVGLAVFACLFLSTLLLVLNKCGRRNKFGINRPAVLAPEDGLAMSLHFMTLGGSSLSPTEGKGSGLQGHIIENPQYFSDACVHHIKRRDIVLKWELGEGAFGKVFLAECHNLLPEQDKMLVAVKALKEASESARQDFQREAELLTMLQHQHIVRFFGVCTEGRPLLMVFEYMRHGDLNRFLRSHGPDAKLLAGGEDVAPGPLGLGQLLAVASQVAAGMVYLAGLHFVHRDLATRNCLVGQGLVVKIGDFGMSRDIYSTDYYRVGGRTMLPIRWMPPESILYRKFTTESDVWSFGVVLWEIFTYGKQPWYQLSNTEAIDCITQGRELERPRACPPEVYAIMRGCWQREPQQRHSIKDVHARLQALAQAPPVYLDVLG' pair. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLRGGRRGQLGWHSWAAGPGSLLAWLILASAGAAPCPDACCPHGSSGLRCTRDGALDSLHHLPGAENLTELYIENQQHLQHLELRDLRGLGELRNLTIVKSGLRFVAPDAFHFTPRLSRLNLSFNALESLSWKTVQGLSLQELVLSGNPLHCSCALRWLQRWEEEGLGGVPEQKLQCHGQGPLAHMPNASCGVPTLKVQVPNASVDVGDDVLLRCQVEGRGLEQAGWILTELEQSATVMKSGGLPSLGLTLANVTSDLNRKNVTCWAENDVGRAEVSVQVNVSFPASVQLHTAVEMHHWCIPFSVDGQPAPSLRWLFNGSVLNETSFIFTEFLEPAANETVRHGCLRLNQPTHVNNGNYTLLAANPFGQASASIMAAFMDNPFEFNPEDPIPDTNSTSGDPVEKKDETPFGVSVAVGLAVFACLFLSTLLLVLNKCGRRNKFGINRPAVLAPEDGLAMSLHFMTLGGSSLSPTEGKGSGLQGHIIENPQYFSDACVHHIKRRDIVLKWELGEGAFGKVFLAECHNLLPEQDKMLVAVKALKEASESARQDFQREAELLTMLQHQHIVRFFGVCTEGRPLLMVFEYMRHGDLNRFLRSHGPDAKLLAGGEDVAPGPLGLGQLLAVASQVAAGMVYLAGLHFVHRDLATRNCLVGQGLVVKIGDFGMSRDIYSTDYYRVGGRTMLPIRWMPPESILYRKFTTESDVWSFGVVLWEIFTYGKQPWYQLSNTEAIDCITQGRELERPRACPPEVYAIMRGCWQREPQQRHSIKDVHARLQALAQAPPVYLDVLG\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLRGGRRGQLGWHSWAAGPGSLLAWLILASAGAAPCPDACCPHGSSGLRCTRDGALDSLHHLPGAENLTELYIENQQHLQHLELRDLRGLGELRNLTIVKSGLRFVAPDAFHFTPRLSRLNLSFNALESLSWKTVQGLSLQELVLSGNPLHCSCALRWLQRWEEEGLGGVPEQKLQCHGQGPLAHMPNASCGVPTLKVQVPNASVDVGDDVLLRCQVEGRGLEQAGWILTELEQSATVMKSGGLPSLGLTLANVTSDLNRKNVTCWAENDVGRAEVSVQVNVSFPASVQLHTAVEMHHWCIPFSVDGQPAPSLRWLFNGSVLNETSFIFTEFLEPAANETVRHGCLRLNQPTHVNNGNYTLLAANPFGQASASIMAAFMDNPFEFNPEDPIPDTNSTSGDPVEKKDETPFGVSVAVGLAVFACLFLSTLLLVLNKCGRRNKFGINRPAVLAPEDGLAMSLHFMTLGGSSLSPTEGKGSGLQGHIIENPQYFSDACVHHIKRRDIVLKWELGEGAFGKVFLAECHNLLPEQDKMLVAVKALKEASESARQDFQREAELLTMLQHQHIVRFFGVCTEGRPLLMVFEYMRHGDLNRFLRSHGPDAKLLAGGEDVAPGPLGLGQLLAVASQVAAGMVYLAGLHFVHRDLATRNCLVGQGLVVKIGDFGMSRDIYSTDYYRVGGRTMLPIRWMPPESILYRKFTTESDVWSFGVVLWEIFTYGKQPWYQLSNTEAIDCITQGRELERPRACPPEVYAIMRGCWQREPQQRHSIKDVHARLQALAQAPPVYLDVLG\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 630.0}, {\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 1000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MLRGGRRGQLGWHSWAAGPGSLLAWLILASAGAAPCPDACCPHGSSGLRCTRDGALDSLHHLPGAENLTELYIENQQHLQHLELRDLRGLGELRNLTIVKSGLRFVAPDAFHFTPRLSRLNLSFNALESLSWKTVQGLSLQELVLSGNPLHCSCALRWLQRWEEEGLGGVPEQKLQCHGQGPLAHMPNASCGVPTLKVQVPNASVDVGDDVLLRCQVEGRGLEQAGWILTELEQSATVMKSGGLPSLGLTLANVTSDLNRKNVTCWAENDVGRAEVSVQVNVSFPASVQLHTAVEMHHWCIPFSVDGQPAPSLRWLFNGSVLNETSFIFTEFLEPAANETVRHGCLRLNQPTHVNNGNYTLLAANPFGQASASIMAAFMDNPFEFNPEDPIPDTNSTSGDPVEKKDETPFGVSVAVGLAVFACLFLSTLLLVLNKCGRRNKFGINRPAVLAPEDGLAMSLHFMTLGGSSLSPTEGKGSGLQGHIIENPQYFSDACVHHIKRRDIVLKWELGEGAFGKVFLAECHNLLPEQDKMLVAVKALKEASESARQDFQREAELLTMLQHQHIVRFFGVCTEGRPLLMVFEYMRHGDLNRFLRSHGPDAKLLAGGEDVAPGPLGLGQLLAVASQVAAGMVYLAGLHFVHRDLATRNCLVGQGLVVKIGDFGMSRDIYSTDYYRVGGRTMLPIRWMPPESILYRKFTTESDVWSFGVVLWEIFTYGKQPWYQLSNTEAIDCITQGRELERPRACPPEVYAIMRGCWQREPQQRHSIKDVHARLQALAQAPPVYLDVLG' are 10000.0, 630.0 and 1000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the generated drugs and the target protein 'MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\", \"value\": 460.0}, {\"smile\": \"COc1cc2ncnc(Oc3cccc(NC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c3)c2cc1OC\", \"value\": 2.0}, {\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGCGCSSHPEDDWMENIDVCENCHYPIVPLDGKGTLLIRNGSEVRDPLVTYEGSNPPASPLQDNLVIALHSYEPSHDGDLGFEKGEQLRILEQSGEWWKAQSLTTGQEGFIPFNFVAKANSLEPEPWFFKNLSRKDAERQLLAPGNTHGSFLIRESESTAGSFSLSVRDFDQNQGEVVKHYKIRNLDNGGFYISPRITFPGLHELVRHYTNASDGLCTRLSRPCQTQKPQKPWWEDEWEVPRETLKLVERLGAGQFGEVWMGYYNGHTKVAVKSLKQGSMSPDAFLAEANLMKQLQHQRLVRLYAVVTQEPIYIITEYMENGSLVDFLKTPSGIKLTINKLLDMAAQIAEGMAFIEERNYIHRDLRAANILVSDTLSCKIADFGLARLIEDNEYTAREGAKFPIKWTAPEAINYGTFTIKSDVWSFGILLTEIVTHGRIPYPGMTNPEVIQNLERGYRMVRPDNCPEELYQLMRLCWKERPEDRPTFDYLRSVLEDFFTATEGQYQPQP' are 460.0, 2.0 and 1200.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the generated drugs and the target protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\", \"result\": [{\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC.O=S(=O)(O)c1ccccc1\", \"value\": 3000.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI' are 10000.0, 3000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the predicted binding value of the optimized drugs for the target protein 'MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSCHDEGVVKEIPITHHVKEGYEKADPAQFELLKVLGQGSFGKVFLVRKKTGPDAGQLYAMKVLKKASLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDVFTRLSKEVLFTEEDVKFYLAELALALDHLHQLGIVYRDLKPENILLDEIGHIKLTDFGLSKESVDQEKKAYSFCGTVEYMAPEVVNRRGHSQSADWWSYGVLMFEMLTGTLPFQGKDRNETMNMILKAKLGMPQFLSAEAQSLLRMLFKRNPANRLGSEGVEEIKRHLFFANIDWDKLYKREVQPPFKPASGKPDDTFCFDPEFTAKTPKDSPGLPASANAHQLFKGFSFVATSIAEEYKITPITSANVLPIVQINGNAAQFGEVYELKEDIGVGSYSVCKRCIHATTNMEFAVKIIDKSKRDPSEEIEILMRYGQHPNIITLKDVFDDGRYVYLVTDLMKGGELLDRILKQKCFSEREASDILYVISKTVDYLHCQGVVHRDLKPSNILYMDESASADSIRICDFGFAKQLRGENGLLLTPCYTANFVAPEVLMQQGYDAACDIWSLGVLFYTMLAGYTPFANGPNDTPEEILLRIGNGKFSLSGGNWDNISDGAKDLLSHMLHMDPHQRYTAEQILKHSWITHRDQLPNDQPKRNDVSHVVKGAMVATYSALTHKTFQPVLEPVAASSLAQRRSMKKRTSTGL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSCHDEGVVKEIPITHHVKEGYEKADPAQFELLKVLGQGSFGKVFLVRKKTGPDAGQLYAMKVLKKASLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDVFTRLSKEVLFTEEDVKFYLAELALALDHLHQLGIVYRDLKPENILLDEIGHIKLTDFGLSKESVDQEKKAYSFCGTVEYMAPEVVNRRGHSQSADWWSYGVLMFEMLTGTLPFQGKDRNETMNMILKAKLGMPQFLSAEAQSLLRMLFKRNPANRLGSEGVEEIKRHLFFANIDWDKLYKREVQPPFKPASGKPDDTFCFDPEFTAKTPKDSPGLPASANAHQLFKGFSFVATSIAEEYKITPITSANVLPIVQINGNAAQFGEVYELKEDIGVGSYSVCKRCIHATTNMEFAVKIIDKSKRDPSEEIEILMRYGQHPNIITLKDVFDDGRYVYLVTDLMKGGELLDRILKQKCFSEREASDILYVISKTVDYLHCQGVVHRDLKPSNILYMDESASADSIRICDFGFAKQLRGENGLLLTPCYTANFVAPEVLMQQGYDAACDIWSLGVLFYTMLAGYTPFANGPNDTPEEILLRIGNGKFSLSGGNWDNISDGAKDLLSHMLHMDPHQRYTAEQILKHSWITHRDQLPNDQPKRNDVSHVVKGAMVATYSALTHKTFQPVLEPVAASSLAQRRSMKKRTSTGL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSCHDEGVVKEIPITHHVKEGYEKADPAQFELLKVLGQGSFGKVFLVRKKTGPDAGQLYAMKVLKKASLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDVFTRLSKEVLFTEEDVKFYLAELALALDHLHQLGIVYRDLKPENILLDEIGHIKLTDFGLSKESVDQEKKAYSFCGTVEYMAPEVVNRRGHSQSADWWSYGVLMFEMLTGTLPFQGKDRNETMNMILKAKLGMPQFLSAEAQSLLRMLFKRNPANRLGSEGVEEIKRHLFFANIDWDKLYKREVQPPFKPASGKPDDTFCFDPEFTAKTPKDSPGLPASANAHQLFKGFSFVATSIAEEYKITPITSANVLPIVQINGNAAQFGEVYELKEDIGVGSYSVCKRCIHATTNMEFAVKIIDKSKRDPSEEIEILMRYGQHPNIITLKDVFDDGRYVYLVTDLMKGGELLDRILKQKCFSEREASDILYVISKTVDYLHCQGVVHRDLKPSNILYMDESASADSIRICDFGFAKQLRGENGLLLTPCYTANFVAPEVLMQQGYDAACDIWSLGVLFYTMLAGYTPFANGPNDTPEEILLRIGNGKFSLSGGNWDNISDGAKDLLSHMLHMDPHQRYTAEQILKHSWITHRDQLPNDQPKRNDVSHVVKGAMVATYSALTHKTFQPVLEPVAASSLAQRRSMKKRTSTGL\", \"result\": [{\"smile\": \"CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1\", \"value\": 1.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 610.0}, {\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MLPFAPQDEPWDREMEVFSGGGASSGEVNGLKMVDEPMEEGEADSCHDEGVVKEIPITHHVKEGYEKADPAQFELLKVLGQGSFGKVFLVRKKTGPDAGQLYAMKVLKKASLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDVFTRLSKEVLFTEEDVKFYLAELALALDHLHQLGIVYRDLKPENILLDEIGHIKLTDFGLSKESVDQEKKAYSFCGTVEYMAPEVVNRRGHSQSADWWSYGVLMFEMLTGTLPFQGKDRNETMNMILKAKLGMPQFLSAEAQSLLRMLFKRNPANRLGSEGVEEIKRHLFFANIDWDKLYKREVQPPFKPASGKPDDTFCFDPEFTAKTPKDSPGLPASANAHQLFKGFSFVATSIAEEYKITPITSANVLPIVQINGNAAQFGEVYELKEDIGVGSYSVCKRCIHATTNMEFAVKIIDKSKRDPSEEIEILMRYGQHPNIITLKDVFDDGRYVYLVTDLMKGGELLDRILKQKCFSEREASDILYVISKTVDYLHCQGVVHRDLKPSNILYMDESASADSIRICDFGFAKQLRGENGLLLTPCYTANFVAPEVLMQQGYDAACDIWSLGVLFYTMLAGYTPFANGPNDTPEEILLRIGNGKFSLSGGNWDNISDGAKDLLSHMLHMDPHQRYTAEQILKHSWITHRDQLPNDQPKRNDVSHVVKGAMVATYSALTHKTFQPVLEPVAASSLAQRRSMKKRTSTGL' are 1.0, 610.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your estimation of the optimized drugs' affinity with the target protein 'MPEIVDTCSLASPASVCRTKHLHLRCSVDFTRRTLTGTAALTVQSQEDNLRSLVLDTKDLTIEKVVINGQEVKYALGERQSYKGSPMEISLPIALSKNQEIVIEISFETSPKSSALQWLTPEQTSGKEHPYLFSQCQAIHCRAILPCQDTPSVKLTYTAEVSVPKELVALMSAIRDGETPDPEDPSRKIYKFIQKVPIPCYLIALVVGALESRQIGPRTLVWSEKEQVEKSAYEFSETESMLKIAEDLGGPYVWGQYDLLVLPPSFPYGGMENPCLTFVTPTLLAGDKSLSNVIAHEISHSWTGNLVTNKTWDHFWLNEGHTVYLERHICGRLFGEKFRHFNALGGWGELQNSVKTFGETHPFTKLVVDLTDIDPDVAYSSVPYEKGFALLFYLEQLLGGPEIFLGFLKAYVEKFSYKSITTDDWKDFLYSYFKDKVDVLNQVDWNAWLYSPGLPPIKPNYDMTLTNACIALSQRWITAKEDDLNSFNATDLKDLSSHQLNEFLAQTLQRAPLPLGHIKRMQEVYNFNAINNSEIRFRWLRLCIQSKWEDAIPLALKMATEQGRMKFTRPLFKDLAAFDKSHDQAVRTYQEHKASMHPVTAMLVGKDLKVD'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPEIVDTCSLASPASVCRTKHLHLRCSVDFTRRTLTGTAALTVQSQEDNLRSLVLDTKDLTIEKVVINGQEVKYALGERQSYKGSPMEISLPIALSKNQEIVIEISFETSPKSSALQWLTPEQTSGKEHPYLFSQCQAIHCRAILPCQDTPSVKLTYTAEVSVPKELVALMSAIRDGETPDPEDPSRKIYKFIQKVPIPCYLIALVVGALESRQIGPRTLVWSEKEQVEKSAYEFSETESMLKIAEDLGGPYVWGQYDLLVLPPSFPYGGMENPCLTFVTPTLLAGDKSLSNVIAHEISHSWTGNLVTNKTWDHFWLNEGHTVYLERHICGRLFGEKFRHFNALGGWGELQNSVKTFGETHPFTKLVVDLTDIDPDVAYSSVPYEKGFALLFYLEQLLGGPEIFLGFLKAYVEKFSYKSITTDDWKDFLYSYFKDKVDVLNQVDWNAWLYSPGLPPIKPNYDMTLTNACIALSQRWITAKEDDLNSFNATDLKDLSSHQLNEFLAQTLQRAPLPLGHIKRMQEVYNFNAINNSEIRFRWLRLCIQSKWEDAIPLALKMATEQGRMKFTRPLFKDLAAFDKSHDQAVRTYQEHKASMHPVTAMLVGKDLKVD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPEIVDTCSLASPASVCRTKHLHLRCSVDFTRRTLTGTAALTVQSQEDNLRSLVLDTKDLTIEKVVINGQEVKYALGERQSYKGSPMEISLPIALSKNQEIVIEISFETSPKSSALQWLTPEQTSGKEHPYLFSQCQAIHCRAILPCQDTPSVKLTYTAEVSVPKELVALMSAIRDGETPDPEDPSRKIYKFIQKVPIPCYLIALVVGALESRQIGPRTLVWSEKEQVEKSAYEFSETESMLKIAEDLGGPYVWGQYDLLVLPPSFPYGGMENPCLTFVTPTLLAGDKSLSNVIAHEISHSWTGNLVTNKTWDHFWLNEGHTVYLERHICGRLFGEKFRHFNALGGWGELQNSVKTFGETHPFTKLVVDLTDIDPDVAYSSVPYEKGFALLFYLEQLLGGPEIFLGFLKAYVEKFSYKSITTDDWKDFLYSYFKDKVDVLNQVDWNAWLYSPGLPPIKPNYDMTLTNACIALSQRWITAKEDDLNSFNATDLKDLSSHQLNEFLAQTLQRAPLPLGHIKRMQEVYNFNAINNSEIRFRWLRLCIQSKWEDAIPLALKMATEQGRMKFTRPLFKDLAAFDKSHDQAVRTYQEHKASMHPVTAMLVGKDLKVD\", \"result\": [{\"smile\": \"O=C(O)CCCN1CCC[C@H]1COc1ccc(Oc2ccc(Cl)cc2)cc1\", \"value\": 25.0}, {\"smile\": \"CN(CCCOc1ccc(Cc2ccccc2)cc1)CCC(=O)O\", \"value\": 60.0}, {\"smile\": \"C[C@H](CS)C(=O)N1CCC[C@H]1C(=O)O\", \"value\": 6250.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MPEIVDTCSLASPASVCRTKHLHLRCSVDFTRRTLTGTAALTVQSQEDNLRSLVLDTKDLTIEKVVINGQEVKYALGERQSYKGSPMEISLPIALSKNQEIVIEISFETSPKSSALQWLTPEQTSGKEHPYLFSQCQAIHCRAILPCQDTPSVKLTYTAEVSVPKELVALMSAIRDGETPDPEDPSRKIYKFIQKVPIPCYLIALVVGALESRQIGPRTLVWSEKEQVEKSAYEFSETESMLKIAEDLGGPYVWGQYDLLVLPPSFPYGGMENPCLTFVTPTLLAGDKSLSNVIAHEISHSWTGNLVTNKTWDHFWLNEGHTVYLERHICGRLFGEKFRHFNALGGWGELQNSVKTFGETHPFTKLVVDLTDIDPDVAYSSVPYEKGFALLFYLEQLLGGPEIFLGFLKAYVEKFSYKSITTDDWKDFLYSYFKDKVDVLNQVDWNAWLYSPGLPPIKPNYDMTLTNACIALSQRWITAKEDDLNSFNATDLKDLSSHQLNEFLAQTLQRAPLPLGHIKRMQEVYNFNAINNSEIRFRWLRLCIQSKWEDAIPLALKMATEQGRMKFTRPLFKDLAAFDKSHDQAVRTYQEHKASMHPVTAMLVGKDLKVD' are 25.0, 60.0 and 6250.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of the binding affinity between the generated drugs and the target protein 'MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI\", \"result\": [{\"smile\": \"CC1(C[C@H](CC(=O)N2CCOCC2)C(=O)N[C@]2(C#N)CCN(C3CCCCC3)C2)CCCCC1\", \"value\": 77.3}, {\"smile\": \"CC(C)C[C@H](NC(=O)N1CCOCC1)C(=O)N[C@H](C#N)Cc1ccc(Cl)c(Cl)c1\", \"value\": 7.0}, {\"smile\": \"N#C[C@H](CCc1ccccc1)NC(=O)[C@H](CC1CCCCC1)NC(=O)N1CCOCC1\", \"value\": 0.8}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MKRLVCVLLVCSSAVAQLHKDPTLDHHWHLWKKTYGKQYKEKNEEAVRRLIWEKNLKFVMLHNLEHSMGMHSYDLGMNHLGDMTSEEVMSLMSSLRVPSQWQRNITYKSNPNRILPDSVDWREKGCVTEVKYQGSCGACWAFSAVGALEAQLKLKTGKLVSLSAQNLVDCSTEKYGNKGCNGGFMTTAFQYIIDNKGIDSDASYPYKAMDQKCQYDSKYRAATCSKYTELPYGREDVLKEAVANKGPVSVGVDARHPSFFLYRSGVYYEPSCTQNVNHGVLVVGYGDLNGKEYWLVKNSWGHNFGEEGYIRMARNKGNHCGIASFPSYPEI' are 77.3, 7.0 and 0.8 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the generated drugs-target protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP' relationship? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"CCCCCC(N=O)c1ccc(OCCCc2cnc[nH]2)cc1\", \"value\": 12589.25}, {\"smile\": \"COc1ccc(CN(CCN(C)CCCCCCNC(=O)c2ccc(-c3c4ccc(=O)cc-4oc4cc(O)ccc34)c(C(=O)O)c2)c2ccccn2)cc1\", \"value\": 21.38}, {\"smile\": \"c1ccc(C(CCc2ncc(CCNCCCCc3ccccn3)[nH]2)c2ccccc2)cc1\", \"value\": 1.62}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP' are 12589.25, 21.38 and 1.62 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted interaction value for the generated drugs and the target protein 'MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF\", \"result\": [{\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGNAAAAKKGSEQESVKEFLAKAKEDFLKKWESPAQNTAHLDQFERIKTLGTGSFGRVMLVKHKETGNHYAMKILDKQKVVKLKQIEHTLNEKRILQAVNFPFLVKLEFSFKDNSNLYMVMEYVPGGEMFSHLRRIGRFSEPHARFYAAQIVLTFEYLHSLDLIYRDLKPENLLIDQQGYIQVTDFGFAKRVKGRTWTLCGTPEYLAPEIILSKGYNKAVDWWALGVLIYEMAAGYPPFFADQPIQIYEKIVSGKVRFPSHFSSDLKDLLRNLLQVDLTKRFGNLKNGVNDIKNHKWFATTDWIAIYQRKVEAPFIPKFKGPGDTSNFDDYEEEEIRVSINEKCGKEFSEF' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the optimized drugs and the target protein 'MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 1100.0}, {\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 160.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 160.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGCIKSKENKSPAIKYRPENTPEPVSTSVSHYGAEPTTVSPCPSSSAKGTAVNFSSLSMTPFGGSSGVTPFGGASSSFSVVPSSYPAGLTGGVTIFVALYDYEARTTEDLSFKKGERFQIINNTEGDWWEARSIATGKNGYIPSNYVAPADSIQAEEWYFGKMGRKDAERLLLNPGNQRGIFLVRESETTKGAYSLSIRDWDEIRGDNVKHYKIRKLDNGGYYITTRAQFDTLQKLVKHYTEHADGLCHKLTTVCPTVKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTKVAIKTLKPGTMMPEAFLQEAQIMKKLRHDKLVPLYAVVSEEPIYIVTEFMSKGSLLDFLKEGDGKYLKLPQLVDMAAQIADGMAYIERMNYIHRDLRAANILVGENLVCKIADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILQTELVTKGRVPYPGMVNREVLEQVERGYRMPCPQGCPESLHELMNLCWKKDPDERPTFEYIQSFLEDYFTATEPQYQPGENL' are 1100.0, 160.0 and 160.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you anticipate the affinity to manifest between the optimized drugs and the target protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL\", \"result\": [{\"smile\": \"COCC(=O)NC/C=C/c1ccc2ncnc(Nc3ccc(Oc4ccc(C)nc4)c(C)c3)c2c1\", \"value\": 10000.0}, {\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}, {\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 0.24}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MAPARGRLPPALWVVTAAAAAATCVSAARGEVNLLDTSTIHGDWGWLTYPAHGWDSINEVDESFQPIHTYQVCNVMSPNQNNWLRTSWVPRDGARRVYAEIKFTLRDCNSMPGVLGTCKETFNLYYLESDRDLGASTQESQFLKIDTIAADESFTGADLGVRRLKLNTEVRSVGPLSKRGFYLAFQDIGACLAILSLRIYYKKCPAMVRNLAAFSEAVTGADSSSLVEVRGQCVRHSEERDTPKMYCSAEGEWLVPIGKCVCSAGYEERRDACVACELGFYKSAPGDQLCARCPPHSHSAAPAAQACHCDLSYYRAALDPPSSACTRPPSAPVNLISSVNGTSVTLEWAPPLDPGGRSDITYNAVCRRCPWALSRCEACGSGTRFVPQQTSLVQASLLVANLLAHMNYSFWIEAVNGVSDLSPEPRRAAVVNITTNQAAPSQVVVIRQERAGQTSVSLLWQEPEQPNGIILEYEIKYYEKDKEMQSYSTLKAVTTRATVSGLKPGTRYVFQVRARTSAGCGRFSQAMEVETGKPRPRYDTRTIVWICLTLITGLVVLLLLLICKKRHCGYSKAFQDSDEEKMHYQNGQAPPPVFLPLHHPPGKLPEPQFYAEPHTYEEPGRAGRSFTREIEASRIHIEKIIGSGDSGEVCYGRLRVPGQRDVPVAIKALKAGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTRGRLAMIVTEYMENGSLDTFLRTHDGQFTIMQLVGMLRGVGAGMRYLSDLGYVHRDLAARNVLVDSNLVCKVSDFGLSRVLEDDPDAAYTTTGGKIPIRWTAPEAIAFRTFSSASDVWSFGVVMWEVLAYGERPYWNMTNRDVISSVEEGYRLPAPMGCPHALHQLMLDCWHKDRAQRPRFSQIVSVLDALIRSPESLRATATVSRCPPPAFVRSCFDLRGGSGGGGGLTVGDWLDSIRMGRYRDHFAAGGYSSLGMVLRMNAQDVRALGITLMGHQKKILGSIQTMRAQLTSTQGPRRHL' are 10000.0, 10000.0 and 0.24 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the projected strength of affinity between the generated drugs and the target protein 'MESEDLSGRELTIDSIMNKVRDIKNKFKNEDLTDELSLNKISADTTDNSGTVNQIMMMANNPEDWLSLLLKLEKNSVPLSDALLNKLIGRYSQAIEALPPDKYGQNESFARIQVRFAELKAIQEPDDARDYFQMARANCKKFAFVHISFAQFELSQGNVKKSKQLLQKAVERGAVPLEMLEIALRNLNLQKKQLLSEEEKKNLSASTVLTAQESFSGSLGHLQNRNNSCDSRGQTTKARFLYGENMPPQDAEIGYRNSLRQTNKTKQSCPFGRVPVNLLNSPDCDVKTDDSVVPCFMKRQTSRSECRDLVVPGSKPSGNDSCELRNLKSVQNSHFKEPLVSDEKSSELIITDSITLKNKTESSLLAKLEETKEYQEPEVPESNQKQWQSKRKSECINQNPAASSNHWQIPELARKVNTEQKHTTFEQPVFSVSKQSPPISTSKWFDPKSICKTPSSNTLDDYMSCFRTPVVKNDFPPACQLSTPYGQPACFQQQQHQILATPLQNLQVLASSSANECISVKGRIYSILKQIGSGGSSKVFQVLNEKKQIYAIKYVNLEEADNQTLDSYRNEIAYLNKLQQHSDKIIRLYDYEITDQYIYMVMECGNIDLNSWLKKKKSIDPWERKSYWKNMLEAVHTIHQHGIVHSDLKPANFLIVDGMLKLIDFGIANQMQPDTTSVVKDSQVGTVNYMPPEAIKDMSSSRENGKSKSKISPKSDVWSLGCILYYMTYGKTPFQQIINQISKLHAIIDPNHEIEFPDIPEKDLQDVLKCCLKRDPKQRISIPELLAHPYVQIQTHPVNQMAKGTTEEMKYVLGQLVGLNSPNSILKAAKTLYEHYSGGESHNSSSSKTFEKKRGKK'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MESEDLSGRELTIDSIMNKVRDIKNKFKNEDLTDELSLNKISADTTDNSGTVNQIMMMANNPEDWLSLLLKLEKNSVPLSDALLNKLIGRYSQAIEALPPDKYGQNESFARIQVRFAELKAIQEPDDARDYFQMARANCKKFAFVHISFAQFELSQGNVKKSKQLLQKAVERGAVPLEMLEIALRNLNLQKKQLLSEEEKKNLSASTVLTAQESFSGSLGHLQNRNNSCDSRGQTTKARFLYGENMPPQDAEIGYRNSLRQTNKTKQSCPFGRVPVNLLNSPDCDVKTDDSVVPCFMKRQTSRSECRDLVVPGSKPSGNDSCELRNLKSVQNSHFKEPLVSDEKSSELIITDSITLKNKTESSLLAKLEETKEYQEPEVPESNQKQWQSKRKSECINQNPAASSNHWQIPELARKVNTEQKHTTFEQPVFSVSKQSPPISTSKWFDPKSICKTPSSNTLDDYMSCFRTPVVKNDFPPACQLSTPYGQPACFQQQQHQILATPLQNLQVLASSSANECISVKGRIYSILKQIGSGGSSKVFQVLNEKKQIYAIKYVNLEEADNQTLDSYRNEIAYLNKLQQHSDKIIRLYDYEITDQYIYMVMECGNIDLNSWLKKKKSIDPWERKSYWKNMLEAVHTIHQHGIVHSDLKPANFLIVDGMLKLIDFGIANQMQPDTTSVVKDSQVGTVNYMPPEAIKDMSSSRENGKSKSKISPKSDVWSLGCILYYMTYGKTPFQQIINQISKLHAIIDPNHEIEFPDIPEKDLQDVLKCCLKRDPKQRISIPELLAHPYVQIQTHPVNQMAKGTTEEMKYVLGQLVGLNSPNSILKAAKTLYEHYSGGESHNSSSSKTFEKKRGKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MESEDLSGRELTIDSIMNKVRDIKNKFKNEDLTDELSLNKISADTTDNSGTVNQIMMMANNPEDWLSLLLKLEKNSVPLSDALLNKLIGRYSQAIEALPPDKYGQNESFARIQVRFAELKAIQEPDDARDYFQMARANCKKFAFVHISFAQFELSQGNVKKSKQLLQKAVERGAVPLEMLEIALRNLNLQKKQLLSEEEKKNLSASTVLTAQESFSGSLGHLQNRNNSCDSRGQTTKARFLYGENMPPQDAEIGYRNSLRQTNKTKQSCPFGRVPVNLLNSPDCDVKTDDSVVPCFMKRQTSRSECRDLVVPGSKPSGNDSCELRNLKSVQNSHFKEPLVSDEKSSELIITDSITLKNKTESSLLAKLEETKEYQEPEVPESNQKQWQSKRKSECINQNPAASSNHWQIPELARKVNTEQKHTTFEQPVFSVSKQSPPISTSKWFDPKSICKTPSSNTLDDYMSCFRTPVVKNDFPPACQLSTPYGQPACFQQQQHQILATPLQNLQVLASSSANECISVKGRIYSILKQIGSGGSSKVFQVLNEKKQIYAIKYVNLEEADNQTLDSYRNEIAYLNKLQQHSDKIIRLYDYEITDQYIYMVMECGNIDLNSWLKKKKSIDPWERKSYWKNMLEAVHTIHQHGIVHSDLKPANFLIVDGMLKLIDFGIANQMQPDTTSVVKDSQVGTVNYMPPEAIKDMSSSRENGKSKSKISPKSDVWSLGCILYYMTYGKTPFQQIINQISKLHAIIDPNHEIEFPDIPEKDLQDVLKCCLKRDPKQRISIPELLAHPYVQIQTHPVNQMAKGTTEEMKYVLGQLVGLNSPNSILKAAKTLYEHYSGGESHNSSSSKTFEKKRGKK\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\", \"value\": 10000.0}, {\"smile\": \"CS(=O)(=O)CCCOc1cccc2c1ccn2-c1ccnc(N[C@H]2CC[C@H](C(=O)N3CCC(O)CC3)CC2)n1\", \"value\": 1900.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MESEDLSGRELTIDSIMNKVRDIKNKFKNEDLTDELSLNKISADTTDNSGTVNQIMMMANNPEDWLSLLLKLEKNSVPLSDALLNKLIGRYSQAIEALPPDKYGQNESFARIQVRFAELKAIQEPDDARDYFQMARANCKKFAFVHISFAQFELSQGNVKKSKQLLQKAVERGAVPLEMLEIALRNLNLQKKQLLSEEEKKNLSASTVLTAQESFSGSLGHLQNRNNSCDSRGQTTKARFLYGENMPPQDAEIGYRNSLRQTNKTKQSCPFGRVPVNLLNSPDCDVKTDDSVVPCFMKRQTSRSECRDLVVPGSKPSGNDSCELRNLKSVQNSHFKEPLVSDEKSSELIITDSITLKNKTESSLLAKLEETKEYQEPEVPESNQKQWQSKRKSECINQNPAASSNHWQIPELARKVNTEQKHTTFEQPVFSVSKQSPPISTSKWFDPKSICKTPSSNTLDDYMSCFRTPVVKNDFPPACQLSTPYGQPACFQQQQHQILATPLQNLQVLASSSANECISVKGRIYSILKQIGSGGSSKVFQVLNEKKQIYAIKYVNLEEADNQTLDSYRNEIAYLNKLQQHSDKIIRLYDYEITDQYIYMVMECGNIDLNSWLKKKKSIDPWERKSYWKNMLEAVHTIHQHGIVHSDLKPANFLIVDGMLKLIDFGIANQMQPDTTSVVKDSQVGTVNYMPPEAIKDMSSSRENGKSKSKISPKSDVWSLGCILYYMTYGKTPFQQIINQISKLHAIIDPNHEIEFPDIPEKDLQDVLKCCLKRDPKQRISIPELLAHPYVQIQTHPVNQMAKGTTEEMKYVLGQLVGLNSPNSILKAAKTLYEHYSGGESHNSSSSKTFEKKRGKK' are 10000.0, 10000.0 and 1900.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the generated drugs and the target protein 'MLERPPALAMPMPTEGTPPPLSGTPIPVPAYFRHAEPGFSLKRPRGLSRSLPPPPPAKGSIPISRLFPPRTPGWHQLQPRRVSFRGEASETLQSPGYDPSRPESFFQQSFQRLSRLGHGSYGEVFKVRSKEDGRLYAVKRSMSPFRGPKDRARKLAEVGSHEKVGQHPCCVRLEQAWEEGGILYLQTELCGPSLQQHCEAWGASLPEAQVWGYLRDTLLALAHLHSQGLVHLDVKPANIFLGPRGRCKLGDFGLLVELGTAGAGEVQEGDPRYMAPELLQGSYGTAADVFSLGLTILEVACNMELPHGGEGWQQLRQGYLPPEFTAGLSSELRSVLVMMLEPDPKLRATAEALLALPVLRQPRAWGVLWCMAAEALSRGWALWQALLALLCWLWHGLAHPASWLQPLGPPATPPGSPPCSLLLDSSLSSNWDDDSLGPSLSPEAVLARTVGSTSTPRSRCTPRDALDLSDINSEPPRGSFPSFEPRNLLSLFEDTLDPT'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MLERPPALAMPMPTEGTPPPLSGTPIPVPAYFRHAEPGFSLKRPRGLSRSLPPPPPAKGSIPISRLFPPRTPGWHQLQPRRVSFRGEASETLQSPGYDPSRPESFFQQSFQRLSRLGHGSYGEVFKVRSKEDGRLYAVKRSMSPFRGPKDRARKLAEVGSHEKVGQHPCCVRLEQAWEEGGILYLQTELCGPSLQQHCEAWGASLPEAQVWGYLRDTLLALAHLHSQGLVHLDVKPANIFLGPRGRCKLGDFGLLVELGTAGAGEVQEGDPRYMAPELLQGSYGTAADVFSLGLTILEVACNMELPHGGEGWQQLRQGYLPPEFTAGLSSELRSVLVMMLEPDPKLRATAEALLALPVLRQPRAWGVLWCMAAEALSRGWALWQALLALLCWLWHGLAHPASWLQPLGPPATPPGSPPCSLLLDSSLSSNWDDDSLGPSLSPEAVLARTVGSTSTPRSRCTPRDALDLSDINSEPPRGSFPSFEPRNLLSLFEDTLDPT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLERPPALAMPMPTEGTPPPLSGTPIPVPAYFRHAEPGFSLKRPRGLSRSLPPPPPAKGSIPISRLFPPRTPGWHQLQPRRVSFRGEASETLQSPGYDPSRPESFFQQSFQRLSRLGHGSYGEVFKVRSKEDGRLYAVKRSMSPFRGPKDRARKLAEVGSHEKVGQHPCCVRLEQAWEEGGILYLQTELCGPSLQQHCEAWGASLPEAQVWGYLRDTLLALAHLHSQGLVHLDVKPANIFLGPRGRCKLGDFGLLVELGTAGAGEVQEGDPRYMAPELLQGSYGTAADVFSLGLTILEVACNMELPHGGEGWQQLRQGYLPPEFTAGLSSELRSVLVMMLEPDPKLRATAEALLALPVLRQPRAWGVLWCMAAEALSRGWALWQALLALLCWLWHGLAHPASWLQPLGPPATPPGSPPCSLLLDSSLSSNWDDDSLGPSLSPEAVLARTVGSTSTPRSRCTPRDALDLSDINSEPPRGSFPSFEPRNLLSLFEDTLDPT\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}, {\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}, {\"smile\": \"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MLERPPALAMPMPTEGTPPPLSGTPIPVPAYFRHAEPGFSLKRPRGLSRSLPPPPPAKGSIPISRLFPPRTPGWHQLQPRRVSFRGEASETLQSPGYDPSRPESFFQQSFQRLSRLGHGSYGEVFKVRSKEDGRLYAVKRSMSPFRGPKDRARKLAEVGSHEKVGQHPCCVRLEQAWEEGGILYLQTELCGPSLQQHCEAWGASLPEAQVWGYLRDTLLALAHLHSQGLVHLDVKPANIFLGPRGRCKLGDFGLLVELGTAGAGEVQEGDPRYMAPELLQGSYGTAADVFSLGLTILEVACNMELPHGGEGWQQLRQGYLPPEFTAGLSSELRSVLVMMLEPDPKLRATAEALLALPVLRQPRAWGVLWCMAAEALSRGWALWQALLALLCWLWHGLAHPASWLQPLGPPATPPGSPPCSLLLDSSLSSNWDDDSLGPSLSPEAVLARTVGSTSTPRSRCTPRDALDLSDINSEPPRGSFPSFEPRNLLSLFEDTLDPT' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the optimized drugs and the target protein 'MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ\", \"result\": [{\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 4700.0}, {\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 42.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MAGPGWGPPRLDGFILTERLGSGTYATVYKAYAKKDTREVVAIKCVAKKSLNKASVENLLTEIEILKGIRHPHIVQLKDFQWDSDNIYLIMEFCAGGDLSRFIHTRRILPEKVARVFMQQLASALQFLHERNISHLDLKPQNILLSSLEKPHLKLADFGFAQHMSPWDEKHVLRGSPLYMAPEMVCQRQYDARVDLWSMGVILYEALFGQPPFASRSFSELEEKIRSNRVIELPLRPLLSRDCRDLLQRLLERDPSRRISFQDFFAHPWVDLEHMPSGESLGRATALVVQAVKKDQEGDSAAALSLYCKALDFFVPALHYEVDAQRKEAIKAKVGQYVSRAEELKAIVSSSNQALLRQGTSARDLLREMARDKPRLLAALEVASAAMAKEEAAGGEQDALDLYQHSLGELLLLLAAEPPGRRRELLHTEVQNLMARAEYLKEQVKMRESRWEADTLDKEGLSESVRSSCTLQ' are 10000.0, 4700.0 and 42.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding score between the optimized drugs and the target protein 'MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS\", \"result\": [{\"smile\": \"CC(C)n1cc2c3c(cccc31)[C@H]1C[C@@H](CO)CN(C)[C@@H]1C2\", \"value\": 1862.09}, {\"smile\": \"CNCCc1cnc(CCC(c2ccccc2)c2ccccc2)[nH]1\", \"value\": 8128.31}, {\"smile\": \"CN(C)CCc1cnc(CCC(c2ccccc2)c2ccccc2)[nH]1\", \"value\": 31622.78}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS' are 1862.09, 8128.31 and 31622.78 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the predicted affinity level for the generated drugs and the target protein 'MGPAPLPLLLGLFLPALWRRAITEAREEAKPYPLFPGPFPGSLQTDHTPLLSLPHASGYQPALMFSPTQPGRPHTGNVAIPQVTSVESKPLPPLAFKHTVGHIILSEHKGVKFNCSISVPNIYQDTTISWWKDGKELLGAHHAITQFYPDDEVTAIIASFSITSVQRSDNGSYICKMKINNEEIVSDPIYIEVQGLPHFTKQPESMNVTRNTAFNLTCQAVGPPEPVNIFWVQNSSRVNEQPEKSPSVLTVPGLTEMAVFSCEAHNDKGLTVSKGVQINIKAIPSPPTEVSIRNSTAHSILISWVPGFDGYSPFRNCSIQVKEADPLSNGSVMIFNTSALPHLYQIKQLQALANYSIGVSCMNEIGWSAVSPWILASTTEGAPSVAPLNVTVFLNESSDNVDIRWMKPPTKQQDGELVGYRISHVWQSAGISKELLEEVGQNGSRARISVQVHNATCTVRIAAVTRGGVGPFSDPVKIFIPAHGWVDYAPSSTPAPGNADPVLIIFGCFCGFILIGLILYISLAIRKRVQETKFGNAFTEEDSELVVNYIAKKSFCRRAIELTLHSLGVSEELQNKLEDVVIDRNLLILGKILGEGEFGSVMEGNLKQEDGTSLKVAVKTMKLDNSSQREIEEFLSEAACMKDFSHPNVIRLLGVCIEMSSQGIPKPMVILPFMKYGDLHTYLLYSRLETGPKHIPLQTLLKFMVDIALGMEYLSNRNFLHRDLAARNCMLRDDMTVCVADFGLSKKIYSGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWAFGVTMWEIATRGMTPYPGVQNHEMYDYLLHGHRLKQPEDCLDELYEIMYSCWRTDPLDRPTFSVLRLQLEKLLESLPDVRNQADVIYVNTQLLESSEGLAQGSTLAPLDLNIDPDSIIASCTPRAAISVVTAEVHDSKPHEGRYILNGGSEEWEDLTSAPSAAVTAEKNSVLPGERLVRNGVSWSHSSMLPLGSSLPDELLFADDSSEGSEVLM'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGPAPLPLLLGLFLPALWRRAITEAREEAKPYPLFPGPFPGSLQTDHTPLLSLPHASGYQPALMFSPTQPGRPHTGNVAIPQVTSVESKPLPPLAFKHTVGHIILSEHKGVKFNCSISVPNIYQDTTISWWKDGKELLGAHHAITQFYPDDEVTAIIASFSITSVQRSDNGSYICKMKINNEEIVSDPIYIEVQGLPHFTKQPESMNVTRNTAFNLTCQAVGPPEPVNIFWVQNSSRVNEQPEKSPSVLTVPGLTEMAVFSCEAHNDKGLTVSKGVQINIKAIPSPPTEVSIRNSTAHSILISWVPGFDGYSPFRNCSIQVKEADPLSNGSVMIFNTSALPHLYQIKQLQALANYSIGVSCMNEIGWSAVSPWILASTTEGAPSVAPLNVTVFLNESSDNVDIRWMKPPTKQQDGELVGYRISHVWQSAGISKELLEEVGQNGSRARISVQVHNATCTVRIAAVTRGGVGPFSDPVKIFIPAHGWVDYAPSSTPAPGNADPVLIIFGCFCGFILIGLILYISLAIRKRVQETKFGNAFTEEDSELVVNYIAKKSFCRRAIELTLHSLGVSEELQNKLEDVVIDRNLLILGKILGEGEFGSVMEGNLKQEDGTSLKVAVKTMKLDNSSQREIEEFLSEAACMKDFSHPNVIRLLGVCIEMSSQGIPKPMVILPFMKYGDLHTYLLYSRLETGPKHIPLQTLLKFMVDIALGMEYLSNRNFLHRDLAARNCMLRDDMTVCVADFGLSKKIYSGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWAFGVTMWEIATRGMTPYPGVQNHEMYDYLLHGHRLKQPEDCLDELYEIMYSCWRTDPLDRPTFSVLRLQLEKLLESLPDVRNQADVIYVNTQLLESSEGLAQGSTLAPLDLNIDPDSIIASCTPRAAISVVTAEVHDSKPHEGRYILNGGSEEWEDLTSAPSAAVTAEKNSVLPGERLVRNGVSWSHSSMLPLGSSLPDELLFADDSSEGSEVLM\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGPAPLPLLLGLFLPALWRRAITEAREEAKPYPLFPGPFPGSLQTDHTPLLSLPHASGYQPALMFSPTQPGRPHTGNVAIPQVTSVESKPLPPLAFKHTVGHIILSEHKGVKFNCSISVPNIYQDTTISWWKDGKELLGAHHAITQFYPDDEVTAIIASFSITSVQRSDNGSYICKMKINNEEIVSDPIYIEVQGLPHFTKQPESMNVTRNTAFNLTCQAVGPPEPVNIFWVQNSSRVNEQPEKSPSVLTVPGLTEMAVFSCEAHNDKGLTVSKGVQINIKAIPSPPTEVSIRNSTAHSILISWVPGFDGYSPFRNCSIQVKEADPLSNGSVMIFNTSALPHLYQIKQLQALANYSIGVSCMNEIGWSAVSPWILASTTEGAPSVAPLNVTVFLNESSDNVDIRWMKPPTKQQDGELVGYRISHVWQSAGISKELLEEVGQNGSRARISVQVHNATCTVRIAAVTRGGVGPFSDPVKIFIPAHGWVDYAPSSTPAPGNADPVLIIFGCFCGFILIGLILYISLAIRKRVQETKFGNAFTEEDSELVVNYIAKKSFCRRAIELTLHSLGVSEELQNKLEDVVIDRNLLILGKILGEGEFGSVMEGNLKQEDGTSLKVAVKTMKLDNSSQREIEEFLSEAACMKDFSHPNVIRLLGVCIEMSSQGIPKPMVILPFMKYGDLHTYLLYSRLETGPKHIPLQTLLKFMVDIALGMEYLSNRNFLHRDLAARNCMLRDDMTVCVADFGLSKKIYSGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWAFGVTMWEIATRGMTPYPGVQNHEMYDYLLHGHRLKQPEDCLDELYEIMYSCWRTDPLDRPTFSVLRLQLEKLLESLPDVRNQADVIYVNTQLLESSEGLAQGSTLAPLDLNIDPDSIIASCTPRAAISVVTAEVHDSKPHEGRYILNGGSEEWEDLTSAPSAAVTAEKNSVLPGERLVRNGVSWSHSSMLPLGSSLPDELLFADDSSEGSEVLM\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 8.5}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"value\": 4600.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGPAPLPLLLGLFLPALWRRAITEAREEAKPYPLFPGPFPGSLQTDHTPLLSLPHASGYQPALMFSPTQPGRPHTGNVAIPQVTSVESKPLPPLAFKHTVGHIILSEHKGVKFNCSISVPNIYQDTTISWWKDGKELLGAHHAITQFYPDDEVTAIIASFSITSVQRSDNGSYICKMKINNEEIVSDPIYIEVQGLPHFTKQPESMNVTRNTAFNLTCQAVGPPEPVNIFWVQNSSRVNEQPEKSPSVLTVPGLTEMAVFSCEAHNDKGLTVSKGVQINIKAIPSPPTEVSIRNSTAHSILISWVPGFDGYSPFRNCSIQVKEADPLSNGSVMIFNTSALPHLYQIKQLQALANYSIGVSCMNEIGWSAVSPWILASTTEGAPSVAPLNVTVFLNESSDNVDIRWMKPPTKQQDGELVGYRISHVWQSAGISKELLEEVGQNGSRARISVQVHNATCTVRIAAVTRGGVGPFSDPVKIFIPAHGWVDYAPSSTPAPGNADPVLIIFGCFCGFILIGLILYISLAIRKRVQETKFGNAFTEEDSELVVNYIAKKSFCRRAIELTLHSLGVSEELQNKLEDVVIDRNLLILGKILGEGEFGSVMEGNLKQEDGTSLKVAVKTMKLDNSSQREIEEFLSEAACMKDFSHPNVIRLLGVCIEMSSQGIPKPMVILPFMKYGDLHTYLLYSRLETGPKHIPLQTLLKFMVDIALGMEYLSNRNFLHRDLAARNCMLRDDMTVCVADFGLSKKIYSGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWAFGVTMWEIATRGMTPYPGVQNHEMYDYLLHGHRLKQPEDCLDELYEIMYSCWRTDPLDRPTFSVLRLQLEKLLESLPDVRNQADVIYVNTQLLESSEGLAQGSTLAPLDLNIDPDSIIASCTPRAAISVVTAEVHDSKPHEGRYILNGGSEEWEDLTSAPSAAVTAEKNSVLPGERLVRNGVSWSHSSMLPLGSSLPDELLFADDSSEGSEVLM' are 8.5, 10000.0 and 4600.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your calculation for the binding affinity between the generated drugs and the target protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}, {\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value between the optimized drugs and the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"COc1cc2ncc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cc2cc1OCCNC(C)=O\", \"value\": 5.0}, {\"smile\": \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 420.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV' are 5.0, 10000.0 and 420.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the optimized drugs to the target protein 'LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 260.0}, {\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)C=CCN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA' are 10000.0, 260.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the generated drugs and the target protein 'MDYRRLLMSRVVPGQFDDADSSDSENRDLKTVKEKDDILFEDLQDNVNENGEGEIEDEEEEGYDDDDDDWDWDEGVGKLAKGYVWNGGSNPQANRQTSDSSSAKMSTPADKVLRKFENKINLDKLNVTDSVINKVTEKSRQKEADMYRIKDKADRATVEQVLDPRTRMILFKMLTRGIITEINGCISTGKEANVYHASTANGESRAIKIYKTSILVFKDRDKYVSGEFRFRHGYCKGNPRKMVKTWAEKEMRNLIRLNTAEIPCPEPIMLRSHVLVMSFIGKDDMPAPLLKNVQLSESKARELYLQVIQYMRRMYQDARLVHADLSEFNMLYHGGGVYIIDVSQSVEHDHPHALEFLRKDCANVNDFFMRHSVAVMTVRELFEFVTDPSITHENMDAYLSKAMEIASQRTKEERSSQDHVDEEVFKRAYIPRTLNEVKNYERDMDIIMKLKEEDMAMNAQQDNILYQTVTGLKKDLSGVQKVPALLENQVEERTCSDSEDIGSSECSDTDSEEQGDHARPKKHTTDPDIDKKERKKMVKEAQREKRKNKIPKHVKKRKEKTAKTKKGK'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MDYRRLLMSRVVPGQFDDADSSDSENRDLKTVKEKDDILFEDLQDNVNENGEGEIEDEEEEGYDDDDDDWDWDEGVGKLAKGYVWNGGSNPQANRQTSDSSSAKMSTPADKVLRKFENKINLDKLNVTDSVINKVTEKSRQKEADMYRIKDKADRATVEQVLDPRTRMILFKMLTRGIITEINGCISTGKEANVYHASTANGESRAIKIYKTSILVFKDRDKYVSGEFRFRHGYCKGNPRKMVKTWAEKEMRNLIRLNTAEIPCPEPIMLRSHVLVMSFIGKDDMPAPLLKNVQLSESKARELYLQVIQYMRRMYQDARLVHADLSEFNMLYHGGGVYIIDVSQSVEHDHPHALEFLRKDCANVNDFFMRHSVAVMTVRELFEFVTDPSITHENMDAYLSKAMEIASQRTKEERSSQDHVDEEVFKRAYIPRTLNEVKNYERDMDIIMKLKEEDMAMNAQQDNILYQTVTGLKKDLSGVQKVPALLENQVEERTCSDSEDIGSSECSDTDSEEQGDHARPKKHTTDPDIDKKERKKMVKEAQREKRKNKIPKHVKKRKEKTAKTKKGK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDYRRLLMSRVVPGQFDDADSSDSENRDLKTVKEKDDILFEDLQDNVNENGEGEIEDEEEEGYDDDDDDWDWDEGVGKLAKGYVWNGGSNPQANRQTSDSSSAKMSTPADKVLRKFENKINLDKLNVTDSVINKVTEKSRQKEADMYRIKDKADRATVEQVLDPRTRMILFKMLTRGIITEINGCISTGKEANVYHASTANGESRAIKIYKTSILVFKDRDKYVSGEFRFRHGYCKGNPRKMVKTWAEKEMRNLIRLNTAEIPCPEPIMLRSHVLVMSFIGKDDMPAPLLKNVQLSESKARELYLQVIQYMRRMYQDARLVHADLSEFNMLYHGGGVYIIDVSQSVEHDHPHALEFLRKDCANVNDFFMRHSVAVMTVRELFEFVTDPSITHENMDAYLSKAMEIASQRTKEERSSQDHVDEEVFKRAYIPRTLNEVKNYERDMDIIMKLKEEDMAMNAQQDNILYQTVTGLKKDLSGVQKVPALLENQVEERTCSDSEDIGSSECSDTDSEEQGDHARPKKHTTDPDIDKKERKKMVKEAQREKRKNKIPKHVKKRKEKTAKTKKGK\", \"result\": [{\"smile\": \"O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"O=c1ncn2nc(Sc3ccc(F)cc3F)ccc2c1-c1c(Cl)cccc1Cl\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MDYRRLLMSRVVPGQFDDADSSDSENRDLKTVKEKDDILFEDLQDNVNENGEGEIEDEEEEGYDDDDDDWDWDEGVGKLAKGYVWNGGSNPQANRQTSDSSSAKMSTPADKVLRKFENKINLDKLNVTDSVINKVTEKSRQKEADMYRIKDKADRATVEQVLDPRTRMILFKMLTRGIITEINGCISTGKEANVYHASTANGESRAIKIYKTSILVFKDRDKYVSGEFRFRHGYCKGNPRKMVKTWAEKEMRNLIRLNTAEIPCPEPIMLRSHVLVMSFIGKDDMPAPLLKNVQLSESKARELYLQVIQYMRRMYQDARLVHADLSEFNMLYHGGGVYIIDVSQSVEHDHPHALEFLRKDCANVNDFFMRHSVAVMTVRELFEFVTDPSITHENMDAYLSKAMEIASQRTKEERSSQDHVDEEVFKRAYIPRTLNEVKNYERDMDIIMKLKEEDMAMNAQQDNILYQTVTGLKKDLSGVQKVPALLENQVEERTCSDSEDIGSSECSDTDSEEQGDHARPKKHTTDPDIDKKERKKMVKEAQREKRKNKIPKHVKKRKEKTAKTKKGK' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the likely interaction affinity for the generated drugs and the target protein 'MTLGSPRKGLLMLLMALVTQGDPVKPSRGPLVTCTCESPHCKGPTCRGAWCTVVLVREEGRHPQEHRGCGNLHRELCRGRPTEFVNHYCCDSHLCNHNVSLVLEATQPPSEQPGTDGQLALILGPVLALLALVALGVLGLWHVRRRQEKQRGLHSELGESSLILKASEQGDSMLGDLLDSDCTTGSGSGLPFLVQRTVARQVALVECVGKGRYGEVWRGLWHGESVAVKIFSSRDEQSWFRETEIYNTVLLRHDNILGFIASDMTSRNSSTQLWLITHYHEHGSLYDFLQRQTLEPHLALRLAVSAACGLAHLHVEIFGTQGKPAIAHRDFKSRNVLVKSNLQCCIADLGLAVMHSQGSDYLDIGNNPRVGTKRYMAPEVLDEQIRTDCFESYKWTDIWAFGLVLWEIARRTIVNGIVEDYRPPFYDVVPNDPSFEDMKKVVCVDQQTPTIPNRLAADPVLSGLAQMMRECWYPNPSARLTALRIKKTLQKISNSPEKPKVIQ'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MTLGSPRKGLLMLLMALVTQGDPVKPSRGPLVTCTCESPHCKGPTCRGAWCTVVLVREEGRHPQEHRGCGNLHRELCRGRPTEFVNHYCCDSHLCNHNVSLVLEATQPPSEQPGTDGQLALILGPVLALLALVALGVLGLWHVRRRQEKQRGLHSELGESSLILKASEQGDSMLGDLLDSDCTTGSGSGLPFLVQRTVARQVALVECVGKGRYGEVWRGLWHGESVAVKIFSSRDEQSWFRETEIYNTVLLRHDNILGFIASDMTSRNSSTQLWLITHYHEHGSLYDFLQRQTLEPHLALRLAVSAACGLAHLHVEIFGTQGKPAIAHRDFKSRNVLVKSNLQCCIADLGLAVMHSQGSDYLDIGNNPRVGTKRYMAPEVLDEQIRTDCFESYKWTDIWAFGLVLWEIARRTIVNGIVEDYRPPFYDVVPNDPSFEDMKKVVCVDQQTPTIPNRLAADPVLSGLAQMMRECWYPNPSARLTALRIKKTLQKISNSPEKPKVIQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTLGSPRKGLLMLLMALVTQGDPVKPSRGPLVTCTCESPHCKGPTCRGAWCTVVLVREEGRHPQEHRGCGNLHRELCRGRPTEFVNHYCCDSHLCNHNVSLVLEATQPPSEQPGTDGQLALILGPVLALLALVALGVLGLWHVRRRQEKQRGLHSELGESSLILKASEQGDSMLGDLLDSDCTTGSGSGLPFLVQRTVARQVALVECVGKGRYGEVWRGLWHGESVAVKIFSSRDEQSWFRETEIYNTVLLRHDNILGFIASDMTSRNSSTQLWLITHYHEHGSLYDFLQRQTLEPHLALRLAVSAACGLAHLHVEIFGTQGKPAIAHRDFKSRNVLVKSNLQCCIADLGLAVMHSQGSDYLDIGNNPRVGTKRYMAPEVLDEQIRTDCFESYKWTDIWAFGLVLWEIARRTIVNGIVEDYRPPFYDVVPNDPSFEDMKKVVCVDQQTPTIPNRLAADPVLSGLAQMMRECWYPNPSARLTALRIKKTLQKISNSPEKPKVIQ\", \"result\": [{\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MTLGSPRKGLLMLLMALVTQGDPVKPSRGPLVTCTCESPHCKGPTCRGAWCTVVLVREEGRHPQEHRGCGNLHRELCRGRPTEFVNHYCCDSHLCNHNVSLVLEATQPPSEQPGTDGQLALILGPVLALLALVALGVLGLWHVRRRQEKQRGLHSELGESSLILKASEQGDSMLGDLLDSDCTTGSGSGLPFLVQRTVARQVALVECVGKGRYGEVWRGLWHGESVAVKIFSSRDEQSWFRETEIYNTVLLRHDNILGFIASDMTSRNSSTQLWLITHYHEHGSLYDFLQRQTLEPHLALRLAVSAACGLAHLHVEIFGTQGKPAIAHRDFKSRNVLVKSNLQCCIADLGLAVMHSQGSDYLDIGNNPRVGTKRYMAPEVLDEQIRTDCFESYKWTDIWAFGLVLWEIARRTIVNGIVEDYRPPFYDVVPNDPSFEDMKKVVCVDQQTPTIPNRLAADPVLSGLAQMMRECWYPNPSARLTALRIKKTLQKISNSPEKPKVIQ' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the binding affinity of the generated drugs with the target protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL\", \"result\": [{\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(NC(=O)c3cn4c(n3)sc3ccccc34)cc2)no1\", \"value\": 4.3}, {\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\", \"value\": 60.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGTSHPAFLVLGCLLTGLSLILCQLSLPSILPNENEKVVQLNSSFSLRCFGESEVSWQYPMSEEESSDVEIRNEENNSGLFVTVLEVSSASAAHTGLYTCYYNHTQTEENELEGRHIYIYVPDPDVAFVPLGMTDYLVIVEDDDSAIIPCRTTDPETPVTLHNSEGVVPASYDSRQGFNGTFTVGPYICEATVKGKKFQTIPFNVYALKATSELDLEMEALKTVYKSGETIVVTCAVFNNEVVDLQWTYPGEVKGKGITMLEEIKVPSIKLVYTLTVPEATVKDSGDYECAARQATREVKEMKKVTISVHEKGFIEIKPTFSQLEAVNLHEVKHFVVEVRAYPPPRISWLKNNLTLIENLTEITTDVEKIQEIRYRSKLKLIRAKEEDSGHYTIVAQNEDAVKSYTFELLTQVPSSILDLVDDHHGSTGGQTVRCTAEGTPLPDIEWMICKDIKKCNNETSWTILANNVSNIITEIHSRDRSTVEGRVTFAKVEETIAVRCLAKNLLGAENRELKLVAPTLRSELTVAAAVLVLLVIVIISLIVLVVIWKQKPRYEIRWRVIESISPDGHEYIYVDPMQLPYDSRWEFPRDGLVLGRVLGSGAFGKVVEGTAYGLSRSQPVMKVAVKMLKPTARSSEKQALMSELKIMTHLGPHLNIVNLLGACTKSGPIYIITEYCFYGDLVNYLHKNRDSFLSHHPEKPKKELDIFGLNPADESTRSYVILSFENNGDYMDMKQADTTQYVPMLERKEVSKYSDIQRSLYDRPASYKKKSMLDSEVKNLLSDDNSEGLTLLDLLSFTYQVARGMEFLASKNCVHRDLAARNVLLAQGKIVKICDFGLARDIMHDSNYVSKGSTFLPVKWMAPESIFDNLYTTLSDVWSYGILLWEIFSLGGTPYPGMMVDSTFYNKIKSGYRMAKPDHATSEVYEIMVKCWNSEPEKRPSFYHLSEIVENLLPGQYKKSYEKIHLDFLKSDHPAVARMRVDSDNAYIGVTYKNEEDKLKDWEGGLDEQRLSADSGYIIPLPDIDPVPEEEDLGKRNRHSSQTSEESAIETGSSSSTFIKREDETIEDIDMMDDIGIDSSDLVEDSFL' are 4.3, 60.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the optimized drugs' interaction with the target protein 'MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK' to be? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK\", \"result\": [{\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\", \"value\": 10000.0}, {\"smile\": \"CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1\", \"value\": 10000.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MPLAAYCYLRVVGKGSYGEVTLVKHRRDGKQYVIKKLNLRNASSRERRAAEQEAQLLSQLKHPNIVTYKESWEGGDGLLYIVMGFCEGGDLYRKLKEQKGQLLPENQVVEWFVQIAMALQYLHEKHILHRDLKTQNVFLTRTNIIKVGDLGIARVLENHCDMASTLIGTPYYMSPELFSNKPYNYKSDVWALGCCVYEMATLKHAFNAKDMNSLVYRIIEGKLPPMPRDYSPELAELIRTMLSKRPEERPSVRSILRQPYIKRQISFFLEATKIKTSKNNIKNGDSQSKPFATVVSGEAESNHEVIHPQPLSSEGSQTYIMGEGKCLSQEKPRASGLLKSPASLKAHTCKQDLSNTTELATISSVNIDILPAKGRDSVSDGFVQENQPRYLDASNELGGICSISQVEEEMLQDNTKSSAQPENLIPMWSSDIVTGEKNEPVKPLQPLKKKK' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the predicted affinity between the generated drugs and the target protein 'AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL\", \"result\": [{\"smile\": \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\", \"value\": 25.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 4.5}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'AASEPDTAGSVRGLPTAHCPVVQDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL' are 25.0, 4.5 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an affinity estimate for the optimized drugs with the target protein 'MKDYDELLKYYELHETIGTGGFAKVKLACHILTGEMVAIKIMDKNTLGSDLPRIKTEIEALKNLRHQHICQLYHVLETANKIFMVLEYCPGGELFDYIISQDRLSEEETRVVFRQIVSAVAYVHSQGYAHRDLKPENLLFDEYHKLKLIDFGLCAKPKGNKDYHLQTCCGSLAYAAPELIQGKSYLGSEADVWSMGILLYVLMCGFLPFDDDNVMALYKKIMRGKYDVPKWLSPSSILLLQQMLQVDPKKRISMKNLLNHPWIMQDYNYPVEWQSKNPFIHLDDDCVTELSVHHRNNRQTMEDLISLWQYDHLTATYLLLLAKKARGKPVRLRLSSFSCGQASATPFTDIKSNNWSLEDVTASDKNYVAGLIDYDWCEDDLSTGAATPRTSQFTKYWTESNGVESKSLTPALCRTPANKLKNKENVYTPKSAVKNEEYFMFPEPKTPVNKNQHKREILTTPNRYTTPSKARNQCLKETPIKIPVNSTGTDKLMTGVISPERRCRSVELDLNQAHMEETPKRKGAKVFGSLERGLDKVITVLTRSKRKGSARDGPRRLKLHYNVTTTRLVNPDQLLNEIMSILPKKHVDFVQKGYTLKCQTQSDFGKVTMQFELEVCQLQKPDVVGIRRQRLKGDAWVYKRLVEDILSSCKV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKDYDELLKYYELHETIGTGGFAKVKLACHILTGEMVAIKIMDKNTLGSDLPRIKTEIEALKNLRHQHICQLYHVLETANKIFMVLEYCPGGELFDYIISQDRLSEEETRVVFRQIVSAVAYVHSQGYAHRDLKPENLLFDEYHKLKLIDFGLCAKPKGNKDYHLQTCCGSLAYAAPELIQGKSYLGSEADVWSMGILLYVLMCGFLPFDDDNVMALYKKIMRGKYDVPKWLSPSSILLLQQMLQVDPKKRISMKNLLNHPWIMQDYNYPVEWQSKNPFIHLDDDCVTELSVHHRNNRQTMEDLISLWQYDHLTATYLLLLAKKARGKPVRLRLSSFSCGQASATPFTDIKSNNWSLEDVTASDKNYVAGLIDYDWCEDDLSTGAATPRTSQFTKYWTESNGVESKSLTPALCRTPANKLKNKENVYTPKSAVKNEEYFMFPEPKTPVNKNQHKREILTTPNRYTTPSKARNQCLKETPIKIPVNSTGTDKLMTGVISPERRCRSVELDLNQAHMEETPKRKGAKVFGSLERGLDKVITVLTRSKRKGSARDGPRRLKLHYNVTTTRLVNPDQLLNEIMSILPKKHVDFVQKGYTLKCQTQSDFGKVTMQFELEVCQLQKPDVVGIRRQRLKGDAWVYKRLVEDILSSCKV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKDYDELLKYYELHETIGTGGFAKVKLACHILTGEMVAIKIMDKNTLGSDLPRIKTEIEALKNLRHQHICQLYHVLETANKIFMVLEYCPGGELFDYIISQDRLSEEETRVVFRQIVSAVAYVHSQGYAHRDLKPENLLFDEYHKLKLIDFGLCAKPKGNKDYHLQTCCGSLAYAAPELIQGKSYLGSEADVWSMGILLYVLMCGFLPFDDDNVMALYKKIMRGKYDVPKWLSPSSILLLQQMLQVDPKKRISMKNLLNHPWIMQDYNYPVEWQSKNPFIHLDDDCVTELSVHHRNNRQTMEDLISLWQYDHLTATYLLLLAKKARGKPVRLRLSSFSCGQASATPFTDIKSNNWSLEDVTASDKNYVAGLIDYDWCEDDLSTGAATPRTSQFTKYWTESNGVESKSLTPALCRTPANKLKNKENVYTPKSAVKNEEYFMFPEPKTPVNKNQHKREILTTPNRYTTPSKARNQCLKETPIKIPVNSTGTDKLMTGVISPERRCRSVELDLNQAHMEETPKRKGAKVFGSLERGLDKVITVLTRSKRKGSARDGPRRLKLHYNVTTTRLVNPDQLLNEIMSILPKKHVDFVQKGYTLKCQTQSDFGKVTMQFELEVCQLQKPDVVGIRRQRLKGDAWVYKRLVEDILSSCKV\", \"result\": [{\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 10000.0}, {\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 1600.0}, {\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MKDYDELLKYYELHETIGTGGFAKVKLACHILTGEMVAIKIMDKNTLGSDLPRIKTEIEALKNLRHQHICQLYHVLETANKIFMVLEYCPGGELFDYIISQDRLSEEETRVVFRQIVSAVAYVHSQGYAHRDLKPENLLFDEYHKLKLIDFGLCAKPKGNKDYHLQTCCGSLAYAAPELIQGKSYLGSEADVWSMGILLYVLMCGFLPFDDDNVMALYKKIMRGKYDVPKWLSPSSILLLQQMLQVDPKKRISMKNLLNHPWIMQDYNYPVEWQSKNPFIHLDDDCVTELSVHHRNNRQTMEDLISLWQYDHLTATYLLLLAKKARGKPVRLRLSSFSCGQASATPFTDIKSNNWSLEDVTASDKNYVAGLIDYDWCEDDLSTGAATPRTSQFTKYWTESNGVESKSLTPALCRTPANKLKNKENVYTPKSAVKNEEYFMFPEPKTPVNKNQHKREILTTPNRYTTPSKARNQCLKETPIKIPVNSTGTDKLMTGVISPERRCRSVELDLNQAHMEETPKRKGAKVFGSLERGLDKVITVLTRSKRKGSARDGPRRLKLHYNVTTTRLVNPDQLLNEIMSILPKKHVDFVQKGYTLKCQTQSDFGKVTMQFELEVCQLQKPDVVGIRRQRLKGDAWVYKRLVEDILSSCKV' are 10000.0, 1600.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the projected binding affinity for the optimized drugs-target protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' complex? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 940.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}, {\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 8000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA' are 940.0, 10000.0 and 8000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you determine the affinity value of the optimized drugs for the target protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}, {\"smile\": \"CN1CCC(c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)C(O)C1\", \"value\": 10000.0}, {\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 670.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK' are 10000.0, 10000.0 and 670.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the generated drugs and the target protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}, {\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCC[C@@H]1CN1CCCC1\", \"value\": 2900.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI' are 10000.0, 10000.0 and 2900.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the optimized drugs-target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' relationship? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"COc1cc(-c2cnc3c(N4CCOCC4)snc3c2)cc(OC)c1OC\", \"value\": 110.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 730.0}, {\"smile\": \"COc1cc(Nc2c(C#N)cnc3cc(OCCCN4CCN(C)CC4)c(OC)cc23)c(Cl)cc1Cl\", \"value\": 1100.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS' are 110.0, 730.0 and 1100.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the expected level of affinity between the optimized drugs and the target protein 'MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI\", \"result\": [{\"smile\": \"Cc1cc(NC(=O)Nc2ccc3c(ccn3C)c2)sn1\", \"value\": 11.22}, {\"smile\": \"O=C(CCCCN1CCN(CC2CC(=O)c3ccoc3C2)CC1)c1ccc(F)cc1\", \"value\": 588.84}, {\"smile\": \"COc1ccc(O)c(CCN(C)C)c1\", \"value\": 145.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MASSYKMSEQSTISEHILQKTCDHLILTDRSGLKAESAAEEMKQTAENQGNTVHWAALLIFAVIIPTIGGNILVILAVSLEKRLQYATNYFLMSLAVADLLVGLFVMPIALLTIMFEATWPLPLALCPAWLFLDVLFSTASIMHLCAISLDRYIAIKKPIQANQCNSRTTAFVKITVVWLISIGIAIPVPIKGIEADVVNAHNITCELTKDRFGSFMLFGSLAAFFAPLTIMIVTYFLTIHALRKKAYLVRNRPPQRLTRWTVSTVLQREDSSFSSPEKMVMLDGSHKDKILPNSTDETLMRRMSSAGKKPAQTISNEQRASKVLGIVFLFFLLMWCPFFITNVTLALCDSCNQTTLKTLLQIFVWVGYVSSGVNPLIYTLFNKTFREAFGRYITCNYQATKSVKVLRKCSSTLYFGNSMVENSKFFTKHGIRNGINPAMYQSPVRLRSSTIQSSSIILLNTFLTENDGDKVEDQVSYI' are 11.22, 588.84 and 145.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction affinity for the optimized drugs and the target protein 'MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 1200.0}, {\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)NC5=CCC(F)C=C5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 45.0}, {\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF' are 1200.0, 45.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the binding affinity of the optimized drugs with the target protein 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}, {\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 310.0}, {\"smile\": \"CCn1cc(-c2cnc3ccc(N4CCN(C)Cc5c(OC)cc(OC)cc54)cc3n2)cn1\", \"value\": 2400.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MWSWKCLLFWAVLVTATLCTARPSPTLPEQAQPWGAPVEVESFLVHPGDLLQLRCRLRDDVQSINWLRDGVQLAESNRTRITGEEVEVQDSVPADSGLYACVTSSPSGSDTTYFSVNVSDALPSSEDDDDDDDSSSEEKETDNTKPNRMPVAPYWTSPEKMEKKLHAVPAAKTVKFKCPSSGTPNPTLRWLKNGKEFKPDHRIGGYKVRYATWSIIMDSVVPSDKGNYTCIVENEYGSINHTYQLDVVERSPHRPILQAGLPANKTVALGSNVEFMCKVYSDPQPHIQWLKHIEVNGSKIGPDNLPYVQILKTAGVNTTDKEMEVLHLRNVSFEDAGEYTCLAGNSIGLSHHSAWLTVLEALEERPAVMTSPLYLEIIIYCTGAFLISCMVGSVIVYKMKSGTKKSDFHSQMAVHKLAKSIPLRRQVTVSADSSASMNSGVLLVRPSRLSSSGTPMLAGVSEYELPEDPRWELPRDRLVLGKPLGEGCFGQVVLAEAIGLDKDKPNRVTKVAVKMLKSDATEKDLSDLISEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLQARRPPGLEYCYNPSHNPEEQLSSKDLVSCAYQVARGMEYLASKKCIHRDLAARNVLVTEDNVMKIADFGLARDIHHIDYYKKTTNGRLPVKWMAPEALFDRIYTHQSDVWSFGVLLWEIFTLGGSPYPGVPVEELFKLLKEGHRMDKPSNCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRIVALTSNQEYLDLSMPLDQYSPSFPDTRSSTCSSGEDSVFSHEPLPEEPCLPRHPAQLANGGLKRR' are 10000.0, 310.0 and 2400.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the predicted value for the optimized drugs' affinity with the target protein 'MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS\", \"result\": [{\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MAEPSGSPVHVQLPQQAAPVTAAAAAAPAAATAAPAPAAPAAPAPAPAPAAQAVGWPICRDAYELQEVIGSGATAVVQAALCKPRQERVAIKRINLEKCQTSMDELLKEIQAMSQCSHPNVVTYYTSFVVKDELWLVMKLLSGGSMLDIIKYIVNRGEHKNGVLEEAIIATILKEVLEGLDYLHRNGQIHRDLKAGNILLGEDGSVQIADFGVSAFLATGGDVTRNKVRKTFVGTPCWMAPEVMEQVRGYDFKADMWSFGITAIELATGAAPYHKYPPMKVLMLTLQNDPPTLETGVEDKEMMKKYGKSFRKLLSLCLQKDPSKRPTAAELLKCKFFQKAKNREYLIEKLLTRTPDIAQRAKKVRRVPGSSGHLHKTEDGDWEWSDDEMDEKSEEGKAAFSQEKSRRVKEENPEIAVSASTIPEQIQSLSVHDSQGPPNANEDYREASSCAVNLVLRLRNSRKELNDIRFEFTPGRDTADGVSQELFSAGLVDGHDVVIVAANLQKIVDDPKALKTLTFKLASGCDGSEIPDEVKLIGFAQLSVS' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the expected binding strength of the generated drugs toward the target protein 'MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI\", \"result\": [{\"smile\": \"CCN1CCN(Cc2ccc(NC(=O)Nc3ccc(Oc4cc(NC)ncn4)cc3)cc2C(F)(F)F)CC1\", \"value\": 10000.0}, {\"smile\": \"CN1CCN(c2ccc3nc(-c4c(N)c5c(F)cccc5[nH]c4=O)[nH]c3c2)CC1\", \"value\": 10000.0}, {\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 7000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MPQLYIYIRLLGAYLFIISRVQGQNLDSMLHGTGMKSDSDQKKSENGVTLAPEDTLPFLKCYCSGHCPDDAINNTCITNGHCFAIIEEDDQGETTLASGCMKYEGSDFQCKDSPKAQLRRTIECCRTNLCNQYLQPTLPPVVIGPFFDGSIRWLVLLISMAVCIIAMIIFSSCFCYKHYCKSISSRRRYNRDLEQDEAFIPVGESLKDLIDQSQSSGSGSGLPLLVQRTIAKQIQMVRQVGKGRYGEVWMGKWRGEKVAVKVFFTTEEASWFRETEIYQTVLMRHENILGFIAADIKGTGSWTQLYLITDYHENGSLYDFLKCATLDTRALLKLAYSAACGLCHLHTEIYGTQGKPAIAHRDLKSKNILIKKNGSCCIADLGLAVKFNSDTNEVDVPLNTRVGTKRYMAPEVLDESLNKNHFQPYIMADIYSFGLIIWEMARRCITGGIVEEYQLPYYNMVPSDPSYEDMREVVCVKRLRPIVSNRWNSDECLRAVLKLMSECWAHNPASRLTALRIKKTLAKMVESQDVKI' are 10000.0, 10000.0 and 7000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the generated drugs and the target protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 2.9}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MSTASAASSSSSSSAGEMIEAPSQVLNFEEIDYKEIEVEEVVGRGAFGVVCKAKWRAKDVAIKQIESESERKAFIVELRQLSRVNHPNIVKLYGACLNPVCLVMEYAEGGSLYNVLHGAEPLPYYTAAHAMSWCLQCSQGVAYLHSMQPKALIHRDLKPPNLLLVAGGTVLKICDFGTACDIQTHMTNNKGSAAWMAPEVFEGSNYSEKCDVFSWGIILWEVITRRKPFDEIGGPAFRIMWAVHNGTRPPLIKNLPKPIESLMTRCWSKDPSQRPSMEEIVKIMTHLMRYFPGADEPLQYPCQYSDEGQSNSATSTGSFMDIASTNTSNKSDTNMEQVPATNDTIKRLESKLLKNQAKQQSESGRLSLGASRGSSVESLPPTSEGKRMSADMSEIEARIAATTAYSKPKRGHRKTASFGNILDVPEIVISGNGQPRRRSIQDLTVTGTEPGQVSSRSSSPSVRMITTSGPTSEKPTRSHPWTPDDSTDTNGSDNSIPMAYLTLDHQLQPLAPCPNSKESMAVFEQHCKMAQEYMKVQTEIALLLQRKQELVAELDQDEKDQQNTSRLVQEHKKLLDENKSLSTYYQQCKKQLEVIRSQQQKRQGTS' are 10000.0, 2.9 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the optimized drugs and the target protein 'MGEKLELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGTTQPMKLNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKGSVSWTGKPVSYYLHTIDRTILENYFSSLKNPKLREEQEAARRRQQRESKSNAATPTKGPEGKVAGPADAPMDSGAEEEKAGAATVKKPSPSKARKKKLNKKGRKMAGRKRGRPKKMNTANPERKPKKNQTALDALHAQTVSQTAASSPQDAYRSPHSPFYQLPPSVQRHSPNPLLVAPTPPALQKLLESFKIQYLQFLAYTKTPQYKASLQELLGQEKEKNAQLLGAAQQLLSHCQAQKEEIRRLFQQKLDELGVKALTYNDLIQAQKEISAHNQQLREQSEQLEQDNRALRGQSLQLLKARCEELQLDWATLSLEKLLKEKQALKSQISEKQRHCLELQISIVELEKSQRQQELLQLKSCVPPDDALSLHLRGKGALGRELEPDASRLHLELDCTKFSLPHLSSMSPELSMNGQAAGYELCGVLSRPSSKQNTPQYLASPLDQEVVPCTPSHVGRPRLEKLSGLAAPDYTRLSPAKIVLRRHLSQDHTVPGRPAASELHSRAEHTKENGLPYQSPSVPGSMKLSPQDPRPLSPGALQLAGEKSSEKGLRERAYGSSGELITSLPISIPLSTVQPNKLPVSIPLASVVLPSRAERARSTPSPVLQPRDPSSTLEKQIGANAHGAGSRSLALAPAGFSYAGSVAISGALAGSPASLTPGAEPATLDESSSSGSLFATVGSRSSTPQHPLLLAQPRNSLPASPAHQLSSSPRLGGAAQGPLPEASKGDLPSDSGFSDPESEAKRRIVFTITTGAGSAKQSPSSKHSPLTASARGDCVPSHGQDSRRRGRRKRASAGTPSLSAGVSPKRRALPSVAGLFTQPSGSPLNLNSMVSNINQPLEITAISSPETSLKSSPVPYQDHDQPPVLKKERPLSQTNGAHYSPLTSDEEPGSEDEPSSARIERKIATISLESKSPPKTLENGGGLAGRKPAPAGEPVNSSKWKSTFSPISDIGLAKSADSPLQASSALSQNSLFTFRPALEEPSADAKLAAHPRKGFPGSLSGADGLSPGTNPANGCTFGGGLAADLSLHSFSDGASLPHKGPEAAGLSSPLSFPSQRGKEGSDANPFLSKRQLDGLAGLKGEGSRGKEAGEGGLPLCGPTDKTPLLSGKAAKARDREVDLKNGHNLFISAAAVPPGSLLSGPGLAPAASSAGGAASSAQTHRSFLGPFPPGPQFALGPMSLQANLGSVAGSSVLQSLFSSVPAAAGLVHVSSAATRLTNSHAMGSFSGVAGGTVGGN'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MGEKLELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGTTQPMKLNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKGSVSWTGKPVSYYLHTIDRTILENYFSSLKNPKLREEQEAARRRQQRESKSNAATPTKGPEGKVAGPADAPMDSGAEEEKAGAATVKKPSPSKARKKKLNKKGRKMAGRKRGRPKKMNTANPERKPKKNQTALDALHAQTVSQTAASSPQDAYRSPHSPFYQLPPSVQRHSPNPLLVAPTPPALQKLLESFKIQYLQFLAYTKTPQYKASLQELLGQEKEKNAQLLGAAQQLLSHCQAQKEEIRRLFQQKLDELGVKALTYNDLIQAQKEISAHNQQLREQSEQLEQDNRALRGQSLQLLKARCEELQLDWATLSLEKLLKEKQALKSQISEKQRHCLELQISIVELEKSQRQQELLQLKSCVPPDDALSLHLRGKGALGRELEPDASRLHLELDCTKFSLPHLSSMSPELSMNGQAAGYELCGVLSRPSSKQNTPQYLASPLDQEVVPCTPSHVGRPRLEKLSGLAAPDYTRLSPAKIVLRRHLSQDHTVPGRPAASELHSRAEHTKENGLPYQSPSVPGSMKLSPQDPRPLSPGALQLAGEKSSEKGLRERAYGSSGELITSLPISIPLSTVQPNKLPVSIPLASVVLPSRAERARSTPSPVLQPRDPSSTLEKQIGANAHGAGSRSLALAPAGFSYAGSVAISGALAGSPASLTPGAEPATLDESSSSGSLFATVGSRSSTPQHPLLLAQPRNSLPASPAHQLSSSPRLGGAAQGPLPEASKGDLPSDSGFSDPESEAKRRIVFTITTGAGSAKQSPSSKHSPLTASARGDCVPSHGQDSRRRGRRKRASAGTPSLSAGVSPKRRALPSVAGLFTQPSGSPLNLNSMVSNINQPLEITAISSPETSLKSSPVPYQDHDQPPVLKKERPLSQTNGAHYSPLTSDEEPGSEDEPSSARIERKIATISLESKSPPKTLENGGGLAGRKPAPAGEPVNSSKWKSTFSPISDIGLAKSADSPLQASSALSQNSLFTFRPALEEPSADAKLAAHPRKGFPGSLSGADGLSPGTNPANGCTFGGGLAADLSLHSFSDGASLPHKGPEAAGLSSPLSFPSQRGKEGSDANPFLSKRQLDGLAGLKGEGSRGKEAGEGGLPLCGPTDKTPLLSGKAAKARDREVDLKNGHNLFISAAAVPPGSLLSGPGLAPAASSAGGAASSAQTHRSFLGPFPPGPQFALGPMSLQANLGSVAGSSVLQSLFSSVPAAAGLVHVSSAATRLTNSHAMGSFSGVAGGTVGGN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGEKLELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGTTQPMKLNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKGSVSWTGKPVSYYLHTIDRTILENYFSSLKNPKLREEQEAARRRQQRESKSNAATPTKGPEGKVAGPADAPMDSGAEEEKAGAATVKKPSPSKARKKKLNKKGRKMAGRKRGRPKKMNTANPERKPKKNQTALDALHAQTVSQTAASSPQDAYRSPHSPFYQLPPSVQRHSPNPLLVAPTPPALQKLLESFKIQYLQFLAYTKTPQYKASLQELLGQEKEKNAQLLGAAQQLLSHCQAQKEEIRRLFQQKLDELGVKALTYNDLIQAQKEISAHNQQLREQSEQLEQDNRALRGQSLQLLKARCEELQLDWATLSLEKLLKEKQALKSQISEKQRHCLELQISIVELEKSQRQQELLQLKSCVPPDDALSLHLRGKGALGRELEPDASRLHLELDCTKFSLPHLSSMSPELSMNGQAAGYELCGVLSRPSSKQNTPQYLASPLDQEVVPCTPSHVGRPRLEKLSGLAAPDYTRLSPAKIVLRRHLSQDHTVPGRPAASELHSRAEHTKENGLPYQSPSVPGSMKLSPQDPRPLSPGALQLAGEKSSEKGLRERAYGSSGELITSLPISIPLSTVQPNKLPVSIPLASVVLPSRAERARSTPSPVLQPRDPSSTLEKQIGANAHGAGSRSLALAPAGFSYAGSVAISGALAGSPASLTPGAEPATLDESSSSGSLFATVGSRSSTPQHPLLLAQPRNSLPASPAHQLSSSPRLGGAAQGPLPEASKGDLPSDSGFSDPESEAKRRIVFTITTGAGSAKQSPSSKHSPLTASARGDCVPSHGQDSRRRGRRKRASAGTPSLSAGVSPKRRALPSVAGLFTQPSGSPLNLNSMVSNINQPLEITAISSPETSLKSSPVPYQDHDQPPVLKKERPLSQTNGAHYSPLTSDEEPGSEDEPSSARIERKIATISLESKSPPKTLENGGGLAGRKPAPAGEPVNSSKWKSTFSPISDIGLAKSADSPLQASSALSQNSLFTFRPALEEPSADAKLAAHPRKGFPGSLSGADGLSPGTNPANGCTFGGGLAADLSLHSFSDGASLPHKGPEAAGLSSPLSFPSQRGKEGSDANPFLSKRQLDGLAGLKGEGSRGKEAGEGGLPLCGPTDKTPLLSGKAAKARDREVDLKNGHNLFISAAAVPPGSLLSGPGLAPAASSAGGAASSAQTHRSFLGPFPPGPQFALGPMSLQANLGSVAGSSVLQSLFSSVPAAAGLVHVSSAATRLTNSHAMGSFSGVAGGTVGGN\", \"result\": [{\"smile\": \"Nc1ncnc2c1ncn2C1OC(CSCCC(N)C(=O)O)[C@@H](O)[C@H]1O\", \"value\": 71.0}, {\"smile\": \"CC(C)N(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)C[C@H]1O[C@@H](n2ccc3c(N)ncnc32)[C@H](O)[C@@H]1O\", \"value\": 0.25}, {\"smile\": \"CC(C)N(CCCNC(=O)Nc1ccc(C(C)(C)C)cc1)C[C@H]1O[C@@H](n2cc(Br)c3c(N)ncnc32)[C@H](O)[C@@H]1O\", \"value\": 0.06}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MGEKLELRLKSPVGAEPAVYPWPLPVYDKHHDAAHEIIETIRWVCEEIPDLKLAMENYVLIDYDTKSFESMQRLCDKYNRAIDSIHQLWKGTTQPMKLNTRPSTGLLRHILQQVYNHSVTDPEKLNNYEPFSPEVYGETSFDLVAQMIDEIKMTDDDLFVDLGSGVGQVVLQVAAATNCKHHYGVEKADIPAKYAETMDREFRKWMKWYGKKHAEYTLERGDFLSEEWRERIANTSVIFVNNFAFGPEVDHQLKERFANMKEGGRIVSSKPFAPLNFRINSRNLSDIGTIMRVVELSPLKGSVSWTGKPVSYYLHTIDRTILENYFSSLKNPKLREEQEAARRRQQRESKSNAATPTKGPEGKVAGPADAPMDSGAEEEKAGAATVKKPSPSKARKKKLNKKGRKMAGRKRGRPKKMNTANPERKPKKNQTALDALHAQTVSQTAASSPQDAYRSPHSPFYQLPPSVQRHSPNPLLVAPTPPALQKLLESFKIQYLQFLAYTKTPQYKASLQELLGQEKEKNAQLLGAAQQLLSHCQAQKEEIRRLFQQKLDELGVKALTYNDLIQAQKEISAHNQQLREQSEQLEQDNRALRGQSLQLLKARCEELQLDWATLSLEKLLKEKQALKSQISEKQRHCLELQISIVELEKSQRQQELLQLKSCVPPDDALSLHLRGKGALGRELEPDASRLHLELDCTKFSLPHLSSMSPELSMNGQAAGYELCGVLSRPSSKQNTPQYLASPLDQEVVPCTPSHVGRPRLEKLSGLAAPDYTRLSPAKIVLRRHLSQDHTVPGRPAASELHSRAEHTKENGLPYQSPSVPGSMKLSPQDPRPLSPGALQLAGEKSSEKGLRERAYGSSGELITSLPISIPLSTVQPNKLPVSIPLASVVLPSRAERARSTPSPVLQPRDPSSTLEKQIGANAHGAGSRSLALAPAGFSYAGSVAISGALAGSPASLTPGAEPATLDESSSSGSLFATVGSRSSTPQHPLLLAQPRNSLPASPAHQLSSSPRLGGAAQGPLPEASKGDLPSDSGFSDPESEAKRRIVFTITTGAGSAKQSPSSKHSPLTASARGDCVPSHGQDSRRRGRRKRASAGTPSLSAGVSPKRRALPSVAGLFTQPSGSPLNLNSMVSNINQPLEITAISSPETSLKSSPVPYQDHDQPPVLKKERPLSQTNGAHYSPLTSDEEPGSEDEPSSARIERKIATISLESKSPPKTLENGGGLAGRKPAPAGEPVNSSKWKSTFSPISDIGLAKSADSPLQASSALSQNSLFTFRPALEEPSADAKLAAHPRKGFPGSLSGADGLSPGTNPANGCTFGGGLAADLSLHSFSDGASLPHKGPEAAGLSSPLSFPSQRGKEGSDANPFLSKRQLDGLAGLKGEGSRGKEAGEGGLPLCGPTDKTPLLSGKAAKARDREVDLKNGHNLFISAAAVPPGSLLSGPGLAPAASSAGGAASSAQTHRSFLGPFPPGPQFALGPMSLQANLGSVAGSSVLQSLFSSVPAAAGLVHVSSAATRLTNSHAMGSFSGVAGGTVGGN' are 71.0, 0.25 and 0.06 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' your evaluation of the generated drugs' affinity for the target protein 'MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV\", \"result\": [{\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc(F)c(NC(=O)Nc2ccc(-c3cccc4[nH]nc(N)c34)cc2)c1\", \"value\": 10000.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MKPATGLWVWVSLLVAAGTVQPSDSQSVCAGTENKLSSLSDLEQQYRALRKYYENCEVVMGNLEITSIEHNRDLSFLRSVREVTGYVLVALNQFRYLPLENLRIIRGTKLYEDRYALAIFLNYRKDGNFGLQELGLKNLTEILNGGVYVDQNKFLCYADTIHWQDIVRNPWPSNLTLVSTNGSSGCGRCHKSCTGRCWGPTENHCQTLTRTVCAEQCDGRCYGPYVSDCCHRECAGGCSGPKDTDCFACMNFNDSGACVTQCPQTFVYNPTTFQLEHNFNAKYTYGAFCVKKCPHNFVVDSSSCVRACPSSKMEVEENGIKMCKPCTDICPKACDGIGTGSLMSAQTVDSSNIDKFINCTKINGNLIFLVTGIHGDPYNAIEAIDPEKLNVFRTVREITGFLNIQSWPPNMTDFSVFSNLVTIGGRVLYSGLSLLILKQQGITSLQFQSLKEISAGNIYITDNSNLCYYHTINWTTLFSTINQRIVIRDNRKAENCTAEGMVCNHLCSSDGCWGPGPDQCLSCRRFSRGRICIESCNLYDGEFREFENGSICVECDPQCEKMEDGLLTCHGPGPDNCTKCSHFKDGPNCVEKCPDGLQGANSFIFKYADPDRECHPCHPNCTQGCNGPTSHDCIYYPWTGHSTLPQHARTPLIAAGVIGGLFILVIVGLTFAVYVRRKSIKKKRALRRFLETELVEPLTPSGTAPNQAQLRILKETELKRVKVLGSGAFGTVYKGIWVPEGETVKIPVAIKILNETTGPKANVEFMDEALIMASMDHPHLVRLLGVCLSPTIQLVTQLMPHGCLLEYVHEHKDNIGSQLLLNWCVQIAKGMMYLEERRLVHRDLAARNVLVKSPNHVKITDFGLARLLEGDEKEYNADGGKMPIKWMALECIHYRKFTHQSDVWSYGVTIWELMTFGGKPYDGIPTREIPDLLEKGERLPQPPICTIDVYMVMVKCWMIDADSRPKFKELAAEFSRMARDPQRYLVIQGDDRMKLPSPNDSKFFQNLLDEEDLEDMMDAEEYLVPQAFNIPPPIYTSRARIDSNRSEIGHSPPPAYTPMSGNQFVYRDGGFAAEQGVSVPYRAPTSTIPEAPVAQGATAEIFDDSCCNGTLRKPVAPHVQEDSSTQRYSADPTVFAPERSPRGELDEEGYMTPMRDKPKQEYLNPVEENPFVSRRKNGDLQALDNPEYHNASNGPPKAEDEYVNEPLYLNTFANTLGKAEYLKNNILSMPEKAKKAFDNPDYWNHSLPPRSTLQHPDYLQEYSTKYFYKQNGRIRPIVAENPEYLSEFSLKPGTVLPPPPYRHRNTVV' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted value of the affinity between the optimized drugs and the target protein 'MFGKRKKRVEISAPSNFEHRVHTGFDQHEQKFTGLPRQWQSLIEESARRPKPLVDPACITSIQPGAPKTIVRGSKGAKDGALTLLLDEFENMSVTRSNSLRRDSPPPPARARQENGMPEEPATTARGGPGKAGSRGRFAGHSEAGGGSGDRRRAGPEKRPKSSREGSGGPQESSRDKRPLSGPDVGTPQPAGLASGAKLAAGRPFNTYPRADTDHPSRGAQGEPHDVAPNGPSAGGLAIPQSSSSSSRPPTRARGAPSPGVLGPHASEPQLAPPACTPAAPAVPGPPGPRSPQREPQRVSHEQFRAALQLVVDPGDPRSYLDNFIKIGEGSTGIVCIATVRSSGKLVAVKKMDLRKQQRRELLFNEVVIMRDYQHENVVEMYNSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIAAVCLAVLQALSVLHAQGVIHRDIKSDSILLTHDGRVKLSDFGFCAQVSKEVPRRKSLVGTPYWMAPELISRLPYGPEVDIWSLGIMVIEMVDGEPPYFNEPPLKAMKMIRDNLPPRLKNLHKVSPSLKGFLDRLLVRDPAQRATAAELLKHPFLAKAGPPASIVPLMRQNRTR'. MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MFGKRKKRVEISAPSNFEHRVHTGFDQHEQKFTGLPRQWQSLIEESARRPKPLVDPACITSIQPGAPKTIVRGSKGAKDGALTLLLDEFENMSVTRSNSLRRDSPPPPARARQENGMPEEPATTARGGPGKAGSRGRFAGHSEAGGGSGDRRRAGPEKRPKSSREGSGGPQESSRDKRPLSGPDVGTPQPAGLASGAKLAAGRPFNTYPRADTDHPSRGAQGEPHDVAPNGPSAGGLAIPQSSSSSSRPPTRARGAPSPGVLGPHASEPQLAPPACTPAAPAVPGPPGPRSPQREPQRVSHEQFRAALQLVVDPGDPRSYLDNFIKIGEGSTGIVCIATVRSSGKLVAVKKMDLRKQQRRELLFNEVVIMRDYQHENVVEMYNSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIAAVCLAVLQALSVLHAQGVIHRDIKSDSILLTHDGRVKLSDFGFCAQVSKEVPRRKSLVGTPYWMAPELISRLPYGPEVDIWSLGIMVIEMVDGEPPYFNEPPLKAMKMIRDNLPPRLKNLHKVSPSLKGFLDRLLVRDPAQRATAAELLKHPFLAKAGPPASIVPLMRQNRTR\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MFGKRKKRVEISAPSNFEHRVHTGFDQHEQKFTGLPRQWQSLIEESARRPKPLVDPACITSIQPGAPKTIVRGSKGAKDGALTLLLDEFENMSVTRSNSLRRDSPPPPARARQENGMPEEPATTARGGPGKAGSRGRFAGHSEAGGGSGDRRRAGPEKRPKSSREGSGGPQESSRDKRPLSGPDVGTPQPAGLASGAKLAAGRPFNTYPRADTDHPSRGAQGEPHDVAPNGPSAGGLAIPQSSSSSSRPPTRARGAPSPGVLGPHASEPQLAPPACTPAAPAVPGPPGPRSPQREPQRVSHEQFRAALQLVVDPGDPRSYLDNFIKIGEGSTGIVCIATVRSSGKLVAVKKMDLRKQQRRELLFNEVVIMRDYQHENVVEMYNSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIAAVCLAVLQALSVLHAQGVIHRDIKSDSILLTHDGRVKLSDFGFCAQVSKEVPRRKSLVGTPYWMAPELISRLPYGPEVDIWSLGIMVIEMVDGEPPYFNEPPLKAMKMIRDNLPPRLKNLHKVSPSLKGFLDRLLVRDPAQRATAAELLKHPFLAKAGPPASIVPLMRQNRTR\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MFGKRKKRVEISAPSNFEHRVHTGFDQHEQKFTGLPRQWQSLIEESARRPKPLVDPACITSIQPGAPKTIVRGSKGAKDGALTLLLDEFENMSVTRSNSLRRDSPPPPARARQENGMPEEPATTARGGPGKAGSRGRFAGHSEAGGGSGDRRRAGPEKRPKSSREGSGGPQESSRDKRPLSGPDVGTPQPAGLASGAKLAAGRPFNTYPRADTDHPSRGAQGEPHDVAPNGPSAGGLAIPQSSSSSSRPPTRARGAPSPGVLGPHASEPQLAPPACTPAAPAVPGPPGPRSPQREPQRVSHEQFRAALQLVVDPGDPRSYLDNFIKIGEGSTGIVCIATVRSSGKLVAVKKMDLRKQQRRELLFNEVVIMRDYQHENVVEMYNSYLVGDELWVVMEFLEGGALTDIVTHTRMNEEQIAAVCLAVLQALSVLHAQGVIHRDIKSDSILLTHDGRVKLSDFGFCAQVSKEVPRRKSLVGTPYWMAPELISRLPYGPEVDIWSLGIMVIEMVDGEPPYFNEPPLKAMKMIRDNLPPRLKNLHKVSPSLKGFLDRLLVRDPAQRATAAELLKHPFLAKAGPPASIVPLMRQNRTR' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What' the anticipated interaction value for the optimized drugs with the target protein 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV'? MemoryPool(arguments=dict_keys(['output_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(output_smiles)\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\", \"result\": [{\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\", \"value\": 10000.0}, {\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}, {\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['output_smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs and the protein 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV' are 10000.0, 10000.0 and 10000.0 respectively."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please predict the affinity of the drugs to the target protein (target_seq1). MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"(target_seq1)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"(target_seq1)\", \"result\": [{\"smile\": \"(drug_smile1)\", \"value\": (value1)}, {\"smile\": \"(drug_smile2)\", \"value\": (value2)}, {\"smile\": \"(drug_smile3)\", \"value\": (value3)}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs (drug_smile1), (drug_smile2), (drug_smile3) and the protein are (value1), (value2) and (value3)."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Please predict the affinity of the drugs (drug_smile1), (drug_smile2) to the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"(drug_smile1)\", \"(drug_smile2)\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"(target_seq1)\", \"result\": [{\"smile\": \"(drug_smile1)\", \"value\": (value1)}, {\"smile\": \"(drug_smile2)\", \"value\": (value2)}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs (drug_smile1), (drug_smile2) and the protein are (value1), (value2)."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Predict the affinity value between the drugs and the target protein 'MEPLKSLFLKSPLGSWNGSGSGGGGGGGGGRPEGSPKAAGYANPVWTALFDYEPSGQDELALRKGDRVEVLSRDAAISGDEGWWAGQVGGQVGIFPSNYVSRGGGPPPCEVASFQELRLEEVIGIGGFGKVYRGSWRGELVAVKAARQDPDEDISVTAESVRQEARLFAMLAHPNIIALKAVCLEEPNLCLVMEYAAGGPLSRALAGRRVPPHVLVNWAVQIARGMHYLHCEALVPVIHRDLKSNNILLLQPIESDDMEHKTLKITDFGLAREWHKTTQMSAAGTYAWMAPEVIKASTFSKGSDVWSFGVLLWELLTGEVPYRGIDCLAVAYGVAVNKLTLPIPSTCPEPFAQLMADCWAQDPHRRPDFASILQQLEALEAQVLREMPRDSFHSMQEGWKREIQGLFDELRAKEKELLSREEELTRAAREQRSQAEQLRRREHLLAQWELEVFERELTLLLQQVDRERPHVRRRRGTFKRSKLRARDGGERISMPLDFKHRITVQASPGLDRRRNVFEVGPGDSPTFPRFRAIQLEPAEPGQAWGRQSPRRLEDSSNGERRACWAWGPSSPKPGEAQNGRRRSRMDEATWYLDSDDSSPLGSPSTPPALNGNPPRPSLEPEEPKRPVPAERGSSSGTPKLIQRALLRGTALLASLGLGRDLQPPGGPGRERGESPTTPPTPTPAPCPTEPPPSPLICFSLKTPDSPPTPAPLLLDLGIPVGQRSAKSPRREEEPRGGTVSPPPGTSRSAPGTPGTPRSPPLGLISRPRPSPLRSRIDPWSFVSAGPRPSPLPSPQPAPRRAPWTLFPDSDPFWDSPPANPFQGGPQDCRAQTKDMGAQAPWVPEAGP'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEPLKSLFLKSPLGSWNGSGSGGGGGGGGGRPEGSPKAAGYANPVWTALFDYEPSGQDELALRKGDRVEVLSRDAAISGDEGWWAGQVGGQVGIFPSNYVSRGGGPPPCEVASFQELRLEEVIGIGGFGKVYRGSWRGELVAVKAARQDPDEDISVTAESVRQEARLFAMLAHPNIIALKAVCLEEPNLCLVMEYAAGGPLSRALAGRRVPPHVLVNWAVQIARGMHYLHCEALVPVIHRDLKSNNILLLQPIESDDMEHKTLKITDFGLAREWHKTTQMSAAGTYAWMAPEVIKASTFSKGSDVWSFGVLLWELLTGEVPYRGIDCLAVAYGVAVNKLTLPIPSTCPEPFAQLMADCWAQDPHRRPDFASILQQLEALEAQVLREMPRDSFHSMQEGWKREIQGLFDELRAKEKELLSREEELTRAAREQRSQAEQLRRREHLLAQWELEVFERELTLLLQQVDRERPHVRRRRGTFKRSKLRARDGGERISMPLDFKHRITVQASPGLDRRRNVFEVGPGDSPTFPRFRAIQLEPAEPGQAWGRQSPRRLEDSSNGERRACWAWGPSSPKPGEAQNGRRRSRMDEATWYLDSDDSSPLGSPSTPPALNGNPPRPSLEPEEPKRPVPAERGSSSGTPKLIQRALLRGTALLASLGLGRDLQPPGGPGRERGESPTTPPTPTPAPCPTEPPPSPLICFSLKTPDSPPTPAPLLLDLGIPVGQRSAKSPRREEEPRGGTVSPPPGTSRSAPGTPGTPRSPPLGLISRPRPSPLRSRIDPWSFVSAGPRPSPLPSPQPAPRRAPWTLFPDSDPFWDSPPANPFQGGPQDCRAQTKDMGAQAPWVPEAGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEPLKSLFLKSPLGSWNGSGSGGGGGGGGGRPEGSPKAAGYANPVWTALFDYEPSGQDELALRKGDRVEVLSRDAAISGDEGWWAGQVGGQVGIFPSNYVSRGGGPPPCEVASFQELRLEEVIGIGGFGKVYRGSWRGELVAVKAARQDPDEDISVTAESVRQEARLFAMLAHPNIIALKAVCLEEPNLCLVMEYAAGGPLSRALAGRRVPPHVLVNWAVQIARGMHYLHCEALVPVIHRDLKSNNILLLQPIESDDMEHKTLKITDFGLAREWHKTTQMSAAGTYAWMAPEVIKASTFSKGSDVWSFGVLLWELLTGEVPYRGIDCLAVAYGVAVNKLTLPIPSTCPEPFAQLMADCWAQDPHRRPDFASILQQLEALEAQVLREMPRDSFHSMQEGWKREIQGLFDELRAKEKELLSREEELTRAAREQRSQAEQLRRREHLLAQWELEVFERELTLLLQQVDRERPHVRRRRGTFKRSKLRARDGGERISMPLDFKHRITVQASPGLDRRRNVFEVGPGDSPTFPRFRAIQLEPAEPGQAWGRQSPRRLEDSSNGERRACWAWGPSSPKPGEAQNGRRRSRMDEATWYLDSDDSSPLGSPSTPPALNGNPPRPSLEPEEPKRPVPAERGSSSGTPKLIQRALLRGTALLASLGLGRDLQPPGGPGRERGESPTTPPTPTPAPCPTEPPPSPLICFSLKTPDSPPTPAPLLLDLGIPVGQRSAKSPRREEEPRGGTVSPPPGTSRSAPGTPGTPRSPPLGLISRPRPSPLRSRIDPWSFVSAGPRPSPLPSPQPAPRRAPWTLFPDSDPFWDSPPANPFQGGPQDCRAQTKDMGAQAPWVPEAGP\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 10000.0}, {\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 10000.0}, {\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C, Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21, CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity between the drugs and the target protein 'MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELAALCRWGLLLALLPPGAASTQVCTGTDMKLRLPASPETHLDMLRHLYQGCQVVQGNLELTYLPTNASLSFLQDIQEVQGYVLIAHNQVRQVPLQRLRIVRGTQLFEDNYALAVLDNGDPLNNTTPVTGASPGGLRELQLRSLTEILKGGVLIQRNPQLCYQDTILWKDIFHKNNQLALTLIDTNRSRACHPCSPMCKGSRCWGESSEDCQSLTRTVCAGGCARCKGPLPTDCCHEQCAAGCTGPKHSDCLACLHFNHSGICELHCPALVTYNTDTFESMPNPEGRYTFGASCVTACPYNYLSTDVGSCTLVCPLHNQEVTAEDGTQRCEKCSKPCARVCYGLGMEHLREVRAVTSANIQEFAGCKKIFGSLAFLPESFDGDPASNTAPLQPEQLQVFETLEEITGYLYISAWPDSLPDLSVFQNLQVIRGRILHNGAYSLTLQGLGISWLGLRSLRELGSGLALIHHNTHLCFVHTVPWDQLFRNPHQALLHTANRPEDECVGEGLACHQLCARGHCWGPGPTQCVNCSQFLRGQECVEECRVLQGLPREYVNARHCLPCHPECQPQNGSVTCFGPEADQCVACAHYKDPPFCVARCPSGVKPDLSYMPIWKFPDEEGACQPCPINCTHSCVDLDDKGCPAEQRASPLTSIISAVVGILLVVVLGVVFGILIKRRQQKIRKYTMRRLLQETELVEPLTPSGAMPNQAQMRILKETELRKVKVLGSGAFGTVYKGIWIPDGENVKIPVAIKVLRENTSPKANKEILDEAYVMAGVGSPYVSRLLGICLTSTVQLVTQLMPYGCLLDHVRENRGRLGSQDLLNWCMQIAKGMSYLEDVRLVHRDLAARNVLVKSPNHVKITDFGLARLLDIDETEYHADGGKVPIKWMALESILRRRFTHQSDVWSYGVTVWELMTFGAKPYDGIPAREIPDLLEKGERLPQPPICTIDVYMIMVKCWMIDSECRPRFRELVSEFSRMARDPQRFVVIQNEDLGPASPLDSTFYRSLLEDDDMGDLVDAEEYLVPQQGFFCPDPAPGAGGMVHHRHRSSSTRSGGGDLTLGLEPSEEEAPRSPLAPSEGAGSDVFDGDLGMGAAKGLQSLPTHDPSPLQRYSEDPTVPLPSETDGYVAPLTCSPQPEYVNQPDVRPQPPSPREGPLPAARPAGATLERPKTLSPGKNGVVKDVFAFGGAVENPEYLTPQGGAAPQPHPPPAFSPAFDNLYYWDQDPPERGAPPSTFKGTPTAENPEYLGLDVPV\", \"result\": [{\"smile\": \"C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 10000.0}, {\"smile\": \"CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@@H]1CSSC[C@H](NC(=O)[C@@H](N)Cc2ccccc2)C(=O)N[C@@H](CC(=O)O)C(=O)NCC(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](Cc2ccc(O)cc2)C(=O)N[C@@H](C)C(=O)N1)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(N)=O)C(C)C\", \"value\": 320.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs C[C@]12O[C@H](C[C@]1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O, CSCC[C@H](NC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)[C@@H]1CSSC[C@H](NC(=O)[C@@H](N)Cc2ccccc2)C(=O)N[C@@H](CC(=O)O)C(=O)NCC(=O)N[C@@H](Cc2ccccc2)C(=O)N[C@@H](Cc2ccc(O)cc2)C(=O)N[C@@H](C)C(=O)N1)C(=O)N[C@@H](CC(=O)O)C(=O)N[C@H](C(N)=O)C(C)C, COC(=O)c1ccc2c(c1)NC(=O)C2=C(Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1 and the protein are 10000.0, 320.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted affinity value for the drugs and the target protein 'MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELENYKQPVVLREDNCRRRRRMKPRSAAASLSSMELIPIEFVLPTSQRKCKSPETALLHVAGHGNVEQMKAQVWLRALETSVAADFYHRLGPHHFLLLYQKKGQWYEIYDKYQVVQTLDCLRYWKATHRSPGQIHLVQRHPPSEESQAFQRQLTALIGYDVTDVSNVHDDELEFTRRGLVTPRMAEVASRDPKLYAMHPWVTSKPLPEYLWKKIANNCIFIVIHRSTTSQTIKVSPDDTPGAILQSFFTKMAKKKSLMDIPESQSEQDFVLRVCGRDEYLVGETPIKNFQWVRHCLKNGEEIHVVLDTPPDPALDEVRKEEWPLVDDCTGVTGYHEQLTIHGKDHESVFTVSLWDCDRKFRVKIRGIDIPVLPRNTDLTVFVEANIQHGQQVLCQRRTSPKPFTEEVLWNVWLEFSIKIKDLPKGALLNLQIYCGKAPALSSKASAESPSSESKGKVQLLYYVNLLLIDHRFLLRRGEYVLHMWQISGKGEDQGSFNADKLTSATNPDKENSMSISILLDNYCHPIALPKHQPTPDPEGDRVRAEMPNQLRKQLEAIIATDPLNPLTAEDKELLWHFRYESLKHPKAYPKLFSSVKWGQQEIVAKTYQLLARREVWDQSALDVGLTMQLLDCNFSDENVRAIAVQKLESLEDDDVLHYLLQLVQAVKFEPYHDSALARFLLKRGLRNKRIGHFLFWFLRSEIAQSRHYQQRFAVILEAYLRGCGTAMLHDFTQQVQVIEMLQKVTLDIKSLSAEKYDVSSQVISQLKQKLENLQNSQLPESFRVPYDPGLKAGALAIEKCKVMASKKKPLWLEFKCADPTALSNETIGIIFKHGDDLRQDMLILQILRIMESIWETESLDLCLLPYGCISTGDKIGMIEIVKDATTIAKIQQSTVGNTGAFKDEVLNHWLKEKSPTEEKFQAAVERFVYSCAGYCVATFVLGIGDRHNDNIMITETGNLFHIDFGHILGNYKSFLGINKERVPFVLTPDFLFVMGTSGKKTSPHFQKFQDICVKAYLALRHHTNLLIILFSMMLMTGMPQLTSKEDIEYIRDALTVGKNEEDAKKYFLDQIEVCRDKGWTVQFNWFLHLVLGIKQGEKHSA\", \"result\": [{\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 10000.0}, {\"smile\": \"Cc1c(Cc2c(CO)nc3c(-c4ncc[nH]4)cc(N4CCOCC4)nn23)cccc1C(F)(F)F\", \"value\": 4786.0}, {\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCN(CC)CCNC(=O)c1c(C)[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c1C, Cc1c(Cc2c(CO)nc3c(-c4ncc[nH]4)cc(N4CCOCC4)nn23)cccc1C(F)(F)F, COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C and the protein are 10000.0, 4786.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Determine the affinity value of the drugs for the target protein 'MTFRDILSVTFEGPRSSSSTGGSGAGGGAGTVGPEGGAVGGVPGATGGGAVVGTGSGEDNQSSTGEPGAAASGEVNGSAAVGGLVVSAQGVGVGVFLAAFILTAVAGNLLVILSVACNRHLQTVTNYFIVNLAVADLLLSAAVLPFSATMEVLGFWAFGRTFCDVWAAVDVLCCTASILSLCTISVDRYVGVRHSLKYPAIMTERKAAAILALLWAVALVVSVGPLLGWKEPVPPDERFCGITEEVGYAIFSSVCSFYLPMAVIVVMYCRVYVVARSTTRSLEAGIKREPGKASEVVLRIHCRGAATSAKGYPGTQSSKGHTLRSSLSVRLLKFSREKKAAKTLAIVVGVFVLCWFPFFFVLPLGSLFPQLKPSEGVFKVIFWLGYFNSCVNPLIYPCSSREFKRAFLRLLRCQCRRRRRRLWAVYGHHWRASTGDARSDCAPSPRIAPPGAPLALTAHPGAGSADTPETQDSVSSSRKPASALREWRLLGPLQRPTTQLRAKVSSLSHKIRSGARRAETACALRSEVEAVSLNVPQDGAEAVICQAYEPGDYSNLRETDI'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTFRDILSVTFEGPRSSSSTGGSGAGGGAGTVGPEGGAVGGVPGATGGGAVVGTGSGEDNQSSTGEPGAAASGEVNGSAAVGGLVVSAQGVGVGVFLAAFILTAVAGNLLVILSVACNRHLQTVTNYFIVNLAVADLLLSAAVLPFSATMEVLGFWAFGRTFCDVWAAVDVLCCTASILSLCTISVDRYVGVRHSLKYPAIMTERKAAAILALLWAVALVVSVGPLLGWKEPVPPDERFCGITEEVGYAIFSSVCSFYLPMAVIVVMYCRVYVVARSTTRSLEAGIKREPGKASEVVLRIHCRGAATSAKGYPGTQSSKGHTLRSSLSVRLLKFSREKKAAKTLAIVVGVFVLCWFPFFFVLPLGSLFPQLKPSEGVFKVIFWLGYFNSCVNPLIYPCSSREFKRAFLRLLRCQCRRRRRRLWAVYGHHWRASTGDARSDCAPSPRIAPPGAPLALTAHPGAGSADTPETQDSVSSSRKPASALREWRLLGPLQRPTTQLRAKVSSLSHKIRSGARRAETACALRSEVEAVSLNVPQDGAEAVICQAYEPGDYSNLRETDI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTFRDILSVTFEGPRSSSSTGGSGAGGGAGTVGPEGGAVGGVPGATGGGAVVGTGSGEDNQSSTGEPGAAASGEVNGSAAVGGLVVSAQGVGVGVFLAAFILTAVAGNLLVILSVACNRHLQTVTNYFIVNLAVADLLLSAAVLPFSATMEVLGFWAFGRTFCDVWAAVDVLCCTASILSLCTISVDRYVGVRHSLKYPAIMTERKAAAILALLWAVALVVSVGPLLGWKEPVPPDERFCGITEEVGYAIFSSVCSFYLPMAVIVVMYCRVYVVARSTTRSLEAGIKREPGKASEVVLRIHCRGAATSAKGYPGTQSSKGHTLRSSLSVRLLKFSREKKAAKTLAIVVGVFVLCWFPFFFVLPLGSLFPQLKPSEGVFKVIFWLGYFNSCVNPLIYPCSSREFKRAFLRLLRCQCRRRRRRLWAVYGHHWRASTGDARSDCAPSPRIAPPGAPLALTAHPGAGSADTPETQDSVSSSRKPASALREWRLLGPLQRPTTQLRAKVSSLSHKIRSGARRAETACALRSEVEAVSLNVPQDGAEAVICQAYEPGDYSNLRETDI\", \"result\": [{\"smile\": \"COc1cccc(OC)c1OCCNCC1CO[C@](C)(c2ccccc2)O1\", \"value\": 128.82}, {\"smile\": \"COc1cc2nc(N(C)CCCCCCN(C)C(=O)c3ccccc3CNCCCCCCN)nc(N)c2cc1OC\", \"value\": 3.16}, {\"smile\": \"COc1cc2nc(N(C)CCCCCCN(C)C(=O)c3ccc(CCl)o3)nc(N)c2cc1OC\", \"value\": 0.407}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cccc(OC)c1OCCNCC1CO[C@](C)(c2ccccc2)O1, COc1cc2nc(N(C)CCCCCCN(C)C(=O)c3ccccc3CNCCCCCCN)nc(N)c2cc1OC, COc1cc2nc(N(C)CCCCCCN(C)C(=O)c3ccc(CCl)o3)nc(N)c2cc1OC and the protein are 128.82, 3.16 and 0.407."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you forecast the affinity between the drugs and the target protein 'MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTRDEALPDSHSAQDFYENYEPKEILGRGVSSVVRRCIHKPTSQEYAVKVIDVTGGGSFSPEEVRELREATLKEVDILRKVSGHPNIIQLKDTYETNTFFFLVFDLMKRGELFDYLTEKVTLSEKETRKIMRALLEVICTLHKLNIVHRDLKPENILLDDNMNIKLTDFGFSCQLEPGERLREVCGTPSYLAPEIIECSMNEDHPGYGKEVDMWSTGVIMYTLLAGSPPFWHRKQMLMLRMIMSGNYQFGSPEWDDYSDTVKDLVSRFLVVQPQNRYTAEEALAHPFFQQYLVEEVRHFSPRGKFKVIALTVLASVRIYYQYRRVKPVTREIVIRDPYALRPLRRLIDAYAFRIYGHWVKKGQQQNRAALFENTPKAVLLSLAEEDY\", \"result\": [{\"smile\": \"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\", \"value\": 10000.0}, {\"smile\": \"CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1, CC(C)N1NC(=C2C=c3cc(O)ccc3=N2)c2c(N)ncnc21, O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drugs and the target protein 'MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 720.0}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 990.0}, {\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C, COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1, Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1 and the protein are 720.0, 990.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the affinity value connecting the drugs to the target protein 'MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASDSPARSLDEIDLSALRDPAGIFELVELVGNGTYGQVYKGRHVKTGQLAAIKVMDVTGDEEEEIKQEINMLKKYSHHRNIATYYGAFIKKNPPGMDDQLWLVMEFCGAGSVTDLIKNTKGNTLKEEWIAYICREILRGLSHLHQHKVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDFKSDLWSLGITAIEMAEGAPPLCDMHPMRALFLIPRNPAPRLKSKKWSKKFQSFIESCLVKNHSQRPATEQLMKHPFIRDQPNERQVRIQLKDHIDRTKKKRGEKDETEYEYSGSEEEEEENDSGEPSSILNLPGESTLRRDFLRLQLANKERSEALRRQQLEQQQRENEEHKRQLLAERQKRIEEQKEQRRRLEEQQRREKELRKQQEREQRRHYEEQMRREEERRRAEHEQEYIRRQLEEEQRQLEILQQQLLHEQALLLEYKRKQLEEQRQAERLQRQLKQERDYLVSLQHQRQEQRPVEKKPLYHYKEGMSPSEKPAWAKEVEERSRLNRQSSPAMPHKVANRISDPNLPPRSESFSISGVQPARTPPMLRPVDPQIPHLVAVKSQGPALTASQSVHEQPTKGLSGFQEALNVTSHRVEMPRQNSDPTSENPPLPTRIEKFDRSSWLRQEEDIPPKVPQRTTSISPALARKNSPGNGSALGPRLGSQPIRASNPDLRRTEPILESPLQRTSSGSSSSSSTPSSQPSSQGGSQPGSQAGSSERTRVRANSKSEGSPVLPHEPAKVKPEESRDITRPSRPASYKKAIDEDLTALAKELRELRIEETNRPMKKVTDYSSSSEESESSEEEEEDGESETHDGTVAVSDIPRLIPTGAPGSNEQYNVGMVGTHGLETSHADSFSGSISREGTLMIRETSGEKKRSGHSDSNGFAGHINLPDLVQQSHSPAGTPTEGLGRVSTHSQEMDSGTEYGMGSSTKASFTPFVDPRVYQTSPTDEDEEDEESSAAALFTSELLRQEQAKLNEARKISVVNVNPTNIRPHSDTPEIRKYKKRFNSEILCAALWGVNLLVGTENGLMLLDRSGQGKVYNLINRRRFQQMDVLEGLNVLVTISGKKNKLRVYYLSWLRNRILHNDPEVEKKQGWITVGDLEGCIHYKVVKYERIKFLVIALKNAVEIYAWAPKPYHKFMAFKSFADLQHKPLLVDLTVEEGQRLKVIFGSHTGFHVIDVDSGNSYDIYIPSHIQGNITPHAIVILPKTDGMEMLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIHSNQIMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVFFMTLNRNSMMNW\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 1200.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21, CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12, COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1 and the protein are 10000.0, 10000.0 and 1200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity value you predict for the drugs and the target protein 'MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDRAPQRQHRASRELLAAKKTHTSQIEVIPCKICGDKSSGIHYGVITCEGCKGFFRRSQRCNAAYSCTRQQNCPIDRTSRNRCQHCRLQKCLALGMSRDAVKFGRMSKKQRDSLHAEVQKQLQQRQQQQQEPVVKTPPAGAQGADTLTYTLGLPDGQLPLGSSPDLPEASACPPGLLKASGSGPSYSNNLAKAGLNGASCHLEYSPERGKAEGRESFYSTGSQLTPDRCGLRFEEHRHPGLGELGQGPDSYGSPSFRSTPEAPYASLTEIEHLVQSVCKSYRETCQLRLEDLLRQRSNIFSREEVTGYQRKSMWEMWERCAHHLTEAIQYVVEFAKRLSGFMELCQNDQIVLLKAGAMEVVLVRMCRAYNADNRTVFFEGKYGGMELFRALGCSELISSIFDFSHSLSALHFSEDEIALYTALVLINAHRPGLQEKRKVEQLQYNLELAFHHHLCKTHRQSILAKLPPKGKLRSLCSQHVERLQIFQHLHPIVVQAAFPPLYKELFSTETESPVGLSK\", \"result\": [{\"smile\": \"COc1cccc(CN(C)C(=O)CCCN2c3ccccc3N(Cc3ccc(C(O)(C(F)(F)F)C(F)(F)F)cc3)S2(=O)=O)c1\", \"value\": 480.0}, {\"smile\": \"COc1nc2ccc([C@@](O)(c3ccc(C)nc3C)c3cnnn3C)cc2c(Cl)c1OCC1CC1(F)F\", \"value\": 8.2}, {\"smile\": \"Cc1ccc(C(O)(c2ccc3nc(Cl)c(CC(F)(F)F)c(N4CCC4)c3c2)c2cnnn2C)c(C)n1\", \"value\": 24000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cccc(CN(C)C(=O)CCCN2c3ccccc3N(Cc3ccc(C(O)(C(F)(F)F)C(F)(F)F)cc3)S2(=O)=O)c1, COc1nc2ccc([C@@](O)(c3ccc(C)nc3C)c3cnnn3C)cc2c(Cl)c1OCC1CC1(F)F, Cc1ccc(C(O)(c2ccc3nc(Cl)c(CC(F)(F)F)c(N4CCC4)c3c2)c2cnnn2C)c(C)n1 and the protein are 480.0, 8.2 and 24000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the binding affinity between the drugs and the target protein 'MSEESDMDKAIKETSILEEYSINWTQKLGAGISGPVRVCVKKSTQERFALKILLDRPKARNEVRLHMMCATHPNIVQIIEVFANSVQFPHESSPRARLLIVMEMMEGGELFHRISQHRHFTEKQASQVTKQIALALRHCHLLNIAHRDLKPENLLFKDNSLDAPVKLCDFGFAKIDQGDLMTPQFTPYYVAPQVLEAQRRHQKEKSGIIPTSPTPYTYNKSCDLWSLGVIIYVMLCGYPPFYSKHHSRTIPKDMRRKIMTGSFEFPEEEWSQISEMAKDVVRKLLKVKPEERLTIEGVLDHPWLNSTEALDNVLPSAQLMMDKAVVAGIQQAHAEQLANMRIQDLKVSLKPLHSVNNPILRKRKLLGTKPKDSVYIHDHENGAEDSNVALEKLRDVIAQCILPQAGKGENEDEKLNEVMQEAWKYNRECKLLRDTLQSFSWNGRGFTDKVDRLKLAEIVKQVIEEQTTSHESQ'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSEESDMDKAIKETSILEEYSINWTQKLGAGISGPVRVCVKKSTQERFALKILLDRPKARNEVRLHMMCATHPNIVQIIEVFANSVQFPHESSPRARLLIVMEMMEGGELFHRISQHRHFTEKQASQVTKQIALALRHCHLLNIAHRDLKPENLLFKDNSLDAPVKLCDFGFAKIDQGDLMTPQFTPYYVAPQVLEAQRRHQKEKSGIIPTSPTPYTYNKSCDLWSLGVIIYVMLCGYPPFYSKHHSRTIPKDMRRKIMTGSFEFPEEEWSQISEMAKDVVRKLLKVKPEERLTIEGVLDHPWLNSTEALDNVLPSAQLMMDKAVVAGIQQAHAEQLANMRIQDLKVSLKPLHSVNNPILRKRKLLGTKPKDSVYIHDHENGAEDSNVALEKLRDVIAQCILPQAGKGENEDEKLNEVMQEAWKYNRECKLLRDTLQSFSWNGRGFTDKVDRLKLAEIVKQVIEEQTTSHESQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSEESDMDKAIKETSILEEYSINWTQKLGAGISGPVRVCVKKSTQERFALKILLDRPKARNEVRLHMMCATHPNIVQIIEVFANSVQFPHESSPRARLLIVMEMMEGGELFHRISQHRHFTEKQASQVTKQIALALRHCHLLNIAHRDLKPENLLFKDNSLDAPVKLCDFGFAKIDQGDLMTPQFTPYYVAPQVLEAQRRHQKEKSGIIPTSPTPYTYNKSCDLWSLGVIIYVMLCGYPPFYSKHHSRTIPKDMRRKIMTGSFEFPEEEWSQISEMAKDVVRKLLKVKPEERLTIEGVLDHPWLNSTEALDNVLPSAQLMMDKAVVAGIQQAHAEQLANMRIQDLKVSLKPLHSVNNPILRKRKLLGTKPKDSVYIHDHENGAEDSNVALEKLRDVIAQCILPQAGKGENEDEKLNEVMQEAWKYNRECKLLRDTLQSFSWNGRGFTDKVDRLKLAEIVKQVIEEQTTSHESQ\", \"result\": [{\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 10000.0}, {\"smile\": \"Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21, Cc1cnc(Nc2ccc(OCCN3CCCC3)cc2)nc1Nc1cccc(S(=O)(=O)NC(C)(C)C)c1, O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted affinity of the drugs for the target protein 'MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGCIKSKGKDSLSDDGVDLKTQPVRNTERTIYVRDPTSNKQQRPVPESQLLPGQRFQTKDPEEQGDIVVALYPYDGIHPDDLSFKKGEKMKVLEEHGEWWKAKSLLTKKEGFIPSNYVAKLNTLETEEWFFKDITRKDAERQLLAPGNSAGAFLIRESETLKGSFSLSVRDFDPVHGDVIKHYKIRSLDNGGYYISPRITFPCISDMIKHYQKQADGLCRRLEKACISPKPQKPWDKDAWEIPRESIKLVKRLGAGQFGEVWMGYYNNSTKVAVKTLKPGTMSVQAFLEEANLMKTLQHDKLVRLYAVVTREEPIYIITEYMAKGSLLDFLKSDEGGKVLLPKLIDFSAQIAEGMAYIERKNYIHRDLRAANVLVSESLMCKIADFGLARVIEDNEYTAREGAKFPIKWTAPEAINFGCFTIKSDVWSFGILLYEIVTYGKIPYPGRTNADVMTALSQGYRMPRVENCPDELYDIMKMCWKEKAEERPTFDYLQSVLDDFYTATEGQYQQQP\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 270.0}, {\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 940.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1, COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1, COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1 and the protein are 270.0, 10000.0 and 940.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding affinity for the drugs and the target protein 'MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA\", \"result\": [{\"smile\": \"CC(C)C1NC(=O)C(Cc2ccccc2)NC(=O)C(Cc2ccc(O)cc2)NC(=O)CC2(CCCCC2)SSCC(C(=O)N2CCCC2C(=O)NC(CCCN=C(N)N)C(=O)NCC(N)=O)NC(=O)C(CC(N)=O)NC1=O\", \"value\": 7.08}, {\"smile\": \"CC(=O)C[C@@H]1NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](C(c2ccccc2)c2ccccc2)NC(=O)[C@@H](N)CSSC[C@@H](C(=O)N2CCC[C@H]2C(=O)N[C@H](CCCN=C(C)C)C(=O)NCC(N)=O)NC1=O\", \"value\": 14.79}, {\"smile\": \"NC(=O)CC[C@@H]1NC(=O)[C@H](Cc2ccc(O)cc2)NC(=O)[C@H](C(c2ccccc2)c2ccccc2)NC(=O)CCSSC[C@H](C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCCN=C(N)N)C(=O)NCC(N)=O)NC(=O)[C@H](CC(N)=O)NC1=O\", \"value\": 58.88}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC(C)C1NC(=O)C(Cc2ccccc2)NC(=O)C(Cc2ccc(O)cc2)NC(=O)CC2(CCCCC2)SSCC(C(=O)N2CCCC2C(=O)NC(CCCN=C(N)N)C(=O)NCC(N)=O)NC(=O)C(CC(N)=O)NC1=O, CC(=O)C[C@@H]1NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H](Cc2ccccc2)NC(=O)[C@@H](C(c2ccccc2)c2ccccc2)NC(=O)[C@@H](N)CSSC[C@@H](C(=O)N2CCC[C@H]2C(=O)N[C@H](CCCN=C(C)C)C(=O)NCC(N)=O)NC1=O, NC(=O)CC[C@@H]1NC(=O)[C@H](Cc2ccc(O)cc2)NC(=O)[C@H](C(c2ccccc2)c2ccccc2)NC(=O)CCSSC[C@H](C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCCN=C(N)N)C(=O)NCC(N)=O)NC(=O)[C@H](CC(N)=O)NC1=O and the protein are 7.08, 14.79 and 58.88."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an estimate of the affinity between the drugs and the target protein 'MAMTGSTPCSSMSNHTKERVTMTKVTLENFYSNLIAQHEEREMRQKKLEKVMEEEGLKDEEKRLRRSAHARKETEFLRLKRTRLGLEDFESLKVIGRGAFGEVRLVQKKDTGHVYAMKILRKADMLEKEQVGHIRAERDILVEADSLWVVKMFYSFQDKLNLYLIMEFLPGGDMMTLLMKKDTLTEEETQFYIAETVLAIDSIHQLGFIHRDIKPDNLLLDSKGHVKLSDFGLCTGLKKAHRTEFYRNLNHSLPSDFTFQNMNSKRKAETWKRNRRQLAFSTVGTPDYIAPEVFMQTGYNKLCDWWSLGVIMYEMLIGYPPFCSETPQETYKKVMNWKETLTFPPEVPISEKAKDLILRFCCEWEHRIGAPGVEEIKSNSFFEGVDWEHIRERPAAISIEIKSIDDTSNFDEFPESDILKPTVATSNHPETDYKNKDWVFINYTYKRFEGLTARGAIPSYMKAAK'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAMTGSTPCSSMSNHTKERVTMTKVTLENFYSNLIAQHEEREMRQKKLEKVMEEEGLKDEEKRLRRSAHARKETEFLRLKRTRLGLEDFESLKVIGRGAFGEVRLVQKKDTGHVYAMKILRKADMLEKEQVGHIRAERDILVEADSLWVVKMFYSFQDKLNLYLIMEFLPGGDMMTLLMKKDTLTEEETQFYIAETVLAIDSIHQLGFIHRDIKPDNLLLDSKGHVKLSDFGLCTGLKKAHRTEFYRNLNHSLPSDFTFQNMNSKRKAETWKRNRRQLAFSTVGTPDYIAPEVFMQTGYNKLCDWWSLGVIMYEMLIGYPPFCSETPQETYKKVMNWKETLTFPPEVPISEKAKDLILRFCCEWEHRIGAPGVEEIKSNSFFEGVDWEHIRERPAAISIEIKSIDDTSNFDEFPESDILKPTVATSNHPETDYKNKDWVFINYTYKRFEGLTARGAIPSYMKAAK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAMTGSTPCSSMSNHTKERVTMTKVTLENFYSNLIAQHEEREMRQKKLEKVMEEEGLKDEEKRLRRSAHARKETEFLRLKRTRLGLEDFESLKVIGRGAFGEVRLVQKKDTGHVYAMKILRKADMLEKEQVGHIRAERDILVEADSLWVVKMFYSFQDKLNLYLIMEFLPGGDMMTLLMKKDTLTEEETQFYIAETVLAIDSIHQLGFIHRDIKPDNLLLDSKGHVKLSDFGLCTGLKKAHRTEFYRNLNHSLPSDFTFQNMNSKRKAETWKRNRRQLAFSTVGTPDYIAPEVFMQTGYNKLCDWWSLGVIMYEMLIGYPPFCSETPQETYKKVMNWKETLTFPPEVPISEKAKDLILRFCCEWEHRIGAPGVEEIKSNSFFEGVDWEHIRERPAAISIEIKSIDDTSNFDEFPESDILKPTVATSNHPETDYKNKDWVFINYTYKRFEGLTARGAIPSYMKAAK\", \"result\": [{\"smile\": \"Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1\", \"value\": 700.0}, {\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 440.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NC[C@H](O)CN1CCOCC1, C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12, COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1 and the protein are 700.0, 10000.0 and 440.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected affinity value for the drugs-target protein 'MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA' interaction? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEGTPAANWSVELDLGSGVPPGEEGNRTAGPPQRNEALARVEVAVLCLILFLALSGNACVLLALRTTRHKHSRLFFFMKHLSIADLVVAVFQVLPQLLWDITFRFYGPDLLCRLVKYLQVVGMFASTYLLLLMSLDRCLAICQPLRSLRRRTDRLAVLGTWLGCLVASAPQVHIFSLREVADGVFDCWAVFIQPWGPKAYVTWITLAVYIVPVIVLAACYGLISFKIWQNLRLKTAAAAAAAEGNDAAGGAGRAALARVSSVKLISKAKIRTVKMTFIIVLAFIVCWTPFFFVQMWSVWDVNAPKEASAFIIAMLLASLNSCCNPWIYMLFTGHLFHELVQRFFCCSARYLKGSRPGETSVSKKSNSSTFVLSRRSSSQRSCSQPSSA\", \"result\": [{\"smile\": \"CC[C@H](C)[C@H](NC(=O)[C@@H](Cc1c[nH]c2ccccc12)NC(=O)CC1(S)CCCCC1)C(=O)N[C@H](CC(N)=O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CS)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(N)=O\", \"value\": 3890.45}, {\"smile\": \"CCOc1ccc(C[C@@H]2NC(=O)CCSSC[C@@H](C(=O)N3Cc4ccccc4C[C@@H]3C(=O)N[C@@H](CC(C)C)C(=O)N3Cc4ccccc4C[C@@H]3C(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H]([C@@H](C)CC)NC2=O)cc1\", \"value\": 8.51}, {\"smile\": \"CCN1C(=O)CC2(CCCCC2)SSC[C@@H](C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(N)=O)NC(=O)[C@@H](CC(N)=O)NC(=O)[C@H](C(C)C)NC(=O)[C@@H](Cc2ccccc2)NC(=O)[C@H]1Cc1ccc(O)cc1\", \"value\": 4.17}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC[C@H](C)[C@H](NC(=O)[C@@H](Cc1c[nH]c2ccccc12)NC(=O)CC1(S)CCCCC1)C(=O)N[C@H](CC(N)=O)C(=O)N[C@@H](CC(N)=O)C(=O)N[C@@H](CS)C(=O)N1CCC[C@@H]1C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(N)=O, CCOc1ccc(C[C@@H]2NC(=O)CCSSC[C@@H](C(=O)N3Cc4ccccc4C[C@@H]3C(=O)N[C@@H](CC(C)C)C(=O)N3Cc4ccccc4C[C@@H]3C(N)=O)NC(=O)[C@H](CC(N)=O)NC(=O)[C@H](CCC(N)=O)NC(=O)[C@H]([C@@H](C)CC)NC2=O)cc1, CCN1C(=O)CC2(CCCCC2)SSC[C@@H](C(=O)N2CCC[C@H]2C(=O)N[C@@H](CCCNC(=N)N)C(=O)NCC(N)=O)NC(=O)[C@@H](CC(N)=O)NC(=O)[C@H](C(C)C)NC(=O)[C@@H](Cc2ccccc2)NC(=O)[C@H]1Cc1ccc(O)cc1 and the protein are 3890.45, 8.51 and 4.17."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drugs and the target protein 'MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MWPNGSSLGPCFRPTNITLEERRLIASPWFAASFCVVGLASNLLALSVLAGARQGGSHTRSSFLTFLCGLVLTDFLGLLVTGTIVVSQHAALFEWHAVDPGCRLCRFMGVVMIFFGLSPLLLGAAMASERYLGITRPFSRPAVASQRRAWATVGLVWAAALALGLLPLLGVGRYTVQYPGSWCFLTLGAESGDVAFGLLFSMLGGLSVGLSFLLNTVSVATLCHVYHGQEAAQQRPRDSEVEMMAQLLGIMVVASVCWLPLLVFIAQTVLRNPPAMSPAGQLSRTTEKELLIYLRVATWNQILDPWVYILFRRAVLRRLQPRLSTRPRSLSLQPQLTQRSGLQ\", \"result\": [{\"smile\": \"O=C(O)CCc1ccccc1CC1C2CCC(O2)C1c1nc(C(=O)NCCc2ccc(Cl)cc2)co1\", \"value\": 1.3}, {\"smile\": \"O=C(O)CC/C=C\\CO[C@@H]1[C@@H](N2CCCCCC2)CC[C@H]1OCc1ccc(-c2ccccc2CO)cc1\", \"value\": 6.31}, {\"smile\": \"O=C1CC(=O)C(Cc2cccc(CCNS(=O)(=O)c3ccc(Cl)cc3)c2)C1\", \"value\": 203.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs O=C(O)CCc1ccccc1CC1C2CCC(O2)C1c1nc(C(=O)NCCc2ccc(Cl)cc2)co1, O=C(O)CC/C=C\\CO[C@@H]1[C@@H](N2CCCCCC2)CC[C@H]1OCc1ccc(-c2ccccc2CO)cc1, O=C1CC(=O)C(Cc2cccc(CCNS(=O)(=O)c3ccc(Cl)cc3)c2)C1 and the protein are 1.3, 6.31 and 203.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value do you project for the drugs and the target protein 'MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDHPSREKDERQRTTKPMAQRSAHCSRPSGSSSSSGVLMVGPNFRVGKKIGCGNFGELRLGKNLYTNEYVAIKLEPIKSRAPQLHLEYRFYKQLGSAGEGLPQVYYFGPCGKYNAMVLELLGPSLEDLFDLCDRTFTLKTVLMIAIQLLSRMEYVHSKNLIYRDVKPENFLIGRQGNKKEHVIHIIDFGLAKEYIDPETKKHIPYREHKSLTGTARYMSINTHLGKEQSRRDDLEALGHMFMYFLRGSLPWQGLKADTLKERYQKIGDTKRNTPIEALCENFPEEMATYLRYVRRLDFFEKPDYEYLRTLFTDLFEKKGYTFDYAYDWVGRPIPTPVGSVHVDSGASAITRESHTHRDRPSQQQPLRNQVVSSTNGELNVDDPTGAHSNAPITAHAEVEVVEEAKCCCFFKRKRKKTAQRHK\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 10000.0}, {\"smile\": \"CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21\", \"value\": 10000.0}, {\"smile\": \"O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1, CCn1c(-c2nonc2N)nc2c(C#CC(C)(C)O)ncc(OC[C@H]3CCCNC3)c21, O=NC1CCc2cc(-c3cn(CCO)nc3-c3ccncc3)ccc21 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the affinity between the drugs and the target protein 'MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MMEELHSLDPRRQELLEARFTGVGVSKGPLNSESSNQSLCSVGSLSDKEVETPEKKQNDQRNRKRKAEPYETSQGKGTPRGHKISDYFEFAGGSAPGTSPGRSVPPVARSSPQHSLSNPLPRRVEQPLYGLDGSAAKEATEEQSALPTLMSVMLAKPRLDTEQLAQRGAGLCFTFVSAQQNSPSSTGSGNTEHSCSSQKQISIQHRQTQSDLTIEKISALENSKNSDLEKKEGRIDDLLRANCDLRRQIDEQQKMLEKYKERLNRCVTMSKKLLIEKSKQEKMACRDKSMQDRLRLGHFTTVRHGASFTEQWTDGYAFQNLIKQQERINSQREEIERQRKMLAKRKPPAMGQAPPATNEQKQRKSKTNGAENETPSSGNTELKDTAPALGAHSLLRLTLAEYHEQEEIFKLRLGHLKKEEAEIQAELERLERVRNLHIRELKRIHNEDNSQFKDHPTLNDRYLLLHLLGRGGFSEVYKAFDLTEQRYVAVKIHQLNKNWRDEKKENYHKHACREYRIHKELDHPRIVKLYDYFSLDTDSFCTVLEYCEGNDLDFYLKQHKLMSEKEARSIIMQIVNALKYLNEIKPPIIHYDLKPGNILLVNGTACGEIKITDFGLSKIMDDDSYNSVDGMELTSQGAGTYWYLPPECFVVGKEPPKISNKVDVWSVGVIFYQCLYGRKPFGHNQSQQDILQENTILKATEVQFPPKPVVTPEAKAFIRRCLAYRKEDRIDVQQLACDPYLLPHIRKSVSTSSPAGAAIASTSGASNNSSSN\", \"result\": [{\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}, {\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1, O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1, CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted binding strength of the drugs with the target protein 'MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNGGHFLRILPDGTVDGTRDRSDQHIQLQLSAESVGEVYIKSTETGQYLAMDTDGLLYGSQTPNEECLFLERLEENHYNTYISKKHAEKNWFVGLKKNGSCKRGPRTHYGQKAILFLPLPVSSD'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNGGHFLRILPDGTVDGTRDRSDQHIQLQLSAESVGEVYIKSTETGQYLAMDTDGLLYGSQTPNEECLFLERLEENHYNTYISKKHAEKNWFVGLKKNGSCKRGPRTHYGQKAILFLPLPVSSD\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAEGEITTFTALTEKFNLPPGNYKKPKLLYCSNGGHFLRILPDGTVDGTRDRSDQHIQLQLSAESVGEVYIKSTETGQYLAMDTDGLLYGSQTPNEECLFLERLEENHYNTYISKKHAEKNWFVGLKKNGSCKRGPRTHYGQKAILFLPLPVSSD\", \"result\": [{\"smile\": \"CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC[C@H]4C[C@@H](O[C@H]5O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@@H]5O[C@H]5O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](O[C@H]6O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](O[C@H]7O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@@H]7OS(=O)(=O)[O-])[C@@H]6OS(=O)(=O)[O-])[C@@H]5OS(=O)(=O)[O-])CC[C@]4(C)[C@H]3CC[C@@]21C\", \"value\": 0.6}, {\"smile\": \"CO[C@H]1O[C@@H](COCC(=O)N(CC(=O)NCCO[C@H]2O[C@@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@H]2OS(=O)(=O)[O-])Cc2ccccc2)[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@H]1OS(=O)(=O)[O-].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+]\", \"value\": 4840.0}, {\"smile\": \"O=P([O-])([O-])OC[C@H]1O[C@H](O[C@@H]2[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]3[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]4[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]5[C@@H](OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H]5OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]4OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]3OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]2OS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H]1OS(=O)(=O)[O-]\", \"value\": 240.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC(C)CCC[C@@H](C)[C@H]1CC[C@H]2[C@@H]3CC[C@H]4C[C@@H](O[C@H]5O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@@H]5O[C@H]5O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](O[C@H]6O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](O[C@H]7O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@@H]7OS(=O)(=O)[O-])[C@@H]6OS(=O)(=O)[O-])[C@@H]5OS(=O)(=O)[O-])CC[C@]4(C)[C@H]3CC[C@@]21C, CO[C@H]1O[C@@H](COCC(=O)N(CC(=O)NCCO[C@H]2O[C@@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@H]2OS(=O)(=O)[O-])Cc2ccccc2)[C@@H](OS(=O)(=O)[O-])[C@H](OS(=O)(=O)[O-])[C@H]1OS(=O)(=O)[O-].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+].[Na+], O=P([O-])([O-])OC[C@H]1O[C@H](O[C@@H]2[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]3[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]4[C@H](OS(=O)(=O)[O-])[C@@H](O[C@@H]5[C@@H](OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H]5OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]4OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]3OS(=O)(=O)[O-])O[C@H](COS(=O)(=O)[O-])[C@H]2OS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H](OS(=O)(=O)[O-])[C@@H]1OS(=O)(=O)[O-] and the protein are 0.6, 4840.0 and 240.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Analyze the affinity value of the drugs toward the target protein 'MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRGARGAWDFLCVLLLLLRVQTGSSQPSVSPGEPSPPSIHPGKSDLIVRVGDEIRLLCTDPGFVKWTFEILDETNENKQNEWITEKAEATNTGKYTCTNKHGLSNSIYVFVRDPAKLFLVDRSLYGKEDNDTLVRCPLTDPEVTNYSLKGCQGKPLPKDLRFIPDPKAGIMIKSVKRAYHRLCLHCSVDQEGKSVLSEKFILKVRPAFKAVPVVSVSKASYLLREGEEFTVTCTIKDVSSSVYSTWKRENSQTKLQEKYNSWHHGDFNYERQATLTISSARVNDSGVFMCYANNTFGSANVTTTLEVVDKGFINIFPMINTTVFVNDGENVDLIVEYEAFPKPEHQQWIYMNRTFTDKWEDYPKSENESNIRYVSELHLTRLKGTEGGTYTFLVSNSDVNAAIAFNVYVNTKPEILTYDRLVNGMLQCVAAGFPEPTIDWYFCPGTEQRCSASVLPVDVQTLNSSGPPFGKLVVQSSIDSSAFKHNGTVECKAYNDVGKTSAYFNFAFKGNNKEQIHPHTLFTPLLIGFVIVAGMMCIIVMILTYKYLQKPMYEVQWKVVEEINGNNYVYIDPTQLPYDHKWEFPRNRLSFGKTLGAGAFGKVVEATAYGLIKSDAAMTVAVKMLKPSAHLTEREALMSELKVLSYLGNHMNIVNLLGACTIGGPTLVITEYCCYGDLLNFLRRKRDSFICSKQEDHAEAALYKNLLHSKESSCSDSTNEYMDMKPGVSYVVPTKADKRRSVRIGSYIERDVTPAIMEDDELALDLEDLLSFSYQVAKGMAFLASKNCIHRDLAARNILLTHGRITKICDFGLARDIKNDSNYVVKGNARLPVKWMAPESIFNCVYTFESDVWSYGIFLWELFSLGSSPYPGMPVDSKFYKMIKEGFRMLSPEHAPAEMYDIMKTCWDADPLKRPTFKQIVQLIEKQISESTNHIYSNLANCSPNRQKPVVDHSVRINSVGSTASSSQPLLVHDDV\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 140.0}, {\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 6.0}, {\"smile\": \"COc1cc(OC)c2cc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)cc3)cnc2c1\", \"value\": 12.5}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1, CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1, COc1cc(OC)c2cc(-c3ccc(CC(=O)Nc4cc(C5(C(F)(F)F)CC5)on4)cc3)cnc2c1 and the protein are 140.0, 6.0 and 12.5."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity rating expected for the drugs and the target protein 'MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGGPGPGEPAAPGAQHFLYEVPPWVMCRFYKVMDALEPADWCQFAALIVRDQTELRLCERSGQRTASVLWPWINRNARVADLVHILTHLQLLRARDIITAWHPPAPLPSPGTTAPRPSSIPAPAEAEAWSPRKLPSSASTFLSPAFPGSQTHSGPELGLVPSPASLWPPPPSPAPSSTKPGPESSVSLLQGARPFPFCWPLCEISRGTHNFSEELKIGEGGFGCVYRAVMRNTVYAVKRLKENADLEWTAVKQSFLTEVEQLSRFRHPNIVDFAGYCAQNGFYCLVYGFLPNGSLEDRLHCQTQACPPLSWPQRLDILLGTARAIQFLHQDSPSLIHGDIKSSNVLLDERLTPKLGDFGLARFSRFAGSSPSQSSMVARTQTVRGTLAYLPEEYIKTGRLAVDTDTFSFGVVVLETLAGQRAVKTHGARTKYLKDLVEEEAEEAGVALRSTQSTLQAGLAADAWAAPIAMQIYKKHLDPRPGPCPPELGLGLGQLACCCLHRRAKRRPPMTQVYERLEKLQAVVAGVPGHSEAASCIPPSPQENSYVSSTGRAHSGAAPWQPLAAPSGASAQAAEQLQRGPNQPVESDESLGGLSAALRSWHLTPSCPLDPAPLREAGCPQGDTAGESSWGSGPGSRPTAVEGLALGSSASSSSEPPQIIINPARQKMVQKLALYEDGALDSLQLLSSSSLPGLGLEQDRQGPEESDEFQS\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\", \"value\": 9.7}, {\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 48.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1, COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC, O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1 and the protein are 10000.0, 9.7 and 48.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the affinity between the drugs and the target protein 'MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1\", \"value\": 790.0}, {\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}, {\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1, CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1, CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1 and the protein are 790.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the affinity between the drugs and the target protein 'MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVSWGRFICLVVVTMATLSLARPSFSLVEDTTLEPEEPPTKYQISQPEVYVAAPGESLEVRCLLKDAAVISWTKDGVHLGPNNRTVLIGEYLQIKGATPRDSGLYACTASRTVDSETWYFMVNVTDAISSGDDEDDTDGAEDFVSENSNNKRAPYWTNTEKMEKRLHAVPAANTVKFRCPAGGNPMPTMRWLKNGKEFKQEHRIGGYKVRNQHWSLIMESVVPSDKGNYTCVVENEYGSINHTYHLDVVERSPHRPILQAGLPANASTVVGGDVEFVCKVYSDAQPHIQWIKHVEKNGSKYGPDGLPYLKVLKAAGVNTTDKEIEVLYIRNVTFEDAGEYTCLAGNSIGISFHSAWLTVLPAPGREKEITASPDYLEIAIYCIGVFLIACMVVTVILCRMKNTTKKPDFSSQPAVHKLTKRIPLRRQVTVSAESSSSMNSNTPLVRITTRLSSTADTPMLAGVSEYELPEDPKWEFPRDKLTLGKPLGEGCFGQVVMAEAVGIDKDKPKEAVTVAVKMLKDDATEKDLSDLVSEMEMMKMIGKHKNIINLLGACTQDGPLYVIVEYASKGNLREYLRARRPPGMEYSYDINRVPEEQMTFKDLVSCTYQLARGMEYLASQKCIHRDLAARNVLVTENNVMKIADFGLARDINNIDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLMWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTNELYMMMRDCWHAVPSQRPTFKQLVEDLDRILTLTTNEEYLDLSQPLEQYSPSYPDTRSSCSSGDDSVFSPDPMPYEPCLPQYPHINGSVKT\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 570.0}, {\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 3600.0}, {\"smile\": \"C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1, CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1, C[C@@H]1CCN(C(=O)CC#N)C[C@@H]1N(C)c1ncnc2[nH]ccc12 and the protein are 570.0, 3600.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction strength between the drugs and the target protein 'MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MELQAARACFALLWGCALAAAAAAQGKEVVLLDFAAAGGELGWLTHPYGKGWDLMQNIMNDMPIYMYSVCNVMSGDQDNWLRTNWVYRGEAERIFIELKFTVRDCNSFPGGASSCKETFNLYYAESDLDYGTNFQKRLFTKIDTIAPDEITVSSDFEARHVKLNVEERSVGPLTRKGFYLAFQDIGACVALLSVRVYYKKCPELLQGLAHFPETIAGSDAPSLATVAGTCVDHAVVPPGGEEPRMHCAVDGEWLVPIGQCLCQAGYEKVEDACQACSPGFFKFEASESPCLECPEHTLPSPEGATSCECEEGFFRAPQDPASMPCTRPPSAPHYLTAVGMGAKVELRWTPPQDSGGREDIVYSVTCEQCWPESGECGPCEASVRYSEPPHGLTRTSVTVSDLEPHMNYTFTVEARNGVSGLVTSRSFRTASVSINQTEPPKVRLEGRSTTSLSVSWSIPPPQQSRVWKYEVTYRKKGDSNSYNVRRTEGFSVTLDDLAPDTTYLVQVQALTQEGQGAGSKVHEFQTLSPEGSGNLAVIGGVAVGVVLLLVLAGVGFFIHRRRKNQRARQSPEDVYFSKSEQLKPLKTYVDPHTYEDPNQAVLKFTTEIHPSCVTRQKVIGAGEFGEVYKGMLKTSSGKKEVPVAIKTLKAGYTEKQRVDFLGEAGIMGQFSHHNIIRLEGVISKYKPMMIITEYMENGALDKFLREKDGEFSVLQLVGMLRGIAAGMKYLANMNYVHRDLAARNILVNSNLVCKVSDFGLSRVLEDDPEATYTTSGGKIPIRWTAPEAISYRKFTSASDVWSFGIVMWEVMTYGERPYWELSNHEVMKAINDGFRLPTPMDCPSAIYQLMMQCWQQERARRPKFADIVSILDKLIRAPDSLKTLADFDPRVSIRLPSTSGSEGVPFRTVSEWLESIKMQQYTEHFMAAGYTAIEKVVQMTNDDIKRIGVRLPGHQKRIAYSLLGLKDQVNTVGIPI\", \"result\": [{\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 10000.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O, CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1, CN(C)CC1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What affinity value is expected between the drugs and the target protein 'MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAEKFESLMNIHGFDLGSRYMDLKPLGCGGNGLVFSAVDNDCDKRVAIKKIVLTDPQSVKHALREIKIIRRLDHDNIVKVFEILGPSGSQLTDDVGSLTELNSVYIVQEYMETDLANVLEQGPLLEEHARLFMYQLLRGLKYIHSANVLHRDLKPANLFINTEDLVLKIGDFGLARIMDPHYSHKGHLSEGLVTKWYRSPRLLLSPNNYTKAIDMWAAGCIFAEMLTGKTLFAGAHELEQMQLILESIPVVHEEDRQELLSVIPVYIRNDMTEPHKPLTQLLPGISREALDFLEQILTFSPMDRLTAEEALSHPYMSIYSFPMDEPISSHPFHIEDEVDDILLMDETHSHIYNWERYHDCQFSEHDWPVHNNFDIDEVQLDPRALSDVTDEEEVQVDPRKYLDGDREKYLEDPAFDTNYSTEPCWQYSDHHENKYCDLECSHTCNYKTRSSSYLDNLVWRESEVNHYYEPKLIIDLSNWKEQSKEKSDKKGKSKCERNGLVKAQIALEEASQQLAGKEREKNQGFDFDSFIAGTIQLSSQHEPTDVVDKLNDLNSSVSQLELKSLISKSVSQEKQEKGMANLAQLEALYQSSWDSQFVSGGEDCFFINQFCEVRKDEQVEKENTYTSYLDKFFSRKEDTEMLETEPVEDGKLGERGHEEGFLNNSGEFLFNKQLESIGIPQFHSPVGSPLKSIQATLTPSAMKSSPQIPHQTYSSILKHLN\", \"result\": [{\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 1700.0}, {\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21, C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1, Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nccc(-c2cccnc2)n1 and the protein are 10000.0, 1700.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted binding value of the drugs with the target protein 'MSSARTPLPTLNERDTEQPTLGHLDSKPSSKSNMIRGRNSATSADEQPHIGNYRLLKTIGKGNFAKVKLARHILTGKEVAVKIIDKTQLNSSSLQKLFREVRIMKVLNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKFIVHRDLKAENLLLDADMNIKIADFGFSNEFTFGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKFLILNPSKRGTLEQIMKDRWMNVGHEDDELKPYVEPLPDYKDPRRTELMVSMGYTREEIQDSLVGQRYNEVMATYLLLGYKSSELEGDTITLKPRPSADLTNSSAPSPSHKVQRSVSANPKQRRFSDQAAGPAIPTSNSYSKKTQSNNAENKRPEEDRESGRKASSTAKVPASPLPGLERKKTTPTPSTNSVLSTSTNRSRNSPLLERASLGQASIQNGKDSLTMPGSRASTASASAAVSAARPRQHQKSMSASVHPNKASGLPPTESNCEVPRPSTAPQRVPVASPSAHNISSSGGAPDRTNFPRGVSSRSTFHAGQLRQVRDQQNLPYGVTPASPSGHSQGRRGASGSIFSKFTSKFVRRNLSFRFARRNLNEPESKDRVETLRPHVVGSGGNDKEKEEFREAKPRSLRFTWSMKTTSSMEPNEMMREIRKVLDANSCQSELHEKYMLLCMHGTPGHEDFVQWEMEVCKLPRLSLNGVRFKRISGTSMAFKNIASKIANELKL'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSARTPLPTLNERDTEQPTLGHLDSKPSSKSNMIRGRNSATSADEQPHIGNYRLLKTIGKGNFAKVKLARHILTGKEVAVKIIDKTQLNSSSLQKLFREVRIMKVLNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKFIVHRDLKAENLLLDADMNIKIADFGFSNEFTFGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKFLILNPSKRGTLEQIMKDRWMNVGHEDDELKPYVEPLPDYKDPRRTELMVSMGYTREEIQDSLVGQRYNEVMATYLLLGYKSSELEGDTITLKPRPSADLTNSSAPSPSHKVQRSVSANPKQRRFSDQAAGPAIPTSNSYSKKTQSNNAENKRPEEDRESGRKASSTAKVPASPLPGLERKKTTPTPSTNSVLSTSTNRSRNSPLLERASLGQASIQNGKDSLTMPGSRASTASASAAVSAARPRQHQKSMSASVHPNKASGLPPTESNCEVPRPSTAPQRVPVASPSAHNISSSGGAPDRTNFPRGVSSRSTFHAGQLRQVRDQQNLPYGVTPASPSGHSQGRRGASGSIFSKFTSKFVRRNLSFRFARRNLNEPESKDRVETLRPHVVGSGGNDKEKEEFREAKPRSLRFTWSMKTTSSMEPNEMMREIRKVLDANSCQSELHEKYMLLCMHGTPGHEDFVQWEMEVCKLPRLSLNGVRFKRISGTSMAFKNIASKIANELKL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSARTPLPTLNERDTEQPTLGHLDSKPSSKSNMIRGRNSATSADEQPHIGNYRLLKTIGKGNFAKVKLARHILTGKEVAVKIIDKTQLNSSSLQKLFREVRIMKVLNHPNIVKLFEVIETEKTLYLVMEYASGGEVFDYLVAHGRMKEKEARAKFRQIVSAVQYCHQKFIVHRDLKAENLLLDADMNIKIADFGFSNEFTFGNKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKKFLILNPSKRGTLEQIMKDRWMNVGHEDDELKPYVEPLPDYKDPRRTELMVSMGYTREEIQDSLVGQRYNEVMATYLLLGYKSSELEGDTITLKPRPSADLTNSSAPSPSHKVQRSVSANPKQRRFSDQAAGPAIPTSNSYSKKTQSNNAENKRPEEDRESGRKASSTAKVPASPLPGLERKKTTPTPSTNSVLSTSTNRSRNSPLLERASLGQASIQNGKDSLTMPGSRASTASASAAVSAARPRQHQKSMSASVHPNKASGLPPTESNCEVPRPSTAPQRVPVASPSAHNISSSGGAPDRTNFPRGVSSRSTFHAGQLRQVRDQQNLPYGVTPASPSGHSQGRRGASGSIFSKFTSKFVRRNLSFRFARRNLNEPESKDRVETLRPHVVGSGGNDKEKEEFREAKPRSLRFTWSMKTTSSMEPNEMMREIRKVLDANSCQSELHEKYMLLCMHGTPGHEDFVQWEMEVCKLPRLSLNGVRFKRISGTSMAFKNIASKIANELKL\", \"result\": [{\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 10000.0}, {\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 10000.0}, {\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F, Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12, CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you rate the affinity of the drugs toward the target protein 'MSSGTMKFNGYLRVRIGEAVGLQPTRWSLRHSLFKKGHQLLDPYLTVSVDQVRVGQTSTKQKTNKPTYNEEFCANVTDGGHLELAVFHETPLGYDHFVANCTLQFQELLRTTGASDTFEGWVDLEPEGKVFVVITLTGSFTEATLQRDRIFKHFTRKRQRAMRRRVHQINGHKFMATYLRQPTYCSHCREFIWGVFGKQGYQCQVCTCVVHKRCHHLIVTACTCQNNINKVDSKIAEQRFGINIPHKFSIHNYKVPTFCDHCGSLLWGIMRQGLQCKICKMNVHIRCQANVAPNCGVNAVELAKTLAGMGLQPGNISPTSKLVSRSTLRRQGKESSKEGNGIGVNSSNRLGIDNFEFIRVLGKGSFGKVMLARVKETGDLYAVKVLKKDVILQDDDVECTMTEKRILSLARNHPFLTQLFCCFQTPDRLFFVMEFVNGGDLMFHIQKSRRFDEARARFYAAEIISALMFLHDKGIIYRDLKLDNVLLDHEGHCKLADFGMCKEGICNGVTTATFCGTPDYIAPEILQEMLYGPAVDWWAMGVLLYEMLCGHAPFEAENEDDLFEAILNDEVVYPTWLHEDATGILKSFMTKNPTMRLGSLTQGGEHAILRHPFFKEIDWAQLNHRQIEPPFRPRIKSREDVSNFDPDFIKEEPVLTPIDEGHLPMINQDEFRNFSYVSPELQP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSGTMKFNGYLRVRIGEAVGLQPTRWSLRHSLFKKGHQLLDPYLTVSVDQVRVGQTSTKQKTNKPTYNEEFCANVTDGGHLELAVFHETPLGYDHFVANCTLQFQELLRTTGASDTFEGWVDLEPEGKVFVVITLTGSFTEATLQRDRIFKHFTRKRQRAMRRRVHQINGHKFMATYLRQPTYCSHCREFIWGVFGKQGYQCQVCTCVVHKRCHHLIVTACTCQNNINKVDSKIAEQRFGINIPHKFSIHNYKVPTFCDHCGSLLWGIMRQGLQCKICKMNVHIRCQANVAPNCGVNAVELAKTLAGMGLQPGNISPTSKLVSRSTLRRQGKESSKEGNGIGVNSSNRLGIDNFEFIRVLGKGSFGKVMLARVKETGDLYAVKVLKKDVILQDDDVECTMTEKRILSLARNHPFLTQLFCCFQTPDRLFFVMEFVNGGDLMFHIQKSRRFDEARARFYAAEIISALMFLHDKGIIYRDLKLDNVLLDHEGHCKLADFGMCKEGICNGVTTATFCGTPDYIAPEILQEMLYGPAVDWWAMGVLLYEMLCGHAPFEAENEDDLFEAILNDEVVYPTWLHEDATGILKSFMTKNPTMRLGSLTQGGEHAILRHPFFKEIDWAQLNHRQIEPPFRPRIKSREDVSNFDPDFIKEEPVLTPIDEGHLPMINQDEFRNFSYVSPELQP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSGTMKFNGYLRVRIGEAVGLQPTRWSLRHSLFKKGHQLLDPYLTVSVDQVRVGQTSTKQKTNKPTYNEEFCANVTDGGHLELAVFHETPLGYDHFVANCTLQFQELLRTTGASDTFEGWVDLEPEGKVFVVITLTGSFTEATLQRDRIFKHFTRKRQRAMRRRVHQINGHKFMATYLRQPTYCSHCREFIWGVFGKQGYQCQVCTCVVHKRCHHLIVTACTCQNNINKVDSKIAEQRFGINIPHKFSIHNYKVPTFCDHCGSLLWGIMRQGLQCKICKMNVHIRCQANVAPNCGVNAVELAKTLAGMGLQPGNISPTSKLVSRSTLRRQGKESSKEGNGIGVNSSNRLGIDNFEFIRVLGKGSFGKVMLARVKETGDLYAVKVLKKDVILQDDDVECTMTEKRILSLARNHPFLTQLFCCFQTPDRLFFVMEFVNGGDLMFHIQKSRRFDEARARFYAAEIISALMFLHDKGIIYRDLKLDNVLLDHEGHCKLADFGMCKEGICNGVTTATFCGTPDYIAPEILQEMLYGPAVDWWAMGVLLYEMLCGHAPFEAENEDDLFEAILNDEVVYPTWLHEDATGILKSFMTKNPTMRLGSLTQGGEHAILRHPFFKEIDWAQLNHRQIEPPFRPRIKSREDVSNFDPDFIKEEPVLTPIDEGHLPMINQDEFRNFSYVSPELQP\", \"result\": [{\"smile\": \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\", \"value\": 10000.0}, {\"smile\": \"Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F\", \"value\": 1.0}, {\"smile\": \"CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1, Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F, CN1CCN(C(=O)c2cc3cc(Cl)ccc3[nH]2)CC1 and the protein are 10000.0, 1.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you foresee between the drugs and the target protein 'METFCFRVSFWVALLGCVISDNPESHSTNLSTHVDDFTTFRGTEFSLVVTTHRPTNLALPSNGSMHNYCPQQTKITSAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFENHDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLVTAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEEHKTCMLNATSKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCYQSKSLMTSVPMNGTSIQWKNHEQNNHNTERSSHKDSIN'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"METFCFRVSFWVALLGCVISDNPESHSTNLSTHVDDFTTFRGTEFSLVVTTHRPTNLALPSNGSMHNYCPQQTKITSAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFENHDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLVTAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEEHKTCMLNATSKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCYQSKSLMTSVPMNGTSIQWKNHEQNNHNTERSSHKDSIN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"METFCFRVSFWVALLGCVISDNPESHSTNLSTHVDDFTTFRGTEFSLVVTTHRPTNLALPSNGSMHNYCPQQTKITSAFKYINTVISCTIFIVGMVGNATLLRIIYQNKCMRNGPNALIASLALGDLIYVVIDLPINVFKLLAGRWPFENHDFGVFLCKLFPFLQKSSVGITVLNLCALSVDRYRAVASWSRVQGIGIPLVTAIEIVSIWILSFILAIPEAIGFVMVPFEYKGEEHKTCMLNATSKFMEFYQDVKDWWLFGFYFCMPLVCTAIFYTLMTCEMLNRRNGSLRIALSEHLKQRREVAKTVFCLVVIFALCWFPLHLSRILKKTVYDEMDKNRCELLSFLLLMDYIGINLATMNSCINPIALYFVSKKFKNCFQSCLCCCCYQSKSLMTSVPMNGTSIQWKNHEQNNHNTERSSHKDSIN\", \"result\": [{\"smile\": \"CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1cnc[nH]1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)[C@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O\", \"value\": 20.0}, {\"smile\": \"CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1cnc[nH]1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)C(=O)N[C@H](C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O)[C@@H](C)CC\", \"value\": 5012.0}, {\"smile\": \"CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1c[nH]cn1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)[C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O\", \"value\": 19.95}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1cnc[nH]1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)[C@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O, CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1cnc[nH]1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)C(=O)N[C@H](C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O)[C@@H](C)CC, CC[C@H](C)[C@H](NC(=O)[C@H](CC(C)(C)C)NC(=O)[C@@H](NC(=O)[C@H](CC(C)C)NC(=O)[C@H](Cc1c[nH]cn1)NC(=O)[C@@H]1CSSC[C@@H](N)C(=O)N[C@@H](CO)C(=O)N[C@@H]2CSSC[C@@H](NC(=O)[C@H](CCC(=O)O)NC(=O)[C@@H](CCCCN)NC(=O)[C@H](CC(=O)O)NC(=O)[C@@H](CCSC)NC(=O)[C@H](CC(C)C)NC(=O)[C@@H](CO)NC(=O)[C@H](CO)NC2=O)C(=O)N[C@@H](C(C)C)C(=O)N[C@H](Cc2ccc(O)cc2)C(=O)N[C@@H](Cc2ccccc2)C(=O)N1)[C@@H](C)O)C(=O)N[C@@H](Cc1c[nH]c2ccccc12)C(=O)O and the protein are 20.0, 5012.0 and 19.95."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated affinity score for the drugs and the target protein 'MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"C[C@@H]1CCCN1CCCOc1ccc(N2CCN(C(=O)c3ccc(F)cc3)CC2=O)cc1\", \"value\": 251.19}, {\"smile\": \"CC(NC(=O)OCCCN1CCCCC1)C(C)(C)C\", \"value\": 39810.72}, {\"smile\": \"N=C(NCCCc1cnc[nH]1)NCCC(c1ccc(C(F)(F)F)cc1)c1ccccn1\", \"value\": 281.84}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs C[C@@H]1CCCN1CCCOc1ccc(N2CCN(C(=O)c3ccc(F)cc3)CC2=O)cc1, CC(NC(=O)OCCCN1CCCCC1)C(C)(C)C, N=C(NCCCc1cnc[nH]1)NCCC(c1ccc(C(F)(F)F)cc1)c1ccccn1 and the protein are 251.19, 39810.72 and 281.84."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you measure the affinity between the drugs and the target protein 'MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNNCGQPKEGKNHSQGCGEGQVACLFEDVVPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCSHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHPERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNNCGQPKEGKNHSQGCGEGQVACLFEDVVPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCSHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHPERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPIMGSSVYITVELAIAVLAILGNVLVCWAVWLNSNLQNVTNYFVVSLAAADIAVGVLAIPFAITISTGFCAACHGCLFIACFVLVLTQSSIFSLLAIAIDRYIAIRIPLRYNGLVTGTRAKGIIAICWVLSFAIGLTPMLGWNNCGQPKEGKNHSQGCGEGQVACLFEDVVPMNYMVYFNFFACVLVPLLLMLGVYLRIFLAARRQLKQMESQPLPGERARSTLQKEVHAAKSLAIIVGLFALCWLPLHIINCFTFFCPDCSHAPLWLMYLAIVLSHTNSVVNPFIYAYRIREFRQTFRKIIRSHVLRQQEPFKAAGTSARVLAAHGSDGEQVSLRLNGHPPGVWANGSAPHPERRPNGYALGLVSGGSAQESQGNTGLPDVELLSHELKGVCPEPPGLDDPLAQDGAGVS\", \"result\": [{\"smile\": \"CCCC[C@H](NC(=O)CCCCCNC(=O)[C@H]1CC[C@H]2c3[nH]c4ccccc4c3CCN2C1)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCCCN)C(N)=O\", \"value\": 16000.0}, {\"smile\": \"CCCC[C@H](NC(=O)CCCCCNC(=O)[C@H]1CC[C@@H]2c3[nH]c4ccccc4c3CCN2C1)C(=O)N[C@@H](Cc1ccc(F)cc1)C(=O)N[C@@H](CCCCN)C(N)=O\", \"value\": 11000.0}, {\"smile\": \"CCNC(=O)[C@H]1O[C@@H](n2cnc3c(N)nc(NCCc4ccc(CCC(=O)Nc5ccc(OC(C)=O)c([N+](=O)[O-])c5)cc4)nc32)[C@H](O)[C@@H]1O\", \"value\": 2.2}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCCC[C@H](NC(=O)CCCCCNC(=O)[C@H]1CC[C@H]2c3[nH]c4ccccc4c3CCN2C1)C(=O)N[C@@H](Cc1ccc(O)cc1)C(=O)N[C@@H](CCCCN)C(N)=O, CCCC[C@H](NC(=O)CCCCCNC(=O)[C@H]1CC[C@@H]2c3[nH]c4ccccc4c3CCN2C1)C(=O)N[C@@H](Cc1ccc(F)cc1)C(=O)N[C@@H](CCCCN)C(N)=O, CCNC(=O)[C@H]1O[C@@H](n2cnc3c(N)nc(NCCc4ccc(CCC(=O)Nc5ccc(OC(C)=O)c([N+](=O)[O-])c5)cc4)nc32)[C@H](O)[C@@H]1O and the protein are 16000.0, 11000.0 and 2.2."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong is the predicted binding affinity of the drugs for the target protein 'MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEKYVRLQKIGEGSFGKAILVKSTEDGRQYVIKEINISRMSSKEREESRREVAVLANMKHPNIVQYRESFEENGSLYIVMDYCEGGDLFKRINAQKGVLFQEDQILDWFVQICLALKHVHDRKILHRDIKSQNIFLTKDGTVQLGDFGIARVLNSTVELARTCIGTPYYLSPEICENKPYNNKSDIWALGCVLYELCTLKHAFEAGSMKNLVLKIISGSFPPVSLHYSYDLRSLVSQLFKRNPRDRPSVNSILEKGFIAKRIEKFLSPQLIAEEFCLKTFSKFGSQPIPAKRPASGQNSISVMPAQKITKPAAKYGIPLAYKKYGDKKLHEKKPLQKHKQAHQTPEKRVNTGEERRKISEEAARKRRLEFIEKEKKQKDQIISLMKAEQMKRQEKERLERINRAREQGWRNVLSAGGSGEVKAPFLGSGGTIAPSSFSSRGQYEHYHAIFDQMQQQRAEDNEAKWKREIYGRGLPERGILPGVRPGFPYGAAGHHHFPDADDIRKTLKRLKAVSKQANANRQKGQLAVERAKQVEEFLQRKREAMQNKARAEGHMVYLARLRQIRLQNFNERQQIKAKLRGEKKEANHSEGQEGSEEADMRRKKIESLKAHANARAAVLKEQLERKRKEAYEREKKVWEEHLVAKGVKSSDVSPPLGQHETGGSPSKQQMRSVISVTSALKEVGVDSSLTDTRETSEEMQKTNNAISSKREILRRLNENLKAQEDEKGKQNLSDTFEINVHEDAKEHEKEKSVSSDRKKWEAGGQLVIPLDELTLDTSFSTTERHTVGEVIKLGPNGSPRRAWGKSPTDSVLKILGEAELQLQTELLENTTIRSEISPEGEKYKPLITGEKKVQCISHEINPSAIVDSPVETKSPEFSEASPQMSLKLEGNLEEPDDLETEILQEPSGTNKDESLPCTITDVWISEEKETKETQSADRITIQENEVSEDGVSSTVDQLSDIHIEPGTNDSQHSKCDVDKSVQPEPFFHKVVHSEHLNLVPQVQSVQCSPEESFAFRSHSHLPPKNKNKNSLLIGLSTGLFDANNPKMLRTCSLPDLSKLFRTLMDVPTVGDVRQDNLEIDEIEDENIKEGPSDSEDIVFEETDTDLQELQASMEQLLREQPGEEYSEEEESVLKNSDVEPTANGTDVADEDDNPSSESALNEEWHSDNSDGEIASECECDSVFNHLEELRLHLEQEMGFEKFFEVYEKIKAIHEDEDENIEICSKIVQNILGNEHQHLYAKILHLVMADGAYQEDNDE\", \"result\": [{\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}, {\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 10000.0}, {\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1, CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1, COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the affinity between the drugs and the target protein 'MQRDHTMDYKESCPSVSIPSSDEHREKKKRFTVYKVLVSVGRSEWFVFRRYAEFDKLYNTLKKQFPAMALKIPAKRIFGDNFDPDFIKQRRAGLNEFIQNLVRYPELYNHPDVRAFLQMDSPKHQSDPSEDEDERSSQKLHSTSQNINLGPSGNPHAKPTDFDFLKVIGKGSFGKVLLAKRKLDGKFYAVKVLQKKIVLNRKEQKHIMAERNVLLKNVKHPFLVGLHYSFQTTEKLYFVLDFVNGGELFFHLQRERSFPEHRARFYAAEIASALGYLHSIKIVYRDLKPENILLDSVGHVVLTDFGLCKEGIAISDTTTTFCGTPEYLAPEVIRKQPYDNTVDWWCLGAVLYEMLYGLPPFYCRDVAEMYDNILHKPLSLRPGVSLTAWSILEELLEKDRQNRLGAKEDFLEIQNHPFFESLSWADLVQKKIPPPFNPNVAGPDDIRNFDTAFTEETVPYSVCVSSDYSIVNASVLEADDAFVGFSYAPPSEDLFL'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQRDHTMDYKESCPSVSIPSSDEHREKKKRFTVYKVLVSVGRSEWFVFRRYAEFDKLYNTLKKQFPAMALKIPAKRIFGDNFDPDFIKQRRAGLNEFIQNLVRYPELYNHPDVRAFLQMDSPKHQSDPSEDEDERSSQKLHSTSQNINLGPSGNPHAKPTDFDFLKVIGKGSFGKVLLAKRKLDGKFYAVKVLQKKIVLNRKEQKHIMAERNVLLKNVKHPFLVGLHYSFQTTEKLYFVLDFVNGGELFFHLQRERSFPEHRARFYAAEIASALGYLHSIKIVYRDLKPENILLDSVGHVVLTDFGLCKEGIAISDTTTTFCGTPEYLAPEVIRKQPYDNTVDWWCLGAVLYEMLYGLPPFYCRDVAEMYDNILHKPLSLRPGVSLTAWSILEELLEKDRQNRLGAKEDFLEIQNHPFFESLSWADLVQKKIPPPFNPNVAGPDDIRNFDTAFTEETVPYSVCVSSDYSIVNASVLEADDAFVGFSYAPPSEDLFL\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQRDHTMDYKESCPSVSIPSSDEHREKKKRFTVYKVLVSVGRSEWFVFRRYAEFDKLYNTLKKQFPAMALKIPAKRIFGDNFDPDFIKQRRAGLNEFIQNLVRYPELYNHPDVRAFLQMDSPKHQSDPSEDEDERSSQKLHSTSQNINLGPSGNPHAKPTDFDFLKVIGKGSFGKVLLAKRKLDGKFYAVKVLQKKIVLNRKEQKHIMAERNVLLKNVKHPFLVGLHYSFQTTEKLYFVLDFVNGGELFFHLQRERSFPEHRARFYAAEIASALGYLHSIKIVYRDLKPENILLDSVGHVVLTDFGLCKEGIAISDTTTTFCGTPEYLAPEVIRKQPYDNTVDWWCLGAVLYEMLYGLPPFYCRDVAEMYDNILHKPLSLRPGVSLTAWSILEELLEKDRQNRLGAKEDFLEIQNHPFFESLSWADLVQKKIPPPFNPNVAGPDDIRNFDTAFTEETVPYSVCVSSDYSIVNASVLEADDAFVGFSYAPPSEDLFL\", \"result\": [{\"smile\": \"Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1\", \"value\": 10000.0}, {\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1\", \"value\": 10000.0}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Nc1nc(N)c2nc(-c3cccc(O)c3)c(-c3cccc(O)c3)nc2n1, CC(C)(C)c1cc(NC(=O)Nc2ccc(-c3cn4c(n3)sc3cc(OCCN5CCOCC5)ccc34)cc2)no1, COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the affinity between the drugs and the target protein 'MDNVLPVDSDLSPNISTNTSEPNQFVQPAWQIVLWAAAYTVIVVTSVVGNVVVMWIILAHKRMRTVTNYFLVNLAFAEASMAAFNTVVNFTYAVHNEWYYGLFYCKFHNFFPIAAVFASIYSMTAVAFDRYMAIIHPLQPRLSATATKVVICVIWVLALLLAFPQGYYSTTETMPSRVVCMIEWPEHPNKIYEKVYHICVTVLIYFLPLLVIGYAYTVVGITLWASEIPGDSSDRYHEQVSAKRKVVKMMIVVVCTFAICWLPFHIFFLLPYINPDLYLKKFIQQVYLAIMWLAMSSTMYNPIIYCCLNDRFRLGFKHAFRCCPFISAGDYEGLEMKSTRYLQTQGSVYKVSRLETTISTVVGAHEEEPEDGPKATPSSLDLTSNCSSRSDSKTMTESFSFSSNVLS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDNVLPVDSDLSPNISTNTSEPNQFVQPAWQIVLWAAAYTVIVVTSVVGNVVVMWIILAHKRMRTVTNYFLVNLAFAEASMAAFNTVVNFTYAVHNEWYYGLFYCKFHNFFPIAAVFASIYSMTAVAFDRYMAIIHPLQPRLSATATKVVICVIWVLALLLAFPQGYYSTTETMPSRVVCMIEWPEHPNKIYEKVYHICVTVLIYFLPLLVIGYAYTVVGITLWASEIPGDSSDRYHEQVSAKRKVVKMMIVVVCTFAICWLPFHIFFLLPYINPDLYLKKFIQQVYLAIMWLAMSSTMYNPIIYCCLNDRFRLGFKHAFRCCPFISAGDYEGLEMKSTRYLQTQGSVYKVSRLETTISTVVGAHEEEPEDGPKATPSSLDLTSNCSSRSDSKTMTESFSFSSNVLS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDNVLPVDSDLSPNISTNTSEPNQFVQPAWQIVLWAAAYTVIVVTSVVGNVVVMWIILAHKRMRTVTNYFLVNLAFAEASMAAFNTVVNFTYAVHNEWYYGLFYCKFHNFFPIAAVFASIYSMTAVAFDRYMAIIHPLQPRLSATATKVVICVIWVLALLLAFPQGYYSTTETMPSRVVCMIEWPEHPNKIYEKVYHICVTVLIYFLPLLVIGYAYTVVGITLWASEIPGDSSDRYHEQVSAKRKVVKMMIVVVCTFAICWLPFHIFFLLPYINPDLYLKKFIQQVYLAIMWLAMSSTMYNPIIYCCLNDRFRLGFKHAFRCCPFISAGDYEGLEMKSTRYLQTQGSVYKVSRLETTISTVVGAHEEEPEDGPKATPSSLDLTSNCSSRSDSKTMTESFSFSSNVLS\", \"result\": [{\"smile\": \"NS(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1\", \"value\": 1.26}, {\"smile\": \"CNS(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1\", \"value\": 3.16}, {\"smile\": \"CN(C)S(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1\", \"value\": 10.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs NS(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1, CNS(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1, CN(C)S(=O)(=O)N1CCN(C2CN(CCC3(c4ccc(Cl)c(Cl)c4)CCC(=O)N(CC4CC4)C3)C2)CC1 and the protein are 1.26, 3.16 and 10.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the strength of interaction between the drugs and the target protein 'MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MASTTTCTRFTDEYQLFEELGKGAFSVVRRCMKIPTGQEYAAKIINTKKLSARDHQKLEREARICRLLKHPNIVRLHDSISEEGFHYLVFDLVTGGELFEDIVAREYYSEADASHCIQQILESVNHCHLNGIVHRDLKPENLLLASKSKGAAVKLADFGLAIEVQGDQQAWFGFAGTPGYLSPEVLRKDPYGKPVDMWACGVILYILLVGYPPFWDEDQHRLYQQIKAGAYDFPSPEWDTVTPEAKDLINKMLTINPAKRITASEALKHPWICQRSTVASMMHRQETVDCLKKFNARRKLKGAILTTMLATRNFSAAKSLLKKPDGVKESTESSNTTIEDEDVKARKQEIIKVTEQLIEAINNGDFEAYTKICDPGLTAFEPEALGNLVEGMDFHRFYFENALSKSNKPIHTIILNPHVHLVGDDAACIAYIRLTQYMDGSGMPKTMQSEETRVWHRRDGKWQNVHFHRSGSPTVPIKPPCIPNGKENFSGGTSLWQNI\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1, C[C@@H](Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl, COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you quantify the predicted affinity for the drugs and the target protein 'MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEEPGAQCAPPPPAGSETWVPQANLSSAPSQNCSAKDYIYQDSISLPWKVLLVMLLALITLATTLSNAFVIATVYRTRKLHTPANYLIASLAVTDLLVSILVMPISTMYTVTGRWTLGQVVCDFWLSSDITCCTASILHLCVIALDRYWAITDAVEYSAKRTPKRAAVMIALVWVFSISISLPPFFWRQAKAEEEVSECVVNTDHILYTVYSTVGAFYFPTLLLIALYGRIYVEARSRILKQTPNRTGKRLTRAQLITDSPGSTSSVTSINSRVPDVPSESGSPVYVNQVKVRVSDALLEKKKLMAARERKATKTLGIILGAFIVCWLPFFIISLVMPICKDACWFHLAIFDFFTWLGYLNSLINPIIYTMSNEDFKQAFHKLIRFKCTS\", \"result\": [{\"smile\": \"CN(C)CCc1c[nH]c2ccc(C[C@H]3COC(=O)N3)cc12\", \"value\": 1.5}, {\"smile\": \"NCCc1cnc(CCC(c2ccccc2)c2ccccc2)[nH]1\", \"value\": 14125.38}, {\"smile\": \"CC(C)n1cc2c3c(cccc31)C1=C[C@@H](CO)CN(C)[C@@H]1C2\", \"value\": 208.93}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CN(C)CCc1c[nH]c2ccc(C[C@H]3COC(=O)N3)cc12, NCCc1cnc(CCC(c2ccccc2)c2ccccc2)[nH]1, CC(C)n1cc2c3c(cccc31)C1=C[C@@H](CO)CN(C)[C@@H]1C2 and the protein are 1.5, 14125.38 and 208.93."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected interaction affinity of the drugs with the target protein 'MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQFPSPPAARSSPAPQAASSSEAAAPATGQPGPSCPVPGTSRRGRPGTPPAGRVEEEEEEEEEDVDKDPHPTQNTCLRCRHFSLRERKREPRRTMGGCEVREFLLQFGFFLPLLTAWPGDCSHVSNNQVVLLDTTTVLGELGWKTYPLNGWDAITEMDEYNRPIHTYQVCNVMEPNQNNWLRTNWISRDAAQKIYVEMKFTLRDCNSIPWVLGTCKETFNLFYMESDESHGIKFKPNQYTKIDTIAADESFTQMDLGDRILKLNTEIREVGPIERKGFYLAFQDIGACIALVSVRVFYKKCPFTVRNLAMFPDTIPRVDSSSLVEVRGSCVKSAEERDTPKLYCGADGDWLVPLGRCICSTGYEEIEGSCHACRPGFYKAFAGNTKCSKCPPHSLTYMEATSVCQCEKGYFRAEKDPPSMACTRPPSAPRNVVFNINETALILEWSPPSDTGGRKDLTYSVICKKCGLDTSQCEDCGGGLRFIPRHTGLINNSVIVLDFVSHVNYTFEIEAMNGVSELSFSPKPFTAITVTTDQDGKFHCCSLKTDP\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1\", \"value\": 2000.0}, {\"smile\": \"CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1\", \"value\": 340.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1[nH]c(C=C2C(=O)Nc3ccc(F)cc32)c(C)c1C(=O)NCC(O)CN1CCOCC1, CN(C)CC=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OC1CCOC1, Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1 and the protein are 2000.0, 340.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the drugs's affinity for the target protein 'MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\", \"result\": [{\"smile\": \"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"value\": 1.0}, {\"smile\": \"O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl\", \"value\": 10000.0}, {\"smile\": \"Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21, O=C(NOCC1CC1)c1ccc(F)c(F)c1Nc1ccc(I)cc1Cl, Cc1nc(Nc2ncc(C(=O)Nc3c(C)cccc3Cl)s2)cc(N2CCN(CCO)CC2)n1 and the protein are 1.0, 10000.0 and 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the affinity between the drugs and the target protein 'MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGAPACALALCVAVAIVAGASSESLGTEQRVVGRAAEVPGPEPGQQEQLVFGSGDAVELSCPPPGGGPMGPTVWVKDGTGLVPSERVLVGPQRLQVLNASHEDSGAYSCRQRLTQRVLCHFSVRVTDAPSSGDDEDGEDEAEDTGVDTGAPYWTRPERMDKKLLAVPAANTVRFRCPAAGNPTPSISWLKNGREFRGEHRIGGIKLRHQQWSLVMESVVPSDRGNYTCVVENKFGSIRQTYTLDVLERSPHRPILQAGLPANQTAVLGSDVEFHCKVYSDAQPHIQWLKHVEVNGSKVGPDGTPYVTVLKTAGANTTDKELEVLSLHNVTFEDAGEYTCLAGNSIGFSHHSAWLVVLPAEEELVEADEAGSVYAGILSYGVGFFLFILVVAAVTLCRLRSPPKKGLGSPTVHKISRFPLKRQVSLESNASMSSNTPLVRIARLSSGEGPTLANVSELELPADPKWELSRARLTLGKPLGEGCFGQVVMAEAIGIDKDRAAKPVTVAVKMLKDDATDKDLSDLVSEMEMMKMIGKHKNIINLLGACTQGGPLYVLVEYAAKGNLREFLRARRPPGLDYSFDTCKPPEEQLTFKDLVSCAYQVARGMEYLASQKCIHRDLAARNVLVTEDNVMKIADFGLARDVHNLDYYKKTTNGRLPVKWMAPEALFDRVYTHQSDVWSFGVLLWEIFTLGGSPYPGIPVEELFKLLKEGHRMDKPANCTHDLYMIMRECWHAAPSQRPTFKQLVEDLDRVLTVTSTDEYLDLSAPFEQYSPGGQDTPSSSSSGDDSVFAHDLLPPAPPSSGGSRT\", \"result\": [{\"smile\": \"COc1cc(OC)c2c(c1)N(c1ccc3ncc(-c4cn[nH]c4)nc3c1)CCN(C(C)C)C2\", \"value\": 68.0}, {\"smile\": \"CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1\", \"value\": 10000.0}, {\"smile\": \"CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1\", \"value\": 1.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc(OC)c2c(c1)N(c1ccc3ncc(-c4cn[nH]c4)nc3c1)CCN(C(C)C)C2, CN(C)C/C=C/C(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1O[C@H]1CCOC1, CN(C)C[C@@H]1CCn2cc(c3ccccc32)C2=C(C(=O)NC2=O)c2cn(c3ccccc23)CCO1 and the protein are 68.0, 10000.0 and 1.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drugs's affinity with the target protein 'LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"LKMNKLPSNRGNTLREVQLMNRLRHPNILRFMGVCVHQGQLHALTEYMNGGTLEXLLSSPEPLSWPVRLHLALDIARGLRYLHSKGVFHRDLTSKNCLVRREDRGFTAVVGDFGLAEKIPVYREGARKEPLAVVGSPYWMAPEVLRGELYDEKADVFAFGIVLCELIARVPADPDYLPRTEDFGLDVPAFRTLVGDDCPLPFLLLAIHCCNLEPSTRAPFTEITQHLEWILEQLPEPAPLTXTA\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}, {\"smile\": \"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"value\": 10000.0}, {\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1, Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21, COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Calculate the expected binding affinity of the drugs for the target protein 'MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDCQLSILLLLSCSVLDSFGELIPQPSNEVNLLDSKTIQGELGWISYPSHGWEEISGVDEHYTPIRTYQVCNVMDHSQNNWLRTNWVPRNSAQKIYVELKFTLRDCNSIPLVLGTCKETFNLYYMESDDDHGVKFREHQFTKIDTIAADESFTQMDLGDRILKLNTEIREVGPVNKKGFYLAFQDVGACVALVSVRVYFKKCPFTVKNLAMFPDTVPMDSQSLVEVRGSCVNNSKEEDPPRMYCSTEGEWLVPIGKCSCNAGYEERGFMCQACRPGFYKALDGNMKCAKCPPHSSTQEDGSMNCRCENNYFRADKDPPSMACTRPPSSPRNVISNINETSVILDWSWPLDTGGRKDVTFNIICKKCGWNIKQCEPCSPNVRFLPRQFGLTNTTVTVTDLLAHTNYTFEIDAVNGVSELSSPPRQFAAVSITTNQAAPSPVLTIKKDRTSRNSISLSWQEPEHPNGIILDYEVKYYEKQEQETSYTILRARGTNVTISSLKPDTIYVFQIRARTAAGYGTNSRKFEFETSPDSFSISGESSQVVMIAISAAVAIILLTVVIYVLIGRFCGYKSKHGADEKRLHFGNGHLKLPGLRTYVDPHTYEDPTQTVHEFAKELDATNISIDKVVGAGEFGEVCSGRLKLPSKKEISVAIKTLKVGYTEKQRRDFLGEASIMGQFDHPNIIRLEGVVTKSKPVMIVTEYMENGSLDSFLRKHDAQFTVIQLVGMLRGIASGMKYLSDMGYVHRDLAARNILINSNLVCKVSDFGLSRVLEDDPEAAYTTRGGKIPIRWTSPEAIAYRKFTSASDVWSYGIVLWEVMSYGERPYWEMSNQDVIKAVDEGYRLPPPMDCPAALYQLMLDCWQKDRNNRPKFEQIVSILDKLIRNPGSLKIITSAAARPSNLLLDQSNVDITTFRTTGDWLNGVWTAHCKEIFTGVEYSSCDTIAKISTDDMKKVGVTVVGPQKKIISSIKALETQSKNGPVPV\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1\", \"value\": 3700.0}, {\"smile\": \"C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1\", \"value\": 2100.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Oc3ccc4[nH]c(C)cc4c3F)ncnc2cc1OCCCN1CCCC1, C=CC(=O)Nc1cc2c(Nc3ccc(F)c(Cl)c3)ncnc2cc1OCCCN1CCOCC1, Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1 and the protein are 3700.0, 2100.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the affinity relationship between the drugs and the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 12.0}, {\"smile\": \"CC(C)(C)c1cc(NC(=O)Nc2ccc(NC(=O)c3cccc(=O)[nH]3)cc2)no1\", \"value\": 2.5}, {\"smile\": \"Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O\", \"value\": 11.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1, CC(C)(C)c1cc(NC(=O)Nc2ccc(NC(=O)c3cccc(=O)[nH]3)cc2)no1, Cc1ccc(Nc2nccc(N(C)c3ccc4c(C)n(C)nc4c3)n2)cc1S(N)(=O)=O and the protein are 12.0, 2.5 and 11.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value for the drugs and the target protein 'MSSCVSSQPSSNRAAPQDELGGRGSSSSESQKPCEALRGLSSLSIHLGMESFIVVTECEPGCAVDLGLARDRPLEADGQEVPLDTSGSQARPHLSGRKLSLQERSQGGLAAGGSLDMNGRCICPSLPYSPVSSPQSSPRLPRRPTVESHHVSITGMQDCVQLNQYTLKDEIGKGSYGVVKLAYNENDNTYYAMKVLSKKKLIRQAGFPRRPPPRGTRPAPGGCIQPRGPIEQVYQEIAILKKLDHPNVVKLVEVLDDPNEDHLYMVFELVNQGPVMEVPTLKPLSEDQARFYFQDLIKGIEYLHYQKIIHRDIKPSNLLVGEDGHIKIADFGVSNEFKGSDALLSNTVGTPAFMAPESLSETRKIFSGKALDVWAMGVTLYCFVFGQCPFMDERIMCLHSKIKSQALEFPDQPDIAEDLKDLITRMLDKNPESRIVVPEIKLHPWVTRHGAEPLPSEDENCTLVEVTEEEVENSVKHIPSLATVILVKTMIRKRSFGNPFEGSRREERSLSAPGNLLTKKPTRECESLSELKEARQRRQPPGHRPAPRGGGGSALVRGSPCVESCWAPAPGSPARMHPLRPEEAMEPE'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSCVSSQPSSNRAAPQDELGGRGSSSSESQKPCEALRGLSSLSIHLGMESFIVVTECEPGCAVDLGLARDRPLEADGQEVPLDTSGSQARPHLSGRKLSLQERSQGGLAAGGSLDMNGRCICPSLPYSPVSSPQSSPRLPRRPTVESHHVSITGMQDCVQLNQYTLKDEIGKGSYGVVKLAYNENDNTYYAMKVLSKKKLIRQAGFPRRPPPRGTRPAPGGCIQPRGPIEQVYQEIAILKKLDHPNVVKLVEVLDDPNEDHLYMVFELVNQGPVMEVPTLKPLSEDQARFYFQDLIKGIEYLHYQKIIHRDIKPSNLLVGEDGHIKIADFGVSNEFKGSDALLSNTVGTPAFMAPESLSETRKIFSGKALDVWAMGVTLYCFVFGQCPFMDERIMCLHSKIKSQALEFPDQPDIAEDLKDLITRMLDKNPESRIVVPEIKLHPWVTRHGAEPLPSEDENCTLVEVTEEEVENSVKHIPSLATVILVKTMIRKRSFGNPFEGSRREERSLSAPGNLLTKKPTRECESLSELKEARQRRQPPGHRPAPRGGGGSALVRGSPCVESCWAPAPGSPARMHPLRPEEAMEPE\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSCVSSQPSSNRAAPQDELGGRGSSSSESQKPCEALRGLSSLSIHLGMESFIVVTECEPGCAVDLGLARDRPLEADGQEVPLDTSGSQARPHLSGRKLSLQERSQGGLAAGGSLDMNGRCICPSLPYSPVSSPQSSPRLPRRPTVESHHVSITGMQDCVQLNQYTLKDEIGKGSYGVVKLAYNENDNTYYAMKVLSKKKLIRQAGFPRRPPPRGTRPAPGGCIQPRGPIEQVYQEIAILKKLDHPNVVKLVEVLDDPNEDHLYMVFELVNQGPVMEVPTLKPLSEDQARFYFQDLIKGIEYLHYQKIIHRDIKPSNLLVGEDGHIKIADFGVSNEFKGSDALLSNTVGTPAFMAPESLSETRKIFSGKALDVWAMGVTLYCFVFGQCPFMDERIMCLHSKIKSQALEFPDQPDIAEDLKDLITRMLDKNPESRIVVPEIKLHPWVTRHGAEPLPSEDENCTLVEVTEEEVENSVKHIPSLATVILVKTMIRKRSFGNPFEGSRREERSLSAPGNLLTKKPTRECESLSELKEARQRRQPPGHRPAPRGGGGSALVRGSPCVESCWAPAPGSPARMHPLRPEEAMEPE\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1\", \"value\": 320.0}, {\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1, CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1, Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl and the protein are 10000.0, 320.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated interaction strength between the drugs and the target protein 'MATEGAAQLGNRVAGMVCSLWVLLLVSSVLALEEVLLDTTGETSEIGWLTYPPGGWDEVSVLDDQRRLTRTFEACHVAGAPPGTGQDNWLQTHFVERRGAQRAHIRLHFSVRACSSLGVSGGTCRETFTLYYRQAEEPDSPDSVSSWHLKRWTKVDTIAADESFPSSSSSSSSSSSAAWAVGPHGAGQRAGLQLNVKERSFGPLTQRGFYVAFQDTGACLALVAVRLFSYTCPAVLRSFASFPETQASGAGGASLVAAVGTCVAHAEPEEDGVGGQAGGSPPRLHCNGEGKWMVAVGGCRCQPGYQPARGDKACQACPRGLYKSSAGNAPCSPCPARSHAPNPAAPVCPCLEGFYRASSDPPEAPCTGPPSAPQELWFEVQGSALMLHWRLPRELGGRGDLLFNVVCKECEGRQEPASGGGGTCHRCRDEVHFDPRQRGLTESRVLVGGLRAHVPYILEVQAVNGVSELSPDPPQAAAINVSTSHEVPSAVPVVHQVSRASNSITVSWPQPDQTNGNILDYQLRYYDQAEDESHSFTLTSETNTATVTQLSPGHIYGFQVRARTAAGHGPYGGKVYFQTLPQGELSSQLPERLSLVIGSILGALAFLLLAAITVLAVVFQRKRRGTGYTEQLQQYSSPGLGVKYYIDPSTYEDPCQAIRELAREVDPAYIKIEEVIGTGSFGEVRQGRLQPRGRREQTVAIQALWAGGAESLQMTFLGRAAVLGQFQHPNILRLEGVVTKSRPLMVLTEFMELGPLDSFLRQREGQFSSLQLVAMQRGVAAAMQYLSSFAFVHRSLSAHSVLVNSHLVCKVARLGHSPQGPSCLLRWAAPEVIAHGKHTTSSDVWSFGILMWEVMSYGERPYWDMSEQEVLNAIEQEFRLPPPPGCPPGLHLLMLDTWQKDRARRPHFDQLVAAFDKMIRKPDTLQAGGDPGERPSQALLTPVALDFPCLDSPQAWLSAIGLECYQDNFSKFGLCTFSDVAQLSLEDLPALGITLAGHQKKLLHHIQLLQQHLRQQGSVEV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MATEGAAQLGNRVAGMVCSLWVLLLVSSVLALEEVLLDTTGETSEIGWLTYPPGGWDEVSVLDDQRRLTRTFEACHVAGAPPGTGQDNWLQTHFVERRGAQRAHIRLHFSVRACSSLGVSGGTCRETFTLYYRQAEEPDSPDSVSSWHLKRWTKVDTIAADESFPSSSSSSSSSSSAAWAVGPHGAGQRAGLQLNVKERSFGPLTQRGFYVAFQDTGACLALVAVRLFSYTCPAVLRSFASFPETQASGAGGASLVAAVGTCVAHAEPEEDGVGGQAGGSPPRLHCNGEGKWMVAVGGCRCQPGYQPARGDKACQACPRGLYKSSAGNAPCSPCPARSHAPNPAAPVCPCLEGFYRASSDPPEAPCTGPPSAPQELWFEVQGSALMLHWRLPRELGGRGDLLFNVVCKECEGRQEPASGGGGTCHRCRDEVHFDPRQRGLTESRVLVGGLRAHVPYILEVQAVNGVSELSPDPPQAAAINVSTSHEVPSAVPVVHQVSRASNSITVSWPQPDQTNGNILDYQLRYYDQAEDESHSFTLTSETNTATVTQLSPGHIYGFQVRARTAAGHGPYGGKVYFQTLPQGELSSQLPERLSLVIGSILGALAFLLLAAITVLAVVFQRKRRGTGYTEQLQQYSSPGLGVKYYIDPSTYEDPCQAIRELAREVDPAYIKIEEVIGTGSFGEVRQGRLQPRGRREQTVAIQALWAGGAESLQMTFLGRAAVLGQFQHPNILRLEGVVTKSRPLMVLTEFMELGPLDSFLRQREGQFSSLQLVAMQRGVAAAMQYLSSFAFVHRSLSAHSVLVNSHLVCKVARLGHSPQGPSCLLRWAAPEVIAHGKHTTSSDVWSFGILMWEVMSYGERPYWDMSEQEVLNAIEQEFRLPPPPGCPPGLHLLMLDTWQKDRARRPHFDQLVAAFDKMIRKPDTLQAGGDPGERPSQALLTPVALDFPCLDSPQAWLSAIGLECYQDNFSKFGLCTFSDVAQLSLEDLPALGITLAGHQKKLLHHIQLLQQHLRQQGSVEV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MATEGAAQLGNRVAGMVCSLWVLLLVSSVLALEEVLLDTTGETSEIGWLTYPPGGWDEVSVLDDQRRLTRTFEACHVAGAPPGTGQDNWLQTHFVERRGAQRAHIRLHFSVRACSSLGVSGGTCRETFTLYYRQAEEPDSPDSVSSWHLKRWTKVDTIAADESFPSSSSSSSSSSSAAWAVGPHGAGQRAGLQLNVKERSFGPLTQRGFYVAFQDTGACLALVAVRLFSYTCPAVLRSFASFPETQASGAGGASLVAAVGTCVAHAEPEEDGVGGQAGGSPPRLHCNGEGKWMVAVGGCRCQPGYQPARGDKACQACPRGLYKSSAGNAPCSPCPARSHAPNPAAPVCPCLEGFYRASSDPPEAPCTGPPSAPQELWFEVQGSALMLHWRLPRELGGRGDLLFNVVCKECEGRQEPASGGGGTCHRCRDEVHFDPRQRGLTESRVLVGGLRAHVPYILEVQAVNGVSELSPDPPQAAAINVSTSHEVPSAVPVVHQVSRASNSITVSWPQPDQTNGNILDYQLRYYDQAEDESHSFTLTSETNTATVTQLSPGHIYGFQVRARTAAGHGPYGGKVYFQTLPQGELSSQLPERLSLVIGSILGALAFLLLAAITVLAVVFQRKRRGTGYTEQLQQYSSPGLGVKYYIDPSTYEDPCQAIRELAREVDPAYIKIEEVIGTGSFGEVRQGRLQPRGRREQTVAIQALWAGGAESLQMTFLGRAAVLGQFQHPNILRLEGVVTKSRPLMVLTEFMELGPLDSFLRQREGQFSSLQLVAMQRGVAAAMQYLSSFAFVHRSLSAHSVLVNSHLVCKVARLGHSPQGPSCLLRWAAPEVIAHGKHTTSSDVWSFGILMWEVMSYGERPYWDMSEQEVLNAIEQEFRLPPPPGCPPGLHLLMLDTWQKDRARRPHFDQLVAAFDKMIRKPDTLQAGGDPGERPSQALLTPVALDFPCLDSPQAWLSAIGLECYQDNFSKFGLCTFSDVAQLSLEDLPALGITLAGHQKKLLHHIQLLQQHLRQQGSVEV\", \"result\": [{\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 10000.0}, {\"smile\": \"Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21\", \"value\": 10000.0}, {\"smile\": \"O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 130.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1, Cn1cnc2c(F)c(Nc3ccc(Br)cc3Cl)c(C(=O)NOCCO)cc21, O=C(c1ccc(/C=C/c2n[nH]c3ccccc23)cc1)N1CCNCC1 and the protein are 10000.0, 10000.0 and 130.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted binding affinity of the drugs with the target protein 'MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQPDMSLNVIKMKSSDFLESAELDSGGFGKVSLCFHRTQGLMIMKTVYKGPNCIEHNEALLEEAKMMNRLRHSRVVKLLGVIIEEGKYSLVMEYMEKGNLMHVLKAEMSTPLSVKGRIILEIIEGMCYLHGKGVIHKDLKPENILVDNDFHIKIADLGLASFKMWSKLNNEEHNELREVDGTAKKNGGTLYYMAPEHLNDVNAKPTEKSDVYSFAVVLWAIFANKEPYENAICEQQLIMCIKSGNRPDVDDITEYCPREIISLMKLCWEANPEARPTFPGIEEKFRPFYLSQLEESVEEDVKSLKKEYSNENAVVKRMQSLQLDCVAVPSSRSNSATEQPGSLHSSQGLGMGPVEESWFAPSLEHPQEENEPSLQSKLQDEANYHLYGSRMDRQTKQQPRQNVAYNREEERRRRVSHDPFAQQRPYENFQNTEGKGTAYSSAASHGNAVHQPSGLTSQPQVLYQNNGLYSSHGFGTRPLDPGTAGPRVWYRPIPSHMPSLHNIPVPETNYLGNTPTMPFSSLPPTDESIKYTIYNSTGIQIGAYNYMEIGGTSSSLLDSTNTNFKEEPAAKYQAIFDNTTSLTDKHLDPIRENLGKHWKNCARKLGFTQSQIDEIDHDYERDGLKEKVYQMLQKWVMREGIKGATVGKLAQALHQCSRIDLLSSLIYVSQN\", \"result\": [{\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O\", \"value\": 10000.0}, {\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)N[C@H]4O, COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1, COc1cc(Oc2ccnc3cc(OC)c(OC)cc23)ccc1NC(=O)NC(C)c1nccs1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drugs's affinity to be for the target protein 'MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSLGASFVQIKFDDLQFFENCGGGSFGSVYRAKWISQDKEVAVKKLLKIEKEAEILSVLSHRNIIQFYGVILEPPNYGIVTEYASLGSLYDYINSNRSEEMDMDHIMTWATDVAKGMHYLHMEAPVKVIHRDLKSRNVVIAADGVLKICDFGASRFHNHTTHMSLVGTFPWMAPEVIQSLPVSETCDTYSYGVVLWEMLTREVPFKGLEGLQVAWLVVEKNERLTIPSSCPRSFAELLHQCWEADAKKRPSFKQIISILESMSNDTSLPDKCNSFLHNKAEWRCEIEATLERLKKLERDLSFKEQELKERERRLKMWEQKLTEQSNTPLLPSFEIGAWTEDDVYCWVQQLVRKGDSSAEMSVYASLFKENNITGKRLLLLEEEDLKDMGIVSKGHIIHFKSAIEKLTHDYINLFHFPPLIKDSGGEPEENEEKIVNLELVFGFHLKPGTGPQDCKWKMYMEMDGDEIAITYIKDVTFNTNLPDAEILKMTKPPFVMEKWIVGIAKSQTVECTVTYESDVRTPKSTKHVHSIQWSRTKPQDEVKAVQLAIQTLFTNSDGNPGSRSDSSADCQWLDTLRMRQIASNTSLQRSQSNPILGSPFFSHFDGQDSYAAAVRRPQVPIKYQQITPVNQSRSSSPTQYGLTKNFSSLHLNSRDSGFSSGNTDTSSERGRYSDRSRNKYGRGSISLNSSPRGRYSGKSQHSTPSRGRYPGKFYRVSQSALNPHQSPDFKRSPRDLHQPNTIPGMPLHPETDSRASEEDSKVSEGGWTKVEYRKKPHRPSPAKTNKERARGDHRGWRNF\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 300.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 4400.0}, {\"smile\": \"CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1, CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1, CC[C@H](CO)Nc1nc(NCc2ccccc2)c2ncn(C(C)C)c2n1 and the protein are 300.0, 4400.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely affinity value for the drugs-target protein 'MAAAETQSLREQPEMEDANSEKSINEENGEVSEDQSQNKHSRHKKKKHKHRSKHKKHKHSSEEDKDKKHKHKHKHKKHKRKEIIDASDKEGMSPAKRTKLDDLALLEDLEKQRALIKAELDNELMEGKVQSGMGLILQGYESGSEEEGEIHEKARNGNRSSTRSSSTKGKLELVDNKITTKKRSKSRSKERTRHRSDKKKSKGGIEIVKEKTTRSKSKERKKSKSPSKRSKSQDQARKSKSPTLRRRSQEKIGKARSPTDDKVKIEDKSKSKDRKKSPIINESRSRDRGKKSRSPVDLRGKSKDRRSRSKERKSKRSETDKEKKPIKSPSKDASSGKENRSPSRRPGRSPKRRSLSPKPRDKSRRSRSPLLNDRRSKQSKSPSRTLSPGRRAKSRSLERKRREPERRRLSSPRTRPRDDILSRRERSKDASPINRWSPTRRRSRSPIRRRSRSPLRRSRSPRRRSRSPRRRDRGRRSRSRLRRRSRSRGGRRRRSRSKVKEDKFKGSLSEGMKVEQESSSDDNLEDFDVEEEDEEALIEQRRIQRQAIVQKYKYLAEDSNMSVPSEPSSPQSSTRTRSPSPDDILERVAADVKEYERENVDTFEASVKAKHNLMTVEQNNGSSQKKLLAPDMFTESDDMFAAYFDSARLRAAGIGKDFKENPNLRDNWTDAEGYYRVNIGEVLDKRYNVYGYTGQGVFSNVVRARDNARANQEVAVKIIRNNELMQKTGLKELEFLKKLNDADPDDKFHCLRLFRHFYHKQHLCLVFEPLSMNLREVLKKYGKDVGLHIKAVRSYSQQLFLALKLLKRCNILHADIKPDNILVNESKTILKLCDFGSASHVADNDITPYLVSRFYRAPEIIIGKSYDYGIDMWSVGCTLYELYTGKILFPGKTNNHMLKLAMDLKGKMPNKMIRKGVFKDQHFDQNLNFMYIEVDKVTEREKVTVMSTINPTKDLLADLIGCQRLPEDQRKKVHQLKDLLDQILMLDPAKRISINQALQHAFIQEKI' interaction? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAAAETQSLREQPEMEDANSEKSINEENGEVSEDQSQNKHSRHKKKKHKHRSKHKKHKHSSEEDKDKKHKHKHKHKKHKRKEIIDASDKEGMSPAKRTKLDDLALLEDLEKQRALIKAELDNELMEGKVQSGMGLILQGYESGSEEEGEIHEKARNGNRSSTRSSSTKGKLELVDNKITTKKRSKSRSKERTRHRSDKKKSKGGIEIVKEKTTRSKSKERKKSKSPSKRSKSQDQARKSKSPTLRRRSQEKIGKARSPTDDKVKIEDKSKSKDRKKSPIINESRSRDRGKKSRSPVDLRGKSKDRRSRSKERKSKRSETDKEKKPIKSPSKDASSGKENRSPSRRPGRSPKRRSLSPKPRDKSRRSRSPLLNDRRSKQSKSPSRTLSPGRRAKSRSLERKRREPERRRLSSPRTRPRDDILSRRERSKDASPINRWSPTRRRSRSPIRRRSRSPLRRSRSPRRRSRSPRRRDRGRRSRSRLRRRSRSRGGRRRRSRSKVKEDKFKGSLSEGMKVEQESSSDDNLEDFDVEEEDEEALIEQRRIQRQAIVQKYKYLAEDSNMSVPSEPSSPQSSTRTRSPSPDDILERVAADVKEYERENVDTFEASVKAKHNLMTVEQNNGSSQKKLLAPDMFTESDDMFAAYFDSARLRAAGIGKDFKENPNLRDNWTDAEGYYRVNIGEVLDKRYNVYGYTGQGVFSNVVRARDNARANQEVAVKIIRNNELMQKTGLKELEFLKKLNDADPDDKFHCLRLFRHFYHKQHLCLVFEPLSMNLREVLKKYGKDVGLHIKAVRSYSQQLFLALKLLKRCNILHADIKPDNILVNESKTILKLCDFGSASHVADNDITPYLVSRFYRAPEIIIGKSYDYGIDMWSVGCTLYELYTGKILFPGKTNNHMLKLAMDLKGKMPNKMIRKGVFKDQHFDQNLNFMYIEVDKVTEREKVTVMSTINPTKDLLADLIGCQRLPEDQRKKVHQLKDLLDQILMLDPAKRISINQALQHAFIQEKI\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAAETQSLREQPEMEDANSEKSINEENGEVSEDQSQNKHSRHKKKKHKHRSKHKKHKHSSEEDKDKKHKHKHKHKKHKRKEIIDASDKEGMSPAKRTKLDDLALLEDLEKQRALIKAELDNELMEGKVQSGMGLILQGYESGSEEEGEIHEKARNGNRSSTRSSSTKGKLELVDNKITTKKRSKSRSKERTRHRSDKKKSKGGIEIVKEKTTRSKSKERKKSKSPSKRSKSQDQARKSKSPTLRRRSQEKIGKARSPTDDKVKIEDKSKSKDRKKSPIINESRSRDRGKKSRSPVDLRGKSKDRRSRSKERKSKRSETDKEKKPIKSPSKDASSGKENRSPSRRPGRSPKRRSLSPKPRDKSRRSRSPLLNDRRSKQSKSPSRTLSPGRRAKSRSLERKRREPERRRLSSPRTRPRDDILSRRERSKDASPINRWSPTRRRSRSPIRRRSRSPLRRSRSPRRRSRSPRRRDRGRRSRSRLRRRSRSRGGRRRRSRSKVKEDKFKGSLSEGMKVEQESSSDDNLEDFDVEEEDEEALIEQRRIQRQAIVQKYKYLAEDSNMSVPSEPSSPQSSTRTRSPSPDDILERVAADVKEYERENVDTFEASVKAKHNLMTVEQNNGSSQKKLLAPDMFTESDDMFAAYFDSARLRAAGIGKDFKENPNLRDNWTDAEGYYRVNIGEVLDKRYNVYGYTGQGVFSNVVRARDNARANQEVAVKIIRNNELMQKTGLKELEFLKKLNDADPDDKFHCLRLFRHFYHKQHLCLVFEPLSMNLREVLKKYGKDVGLHIKAVRSYSQQLFLALKLLKRCNILHADIKPDNILVNESKTILKLCDFGSASHVADNDITPYLVSRFYRAPEIIIGKSYDYGIDMWSVGCTLYELYTGKILFPGKTNNHMLKLAMDLKGKMPNKMIRKGVFKDQHFDQNLNFMYIEVDKVTEREKVTVMSTINPTKDLLADLIGCQRLPEDQRKKVHQLKDLLDQILMLDPAKRISINQALQHAFIQEKI\", \"result\": [{\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}, {\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 55.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1, COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4, Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1 and the protein are 10000.0, 55.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide a prediction for the affinity between the drugs and the target protein 'MAFANFRRILRLSTFEKRKSREYEHVRRDLDPNEVWEIVGELGDGAFGKVYKAKNKETGALAAAKVIETKSEEELEDYIVEIEILATCDHPYIVKLLGAYYHDGKLWIMIEFCPGGAVDAIMLELDRGLTEPQIQVVCRQMLEALNFLHSKRIIHRDLKAGNVLMTLEGDIRLADFGVSAKNLKTLQKRDSFIGTPYWMAPEVVMCETMKDTPYDYKADIWSLGITLIEMAQIEPPHHELNPMRVLLKIAKSDPPTLLTPSKWSVEFRDFLKIALDKNPETRPSAAQLLEHPFVSSITSNKALRELVAEAKAEVMEEIEDGRDEGEEEDAVDAASTLENHTQNSSEVSPPSLNADKPLEESPSTPLAPSQSQDSVNEPCSQPSGDRSLQTTSPPVVAPGNENGLAVPVPLRKSRPVSMDARIQVAQEKQVAEQGGDLSPAANRSQKASQSRPNSSALETLGGEKLANGSLEPPAQAAPGPSKRDSDCSSLCTSESMDYGTNLSTDLSLNKEMGSLSIKDPKLYKKTLKRTRKFVVDGVEVSITTSKIISEDEKKDEEMRFLRRQELRELRLLQKEEHRNQTQLSNKHELQLEQMHKRFEQEINAKKKFFDTELENLERQQKQQVEKMEQDHAVRRREEARRIRLEQDRDYTRFQEQLKLMKKEVKNEVEKLPRQQRKESMKQKMEEHTQKKQLLDRDFVAKQKEDLELAMKRLTTDNRREICDKERECLMKKQELLRDREAALWEMEEHQLQERHQLVKQQLKDQYFLQRHELLRKHEKEREQMQRYNQRMIEQLKVRQQQEKARLPKIQRSEGKTRMAMYKKSLHINGGGSAAEQREKIKQFSQQEEKRQKSERLQQQQKHENQMRDMLAQCESNMSELQQLQNEKCHLLVEHETQKLKALDESHNQNLKEWRDKLRPRKKALEEDLNQKKREQEMFFKLSEEAECPNPSTPSKAAKFFPYSSADAS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAFANFRRILRLSTFEKRKSREYEHVRRDLDPNEVWEIVGELGDGAFGKVYKAKNKETGALAAAKVIETKSEEELEDYIVEIEILATCDHPYIVKLLGAYYHDGKLWIMIEFCPGGAVDAIMLELDRGLTEPQIQVVCRQMLEALNFLHSKRIIHRDLKAGNVLMTLEGDIRLADFGVSAKNLKTLQKRDSFIGTPYWMAPEVVMCETMKDTPYDYKADIWSLGITLIEMAQIEPPHHELNPMRVLLKIAKSDPPTLLTPSKWSVEFRDFLKIALDKNPETRPSAAQLLEHPFVSSITSNKALRELVAEAKAEVMEEIEDGRDEGEEEDAVDAASTLENHTQNSSEVSPPSLNADKPLEESPSTPLAPSQSQDSVNEPCSQPSGDRSLQTTSPPVVAPGNENGLAVPVPLRKSRPVSMDARIQVAQEKQVAEQGGDLSPAANRSQKASQSRPNSSALETLGGEKLANGSLEPPAQAAPGPSKRDSDCSSLCTSESMDYGTNLSTDLSLNKEMGSLSIKDPKLYKKTLKRTRKFVVDGVEVSITTSKIISEDEKKDEEMRFLRRQELRELRLLQKEEHRNQTQLSNKHELQLEQMHKRFEQEINAKKKFFDTELENLERQQKQQVEKMEQDHAVRRREEARRIRLEQDRDYTRFQEQLKLMKKEVKNEVEKLPRQQRKESMKQKMEEHTQKKQLLDRDFVAKQKEDLELAMKRLTTDNRREICDKERECLMKKQELLRDREAALWEMEEHQLQERHQLVKQQLKDQYFLQRHELLRKHEKEREQMQRYNQRMIEQLKVRQQQEKARLPKIQRSEGKTRMAMYKKSLHINGGGSAAEQREKIKQFSQQEEKRQKSERLQQQQKHENQMRDMLAQCESNMSELQQLQNEKCHLLVEHETQKLKALDESHNQNLKEWRDKLRPRKKALEEDLNQKKREQEMFFKLSEEAECPNPSTPSKAAKFFPYSSADAS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAFANFRRILRLSTFEKRKSREYEHVRRDLDPNEVWEIVGELGDGAFGKVYKAKNKETGALAAAKVIETKSEEELEDYIVEIEILATCDHPYIVKLLGAYYHDGKLWIMIEFCPGGAVDAIMLELDRGLTEPQIQVVCRQMLEALNFLHSKRIIHRDLKAGNVLMTLEGDIRLADFGVSAKNLKTLQKRDSFIGTPYWMAPEVVMCETMKDTPYDYKADIWSLGITLIEMAQIEPPHHELNPMRVLLKIAKSDPPTLLTPSKWSVEFRDFLKIALDKNPETRPSAAQLLEHPFVSSITSNKALRELVAEAKAEVMEEIEDGRDEGEEEDAVDAASTLENHTQNSSEVSPPSLNADKPLEESPSTPLAPSQSQDSVNEPCSQPSGDRSLQTTSPPVVAPGNENGLAVPVPLRKSRPVSMDARIQVAQEKQVAEQGGDLSPAANRSQKASQSRPNSSALETLGGEKLANGSLEPPAQAAPGPSKRDSDCSSLCTSESMDYGTNLSTDLSLNKEMGSLSIKDPKLYKKTLKRTRKFVVDGVEVSITTSKIISEDEKKDEEMRFLRRQELRELRLLQKEEHRNQTQLSNKHELQLEQMHKRFEQEINAKKKFFDTELENLERQQKQQVEKMEQDHAVRRREEARRIRLEQDRDYTRFQEQLKLMKKEVKNEVEKLPRQQRKESMKQKMEEHTQKKQLLDRDFVAKQKEDLELAMKRLTTDNRREICDKERECLMKKQELLRDREAALWEMEEHQLQERHQLVKQQLKDQYFLQRHELLRKHEKEREQMQRYNQRMIEQLKVRQQQEKARLPKIQRSEGKTRMAMYKKSLHINGGGSAAEQREKIKQFSQQEEKRQKSERLQQQQKHENQMRDMLAQCESNMSELQQLQNEKCHLLVEHETQKLKALDESHNQNLKEWRDKLRPRKKALEEDLNQKKREQEMFFKLSEEAECPNPSTPSKAAKFFPYSSADAS\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)NC5=CCC(F)C=C5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 0.53}, {\"smile\": \"CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1\", \"value\": 150.0}, {\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1\", \"value\": 220.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)NC5=CCC(F)C=C5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1, CNC(=O)c1cc(Oc2ccc(NC(=O)Nc3ccc(Cl)c(C(F)(F)F)c3)cc2)ccn1, Cc1cc2c(F)c(Oc3ncnn4cc(OC[C@@H](C)O)c(C)c34)ccc2[nH]1 and the protein are 0.53, 150.0 and 220.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What binding affinity do you anticipate for the drugs and the target protein 'MDEQEALNSIMNDLVALQMNRRHRMPGYETMKNKDTGHSNRQSDVRIKFEHNGERRIIAFSRPVKYEDVEHKVTTVFGQPLDLHYMNNELSILLKNQDDLDKAIDILDRSSSMKSLRILLLSQDRNHNSSSPHSGVSRQVRIKASQSAGDINTIYQPPEPRSRHLSVSSQNPGRSSPPPGYVPERQQHIARQGSYTSINSEGEFIPETSEQCMLDPLSSAENSLSGSCQSLDRSADSPSFRKSRMSRAQSFPDNRQEYSDRETQLYDKGVKGGTYPRRYHVSVHHKDYSDGRRTFPRIRRHQGNLFTLVPSSRSLSTNGENMGLAVQYLDPRGRLRSADSENALSVQERNVPTKSPSAPINWRRGKLLGQGAFGRVYLCYDVDTGRELASKQVQFDPDSPETSKEVSALECEIQLLKNLQHERIVQYYGCLRDRAEKTLTIFMEYMPGGSVKDQLKAYGALTESVTRKYTRQILEGMSYLHSNMIVHRDIKGANILRDSAGNVKLGDFGASKRLQTICMSGTGMRSVTGTPYWMSPEVISGEGYGRKADVWSLGCTVVEMLTEKPPWAEYEAMAAIFKIATQPTNPQLPSHISEHGRDFLRRIFVEARQRPSAEELLTHHFAQLMY'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MDEQEALNSIMNDLVALQMNRRHRMPGYETMKNKDTGHSNRQSDVRIKFEHNGERRIIAFSRPVKYEDVEHKVTTVFGQPLDLHYMNNELSILLKNQDDLDKAIDILDRSSSMKSLRILLLSQDRNHNSSSPHSGVSRQVRIKASQSAGDINTIYQPPEPRSRHLSVSSQNPGRSSPPPGYVPERQQHIARQGSYTSINSEGEFIPETSEQCMLDPLSSAENSLSGSCQSLDRSADSPSFRKSRMSRAQSFPDNRQEYSDRETQLYDKGVKGGTYPRRYHVSVHHKDYSDGRRTFPRIRRHQGNLFTLVPSSRSLSTNGENMGLAVQYLDPRGRLRSADSENALSVQERNVPTKSPSAPINWRRGKLLGQGAFGRVYLCYDVDTGRELASKQVQFDPDSPETSKEVSALECEIQLLKNLQHERIVQYYGCLRDRAEKTLTIFMEYMPGGSVKDQLKAYGALTESVTRKYTRQILEGMSYLHSNMIVHRDIKGANILRDSAGNVKLGDFGASKRLQTICMSGTGMRSVTGTPYWMSPEVISGEGYGRKADVWSLGCTVVEMLTEKPPWAEYEAMAAIFKIATQPTNPQLPSHISEHGRDFLRRIFVEARQRPSAEELLTHHFAQLMY\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDEQEALNSIMNDLVALQMNRRHRMPGYETMKNKDTGHSNRQSDVRIKFEHNGERRIIAFSRPVKYEDVEHKVTTVFGQPLDLHYMNNELSILLKNQDDLDKAIDILDRSSSMKSLRILLLSQDRNHNSSSPHSGVSRQVRIKASQSAGDINTIYQPPEPRSRHLSVSSQNPGRSSPPPGYVPERQQHIARQGSYTSINSEGEFIPETSEQCMLDPLSSAENSLSGSCQSLDRSADSPSFRKSRMSRAQSFPDNRQEYSDRETQLYDKGVKGGTYPRRYHVSVHHKDYSDGRRTFPRIRRHQGNLFTLVPSSRSLSTNGENMGLAVQYLDPRGRLRSADSENALSVQERNVPTKSPSAPINWRRGKLLGQGAFGRVYLCYDVDTGRELASKQVQFDPDSPETSKEVSALECEIQLLKNLQHERIVQYYGCLRDRAEKTLTIFMEYMPGGSVKDQLKAYGALTESVTRKYTRQILEGMSYLHSNMIVHRDIKGANILRDSAGNVKLGDFGASKRLQTICMSGTGMRSVTGTPYWMSPEVISGEGYGRKADVWSLGCTVVEMLTEKPPWAEYEAMAAIFKIATQPTNPQLPSHISEHGRDFLRRIFVEARQRPSAEELLTHHFAQLMY\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)n[nH]1\", \"value\": 1900.0}, {\"smile\": \"CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C\", \"value\": 220.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)n[nH]1, CCN(CC)CCNC(=O)c1c(C)[nH]c(/C=C2\\C(=O)Nc3ccc(F)cc32)c1C, COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1 and the protein are 1900.0, 220.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the expected affinity of the drugs towards the target protein 'MAGPCPRSGAERAGSCWQDPLAVALSRGRQLAAPPGRGCARSRPLSVVYVLTREPQPGLEPREGTEAEPLPLRCLREACAQVPRPRPPPQLRSLPFGTLELGDTAALDAFYNADVVVLEVSSSLVQPSLFYHLGVRESFSMTNNVLLCSQADLPDLQALREDVFQKNSDCVGSYTLIPYVVTATGRVLCGDAGLLRGLADGLVQAGVGTEALLTPLVGRLARLLEATPTDSCGYFRETIRRDIRQARERFSGPQLRQELARLQRRLDSVELLSPDIIMNLLLSYRDVQDYSAIIELVETLQALPTCDVAEQHNVCFHYTFALNRRNRPGDRAKALSVLLPLVQLEGSVAPDLYCMCGRIYKDMFFSSGFQDAGHREQAYHWYRKAFDVEPSLHSGINAAVLLIAAGQHFEDSKELRLIGMKLGCLLARKGCVEKMQYYWDVGFYLGAQILANDPTQVVLAAEQLYKLNAPIWYLVSVMETFLLYQHFRPTPEPPGGPPRRAHFWLHFLLQSCQPFKTACAQGDQCLVLVLEMNKVLLPAKLEVRGTDPVSTVTLSLLEPETQDIPSSWTFPVASICGVSASKRDERCCFLYALPPAQDVQLCFPSVGHCQWFCGLIQAWVTNPDSTAPAEEAEGAGEMLEFDYEYTETGERLVLGKGTYGVVYAGRDRHTRVRIAIKEIPERDSRFSQPLHEEIALHRRLRHKNIVRYLGSASQGGYLKIFMEEVPGGSLSSLLRSVWGPLKDNESTISFYTRQILQGLGYLHDNHIVHRDIKGDNVLINTFSGLLKISDFGTSKRLAGITPCTETFTGTLQYMAPEIIDQGPRGYGKAADIWSLGCTVIEMATGRPPFHELGSPQAAMFQVGMYKVHPPMPSSLSAEAQAFLLRTFEPDPRLRASAQTLLGDPFLQPGKRSRSPSSPRHAPRPSDAPSASPTPSANSTTQSQTFPCPQAPSQHPPSPPKRCLSYGGTSQLRVPEEPAAEEPASPEESSGLSLLHQESKRRAMLAAVLEQELPALAENLHQEQKQEQGARLGRNHVEELLRCLGAHIHTPNRRQLAQELRALQGRLRAQGLGPALLHRPLFAFPDAVKQILRKRQIRPHWMFVLDSLLSRAVRAALGVLGPEVEKEAVSPRSEELSNEGDSQQSPGQQSPLPVEPEQGPAPLMVQLSLLRAETDRLREILAGKEREYQALVQRALQRLNEEARTYVLAPEPPTALSTDQGLVQWLQELNVDSGTIQMLLNHSFTLHTLLTYATRDDLIYTRIRGGMVCRIWRAILAQRAGSTPVTSGP'. MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MAGPCPRSGAERAGSCWQDPLAVALSRGRQLAAPPGRGCARSRPLSVVYVLTREPQPGLEPREGTEAEPLPLRCLREACAQVPRPRPPPQLRSLPFGTLELGDTAALDAFYNADVVVLEVSSSLVQPSLFYHLGVRESFSMTNNVLLCSQADLPDLQALREDVFQKNSDCVGSYTLIPYVVTATGRVLCGDAGLLRGLADGLVQAGVGTEALLTPLVGRLARLLEATPTDSCGYFRETIRRDIRQARERFSGPQLRQELARLQRRLDSVELLSPDIIMNLLLSYRDVQDYSAIIELVETLQALPTCDVAEQHNVCFHYTFALNRRNRPGDRAKALSVLLPLVQLEGSVAPDLYCMCGRIYKDMFFSSGFQDAGHREQAYHWYRKAFDVEPSLHSGINAAVLLIAAGQHFEDSKELRLIGMKLGCLLARKGCVEKMQYYWDVGFYLGAQILANDPTQVVLAAEQLYKLNAPIWYLVSVMETFLLYQHFRPTPEPPGGPPRRAHFWLHFLLQSCQPFKTACAQGDQCLVLVLEMNKVLLPAKLEVRGTDPVSTVTLSLLEPETQDIPSSWTFPVASICGVSASKRDERCCFLYALPPAQDVQLCFPSVGHCQWFCGLIQAWVTNPDSTAPAEEAEGAGEMLEFDYEYTETGERLVLGKGTYGVVYAGRDRHTRVRIAIKEIPERDSRFSQPLHEEIALHRRLRHKNIVRYLGSASQGGYLKIFMEEVPGGSLSSLLRSVWGPLKDNESTISFYTRQILQGLGYLHDNHIVHRDIKGDNVLINTFSGLLKISDFGTSKRLAGITPCTETFTGTLQYMAPEIIDQGPRGYGKAADIWSLGCTVIEMATGRPPFHELGSPQAAMFQVGMYKVHPPMPSSLSAEAQAFLLRTFEPDPRLRASAQTLLGDPFLQPGKRSRSPSSPRHAPRPSDAPSASPTPSANSTTQSQTFPCPQAPSQHPPSPPKRCLSYGGTSQLRVPEEPAAEEPASPEESSGLSLLHQESKRRAMLAAVLEQELPALAENLHQEQKQEQGARLGRNHVEELLRCLGAHIHTPNRRQLAQELRALQGRLRAQGLGPALLHRPLFAFPDAVKQILRKRQIRPHWMFVLDSLLSRAVRAALGVLGPEVEKEAVSPRSEELSNEGDSQQSPGQQSPLPVEPEQGPAPLMVQLSLLRAETDRLREILAGKEREYQALVQRALQRLNEEARTYVLAPEPPTALSTDQGLVQWLQELNVDSGTIQMLLNHSFTLHTLLTYATRDDLIYTRIRGGMVCRIWRAILAQRAGSTPVTSGP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGPCPRSGAERAGSCWQDPLAVALSRGRQLAAPPGRGCARSRPLSVVYVLTREPQPGLEPREGTEAEPLPLRCLREACAQVPRPRPPPQLRSLPFGTLELGDTAALDAFYNADVVVLEVSSSLVQPSLFYHLGVRESFSMTNNVLLCSQADLPDLQALREDVFQKNSDCVGSYTLIPYVVTATGRVLCGDAGLLRGLADGLVQAGVGTEALLTPLVGRLARLLEATPTDSCGYFRETIRRDIRQARERFSGPQLRQELARLQRRLDSVELLSPDIIMNLLLSYRDVQDYSAIIELVETLQALPTCDVAEQHNVCFHYTFALNRRNRPGDRAKALSVLLPLVQLEGSVAPDLYCMCGRIYKDMFFSSGFQDAGHREQAYHWYRKAFDVEPSLHSGINAAVLLIAAGQHFEDSKELRLIGMKLGCLLARKGCVEKMQYYWDVGFYLGAQILANDPTQVVLAAEQLYKLNAPIWYLVSVMETFLLYQHFRPTPEPPGGPPRRAHFWLHFLLQSCQPFKTACAQGDQCLVLVLEMNKVLLPAKLEVRGTDPVSTVTLSLLEPETQDIPSSWTFPVASICGVSASKRDERCCFLYALPPAQDVQLCFPSVGHCQWFCGLIQAWVTNPDSTAPAEEAEGAGEMLEFDYEYTETGERLVLGKGTYGVVYAGRDRHTRVRIAIKEIPERDSRFSQPLHEEIALHRRLRHKNIVRYLGSASQGGYLKIFMEEVPGGSLSSLLRSVWGPLKDNESTISFYTRQILQGLGYLHDNHIVHRDIKGDNVLINTFSGLLKISDFGTSKRLAGITPCTETFTGTLQYMAPEIIDQGPRGYGKAADIWSLGCTVIEMATGRPPFHELGSPQAAMFQVGMYKVHPPMPSSLSAEAQAFLLRTFEPDPRLRASAQTLLGDPFLQPGKRSRSPSSPRHAPRPSDAPSASPTPSANSTTQSQTFPCPQAPSQHPPSPPKRCLSYGGTSQLRVPEEPAAEEPASPEESSGLSLLHQESKRRAMLAAVLEQELPALAENLHQEQKQEQGARLGRNHVEELLRCLGAHIHTPNRRQLAQELRALQGRLRAQGLGPALLHRPLFAFPDAVKQILRKRQIRPHWMFVLDSLLSRAVRAALGVLGPEVEKEAVSPRSEELSNEGDSQQSPGQQSPLPVEPEQGPAPLMVQLSLLRAETDRLREILAGKEREYQALVQRALQRLNEEARTYVLAPEPPTALSTDQGLVQWLQELNVDSGTIQMLLNHSFTLHTLLTYATRDDLIYTRIRGGMVCRIWRAILAQRAGSTPVTSGP\", \"result\": [{\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1\", \"value\": 10000.0}, {\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)n[nH]3)ncnc2c1, N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1, CS(=O)c1ccc(-c2nc(-c3ccc(F)cc3)c(-c3ccncc3)[nH]2)cc1 and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the binding affinity between the drugs and the target protein 'MLWLALGPFPAMENQVLVIRIKIPNSGAVDWTVHSGPQLLFRDVLDVIGQVLPEATTTAFEYEDEDGDRITVRSDEEMKAMLSYYYSTVMEQQVNGQLIEPLQIFPRACKPPGERNIHGLKVNTRAGPSQHSSPAVSDSLPSNSLKKSSAELKKILANGQMNEQDIRYRDTLGHGNGGTVYKAYHVPSGKILAVKVILLDITLELQKQIMSELEILYKCDSSYIIGFYGAFFVENRISICTEFMDGGSLDVYRKMPEHVLGRIAVAVVKGLTYLWSLKILHRDVKPSNMLVNTRGQVKLCDFGVSTQLVNSIAKTYVGTNAYMAPERISGEQYGIHSDVWSLGISFMELALGRFPYPQIQKNQGSLMPLQLLQCIVDEDSPVLPVGEFSEPFVHFITQCMRKQPKERPAPEELMGHPFIVQFNDGNAAVVSMWVCRALEERRSQQGPP'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MLWLALGPFPAMENQVLVIRIKIPNSGAVDWTVHSGPQLLFRDVLDVIGQVLPEATTTAFEYEDEDGDRITVRSDEEMKAMLSYYYSTVMEQQVNGQLIEPLQIFPRACKPPGERNIHGLKVNTRAGPSQHSSPAVSDSLPSNSLKKSSAELKKILANGQMNEQDIRYRDTLGHGNGGTVYKAYHVPSGKILAVKVILLDITLELQKQIMSELEILYKCDSSYIIGFYGAFFVENRISICTEFMDGGSLDVYRKMPEHVLGRIAVAVVKGLTYLWSLKILHRDVKPSNMLVNTRGQVKLCDFGVSTQLVNSIAKTYVGTNAYMAPERISGEQYGIHSDVWSLGISFMELALGRFPYPQIQKNQGSLMPLQLLQCIVDEDSPVLPVGEFSEPFVHFITQCMRKQPKERPAPEELMGHPFIVQFNDGNAAVVSMWVCRALEERRSQQGPP\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLWLALGPFPAMENQVLVIRIKIPNSGAVDWTVHSGPQLLFRDVLDVIGQVLPEATTTAFEYEDEDGDRITVRSDEEMKAMLSYYYSTVMEQQVNGQLIEPLQIFPRACKPPGERNIHGLKVNTRAGPSQHSSPAVSDSLPSNSLKKSSAELKKILANGQMNEQDIRYRDTLGHGNGGTVYKAYHVPSGKILAVKVILLDITLELQKQIMSELEILYKCDSSYIIGFYGAFFVENRISICTEFMDGGSLDVYRKMPEHVLGRIAVAVVKGLTYLWSLKILHRDVKPSNMLVNTRGQVKLCDFGVSTQLVNSIAKTYVGTNAYMAPERISGEQYGIHSDVWSLGISFMELALGRFPYPQIQKNQGSLMPLQLLQCIVDEDSPVLPVGEFSEPFVHFITQCMRKQPKERPAPEELMGHPFIVQFNDGNAAVVSMWVCRALEERRSQQGPP\", \"result\": [{\"smile\": \"COc1cc(-c2cnc3c(N4CCOCC4)snc3c2)cc(OC)c1OC\", \"value\": 150.0}, {\"smile\": \"Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21\", \"value\": 1900.0}, {\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 1400.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs COc1cc(-c2cnc3c(N4CCOCC4)snc3c2)cc(OC)c1OC, Cn1c(Nc2ccc(C(F)(F)F)cc2)nc2cc(Oc3ccnc(-c4ncc(C(F)(F)F)[nH]4)c3)ccc21, CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1 and the protein are 150.0, 1900.0 and 1400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you assess the drugs's affinity for the target protein 'MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"Cc1cn(-c2cccc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)c2)cn1\", \"value\": 1400.0}, {\"smile\": \"COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OC1CN(C)C1\", \"value\": 5.0}, {\"smile\": \"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\", \"value\": 2400.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs Cc1cn(-c2cccc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)c2)cn1, COc1cc2nc(-c3ccc(CC(=O)Nc4cc(C(C)(C)C(F)(F)F)on4)c(F)c3)cnc2cc1OC1CN(C)C1, CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12 and the protein are 1400.0, 5.0 and 2400.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected affinity score for the drugs and the target protein 'MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV'? MemoryPool(arguments=dict_keys(['user_smiles']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": \"(user_smiles)\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPKKKPTPIQLNPAPDGSAVNGTSSAETNLEALQKKLEELELDEQQRKRLEAFLTQKQKVGELKDDDFEKISELGAGNGGVVFKVSHKPSGLVMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKKAGRIPEQILGKVSIAVIKGLTYLREKHKIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMSPERLQGTHYSVQSDIWSMGLSLVEMAVGRYPIPPPDAKELELMFGCQVEGDAAETPPRPRTPGRPLSSYGMDSRPPMAIFELLDYIVNEPPPKLPSGVFSLEFQDFVNKCLIKNPAERADLKQLMVHAFIKRSDAEEVDFAGWLCSTIGLNQPSTPTHAAGV\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}, {\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1, Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1, Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl and the protein are 10000.0, 10000.0 and 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you evaluate the affinity between the drug 'C/C=C/C[C@@H](C)[C@@H](O)[C@H]1C(=O)N[C@@H](CC)C(=O)N(C)[C@H](C)C(=O)N(C)[C@@H]([C@H](C)CN(C)CCOC)C(=O)N[C@@H](C(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](C(C)C)C(=O)N1C', 'N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N1CCC[C@H]1C(=O)O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C/C=C/C[C@@H](C)[C@@H](O)[C@H]1C(=O)N[C@@H](CC)C(=O)N(C)[C@H](C)C(=O)N(C)[C@@H]([C@H](C)CN(C)CCOC)C(=O)N[C@@H](C(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](C(C)C)C(=O)N1C\", \"N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N1CCC[C@H]1C(=O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MVNPTVFFDIAVDGEPLGRVSFELFADKVPKTAENFRALSTGEKGFGYKGSCFHRIIPGFMCQGGDFTRHNGTGGKSIYGEKFEDENFILKHTGPGILSMANAGPNTNGSQFFICTAKTEWLDGKHVVFGKVKEGMNIVEAMERFGSRNGKTSKKITIADCGQLE\", \"result\": [{\"smile\": \"C/C=C/C[C@@H](C)[C@@H](O)[C@H]1C(=O)N[C@@H](CC)C(=O)N(C)[C@H](C)C(=O)N(C)[C@@H]([C@H](C)CN(C)CCOC)C(=O)N[C@@H](C(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](C(C)C)C(=O)N1C\", \"value\": 1.8}, {\"smile\": \"N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N1CCC[C@H]1C(=O)O\", \"value\": 3410.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C/C=C/C[C@@H](C)[C@@H](O)[C@H]1C(=O)N[C@@H](CC)C(=O)N(C)[C@H](C)C(=O)N(C)[C@@H]([C@H](C)CN(C)CCOC)C(=O)N[C@@H](C(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N[C@@H](C)C(=O)N[C@H](C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](CC(C)C)C(=O)N(C)[C@@H](C(C)C)C(=O)N1C', 'N[C@@H](Cc1c[nH]c2ccccc12)C(=O)NCC(=O)N1CCC[C@H]1C(=O)O' and the protein are 1.8, 3410.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you determine the affinity value for the drug 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1', 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' with respect to the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGPCPRSGAERAGSCWQDPLAVALSRGRQLAAPPGRGCARSRPLSVVYVLTREPQPGLEPREGTEAEPLPLRCLREACAQVPRPRPPPQLRSLPFGTLELGDTAALDAFYNADVVVLEVSSSLVQPSLFYHLGVRESFSMTNNVLLCSQADLPDLQALREDVFQKNSDCVGSYTLIPYVVTATGRVLCGDAGLLRGLADGLVQAGVGTEALLTPLVGRLARLLEATPTDSCGYFRETIRRDIRQARERFSGPQLRQELARLQRRLDSVELLSPDIIMNLLLSYRDVQDYSAIIELVETLQALPTCDVAEQHNVCFHYTFALNRRNRPGDRAKALSVLLPLVQLEGSVAPDLYCMCGRIYKDMFFSSGFQDAGHREQAYHWYRKAFDVEPSLHSGINAAVLLIAAGQHFEDSKELRLIGMKLGCLLARKGCVEKMQYYWDVGFYLGAQILANDPTQVVLAAEQLYKLNAPIWYLVSVMETFLLYQHFRPTPEPPGGPPRRAHFWLHFLLQSCQPFKTACAQGDQCLVLVLEMNKVLLPAKLEVRGTDPVSTVTLSLLEPETQDIPSSWTFPVASICGVSASKRDERCCFLYALPPAQDVQLCFPSVGHCQWFCGLIQAWVTNPDSTAPAEEAEGAGEMLEFDYEYTETGERLVLGKGTYGVVYAGRDRHTRVRIAIKEIPERDSRFSQPLHEEIALHRRLRHKNIVRYLGSASQGGYLKIFMEEVPGGSLSSLLRSVWGPLKDNESTISFYTRQILQGLGYLHDNHIVHRDIKGDNVLINTFSGLLKISDFGTSKRLAGITPCTETFTGTLQYMAPEIIDQGPRGYGKAADIWSLGCTVIEMATGRPPFHELGSPQAAMFQVGMYKVHPPMPSSLSAEAQAFLLRTFEPDPRLRASAQTLLGDPFLQPGKRSRSPSSPRHAPRPSDAPSASPTPSANSTTQSQTFPCPQAPSQHPPSPPKRCLSYGGTSQLRVPEEPAAEEPASPEESSGLSLLHQESKRRAMLAAVLEQELPALAENLHQEQKQEQGARLGRNHVEELLRCLGAHIHTPNRRQLAQELRALQGRLRAQGLGPALLHRPLFAFPDAVKQILRKRQIRPHWMFVLDSLLSRAVRAALGVLGPEVEKEAVSPRSEELSNEGDSQQSPGQQSPLPVEPEQGPAPLMVQLSLLRAETDRLREILAGKEREYQALVQRALQRLNEEARTYVLAPEPPTALSTDQGLVQWLQELNVDSGTIQMLLNHSFTLHTLLTYATRDDLIYTRIRGGMVCRIWRAILAQRAGSTPVTSGP\", \"result\": [{\"smile\": \"CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CSc1cccc(Nc2ncc3cc(-c4c(Cl)cccc4Cl)c(=O)n(C)c3n2)c1', 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected value for the affinity between the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRHSKRTHCPDWDSRESWGHESYRGSHKRKRRSHSSTQENRHCKPHHQFKESDCHYLEARSLNERDYRDRRYVDEYRNDYCEGYVPRHYHRDIESGYRIHCSKSSVRSRRSSPKRKRNRHCSSHQSRSKSHRRKRSRSIEDDEEGHLICQSGDVLRARYEIVDTLGEGAFGKVVECIDHGMDGMHVAVKIVKNVGRYREAARSEIQVLEHLNSTDPNSVFRCVQMLEWFDHHGHVCIVFELLGLSTYDFIKENSFLPFQIDHIRQMAYQICQSINFLHHNKLTHTDLKPENILFVKSDYVVKYNSKMKRDERTLKNTDIKVVDFGSATYDDEHHSTLVSTRHYRAPEVILALGWSQPCDVWSIGCILIEYYLGFTVFQTHDSKEHLAMMERILGPIPQHMIQKTRKRKYFHHNQLDWDEHSSAGRYVRRRCKPLKEFMLCHDEEHEKLFDLVRRMLEYDPTQRITLDEALQHPFFDLLKKK\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 850.0}, {\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 1600.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21' and the protein are 850.0, 1600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding strength of the drug 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1', 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' to the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAWRCPRMGRVPLAWCLALCGWACMAPRGTQAEESPFVGNPGNITGARGLTGTLRCQLQVQGEPPEVHWLRDGQILELADSTQTQVPLGEDEQDDWIVVSQLRITSLQLSDTGQYQCLVFLGHQTFVSQPGYVGLEGLPYFLEEPEDRTVAANTPFNLSCQAQGPPEPVDLLWLQDAVPLATAPGHGPQRSLHVPGLNKTSSFSCEAHNAKGVTTSRTATITVLPQQPRNLHLVSRQPTELEVAWTPGLSGIYPLTHCTLQAVLSDDGMGIQAGEPDPPEEPLTSQASVPPHQLRLGSLHPHTPYHIRVACTSSQGPSSWTHWLPVETPEGVPLGPPENISATRNGSQAFVHWQEPRAPLQGTLLGYRLAYQGQDTPEVLMDIGLRQEVTLELQGDGSVSNLTVCVAAYTAAGDGPWSLPVPLEAWRPGQAQPVHQLVKEPSTPAFSWPWWYVLLGAVVAAACVLILALFLVHRRKKETRYGEVFEPTVERGELVVRYRVRKSYSRRTTEATLNSLGISEELKEKLRDVMVDRHKVALGKTLGEGEFGAVMEGQLNQDDSILKVAVKTMKIAICTRSELEDFLSEAVCMKEFDHPNVMRLIGVCFQGSERESFPAPVVILPFMKHGDLHSFLLYSRLGDQPVYLPTQMLVKFMADIASGMEYLSTKRFIHRDLAARNCMLNENMSVCVADFGLSKKIYNGDYYRQGRIAKMPVKWIAIESLADRVYTSKSDVWSFGVTMWEIATRGQTPYPGVENSEIYDYLRQGNRLKQPADCLDGLYALMSRCWELNPQDRPSFTELREDLENTLKALPPAQEPDEILYVNMDEGGGYPEPPGAAGGADPPTQPDPKDSCSCLTAAEVHPAGRYVLCPSTTPSPAQPADRGSPAAPGQEDGA\", \"result\": [{\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 210.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 12.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1', 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein are 210.0, 12.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you predict the interaction affinity for the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MALDVKSRAKRYEKLDFLGEGQFATVYKARDKNTNQIVAIKKIKLGHRSEAKDGINRTALREIKLLQELSHPNIIGLLDAFGHKSNISLVFDFMETDLEVIIKDNSLVLTPSHIKAYMLMTLQGLEYLHQHWILHRDLKPNNLLLDENGVLKLADFGLAKSFGSPNRAYTHQVVTRWYRAPELLFGARMYGVGVDMWAVGCILAELLLRVPFLPGDSDLDQLTRIFETLGTPTEEQWPDMCSLPDYVTFKSFPGIPLHHIFSAAGDDLLDLIQGLFLFNPCARITATQALKMKYFSNRPGPTPGCQLPRPNCPVETLKEQSNPALAIKRKRTEALEQGGLPKKLIF\", \"result\": [{\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 7200.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein are 10000.0, 7200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the likely binding affinity for the drug 'C/C(=C\\c1cc(C(=O)O)no1)c1ccc2c(c1)C(C)(C)CCC2(C)C', 'CC(/C=C/[C@H]1C[C@@]1(C)c1ccc2c(c1)C(C)(C)CCC2(C)C)=C\\C(=O)O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C/C(=C\\c1cc(C(=O)O)no1)c1ccc2c(c1)C(C)(C)CCC2(C)C\", \"CC(/C=C/[C@H]1C[C@@]1(C)c1ccc2c(c1)C(C)(C)CCC2(C)C)=C\\C(=O)O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MYGNYSHFMKFPAGYGGSPGHTGSTSMSPSAALSTGKPMDSHPSYTDTPVSAPRTLSAVGTPLNALGSPYRVITSAMGPPSGALAAPPGINLVAPPSSQLNVVNSVSSSEDIKPLPGLPGIGNMNYPSTSPGSLVKHICAICGDRSSGKHYGVYSCEGCKGFFKRTIRKDLIYTCRDNKDCLIDKRQRNRCQYCRYQKCLVMGMKREAVQEERQRSRERAESEAECATSGHEDMPVERILEAELAVEPKTESYGDMNMENSTNDPVTNICHAADKQLFTLVEWAKRIPHFSDLTLEDQVILLRAGWNELLIASFSHRSVSVQDGILLATGLHVHRSSAHSAGVGSIFDRVLTELVSKMKDMQMDKSELGCLRAIVLFNPDAKGLSNPSEVETLREKVYATLEAYTKQKYPEQPGRFAKLLLRLPALRSIGLKCLEHLFFFKLIGDTPIDTFLMEMLETPLQIT\", \"result\": [{\"smile\": \"C/C(=C\\c1cc(C(=O)O)no1)c1ccc2c(c1)C(C)(C)CCC2(C)C\", \"value\": 1400.0}, {\"smile\": \"CC(/C=C/[C@H]1C[C@@]1(C)c1ccc2c(c1)C(C)(C)CCC2(C)C)=C\\C(=O)O\", \"value\": 180.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C/C(=C\\c1cc(C(=O)O)no1)c1ccc2c(c1)C(C)(C)CCC2(C)C', 'CC(/C=C/[C@H]1C[C@@]1(C)c1ccc2c(c1)C(C)(C)CCC2(C)C)=C\\C(=O)O' and the protein are 1400.0, 180.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the predicted affinity value between the drug 'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1', 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDEQEALNSIMNDLVALQMNRRHRMPGYETMKNKDTGHSNRQSDVRIKFEHNGERRIIAFSRPVKYEDVEHKVTTVFGQPLDLHYMNNELSILLKNQDDLDKAIDILDRSSSMKSLRILLLSQDRNHNSSSPHSGVSRQVRIKASQSAGDINTIYQPPEPRSRHLSVSSQNPGRSSPPPGYVPERQQHIARQGSYTSINSEGEFIPETSEQCMLDPLSSAENSLSGSCQSLDRSADSPSFRKSRMSRAQSFPDNRQEYSDRETQLYDKGVKGGTYPRRYHVSVHHKDYSDGRRTFPRIRRHQGNLFTLVPSSRSLSTNGENMGLAVQYLDPRGRLRSADSENALSVQERNVPTKSPSAPINWRRGKLLGQGAFGRVYLCYDVDTGRELASKQVQFDPDSPETSKEVSALECEIQLLKNLQHERIVQYYGCLRDRAEKTLTIFMEYMPGGSVKDQLKAYGALTESVTRKYTRQILEGMSYLHSNMIVHRDIKGANILRDSAGNVKLGDFGASKRLQTICMSGTGMRSVTGTPYWMSPEVISGEGYGRKADVWSLGCTVVEMLTEKPPWAEYEAMAAIFKIATQPTNPQLPSHISEHGRDFLRRIFVEARQRPSAEELLTHHFAQLMY\", \"result\": [{\"smile\": \"COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1\", \"value\": 10000.0}, {\"smile\": \"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1ccc(COc2ccc(Cc3cnc(N)nc3N)cc2OC)cc1', 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your forecast for the drug 'Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1''s affinity towards the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F\", \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERKVLALQARKKRTKAKKDKAQRKSETQHRGSAPHSESDLPEQEEEILGSDDDEQEDPNDYCKGGYHLVKIGDLFNGRYHVIRKLGWGHFSTVWLSWDIQGKKFVAMKVVKSAEHYTETALDEIRLLKSVRNSDPNDPNREMVVQLLDDFKISGVNGTHICMVFEVLGHHLLKWIIKSNYQGLPLPCVKKIIQQVLQGLDYLHTKCRIIHTDIKPENILLSVNEQYIRRLAAEATEWQRSGAPPPSGSAVSTAPQPKPADKMSKNKKKKLKKKQKRQAELLEKRMQEIEEMEKESGPGQKRPNKQEESESPVERPLKENPPNKMTQEKLEESSTIGQDQTLMERDTEGGAAEINCNGVIEVINYTQNSNNETLRHKEDLHNANDCDVQNLNQESSFLSSQNGDSSTSQETDSCTPITSEVSDTMVCQSSSTVGQSFSEQHISQLQESIRAEIPCEDEQEQEHNGPLDNKGKSTAGNFLVNPLEPKNAEKLKVKIADLGNACWVHKHFTEDIQTRQYRSLEVLIGSGYNTPADIWSTACMAFELATGDYLFEPHSGEEYTRDEDHIALIIELLGKVPRKLIVAGKYSKEFFTKKGDLKHITKLKPWGLFEVLVEKYEWSQEEAAGFTDFLLPMLELIPEKRATAAECLRHPWLNS\", \"result\": [{\"smile\": \"Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F\", \"value\": 2800.0}, {\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Nc1nc(Nc2ccc(S(N)(=O)=O)cc2)nn1C(=O)c1c(F)cccc1F', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein are 2800.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the projected affinity value between the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1', 'CNC(=O)c1nn(C)c2c1C(C)(C)Cc1cnc(Nc3ccc(N4CCN(C)CC4)cc3)nc1-2' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"CNC(=O)c1nn(C)c2c1C(C)(C)Cc1cnc(Nc3ccc(N4CCN(C)CC4)cc3)nc1-2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLSRQQPPPPRRAGAACTLRQKLIFSPCSDCEEEEEEEEEEGSGHSTGEDSAFQEPDSPLPPARSPTEPGPERRRSPGPAPGSPGELEEDLLLPGACPGADEAGGGAEGDSWEEEGFGSSSPVKSPAAPYFLGSSFSPVRCGGPGDASPRGCGARRAGEGRRSPRPDHPGTPPHKTFRKLRLFDTPHTPKSLLSKARGIDSSSVKLRGSSLFMDTEKSGKREFDVRQTPQVNINPFTPDSLLLHSSGQCRRRKRTYWNDSCGEDMEASDYELEDETRPAKRITITESNMKSRYTTEFHELEKIGSGEFGSVFKCVKRLDGCIYAIKRSKKPLAGSVDEQNALREVYAHAVLGQHSHVVRYFSAWAEDDHMLIQNEYCNGGSLADAISENYRIMSYFKEAELKDLLLQVGRGLRYIHSMSLVHMDIKPSNIFISRTSIPNAASEEGDEDDWASNKVMFKIGDLGHVTRISSPQVEEGDSRFLANEVLQENYTHLPKADIFALALTVVCAAGAEPLPRNGDQWHEIRQGRLPRIPQVLSQEFTELLKVMIHPDPERRPSAMALVKHSVLLSASRKSAEQLRIELNAEKFKNSLLQKELKKAQMAKAAAEERALFTDRMATRSTTQSNRTSRLIGKKMNRSVSLTIY\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1\", \"value\": 10000.0}, {\"smile\": \"CNC(=O)c1nn(C)c2c1C(C)(C)Cc1cnc(Nc3ccc(N4CCN(C)CC4)cc3)nc1-2\", \"value\": 14.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1', 'CNC(=O)c1nn(C)c2c1C(C)(C)Cc1cnc(Nc3ccc(N4CCN(C)CC4)cc3)nc1-2' and the protein are 10000.0, 14.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the interaction value between the drug 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKIPNIGNVMNKFEILGVVGEGAYGVVLKCRHKETHEIVAIKKFKDSEENEEVKETTLRELKMLRTLKQENIVELKEAFRRRGKLYLVFEYVEKNMLELLEEMPNGVPPEKVKSYIYQLIKAIHWCHKNDIVHRDIKPENLLISHNDVLKLCDFGFARNLSEGNNANYTEYVATRWYRSPELLLGAPYGKSVDMWSVGCILGELSDGQPLFPGESEIDQLFTIQKVLGPLPSEQMKLFYSNPRFHGLRFPAVNHPQSLERRYLGILNSVLLDLMKNLLKLDPADRYLTEQCLNHPTFQTQRLLDRSPSRSAKRKPYHVESSTLSNRNQAGKSTALQSHHRSNSKDIQNLSVGLPRADEGLPANESFLNGNLAGASLSPLHTKTYQASSQPGSTSKDLTNNNIPHLLSPKEAKSKTEFDFNIDPKPSEGPGTKYLKSNSRSQQNRHSFMESSQSKAGTLQPNEKQSRHSYIDTIPQSSRSPSYRTKAKSHGALSDSKSVSNLSEARAQIAEPSTSRYFPSSCLDLNSPTSPTPTRHSDTRTLLSPSGRNNRNEGTLDSRRTTTRHSKTMEELKLPEHMDSSHSHSLSAPHESFSYGLGYTSPFSSQQRPHRHSMYVTRDKVRAKGLDGSLSIGQGMAARANSLQLLSPQPGEQLPPEMTVARSSVKETSREGTSSFHTRQKSEGGVYHDPHSDDGTAPKENRHLYNDPVPRRVGSFYRVPSPRPDNSFHENNVSTRVSSLPSESSSGTNHSKRQPAFDPWKSPENISHSEQLKEKEKQGFFRSMKKKKKKSQTVPNSDSPDLLTLQKSIHSASTPSSRPKEWRPEKISDLQTQSQPLKSLRKLLHLSSASNHPASSDPRFQPLTAQQTKNSFSEIRIHPLSQASGGSSNIRQEPAPKGRPALQLPDGGCDGRRQRHHSGPQDRRFMLRTTEQQGEYFCCGDPKKPHTPCVPNRALHRPISSPAPYPVLQVRGTSMCPTLQVRGTDAFSCPTQQSGFSFFVRHVMREALIHRAQVNQAALLTYHENAALTGK\", \"result\": [{\"smile\": \"COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 4000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1cc2ncnc(Nc3ccc(F)c(Cl)c3)c2cc1OCCCN1CCOCC1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein are 10000.0, 4000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the binding affinity you would expect for the drug 'CCc1cc(OCc2ccc(-c3ccccc3-c3nnn[nH]3)cc2)c2ccccc2n1', 'COC(=O)c1cccc2nc(C)n(Cc3ccc(-n4cccc4-c4nnn[nH]4)cc3)c12' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCc1cc(OCc2ccc(-c3ccccc3-c3nnn[nH]3)cc2)c2ccccc2n1\", \"COC(=O)c1cccc2nc(C)n(Cc3ccc(-n4cccc4-c4nnn[nH]4)cc3)c12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MMLNSSTEDGIKRIQDDCPKAGRHNYIFVMIPTLYSIIFVVGIFGNSLAVIVIYFYMKLKTVASVFLLNLALADLCFLLTLPLWAVYTAMEYRWPFGNYLCKIASASVSFNLYASVFLLTCLSIDRYLAIVHPMKSRLRRTMLVAKVTCIIIWLLAGLASLPAIIHRNVFFIENTNITVCAFHYESQNSTLPIGLGLTKNILGFLFPFLIILTSYTLIWKALKKAYEIQKNKPRNDDIFKIIMAIVLFFFFSWVPHQIFTFLDVLIQLGVIHDCRIADIVDTAMPITICIAYFNNCLNPLFYGFLGKKFKKYFLQLLKYIPPKAKSHSNLSTKMSTLSYRPSDNVSSSSKKPVPCFEVE\", \"result\": [{\"smile\": \"CCc1cc(OCc2ccc(-c3ccccc3-c3nnn[nH]3)cc2)c2ccccc2n1\", \"value\": 5.01}, {\"smile\": \"COC(=O)c1cccc2nc(C)n(Cc3ccc(-n4cccc4-c4nnn[nH]4)cc3)c12\", \"value\": 100.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CCc1cc(OCc2ccc(-c3ccccc3-c3nnn[nH]3)cc2)c2ccccc2n1', 'COC(=O)c1cccc2nc(C)n(Cc3ccc(-n4cccc4-c4nnn[nH]4)cc3)c12' and the protein are 5.01, 100.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the affinity value for the drug 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1'-target protein pair. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSSMPKPERHAESLLDICHDTNSSPTDLMTVTKNQNIILQSISRSEEFDQDGDCSHSTLVNEEEDPSGGRQDWQPRTEGVEITVTFPRDVSPPQEMSQEDLKEKNLINSSLQEWAQAHAVSHPNEIETVELRKKKLTMRPLVLQKEESSRELCNVNLGFLLPRSCLELNISKSVTREDAPHFLKEQQRKSEEFSTSHMKYSGRSIKFLLPPLSLLPTRSGVLTIPQNHKFPKEKERNIPSLTSFVPKLSVSVRQSDELSPSNEPPGALVKSLMDPTLRSSDGFIWSRNMCSFPKTNHHRQCLEKEENWKSKEIEECNKIEITHFEKGQSLVSFENLKEGNIPAVREEDIDCHGSKTRKPEEENSQYLSSRKNESSVAKNYEQDPEIVCTIPSKFQETQHSEITPSQDEEMRNNKAASKRVSLHKNEAMEPNNILEECTVLKSLSSVVFDDPIDKLPEGCSSMETNIKISIAERAKPEMSRMVPLIHITFPVDGSPKEPVIAKPSLQTRKGTIHNNHSVNIPVHQENDKHKMNSHRSKLDSKTKTSKKTPQNFVISTEGPIKPTMHKTSIKTQIFPALGLVDPRPWQLPRFQKKMPQIAKKQSTHRTQKPKKQSFPCICKNPGTQKSCVPLSVQPTEPRLNYLDLKYSDMFKEINSTANGPGIYEMFGTPVYCHVRETERDENTYYREICSAPSGRRITNKCRSSHSERKSNIRTRLSQKKTHMKCPKTSFGIKQEHKVLISKEKSSKAVHSNLHDIENGDGISEPDWQIKSSGNEFLSSKDEIHPMNLAQTPEQSMKQNEFPPVSDLSIVEEVSMEESTGDRDISNNQILTTSLRDLQELEELHHQIPFIPSEDSWAVPSEKNSNKYVQQEKQNTASLSKVNASRILTNDLEFDSVSDHSKTLTNFSFQAKQESASSQTYQYWVHYLDHDSLANKSITYQMFGKTLSGTNSISQEIMDSVNNEELTDELLGCLAAELLALDEKDNNSCQKMANETDPENLNLVLRWRGSTPKEMGRETTKVKIQRHSSGLRIYDREEKFLISNEKKIFSENSLKSEEPILWTKGEILGKGAYGTVYCGLTSQGQLIAVKQVALDTSNKLAAEKEYRKLQEEVDLLKALKHVNIVAYLGTCLQENTVSIFMEFVPGGSISSIINRFGPLPEMVFCKYTKQILQGVAYLHENCVVHRDIKGNNVMLMPTGIIKLIDFGCARRLAWAGLNGTHSDMLKSMHGTPYWMAPEVINESGYGRKSDIWSIGCTVFEMATGKPPLASMDRMAAMFYIGAHRGLMPPLPDHFSENAADFVRMCLTRDQHERPSALQLLKHSFLERSH\", \"result\": [{\"smile\": \"N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O\", \"value\": 270.0}, {\"smile\": \"COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1\", \"value\": 2800.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'N#CCC(C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1.O=P(O)(O)O', 'COc1cc(N2CCC(N3CCN(C)CC3)CC2)ccc1Nc1ncc(Cl)c(Nc2ccccc2S(=O)(=O)C(C)C)n1' and the protein are 270.0, 2800.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you foresee the affinity between the drug 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21', 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLLSKINSLAHLRAAPCNDLHATKLAPGKEKEPLESQYQVGPLLGSGGFGSVYSGIRVSDNLPVAIKHVEKDRISDWGELPNGTRVPMEVVLLKKVSSGFSGVIRLLDWFERPDSFVLILERPEPVQDLFDFITERGALQEELARSFFWQVLEAVRHCHNCGVLHRDIKDENILIDLNRGELKLIDFGSGALLKDTVYTDFDGTRVYSPPEWIRYHRYHGRSAAVWSLGILLYDMVCGDIPFEHDEEIIRGQVFFRQRVSSECQHLIRWCLALRPSDRPTFEEIQNHPWMQDVLLPQETAEIHLHSLSPGPSK\", \"result\": [{\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 200.0}, {\"smile\": \"Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21', 'Cc1cc(Nc2cc(N3CCN(C)CC3)nc(Sc3ccc(NC(=O)C4CC4)cc3)n2)[nH]n1' and the protein are 200.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity level for the drug 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQYLNIKEDCNAMAFCAKMRSSKKTEVNLEAPEPGVEVIFYLSDREPLRLGSGEYTAEELCIRAAQACRISPLCHNLFALYDENTKLWYAPNRTITVDDKMSLRLHYRMRFYFTNWHGTNDNEQSVWRHSPKKQKNGYEKKKIPDATPLLDASSLEYLFAQGQYDLVKCLAPIRDPKTEQDGHDIENECLGMAVLAISHYAMMKKMQLPELPKDISYKRYIPETLNKSIRQRNLLTRMRINNVFKDFLKEFNNKTICDSSVSTHDLKVKYLATLETLTKHYGAEIFETSMLLISSENEMNWFHSNDGGNVLYYEVMVTGNLGIQWRHKPNVVSVEKEKNKLKRKKLENKHKKDEEKNKIREEWNNFSYFPEITHIVIKESVVSINKQDNKKMELKLSSHEEALSFVSLVDGYFRLTADAHHYLCTDVAPPLIVHNIQNGCHGPICTEYAINKLRQEGSEEGMYVLRWSCTDFDNILMTVTCFEKSEQVQGAQKQFKNFQIEVQKGRYSLHGSDRSFPSLGDLMSHLKKQILRTDNISFMLKRCCQPKPREISNLLVATKKAQEWQPVYPMSQLSFDRILKKDLVQGEHLGRGTRTHIYSGTLMDYKDDEGTSEEKKIKVILKVLDPSHRDISLAFFEAASMMRQVSHKHIVYLYGVCVRDVENIMVEEFVEGGPLDLFMHRKSDVLTTPWKFKVAKQLASALSYLEDKDLVHGNVCTKNLLLAREGIDSECGPFIKLSDPGIPITVLSRQECIERIPWIAPECVEDSKNLSVAADKWSFGTTLWEICYNGEIPLKDKTLIEKERFYESRCRPVTPSCKELADLMTRCMNYDPNQRPFFRAIMRDINKLEEQNPDIVSEKKPATEVDPTHFEKRFLKRIRDLGEGHFGKVELCRYDPEGDNTGEQVAVKSLKPESGGNHIADLKKEIEILRNLYHENIVKYKGICTEDGGNGIKLIMEFLPSGSLKEYLPKNKNKINLKQQLKYAVQICKGMDYLGSRQYVHRDLAARNVLVESEHQVKIGDFGLTKAIETDKEYYTVKDDRDSPVFWYAPECLMQSKFYIASDVWSFGVTLHELLTYCDSDSSPMALFLKMIGPTHGQMTVTRLVNTLKEGKRLPCPPNCPDEVYQLMRKCWEFQPSNRTSFQNLIEGFEALLK\", \"result\": [{\"smile\": \"Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1\", \"value\": 10000.0}, {\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 380.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1ccc(-n2nc(C(C)(C)C)cc2NC(=O)Nc2ccc(OCCN3CCOCC3)c3ccccc23)cc1', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the protein are 10000.0, 380.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you outline the predicted binding value of the drug 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1', 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' for the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSTRTPLPTVNERDTENHTSHGDGRQEVTSRTSRSGARCRNSIASCADEQPHIGNYRLLKTIGKGNFAKVKLARHILTGREVAIKIIDKTQLNPTSLQKLFREVRIMKILNHPNIVKLFEVIETEKTLYLIMEYASGGEVFDYLVAHGRMKEKEARSKFRQIVSAVQYCHQKRIVHRDLKAENLLLDADMNIKIADFGFSNEFTVGGKLDTFCGSPPYAAPELFQGKKYDGPEVDVWSLGVILYTLVSGSLPFDGQNLKELRERVLRGKYRIPFYMSTDCENLLKRFLVLNPIKRGTLEQIMKDRWINAGHEEDELKPFVEPELDISDQKRIDIMVGMGYSQEEIQESLSKMKYDEITATYLLLGRKSSELDASDSSSSSNLSLAKVRPSSDLNNSTGQSPHHKVQRSVFSSQKQRRYSDHAGPAIPSVVAYPKRSQTSTADSDLKEDGISSRKSSGSAVGGKGIAPASPMLGNASNPNKADIPERKKSSTVPSSNTASGGMTRRNTYVCSERTTADRHSVIQNGKENSTIPDQRTPVASTHSISSAATPDRIRFPRGTASRSTFHGQPRERRTATYNGPPASPSLSHEATPLSQTRSRGSTNLFSKLTSKLTRRNMSFRFIKRLPTEYERNGRYEGSSRNVSAEQKDENKEAKPRSLRFTWSMKTTSSMDPGDMMREIRKVLDANNCDYEQRERFLLFCVHGDGHAENLVQWEMEVCKLPRLSLNGVRFKRISGTSIAFKNIASKIANELKL\", \"result\": [{\"smile\": \"Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1[nH]c(C=C2C(=O)Nc3ccc(S(=O)(=O)Cc4c(Cl)cccc4Cl)cc32)c(C)c1C(=O)N1CCCC1CN1CCCC1', 'COc1cc2c(N3CCN(C(=O)Nc4ccc(OC(C)C)cc4)CC3)ncnc2cc1OCCCN1CCCCC1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your estimation of the drug 'NC(=O)CC(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(Cc1ccc(OP(=O)(O)O)cc1)NC(=O)OCc1cccc(N)c1)C(N)=O', 'NC(=O)C[C@H](NC(=O)C1(NC(=O)[C@@H](CC(=O)O)Cc2ccc(CP(=O)(O)O)cc2)CCN(C(=O)OCCc2ccccc2)CC1)C(=O)NCCCc1ccc2ccccc2c1''s affinity with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)CC(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(Cc1ccc(OP(=O)(O)O)cc1)NC(=O)OCc1cccc(N)c1)C(N)=O\", \"NC(=O)C[C@H](NC(=O)C1(NC(=O)[C@@H](CC(=O)O)Cc2ccc(CP(=O)(O)O)cc2)CCN(C(=O)OCCc2ccccc2)CC1)C(=O)NCCCc1ccc2ccccc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEAIAKYDFKATADDELSFKRGDILKVLNEECDQNWYKAELNGKDGFIPKNYIEMKPHPWFFGKIPRAKAEEMLSKQRHDGAFLIRESESAPGDFSLSVKFGNDVQHFKVLRDGAGKYFLWVVKFNSLNELVDYHRSTSVSRNQQIFLRDIEQVPQQPTYVQALFDFDPQEDGELGFRRGDFIHVMDNSDPNWWKGACHGQTGMFPRNYVTPVNRNV\", \"result\": [{\"smile\": \"NC(=O)CC(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(Cc1ccc(OP(=O)(O)O)cc1)NC(=O)OCc1cccc(N)c1)C(N)=O\", \"value\": 155.0}, {\"smile\": \"NC(=O)C[C@H](NC(=O)C1(NC(=O)[C@@H](CC(=O)O)Cc2ccc(CP(=O)(O)O)cc2)CCN(C(=O)OCCc2ccccc2)CC1)C(=O)NCCCc1ccc2ccccc2c1\", \"value\": 36.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'NC(=O)CC(NC(=O)C(Cc1ccc(O)cc1)NC(=O)C(Cc1ccc(OP(=O)(O)O)cc1)NC(=O)OCc1cccc(N)c1)C(N)=O', 'NC(=O)C[C@H](NC(=O)C1(NC(=O)[C@@H](CC(=O)O)Cc2ccc(CP(=O)(O)O)cc2)CCN(C(=O)OCCc2ccccc2)CC1)C(=O)NCCCc1ccc2ccccc2c1' and the protein are 155.0, 36.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the strength of the binding affinity between the drug 'CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEELELDEQQKKRLEAFLTQKAKVGELKDDDFERISELGAGNGGVVTKVQHRPSGLIMARKLIHLEIKPAIRNQIIRELQVLHECNSPYIVGFYGAFYSDGEISICMEHMDGGSLDQVLKEAKRIPEEILGKVSIAVLRGLAYLREKHQIMHRDVKPSNILVNSRGEIKLCDFGVSGQLIDSMANSFVGTRSYMAPERLQGTHYSVQSDIWSMGLSLVELAVGRYPIPPPDAKELEAIFGRPVVDGEEGEPHSISPRPRPPGRPVSGHGMDSRPAMAIFELLDYIVNEPPPKLPNGVFTPDFQEFVNKCLIKNPAERADLKMLTNHTFIKRSEVEEVDFAGWLCKTLRLNQPGTPTRTAV\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 9600.0}, {\"smile\": \"CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CS(=O)(=O)N1CCN(Cc2cc3nc(-c4cccc5[nH]ncc45)nc(N4CCOCC4)c3s2)CC1' and the protein are 9600.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you quantify the predicted affinity for the drug 'CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N'-target protein relationship? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAAILGDTIMVAKGLVKLTQAAVETHLQHLGIGGELIMAARALQSTAVEQIGMFLGKVQGQDKHEEYFAENFGGPEGEFHFSVPHAAGASTDFSSASAPDQSAPPSLGHAHSEGPAPAYVASGPFREAGFPGQASSPLGRANGRLFANPRDSFSAMGFQRRFFHQDQSPVGGLTAEDIEKARQAKARPENKQHKQTLSEHARERKVPVTRIGRLANFGGLAVGLGFGALAEVAKKSLRSEDPSGKKAVLGSSPFLSEANAERIVRTLCKVRGAALKLGQMLSIQDDAFINPHLAKIFERVRQSADFMPLKQMMKTLNNDLGPNWRDKLEYFEERPFAAASIGQVHLARMKGGREVAMKIQYPGVAQSINSDVNNLMAVLNMSNMLPEGLFPEHLIDVLRRELALECDYQREAACARKFRDLLKGHPFFYVPEIVDELCSPHVLTTELVSGFPLDQAEGLSQEIRNEICYNILVLCLRELFEFHFMQTDPNWSNFFYDPQQHKVALLDFGATREYDRSFTDLYIQIIRAAADRDRETVRAKSIEMKFLTGYEVKVMEDAHLDAILILGEAFASDEPFDFGTQSTTEKIHNLIPVMLRHRLVPPPEETYSLHRKMGGSFLICSKLKARFPCKAMFEEAYSNYCKRQAQQ\", \"result\": [{\"smile\": \"CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CS(=O)c1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'COc1ccc(F)c(F)c1C(=O)c1cnc(NC2CCN(S(C)(=O)=O)CC2)nc1N' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the predicted interaction value for the drug 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1', 'Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEPGRGGTETVGKFEFSRKDLIGHGAFAVVFKGRHREKHDLEVAVKCINKKNLAKSQTLLGKEIKILKELKHENIVALYDFQEMANSVYLVMEYCNGGDLADYLHAMRTLSEDTIRLFLQQIAGAMRLLHSKGIIHRDLKPQNILLSNPAGRRANPNSIRVKIADFGFARYLQSNMMAATLCGSPMYMAPEVIMSQHYDGKADLWSIGTIVYQCLTGKAPFQASSPQDLRLFYEKNKTLVPTIPRETSAPLRQLLLALLQRNHKDRMDFDEFFHHPFLDASPSVRKSPPVPVPSYPSSGSGSSSSSSSTSHLASPPSLGEMQQLQKTLASPADTAGFLHSSRDSGGSKDSSCDTDDFVMVPAQFPGDLVAEAPSAKPPPDSLMCSGSSLVASAGLESHGRTPSPSPPCSSSPSPSGRAGPFSSSRCGASVPIPVPTQVQNYQRIERNLQSPTQFQTPRSSAIRRSGSTSPLGFARASPSPPAHAEHGGVLARKMSLGGGRPYTPSPQVGTIPERPGWSGTPSPQGAEMRGGRSPRPGSSAPEHSPRTSGLGCRLHSAPNLSDLHVVRPKLPKPPTDPLGAVFSPPQASPPQPSHGLQSCRNLRGSPKLPDFLQRNPLPPILGSPTKAVPSFDFPKTPSSQNLLALLARQGVVMTPPRNRTLPDLSEVGPFHGQPLGPGLRPGEDPKGPFGRSFSTSRLTDLLLKAAFGTQAPDPGSTESLQEKPMEIAPSAGFGGSLHPGARAGGTSSPSPVVFTVGSPPSGSTPPQGPRTRMFSAGPTGSASSSARHLVPGPCSEAPAPELPAPGHGCSFADPITANLEGAVTFEAPDLPEETLMEQEHTEILRGLRFTLLFVQHVLEIAALKGSASEAAGGPEYQLQESVVADQISLLSREWGFAEQLVLYLKVAELLSSGLQSAIDQIRAGKLCLSSTVKQVVRRLNELYKASVVSCQGLSLRLQRFFLDKQRLLDRIHSITAERLIFSHAVQMVQSAALDEMFQHREGCVPRYHKALLLLEGLQHMLSDQADIENVTKCKLCIERRLSALLTGICA\", \"result\": [{\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 53.0}, {\"smile\": \"Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1', 'Clc1ccc(Nc2nnc(Cc3ccncc3)c3ccccc23)cc1' and the protein are 53.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the binding affinity that exists between the drug 'N=C(NCCCc1cnc[nH]1)NCCC(c1cc(F)cc(F)c1)c1ccccn1', 'c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N=C(NCCCc1cnc[nH]1)NCCC(c1cc(F)cc(F)c1)c1ccccn1\", \"c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"N=C(NCCCc1cnc[nH]1)NCCC(c1cc(F)cc(F)c1)c1ccccn1\", \"value\": 275.42}, {\"smile\": \"c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1\", \"value\": 3.24}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'N=C(NCCCc1cnc[nH]1)NCCC(c1cc(F)cc(F)c1)c1ccccn1', 'c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1' and the protein are 275.42, 3.24."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you anticipate the affinity to manifest between the drug 'c1ccc2cc(-c3nc4ccccc4n3-c3ccnc(N[C@@H]4CCCNC4)n3)ccc2c1', 'O/N=C1/c2ccccc2-c2nc3ccccc3nc21' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c1ccc2cc(-c3nc4ccccc4n3-c3ccnc(N[C@@H]4CCCNC4)n3)ccc2c1\", \"O/N=C1/c2ccccc2-c2nc3ccccc3nc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSLHFLYYCSEPTLDVKIAFCQGFDKQVDVSYIAKHYNMSKSKVDNQFYSVEVGDSTFTVLKRYQNLKPIGSGAQGIVCAAYDAVLDRNVAIKKLSRPFQNQTHAKRAYRELVLMKCVNHKNIISLLNVFTPQKTLEEFQDVYLVMELMDANLCQVIQMELDHERMSYLLYQMLCGIKHLHSAGIIHRDLKPSNIVVKSDCTLKILDFGLARTAGTSFMMTPYVVTRYYRAPEVILGMGYKENVDIWSVGCIMGEMVRHKILFPGRDYIDQWNKVIEQLGTPCPEFMKKLQPTVRNYVENRPKYAGLTFPKLFPDSLFPADSEHNKLKASQARDLLSKMLVIDPAKRISVDDALQHPYINVWYDPAEVEAPPPQIYDKQLDEREHTIEEWKELIYKEVMNSEEKTKNGVVKGQPSPSGAAVNSSESLPPSSSVNDISSMSTDQTLASDTDSSLEASAGPLGCCR\", \"result\": [{\"smile\": \"c1ccc2cc(-c3nc4ccccc4n3-c3ccnc(N[C@@H]4CCCNC4)n3)ccc2c1\", \"value\": 2980.0}, {\"smile\": \"O/N=C1/c2ccccc2-c2nc3ccccc3nc21\", \"value\": 100.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'c1ccc2cc(-c3nc4ccccc4n3-c3ccnc(N[C@@H]4CCCNC4)n3)ccc2c1', 'O/N=C1/c2ccccc2-c2nc3ccccc3nc21' and the protein are 2980.0, 100.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the projected strength of affinity between the drug 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1', 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MNKPITPSTYVRCLNVGLIRKLSDFIDPQEGWKKLAVAIKKPSGDDRYNQFHIRRFEALLQTGKSPTSELLFDWGTTNCTVGDLVDLLIQNEFFAPASLLLPDAVPKTANTLPSKEAITVQQKQMPFCDKDRTLMTPVQNLEQSYMPPDSSSPENKSLEVSDTRFHSFSFYELKNVTNNFDERPISVGGNKMGEGGFGVVYKGYVNNTTVAVKKLAAMVDITTEELKQQFDQEIKVMAKCQHENLVELLGFSSDGDDLCLVYVYMPNGSLLDRLSCLDGTPPLSWHMRCKIAQGAANGINFLHENHHIHRDIKSANILLDEAFTAKISDFGLARASEKFAQTVMTSRIVGTTAYMAPEALRGEITPKSDIYSFGVVLLEIITGLPAVDEHREPQLLLDIKEEIEDEEKTIEDYIDKKMNDADSTSVEAMYSVASQCLHEKKNKRPDIKKVQQLLQEMTAS\", \"result\": [{\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 860.0}, {\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1', 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C' and the protein are 860.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you give a forecast of the affinity value for the drug 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1', 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\", \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MEMYETLGKVGEGSYGTVMKCKHKNTGQIVAIKIFYERPEQSVNKIAMREIKFLKQFHHENLVNLIEVFRQKKKIHLVFEFIDHTVLDELQHYCHGLESKRLRKYLFQILRAIDYLHSNNIIHRDIKPENILVSQSGITKLCDFGFARTLAAPGDIYTDYVATRWYRAPELVLKDTSYGKPVDIWALGCMIIEMATGNPYLPSSSDLDLLHKIVLKVGNLSPHLQNIFSKSPIFAGVVLPQVQHPKNARKKYPKLNGLLADIVHACLQIDPADRISSSDLLHHEYFTRDGFIEKFMPELKAKLLQEAKVNSLIKPKESSKENELRKDERKTVYTNTLLSSSVLGKEIEKEKKPKEIKVRVIKVKGGRGDISEPKKKEYEGGLGQQDANENVHPMSPDTKLVTIEPPNPINPSTNCNGLKENPHCGGSVTMPPINLTNSNLMAANLSSNLFHPSVRLTERAKKRRTSSQSIGQVMPNSRQEDPGPIQSQMEKGIFNERTGHSDQMANENKRKLNFSRSDRKEFHFPELPVTIQSKDTKGMEVKQIKMLKRESKKTESSKIPTLLNVDQNQEKQEGGDGHCEGKNLKRNRFFFW\", \"result\": [{\"smile\": \"OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1\", \"value\": 10000.0}, {\"smile\": \"COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'OCCn1cc(-c2ccc3c(c2)CCC3=NO)c(-c2ccncc2)n1', 'COc1cc(Nc2ncc(F)c(Nc3ccc4c(n3)NC(=O)C(C)(C)O4)n2)cc(OC)c1OC' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you measure the expected affinity between the drug 'CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12', 'CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\", \"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSASTGGGGDSGGSGGSSSSSQASCGPESSGSELALATPVPQMLQGLLGSDDEEQEDPKDYCKGGYHPVKIGDVFNGRYHVVRKLGWGHFSTVWLCWDIQRKRFVALKVVKSAGHYTETAVDEIKLLKCVRDSDPSDPKRETIVQLIDDFRISGVNGVHVCMVLEVLGHQLLKWIIKSNYQGLPVPCVKSIVRQVLHGLDYLHTKCKIIHTDIKPENILLCVGDAYIRRLAAEATEWQQAGAPPPSRSIVSTAPQEVLQTGKLSKNKRKKMRRKRKQQKRLLEERLRDLQRLEAMEAATQAEDSGLRLDGGSGSTSSSGCHPGGARAGPSPASSSPAPGGGRSLSAGSQTSGFSGSLFSPASCSILSGSSNQRETGGLLSPSTPFGASNLLVNPLEPQNADKIKIKIADLGNACWVHKHFTEDIQTRQYRAVEVLIGAEYGPPADIWSTACMAFELATGDYLFEPHSGEDYSRDEDHIAHIVELLGDIPPAFALSGRYSREFFNRRGELRHIHNLKHWGLYEVLMEKYEWPLEQATQFSAFLLPMMEYIPEKRASAADCLQHPWLNP\", \"result\": [{\"smile\": \"CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12\", \"value\": 10000.0}, {\"smile\": \"CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl\", \"value\": 8200.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CNC(=O)c1c(F)cccc1Nc1nc(Nc2cc3c(cc2OC)CCN3C(=O)CN(C)C)nc2[nH]ccc12', 'CC(Oc1cc(-c2cnn(C3CCNCC3)c2)cnc1N)c1c(Cl)ccc(F)c1Cl' and the protein are 10000.0, 8200.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated binding score between the drug 'NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1', 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1\", \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGSNKSKPKDASQRRRSLEPAENVHGAGGGAFPASQTPSKPASADGHRGPSAAFAPAAAEPKLFGGFNSSDTVTSPQRAGPLAGGVTTFVALYDYESRTETDLSFKKGERLQIVNNTEGDWWLAHSLSTGQTGYIPSNYVAPSDSIQAEEWYFGKITRRESERLLLNAENPRGTFLVRESETTKGAYCLSVSDFDNAKGLNVKHYKIRKLDSGGFYITSRTQFNSLQQLVAYYSKHADGLCHRLTTVCPTSKPQTQGLAKDAWEIPRESLRLEVKLGQGCFGEVWMGTWNGTTRVAIKTLKPGTMSPEAFLQEAQVMKKLRHEKLVQLYAVVSEEPIYIVTEYMSKGSLLDFLKGETGKYLRLPQLVDMAAQIASGMAYVERMNYVHRDLRAANILVGENLVCKVADFGLARLIEDNEYTARQGAKFPIKWTAPEAALYGRFTIKSDVWSFGILLTELTTKGRVPYPGMVNREVLDQVERGYRMPCPPECPESLHDLMCQCWRKEPEERPTFEYLQAFLEDYFTSTEPQYQPGENL\", \"result\": [{\"smile\": \"NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 12.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'NC(=O)c1ccc(-c2nc(-c3ccccn3)c(-c3ccc4c(c3)OCO4)[nH]2)cc1', 'CO[C@@H]1[C@H](N(C)C(=O)c2ccccc2)C[C@H]2O[C@]1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein are 10000.0, 12.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Infer the predicted affinity level for the drug 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDLVGVASPEPGTAAAWGPSKCPWAIPQNTISCSLADVMSEQLAKELQLEEEAAVFPEVAVAEGPFITGENIDTSSDLMLAQMLQMEYDREYDAQLRREEKKFNGDSKVSISFENYRKVHPYEDSDSSEDEVDWQDTRDDPYRPAKPVPTPKKGFIGKGKDITTKHDEVVCGRKNTARMENFAPEFQVGDGIGMDLKLSNHVFNALKQHAYSEERRSARLHEKKEHSTAEKAVDPKTRLLMYKMVNSGMLETITGCISTGKESVVFHAYGGSMEDEKEDSKVIPTECAIKVFKTTLNEFKNRDKYIKDDFRFKDRFSKLNPRKIIRMWAEKEMHNLARMQRAGIPCPTVVLLKKHILVMSFIGHDQVPAPKLKEVKLNSEEMKEAYYQTLHLMRQLYHECTLVHADLSEYNMLWHAGKVWLIDVSQSVEPTHPHGLEFLFRDCRNVSQFFQKGGVKEALSERELFNAVSGLNITADNEADFLAEIEALEKMNEDHVQKNGRKAASFLKDDGDPPLLYDE\", \"result\": [{\"smile\": \"Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1\", \"value\": 10000.0}, {\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Oc1ccc(-c2nc(-c3ccncc3)c(-c3ccc(F)cc3)[nH]2)cc1', 'CCOc1cc2ncc(C#N)c(Nc3ccc(OCc4ccccn4)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your calculation for the binding affinity between the drug 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1', 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MCTVVDPRIVRRYLLRRQLGQGAYGIVWKAVDRRTGEVVAIKKIFDAFRDKTDAQRTFREITLLQEFGDHPNIISLLDVIRAENDRDIYLVFEFMDTDLNAVIRKGGLLQDVHVRSIFYQLLRATRFLHSGHVVHRDQKPSNVLLDANCTVKLCDFGLARSLGDLPEGPEDQAVTEYVATRWYRAPEVLLSSHRYTLGVDMWSLGCILGEMLRGRPLFPGTSTLHQLELILETIPPPSEEDLLALGSGCRASVLHQLGSRPRQTLDALLPPDTSPEALDLLRRLLVFAPDKRLSATQALQHPYVQRFHCPSDEWAREADVRPRAHEGVQLSVPEYRSRVYQMILECGGSSGTSREKGPEGVSPSQAHLHKPRADPQLPSRTPVQGPRPRPQSSPGHDPAEHESPRAAKNVPRQNSAPLLQTALLGNGERPPGAKEAPPLTLSLVKPSGRGAAPSLTSQAAAQVANQALIRGDWNRGGGVRVASVQQVPPRLPPEARPGRRMFSTSALQGAQGGARALLGGYSQAYGTVCHSALGHLPLLEGHHV\", \"result\": [{\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 10000.0}, {\"smile\": \"COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1', 'COC1C(N(C)C(=O)c2ccccc2)CC2OC1(C)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)C(=O)NC4' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you project the affinity value between the drug 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1', 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDKYDVIKAIGQGAFGKAYLAKGKSDSKHCVIKEINFEKMPIQEKEASKKEVILLEKMKHPNIVAFFNSFQENGRLFIVMEYCDGGDLMKRINRQRGVLFSEDQILGWFVQISLGLKHIHDRKILHRDIKAQNIFLSKNGMVAKLGDFGIARVLNNSMELARTCIGTPYYLSPEICQNKPYNNKTDIWSLGCVLYELCTLKHPFEGNNLQQLVLKICQAHFAPISPGFSRELHSLISQLFQVSPRDRPSINSILKRPFLENLIPKYLTPEVIQEEFSHMLICRAGAPASRHAGKVVQKCKIQKVRFQGKCPPRSRISVPIKRNAILHRNEWRPPAGAQKARSIKMIERPKIAAVCGHYDYYYAQLDMLRRRAHKPSYHPIPQENTGVEDYGQETRHGPSPSQWPAEYLQRKFEAQQYKLKVEKQLGLRPSSAEPNYNQRQELRSNGEEPRFQELPFRKNEMKEQEYWKQLEEIRQQYHNDMKEIRKKMGREPEENSKISHKTYLVKKSNLPVHQDASEGEAPVQMEFRSCCPGWSAMARSWLTATSASQDIEKDLKQMRLQNTKESKNPEQKYKAKKGVKFEINLDKCISDENILQEEEAMDIPNETLTFEDGMKFKEYECVKEHGDYTDKAFEKLHCPEAGFSTQTVAAVGNRRQWDGGAPQTLLQMMAVADITSTCPTGPDSESVLSVSRQEGKTKDPYSPVLILM\", \"result\": [{\"smile\": \"Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1\", \"value\": 10000.0}, {\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1cn(-c2cc(NC(=O)c3ccc(C)c(Nc4nccc(-c5cccnc5)n4)c3)cc(C(F)(F)F)c2)cn1', 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity value when relating the drug 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](OC(=O)NCCN=C=S)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C', 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(=O)NCCNC(=O)CBr)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' to the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](OC(=O)NCCN=C=S)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\", \"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(=O)NCCNC(=O)CBr)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MAGAPRGRGGGGGGGGAGESGGAERAAGPGGRRGLRACDEEFACPELEALFRGYTLRLEQAATLKALAVLSLLAGALALAELLGAPGPAPGLAKGSHPVHCVLFLALLVVTNVRSLQVPQLQQVGQLALLFSLTFALLCCPFALGGPAGAHAGAAAVPATADQGVWQLLLVTFVSYALLPVRSLLAIGFGLVVAASHLLVTATLVPAKRPRLWRTLGANALLFLGVNVYGIFVRILAERAQRKAFLQARNCIEDRLRLEDENEKQERLLMSLLPRNVAMEMKEDFLKPPERIFHKIYIQRHDNVSILFADIVGFTGLASQCTAQELVKLLNELFGKFDELATENHCRRIKILGDCYYCVSGLTQPKTDHAHCCVEMGLDMIDTITSVAEATEVDLNMRVGLHTGRVLCGVLGLRKWQYDVWSNDVTLANVMEAAGLPGKVHITKTTLACLNGDYEVEPGHGHERNSFLKTHNIETFFIVPSHRRKIFPGLILSDIKPAKRMKFKTVCYLLVQLMHCRKMFKAEIPFSNVMTCEDDDKRRALRTASEKLRNRSSFSTNVVQTTPGTRVNRYIGRLLEARQMELEMADLNFFTLKYKQAERERKYHQLQDEYFTSAVVLALILAALFGLVYLLIIPQSVAVLLLLVFCICFLVACVLYLHITRVQCFPGCLTIQIRTVLCIFIVVLIYSVAQGCVVGCLPWSWSSSPNGSLVVLSSGGRDPVLPVPPCESAPHALLCGLVGTLPLAIFLRVSSLPKMILLAVLTTSYILVLELSGYTKAMGAGAISGRSFEPIMAILLFSCTLALHARQVDVKLRLDYLWAAQAEEERDDMEKVKLDNKRILFNLLPAHVAQHFLMSNPRNMDLYYQSYSQVGVMFASIPNFNDFYIELDGNNMGVECLRLLNEIIADFDELMDKDFYKDLEKIKTIGSTYMAAVGLAPTAGTKAKKCISSHLSTLADFAIEMFDVLDEINYQSYNDFVLRVGINVGPVVAGVIGARRPQYDIWGNTVNVASRMDSTGVQGRIQVTEEVHRLLRRGSYRFVCRGKVSVKGKGEMLTYFLEGRTDGNGSQTRSLNSERKMYPFGRAGLQTRLAAGHPPVPPAAGLPVGAGPGALQGSGLAPGPPGQHLPPGASGKEA\", \"result\": [{\"smile\": \"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](OC(=O)NCCN=C=S)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\", \"value\": 130.0}, {\"smile\": \"C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(=O)NCCNC(=O)CBr)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C\", \"value\": 10.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(C)=O)[C@@H](OC(=O)NCCN=C=S)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C', 'C=C[C@@]1(C)CC(=O)[C@@]2(O)[C@](C)(O1)[C@@H](OC(=O)NCCNC(=O)CBr)[C@@H](O)[C@H]1C(C)(C)CC[C@H](O)[C@@]12C' and the protein are 130.0, 10.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you estimate the strength of binding between the drug 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F', 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRLTLLCCTWREERMGEEGSELPVCASCGQRIYDGQYLQALNADWHADCFRCCDCSASLSHQYYEKDGQLFCKKDYWARYGESCHGCSEQITKGLVMVAGELKYHPECFICLTCGTFIGDGDTYTLVEHSKLYCGHCYYQTVVTPVIEQILPDSPGSHLPHTVTLVSIPASSHGKRGLSVSIDPPHGPPGCGTEHSHTVRVQGVDPGCMSPDVKNSIHVGDRILEINGTPIRNVPLDEIDLLIQETSRLLQLTLEHDPHDTLGHGLGPETSPLSSPAYTPSGEAGSSARQKPVLRSCSIDRSPGAGSLGSPASQRKDLGRSESLRVVCRPHRIFRPSDLIHGEVLGKGCFGQAIKVTHRETGEVMVMKELIRFDEETQRTFLKEVKVMRCLEHPNVLKFIGVLYKDKRLNFITEYIKGGTLRGIIKSMDSQYPWSQRVSFAKDIASGMAYLHSMNIIHRDLNSHNCLVRENKNVVVADFGLARLMVDEKTQPEGLRSLKKPDRKKRYTVVGNPYWMAPEMINGRSYDEKVDVFSFGIVLCEIIGRVNADPDYLPRTMDFGLNVRGFLDRYCPPNCPPSFFPITVRCCDLDPEKRPSFVKLEHWLETLRMHLAGHLPLGPQLEQLDRGFWETYRRGESGLPAHPEVPD\", \"result\": [{\"smile\": \"C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F\", \"value\": 10000.0}, {\"smile\": \"COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C[C@@H](Oc1cc(-n2cnc3ccc(CN4CCN(C)CC4)cc32)sc1C(N)=O)c1ccccc1C(F)(F)F', 'COc1cc2c(Nc3ccc(Br)cc3F)ncnc2cc1OCC1CCN(C)CC1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the likely interaction affinity for the drug 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C', 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARTTSQLYDAVPIQSSVVLCSCPSPSMVRTQTESSTPPGIPGGSRQGPAMDGTAAEPRPGAGSLQHAQPPPQPRKKRPEDFKFGKILGEGSFSTVVLARELATSREYAIKILEKRHIIKENKVPYVTRERDVMSRLDHPFFVKLYFTFQDDEKLYFGLSYAKNGELLKYIRKIGSFDETCTRFYTAEIVSALEYLHGKGIIHRDLKPENILLNEDMHIQITDFGTAKVLSPESKQARANSFVGTAQYVSPELLTEKSACKSSDLWALGCIIYQLVAGLPPFRAGNEYLIFQKIIKLEYDFPEKFFPKARDLVEKLLVLDATKRLGCEEMEGYGPLKAHPFFESVTWENLHQQTPPKLTAYLPAMSEDDEDCYGNYDNLLSQFGCMQVSSSSSSHSLSASDTGLPQRSGSNIEQYIHDLDSNSFELDLQFSEDEKRLLLEKQAGGNPWHQFVENNLILKMGPVDKRKGLFARRRQLLLTEGPHLYYVDPVNKVLKGEIPWSQELRPEAKNFKTFFVHTPNRTYYLMDPSGNAHKWCRKIQEVWRQRYQSHPDAAVQ\", \"result\": [{\"smile\": \"CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C\", \"value\": 10000.0}, {\"smile\": \"O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1\", \"value\": 650.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CCOc1cc2ncc(C#N)c(Nc3ccc(F)c(Cl)c3)c2cc1NC(=O)/C=C/CN(C)C', 'O=C(c1ccc(C=Cc2n[nH]c3ccccc23)cc1)N1CCNCC1' and the protein are 10000.0, 650.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the binding affinity of the drug 'C[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2', 'CC(=O)O[C@H]1O[C@@H](C)[C@@H]2[C@H](/C=C/[C@H]3CCC[C@H](C)N3C)[C@H]3CCCC[C@@H]3C[C@H]12' with the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2\", \"CC(=O)O[C@H]1O[C@@H](C)[C@@H]2[C@H](/C=C/[C@H]3CCC[C@H](C)N3C)[C@H]3CCCC[C@@H]3C[C@H]12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MNNSTNSSNNGLAITSPYKTFEVVFIVLVAGSLSLVTIIGNILVMVSIKVNRHLQTVNNYFLFSLACADLIIGVFSMNLYTLYTVIGYWPLGPVVCDLWLALDYVVSNASVMNLLIISFDRYFCVTKPLTYPVKRTTKMAGMMIAAAWVLSFILWAPAILFWQFIVGVRTVEDGECYIQFFSNAAVTFGTAIAAFYLPVIIMTVLYWHISRASKSRIKKEKKEPVANQDPVSPSLVQGRIVKPNNNNMPGGDGGLEHNKIQNGKAPRDGVTENCVQGEEKESSNDSTSVSAVASNMRDDEITQDENTVSTSLGHSRDDNSKQTCIKIVTKAQKGDVCTPTSTTVELVGSSGQNGDEKQNIVARKIVKMTKQPAKKKPPPSREKKVTRTILAILLAFIITWAPYNVMVLINTFCAPCIPNTVWTIGYWLCYINSTINPACYALCNATFKKTFKHLLMCHYKNIGATR\", \"result\": [{\"smile\": \"C[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2\", \"value\": 2.7}, {\"smile\": \"CC(=O)O[C@H]1O[C@@H](C)[C@@H]2[C@H](/C=C/[C@H]3CCC[C@H](C)N3C)[C@H]3CCCC[C@@H]3C[C@H]12\", \"value\": 89.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C[N+]1(C)C2CCC1CC(OC(=O)C(CO)c1ccccc1)C2', 'CC(=O)O[C@H]1O[C@@H](C)[C@@H]2[C@H](/C=C/[C@H]3CCC[C@H](C)N3C)[C@H]3CCCC[C@@H]3C[C@H]12' and the protein are 2.7, 89.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How strong do you expect the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C''s interaction with the target protein to be? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSALAGEDVWRCPGCGDHIAPSQIWYRTVNETWHGSCFRCSECQDSLTNWYYEKDGKLYCPKDYWGKFGEFCHGCSLLMTGPFMVAGEFKYHPECFACMSCKVIIEDGDAYALVQHATLYCGKCHNEVVLAPMFERLSTESVQEQLPYSVTLISMPATTEGRRGFSVSVESACSNYATTVQVKEVNRMHISPNNRNAIHPGDRILEINGTPVRTLRVEEVEDAISQTSQTLQLLIEHDPVSQRLDQLRLEARLAPHMQNAGHPHALSTLDTKENLEGTLRRRSLRRSNSISKSPGPSSPKEPLLFSRDISRSESLRCSSSYSQQIFRPCDLIHGEVLGKGFFGQAIKVTHKATGKVMVMKELIRCDEETQKTFLTEVKVMRSLDHPNVLKFIGVLYKDKKLNLLTEYIEGGTLKDFLRSMDPFPWQQKVRFAKGIASGMAYLHSMCIIHRDLNSHNCLIKLDKTVVVADFGLSRLIVEERKRAPMEKATTKKRTLRKNDRKKRYTVVGNPYWMAPEMLNGKSYDETVDIFSFGIVLCEIIGQVYADPDCLPRTLDFGLNVKLFWEKFVPTDCPPAFFPLAAICCRLEPESRPAFSKLEDSFEALSLYLGELGIPLPAELEELDHTVSMQYGLTRDSPP\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 1700.0}, {\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C' and the protein are 1700.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is your assessment of the predicted affinity between the drug 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPALARDGGQLPLLVVFSAMIFGTITNQDLPVIKCVLINHKNNDSSVGKSSSYPMVSESPEDLGCALRPQSSGTVYEAAAVEVDVSASITLQVLVDAPGNISCLWVFKHSSLNCQPHFDLQNRGVVSMVILKMTETQAGEYLLFIQSEATNYTILFTVSIRNTLLYTLRRPYFRKMENQDALVCISESVPEPIVEWVLCDSQGESCKEESPAVVKKEEKVLHELFGTDIRCCARNELGRECTRLFTIDLNQTPQTTLPQLFLKVGEPLWIRCKAVHVNHGFGLTWELENKALEEGNYFEMSTYSTNRTMIRILFAFVSSVARNDTGYYTCSSSKHPSQSALVTIVEKGFINATNSSEDYEIDQYEEFCFSVRFKAYPQIRCTWTFSRKSFPCEQKGLDNGYSISKFCNHKHQPGEYIFHAENDDAQFTKMFTLNIRRKPQVLAEASASQASCFSDGYPLPSWTWKKCSDKSPNCTEEITEGVWNRKANRKVFGQWVSSSTLNMSEAIKGFLVKCCAYNSLGTSCETILLNSPGPFPFIQDNISFYATIGVCLLFIVVLTLLICHKYKKQFRYESQLQMVQVTGSSDNEYFYVDFREYEYDLKWEFPRENLEFGKVLGSGAFGKVMNATAYGISKTGVSIQVAVKMLKEKADSSEREALMSELKMMTQLGSHENIVNLLGACTLSGPIYLIFEYCCYGDLLNYLRSKREKFHRTWTEIFKEHNFSFYPTFQSHPNSSMPGSREVQIHPDSDQISGLHGNSFHSEDEIEYENQKRLEEEEDLNVLTFEDLLCFAYQVAKGMEFLEFKSCVHRDLAARNVLVTHGKVVKICDFGLARDIMSDSNYVVRGNARLPVKWMAPESLFEGIYTIKSDVWSYGILLWEIFSLGVNPYPGIPVDANFYKLIQNGFKMDQPFYATEEIYIIMQSCWAFDSRKRPSFPNLTSFLGCQLADAEEAMYQNVDGRVSECPHTYQNRRPFSREMDLGLLSPQAQVEDS\", \"result\": [{\"smile\": \"COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1\", \"value\": 11.0}, {\"smile\": \"Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1\", \"value\": 5900.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1cc2c(Oc3ccc(NC(=O)C4(C(=O)Nc5ccc(F)cc5)CC4)cc3F)ccnc2cc1OCCCN1CCOCC1', 'Oc1cccc(-c2nc(N3CCOCC3)c3oc4ncccc4c3n2)c1' and the protein are 11.0, 5900.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you provide an affinity estimate for the drug 'O=C(NCCC1CCCCC1)OCCCc1cnc[nH]1', 'c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1' with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NCCC1CCCCC1)OCCCc1cnc[nH]1\", \"c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MSFLPGMTPVTLSNFSWALEDRMLEGNSTTTPTRQLMPLVVVLSSVSLVTVALNLLVLYAVRSERKLHTVGNLYIVSLSVADLIVGAVVMPMSILYLHRSAWILGRPLCLFWLSMDYVASTASIFSVFILCIDRYRSVQQPLRYLRYRTKTRASATILGAWLLSFLWVIPILGWHHFMAPTSEPREKKCETDFYDVTWFKVMTAIINFYLPTLLMLWFYIRIYKAVRRHCQHRQLINSSLPSFSEMKLKLENAKVDTRRMGKESPWEDPKRCSKDASGVHTPMPSSQHLVDMPCAAVLSEDEGGEVGTRQMPMLAVGDGRCCEALNHMHSQLELSGQSRATHSISARPEEWTVVDGQSFPITDSDTSTEAAPMGGQPRSGSNSGLDYIKFTWRRLRSHSRQYTSGLHLNRERKAAKQLGCIMAAFILCWIPYFVFFMVIAFCKSCSNEPVHMFTIWLGYLNSTLNPLIYPLCNENFRKTFKRILRIPP\", \"result\": [{\"smile\": \"O=C(NCCC1CCCCC1)OCCCc1cnc[nH]1\", \"value\": 109647.82}, {\"smile\": \"c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1\", \"value\": 3.24}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'O=C(NCCC1CCCCC1)OCCCc1cnc[nH]1', 'c1ccc(C(CCc2ncc(CCNCCc3cccnc3)[nH]2)c2ccccc2)cc1' and the protein are 109647.82, 3.24."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the projected binding affinity for the drug 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O'-target protein complex? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPLAQLADPWQKMAVESPSDSAENGQQIMDEPMGEEEINPQTEEVSIKEIAITHHVKEGHEKADPSQFELLKVLGQGSFGKVFLVKKISGSDARQLYAMKVLKKATLKVRDRVRTKMERDILVEVNHPFIVKLHYAFQTEGKLYLILDFLRGGDLFTRLSKEVMFTEEDVKFYLAELALALDHLHSLGIIYRDLKPENILLDEEGHIKLTDFGLSKESIDHEKKAYSFCGTVEYMAPEVVNRRGHTQSADWWSFGVLMFEMLTGTLPFQGKDRKETMTMILKAKLGMPQFLSPEAQSLLRMLFKRNPANRLGAGPDGVEEIKRHSFFSTIDWNKLYRREIHPPFKPATGRPEDTFYFDPEFTAKTPKDSPGIPPSANAHQLFRGFSFVAITSDDESQAMQTVGVHSIVQQLHRNSIQFTDGYEVKEDIGVGSYSVCKRCIHKATNMEFAVKIIDKSKRDPTEEIEILLRYGQHPNIITLKDVYDDGKYVYVVTELMKGGELLDKILRQKFFSEREASAVLFTITKTVEYLHAQGVVHRDLKPSNILYVDESGNPESIRICDFGFAKQLRAENGLLMTPCYTANFVAPEVLKRQGYDAACDIWSLGVLLYTMLTGYTPFANGPDDTPEEILARIGSGKFSLSGGYWNSVSDTAKDLVSKMLHVDPHQRLTAALVLRHPWIVHWDQLPQYQLNRQDAPHLVKGAMAATYSALNRNQSPVLEPVGRSTLAQRRGIKKITSTAL\", \"result\": [{\"smile\": \"O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl\", \"value\": 10000.0}, {\"smile\": \"CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O\", \"value\": 5600.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'O=C(NC1CCNCC1)c1[nH]ncc1NC(=O)c1c(Cl)cccc1Cl', 'CC(O)C(=O)O.CN1CCN(c2ccc3c(c2)NC(=C2C(=O)N=c4cccc(F)c4=C2N)N3)CC1.O' and the protein are 10000.0, 5600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you determine the affinity value of the drug 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1', 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' for the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRPSGTAGAALLALLAALCPASRALEEKKVCQGTSNKLTQLGTFEDHFLSLQRMFNNCEVVLGNLEITYVQRNYDLSFLKTIQEVAGYVLIALNTVERIPLENLQIIRGNMYYENSYALAVLSNYDANKTGLKELPMRNLQEILHGAVRFSNNPALCNVESIQWRDIVSSDFLSNMSMDFQNHLGSCQKCDPSCPNGSCWGAGEENCQKLTKIICAQQCSGRCRGKSPSDCCHNQCAAGCTGPRESDCLVCRKFRDEATCKDTCPPLMLYNPTTYQMDVNPEGKYSFGATCVKKCPRNYVVTDHGSCVRACGADSYEMEEDGVRKCKKCEGPCRKVCNGIGIGEFKDSLSINATNIKHFKNCTSISGDLHILPVAFRGDSFTHTPPLDPQELDILKTVKEITGFLLIQAWPENRTDLHAFENLEIIRGRTKQHGQFSLAVVSLNITSLGLRSLKEISDGDVIISGNKNLCYANTINWKKLFGTSGQKTKIISNRGENSCKATGQVCHALCSPEGCWGPEPRDCVSCRNVSRGRECVDKCNLLEGEPREFVENSECIQCHPECLPQAMNITCTGRGPDNCIQCAHYIDGPHCVKTCPAGVMGENNTLVWKYADAGHVCHLCHPNCTYGCTGPGLEGCPTNGPKIPSIATGMVGALLLLLVVALGIGLFMRRRHIVRKRTLRRLLQERELVEPLTPSGEAPNQALLRILKETEFKKIKVLGSGAFGTVYKGLWIPEGEKVKIPVAIKELREATSPKANKEILDEAYVMASVDNPHVCRLLGICLTSTVQLITQLMPFGCLLDYVREHKDNIGSQYLLNWCVQIAKGMNYLEDRRLVHRDLAARNVLVKTPQHVKITDFGLAKLLGAEEKEYHAEGGKVPIKWMALESILHRIYTHQSDVWSYGVTVWELMTFGSKPYDGIPASEISSILEKGERLPQPPICTIDVYMIMVKCWMIDADSRPKFRELIIEFSKMARDPQRYLVIQGDERMHLPSPTDSNFYRALMDEEDMDDVVDADEYLIPQQGFFSSPSTSRTPLLSSLSATSNNSTVACIDRNGLQSCPIKEDSFLQRYSSDPTGALTEDSIDDTFLPVPEYINQSVPKRPAGSVQNPVYHNQPLNPAPSRDPHYQDPHSTAVGNPEYLNTVQPTCVNSTFDSPAHWAQKGSHQISLDNPDYQQDFFPKEAKPNGIFKGSTAENAEYLRVAPQSSEFIGA\", \"result\": [{\"smile\": \"Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1\", \"value\": 2600.0}, {\"smile\": \"CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21\", \"value\": 2300.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1cc2c(F)c(Oc3ncnn4cc(OCC(C)O)c(C)c34)ccc2[nH]1', 'CC1(C)CNc2cc(NC(=O)c3cccnc3NCc3ccncc3)ccc21' and the protein are 2600.0, 2300.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the anticipated affinity strength between the drug 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1', 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MQRDHTMDYKESCPSVSIPSSDEHREKKKRFTVYKVLVSVGRSEWFVFRRYAEFDKLYNTLKKQFPAMALKIPAKRIFGDNFDPDFIKQRRAGLNEFIQNLVRYPELYNHPDVRAFLQMDSPKHQSDPSEDEDERSSQKLHSTSQNINLGPSGNPHAKPTDFDFLKVIGKGSFGKVLLAKRKLDGKFYAVKVLQKKIVLNRKEQKHIMAERNVLLKNVKHPFLVGLHYSFQTTEKLYFVLDFVNGGELFFHLQRERSFPEHRARFYAAEIASALGYLHSIKIVYRDLKPENILLDSVGHVVLTDFGLCKEGIAISDTTTTFCGTPEYLAPEVIRKQPYDNTVDWWCLGAVLYEMLYGLPPFYCRDVAEMYDNILHKPLSLRPGVSLTAWSILEELLEKDRQNRLGAKEDFLEIQNHPFFESLSWADLVQKKIPPPFNPNVAGPDDIRNFDTAFTEETVPYSVCVSSDYSIVNASVLEADDAFVGFSYAPPSEDLFL\", \"result\": [{\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 10000.0}, {\"smile\": \"CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1', 'CCN(CCO)CCCOc1ccc2c(Nc3cc(CC(=O)Nc4cccc(F)c4)[nH]n3)ncnc2c1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you gauge the expected affinity value for the drug 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1'-target protein relationship? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"PFWKILNPLLERGTYYYFMGQQPGKVLGDQRRPSLPALHFIKGAGKKESSRHGGPHCNVFVEHEALQRPVASDFEPQGLSEAARWNSKENLLAGPSENDPNLFVALYDFVASGDNTLSITKGEKLRVLGYNHNGEWCEAQTKNGQGWVPSNYITPVNSLEKHSWYHGPVSRNAAEYLLSSGINGSFLVRESESSPGQRSISLRYEGRVYHYRINTASDGKLYVSSESRFNTLAELVHHHSTVADGLITTLHYPAPKRNKPTVYGVSPNYDKWEMERTDITMKHKLGGGQYGEVYEGVWKKYSLTVAVKTLKEDTMEVEEFLKEAAVMKEIKHPNLVQLLGVCTREPPFYIITEFMTYGNLLDYLRECNRQEVNAVVLLYMATQISSAMEYLEKKNFIHRDLAARNCLVGENHLVKVADFGLSRLMTGDTYTAHAGAKFPIKWTAPESLAYNKFSIKSDVWAFGVLLWEIATYGMSPYPGIDLSQVYELLEKDYRMERPEGCPEKVYELMRACWQWNPSDRPSFAEIHQAFETMFQESSISDEVEKELGKQGVRGAVSTLLQAPELPTKTRTSRRAAEHRDTTDVPEMPHSKGQGESDPLDHEPAVSPLLPRKERGPPEGGLNEDERLLPKDKKTNLFSALIKKKKKTAPTPPKRSSSFREMDGQPERRGAGEEEGRDISNGALAFTPLDTADPAKSPKPSNGAGVPNGALRESGGSGFRSPHLWKKSSTLTSSRLATGEEEGGGSSSKRFLRSCSASCVPHGAKDTEWRSVTLPRDLQSTGRQFDSSTFGGHKSEKPALPRKRAGENRSDQVTRGTVTPPPRLVKKNEEAADEVFKDIMESSPGSSPPNLTPKPLRRQVTVAPASGLPHKEEAGKGSALGTPAAAEPVTPTSKAGSGAPGGTSKGPAEESRVRRHKHSSESPGRDKGKLSRLKPAPPPPPAASAGKAGGKPSQSPSQEAAGEAVLGAKTKATSLVDAVNSDAAKPSQPGEGLKKPVLPATPKPQSAKPSGTPISPAPVPSTLPSASSALAGDQPSSTAFIPLISTRVSLRKTRQPPERIASGAITKGVVLDSTEALCLAISRNSEQMASHSAVLEAGKNLYTFCVSYVDSIQQMRNKFAFREAINKLENNLRELQICPATAGSGPAATQDFSKLLSSVKEISDIVQR\", \"result\": [{\"smile\": \"CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F\", \"value\": 4000.0}, {\"smile\": \"Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1\", \"value\": 11.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CCCS(=O)(=O)Nc1ccc(F)c(C(=O)c2c[nH]c3ncc(Cl)cc23)c1F', 'Cc1ccc(NC(=O)c2ccc(CN3CCN(C)CC3)cc2)cc1Nc1nc(-c2cccnc2)cs1' and the protein are 4000.0, 11.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the expected level of affinity between the drug 'CC(=O)N[C@@H](CC(=O)O)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2', 'O=C(O)C[C@H](NC(=O)OCC1c2ccccc2-c2ccccc21)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC(=O)N[C@@H](CC(=O)O)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2\", \"O=C(O)C[C@H](NC(=O)OCC1c2ccccc2-c2ccccc21)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPPTVIAEPVASGAHASYSGGPGETDYHALNAMLNLYDADGKIQFDKDREAAHQYFLQHVNQNTVFFHNQDEKLDYLIRENYYEREVLDQYSRNFVKTLLDRAYAKKFRFPTFLGAFKYYTSYTLKTFDGKRYLERFEDRVVMVALTLAAGDTALAELLVDEIIDGRFQPATPTFLNSGKKQRGEPVSCFLLRVEDNMESIGRSINSALQLSKRGGGVALLLTNIREHGAPIKNIENQSSGVIPIMKLLEDAFSYANQLGARQGAGAVYLHAHHPDIYRFLDTKRENADEKIRIKTLSLGVVIPDITFELAKRNDDMYLFSPYDVERVYGVPFADISVTEKYYEMVDDARIRKTKIKAREFFQTLAELQFESGYPYIMFEDTVNRANPIDGKITHSNLCSEILQVSTPSLFNEDLSYAKVGKDISCNLGSLNIAKTMDSPDFAQTIEVAIRALTAVSDQTHIKSVPSIEQGNNDSHAIGLGQMNLHGYLARERIFYGSDEGIDFTNIYFYTVLYHALRASNRIAIERGTHFKGFERSKYASGEFFDKYTDQIWEPKTQKVRQLFADAGIRIPTQDDWRRLKESVQAHGIYNQNLQAVPPTGSISYINHSTSSIHPIVSKVEIRKEGKIGRVYYPAPYMTNDNLEYYEDAYEIGYEKIIDTYAAATQHVDQGLSLTLFFKDTATTRDVNKAQIYAWRKGIKTLYYIRLRQMALEGTEVEGCVSCML\", \"result\": [{\"smile\": \"CC(=O)N[C@@H](CC(=O)O)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2\", \"value\": 150000.0}, {\"smile\": \"O=C(O)C[C@H](NC(=O)OCC1c2ccccc2-c2ccccc21)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2\", \"value\": 1600.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CC(=O)N[C@@H](CC(=O)O)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2', 'O=C(O)C[C@H](NC(=O)OCC1c2ccccc2-c2ccccc21)C(=O)Nc1cccc2c1N[C@@H](Cc1c[nH]c3ccccc13)C(=O)N(CC(=O)N[C@@H](Cc1ccccc1)C(=O)O)C2' and the protein are 150000.0, 1600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Estimate the interaction affinity for the drug 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl', 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MDVVDPDIFNRDPRDHYDLLQRLGGGTYGEVFKARDKVSGDLVALKMVKMEPDDDVSTLQKEILILKTCRHANIVAYHGSYLWLQKLWICMEFCGAGSLQDIYQVTGSLSELQISYVCREVLQGLAYLHSQKKIHRDIKGANILINDAGEVRLADFGISAQIGATLARRLSFIGTPYWMAPEVAAVALKGGYNELCDIWSLGITAIELAELQPPLFDVHPLRVLFLMTKSGYQPPRLKEKGKWSAAFHNFIKVTLTKSPKKRPSATKMLSHQLVSQPGLNRGLILDLLDKLKNPGKGPSIGDIEDEEPELPPAIPRRIRSTHRSSSLGIPDADCCRRHMEFRKLRGMETRPPANTARLQPPRDLRSSSPRKQLSESSDDDYDDVDIPTPAEDTPPPLPPKPKFRSPSDEGPGSMGDDGQLSPGVLVRCASGPPPNSPRPGPPPSTSSPHLTAHSEPSLWNPPSRELDKPPLLPPKKEKMKRKGCALLVKLFNGCPLRIHSTAAWTHPSTKDQHLLLGAEEGIFILNRNDQEATLEMLFPSRTTWVYSINNVLMSLSGKTPHLYSHSILGLLERKETRAGNPIAHISPHRLLARKNMVSTKIQDTKGCRACCVAEGASSGGPFLCGALETSVVLLQWYQPMNKFLLVRQVLFPLPTPLSVFALLTGPGSELPAVCIGVSPGRPGKSVLFHTVRFGALSCWLGEMSTEHRGPVQVTQVEEDMVMVLMDGSVKLVTPEGSPVRGLRTPEIPMTEAVEAVAMVGGQLQAFWKHGVQVWALGSDQLLQELRDPTLTFRLLGSPRLECSGTISPHCNLLLPGSSNSPASASRVAGITGL\", \"result\": [{\"smile\": \"Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl\", \"value\": 10000.0}, {\"smile\": \"CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1\", \"value\": 370.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cc1ccc2nc(NCCN)c3ncc(C)n3c2c1.Cl', 'CN(C)CC(=O)N1CCC(c2ccc(NC(=O)c3ncc(C#N)[nH]3)c(C3=CCCCC3)c2)CC1' and the protein are 10000.0, 370.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How would you define the binding affinity of the drug 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O', 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MARARPPPPPSPPPGLLPLLPPLLLLPLLLLPAGCRALEETLMDTKWVTSELAWTSHPESGWEEVSGYDEAMNPIRTYQVCNVRESSQNNWLRTGFIWRRDVQRVYVELKFTVRDCNSIPNIPGSCKETFNLFYYEADSDVASASSPFWMENPYVKVDTIAPDESFSRLDAGRVNTKVRSFGPLSKAGFYLAFQDQGACMSLISVRAFYKKCASTTAGFALFPETLTGAEPTSLVIAPGTCIPNAVEVSVPLKLYCNGDGEWMVPVGACTCATGHEPAAKESQCRPCPPGSYKAKQGEGPCLPCPPNSRTTSPAASICTCHNNFYRADSDSADSACTTVPSPPRGVISNVNETSLILEWSEPRDLGGRDDLLYNVICKKCHGAGGASACSRCDDNVEFVPRQLGLTERRVHISHLLAHTRYTFEVQAVNGVSGKSPLPPRYAAVNITTNQAAPSEVPTLRLHSSSGSSLTLSWAPPERPNGVILDYEMKYFEKSEGIASTVTSQMNSVQLDGLRPDARYVVQVRARTVAGYGQYSRPAEFETTSERGSGAQQLQEQLPLIVGSATAGLVFVVAVVVIAIVCLRKQRHGSDSEYTEKLQQYIAPGMKVYIDPFTYEDPNEAVREFAKEIDVSCVKIEEVIGAGEFGEVCRGRLKQPGRREVFVAIKTLKVGYTERQRRDFLSEASIMGQFDHPNIIRLEGVVTKSRPVMILTEFMENCALDSFLRLNDGQFTVIQLVGMLRGIAAGMKYLSEMNYVHRDLAARNILVNSNLVCKVSDFGLSRFLEDDPSDPTYTSSLGGKIPIRWTAPEAIAYRKFTSASDVWSYGIVMWEVMSYGERPYWDMSNQDVINAVEQDYRLPPPMDCPTALHQLMLDCWVRDRNLRPKFSQIVNTLDKLIRNAASLKVIASAQSGMSQPLLDRTVPDYTTFTTVGDWLDAIKMGRYKESFVSAGFASFDLVAQMTAEDLLRIGVTLAGHQKKILSSIQDMRLQMNQTLPVQV\", \"result\": [{\"smile\": \"CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O\", \"value\": 10000.0}, {\"smile\": \"CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CC12OC(CC1(O)CO)n1c3ccccc3c3c4c(c5c6ccccc6n2c5c31)CNC4=O', 'CCC1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the predicted value for the drug 'CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1', 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1''s affinity with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1\", \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MPTQRDSSTMSHTVAGGGSGDHSHQVRVKAYYRGDIMITHFEPSISFEGLCNEVRDMCSFDNEQLFTMKWIDEEGDPCTVSSQLELEEAFRLYELNKDSELLIHVFPCVPERPGMPCPGEDKSIYRRGARRWRKLYCANGHTFQAKRFNRRAHCAICTDRIWGLGRQGYKCINCKLLVHKKCHKLVTIECGRHSLPQEPVMPMDQSSMHSDHAQTVIPYNPSSHESLDQVGEEKEAMNTRESGKASSSLGLQDFDLLRVIGRGSYAKVLLVRLKKTDRIYAMKVVKKELVNDDEDIDWVQTEKHVFEQASNHPFLVGLHSCFQTESRLFFVIEYVNGGDLMFHMQRQRKLPEEHARFYSAEISLALNYLHERGIIYRDLKLDNVLLDSEGHIKLTDYGMCKEGLRPGDTTSTFCGTPNYIAPEILRGEDYGFSVDWWALGVLMFEMMAGRSPFDIVGSSDNPDQNTEDYLFQVILEKQIRIPRSLSVKAASVLKSFLNKDPKERLGCHPQTGFADIQGHPFFRNVDWDMMEQKQVVPPFKPNISGEFGLDNFDSQFTNEPVQLTPDDDDIVRKIDQSEFEGFEYINPLLMSAEECV\", \"result\": [{\"smile\": \"CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1\", \"value\": 10000.0}, {\"smile\": \"O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CN1CC[C@H](c2c(O)cc(O)c3c(=O)cc(-c4ccccc4Cl)oc23)[C@H](O)C1', 'O=C(O)c1ccc(Nc2ncc3c(n2)-c2ccc(Cl)cc2C(c2c(F)cccc2F)=NC3)cc1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Can you calculate the expected binding strength of the drug 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' toward the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MGFGSDLKNSHEAVLKLQDWELRLLETVKKFMALRIKSDKEYASTLQNLCNQVDKESTVQMNYVSNVSKSWLLMIQQTEQLSRIMKTHAEDLNSGPLHRLTMMIKDKQQVKKSYIGVHQQIEAEMIKVTKTELEKLKCSYRQLIKEMNSAKEKYKEALAKGKETEKAKERYDKATMKLHMLHNQYVLALKGAQLHQNQYYDITLPLLLDSLQKMQEEMIKALKGIFDEYSQITSLVTEEIVNVHKEIQMSVEQIDPSTEYNNFIDVHRTTAAKEQEIEFDTSLLEENENLQANEIMWNNLTAESLQVMLKTLAEELMQTQQMLLNKEEAVLELEKRIEESSETCEKKSDIVLLLSQKQALEELKQSVQQLRCTEAKFSAQKELLEQKVQENDGKEPPPVVNYEEDARSVTSMERKERLSKFESIRHSIAGIIRSPKSALGSSALSDMISISEKPLAEQDWYHGAIPRIEAQELLKKQGDFLVRESHGKPGEYVLSVYSDGQRRHFIIQYVDNMYRFEGTGFSNIPQLIDHHYTTKQVITKKSGVVLLNPIPKDKKWILSHEDVILGELLGKGNFGEVYKGTLKDKTSVAVKTCKEDLPQELKIKFLQEAKILKQYDHPNIVKLIGVCTQRQPVYIIMELVSGGDFLTFLRRKKDELKLKQLVKFSLDAAAGMLYLESKNCIHRDLAARNCLVGENNVLKISDFGMSRQEDGGVYSSSGLKQIPIKWTAPEALNYGRYSSESDVWSFGILLWETFSLGVCPYPGMTNQQAREQVERGYRMSAPQHCPEDISKIMMKCWDYKPENRPKFSELQKELTIIKRKLT\", \"result\": [{\"smile\": \"COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C\", \"value\": 10000.0}, {\"smile\": \"Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1\", \"value\": 10000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'COc1c(Cl)cc2c([nH]c3cnccc32)c1NC(=O)c1cccnc1C', 'Cn1cc(-c2ccc3nnc(Sc4ccc5ncccc5c4)n3n2)cn1' and the protein are 10000.0, 10000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What is the expected affinity between the drug 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\", \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MRLPGAMPALALKGELLLLSLLLLLEPQISQGLVVTPPGPELVLNVSSTFVLTCSGSAPVVWERMSQEPPQEMAKAQDGTFSSVLTLTNLTGLDTGEYFCTHNDSRGLETDERKRLYIFVPDPTVGFLPNDAEELFIFLTEITEITIPCRVTDPQLVVTLHEKKGDVALPVPYDHQRGFSGIFEDRSYICKTTIGDREVDSDAYYVYRLQVSSINVSVNAVQTVVRQGENITLMCIVIGNEVVNFEWTYPRKESGRLVEPVTDFLLDMPYHIRSILHIPSAELEDSGTYTCNVTESVNDHQDEKAINITVVESGYVRLLGEVGTLQFAELHRSRTLQVVFEAYPPPTVLWFKDNRTLGDSSAGEIALSTRNVSETRYVSELTLVRVKVAEAGHYTMRAFHEDAEVQLSFQLQINVPVRVLELSESHPDSGEQTVRCRGRGMPQPNIIWSACRDLKRCPRELPPTLLGNSSEEESQLETNVTYWEEEQEFEVVSTLRLQHVDRPLSVRCTLRNAVGQDTQEVIVVPHSLPFKVVVISAILALVVLTIISLIILIMLWQKKPRYEIRWKVIESVSSDGHEYIYVDPMQLPYDSTWELPRDQLVLGRTLGSGAFGQVVEATAHGLSHSQATMKVAVKMLKSTARSSEKQALMSELKIMSHLGPHLNVVNLLGACTKGGPIYIITEYCRYGDLVDYLHRNKHTFLQHHSDKRRPPSAELYSNALPVGLPLPSHVSLTGESDGGYMDMSKDESVDYVPMLDMKGDVKYADIESSNYMAPYDNYVPSAPERTCRATLINESPVLSYMDLVGFSYQVANGMEFLASKNCVHRDLAARNVLICEGKLVKICDFGLARDIMRDSNYISKGSTFLPLKWMAPESIFNSLYTTLSDVWSFGILLWEIFTLGGTPYPELPMNEQFYNAIKRGYRMAQPAHASDEIYEIMQKCWEEKFEIRPPFSQLVLLLERLLGEGYKKKYQQVDEEFLRSDHPAILRSQARLPGFHGLRSPLDTSSVLYTAVQPNEGDNDYIIPLPDPKPEVADEGPLEGSPSLASSTLNEVNTSSTISCDSPLEPQDEPEPEPQLELQVEPEPELEQLPDSGCPAPRAEAEDSFL\", \"result\": [{\"smile\": \"C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1\", \"value\": 1400.0}, {\"smile\": \"COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1\", \"value\": 15.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'C#Cc1cccc(Nc2ncnc3cc(OCCOC)c(OCCOC)cc23)c1', 'COC(=O)c1ccc2c(c1)NC(=O)/C2=C(\\Nc1ccc(N(C)C(=O)CN2CCN(C)CC2)cc1)c1ccccc1' and the protein are 1400.0, 15.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "How do you estimate the affinity level for the drug 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MKIPNIGNVMNKFEILGVVGEGAYGVVLKCRHKETHEIVAIKKFKDSEENEEVKETTLRELKMLRTLKQENIVELKEAFRRRGKLYLVFEYVEKNMLELLEEMPNGVPPEKVKSYIYQLIKAIHWCHKNDIVHRDIKPENLLISHNDVLKLCDFGFARNLSEGNNANYTEYVATRWYRSPELLLGAPYGKSVDMWSVGCILGELSDGQPLFPGESEIDQLFTIQKVLGPLPSEQMKLFYSNPRFHGLRFPAVNHPQSLERRYLGILNSVLLDLMKNLLKLDPADRYLTEQCLNHPTFQTQRLLDRSPSRSAKRKPYHVESSTLSNRNQAGKSTALQSHHRSNSKDIQNLSVGLPRADEGLPANESFLNGNLAGASLSPLHTKTYQASSQPGSTSKDLTNNNIPHLLSPKEAKSKTEFDFNIDPKPSEGPGTKYLKSNSRSQQNRHSFMESSQSKAGTLQPNEKQSRHSYIDTIPQSSRSPSYRTKAKSHGALSDSKSVSNLSEARAQIAEPSTSRYFPSSCLDLNSPTSPTPTRHSDTRTLLSPSGRNNRNEGTLDSRRTTTRHSKTMEELKLPEHMDSSHSHSLSAPHESFSYGLGYTSPFSSQQRPHRHSMYVTRDKVRAKGLDGSLSIGQGMAARANSLQLLSPQPGEQLPPEMTVARSSVKETSREGTSSFHTRQKSEGGVYHDPHSDDGTAPKENRHLYNDPVPRRVGSFYRVPSPRPDNSFHENNVSTRVSSLPSESSSGTNHSKRQPAFDPWKSPENISHSEQLKEKEKQGFFRSMKKKKKKSQTVPNSDSPDLLTLQKSIHSASTPSSRPKEWRPEKISDLQTQSQPLKSLRKLLHLSSASNHPASSDPRFQPLTAQQTKNSFSEIRIHPLSQASGGSSNIRQEPAPKGRPALQLPDGGCDGRRQRHHSGPQDRRFMLRTTEQQGEYFCCGDPKKPHTPCVPNRALHRPISSPAPYPVLQVRGTSMCPTLQVRGTDAFSCPTQQSGFSFFVRHVMREALIHRAQVNQAALLTYHENAALTGK\", \"result\": [{\"smile\": \"N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1\", \"value\": 10000.0}, {\"smile\": \"CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1\", \"value\": 1.7}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'N#CC[C@H](C1CCCC1)n1cc(-c2ncnc3[nH]ccc23)cn1', 'CC(C)(C)c1cnc(CSc2cnc(NC(=O)C3CCNCC3)s2)o1' and the protein are 10000.0, 1.7."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's your evaluation of the drug 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21', 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12''s affinity for the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MTLDVGPEDELPDWAAAKEFYQKYDPKDVIGRGVSSVVRRCVHRATGHEFAVKIMEVTAERLSPEQLEEVREATRRETHILRQVAGHPHIITLIDSYESSSFMFLVFDLMRKGELFDYLTEKVALSEKETRSIMRSLLEAVSFLHANNIVHRDLKPENILLDDNMQIRLSDFGFSCHLEPGEKLRELCGTPGYLAPEILKCSMDETHPGYGKEVDLWACGVILFTLLAGSPPFWHRRQILMLRMIMEGQYQFSSPEWDDRSSTVKDLISRLLQVDPEARLTAEQALQHPFFERCEGSQPWNLTPRQRFRVAVWTVLAAGRVALSTHRVRPLTKNALLRDPYALRSVRHLIDNCAFRLYGHWVKKGEQQNRAALFQHRPPGPFPIMGPEEEGDSAAITEDEAVLVLG\", \"result\": [{\"smile\": \"Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21\", \"value\": 10000.0}, {\"smile\": \"Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12\", \"value\": 2600.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'Cn1cc(C2=C(c3cn(C4CCN(Cc5ccccn5)CC4)c4ccccc34)C(=O)NC2=O)c2ccccc21', 'Cc1n[nH]c2ccc(-c3cncc(OC[C@@H](N)Cc4ccccc4)c3)cc12' and the protein are 10000.0, 2600.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "Assess the predicted value of the affinity between the drug 'c1ncc(CCCOCCC2CCCCC2)[nH]1', 'NCCc1cnc(-c2cccc(C(F)(F)F)c2)[nH]1' and the target protein. MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"c1ncc(CCCOCCC2CCCCC2)[nH]1\", \"NCCc1cnc(-c2cccc(C(F)(F)F)c2)[nH]1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MERAPPDGLMNASGALAGEAAAAAGGARTFSAAWTAVLAALMALLIVATVLGNALVMLAFVADSSLRTQNNFFLLNLAISDFLVGVFCIPLYVPYVLTGRWTFGRGLCKLWLVVDYLLCTSSVFNIVLISYDRFLSVTRAVSYRAQQGDTRRAVRKMVLVWVLAFLLYGPAILSWEYLSGGSSIPEGHCYAEFFYNWYFLITASTLEFFTPFLSVTFFNLSIYLNIQRRTRLRLDGGAREAGPDPLPEAQSSPPQPPPGCWGCWPKGQGESMPLHRYGVGEAGPGAEAGEAALGGGSGAAASPTSSSGSSSRGTERPRSLKRGSKPSASSASLEKRMKMVSQSITQRFRLSRDKKVAKSLAIIVSIFGLCWAPYTLLMIIRAACHGHCVPDYWYETSFWLLWANSAVNPVLYPLCHYSFRRAFTKLLCPQKLKVQPHSSLEHCWK\", \"result\": [{\"smile\": \"c1ncc(CCCOCCC2CCCCC2)[nH]1\", \"value\": 15.85}, {\"smile\": \"NCCc1cnc(-c2cccc(C(F)(F)F)c2)[nH]1\", \"value\": 6165.95}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'c1ncc(CCCOCCC2CCCCC2)[nH]1', 'NCCc1cnc(-c2cccc(C(F)(F)F)c2)[nH]1' and the protein are 15.85, 6165.95."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    },
    {
        "conversations": [
            {
                "from": "human",
                "value": "What's the anticipated interaction value for the drug 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1', 'c1cc2c(Nc3cn[nH]c3)ncnn2c1' with the target protein? MemoryPool(arguments=dict_keys(['user_target_sequence']))"
            },
            {
                "from": "function_call",
                "value": "{\"name\": \"drug_target_affinity_regression_predict\", \"arguments\": {\"drug_smiles\": [\"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"c1cc2c(Nc3cn[nH]c3)ncnn2c1\"], \"target_seq\": \"(user_target_sequence)\"}}"
            },
            {
                "from": "observation",
                "value": "{\"task\": \"Predict the affinity regression value between the drug and the target sequence.\", \"target_seq\": \"MANDSPAKSLVDIDLSSLRDPAGIFELVEVVGNGTYGQVYKGRHVKTGQLAAIKVMDVTEDEEEEIKLEINMLKKYSHHRNIATYYGAFIKKSPPGHDDQLWLVMEFCGAGSITDLVKNTKGNTLKEDWIAYISREILRGLAHLHIHHVIHRDIKGQNVLLTENAEVKLVDFGVSAQLDRTVGRRNTFIGTPYWMAPEVIACDENPDATYDYRSDLWSCGITAIEMAEGAPPLCDMHPMRALFLIPRNPPPRLKSKKWSKKFFSFIEGCLVKNYMQRPSTEQLLKHPFIRDQPNERQVRIQLKDHIDRTRKKRGEKDETEYEYSGSEEEEEEVPEQEGEPSSIVNVPGESTLRRDFLRLQQENKERSEALRRQQLLQEQQLREQEEYKRQLLAERQKRIEQQKEQRRRLEEQQRREREARRQQEREQRRREQEEKRRLEELERRRKEEEERRRAEEEKRRVEREQEYIRRQLEEEQRHLEVLQQQLLQEQAMLLECRWREMEEHRQAERLQRQLQQEQAYLLSLQHDHRRPHPQHSQQPPPPQQERSKPSFHAPEPKAHYEPADRAREVEDRFRKTNHSSPEAQSKQTGRVLEPPVPSRSESFSNGNSESVHPALQRPAEPQVPVRTTSRSPVLSRRDSPLQGSGQQNSQAGQRNSTSIEPRLLWERVEKLVPRPGSGSSSGSSNSGSQPGSHPGSQSGSGERFRVRSSSKSEGSPSQRLENAVKKPEDKKEVFRPLKPADLTALAKELRAVEDVRPPHKVTDYSSSSEESGTTDEEDDDVEQEGADESTSGPEDTRAASSLNLSNGETESVKTMIVHDDVESEPAMTPSKEGTLIVRQTQSASSTLQKHKSSSSFTPFIDPRLLQISPSSGTTVTSVVGFSCDGMRPEAIRQDPTRKGSVVNVNPTNTRPQSDTPEIRKYKKRFNSEILCAALWGVNLLVGTESGLMLLDRSGQGKVYPLINRRRFQQMDVLEGLNVLVTISGKKDKLRVYYLSWLRNKILHNDPEVEKKQGWTTVGDLEGCVHYKVVKYERIKFLVIALKSSVEVYAWAPKPYHKFMAFKSFGELVHKPLLVDLTVEEGQRLKVIYGSCAGFHAVDVDSGSVYDIYLPTHIQCSIKPHAIIILPNTDGMELLVCYEDEGVYVNTYGRITKDVVLQWGEMPTSVAYIRSNQTMGWGEKAIEIRSVETGHLDGVFMHKRAQRLKFLCERNDKVFFASVRSGGSSQVYFMTLGRTSLLSW\", \"result\": [{\"smile\": \"CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1\", \"value\": 10000.0}, {\"smile\": \"c1cc2c(Nc3cn[nH]c3)ncnn2c1\", \"value\": 17000.0}]} MemoryPool(arguments=dict_keys(['user'smiles', 'user_target_sequence', 'drug_target_affinity_regression_result']))"
            },
            {
                "from": "gpt",
                "value": "The affinity between the drugs 'CC[C@@H]1C(=O)N(C)c2cnc(Nc3ccc(C(=O)NC4CCN(C)CC4)cc3OC)nc2N1C1CCCC1', 'c1cc2c(Nc3cn[nH]c3)ncnn2c1' and the protein are 10000.0, 17000.0."
            }
        ],
        "system": "You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows: <tool_call>{\"name\": <function-name>,\"arguments\": <args-dict>}</tool_call>",
        "tools": "[{\"name\": \"drug_target_affinity_regression_predict\", \"description\": \"Predict drug-target affinity using a specified model\", \"parameters\": {\"type\": \"object\", \"properties\": {\"drug_smiles\": {\"type\": \"array\", \"items\": {\"type\": \"string\"}, \"description\": \"Drug smiles organized as a list\"}, \"target_seq\": {\"type\": \"string\", \"description\": \"Protein target string\"}}}, \"required\": [\"drug_smiles\", \"target_seq\"]}]"
    }
]